diff --git a/.github/workflows/python-test.yml b/.github/workflows/python-test.yml
index 9017f795a..0e51c6de1 100644
--- a/.github/workflows/python-test.yml
+++ b/.github/workflows/python-test.yml
@@ -14,7 +14,7 @@ jobs:
         python-version: ['3.9', '3.10', '3.11', '3.12']
 
     steps:
-      - uses: actions/checkout@v3 # Updated to the latest version
+      - uses: actions/checkout@v4 # Updated to the latest version
       - name: Set up Python ${{ matrix.python-version }}
         uses: actions/setup-python@v4 # Updated to the latest version
         with:
@@ -37,7 +37,7 @@ jobs:
           poetry run pytest
 
       - name: Upload pytest results as an artifact (optional)
-        uses: actions/upload-artifact@v3 # Updated to the latest version
+        uses: actions/upload-artifact@v4 # Updated to the latest version
         if: always() # Always run this step to ensure test results are saved even if previous steps fail
         with:
           name: pytest-results
diff --git a/adalflow/CHANGELOG.md b/adalflow/CHANGELOG.md
index df1140ce9..1b0bef631 100644
--- a/adalflow/CHANGELOG.md
+++ b/adalflow/CHANGELOG.md
@@ -1,16 +1,23 @@
-## [0.2.7] - 2024-09-23
+## [0.2.7] - 2025-01-16
 
-### Improved
-- Better diagnose report for `Trainer.diagnose`.
-- Multi-hop RAG with handling of Cycle.
-
-## [0.2.7] - TO Be Released
 ### Added
 - `Memory` is completed with `call` and `add_dialog_turn` methods.
 - Integrated `LanceDB` in the `Retriever`
+- Multi-modal (image input and generation) in `OpenAIClient` along with tests.
+- `ComponentList` to support a list of components registered in a component. Added `test_componentlist` to test the `ComponentList`.
+
 ### Improved
+- Better diagnose report for `Trainer.diagnose`.
 - `BedrockAPIClient` added more details on setup, yet it is still in experimental stage.
 - `AzureAPIClient` added more details on setup, yet it is still in experimental stage.
+-  `Retriever` class:
+  - Support data id (field).
+- `GradComponent`: Support pass-through gradient for the `forward` method.
+
+Optimization
+- Aggregated all backward engine prompts in `backward_engine_prompt`.
+- Added `TGDData` for the optimizer to support reasoning at proposing new prompt.
+- Added `sequential_order` in the `Trainer` to support the sequential training order. Reorganized the trainer code.
 ## [0.2.6] - 2024-11-25
 ### Improved
 - Add default `max_tokens=512` to the `AnthropicAPIClient` to avoid the error when the user does not provide the `max_tokens` in the prompt.
diff --git a/adalflow/adalflow/__init__.py b/adalflow/adalflow/__init__.py
index 4c9b45bae..75f26ffad 100644
--- a/adalflow/adalflow/__init__.py
+++ b/adalflow/adalflow/__init__.py
@@ -1,4 +1,4 @@
-__version__ = "0.2.6"
+__version__ = "0.2.7"
 
 from adalflow.core.component import Component, fun_to_component
 from adalflow.core.container import Sequential, ComponentList
@@ -61,6 +61,7 @@
     AnthropicAPIClient,
     CohereAPIClient,
     BedrockAPIClient,
+    DeepSeekClient,
 )
 
 # data pipeline
@@ -124,6 +125,7 @@
     "OpenAIClient",
     "GoogleGenAIClient",
     "GroqAPIClient",
+    "DeepSeekClient",
     "OllamaClient",
     "TransformersClient",
     "AnthropicAPIClient",
diff --git a/adalflow/adalflow/components/model_client/__init__.py b/adalflow/adalflow/components/model_client/__init__.py
index ae508ece4..1ea3c40ad 100644
--- a/adalflow/adalflow/components/model_client/__init__.py
+++ b/adalflow/adalflow/components/model_client/__init__.py
@@ -52,6 +52,12 @@
     "adalflow.components.model_client.ollama_client.OllamaClient",
     OptionalPackages.OLLAMA,
 )
+
+# no imports are needed for DeepSeek
+DeepSeekClient = LazyImport(
+    "adalflow.components.model_client.deepseek_client.DeepSeekClient",
+    None
+)
 get_first_message_content = LazyImport(
     "adalflow.components.model_client.openai_client.get_first_message_content",
     OptionalPackages.OPENAI,
@@ -76,6 +82,7 @@
     "GroqAPIClient",
     "OpenAIClient",
     "GoogleGenAIClient",
+    "DeepSeekClient",
 ]
 
 for name in __all__:
diff --git a/adalflow/adalflow/components/model_client/bedrock_client.py b/adalflow/adalflow/components/model_client/bedrock_client.py
index b10098bb8..493147e63 100644
--- a/adalflow/adalflow/components/model_client/bedrock_client.py
+++ b/adalflow/adalflow/components/model_client/bedrock_client.py
@@ -1,7 +1,8 @@
 """AWS Bedrock ModelClient integration."""
 
+import json
 import os
-from typing import Dict, Optional, Any, Callable
+from typing import Dict, Optional, Any, Callable, Generator as GeneratorType
 import backoff
 import logging
 
@@ -26,7 +27,6 @@ def get_first_message_content(completion: Dict) -> str:
     r"""When we only need the content of the first message.
     It is the default parser for chat completion."""
     return completion["output"]["message"]["content"][0]["text"]
-    return completion["output"]["message"]["content"][0]["text"]
 
 
 __all__ = [
@@ -117,6 +117,7 @@ def __init__(
         self._aws_connection_timeout = aws_connection_timeout
         self._aws_read_timeout = aws_read_timeout
 
+        self._client = None
         self.session = None
         self.sync_client = self.init_sync_client()
         self.chat_completion_parser = (
@@ -158,16 +159,51 @@ def init_sync_client(self):
     def init_async_client(self):
         raise NotImplementedError("Async call not implemented yet.")
 
-    def parse_chat_completion(self, completion):
-        log.debug(f"completion: {completion}")
+    def handle_stream_response(self, stream: dict) -> GeneratorType:
+        r"""Handle the stream response from bedrock. Yield the chunks.
+
+        Args:
+            stream (dict): The stream response generator from bedrock.
+
+        Returns:
+            GeneratorType: A generator that yields the chunks from bedrock stream.
+        """
+        try:
+            stream: GeneratorType = stream["stream"]
+            for chunk in stream:
+                log.debug(f"Raw chunk: {chunk}")
+                yield chunk
+        except Exception as e:
+            log.debug(f"Error in handle_stream_response: {e}")  # Debug print
+            raise
+
+    def parse_chat_completion(self, completion: dict) -> "GeneratorOutput":
+        r"""Parse the completion, and assign it into the raw_response attribute.
+
+        If the completion is a stream, it will be handled by the handle_stream_response
+        method that returns a Generator. Otherwise, the completion will be parsed using
+        the get_first_message_content method.
+
+        Args:
+            completion (dict): The completion response from bedrock API call.
+
+        Returns:
+            GeneratorOutput: A generator output object with the parsed completion. May
+                return a generator if the completion is a stream.
+        """
         try:
-            data = completion["output"]["message"]["content"][0]["text"]
-            usage = self.track_completion_usage(completion)
-            return GeneratorOutput(data=None, usage=usage, raw_response=data)
+            usage = None
+            data = self.chat_completion_parser(completion)
+            if not isinstance(data, GeneratorType):
+                # Streaming completion usage tracking is not implemented.
+                usage = self.track_completion_usage(completion)
+            return GeneratorOutput(
+                data=None, error=None, raw_response=data, usage=usage
+            )
         except Exception as e:
-            log.error(f"Error parsing completion: {e}")
+            log.error(f"Error parsing the completion: {e}")
             return GeneratorOutput(
-                data=None, error=str(e), raw_response=str(completion)
+                data=None, error=str(e), raw_response=json.dumps(completion)
             )
 
     def track_completion_usage(self, completion: Dict) -> CompletionUsage:
@@ -191,6 +227,7 @@ def list_models(self):
                 print(f"  Description: {model['description']}")
                 print(f"  Provider: {model['provider']}")
                 print("")
+
         except Exception as e:
             print(f"Error listing models: {e}")
 
@@ -222,14 +259,27 @@ def convert_inputs_to_api_kwargs(
             bedrock_runtime_exceptions.ModelErrorException,
             bedrock_runtime_exceptions.ValidationException,
         ),
-        max_time=5,
+        max_time=2,
     )
-    def call(self, api_kwargs: Dict = {}, model_type: ModelType = ModelType.UNDEFINED):
+    def call(
+        self,
+        api_kwargs: Dict = {},
+        model_type: ModelType = ModelType.UNDEFINED,
+    ) -> dict:
         """
         kwargs is the combined input and model_kwargs
         """
         if model_type == ModelType.LLM:
-            return self.sync_client.converse(**api_kwargs)
+            if "stream" in api_kwargs and api_kwargs.get("stream", False):
+                log.debug("Streaming call")
+                api_kwargs.pop(
+                    "stream", None
+                )  # stream is not a valid parameter for bedrock
+                self.chat_completion_parser = self.handle_stream_response
+                return self.sync_client.converse_stream(**api_kwargs)
+            else:
+                api_kwargs.pop("stream", None)
+                return self.sync_client.converse(**api_kwargs)
         else:
             raise ValueError(f"model_type {model_type} is not supported")
 
diff --git a/adalflow/adalflow/components/model_client/deepseek_client.py b/adalflow/adalflow/components/model_client/deepseek_client.py
new file mode 100644
index 000000000..64e85a00d
--- /dev/null
+++ b/adalflow/adalflow/components/model_client/deepseek_client.py
@@ -0,0 +1,72 @@
+import os
+import logging
+import backoff
+from typing import (
+    Dict,
+    Sequence,
+    Optional,
+    List,
+    Any,
+    TypeVar,
+    Callable,
+    Literal,
+)
+
+from adalflow.utils.lazy_import import safe_import, OptionalPackages
+from adalflow.components.model_client.openai_client import OpenAIClient
+from openai.types import Completion
+
+openai = safe_import(OptionalPackages.OPENAI.value[0], OptionalPackages.OPENAI.value[1])
+
+class DeepSeekClient(OpenAIClient):
+    """
+    A component wrapper for the DeepSeek API client.
+
+    DeepSeek's API is compatible with OpenAI's API, making it possible to use OpenAI SDKs 
+    or OpenAI-compatible software with DeepSeek by adjusting the API base URL.
+
+    This client extends `OpenAIClient` but modifies the default `base_url` to use DeepSeek's API.
+
+    Documentation: https://api-docs.deepseek.com/guides/reasoning_model
+
+    Args:
+        api_key (Optional[str], optional): DeepSeek API key. Defaults to `None`.
+        chat_completion_parser (Callable[[Completion], Any], optional): A function to parse API responses.
+        input_type (Literal["text", "messages"], optional): Defines how input is handled. Defaults to `"text"`.
+        base_url (str, optional): API base URL, defaults to `"https://api.deepseek.com/v1/"`.
+    """
+
+    def __init__(
+        self,
+        api_key: Optional[str] = None,
+        chat_completion_parser: Callable[[Completion], Any] = None,
+        input_type: Literal["text", "messages"] = "messages",
+        base_url: str = "https://api.deepseek.com/v1/",
+        env_api_key_name: str = "DEEPSEEK_API_KEY"
+    ):
+        """Initializes DeepSeek API client with the correct base URL. The input_type is set to "messages" by default to be compatible with DeepSeek reasoner. 
+        """
+        super().__init__(api_key=api_key, chat_completion_parser=chat_completion_parser, input_type=input_type, base_url=base_url, env_api_key_name=env_api_key_name)
+
+# Example usage:
+if __name__ == "__main__":
+    from adalflow.core import Generator
+    from adalflow.utils import setup_env, get_logger
+
+    log = get_logger(level="DEBUG")
+
+    prompt_kwargs = {"input_str": "What is the meaning of life?"}
+
+    setup_env()
+
+    gen = Generator(
+        model_client=DeepSeekClient(),
+        model_kwargs={"model": "deepseek-reasoner", "stream": True},
+    )
+    
+    gen_response = gen(prompt_kwargs)
+    print(f"gen_response: {gen_response}")
+
+    for genout in gen_response.data:
+        print(f"genout: {genout}")
+
diff --git a/adalflow/adalflow/components/model_client/openai_client.py b/adalflow/adalflow/components/model_client/openai_client.py
index c3750667d..ba767ecc3 100644
--- a/adalflow/adalflow/components/model_client/openai_client.py
+++ b/adalflow/adalflow/components/model_client/openai_client.py
@@ -64,6 +64,23 @@ def get_first_message_content(completion: ChatCompletion) -> str:
 # def _get_chat_completion_usage(completion: ChatCompletion) -> OpenAICompletionUsage:
 #     return completion.usage
 
+# A simple heuristic to estimate token count for estimating number of tokens in a Streaming response
+def estimate_token_count(text: str) -> int:
+    """
+    Estimate the token count of a given text.
+
+    Args:
+        text (str): The text to estimate token count for.
+
+    Returns:
+        int: Estimated token count.
+    """
+    # Split the text into tokens using spaces as a simple heuristic
+    tokens = text.split()
+
+    # Return the number of tokens
+    return len(tokens)
+
 
 def parse_stream_response(completion: ChatCompletionChunk) -> str:
     r"""Parse the response of the stream API."""
@@ -101,72 +118,83 @@ def get_probabilities(completion: ChatCompletion) -> List[List[TokenLogProb]]:
 class OpenAIClient(ModelClient):
     __doc__ = r"""A component wrapper for the OpenAI API client.
 
-    Support both embedding and chat completion API, including multimodal capabilities.
+    Supports both embedding and chat completion APIs, including multimodal capabilities.
 
-    Users (1) simplify use ``Embedder`` and ``Generator`` components by passing OpenAIClient() as the model_client.
-    (2) can use this as an example to create their own API client or extend this class(copying and modifing the code) in their own project.
+    Users can:
+    1. Simplify use of ``Embedder`` and ``Generator`` components by passing `OpenAIClient()` as the `model_client`.
+    2. Use this as a reference to create their own API client or extend this class by copying and modifying the code.
 
     Note:
-        We suggest users not to use `response_format` to enforce output data type or `tools` and `tool_choice`  in your model_kwargs when calling the API.
-        We do not know how OpenAI is doing the formating or what prompt they have added.
-        Instead
-        - use :ref:`OutputParser<components-output_parsers>` for response parsing and formating.
+        We recommend avoiding `response_format` to enforce output data type or `tools` and `tool_choice` in `model_kwargs` when calling the API.
+        OpenAI's internal formatting and added prompts are unknown. Instead:
+        - Use :ref:`OutputParser<components-output_parsers>` for response parsing and formatting.
 
-        For multimodal inputs, provide images in model_kwargs["images"] as a path, URL, or list of them.
-        The model must support vision capabilities (e.g., gpt-4o, gpt-4o-mini, o1, o1-mini).
+        For multimodal inputs, provide images in `model_kwargs["images"]` as a path, URL, or list of them.
+        The model must support vision capabilities (e.g., `gpt-4o`, `gpt-4o-mini`, `o1`, `o1-mini`).
 
-        For image generation, use model_type=ModelType.IMAGE_GENERATION and provide:
-        - model: "dall-e-3" or "dall-e-2"
+        For image generation, use `model_type=ModelType.IMAGE_GENERATION` and provide:
+        - model: `"dall-e-3"` or `"dall-e-2"`
         - prompt: Text description of the image to generate
-        - size: "1024x1024", "1024x1792", or "1792x1024" for DALL-E 3; "256x256", "512x512", or "1024x1024" for DALL-E 2
-        - quality: "standard" or "hd" (DALL-E 3 only)
+        - size: `"1024x1024"`, `"1024x1792"`, or `"1792x1024"` for DALL-E 3; `"256x256"`, `"512x512"`, or `"1024x1024"` for DALL-E 2
+        - quality: `"standard"` or `"hd"` (DALL-E 3 only)
         - n: Number of images to generate (1 for DALL-E 3, 1-10 for DALL-E 2)
-        - response_format: "url" or "b64_json"
+        - response_format: `"url"` or `"b64_json"`
 
     Args:
-        api_key (Optional[str], optional): OpenAI API key. Defaults to None.
-        chat_completion_parser (Callable[[Completion], Any], optional): A function to parse the chat completion to a str. Defaults to None.
-            Default is `get_first_message_content`.
-
+        api_key (Optional[str], optional): OpenAI API key. Defaults to `None`.
+        chat_completion_parser (Callable[[Completion], Any], optional): A function to parse the chat completion into a `str`. Defaults to `None`.
+            The default parser is `get_first_message_content`.
+        base_url (str): The API base URL to use when initializing the client.
+            Defaults to `"https://api.openai.com"`, but can be customized for third-party API providers or self-hosted models.
+        env_api_key_name (str): The environment variable name for the API key. Defaults to `"OPENAI_API_KEY"`.
+        
     References:
-        - Embeddings models: https://platform.openai.com/docs/guides/embeddings
-        - Chat models: https://platform.openai.com/docs/guides/text-generation
-        - Vision models: https://platform.openai.com/docs/guides/vision
-        - Image models: https://platform.openai.com/docs/guides/images
-        - OpenAI docs: https://platform.openai.com/docs/introduction
+        - OpenAI API Overview: https://platform.openai.com/docs/introduction
+        - Embeddings Guide: https://platform.openai.com/docs/guides/embeddings
+        - Chat Completion Models: https://platform.openai.com/docs/guides/text-generation
+        - Vision Models: https://platform.openai.com/docs/guides/vision
+        - Image Generation: https://platform.openai.com/docs/guides/images
     """
 
+
     def __init__(
         self,
         api_key: Optional[str] = None,
         chat_completion_parser: Callable[[Completion], Any] = None,
         input_type: Literal["text", "messages"] = "text",
+        base_url: str = "https://api.openai.com/v1/", 
+        env_api_key_name: str = "OPENAI_API_KEY"
     ):
         r"""It is recommended to set the OPENAI_API_KEY environment variable instead of passing it as an argument.
 
         Args:
             api_key (Optional[str], optional): OpenAI API key. Defaults to None.
+            base_url (str): The API base URL to use when initializing the client.
+            env_api_key_name (str): The environment variable name for the API key. Defaults to `"OPENAI_API_KEY"`.
         """
         super().__init__()
         self._api_key = api_key
+        self._env_api_key_name = env_api_key_name
+        self.base_url = base_url
         self.sync_client = self.init_sync_client()
         self.async_client = None  # only initialize if the async call is called
         self.chat_completion_parser = (
             chat_completion_parser or get_first_message_content
         )
         self._input_type = input_type
+        self._api_kwargs = {} # add api kwargs when the OpenAI Client is called
 
     def init_sync_client(self):
-        api_key = self._api_key or os.getenv("OPENAI_API_KEY")
+        api_key = self._api_key or os.getenv(self._env_api_key_name)
         if not api_key:
-            raise ValueError("Environment variable OPENAI_API_KEY must be set")
-        return OpenAI(api_key=api_key)
+            raise ValueError(f"Environment variable {self._env_api_key_name} must be set")
+        return OpenAI(api_key=api_key, base_url=self.base_url)
 
     def init_async_client(self):
-        api_key = self._api_key or os.getenv("OPENAI_API_KEY")
+        api_key = self._api_key or os.getenv(self._env_api_key_name)
         if not api_key:
-            raise ValueError("Environment variable OPENAI_API_KEY must be set")
-        return AsyncOpenAI(api_key=api_key)
+            raise ValueError(f"Environment variable {self._env_api_key_name} must be set")
+        return AsyncOpenAI(api_key=api_key, base_url=self.base_url)
 
     # def _parse_chat_completion(self, completion: ChatCompletion) -> "GeneratorOutput":
     #     # TODO: raw output it is better to save the whole completion as a source of truth instead of just the message
@@ -207,10 +235,34 @@ def track_completion_usage(
                 total_tokens=completion.usage.total_tokens,
             )
             return usage
-        else:
-            raise NotImplementedError(
-                "streaming completion usage tracking is not implemented"
+        elif isinstance(completion, Stream):
+            # Streaming response
+            completion_tokens = 0
+            prompt_tokens = 0
+
+            for message in self._api_kwargs.get("messages", []):
+                # add to prompt_tokens if the message role is 'system' which contains system prompt 
+                if message.get("role") == "system": 
+                    content = message.get("content", '') 
+                    prompt_tokens += estimate_token_count(content)
+                    break 
+
+            for chunk in completion:
+                if hasattr(chunk.choices[0].delta, "content"):
+                    # Estimate token count from streamed content
+                    completion_tokens += estimate_token_count(parse_stream_response(chunk))
+            # Since prompt tokens are known in advance, retrieve from model kwargs or a known value
+            total_tokens = prompt_tokens + completion_tokens
+
+            usage: CompletionUsage = CompletionUsage(
+                completion_tokens=completion_tokens,
+                prompt_tokens=prompt_tokens,
+                total_tokens=total_tokens,
             )
+            return usage
+
+        else:
+            raise ValueError(f"Unsupported completion type: {type(completion)}")
 
     def parse_embedding_response(
         self, response: CreateEmbeddingResponse
@@ -268,11 +320,19 @@ def convert_inputs_to_api_kwargs(
                 system_end_tag = "<END_OF_SYSTEM_PROMPT>"
                 user_start_tag = "<START_OF_USER_PROMPT>"
                 user_end_tag = "<END_OF_USER_PROMPT>"
-                pattern = f"{system_start_tag}(.*?){system_end_tag}{user_start_tag}(.*?){user_end_tag}"
+                
+                # new regex pattern to ignore special characters such as \n 
+                pattern = (
+                    rf"{system_start_tag}\s*(.*?)\s*{system_end_tag}\s*"
+                    rf"{user_start_tag}\s*(.*?)\s*{user_end_tag}"
+                )
+
                 # Compile the regular expression
-                regex = re.compile(pattern)
+                
+                # re.DOTALL is to allow . to match newline so that (.*?) does not match in a single line 
+                regex = re.compile(pattern, re.DOTALL)
                 # Match the pattern
-                match = regex.search(input)
+                match = regex.match(input)
                 system_prompt, input_str = None, None
 
                 if match:
@@ -310,20 +370,27 @@ def convert_inputs_to_api_kwargs(
                 raise ValueError("model must be specified for image generation")
             # Set defaults for DALL-E 3 if not specified
             final_model_kwargs["size"] = final_model_kwargs.get("size", "1024x1024")
-            final_model_kwargs["quality"] = final_model_kwargs.get("quality", "standard")
+            final_model_kwargs["quality"] = final_model_kwargs.get(
+                "quality", "standard"
+            )
             final_model_kwargs["n"] = final_model_kwargs.get("n", 1)
-            final_model_kwargs["response_format"] = final_model_kwargs.get("response_format", "url")
+            final_model_kwargs["response_format"] = final_model_kwargs.get(
+                "response_format", "url"
+            )
 
             # Handle image edits and variations
             image = final_model_kwargs.get("image")
             if isinstance(image, str) and os.path.isfile(image):
                 final_model_kwargs["image"] = self._encode_image(image)
-            
+
             mask = final_model_kwargs.get("mask")
             if isinstance(mask, str) and os.path.isfile(mask):
                 final_model_kwargs["mask"] = self._encode_image(mask)
         else:
             raise ValueError(f"model_type {model_type} is not supported")
+        
+        print(f"final_model_kwargs: {final_model_kwargs}")
+
         return final_model_kwargs
 
     def parse_image_generation_response(self, response: List[Image]) -> GeneratorOutput:
@@ -340,11 +407,7 @@ def parse_image_generation_response(self, response: List[Image]) -> GeneratorOut
             )
         except Exception as e:
             log.error(f"Error parsing image generation response: {e}")
-            return GeneratorOutput(
-                data=None,
-                error=str(e),
-                raw_response=str(response)
-            )
+            return GeneratorOutput(data=None, error=str(e), raw_response=str(response))
 
     @backoff.on_exception(
         backoff.expo,
@@ -362,6 +425,7 @@ def call(self, api_kwargs: Dict = {}, model_type: ModelType = ModelType.UNDEFINE
         kwargs is the combined input and model_kwargs.  Support streaming call.
         """
         log.info(f"api_kwargs: {api_kwargs}")
+        self._api_kwargs = api_kwargs
         if model_type == ModelType.EMBEDDER:
             return self.sync_client.embeddings.create(**api_kwargs)
         elif model_type == ModelType.LLM:
@@ -403,6 +467,8 @@ async def acall(
         """
         kwargs is the combined input and model_kwargs
         """
+        # store the api kwargs in the client 
+        self._api_kwargs = api_kwargs
         if self.async_client is None:
             self.async_client = self.init_async_client()
         if model_type == ModelType.EMBEDDER:
@@ -417,7 +483,9 @@ async def acall(
                     response = await self.async_client.images.edit(**api_kwargs)
                 else:
                     # Image variation
-                    response = await self.async_client.images.create_variation(**api_kwargs)
+                    response = await self.async_client.images.create_variation(
+                        **api_kwargs
+                    )
             else:
                 # Image generation
                 response = await self.async_client.images.generate(**api_kwargs)
@@ -496,21 +564,21 @@ def _prepare_image_content(
 
 
 # Example usage:
-# if __name__ == "__main__":
-#     from adalflow.core import Generator
-#     from adalflow.utils import setup_env, get_logger
-#
-#     log = get_logger(level="DEBUG")
-#
-#     setup_env()
-#     prompt_kwargs = {"input_str": "What is the meaning of life?"}
-#
-#     gen = Generator(
-#         model_client=OpenAIClient(),
-#         model_kwargs={"model": "gpt-3.5-turbo", "stream": True},
-#     )
-#     gen_response = gen(prompt_kwargs)
-#     print(f"gen_response: {gen_response}")
-#
-#     for genout in gen_response.data:
-#         print(f"genout: {genout}")
+if __name__ == "__main__":
+    from adalflow.core import Generator
+    from adalflow.utils import setup_env, get_logger
+
+    log = get_logger(level="DEBUG")
+
+    setup_env()
+    prompt_kwargs = {"input_str": "What is the meaning of life?"}
+
+    gen = Generator(
+        model_client=OpenAIClient(),
+        model_kwargs={"model": "gpt-3.5-turbo", "stream": True},
+    )
+    gen_response = gen(prompt_kwargs)
+    print(f"gen_response: {gen_response}")
+
+    for genout in gen_response.data:
+        print(f"genout: {genout}")
diff --git a/adalflow/adalflow/utils/lazy_import.py b/adalflow/adalflow/utils/lazy_import.py
index 16ad8d1f0..b1efbde3f 100644
--- a/adalflow/adalflow/utils/lazy_import.py
+++ b/adalflow/adalflow/utils/lazy_import.py
@@ -42,6 +42,7 @@ class OptionalPackages(Enum):
     # model sdk
     GROQ = ("groq", "Please install groq with: pip install groq")
     OPENAI = ("openai", "Please install openai with: pip install openai")
+    DEEPSEEK = ("openai", "Please install deepseek with: pip install openai")
     ANTHROPIC = ("anthropic", "Please install anthropic with: pip install anthropic")
     GOOGLE_GENERATIVEAI = (
         "google.generativeai",
diff --git a/adalflow/poetry.lock b/adalflow/poetry.lock
index 92d3c9cbd..a9e93bd0d 100644
--- a/adalflow/poetry.lock
+++ b/adalflow/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand.
 
 [[package]]
 name = "absl-py"
@@ -24,87 +24,87 @@ files = [
 
 [[package]]
 name = "aiohttp"
-version = "3.11.10"
+version = "3.11.11"
 description = "Async http client/server framework (asyncio)"
 optional = false
 python-versions = ">=3.9"
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 [package.dependencies]
@@ -122,13 +122,13 @@ speedups = ["Brotli", "aiodns (>=3.2.0)", "brotlicffi"]
 
 [[package]]
 name = "aiosignal"
-version = "1.3.1"
+version = "1.3.2"
 description = "aiosignal: a list of registered asynchronous callbacks"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.9"
 files = [
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 ]
 
 [package.dependencies]
@@ -172,13 +172,13 @@ vertex = ["google-auth (>=2,<3)"]
 
 [[package]]
 name = "anyio"
-version = "4.7.0"
+version = "4.8.0"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
 python-versions = ">=3.9"
 files = [
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 ]
 
 [package.dependencies]
@@ -189,7 +189,7 @@ typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""}
 
 [package.extras]
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-test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21)"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21)"]
 trio = ["trio (>=0.26.1)"]
 
 [[package]]
@@ -220,19 +220,19 @@ files = [
 
 [[package]]
 name = "attrs"
-version = "24.2.0"
+version = "24.3.0"
 description = "Classes Without Boilerplate"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
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 docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier (<24.7)"]
 tests = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
 tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"]
@@ -287,32 +287,32 @@ files = [
 
 [[package]]
 name = "boto3"
-version = "1.35.80"
+version = "1.36.0"
 description = "The AWS SDK for Python"
-optional = true
+optional = false
 python-versions = ">=3.8"
 files = [
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+botocore = ">=1.36.0,<1.37.0"
 jmespath = ">=0.7.1,<2.0.0"
-s3transfer = ">=0.10.0,<0.11.0"
+s3transfer = ">=0.11.0,<0.12.0"
 
 [package.extras]
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 [[package]]
 name = "botocore"
-version = "1.35.80"
+version = "1.36.0"
 description = "Low-level, data-driven core of boto 3."
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 python-versions = ">=3.8"
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 [package.dependencies]
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 ]
 
 [package.extras]
-crt = ["awscrt (==0.22.0)"]
+crt = ["awscrt (==0.23.4)"]
 
 [[package]]
 name = "cachetools"
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 name = "certifi"
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+version = "2024.12.14"
 description = "Python package for providing Mozilla's CA Bundle."
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+version = "3.4.1"
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 ]
 
 [[package]]
 name = "click"
-version = "8.1.7"
+version = "8.1.8"
 description = "Composable command line interface toolkit"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -568,20 +555,19 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
 [[package]]
 name = "cohere"
-version = "5.13.3"
+version = "5.13.8"
 description = ""
 optional = true
 python-versions = "<4.0,>=3.9"
 files = [
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 ]
 
 [package.dependencies]
 fastavro = ">=1.9.4,<2.0.0"
 httpx = ">=0.21.2"
 httpx-sse = "0.4.0"
-numpy = ">=1.26,<2.0"
 parameterized = ">=0.9.0,<0.10.0"
 pydantic = ">=1.9.2"
 pydantic-core = ">=2.18.2,<3.0.0"
@@ -590,9 +576,6 @@ tokenizers = ">=0.15,<1"
 types-requests = ">=2.0.0,<3.0.0"
 typing_extensions = ">=4.0.0"
 
-[package.extras]
-aws = ["boto3 (>=1.34.0,<2.0.0)", "sagemaker (>=2.232.1,<3.0.0)"]
-
 [[package]]
 name = "colorama"
 version = "0.4.6"
@@ -938,42 +921,42 @@ packaging = "*"
 
 [[package]]
 name = "fastavro"
-version = "1.9.7"
+version = "1.10.0"
 description = "Fast read/write of AVRO files"
 optional = true
-python-versions = ">=3.8"
+python-versions = ">=3.9"
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-protobuf = ["grpcio-tools (>=1.68.1)"]
+protobuf = ["grpcio-tools (>=1.69.0)"]
 
 [[package]]
 name = "grpcio-status"
@@ -1651,13 +1634,13 @@ files = [
 
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 [[package]]
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 [package.dependencies]
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 python-dateutil = ">=2.7"
 
 [package.extras]
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 [[package]]
 name = "matplotlib-inline"
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 [package.extras]
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 [package.extras]
 docs = ["furo", "olefile", "sphinx (>=8.1)", "sphinx-copybutton", "sphinx-inline-tabs", "sphinxext-opengraph"]
 fpx = ["olefile"]
 mic = ["olefile"]
-tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
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 typing = ["typing-extensions"]
 xmp = ["defusedxml"]
 
@@ -3317,53 +3312,53 @@ files = [
 
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 [[package]]
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@@ -3597,13 +3592,13 @@ torch = ["torch"]
 
 [[package]]
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 [[package]]
 name = "six"
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-    {file = "tiktoken-0.7.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:8f5f6afb52fb8a7ea1c811e435e4188f2bef81b5e0f7a8635cc79b0eef0193d6"},
-    {file = "tiktoken-0.7.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:861f9ee616766d736be4147abac500732b505bf7013cfaf019b85892637f235e"},
-    {file = "tiktoken-0.7.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:54031f95c6939f6b78122c0aa03a93273a96365103793a22e1793ee86da31685"},
-    {file = "tiktoken-0.7.0-cp38-cp38-musllinux_1_2_aarch64.whl", hash = "sha256:fffdcb319b614cf14f04d02a52e26b1d1ae14a570f90e9b55461a72672f7b13d"},
-    {file = "tiktoken-0.7.0-cp38-cp38-musllinux_1_2_x86_64.whl", hash = "sha256:c72baaeaefa03ff9ba9688624143c858d1f6b755bb85d456d59e529e17234769"},
-    {file = "tiktoken-0.7.0-cp38-cp38-win_amd64.whl", hash = "sha256:131b8aeb043a8f112aad9f46011dced25d62629091e51d9dc1adbf4a1cc6aa98"},
-    {file = "tiktoken-0.7.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:cabc6dc77460df44ec5b879e68692c63551ae4fae7460dd4ff17181df75f1db7"},
-    {file = "tiktoken-0.7.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:8d57f29171255f74c0aeacd0651e29aa47dff6f070cb9f35ebc14c82278f3b25"},
-    {file = "tiktoken-0.7.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2ee92776fdbb3efa02a83f968c19d4997a55c8e9ce7be821ceee04a1d1ee149c"},
-    {file = "tiktoken-0.7.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e215292e99cb41fbc96988ef62ea63bb0ce1e15f2c147a61acc319f8b4cbe5bf"},
-    {file = "tiktoken-0.7.0-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:8a81bac94769cab437dd3ab0b8a4bc4e0f9cf6835bcaa88de71f39af1791727a"},
-    {file = "tiktoken-0.7.0-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:d6d73ea93e91d5ca771256dfc9d1d29f5a554b83821a1dc0891987636e0ae226"},
-    {file = "tiktoken-0.7.0-cp39-cp39-win_amd64.whl", hash = "sha256:2bcb28ddf79ffa424f171dfeef9a4daff61a94c631ca6813f43967cb263b83b9"},
-    {file = "tiktoken-0.7.0.tar.gz", hash = "sha256:1077266e949c24e0291f6c350433c6f0971365ece2b173a23bc3b9f9defef6b6"},
+    {file = "tiktoken-0.8.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:b07e33283463089c81ef1467180e3e00ab00d46c2c4bbcef0acab5f771d6695e"},
+    {file = "tiktoken-0.8.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:9269348cb650726f44dd3bbb3f9110ac19a8dcc8f54949ad3ef652ca22a38e21"},
+    {file = "tiktoken-0.8.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:25e13f37bc4ef2d012731e93e0fef21dc3b7aea5bb9009618de9a4026844e560"},
+    {file = "tiktoken-0.8.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f13d13c981511331eac0d01a59b5df7c0d4060a8be1e378672822213da51e0a2"},
+    {file = "tiktoken-0.8.0-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:6b2ddbc79a22621ce8b1166afa9f9a888a664a579350dc7c09346a3b5de837d9"},
+    {file = "tiktoken-0.8.0-cp310-cp310-win_amd64.whl", hash = "sha256:d8c2d0e5ba6453a290b86cd65fc51fedf247e1ba170191715b049dac1f628005"},
+    {file = "tiktoken-0.8.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:d622d8011e6d6f239297efa42a2657043aaed06c4f68833550cac9e9bc723ef1"},
+    {file = "tiktoken-0.8.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:2efaf6199717b4485031b4d6edb94075e4d79177a172f38dd934d911b588d54a"},
+    {file = "tiktoken-0.8.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5637e425ce1fc49cf716d88df3092048359a4b3bbb7da762840426e937ada06d"},
+    {file = "tiktoken-0.8.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9fb0e352d1dbe15aba082883058b3cce9e48d33101bdaac1eccf66424feb5b47"},
+    {file = "tiktoken-0.8.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:56edfefe896c8f10aba372ab5706b9e3558e78db39dd497c940b47bf228bc419"},
+    {file = "tiktoken-0.8.0-cp311-cp311-win_amd64.whl", hash = "sha256:326624128590def898775b722ccc327e90b073714227175ea8febbc920ac0a99"},
+    {file = "tiktoken-0.8.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:881839cfeae051b3628d9823b2e56b5cc93a9e2efb435f4cf15f17dc45f21586"},
+    {file = "tiktoken-0.8.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:fe9399bdc3f29d428f16a2f86c3c8ec20be3eac5f53693ce4980371c3245729b"},
+    {file = "tiktoken-0.8.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9a58deb7075d5b69237a3ff4bb51a726670419db6ea62bdcd8bd80c78497d7ab"},
+    {file = "tiktoken-0.8.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d2908c0d043a7d03ebd80347266b0e58440bdef5564f84f4d29fb235b5df3b04"},
+    {file = "tiktoken-0.8.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:294440d21a2a51e12d4238e68a5972095534fe9878be57d905c476017bff99fc"},
+    {file = "tiktoken-0.8.0-cp312-cp312-win_amd64.whl", hash = "sha256:d8f3192733ac4d77977432947d563d7e1b310b96497acd3c196c9bddb36ed9db"},
+    {file = "tiktoken-0.8.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:02be1666096aff7da6cbd7cdaa8e7917bfed3467cd64b38b1f112e96d3b06a24"},
+    {file = "tiktoken-0.8.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:c94ff53c5c74b535b2cbf431d907fc13c678bbd009ee633a2aca269a04389f9a"},
+    {file = "tiktoken-0.8.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6b231f5e8982c245ee3065cd84a4712d64692348bc609d84467c57b4b72dcbc5"},
+    {file = "tiktoken-0.8.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4177faa809bd55f699e88c96d9bb4635d22e3f59d635ba6fd9ffedf7150b9953"},
+    {file = "tiktoken-0.8.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:5376b6f8dc4753cd81ead935c5f518fa0fbe7e133d9e25f648d8c4dabdd4bad7"},
+    {file = "tiktoken-0.8.0-cp313-cp313-win_amd64.whl", hash = "sha256:18228d624807d66c87acd8f25fc135665617cab220671eb65b50f5d70fa51f69"},
+    {file = "tiktoken-0.8.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:7e17807445f0cf1f25771c9d86496bd8b5c376f7419912519699f3cc4dc5c12e"},
+    {file = "tiktoken-0.8.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:886f80bd339578bbdba6ed6d0567a0d5c6cfe198d9e587ba6c447654c65b8edc"},
+    {file = "tiktoken-0.8.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6adc8323016d7758d6de7313527f755b0fc6c72985b7d9291be5d96d73ecd1e1"},
+    {file = "tiktoken-0.8.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b591fb2b30d6a72121a80be24ec7a0e9eb51c5500ddc7e4c2496516dd5e3816b"},
+    {file = "tiktoken-0.8.0-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:845287b9798e476b4d762c3ebda5102be87ca26e5d2c9854002825d60cdb815d"},
+    {file = "tiktoken-0.8.0-cp39-cp39-win_amd64.whl", hash = "sha256:1473cfe584252dc3fa62adceb5b1c763c1874e04511b197da4e6de51d6ce5a02"},
+    {file = "tiktoken-0.8.0.tar.gz", hash = "sha256:9ccbb2740f24542534369c5635cfd9b2b3c2490754a78ac8831d99f89f94eeb2"},
 ]
 
 [package.dependencies]
@@ -4447,13 +4437,13 @@ tutorials = ["matplotlib", "pandas", "tabulate"]
 
 [[package]]
 name = "types-pyyaml"
-version = "6.0.12.20240917"
+version = "6.0.12.20241230"
 description = "Typing stubs for PyYAML"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "types-PyYAML-6.0.12.20240917.tar.gz", hash = "sha256:d1405a86f9576682234ef83bcb4e6fff7c9305c8b1fbad5e0bcd4f7dbdc9c587"},
-    {file = "types_PyYAML-6.0.12.20240917-py3-none-any.whl", hash = "sha256:392b267f1c0fe6022952462bf5d6523f31e37f6cea49b14cee7ad634b6301570"},
+    {file = "types_PyYAML-6.0.12.20241230-py3-none-any.whl", hash = "sha256:fa4d32565219b68e6dee5f67534c722e53c00d1cfc09c435ef04d7353e1e96e6"},
+    {file = "types_pyyaml-6.0.12.20241230.tar.gz", hash = "sha256:7f07622dbd34bb9c8b264fe860a17e0efcad00d50b5f27e93984909d9363498c"},
 ]
 
 [[package]]
@@ -4486,13 +4476,13 @@ urllib3 = ">=2"
 
 [[package]]
 name = "types-tqdm"
-version = "4.67.0.20241119"
+version = "4.67.0.20241221"
 description = "Typing stubs for tqdm"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "types-tqdm-4.67.0.20241119.tar.gz", hash = "sha256:1769e0e94d5e6d8fa814965f9cf3d9928376dd15dabcbcb784bb8769081092b4"},
-    {file = "types_tqdm-4.67.0.20241119-py3-none-any.whl", hash = "sha256:a18d4eb62db0d35c52707ae13d821b5a57970755273ecb56e133ccc0ac7e7c79"},
+    {file = "types_tqdm-4.67.0.20241221-py3-none-any.whl", hash = "sha256:a1f1c9cda5c2d8482d2c73957a5398bfdedda10f6bc7b3b4e812d5c910486d29"},
+    {file = "types_tqdm-4.67.0.20241221.tar.gz", hash = "sha256:e56046631056922385abe89aeb18af5611f471eadd7918a0ad7f34d84cd4c8cc"},
 ]
 
 [package.dependencies]
@@ -4560,13 +4550,13 @@ socks = ["PySocks (>=1.5.6,!=1.5.7,<2.0)"]
 
 [[package]]
 name = "urllib3"
-version = "2.2.3"
+version = "2.3.0"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "urllib3-2.2.3-py3-none-any.whl", hash = "sha256:ca899ca043dcb1bafa3e262d73aa25c465bfb49e0bd9dd5d59f1d0acba2f8fac"},
-    {file = "urllib3-2.2.3.tar.gz", hash = "sha256:e7d814a81dad81e6caf2ec9fdedb284ecc9c73076b62654547cc64ccdcae26e9"},
+    {file = "urllib3-2.3.0-py3-none-any.whl", hash = "sha256:1cee9ad369867bfdbbb48b7dd50374c0967a0bb7710050facf0dd6911440e3df"},
+    {file = "urllib3-2.3.0.tar.gz", hash = "sha256:f8c5449b3cf0861679ce7e0503c7b44b5ec981bec0d1d3795a07f1ba96f0204d"},
 ]
 
 [package.extras]
@@ -4577,13 +4567,13 @@ zstd = ["zstandard (>=0.18.0)"]
 
 [[package]]
 name = "virtualenv"
-version = "20.28.0"
+version = "20.29.0"
 description = "Virtual Python Environment builder"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "virtualenv-20.28.0-py3-none-any.whl", hash = "sha256:23eae1b4516ecd610481eda647f3a7c09aea295055337331bb4e6892ecce47b0"},
-    {file = "virtualenv-20.28.0.tar.gz", hash = "sha256:2c9c3262bb8e7b87ea801d715fae4495e6032450c71d2309be9550e7364049aa"},
+    {file = "virtualenv-20.29.0-py3-none-any.whl", hash = "sha256:c12311863497992dc4b8644f8ea82d3b35bb7ef8ee82e6630d76d0197c39baf9"},
+    {file = "virtualenv-20.29.0.tar.gz", hash = "sha256:6345e1ff19d4b1296954cee076baaf58ff2a12a84a338c62b02eda39f20aa982"},
 ]
 
 [package.dependencies]
@@ -4889,4 +4879,4 @@ torch = ["torch"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9, <4.0"
-content-hash = "86d5f192585121c048dae33edff42527cf40f8a0398e1c3e5b60c1c8ab0af363"
+content-hash = "d824db4561b03ced4ab2c5d584a945b85ef26917d78d423e41cbe82b9fcb6e71"
diff --git a/adalflow/pyproject.toml b/adalflow/pyproject.toml
index 08947d81a..bbe78133d 100644
--- a/adalflow/pyproject.toml
+++ b/adalflow/pyproject.toml
@@ -1,14 +1,14 @@
 [tool.poetry]
 name = "adalflow"
 
-version = "0.2.6"
+version = "0.2.7"
 description = "The Library to Build and Auto-optimize LLM Applications"
 authors = ["Li Yin <li@sylphai.com>"]
 readme = "README.md"
 repository = "https://github.com/SylphAI-Inc/AdalFlow"
-
 license = "MIT"
-maintainers = ["Li Yin <li@sylphai.com>"]
+
+maintainers = ["Li Yin <li@sylphai.com>", "Filip Makraduli <filip@sylphai.com>"]
 classifiers = [
     "Topic :: Software Development :: Build Tools",
     "Topic :: Software Development :: Libraries :: Python Modules",
@@ -36,35 +36,30 @@ python-dotenv = "^1.0.1"
 backoff = "^2.2.1"
 jinja2 = "^3.1.3"
 jsonlines = "^4.0.0"
-tiktoken = "^0.7.0"
-numpy = "^1.26.4"
+tiktoken = ">=0.3.3"
+numpy = "*"
 tqdm = "^4.66.4"
-pyyaml = "^6.0.1"
+PyYAML = ">=6.0.1"
 nest-asyncio = "^1.6.0"
 colorama = "^0.4.6"
-botocore = "^1.34.149"   # if not specified, it will fail to resolve the dependency, we need to figure out a way to get rid of botocore dependency
 diskcache = "^5.6.3"
 
 
 # Optional dependencies
-openai = { version = "^1.12.0", optional = true }
-groq = { version = "^0.9.0", optional = true }
-faiss-cpu = { version = "^1.8.0", optional = true }
-sqlalchemy = { version = "^2.0.30", optional = true }
-pgvector = { version = "^0.3.1", optional = true }
-torch = { version = "^2.3.1", optional = true }
-anthropic = { version = "^0.31.1", optional = true }
-google-generativeai = { version = "^0.7.2", optional = true }
-cohere = { version = "^5.5.8", optional = true }
-ollama = { version = "^0.2.1", optional = true }
-lancedb = { version = "^0.5.2", optional = true }
-
-
+openai = { version = ">=1.12.0", optional = true }
+groq = { version = ">=0.9.0", optional = true }
+faiss-cpu = { version = ">=1.8.0", optional = true }
+sqlalchemy = { version = ">=2.0.30", optional = true }
+pgvector = { version = ">=0.3.1", optional = true }
+torch = { version = ">=2.3.1", optional = true }
+anthropic = { version = ">=0.31.1", optional = true }
+google-generativeai = { version = ">=0.7.2", optional = true }
+cohere = { version = ">=5.5.8", optional = true }
+ollama = { version = ">=0.2.1", optional = true }
+lancedb = { version = ">=0.5.2", optional = true }
 # Azure dependencies
-azure-core = { version = "^1.24.0", optional = true }
-azure-identity = { version = "^1.12.0", optional = true }
-# azure-ai-formrecognizer = { version = "^3.2.0", optional = true }
-# azure-ai-textanalytics = { version = "^5.3.0", optional = true }
+azure-core = { version = ">=1.24.0", optional = true }
+azure-identity = { version = ">=1.12.0", optional = true }
 
 # amazon bedrock
 boto3 = { version = "^1.35.19", optional = true }
@@ -80,7 +75,13 @@ groq = "^0.9.0"
 google-generativeai = "^0.7.2"
 anthropic = "^0.31.1"
 lancedb = "^0.5.2"
+boto3 = "^1.35.19"
 
+# TODO: cant make qdrant work here
+# qdrant_client = [
+#     { version = ">=1.12.2,<2.0.0", optional = true, markers = "python_version >= '3.10'" },
+#     { version = ">=1.8.0,<1.12.2", optional = true, markers = "python_version < '3.10'" },
+# ]
 
 
 [tool.poetry.group.typing.dependencies]
@@ -103,17 +104,14 @@ pyvis = "^0.3.2"
 datasets = "^2.21.0"
 
 [tool.poetry.extras] # allow pip install adalflow[openai, groq]
+
+# model providers
 openai = ["openai"]
 groq = ["groq"]
 anthropic = ["anthropic"]
 cohere = ["cohere"]
 google-generativeai = ["google-generativeai"]
-pgvector = ["pgvector"]
-faiss-cpu = ["faiss-cpu"]
-sqlalchemy = ["sqlalchemy"]
-torch = ["torch"]
 ollama = ["ollama"]
-lancedb = ["lancedb"]
 azure = [
     "azure-core",
     "azure-identity",
@@ -122,8 +120,22 @@ azure = [
 ]
 
 bedrock = ["boto3"]
+
+# vector dbs
+lancedb = ["lancedb"]
+pgvector = ["pgvector"]
+# qdrant = ["qdrant-client"]
+
 datasets = ["datasets"]
 
+# similarity search local
+faiss-cpu = ["faiss-cpu"]
+torch = ["torch"]
+
+# data modeling
+sqlalchemy = ["sqlalchemy"]
+
+
 [[tool.poetry.source]]
 name = "nvidia-pypi"
 priority = "supplemental"
diff --git a/adalflow/tests/test_aws_bedrock_client.py b/adalflow/tests/test_aws_bedrock_client.py
new file mode 100644
index 000000000..9f21682a3
--- /dev/null
+++ b/adalflow/tests/test_aws_bedrock_client.py
@@ -0,0 +1,139 @@
+import unittest
+from unittest.mock import Mock, patch
+from adalflow.core.types import ModelType, GeneratorOutput
+from adalflow.components.model_client import BedrockAPIClient
+
+
+class TestBedrockClient(unittest.TestCase):
+    def setUp(self) -> None:
+        """Set up mocks and test data.
+
+        Mocks the boto3 session and the init_sync_client method. Mocks will create a
+        mock bedrock client and mock responses that can be reused across tests.
+        """
+        self.session_patcher = patch(
+            "adalflow.components.model_client.bedrock_client.boto3.Session"
+        )
+        self.mock_session = self.session_patcher.start()
+        self.mock_boto3_client = Mock()
+        self.mock_session.return_value.client.return_value = self.mock_boto3_client
+        self.init_sync_patcher = patch.object(BedrockAPIClient, "init_sync_client")
+        self.mock_init_sync_client = self.init_sync_patcher.start()
+        self.mock_sync_client = Mock()
+        self.mock_init_sync_client.return_value = self.mock_sync_client
+        self.mock_sync_client.converse = Mock()
+        self.mock_sync_client.converse_stream = Mock()
+        self.client = BedrockAPIClient()
+        self.client.sync_client = self.mock_sync_client
+
+        self.mock_response = {
+            "ResponseMetadata": {
+                "RequestId": "43aec10a-9780-4bd5-abcc-857d12460569",
+                "HTTPStatusCode": 200,
+                "HTTPHeaders": {
+                    "date": "Sat, 30 Nov 2024 14:27:44 GMT",
+                    "content-type": "application/json",
+                    "content-length": "273",
+                    "connection": "keep-alive",
+                    "x-amzn-requestid": "43aec10a-9780-4bd5-abcc-857d12460569",
+                },
+                "RetryAttempts": 0,
+            },
+            "output": {
+                "message": {"role": "assistant", "content": [{"text": "Hello, world!"}]}
+            },
+            "stopReason": "end_turn",
+            "usage": {"inputTokens": 20, "outputTokens": 10, "totalTokens": 30},
+            "metrics": {"latencyMs": 430},
+        }
+        self.mock_stream_response = {
+            "ResponseMetadata": {
+                "RequestId": "c76d625e-9fdb-4173-8138-debdd724fc56",
+                "HTTPStatusCode": 200,
+                "HTTPHeaders": {
+                    "date": "Sun, 12 Jan 2025 15:10:00 GMT",
+                    "content-type": "application/vnd.amazon.eventstream",
+                    "transfer-encoding": "chunked",
+                    "connection": "keep-alive",
+                    "x-amzn-requestid": "c76d625e-9fdb-4173-8138-debdd724fc56",
+                },
+                "RetryAttempts": 0,
+            },
+            "stream": iter(()),
+        }
+        self.api_kwargs = {
+            "messages": [{"role": "user", "content": "Hello"}],
+            "model": "gpt-3.5-turbo",
+        }
+
+    def tearDown(self) -> None:
+        """Stop the patchers."""
+        self.init_sync_patcher.stop()
+
+    def test_call(self) -> None:
+        """Tests that the call method calls the converse method correctly."""
+        self.mock_sync_client.converse = Mock(return_value=self.mock_response)
+        self.mock_sync_client.converse_stream = Mock(return_value=self.mock_response)
+
+        result = self.client.call(api_kwargs=self.api_kwargs, model_type=ModelType.LLM)
+
+        # Assertions: converse is called once and stream is not called
+        self.mock_sync_client.converse.assert_called_once_with(**self.api_kwargs)
+        self.mock_sync_client.converse_stream.assert_not_called()
+        self.assertEqual(result, self.mock_response)
+
+    def test_parse_chat_completion(self) -> None:
+        """Tests that the parse_chat_completion method returns expected object."""
+        output = self.client.parse_chat_completion(completion=self.mock_response)
+        self.assertTrue(isinstance(output, GeneratorOutput))
+        self.assertEqual(output.raw_response, "Hello, world!")
+        self.assertEqual(output.usage.prompt_tokens, 20)
+        self.assertEqual(output.usage.completion_tokens, 10)
+        self.assertEqual(output.usage.total_tokens, 30)
+
+    def test_parse_chat_completion_call_usage(self) -> None:
+        """Test that the parse_chat_completion calls usage completion when not
+        streaming."""
+        mock_track_completion_usage = Mock()
+        self.client.track_completion_usage = mock_track_completion_usage
+        generator_output = self.client.parse_chat_completion(self.mock_response)
+
+        mock_track_completion_usage.assert_called_once()
+        assert isinstance(generator_output, GeneratorOutput)
+
+    def test_streaming_call(self) -> None:
+        """Test that a streaming call calls the converse_stream method."""
+        self.mock_sync_client.converse = Mock(return_value=self.mock_response)
+        self.mock_sync_client.converse_stream = Mock(return_value=self.mock_response)
+
+        # Call the call method.
+        stream_kwargs = self.api_kwargs | {"stream": True}
+        self.client.call(api_kwargs=stream_kwargs, model_type=ModelType.LLM)
+
+        # Assertions: Streaming method is called
+        self.mock_sync_client.converse_stream.assert_called_once_with(**stream_kwargs)
+        self.mock_sync_client.converse.assert_not_called()
+
+    def test_call_value_error(self) -> None:
+        """Test that a ValueError is raised when an invalid model_type is passed."""
+        with self.assertRaises(ValueError):
+            self.client.call(
+                api_kwargs={},
+                model_type=ModelType.UNDEFINED,  # This should trigger ValueError
+            )
+
+    def test_parse_streaming_chat_completion(self) -> None:
+        """Test that the parse_chat_completion does not call usage completion when
+        streaming."""
+        mock_track_completion_usage = Mock()
+        self.client.track_completion_usage = mock_track_completion_usage
+
+        self.client.chat_completion_parser = self.client.handle_stream_response
+        generator_output = self.client.parse_chat_completion(self.mock_stream_response)
+
+        mock_track_completion_usage.assert_not_called()
+        assert isinstance(generator_output, GeneratorOutput)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adalflow/tests/test_deepseek_client.py b/adalflow/tests/test_deepseek_client.py
new file mode 100644
index 000000000..24df9307b
--- /dev/null
+++ b/adalflow/tests/test_deepseek_client.py
@@ -0,0 +1,52 @@
+import unittest
+from unittest.mock import patch, Mock
+import os
+
+from openai import Stream 
+from openai.types import CompletionUsage
+from openai.types.chat import ChatCompletion, ChatCompletionChunk 
+from adalflow.core.types import ModelType, GeneratorOutput
+from adalflow.components.model_client.deepseek_client import DeepSeekClient 
+from unittest.mock import AsyncMock
+
+def getenv_side_effect(key):
+    env_vars = {"DEEPSEEK_API_KEY": "fake_api_key"}
+    return env_vars.get(key, None)
+
+# Mock the Stream object
+class MockStream(Stream):
+    def __init__(self, chunks):
+        self.chunks = iter(chunks)
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        return next(self.chunks)
+
+class TestDeepSeekClient(unittest.TestCase):
+    def setUp(self):
+        self.client = DeepSeekClient(api_key="fake_api_key")
+
+    def test_deepseek_init(self): 
+        self.assertEqual(self.client.base_url, "https://api.deepseek.com/v1/")
+        self.assertEqual(self.client._input_type, "messages")
+        self.assertEqual(self.client._env_api_key_name, "DEEPSEEK_API_KEY")
+
+    # mock os.getenv(self._env_api_key_name) with getenv_side_effect
+    @patch("os.getenv")
+    def test_deepseek_init_sync_client(self, mock_os_getenv):
+        mock_os_getenv.return_value = "fake_api_key"
+        self.client.init_sync_client()
+        self.assertEqual(self.client.sync_client.api_key, "fake_api_key")
+        self.assertEqual(self.client.sync_client.base_url, "https://api.deepseek.com/v1/")
+
+    @patch("os.getenv")
+    def test_deepseek_init_async_client(self, mock_os_getenv):
+        mock_os_getenv.return_value = "fake_api_key"
+        self.client.async_client = self.client.init_async_client()
+        self.assertEqual(self.client.async_client.api_key, "fake_api_key")
+        self.assertEqual(self.client.async_client.base_url, "https://api.deepseek.com/v1/") 
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/adalflow/tests/test_openai_client.py b/adalflow/tests/test_openai_client.py
index 9167c8200..823f3ec60 100644
--- a/adalflow/tests/test_openai_client.py
+++ b/adalflow/tests/test_openai_client.py
@@ -362,7 +362,7 @@ async def test_acall_image_generation(self, MockAsyncOpenAI):
 
         # Mock the image generation response
         mock_async_client.images.generate = AsyncMock(
-            return_value=type('Response', (), {'data': self.mock_image_response})()
+            return_value=type("Response", (), {"data": self.mock_image_response})()
         )
 
         # Call the acall method with image generation
@@ -394,7 +394,7 @@ def test_call_image_generation(self, MockSyncOpenAI, mock_init_sync_client):
 
         # Mock the image generation response
         mock_sync_client.images.generate = Mock(
-            return_value=type('Response', (), {'data': self.mock_image_response})()
+            return_value=type("Response", (), {"data": self.mock_image_response})()
         )
 
         # Set the sync client
diff --git a/docs/CHANGLOG.md b/docs/CHANGLOG.md
new file mode 100644
index 000000000..585596ab5
--- /dev/null
+++ b/docs/CHANGLOG.md
@@ -0,0 +1,3 @@
+## [0.2.7] - 2025-01-16
+
+- Added multimodal support in Generator tutorial and more explanation by Filip.
diff --git a/notebooks/tutorials/adalflow_classification_optimization.ipynb b/notebooks/tutorials/adalflow_classification_optimization.ipynb
index 363323878..0d3034f71 100644
--- a/notebooks/tutorials/adalflow_classification_optimization.ipynb
+++ b/notebooks/tutorials/adalflow_classification_optimization.ipynb
@@ -1,967 +1,968 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3"
+  },
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# 🤗 Welcome to AdalFlow!\n",
+    "## The PyTorch library to auto-optimize any LLM task pipelines\n",
+    "\n",
+    "Thanks for trying us out, we're here to provide you with the best LLM application development experience you can dream of 😊 any questions or concerns you may have, [come talk to us on discord,](https://discord.gg/ezzszrRZvT) we're always here to help! ⭐ <i>Star us on <a href=\"https://github.com/SylphAI-Inc/AdalFlow\">Github</a> </i> ⭐\n",
+    "\n",
+    "\n",
+    "# Quick Links\n",
+    "\n",
+    "Github repo: https://github.com/SylphAI-Inc/AdalFlow\n",
+    "\n",
+    "Full Tutorials: https://adalflow.sylph.ai/index.html#.\n",
+    "\n",
+    "Deep dive on each API: check out the [developer notes](https://adalflow.sylph.ai/tutorials/index.html).\n",
+    "\n",
+    "Common use cases along with the auto-optimization:  check out [Use cases](https://adalflow.sylph.ai/use_cases/index.html).\n",
+    "\n",
+    "## 📖 Outline\n",
+    "\n",
+    "This is the code for a classification optimization tutorial ![image.png](data:image/png;base64,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)\n"
+   ],
+   "metadata": {
+    "id": "xHF95Kr4CzGq"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "\n",
+    "# Installation\n",
+    "\n",
+    "1. Use `pip` to install the `adalflow` Python package. We will need `openai`, `groq` from the extra packages.\n",
+    "\n",
+    "  ```bash\n",
+    "  pip install adalflow[openai,groq]\n",
+    "  ```\n",
+    "2. Setup  `openai` and `groq` API key in the environment variables\n",
+    "\n",
+    "You can choose to use different client. You can import the model client you prefer. We support `Anthropic`, `Cohere`, `Google`, `GROQ`, `OpenAI`, `Transformer` and more in development. We will use OpenAI here as an example.Please refer to our [full installation guide](https://adalflow.sylph.ai/get_started/installation.html)"
+   ],
+   "metadata": {
+    "id": "Kof5M6DRaKhh"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "tAp3eDjOCma1"
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.display import clear_output\n",
+    "\n",
+    "!pip install -U adalflow[openai] # also install the package for the model client you'll use\n",
+    "!pip install datasets\n",
+    "clear_output()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "!pip uninstall httpx anyio -y\n",
+    "!pip install “anyio>=3.1.0,<4.0”\n",
+    "!pip install httpx==0.24.1"
+   ],
+   "metadata": {
     "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
+     "base_uri": "https://localhost:8080/"
     },
-    "language_info": {
-      "name": "python"
+    "id": "CU672Gt4bY7b",
+    "outputId": "532c84d2-c7bd-40ac-c050-e2c5dddc8946"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Found existing installation: httpx 0.28.1\n",
+      "Uninstalling httpx-0.28.1:\n",
+      "  Successfully uninstalled httpx-0.28.1\n",
+      "Found existing installation: anyio 3.7.1\n",
+      "Uninstalling anyio-3.7.1:\n",
+      "  Successfully uninstalled anyio-3.7.1\n",
+      "/bin/bash: line 1: 4.0”: No such file or directory\n",
+      "Collecting httpx==0.24.1\n",
+      "  Downloading httpx-0.24.1-py3-none-any.whl.metadata (7.4 kB)\n",
+      "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from httpx==0.24.1) (2024.8.30)\n",
+      "Collecting httpcore<0.18.0,>=0.15.0 (from httpx==0.24.1)\n",
+      "  Downloading httpcore-0.17.3-py3-none-any.whl.metadata (18 kB)\n",
+      "Requirement already satisfied: idna in /usr/local/lib/python3.10/dist-packages (from httpx==0.24.1) (3.10)\n",
+      "Requirement already satisfied: sniffio in /usr/local/lib/python3.10/dist-packages (from httpx==0.24.1) (1.3.1)\n",
+      "Requirement already satisfied: h11<0.15,>=0.13 in /usr/local/lib/python3.10/dist-packages (from httpcore<0.18.0,>=0.15.0->httpx==0.24.1) (0.14.0)\n",
+      "Collecting anyio<5.0,>=3.0 (from httpcore<0.18.0,>=0.15.0->httpx==0.24.1)\n",
+      "  Downloading anyio-4.7.0-py3-none-any.whl.metadata (4.7 kB)\n",
+      "Requirement already satisfied: exceptiongroup>=1.0.2 in /usr/local/lib/python3.10/dist-packages (from anyio<5.0,>=3.0->httpcore<0.18.0,>=0.15.0->httpx==0.24.1) (1.2.2)\n",
+      "Requirement already satisfied: typing_extensions>=4.5 in /usr/local/lib/python3.10/dist-packages (from anyio<5.0,>=3.0->httpcore<0.18.0,>=0.15.0->httpx==0.24.1) (4.12.2)\n",
+      "Downloading httpx-0.24.1-py3-none-any.whl (75 kB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.4/75.4 kB\u001b[0m \u001b[31m2.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hDownloading httpcore-0.17.3-py3-none-any.whl (74 kB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m74.5/74.5 kB\u001b[0m \u001b[31m6.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hDownloading anyio-4.7.0-py3-none-any.whl (93 kB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m93.1/93.1 kB\u001b[0m \u001b[31m8.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hInstalling collected packages: anyio, httpcore, httpx\n",
+      "  Attempting uninstall: httpcore\n",
+      "    Found existing installation: httpcore 1.0.7\n",
+      "    Uninstalling httpcore-1.0.7:\n",
+      "      Successfully uninstalled httpcore-1.0.7\n",
+      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+      "jupyter-server 1.24.0 requires anyio<4,>=3.1.0, but you have anyio 4.7.0 which is incompatible.\u001b[0m\u001b[31m\n",
+      "\u001b[0mSuccessfully installed anyio-4.7.0 httpcore-0.17.3 httpx-0.24.1\n"
+     ]
     }
+   ]
   },
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 🤗 Welcome to AdalFlow!\n",
-        "## The PyTorch library to auto-optimize any LLM task pipelines\n",
-        "\n",
-        "Thanks for trying us out, we're here to provide you with the best LLM application development experience you can dream of 😊 any questions or concerns you may have, [come talk to us on discord,](https://discord.gg/ezzszrRZvT) we're always here to help! ⭐ <i>Star us on <a href=\"https://github.com/SylphAI-Inc/AdalFlow\">Github</a> </i> ⭐\n",
-        "\n",
-        "\n",
-        "# Quick Links\n",
-        "\n",
-        "Github repo: https://github.com/SylphAI-Inc/AdalFlow\n",
-        "\n",
-        "Full Tutorials: https://adalflow.sylph.ai/index.html#.\n",
-        "\n",
-        "Deep dive on each API: check out the [developer notes](https://adalflow.sylph.ai/tutorials/index.html).\n",
-        "\n",
-        "Common use cases along with the auto-optimization:  check out [Use cases](https://adalflow.sylph.ai/use_cases/index.html).\n",
-        "\n",
-        "## 📖 Outline\n",
-        "\n",
-        "This is the code for a classification optimization tutorial ![image.png](data:image/png;base64,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)\n"
-      ],
-      "metadata": {
-        "id": "xHF95Kr4CzGq"
-      }
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "\n",
-        "# Installation\n",
-        "\n",
-        "1. Use `pip` to install the `adalflow` Python package. We will need `openai`, `groq` from the extra packages.\n",
-        "\n",
-        "  ```bash\n",
-        "  pip install adalflow[openai,groq]\n",
-        "  ```\n",
-        "2. Setup  `openai` and `groq` API key in the environment variables\n",
-        "\n",
-        "You can choose to use different client. You can import the model client you prefer. We support `Anthropic`, `Cohere`, `Google`, `GROQ`, `OpenAI`, `Transformer` and more in development. We will use OpenAI here as an example.Please refer to our [full installation guide](https://adalflow.sylph.ai/get_started/installation.html)"
-      ],
-      "metadata": {
-        "id": "Kof5M6DRaKhh"
-      }
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Set Environment Variables\n",
+    "\n",
+    "Run the following code and pass your api key.\n",
+    "\n",
+    "Note: for normal `.py` projects, follow our [official installation guide](https://lightrag.sylph.ai/get_started/installation.html).\n",
+    "\n",
+    "*Go to [OpenAI](https://platform.openai.com/docs/introduction) to get API keys if you don't already have.*"
+   ],
+   "metadata": {
+    "id": "KapUyHMM07pJ"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import os\n",
+    "\n",
+    "from getpass import getpass\n",
+    "\n",
+    "# Prompt user to enter their API keys securely\n",
+    "openai_api_key = getpass(\"Please enter your OpenAI API key: \")\n",
+    "\n",
+    "\n",
+    "# Set environment variables\n",
+    "os.environ[\"OPENAI_API_KEY\"] = openai_api_key\n",
+    "\n",
+    "print(\"API keys have been set.\")"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "ONfzF9Puzdd_",
+    "outputId": "a8ca0388-be6e-4b7a-cd05-d4ec52f64e95"
+   },
+   "execution_count": null,
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "tAp3eDjOCma1"
-      },
-      "outputs": [],
-      "source": [
-        "from IPython.display import clear_output\n",
-        "\n",
-        "!pip install -U adalflow[openai] # also install the package for the model client you'll use\n",
-        "!pip install datasets\n",
-        "clear_output()"
-      ]
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Please enter your OpenAI API key: ··········\n",
+      "API keys have been set.\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Prepare data structures and prompt template"
+   ],
+   "metadata": {
+    "id": "4W3yEpRpepNK"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "from dataclasses import dataclass, field\n",
+    "from typing import List, Dict, Union, Optional, Tuple, Any, Callable\n",
+    "from datasets import load_dataset\n",
+    "from adalflow.components.model_client import OpenAIClient\n",
+    "import adalflow as adal\n",
+    "from adalflow.core.component import Component\n",
+    "from adalflow.datasets.types import TrecData\n",
+    "from adalflow.datasets.trec import TrecDataset\n",
+    "\n",
+    "from adalflow.eval.answer_match_acc import AnswerMatchAcc\n",
+    "\n",
+    "\n",
+    "_COARSE_LABELS = [\"ABBR\", \"DESC\", \"ENTY\", \"HUM\", \"LOC\", \"NUM\"]\n",
+    "\n",
+    "_COARSE_LABELS_DESC = [\n",
+    "    \"Abbreviation: Questions about abbreviations and their meanings\",\n",
+    "    \"Description: Questions seeking descriptions of people, things, or concepts\",\n",
+    "    \"Entity: Questions about entities (e.g., animals, colors, inventions)\",\n",
+    "    \"Human: Questions about people or organizations\",\n",
+    "    \"Location: Questions about places, cities, countries\",\n",
+    "    \"Numeric: Questions seeking numeric answers (e.g., dates, amounts, distances)\",\n",
+    "]\n",
+    "\n",
+    "\n",
+    "template = r\"\"\"<START_OF_SYSTEM_MESSAGE>\n",
+    " {{system_prompt}}\n",
+    " {% if output_format_str is not none %}\n",
+    " {{output_format_str}}\n",
+    " {% endif %}\n",
+    " {% if few_shot_demos is not none %}\n",
+    " Here are some examples:\n",
+    " {{few_shot_demos}}\n",
+    " {% endif %}\n",
+    " <END_OF_SYSTEM_MESSAGE>\n",
+    " <START_OF_USER_MESSAGE>\n",
+    " {{input_str}}\n",
+    " <END_OF_USER_MESSAGE>\n",
+    " \"\"\"\n",
+    "\n",
+    "task_desc_template = r\"\"\"You are a classifier. Given a question, you need to classify it into one of the following classes:\n",
+    " Format: class_index. class_name, class_description\n",
+    " {% if classes %}\n",
+    " {% for class in classes %}\n",
+    " {{loop.index-1}}. {{class.label}}, {{class.desc}}\n",
+    " {% endfor %}\n",
+    " {% endif %}\n",
+    " - Do not try to answer the question:\n",
+    " \"\"\"\n",
+    "\n",
+    "\n",
+    "@dataclass\n",
+    "class TRECExtendedData(TrecData):\n",
+    "    rationale: str = field(\n",
+    "        metadata={\n",
+    "            \"desc\": \"Your step-by-step reasoning to classify the question to class_name\"\n",
+    "        },\n",
+    "        default=None,\n",
+    "    )\n",
+    "    __input_fields__ = [\"question\"]\n",
+    "    __output_fields__ = [\n",
+    "        \"rationale\",\n",
+    "        \"class_name\",\n",
+    "    ]  # it is important to have the rationale before the class_name\n",
+    "\n",
+    "\n",
+    "def load_datasets():\n",
+    "    \"\"\"Load the dataset\"\"\"\n",
+    "    train_data = TrecDataset(split=\"train\")\n",
+    "    val_data = TrecDataset(split=\"val\")\n",
+    "    test_data = TrecDataset(split=\"test\")\n",
+    "    return train_data, val_data, test_data  # 0.694, 0.847"
+   ],
+   "metadata": {
+    "id": "ZZIEtZYHNVjo"
+   },
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# prepare models\n",
+    "\n",
+    "from adalflow.components.model_client.openai_client import OpenAIClient\n",
+    "\n",
+    "# used as the target model\n",
+    "gpt_3_model = {\n",
+    "    \"model_client\": OpenAIClient(),\n",
+    "    \"model_kwargs\": {\n",
+    "        \"model\": \"gpt-3.5-turbo\",\n",
+    "        \"max_tokens\": 2000,\n",
+    "        \"temperature\": 0.0,\n",
+    "        \"top_p\": 0.99,\n",
+    "        \"frequency_penalty\": 0,\n",
+    "        \"presence_penalty\": 0,\n",
+    "        \"stop\": None,\n",
+    "    },\n",
+    "}\n",
+    "\n",
+    "# used as optimizer and backward engine\n",
+    "gpt_4o_mini_model = {\n",
+    "    \"model_client\": OpenAIClient(),\n",
+    "    \"model_kwargs\": {\n",
+    "        \"model\": \"gpt-4o-mini\",\n",
+    "        \"temperature\": 1,\n",
+    "        \"top_p\": 0.99,\n",
+    "        \"max_tokens\": 1000,\n",
+    "        # \"frequency_penalty\": 1,  # high for nto repeating prompt\n",
+    "    },\n",
+    "}"
+   ],
+   "metadata": {
+    "id": "yAvzn7DZeUX-"
+   },
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Create the task pipeline"
+   ],
+   "metadata": {
+    "id": "G664uy9MgDdC"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "class TRECClassifierStructuredOutput(adal.Component):\n",
+    "\n",
+    "    def __init__(self, model_client: adal.ModelClient, model_kwargs: Dict):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        label_desc = [\n",
+    "            {\"label\": label, \"desc\": desc}\n",
+    "            for label, desc in zip(_COARSE_LABELS, _COARSE_LABELS_DESC)\n",
+    "        ]\n",
+    "\n",
+    "        task_desc_str = adal.Prompt(\n",
+    "            template=task_desc_template, prompt_kwargs={\"classes\": label_desc}\n",
+    "        )()\n",
+    "\n",
+    "        self.data_class = TRECExtendedData\n",
+    "        self.data_class.set_task_desc(task_desc_str)\n",
+    "\n",
+    "        self.parser = adal.DataClassParser(\n",
+    "            data_class=self.data_class, return_data_class=True, format_type=\"yaml\"\n",
+    "        )\n",
+    "\n",
+    "        prompt_kwargs = {\n",
+    "            \"system_prompt\": adal.Parameter(\n",
+    "                data=self.parser.get_task_desc_str(),\n",
+    "                role_desc=\"Task description\",\n",
+    "                requires_opt=True,\n",
+    "                param_type=adal.ParameterType.PROMPT,\n",
+    "            ),\n",
+    "            \"output_format_str\": adal.Parameter(\n",
+    "                data=self.parser.get_output_format_str(),\n",
+    "                role_desc=\"Output format requirements\",\n",
+    "                requires_opt=False,\n",
+    "                param_type=adal.ParameterType.PROMPT,\n",
+    "            ),\n",
+    "            \"few_shot_demos\": adal.Parameter(\n",
+    "                data=None,\n",
+    "                requires_opt=True,\n",
+    "                role_desc=\"Few shot examples to help the model\",\n",
+    "                param_type=adal.ParameterType.DEMOS,\n",
+    "            ),\n",
+    "        }\n",
+    "\n",
+    "        self.llm = adal.Generator(\n",
+    "            model_client=model_client,\n",
+    "            model_kwargs=model_kwargs,\n",
+    "            prompt_kwargs=prompt_kwargs,\n",
+    "            template=template,\n",
+    "            output_processors=self.parser,\n",
+    "            use_cache=True,\n",
+    "        )\n",
+    "\n",
+    "    def _prepare_input(self, question: str):\n",
+    "        input_data = self.data_class(question=question)\n",
+    "        input_str = self.parser.get_input_str(input_data)\n",
+    "        prompt_kwargs = {\n",
+    "            \"input_str\": adal.Parameter(\n",
+    "                data=input_str, requires_opt=False, role_desc=\"input to the LLM\"\n",
+    "            )\n",
+    "        }\n",
+    "        return prompt_kwargs\n",
+    "\n",
+    "    def call(\n",
+    "        self, question: str, id: Optional[str] = None\n",
+    "    ) -> Union[adal.GeneratorOutput, adal.Parameter]:\n",
+    "        prompt_kwargs = self._prepare_input(question)\n",
+    "        output = self.llm(prompt_kwargs=prompt_kwargs, id=id)\n",
+    "        return output"
+   ],
+   "metadata": {
+    "id": "3Q3H9XC4Ncfi"
+   },
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Inference the task pipeline and draw the computation graph"
+   ],
+   "metadata": {
+    "id": "gj08oOqqgGyr"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# load dataset to get one example\n",
+    "\n",
+    "train_dataset, val_dataset, test_dataset = load_datasets()\n",
+    "example = train_dataset[0]\n",
+    "print(example)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "qtvLN8zOgnSg",
+    "outputId": "9996f8c3-371d-4b5c-ec48-e8cf6d6c396b"
+   },
+   "execution_count": null,
+   "outputs": [
     {
-      "cell_type": "code",
-      "source": [
-        "!pip uninstall httpx anyio -y\n",
-        "!pip install “anyio>=3.1.0,<4.0”\n",
-        "!pip install httpx==0.24.1"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "CU672Gt4bY7b",
-        "outputId": "532c84d2-c7bd-40ac-c050-e2c5dddc8946"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Found existing installation: httpx 0.28.1\n",
-            "Uninstalling httpx-0.28.1:\n",
-            "  Successfully uninstalled httpx-0.28.1\n",
-            "Found existing installation: anyio 3.7.1\n",
-            "Uninstalling anyio-3.7.1:\n",
-            "  Successfully uninstalled anyio-3.7.1\n",
-            "/bin/bash: line 1: 4.0”: No such file or directory\n",
-            "Collecting httpx==0.24.1\n",
-            "  Downloading httpx-0.24.1-py3-none-any.whl.metadata (7.4 kB)\n",
-            "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from httpx==0.24.1) (2024.8.30)\n",
-            "Collecting httpcore<0.18.0,>=0.15.0 (from httpx==0.24.1)\n",
-            "  Downloading httpcore-0.17.3-py3-none-any.whl.metadata (18 kB)\n",
-            "Requirement already satisfied: idna in /usr/local/lib/python3.10/dist-packages (from httpx==0.24.1) (3.10)\n",
-            "Requirement already satisfied: sniffio in /usr/local/lib/python3.10/dist-packages (from httpx==0.24.1) (1.3.1)\n",
-            "Requirement already satisfied: h11<0.15,>=0.13 in /usr/local/lib/python3.10/dist-packages (from httpcore<0.18.0,>=0.15.0->httpx==0.24.1) (0.14.0)\n",
-            "Collecting anyio<5.0,>=3.0 (from httpcore<0.18.0,>=0.15.0->httpx==0.24.1)\n",
-            "  Downloading anyio-4.7.0-py3-none-any.whl.metadata (4.7 kB)\n",
-            "Requirement already satisfied: exceptiongroup>=1.0.2 in /usr/local/lib/python3.10/dist-packages (from anyio<5.0,>=3.0->httpcore<0.18.0,>=0.15.0->httpx==0.24.1) (1.2.2)\n",
-            "Requirement already satisfied: typing_extensions>=4.5 in /usr/local/lib/python3.10/dist-packages (from anyio<5.0,>=3.0->httpcore<0.18.0,>=0.15.0->httpx==0.24.1) (4.12.2)\n",
-            "Downloading httpx-0.24.1-py3-none-any.whl (75 kB)\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.4/75.4 kB\u001b[0m \u001b[31m2.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25hDownloading httpcore-0.17.3-py3-none-any.whl (74 kB)\n",
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-            "\u001b[?25hDownloading anyio-4.7.0-py3-none-any.whl (93 kB)\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m93.1/93.1 kB\u001b[0m \u001b[31m8.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25hInstalling collected packages: anyio, httpcore, httpx\n",
-            "  Attempting uninstall: httpcore\n",
-            "    Found existing installation: httpcore 1.0.7\n",
-            "    Uninstalling httpcore-1.0.7:\n",
-            "      Successfully uninstalled httpcore-1.0.7\n",
-            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
-            "jupyter-server 1.24.0 requires anyio<4,>=3.1.0, but you have anyio 4.7.0 which is incompatible.\u001b[0m\u001b[31m\n",
-            "\u001b[0mSuccessfully installed anyio-4.7.0 httpcore-0.17.3 httpx-0.24.1\n"
-          ]
-        }
-      ]
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "TrecData(id='e73a82a7-6a3d-4947-90f5-03739e169db0', question='When reading classified ads , what does EENTY : other stand for ?', class_name='ABBR', class_index=0)\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "task = TRECClassifierStructuredOutput(\n",
+    "    model_client=gpt_3_model[\"model_client\"],\n",
+    "    model_kwargs=gpt_3_model[\"model_kwargs\"],\n",
+    ")\n",
+    "task.train()\n",
+    "\n",
+    "output = task(question=example.question, id=example.id)\n",
+    "print(output)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "cKuW3QlhgLTG",
+    "outputId": "7f1f9cd6-9615-4b41-ecc5-5901626d57ae"
+   },
+   "execution_count": null,
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "source": [
-        "## Set Environment Variables\n",
-        "\n",
-        "Run the following code and pass your api key.\n",
-        "\n",
-        "Note: for normal `.py` projects, follow our [official installation guide](https://lightrag.sylph.ai/get_started/installation.html).\n",
-        "\n",
-        "*Go to [OpenAI](https://platform.openai.com/docs/introduction) to get API keys if you don't already have.*"
-      ],
-      "metadata": {
-        "id": "KapUyHMM07pJ"
-      }
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Parameter(name=Generator_output, requires_opt=True, param_type=generator_output (The output of the generator.), role_desc=Output from (llm) Generator, data=```\n",
+      "rationale: The question is asking for the meaning of the abbreviation \"EENTY\" in classified ads, which falls under the ABBR class.\n",
+      "class_name: ABBR\n",
+      "```, predecessors={Parameter(name=Output_for, requires_opt=False, param_type=prompt (Instruction to the language model on task, data, and format.), role_desc=Output format requirements, data=Your output should be formatted as a standard YAML instance with the following schema:\n",
+      "```\n",
+      "rationale: Your step-by-step reasoning to classify the question to class_name (str) (optional)\n",
+      "class_name: One of {ABBR, ENTY, DESC, HUM, LOC, NUM} (str) (optional)\n",
+      "```\n",
+      "-Make sure to always enclose the YAML output in triple backticks (```). Please do not add anything other than valid YAML output!\n",
+      "-Follow the YAML formatting conventions with an indent of 2 spaces.\n",
+      "-DO NOT mistaken the \"properties\" and \"type\" in the schema as the actual fields in the YAML output.\n",
+      "-Quote the string values properly., predecessors=set(), gradients=[],            raw_response=None, input_args=None, traces={}), Parameter(name=Few_shot_e, requires_opt=True, param_type=demos (A few examples to guide the language model.), role_desc=Few shot examples to help the model, data=None, predecessors=set(), gradients=[],            raw_response=None, input_args=None, traces={}), Parameter(name=Input_to_t, requires_opt=False, param_type=none (), role_desc=input to the LLM, data=question: 'When reading classified ads , what does EENTY : other stand for ?', predecessors=set(), gradients=[],            raw_response=None, input_args=None, traces={}), Parameter(name=Task_descr, requires_opt=True, param_type=prompt (Instruction to the language model on task, data, and format.), role_desc=Task description, data=You are a classifier. Given a question, you need to classify it into one of the following classes:\n",
+      " Format: class_index. class_name, class_description\n",
+      " 0. ABBR, Abbreviation: Questions about abbreviations and their meanings\n",
+      " 1. DESC, Description: Questions seeking descriptions of people, things, or concepts\n",
+      " 2. ENTY, Entity: Questions about entities (e.g., animals, colors, inventions)\n",
+      " 3. HUM, Human: Questions about people or organizations\n",
+      " 4. LOC, Location: Questions about places, cities, countries\n",
+      " 5. NUM, Numeric: Questions seeking numeric answers (e.g., dates, amounts, distances)\n",
+      " - Do not try to answer the question:\n",
+      " , predecessors=set(), gradients=[],            raw_response=None, input_args=None, traces={})}, gradients=[],            raw_response=None, input_args={'prompt_kwargs': {'system_prompt': Parameter(name=Task_descr, requires_opt=True, param_type=prompt (Instruction to the language model on task, data, and format.), role_desc=Task description, data=You are a classifier. Given a question, you need to classify it into one of the following classes:\n",
+      " Format: class_index. class_name, class_description\n",
+      " 0. ABBR, Abbreviation: Questions about abbreviations and their meanings\n",
+      " 1. DESC, Description: Questions seeking descriptions of people, things, or concepts\n",
+      " 2. ENTY, Entity: Questions about entities (e.g., animals, colors, inventions)\n",
+      " 3. HUM, Human: Questions about people or organizations\n",
+      " 4. LOC, Location: Questions about places, cities, countries\n",
+      " 5. NUM, Numeric: Questions seeking numeric answers (e.g., dates, amounts, distances)\n",
+      " - Do not try to answer the question:\n",
+      " , predecessors=set(), gradients=[],            raw_response=None, input_args=None, traces={}), 'output_format_str': Parameter(name=Output_for, requires_opt=False, param_type=prompt (Instruction to the language model on task, data, and format.), role_desc=Output format requirements, data=Your output should be formatted as a standard YAML instance with the following schema:\n",
+      "```\n",
+      "rationale: Your step-by-step reasoning to classify the question to class_name (str) (optional)\n",
+      "class_name: One of {ABBR, ENTY, DESC, HUM, LOC, NUM} (str) (optional)\n",
+      "```\n",
+      "-Make sure to always enclose the YAML output in triple backticks (```). Please do not add anything other than valid YAML output!\n",
+      "-Follow the YAML formatting conventions with an indent of 2 spaces.\n",
+      "-DO NOT mistaken the \"properties\" and \"type\" in the schema as the actual fields in the YAML output.\n",
+      "-Quote the string values properly., predecessors=set(), gradients=[],            raw_response=None, input_args=None, traces={}), 'few_shot_demos': Parameter(name=Few_shot_e, requires_opt=True, param_type=demos (A few examples to guide the language model.), role_desc=Few shot examples to help the model, data=None, predecessors=set(), gradients=[],            raw_response=None, input_args=None, traces={}), 'input_str': Parameter(name=Input_to_t, requires_opt=False, param_type=none (), role_desc=input to the LLM, data=question: 'When reading classified ads , what does EENTY : other stand for ?', predecessors=set(), gradients=[],            raw_response=None, input_args=None, traces={})}, 'model_kwargs': {'model': 'gpt-3.5-turbo', 'max_tokens': 2000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}}, traces={})\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "class TrecClassifierAdal(adal.AdalComponent):\n",
+    "    def __init__(\n",
+    "        self,\n",
+    "        model_client: adal.ModelClient,\n",
+    "        model_kwargs: Dict,\n",
+    "        teacher_model_config: Dict,\n",
+    "        backward_engine_model_config: Dict,\n",
+    "        text_optimizer_model_config: Dict,\n",
+    "    ):\n",
+    "        task = TRECClassifierStructuredOutput(model_client, model_kwargs)\n",
+    "        eval_fn = AnswerMatchAcc(type=\"exact_match\").compute_single_item\n",
+    "        loss_fn = adal.EvalFnToTextLoss(\n",
+    "            eval_fn=eval_fn,\n",
+    "            eval_fn_desc=\"exact_match: 1 if str(y) == str(y_gt) else 0\",\n",
+    "        )\n",
+    "        super().__init__(\n",
+    "            task=task,\n",
+    "            eval_fn=eval_fn,\n",
+    "            loss_fn=loss_fn,\n",
+    "            backward_engine_model_config=backward_engine_model_config,\n",
+    "            text_optimizer_model_config=text_optimizer_model_config,\n",
+    "            teacher_model_config=teacher_model_config,\n",
+    "        )\n",
+    "\n",
+    "    def prepare_task(self, sample: TRECExtendedData):\n",
+    "        return self.task.call, {\"question\": sample.question, \"id\": sample.id}\n",
+    "\n",
+    "    def prepare_eval(\n",
+    "        self, sample: TRECExtendedData, y_pred: adal.GeneratorOutput\n",
+    "    ) -> float:\n",
+    "        y_label = -1\n",
+    "        if y_pred and y_pred.data is not None and y_pred.data.class_name is not None:\n",
+    "            y_label = y_pred.data.class_name\n",
+    "        return self.eval_fn, {\"y\": y_label, \"y_gt\": sample.class_name}\n",
+    "\n",
+    "    def prepare_loss(\n",
+    "        self, sample: TRECExtendedData, y_pred: adal.Parameter, *args, **kwargs\n",
+    "    ) -> Tuple[Callable[..., Any], Dict]:\n",
+    "        full_response = y_pred.full_response\n",
+    "        y_label = -1\n",
+    "        if (\n",
+    "            full_response\n",
+    "            and full_response.data is not None\n",
+    "            and full_response.data.class_name is not None\n",
+    "        ):\n",
+    "            y_label = full_response.data.class_name\n",
+    "\n",
+    "        y_pred.eval_input = y_label\n",
+    "        y_gt = adal.Parameter(\n",
+    "            name=\"y_gt\",\n",
+    "            data=sample.class_name,\n",
+    "            eval_input=sample.class_name,\n",
+    "            requires_opt=False,\n",
+    "        )\n",
+    "        return self.loss_fn, {\"kwargs\": {\"y\": y_pred, \"y_gt\": y_gt}}"
+   ],
+   "metadata": {
+    "id": "HpkQYsh2NevT"
+   },
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "def train(\n",
+    "    model_client: adal.ModelClient,\n",
+    "    model_kwargs: Dict,\n",
+    "    train_batch_size=4,\n",
+    "    raw_shots: int = 0,\n",
+    "    bootstrap_shots: int = 1,\n",
+    "    max_steps=12,\n",
+    "    num_workers=4,\n",
+    "    strategy=\"constrained\",\n",
+    "    optimization_order=\"sequential\",\n",
+    "    debug=False,\n",
+    "):\n",
+    "    print(\"Starting training process...\")\n",
+    "\n",
+    "    # Define the model configuration for all components\n",
+    "    gpt_4o_model = {\n",
+    "        \"model_client\": OpenAIClient(),\n",
+    "        \"model_kwargs\": {\n",
+    "            \"model\": \"gpt-4o-mini\",\n",
+    "            \"temperature\": 1,\n",
+    "            \"top_p\": 0.99,\n",
+    "            \"max_tokens\": 1000,\n",
+    "            # \"frequency_penalty\": 1,  # high for nto repeating prompt\n",
+    "        },\n",
+    "    }\n",
+    "\n",
+    "    print(f\"Component model configuration: {gpt_4o_model}\")\n",
+    "\n",
+    "    try:\n",
+    "        print(\"Initializing ADAL component...\")\n",
+    "        adal_component = TrecClassifierAdal(\n",
+    "            model_client=model_client,\n",
+    "            model_kwargs=model_kwargs,\n",
+    "            text_optimizer_model_config=gpt_4o_model,\n",
+    "            backward_engine_model_config=gpt_4o_model,\n",
+    "            teacher_model_config=gpt_4o_model,\n",
+    "        )\n",
+    "        print(\"ADAL component initialized successfully\")\n",
+    "\n",
+    "        print(\"Initializing trainer...\")\n",
+    "        trainer = adal.Trainer(\n",
+    "            train_batch_size=train_batch_size,\n",
+    "            adaltask=adal_component,\n",
+    "            strategy=strategy,\n",
+    "            max_steps=max_steps,\n",
+    "            num_workers=num_workers,\n",
+    "            raw_shots=raw_shots,\n",
+    "            bootstrap_shots=bootstrap_shots,\n",
+    "            debug=debug,\n",
+    "            weighted_sampling=True,\n",
+    "            optimization_order=optimization_order,\n",
+    "            exclude_input_fields_from_bootstrap_demos=True,\n",
+    "        )\n",
+    "        print(\"Trainer initialized successfully\")\n",
+    "\n",
+    "        print(\"Loading datasets...\")\n",
+    "        train_dataset, val_dataset, test_dataset = load_datasets()\n",
+    "        print(\n",
+    "            f\"Datasets loaded - Train size: {len(train_dataset)}, Val size: {len(val_dataset)}, Test size: {len(test_dataset)}\"\n",
+    "        )\n",
+    "\n",
+    "        print(\"Starting model training...\")\n",
+    "        trainer.fit(\n",
+    "            train_dataset=train_dataset,\n",
+    "            val_dataset=test_dataset,\n",
+    "            debug=debug,\n",
+    "        )\n",
+    "        print(\"Training completed successfully\")\n",
+    "\n",
+    "    except Exception as e:\n",
+    "        print(f\"Error occurred: {str(e)}\")\n",
+    "        raise"
+   ],
+   "metadata": {
+    "id": "PEj6xiZ5dVaj"
+   },
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "train(**gpt_3_model)"
+   ],
+   "metadata": {
+    "id": "GnlZBQOMEj6E",
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
     },
+    "outputId": "055a95c4-ccae-4028-d904-86b839bc1c14"
+   },
+   "execution_count": null,
+   "outputs": [
     {
-      "cell_type": "code",
-      "source": [
-        "import os\n",
-        "\n",
-        "from getpass import getpass\n",
-        "\n",
-        "# Prompt user to enter their API keys securely\n",
-        "openai_api_key = getpass(\"Please enter your OpenAI API key: \")\n",
-        "\n",
-        "\n",
-        "# Set environment variables\n",
-        "os.environ[\"OPENAI_API_KEY\"] = openai_api_key\n",
-        "\n",
-        "print(\"API keys have been set.\")"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "ONfzF9Puzdd_",
-        "outputId": "a8ca0388-be6e-4b7a-cd05-d4ec52f64e95"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Please enter your OpenAI API key: ··········\n",
-            "API keys have been set.\n"
-          ]
-        }
-      ]
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Starting training process...\n",
+      "Component model configuration: {'model_client': OpenAIClient(), 'model_kwargs': {'model': 'gpt-4o-mini', 'temperature': 1, 'top_p': 0.99, 'max_tokens': 1000}}\n",
+      "Initializing ADAL component...\n",
+      "ADAL component initialized successfully\n",
+      "Initializing trainer...\n",
+      "Trainer initialized successfully\n",
+      "Loading datasets...\n",
+      "Datasets loaded - Train size: 120, Val size: 36, Test size: 144\n",
+      "Starting model training...\n",
+      "raw_shots: 0, bootstrap_shots: 1\n",
+      "Configuring teacher generator.\n",
+      "Configuring teacher generator for Generator(\n",
+      "  model_kwargs={'model': 'gpt-4o-mini', 'temperature': 1, 'top_p': 0.99, 'max_tokens': 1000}, trainable_prompt_kwargs=[]\n",
+      "  (prompt): Prompt(\n",
+      "    template: <START_OF_SYSTEM_MESSAGE>\n",
+      "     {{system_prompt}}\n",
+      "     {% if output_format_str is not none %}\n",
+      "     {{output_format_str}}\n",
+      "     {% endif %}\n",
+      "     {% if few_shot_demos is not none %}\n",
+      "     Here are some examples:\n",
+      "     {{few_shot_demos}}\n",
+      "     {% endif %}\n",
+      "     <END_OF_SYSTEM_MESSAGE>\n",
+      "     <START_OF_USER_MESSAGE>\n",
+      "     {{input_str}}\n",
+      "     <END_OF_USER_MESSAGE>\n",
+      "     , prompt_kwargs: {'system_prompt': 'You are a classifier. Given a question, you need to classify it into one of the following classes:\\n Format: class_index. class_name, class_description\\n 0. ABBR, Abbreviation: Questions about abbreviations and their meanings\\n 1. DESC, Description: Questions seeking descriptions of people, things, or concepts\\n 2. ENTY, Entity: Questions about entities (e.g., animals, colors, inventions)\\n 3. HUM, Human: Questions about people or organizations\\n 4. LOC, Location: Questions about places, cities, countries\\n 5. NUM, Numeric: Questions seeking numeric answers (e.g., dates, amounts, distances)\\n - Do not try to answer the question:\\n ', 'output_format_str': 'Your output should be formatted as a standard YAML instance with the following schema:\\n```\\nrationale: Your step-by-step reasoning to classify the question to class_name (str) (optional)\\nclass_name: One of {ABBR, ENTY, DESC, HUM, LOC, NUM} (str) (optional)\\n```\\n-Make sure to always enclose the YAML output in triple backticks (```). Please do not add anything other than valid YAML output!\\n-Follow the YAML formatting conventions with an indent of 2 spaces.\\n-DO NOT mistaken the \"properties\" and \"type\" in the schema as the actual fields in the YAML output.\\n-Quote the string values properly.', 'few_shot_demos': 'None'}, prompt_variables: ['output_format_str', 'system_prompt', 'input_str', 'few_shot_demos']\n",
+      "  )\n",
+      "  (model_client): OpenAIClient()\n",
+      "  (output_processors): DataClassParser(\n",
+      "    data_class=TRECExtendedData, format_type=yaml,            return_data_class=True, input_fields=['question'],            output_fields=['rationale', 'class_name']\n",
+      "    (_output_processor): YamlParser()\n",
+      "    (output_format_prompt): Prompt(\n",
+      "      template: Your output should be formatted as a standard YAML instance with the following schema:\n",
+      "      ```\n",
+      "      {{schema}}\n",
+      "      ```\n",
+      "      -Make sure to always enclose the YAML output in triple backticks (```). Please do not add anything other than valid YAML output!\n",
+      "      -Follow the YAML formatting conventions with an indent of 2 spaces.\n",
+      "      -DO NOT mistaken the \"properties\" and \"type\" in the schema as the actual fields in the YAML output.\n",
+      "      -Quote the string values properly., prompt_variables: ['schema']\n",
+      "    )\n",
+      "  )\n",
+      ")\n",
+      "Teacher generator set: Generator(\n",
+      "  model_kwargs={'model': 'gpt-4o-mini', 'temperature': 1, 'top_p': 0.99, 'max_tokens': 1000}, trainable_prompt_kwargs=[]\n",
+      "  (prompt): Prompt(\n",
+      "    template: <START_OF_SYSTEM_MESSAGE>\n",
+      "     {{system_prompt}}\n",
+      "     {% if output_format_str is not none %}\n",
+      "     {{output_format_str}}\n",
+      "     {% endif %}\n",
+      "     {% if few_shot_demos is not none %}\n",
+      "     Here are some examples:\n",
+      "     {{few_shot_demos}}\n",
+      "     {% endif %}\n",
+      "     <END_OF_SYSTEM_MESSAGE>\n",
+      "     <START_OF_USER_MESSAGE>\n",
+      "     {{input_str}}\n",
+      "     <END_OF_USER_MESSAGE>\n",
+      "     , prompt_kwargs: {'system_prompt': 'You are a classifier. Given a question, you need to classify it into one of the following classes:\\n Format: class_index. class_name, class_description\\n 0. ABBR, Abbreviation: Questions about abbreviations and their meanings\\n 1. DESC, Description: Questions seeking descriptions of people, things, or concepts\\n 2. ENTY, Entity: Questions about entities (e.g., animals, colors, inventions)\\n 3. HUM, Human: Questions about people or organizations\\n 4. LOC, Location: Questions about places, cities, countries\\n 5. NUM, Numeric: Questions seeking numeric answers (e.g., dates, amounts, distances)\\n - Do not try to answer the question:\\n ', 'output_format_str': 'Your output should be formatted as a standard YAML instance with the following schema:\\n```\\nrationale: Your step-by-step reasoning to classify the question to class_name (str) (optional)\\nclass_name: One of {ABBR, ENTY, DESC, HUM, LOC, NUM} (str) (optional)\\n```\\n-Make sure to always enclose the YAML output in triple backticks (```). Please do not add anything other than valid YAML output!\\n-Follow the YAML formatting conventions with an indent of 2 spaces.\\n-DO NOT mistaken the \"properties\" and \"type\" in the schema as the actual fields in the YAML output.\\n-Quote the string values properly.', 'few_shot_demos': 'None'}, prompt_variables: ['output_format_str', 'system_prompt', 'input_str', 'few_shot_demos']\n",
+      "  )\n",
+      "  (model_client): OpenAIClient()\n",
+      "  (output_processors): DataClassParser(\n",
+      "    data_class=TRECExtendedData, format_type=yaml,            return_data_class=True, input_fields=['question'],            output_fields=['rationale', 'class_name']\n",
+      "    (_output_processor): YamlParser()\n",
+      "    (output_format_prompt): Prompt(\n",
+      "      template: Your output should be formatted as a standard YAML instance with the following schema:\n",
+      "      ```\n",
+      "      {{schema}}\n",
+      "      ```\n",
+      "      -Make sure to always enclose the YAML output in triple backticks (```). Please do not add anything other than valid YAML output!\n",
+      "      -Follow the YAML formatting conventions with an indent of 2 spaces.\n",
+      "      -DO NOT mistaken the \"properties\" and \"type\" in the schema as the actual fields in the YAML output.\n",
+      "      -Quote the string values properly., prompt_variables: ['schema']\n",
+      "    )\n",
+      "  )\n",
+      "), teacher Generator(\n",
+      "  model_kwargs={'model': 'gpt-4o-mini', 'temperature': 1, 'top_p': 0.99, 'max_tokens': 1000}, trainable_prompt_kwargs=[]\n",
+      "  (prompt): Prompt(\n",
+      "    template: <START_OF_SYSTEM_MESSAGE>\n",
+      "     {{system_prompt}}\n",
+      "     {% if output_format_str is not none %}\n",
+      "     {{output_format_str}}\n",
+      "     {% endif %}\n",
+      "     {% if few_shot_demos is not none %}\n",
+      "     Here are some examples:\n",
+      "     {{few_shot_demos}}\n",
+      "     {% endif %}\n",
+      "     <END_OF_SYSTEM_MESSAGE>\n",
+      "     <START_OF_USER_MESSAGE>\n",
+      "     {{input_str}}\n",
+      "     <END_OF_USER_MESSAGE>\n",
+      "     , prompt_kwargs: {'system_prompt': 'You are a classifier. Given a question, you need to classify it into one of the following classes:\\n Format: class_index. class_name, class_description\\n 0. ABBR, Abbreviation: Questions about abbreviations and their meanings\\n 1. DESC, Description: Questions seeking descriptions of people, things, or concepts\\n 2. ENTY, Entity: Questions about entities (e.g., animals, colors, inventions)\\n 3. HUM, Human: Questions about people or organizations\\n 4. LOC, Location: Questions about places, cities, countries\\n 5. NUM, Numeric: Questions seeking numeric answers (e.g., dates, amounts, distances)\\n - Do not try to answer the question:\\n ', 'output_format_str': 'Your output should be formatted as a standard YAML instance with the following schema:\\n```\\nrationale: Your step-by-step reasoning to classify the question to class_name (str) (optional)\\nclass_name: One of {ABBR, ENTY, DESC, HUM, LOC, NUM} (str) (optional)\\n```\\n-Make sure to always enclose the YAML output in triple backticks (```). Please do not add anything other than valid YAML output!\\n-Follow the YAML formatting conventions with an indent of 2 spaces.\\n-DO NOT mistaken the \"properties\" and \"type\" in the schema as the actual fields in the YAML output.\\n-Quote the string values properly.', 'few_shot_demos': 'None'}, prompt_variables: ['output_format_str', 'system_prompt', 'input_str', 'few_shot_demos']\n",
+      "  )\n",
+      "  (model_client): OpenAIClient()\n",
+      "  (output_processors): DataClassParser(\n",
+      "    data_class=TRECExtendedData, format_type=yaml,            return_data_class=True, input_fields=['question'],            output_fields=['rationale', 'class_name']\n",
+      "    (_output_processor): YamlParser()\n",
+      "    (output_format_prompt): Prompt(\n",
+      "      template: Your output should be formatted as a standard YAML instance with the following schema:\n",
+      "      ```\n",
+      "      {{schema}}\n",
+      "      ```\n",
+      "      -Make sure to always enclose the YAML output in triple backticks (```). Please do not add anything other than valid YAML output!\n",
+      "      -Follow the YAML formatting conventions with an indent of 2 spaces.\n",
+      "      -DO NOT mistaken the \"properties\" and \"type\" in the schema as the actual fields in the YAML output.\n",
+      "      -Quote the string values properly., prompt_variables: ['schema']\n",
+      "    )\n",
+      "  )\n",
+      ")\n",
+      "Teacher generator configured.\n",
+      "Configured demo optimizers\n",
+      "Backward engine configured for all generators.\n"
+     ]
     },
     {
-      "cell_type": "markdown",
-      "source": [
-        "Prepare data structures and prompt template"
-      ],
-      "metadata": {
-        "id": "4W3yEpRpepNK"
-      }
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "\n",
+      "Loading Data: 100%|██████████| 144/144 [00:00<00:00, 9161.62it/s]\n",
+      "Predicting: step(0): 0.8264 across 144 samples, Max potential: 0.8264: 100%|██████████| 144/144 [00:19<00:00,  7.39it/s]\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "source": [
-        "from dataclasses import dataclass, field\n",
-        "from typing import List, Dict, Union, Optional, Tuple, Any, Callable\n",
-        "from datasets import load_dataset\n",
-        "from adalflow.components.model_client import OpenAIClient\n",
-        "import adalflow as adal\n",
-        "from adalflow.core.component import Component\n",
-        "from adalflow.datasets.types import TrecData\n",
-        "from adalflow.datasets.trec import TrecDataset\n",
-        "\n",
-        "from adalflow.eval.answer_match_acc import AnswerMatchAcc\n",
-        "\n",
-        "\n",
-        "_COARSE_LABELS = [\"ABBR\", \"DESC\", \"ENTY\", \"HUM\", \"LOC\", \"NUM\"]\n",
-        "\n",
-        "_COARSE_LABELS_DESC = [\n",
-        "    \"Abbreviation: Questions about abbreviations and their meanings\",\n",
-        "    \"Description: Questions seeking descriptions of people, things, or concepts\",\n",
-        "    \"Entity: Questions about entities (e.g., animals, colors, inventions)\",\n",
-        "    \"Human: Questions about people or organizations\",\n",
-        "    \"Location: Questions about places, cities, countries\",\n",
-        "    \"Numeric: Questions seeking numeric answers (e.g., dates, amounts, distances)\",\n",
-        "]\n",
-        "\n",
-        "\n",
-        "template = r\"\"\"<START_OF_SYSTEM_MESSAGE>\n",
-        " {{system_prompt}}\n",
-        " {% if output_format_str is not none %}\n",
-        " {{output_format_str}}\n",
-        " {% endif %}\n",
-        " {% if few_shot_demos is not none %}\n",
-        " Here are some examples:\n",
-        " {{few_shot_demos}}\n",
-        " {% endif %}\n",
-        " <END_OF_SYSTEM_MESSAGE>\n",
-        " <START_OF_USER_MESSAGE>\n",
-        " {{input_str}}\n",
-        " <END_OF_USER_MESSAGE>\n",
-        " \"\"\"\n",
-        "\n",
-        "task_desc_template = r\"\"\"You are a classifier. Given a question, you need to classify it into one of the following classes:\n",
-        " Format: class_index. class_name, class_description\n",
-        " {% if classes %}\n",
-        " {% for class in classes %}\n",
-        " {{loop.index-1}}. {{class.label}}, {{class.desc}}\n",
-        " {% endfor %}\n",
-        " {% endif %}\n",
-        " - Do not try to answer the question:\n",
-        " \"\"\"\n",
-        "\n",
-        "\n",
-        "@dataclass\n",
-        "class TRECExtendedData(TrecData):\n",
-        "    rationale: str = field(\n",
-        "        metadata={\n",
-        "            \"desc\": \"Your step-by-step reasoning to classify the question to class_name\"\n",
-        "        },\n",
-        "        default=None,\n",
-        "    )\n",
-        "    __input_fields__ = [\"question\"]\n",
-        "    __output_fields__ = [\n",
-        "        \"rationale\",\n",
-        "        \"class_name\",\n",
-        "    ]  # it is important to have the rationale before the class_name\n",
-        "\n",
-        "def load_datasets():\n",
-        "    \"\"\"Load the dataset\"\"\"\n",
-        "    train_data = TrecDataset(split=\"train\")\n",
-        "    val_data = TrecDataset(split=\"val\")\n",
-        "    test_data = TrecDataset(split=\"test\")\n",
-        "    return train_data, val_data, test_data  # 0.694, 0.847"
-      ],
-      "metadata": {
-        "id": "ZZIEtZYHNVjo"
-      },
-      "execution_count": null,
-      "outputs": []
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "completed_samples: 144, len: 144\n",
+      "Initial validation score: 0.8263888888888888\n",
+      "Initial test score: None\n",
+      "Checkpoint path: /root/.adalflow/ckpt/TrecClassifierAdal\n",
+      "save to /root/.adalflow/ckpt/TrecClassifierAdal/constrained_max_steps_12_a6e76_run_1.json\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "source": [
-        "# prepare models\n",
-        "\n",
-        "from adalflow.components.model_client.openai_client import OpenAIClient\n",
-        "\n",
-        "# used as the target model\n",
-        "gpt_3_model = {\n",
-        "    \"model_client\": OpenAIClient(),\n",
-        "    \"model_kwargs\": {\n",
-        "        \"model\": \"gpt-3.5-turbo\",\n",
-        "        \"max_tokens\": 2000,\n",
-        "        \"temperature\": 0.0,\n",
-        "        \"top_p\": 0.99,\n",
-        "        \"frequency_penalty\": 0,\n",
-        "        \"presence_penalty\": 0,\n",
-        "        \"stop\": None,\n",
-        "    },\n",
-        "}\n",
-        "\n",
-        "# used as optimizer and backward engine\n",
-        "gpt_4o_mini_model = {\n",
-        "    \"model_client\": OpenAIClient(),\n",
-        "    \"model_kwargs\": {\n",
-        "        \"model\": \"gpt-4o-mini\",\n",
-        "        \"temperature\": 1,\n",
-        "        \"top_p\": 0.99,\n",
-        "        \"max_tokens\": 1000,\n",
-        "        # \"frequency_penalty\": 1,  # high for nto repeating prompt\n",
-        "    },\n",
-        "}"
-      ],
-      "metadata": {
-        "id": "yAvzn7DZeUX-"
-      },
-      "execution_count": null,
-      "outputs": []
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "\n",
+      "Training Step: 1:   0%|          | 0/30 [00:00<?, ?it/s]\n",
+      "\n",
+      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 328.98it/s]\n",
+      "Training: 100%|██████████| 4/4 [00:00<00:00,  5.31it/s]\n",
+      "\n",
+      "\n",
+      "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 548.94it/s]\n",
+      "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 6197.72it/s]\n",
+      "Evaluating: 100%|██████████| 4/4 [00:00<00:00, 5187.76it/s]\n"
+     ]
     },
     {
-      "cell_type": "markdown",
-      "source": [
-        "Create the task pipeline"
-      ],
-      "metadata": {
-        "id": "G664uy9MgDdC"
-      }
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Moving batch acc: 0.75\n",
+      "Moving batch correct size: 3\n",
+      "Moving batch error size: 1\n",
+      "Subset Error size: 1\n",
+      "Subset Correct size: 2\n",
+      "Subset score: 0.6666666666666666\n",
+      "Subset batch acc: 0.6666666666666666\n",
+      "Subset loss backward...\n",
+      "setting pred name Generator_outputy_pred_3 score to 1.0\n",
+      "setting pred name Generator_outputy_pred_1 score to 1.0\n",
+      "setting pred name Generator_outputy_pred_2 score to 0.0\n",
+      "Subset loss backward time: 10.694303750991821\n",
+      "Optimizer propose...\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "source": [
-        "class TRECClassifierStructuredOutput(adal.Component):\n",
-        "\n",
-        "    def __init__(self, model_client: adal.ModelClient, model_kwargs: Dict):\n",
-        "        super().__init__()\n",
-        "\n",
-        "        label_desc = [\n",
-        "            {\"label\": label, \"desc\": desc}\n",
-        "            for label, desc in zip(_COARSE_LABELS, _COARSE_LABELS_DESC)\n",
-        "        ]\n",
-        "\n",
-        "        task_desc_str = adal.Prompt(\n",
-        "            template=task_desc_template, prompt_kwargs={\"classes\": label_desc}\n",
-        "        )()\n",
-        "\n",
-        "        self.data_class = TRECExtendedData\n",
-        "        self.data_class.set_task_desc(task_desc_str)\n",
-        "\n",
-        "        self.parser = adal.DataClassParser(\n",
-        "            data_class=self.data_class, return_data_class=True, format_type=\"yaml\"\n",
-        "        )\n",
-        "\n",
-        "        prompt_kwargs = {\n",
-        "            \"system_prompt\": adal.Parameter(\n",
-        "                data=self.parser.get_task_desc_str(),\n",
-        "                role_desc=\"Task description\",\n",
-        "                requires_opt=True,\n",
-        "                param_type=adal.ParameterType.PROMPT,\n",
-        "            ),\n",
-        "            \"output_format_str\": adal.Parameter(\n",
-        "                data=self.parser.get_output_format_str(),\n",
-        "                role_desc=\"Output format requirements\",\n",
-        "                requires_opt=False,\n",
-        "                param_type=adal.ParameterType.PROMPT,\n",
-        "            ),\n",
-        "            \"few_shot_demos\": adal.Parameter(\n",
-        "                data=None,\n",
-        "                requires_opt=True,\n",
-        "                role_desc=\"Few shot examples to help the model\",\n",
-        "                param_type=adal.ParameterType.DEMOS,\n",
-        "            ),\n",
-        "        }\n",
-        "\n",
-        "        self.llm = adal.Generator(\n",
-        "            model_client=model_client,\n",
-        "            model_kwargs=model_kwargs,\n",
-        "            prompt_kwargs=prompt_kwargs,\n",
-        "            template=template,\n",
-        "            output_processors=self.parser,\n",
-        "            use_cache=True,\n",
-        "        )\n",
-        "\n",
-        "    def _prepare_input(self, question: str):\n",
-        "        input_data = self.data_class(question=question)\n",
-        "        input_str = self.parser.get_input_str(input_data)\n",
-        "        prompt_kwargs = {\n",
-        "            \"input_str\": adal.Parameter(\n",
-        "                data=input_str, requires_opt=False, role_desc=\"input to the LLM\"\n",
-        "            )\n",
-        "        }\n",
-        "        return prompt_kwargs\n",
-        "\n",
-        "    def call(\n",
-        "        self, question: str, id: Optional[str] = None\n",
-        "    ) -> Union[adal.GeneratorOutput, adal.Parameter]:\n",
-        "        prompt_kwargs = self._prepare_input(question)\n",
-        "        output = self.llm(prompt_kwargs=prompt_kwargs, id=id)\n",
-        "        return output"
-      ],
-      "metadata": {
-        "id": "3Q3H9XC4Ncfi"
-      },
-      "execution_count": null,
-      "outputs": []
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "\n",
+      "\n",
+      "Proposing:   0%|          | 0/5 [00:00<?, ?it/s]\u001b[A\u001b[A"
+     ]
     },
     {
-      "cell_type": "markdown",
-      "source": [
-        "Inference the task pipeline and draw the computation graph"
-      ],
-      "metadata": {
-        "id": "gj08oOqqgGyr"
-      }
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "New prompts:  [PromptData(id='53b75924-9350-4ffb-9710-64652feabf23', name='llm.system_prompt', data='You are a classifier. Given a question, classify it into one of the classes:\\nFormat: class_index. class_name, class_description\\n0. ABBR, Abbreviation: Questions about abbreviations and their meanings\\n1. DESC, Description: Questions seeking descriptions of people, things, or concepts\\n2. ENTY, Entity: Questions about entities (e.g., animals, colors, inventions)\\n3. HUM, Human: Questions about people or organizations\\n4. LOC, Location: Questions about places, cities, countries\\n5. NUM, Numeric: Questions seeking numeric answers (e.g., dates, amounts, distances)\\n- Do not attempt to answer the question directly. Ensure your classification is precise and reflects the specific focus of the inquiry:', requires_opt=True), PromptData(id='2218906f-9600-4ff8-8532-a8038ef6cb63', name='llm.output_format_str', data='Your output should be formatted as a standard YAML instance with the following schema:\\n```\\nrationale: Your step-by-step reasoning to classify the question to class_name (str) (optional)\\nclass_name: One of {ABBR, ENTY, DESC, HUM, LOC, NUM} (str) (optional)\\n```\\n-Make sure to always enclose the YAML output in triple backticks (```). Please do not add anything other than valid YAML output!\\n-Follow the YAML formatting conventions with an indent of 2 spaces.\\n-DO NOT mistaken the \"properties\" and \"type\" in the schema as the actual fields in the YAML output.\\n-Quote the string values properly.', requires_opt=False), PromptData(id='8640ed64-3658-445d-ad82-011d398499f2', name='llm.few_shot_demos', data=None, requires_opt=True)]\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "source": [
-        "# load dataset to get one example\n",
-        "\n",
-        "train_dataset, val_dataset, test_dataset = load_datasets()\n",
-        "example = train_dataset[0]\n",
-        "print(example)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "qtvLN8zOgnSg",
-        "outputId": "9996f8c3-371d-4b5c-ec48-e8cf6d6c396b"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "TrecData(id='e73a82a7-6a3d-4947-90f5-03739e169db0', question='When reading classified ads , what does EENTY : other stand for ?', class_name='ABBR', class_index=0)\n"
-          ]
-        }
-      ]
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "\n",
+      "\n",
+      "\n",
+      "Loading Data: 100%|██████████| 3/3 [00:00<00:00, 651.26it/s]\n",
+      "Predicting: step(0): 0.6667 across 3 samples, Max potential: 0.6667: 100%|██████████| 3/3 [00:00<00:00,  3.59it/s]\n",
+      "\n",
+      "\n",
+      "Proposing:  20%|██        | 1/5 [00:03<00:13,  3.27s/it]\u001b[A\u001b[A"
+     ]
     },
     {
-      "cell_type": "code",
-      "source": [
-        "task = TRECClassifierStructuredOutput(\n",
-        "    model_client=gpt_3_model[\"model_client\"],\n",
-        "    model_kwargs=gpt_3_model[\"model_kwargs\"],\n",
-        ")\n",
-        "task.train()\n",
-        "\n",
-        "output = task(question=example.question, id=example.id)\n",
-        "print(output)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "cKuW3QlhgLTG",
-        "outputId": "7f1f9cd6-9615-4b41-ecc5-5901626d57ae"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Parameter(name=Generator_output, requires_opt=True, param_type=generator_output (The output of the generator.), role_desc=Output from (llm) Generator, data=```\n",
-            "rationale: The question is asking for the meaning of the abbreviation \"EENTY\" in classified ads, which falls under the ABBR class.\n",
-            "class_name: ABBR\n",
-            "```, predecessors={Parameter(name=Output_for, requires_opt=False, param_type=prompt (Instruction to the language model on task, data, and format.), role_desc=Output format requirements, data=Your output should be formatted as a standard YAML instance with the following schema:\n",
-            "```\n",
-            "rationale: Your step-by-step reasoning to classify the question to class_name (str) (optional)\n",
-            "class_name: One of {ABBR, ENTY, DESC, HUM, LOC, NUM} (str) (optional)\n",
-            "```\n",
-            "-Make sure to always enclose the YAML output in triple backticks (```). Please do not add anything other than valid YAML output!\n",
-            "-Follow the YAML formatting conventions with an indent of 2 spaces.\n",
-            "-DO NOT mistaken the \"properties\" and \"type\" in the schema as the actual fields in the YAML output.\n",
-            "-Quote the string values properly., predecessors=set(), gradients=[],            raw_response=None, input_args=None, traces={}), Parameter(name=Few_shot_e, requires_opt=True, param_type=demos (A few examples to guide the language model.), role_desc=Few shot examples to help the model, data=None, predecessors=set(), gradients=[],            raw_response=None, input_args=None, traces={}), Parameter(name=Input_to_t, requires_opt=False, param_type=none (), role_desc=input to the LLM, data=question: 'When reading classified ads , what does EENTY : other stand for ?', predecessors=set(), gradients=[],            raw_response=None, input_args=None, traces={}), Parameter(name=Task_descr, requires_opt=True, param_type=prompt (Instruction to the language model on task, data, and format.), role_desc=Task description, data=You are a classifier. Given a question, you need to classify it into one of the following classes:\n",
-            " Format: class_index. class_name, class_description\n",
-            " 0. ABBR, Abbreviation: Questions about abbreviations and their meanings\n",
-            " 1. DESC, Description: Questions seeking descriptions of people, things, or concepts\n",
-            " 2. ENTY, Entity: Questions about entities (e.g., animals, colors, inventions)\n",
-            " 3. HUM, Human: Questions about people or organizations\n",
-            " 4. LOC, Location: Questions about places, cities, countries\n",
-            " 5. NUM, Numeric: Questions seeking numeric answers (e.g., dates, amounts, distances)\n",
-            " - Do not try to answer the question:\n",
-            " , predecessors=set(), gradients=[],            raw_response=None, input_args=None, traces={})}, gradients=[],            raw_response=None, input_args={'prompt_kwargs': {'system_prompt': Parameter(name=Task_descr, requires_opt=True, param_type=prompt (Instruction to the language model on task, data, and format.), role_desc=Task description, data=You are a classifier. Given a question, you need to classify it into one of the following classes:\n",
-            " Format: class_index. class_name, class_description\n",
-            " 0. ABBR, Abbreviation: Questions about abbreviations and their meanings\n",
-            " 1. DESC, Description: Questions seeking descriptions of people, things, or concepts\n",
-            " 2. ENTY, Entity: Questions about entities (e.g., animals, colors, inventions)\n",
-            " 3. HUM, Human: Questions about people or organizations\n",
-            " 4. LOC, Location: Questions about places, cities, countries\n",
-            " 5. NUM, Numeric: Questions seeking numeric answers (e.g., dates, amounts, distances)\n",
-            " - Do not try to answer the question:\n",
-            " , predecessors=set(), gradients=[],            raw_response=None, input_args=None, traces={}), 'output_format_str': Parameter(name=Output_for, requires_opt=False, param_type=prompt (Instruction to the language model on task, data, and format.), role_desc=Output format requirements, data=Your output should be formatted as a standard YAML instance with the following schema:\n",
-            "```\n",
-            "rationale: Your step-by-step reasoning to classify the question to class_name (str) (optional)\n",
-            "class_name: One of {ABBR, ENTY, DESC, HUM, LOC, NUM} (str) (optional)\n",
-            "```\n",
-            "-Make sure to always enclose the YAML output in triple backticks (```). Please do not add anything other than valid YAML output!\n",
-            "-Follow the YAML formatting conventions with an indent of 2 spaces.\n",
-            "-DO NOT mistaken the \"properties\" and \"type\" in the schema as the actual fields in the YAML output.\n",
-            "-Quote the string values properly., predecessors=set(), gradients=[],            raw_response=None, input_args=None, traces={}), 'few_shot_demos': Parameter(name=Few_shot_e, requires_opt=True, param_type=demos (A few examples to guide the language model.), role_desc=Few shot examples to help the model, data=None, predecessors=set(), gradients=[],            raw_response=None, input_args=None, traces={}), 'input_str': Parameter(name=Input_to_t, requires_opt=False, param_type=none (), role_desc=input to the LLM, data=question: 'When reading classified ads , what does EENTY : other stand for ?', predecessors=set(), gradients=[],            raw_response=None, input_args=None, traces={})}, 'model_kwargs': {'model': 'gpt-3.5-turbo', 'max_tokens': 2000, 'temperature': 0.0, 'top_p': 0.99, 'frequency_penalty': 0, 'presence_penalty': 0, 'stop': None}}, traces={})\n"
-          ]
-        }
-      ]
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "completed_samples: 3, len: 3\n",
+      "Fail subset check, try next proposal: 0.6666666666666666 <= 0.6666666666666666\n",
+      "New prompts:  [PromptData(id='53b75924-9350-4ffb-9710-64652feabf23', name='llm.system_prompt', data='You are a classifier. Given a question, classify it into one of the following classes by following these guidelines: \\nFormat: class_index. class_name, class_description \\n0. ABBR, Abbreviation: Questions about abbreviations and their meanings \\n1. DESC, Description: Questions seeking descriptions of people, things, or concepts \\n2. ENTY, Entity: Questions about entities (e.g., animals, colors, inventions) \\n3. HUM, Human: Questions about people or organizations \\n4. LOC, Location: Questions about places, cities, countries \\n5. NUM, Numeric: Questions seeking numeric answers (e.g., dates, amounts, distances) \\n- Avoid attempting to directly answer the question; instead, focus on accurate classification based on specific criteria: \\n- Ensure the category accurately represents the essence of the question to avoid misclassification.', requires_opt=True), PromptData(id='2218906f-9600-4ff8-8532-a8038ef6cb63', name='llm.output_format_str', data='Your output should be formatted as a standard YAML instance with the following schema:\\n```\\nrationale: Your step-by-step reasoning to classify the question to class_name (str) (optional)\\nclass_name: One of {ABBR, ENTY, DESC, HUM, LOC, NUM} (str) (optional)\\n```\\n-Make sure to always enclose the YAML output in triple backticks (```). Please do not add anything other than valid YAML output!\\n-Follow the YAML formatting conventions with an indent of 2 spaces.\\n-DO NOT mistaken the \"properties\" and \"type\" in the schema as the actual fields in the YAML output.\\n-Quote the string values properly.', requires_opt=False), PromptData(id='8640ed64-3658-445d-ad82-011d398499f2', name='llm.few_shot_demos', data=None, requires_opt=True)]\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "source": [
-        "class TrecClassifierAdal(adal.AdalComponent):\n",
-        "    def __init__(\n",
-        "        self,\n",
-        "        model_client: adal.ModelClient,\n",
-        "        model_kwargs: Dict,\n",
-        "        teacher_model_config: Dict,\n",
-        "        backward_engine_model_config: Dict,\n",
-        "        text_optimizer_model_config: Dict,\n",
-        "    ):\n",
-        "        task = TRECClassifierStructuredOutput(model_client, model_kwargs)\n",
-        "        eval_fn = AnswerMatchAcc(type=\"exact_match\").compute_single_item\n",
-        "        loss_fn = adal.EvalFnToTextLoss(\n",
-        "            eval_fn=eval_fn,\n",
-        "            eval_fn_desc=\"exact_match: 1 if str(y) == str(y_gt) else 0\",\n",
-        "        )\n",
-        "        super().__init__(\n",
-        "            task=task,\n",
-        "            eval_fn=eval_fn,\n",
-        "            loss_fn=loss_fn,\n",
-        "            backward_engine_model_config=backward_engine_model_config,\n",
-        "            text_optimizer_model_config=text_optimizer_model_config,\n",
-        "            teacher_model_config=teacher_model_config,\n",
-        "        )\n",
-        "\n",
-        "    def prepare_task(self, sample: TRECExtendedData):\n",
-        "        return self.task.call, {\"question\": sample.question, \"id\": sample.id}\n",
-        "\n",
-        "    def prepare_eval(\n",
-        "        self, sample: TRECExtendedData, y_pred: adal.GeneratorOutput\n",
-        "    ) -> float:\n",
-        "        y_label = -1\n",
-        "        if y_pred and y_pred.data is not None and y_pred.data.class_name is not None:\n",
-        "            y_label = y_pred.data.class_name\n",
-        "        return self.eval_fn, {\"y\": y_label, \"y_gt\": sample.class_name}\n",
-        "\n",
-        "    def prepare_loss(\n",
-        "        self, sample: TRECExtendedData, y_pred: adal.Parameter, *args, **kwargs\n",
-        "    ) -> Tuple[Callable[..., Any], Dict]:\n",
-        "        full_response = y_pred.full_response\n",
-        "        y_label = -1\n",
-        "        if (\n",
-        "            full_response\n",
-        "            and full_response.data is not None\n",
-        "            and full_response.data.class_name is not None\n",
-        "        ):\n",
-        "            y_label = full_response.data.class_name\n",
-        "\n",
-        "        y_pred.eval_input = y_label\n",
-        "        y_gt = adal.Parameter(\n",
-        "            name=\"y_gt\",\n",
-        "            data=sample.class_name,\n",
-        "            eval_input=sample.class_name,\n",
-        "            requires_opt=False,\n",
-        "        )\n",
-        "        return self.loss_fn, {\"kwargs\": {\"y\": y_pred, \"y_gt\": y_gt}}"
-      ],
-      "metadata": {
-        "id": "HpkQYsh2NevT"
-      },
-      "execution_count": null,
-      "outputs": []
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "\n",
+      "\n",
+      "\n",
+      "Loading Data: 100%|██████████| 3/3 [00:00<00:00, 302.95it/s]\n",
+      "Predicting: step(0): 0.6667 across 3 samples, Max potential: 0.6667: 100%|██████████| 3/3 [00:00<00:00,  4.25it/s]\n",
+      "\n",
+      "\n",
+      "Proposing:  40%|████      | 2/5 [00:06<00:09,  3.24s/it]\u001b[A\u001b[A"
+     ]
     },
     {
-      "cell_type": "code",
-      "source": [
-        "def train(\n",
-        "    model_client: adal.ModelClient,\n",
-        "    model_kwargs: Dict,\n",
-        "    train_batch_size=4,\n",
-        "    raw_shots: int = 0,\n",
-        "    bootstrap_shots: int = 1,\n",
-        "    max_steps=12,\n",
-        "    num_workers=4,\n",
-        "    strategy=\"constrained\",\n",
-        "    optimization_order=\"sequential\",\n",
-        "    debug=False,\n",
-        "):\n",
-        "    print(\"Starting training process...\")\n",
-        "\n",
-        "    # Define the model configuration for all components\n",
-        "    gpt_4o_model = {\n",
-        "    \"model_client\": OpenAIClient(),\n",
-        "    \"model_kwargs\": {\n",
-        "        \"model\": \"gpt-4o-mini\",\n",
-        "        \"temperature\": 1,\n",
-        "        \"top_p\": 0.99,\n",
-        "        \"max_tokens\": 1000,\n",
-        "        # \"frequency_penalty\": 1,  # high for nto repeating prompt\n",
-        "    },\n",
-        "  }\n",
-        "\n",
-        "    print(f\"Component model configuration: {gpt_4o_model}\")\n",
-        "\n",
-        "    try:\n",
-        "        print(\"Initializing ADAL component...\")\n",
-        "        adal_component = TrecClassifierAdal(\n",
-        "            model_client=model_client,\n",
-        "            model_kwargs=model_kwargs,\n",
-        "            text_optimizer_model_config=gpt_4o_model,\n",
-        "            backward_engine_model_config=gpt_4o_model,\n",
-        "            teacher_model_config=gpt_4o_model,\n",
-        "        )\n",
-        "        print(\"ADAL component initialized successfully\")\n",
-        "\n",
-        "        print(\"Initializing trainer...\")\n",
-        "        trainer = adal.Trainer(\n",
-        "            train_batch_size=train_batch_size,\n",
-        "            adaltask=adal_component,\n",
-        "            strategy=strategy,\n",
-        "            max_steps=max_steps,\n",
-        "            num_workers=num_workers,\n",
-        "            raw_shots=raw_shots,\n",
-        "            bootstrap_shots=bootstrap_shots,\n",
-        "            debug=debug,\n",
-        "            weighted_sampling=True,\n",
-        "            optimization_order=optimization_order,\n",
-        "            exclude_input_fields_from_bootstrap_demos=True,\n",
-        "        )\n",
-        "        print(\"Trainer initialized successfully\")\n",
-        "\n",
-        "        print(\"Loading datasets...\")\n",
-        "        train_dataset, val_dataset, test_dataset = load_datasets()\n",
-        "        print(\n",
-        "            f\"Datasets loaded - Train size: {len(train_dataset)}, Val size: {len(val_dataset)}, Test size: {len(test_dataset)}\"\n",
-        "        )\n",
-        "\n",
-        "        print(\"Starting model training...\")\n",
-        "        trainer.fit(\n",
-        "            train_dataset=train_dataset,\n",
-        "            val_dataset=test_dataset,\n",
-        "            debug=debug,\n",
-        "        )\n",
-        "        print(\"Training completed successfully\")\n",
-        "\n",
-        "    except Exception as e:\n",
-        "        print(f\"Error occurred: {str(e)}\")\n",
-        "        raise"
-      ],
-      "metadata": {
-        "id": "PEj6xiZ5dVaj"
-      },
-      "execution_count": null,
-      "outputs": []
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "completed_samples: 3, len: 3\n",
+      "Fail subset check, try next proposal: 0.6666666666666666 <= 0.6666666666666666\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "source": [
-        "train(**gpt_3_model)"
-      ],
-      "metadata": {
-        "id": "GnlZBQOMEj6E",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 1000
-        },
-        "outputId": "055a95c4-ccae-4028-d904-86b839bc1c14"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Starting training process...\n",
-            "Component model configuration: {'model_client': OpenAIClient(), 'model_kwargs': {'model': 'gpt-4o-mini', 'temperature': 1, 'top_p': 0.99, 'max_tokens': 1000}}\n",
-            "Initializing ADAL component...\n",
-            "ADAL component initialized successfully\n",
-            "Initializing trainer...\n",
-            "Trainer initialized successfully\n",
-            "Loading datasets...\n",
-            "Datasets loaded - Train size: 120, Val size: 36, Test size: 144\n",
-            "Starting model training...\n",
-            "raw_shots: 0, bootstrap_shots: 1\n",
-            "Configuring teacher generator.\n",
-            "Configuring teacher generator for Generator(\n",
-            "  model_kwargs={'model': 'gpt-4o-mini', 'temperature': 1, 'top_p': 0.99, 'max_tokens': 1000}, trainable_prompt_kwargs=[]\n",
-            "  (prompt): Prompt(\n",
-            "    template: <START_OF_SYSTEM_MESSAGE>\n",
-            "     {{system_prompt}}\n",
-            "     {% if output_format_str is not none %}\n",
-            "     {{output_format_str}}\n",
-            "     {% endif %}\n",
-            "     {% if few_shot_demos is not none %}\n",
-            "     Here are some examples:\n",
-            "     {{few_shot_demos}}\n",
-            "     {% endif %}\n",
-            "     <END_OF_SYSTEM_MESSAGE>\n",
-            "     <START_OF_USER_MESSAGE>\n",
-            "     {{input_str}}\n",
-            "     <END_OF_USER_MESSAGE>\n",
-            "     , prompt_kwargs: {'system_prompt': 'You are a classifier. Given a question, you need to classify it into one of the following classes:\\n Format: class_index. class_name, class_description\\n 0. ABBR, Abbreviation: Questions about abbreviations and their meanings\\n 1. DESC, Description: Questions seeking descriptions of people, things, or concepts\\n 2. ENTY, Entity: Questions about entities (e.g., animals, colors, inventions)\\n 3. HUM, Human: Questions about people or organizations\\n 4. LOC, Location: Questions about places, cities, countries\\n 5. NUM, Numeric: Questions seeking numeric answers (e.g., dates, amounts, distances)\\n - Do not try to answer the question:\\n ', 'output_format_str': 'Your output should be formatted as a standard YAML instance with the following schema:\\n```\\nrationale: Your step-by-step reasoning to classify the question to class_name (str) (optional)\\nclass_name: One of {ABBR, ENTY, DESC, HUM, LOC, NUM} (str) (optional)\\n```\\n-Make sure to always enclose the YAML output in triple backticks (```). Please do not add anything other than valid YAML output!\\n-Follow the YAML formatting conventions with an indent of 2 spaces.\\n-DO NOT mistaken the \"properties\" and \"type\" in the schema as the actual fields in the YAML output.\\n-Quote the string values properly.', 'few_shot_demos': 'None'}, prompt_variables: ['output_format_str', 'system_prompt', 'input_str', 'few_shot_demos']\n",
-            "  )\n",
-            "  (model_client): OpenAIClient()\n",
-            "  (output_processors): DataClassParser(\n",
-            "    data_class=TRECExtendedData, format_type=yaml,            return_data_class=True, input_fields=['question'],            output_fields=['rationale', 'class_name']\n",
-            "    (_output_processor): YamlParser()\n",
-            "    (output_format_prompt): Prompt(\n",
-            "      template: Your output should be formatted as a standard YAML instance with the following schema:\n",
-            "      ```\n",
-            "      {{schema}}\n",
-            "      ```\n",
-            "      -Make sure to always enclose the YAML output in triple backticks (```). Please do not add anything other than valid YAML output!\n",
-            "      -Follow the YAML formatting conventions with an indent of 2 spaces.\n",
-            "      -DO NOT mistaken the \"properties\" and \"type\" in the schema as the actual fields in the YAML output.\n",
-            "      -Quote the string values properly., prompt_variables: ['schema']\n",
-            "    )\n",
-            "  )\n",
-            ")\n",
-            "Teacher generator set: Generator(\n",
-            "  model_kwargs={'model': 'gpt-4o-mini', 'temperature': 1, 'top_p': 0.99, 'max_tokens': 1000}, trainable_prompt_kwargs=[]\n",
-            "  (prompt): Prompt(\n",
-            "    template: <START_OF_SYSTEM_MESSAGE>\n",
-            "     {{system_prompt}}\n",
-            "     {% if output_format_str is not none %}\n",
-            "     {{output_format_str}}\n",
-            "     {% endif %}\n",
-            "     {% if few_shot_demos is not none %}\n",
-            "     Here are some examples:\n",
-            "     {{few_shot_demos}}\n",
-            "     {% endif %}\n",
-            "     <END_OF_SYSTEM_MESSAGE>\n",
-            "     <START_OF_USER_MESSAGE>\n",
-            "     {{input_str}}\n",
-            "     <END_OF_USER_MESSAGE>\n",
-            "     , prompt_kwargs: {'system_prompt': 'You are a classifier. Given a question, you need to classify it into one of the following classes:\\n Format: class_index. class_name, class_description\\n 0. ABBR, Abbreviation: Questions about abbreviations and their meanings\\n 1. DESC, Description: Questions seeking descriptions of people, things, or concepts\\n 2. ENTY, Entity: Questions about entities (e.g., animals, colors, inventions)\\n 3. HUM, Human: Questions about people or organizations\\n 4. LOC, Location: Questions about places, cities, countries\\n 5. NUM, Numeric: Questions seeking numeric answers (e.g., dates, amounts, distances)\\n - Do not try to answer the question:\\n ', 'output_format_str': 'Your output should be formatted as a standard YAML instance with the following schema:\\n```\\nrationale: Your step-by-step reasoning to classify the question to class_name (str) (optional)\\nclass_name: One of {ABBR, ENTY, DESC, HUM, LOC, NUM} (str) (optional)\\n```\\n-Make sure to always enclose the YAML output in triple backticks (```). Please do not add anything other than valid YAML output!\\n-Follow the YAML formatting conventions with an indent of 2 spaces.\\n-DO NOT mistaken the \"properties\" and \"type\" in the schema as the actual fields in the YAML output.\\n-Quote the string values properly.', 'few_shot_demos': 'None'}, prompt_variables: ['output_format_str', 'system_prompt', 'input_str', 'few_shot_demos']\n",
-            "  )\n",
-            "  (model_client): OpenAIClient()\n",
-            "  (output_processors): DataClassParser(\n",
-            "    data_class=TRECExtendedData, format_type=yaml,            return_data_class=True, input_fields=['question'],            output_fields=['rationale', 'class_name']\n",
-            "    (_output_processor): YamlParser()\n",
-            "    (output_format_prompt): Prompt(\n",
-            "      template: Your output should be formatted as a standard YAML instance with the following schema:\n",
-            "      ```\n",
-            "      {{schema}}\n",
-            "      ```\n",
-            "      -Make sure to always enclose the YAML output in triple backticks (```). Please do not add anything other than valid YAML output!\n",
-            "      -Follow the YAML formatting conventions with an indent of 2 spaces.\n",
-            "      -DO NOT mistaken the \"properties\" and \"type\" in the schema as the actual fields in the YAML output.\n",
-            "      -Quote the string values properly., prompt_variables: ['schema']\n",
-            "    )\n",
-            "  )\n",
-            "), teacher Generator(\n",
-            "  model_kwargs={'model': 'gpt-4o-mini', 'temperature': 1, 'top_p': 0.99, 'max_tokens': 1000}, trainable_prompt_kwargs=[]\n",
-            "  (prompt): Prompt(\n",
-            "    template: <START_OF_SYSTEM_MESSAGE>\n",
-            "     {{system_prompt}}\n",
-            "     {% if output_format_str is not none %}\n",
-            "     {{output_format_str}}\n",
-            "     {% endif %}\n",
-            "     {% if few_shot_demos is not none %}\n",
-            "     Here are some examples:\n",
-            "     {{few_shot_demos}}\n",
-            "     {% endif %}\n",
-            "     <END_OF_SYSTEM_MESSAGE>\n",
-            "     <START_OF_USER_MESSAGE>\n",
-            "     {{input_str}}\n",
-            "     <END_OF_USER_MESSAGE>\n",
-            "     , prompt_kwargs: {'system_prompt': 'You are a classifier. Given a question, you need to classify it into one of the following classes:\\n Format: class_index. class_name, class_description\\n 0. ABBR, Abbreviation: Questions about abbreviations and their meanings\\n 1. DESC, Description: Questions seeking descriptions of people, things, or concepts\\n 2. ENTY, Entity: Questions about entities (e.g., animals, colors, inventions)\\n 3. HUM, Human: Questions about people or organizations\\n 4. LOC, Location: Questions about places, cities, countries\\n 5. NUM, Numeric: Questions seeking numeric answers (e.g., dates, amounts, distances)\\n - Do not try to answer the question:\\n ', 'output_format_str': 'Your output should be formatted as a standard YAML instance with the following schema:\\n```\\nrationale: Your step-by-step reasoning to classify the question to class_name (str) (optional)\\nclass_name: One of {ABBR, ENTY, DESC, HUM, LOC, NUM} (str) (optional)\\n```\\n-Make sure to always enclose the YAML output in triple backticks (```). Please do not add anything other than valid YAML output!\\n-Follow the YAML formatting conventions with an indent of 2 spaces.\\n-DO NOT mistaken the \"properties\" and \"type\" in the schema as the actual fields in the YAML output.\\n-Quote the string values properly.', 'few_shot_demos': 'None'}, prompt_variables: ['output_format_str', 'system_prompt', 'input_str', 'few_shot_demos']\n",
-            "  )\n",
-            "  (model_client): OpenAIClient()\n",
-            "  (output_processors): DataClassParser(\n",
-            "    data_class=TRECExtendedData, format_type=yaml,            return_data_class=True, input_fields=['question'],            output_fields=['rationale', 'class_name']\n",
-            "    (_output_processor): YamlParser()\n",
-            "    (output_format_prompt): Prompt(\n",
-            "      template: Your output should be formatted as a standard YAML instance with the following schema:\n",
-            "      ```\n",
-            "      {{schema}}\n",
-            "      ```\n",
-            "      -Make sure to always enclose the YAML output in triple backticks (```). Please do not add anything other than valid YAML output!\n",
-            "      -Follow the YAML formatting conventions with an indent of 2 spaces.\n",
-            "      -DO NOT mistaken the \"properties\" and \"type\" in the schema as the actual fields in the YAML output.\n",
-            "      -Quote the string values properly., prompt_variables: ['schema']\n",
-            "    )\n",
-            "  )\n",
-            ")\n",
-            "Teacher generator configured.\n",
-            "Configured demo optimizers\n",
-            "Backward engine configured for all generators.\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "\n",
-            "Loading Data: 100%|██████████| 144/144 [00:00<00:00, 9161.62it/s]\n",
-            "Predicting: step(0): 0.8264 across 144 samples, Max potential: 0.8264: 100%|██████████| 144/144 [00:19<00:00,  7.39it/s]\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "completed_samples: 144, len: 144\n",
-            "Initial validation score: 0.8263888888888888\n",
-            "Initial test score: None\n",
-            "Checkpoint path: /root/.adalflow/ckpt/TrecClassifierAdal\n",
-            "save to /root/.adalflow/ckpt/TrecClassifierAdal/constrained_max_steps_12_a6e76_run_1.json\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "\n",
-            "Training Step: 1:   0%|          | 0/30 [00:00<?, ?it/s]\n",
-            "\n",
-            "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 328.98it/s]\n",
-            "Training: 100%|██████████| 4/4 [00:00<00:00,  5.31it/s]\n",
-            "\n",
-            "\n",
-            "Loading Data: 100%|██████████| 4/4 [00:00<00:00, 548.94it/s]\n",
-            "Calculating Loss: 100%|██████████| 4/4 [00:00<00:00, 6197.72it/s]\n",
-            "Evaluating: 100%|██████████| 4/4 [00:00<00:00, 5187.76it/s]\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Moving batch acc: 0.75\n",
-            "Moving batch correct size: 3\n",
-            "Moving batch error size: 1\n",
-            "Subset Error size: 1\n",
-            "Subset Correct size: 2\n",
-            "Subset score: 0.6666666666666666\n",
-            "Subset batch acc: 0.6666666666666666\n",
-            "Subset loss backward...\n",
-            "setting pred name Generator_outputy_pred_3 score to 1.0\n",
-            "setting pred name Generator_outputy_pred_1 score to 1.0\n",
-            "setting pred name Generator_outputy_pred_2 score to 0.0\n",
-            "Subset loss backward time: 10.694303750991821\n",
-            "Optimizer propose...\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "\n",
-            "\n",
-            "Proposing:   0%|          | 0/5 [00:00<?, ?it/s]\u001b[A\u001b[A"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "New prompts:  [PromptData(id='53b75924-9350-4ffb-9710-64652feabf23', name='llm.system_prompt', data='You are a classifier. Given a question, classify it into one of the classes:\\nFormat: class_index. class_name, class_description\\n0. ABBR, Abbreviation: Questions about abbreviations and their meanings\\n1. DESC, Description: Questions seeking descriptions of people, things, or concepts\\n2. ENTY, Entity: Questions about entities (e.g., animals, colors, inventions)\\n3. HUM, Human: Questions about people or organizations\\n4. LOC, Location: Questions about places, cities, countries\\n5. NUM, Numeric: Questions seeking numeric answers (e.g., dates, amounts, distances)\\n- Do not attempt to answer the question directly. Ensure your classification is precise and reflects the specific focus of the inquiry:', requires_opt=True), PromptData(id='2218906f-9600-4ff8-8532-a8038ef6cb63', name='llm.output_format_str', data='Your output should be formatted as a standard YAML instance with the following schema:\\n```\\nrationale: Your step-by-step reasoning to classify the question to class_name (str) (optional)\\nclass_name: One of {ABBR, ENTY, DESC, HUM, LOC, NUM} (str) (optional)\\n```\\n-Make sure to always enclose the YAML output in triple backticks (```). Please do not add anything other than valid YAML output!\\n-Follow the YAML formatting conventions with an indent of 2 spaces.\\n-DO NOT mistaken the \"properties\" and \"type\" in the schema as the actual fields in the YAML output.\\n-Quote the string values properly.', requires_opt=False), PromptData(id='8640ed64-3658-445d-ad82-011d398499f2', name='llm.few_shot_demos', data=None, requires_opt=True)]\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "\n",
-            "\n",
-            "\n",
-            "Loading Data: 100%|██████████| 3/3 [00:00<00:00, 651.26it/s]\n",
-            "Predicting: step(0): 0.6667 across 3 samples, Max potential: 0.6667: 100%|██████████| 3/3 [00:00<00:00,  3.59it/s]\n",
-            "\n",
-            "\n",
-            "Proposing:  20%|██        | 1/5 [00:03<00:13,  3.27s/it]\u001b[A\u001b[A"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "completed_samples: 3, len: 3\n",
-            "Fail subset check, try next proposal: 0.6666666666666666 <= 0.6666666666666666\n",
-            "New prompts:  [PromptData(id='53b75924-9350-4ffb-9710-64652feabf23', name='llm.system_prompt', data='You are a classifier. Given a question, classify it into one of the following classes by following these guidelines: \\nFormat: class_index. class_name, class_description \\n0. ABBR, Abbreviation: Questions about abbreviations and their meanings \\n1. DESC, Description: Questions seeking descriptions of people, things, or concepts \\n2. ENTY, Entity: Questions about entities (e.g., animals, colors, inventions) \\n3. HUM, Human: Questions about people or organizations \\n4. LOC, Location: Questions about places, cities, countries \\n5. NUM, Numeric: Questions seeking numeric answers (e.g., dates, amounts, distances) \\n- Avoid attempting to directly answer the question; instead, focus on accurate classification based on specific criteria: \\n- Ensure the category accurately represents the essence of the question to avoid misclassification.', requires_opt=True), PromptData(id='2218906f-9600-4ff8-8532-a8038ef6cb63', name='llm.output_format_str', data='Your output should be formatted as a standard YAML instance with the following schema:\\n```\\nrationale: Your step-by-step reasoning to classify the question to class_name (str) (optional)\\nclass_name: One of {ABBR, ENTY, DESC, HUM, LOC, NUM} (str) (optional)\\n```\\n-Make sure to always enclose the YAML output in triple backticks (```). Please do not add anything other than valid YAML output!\\n-Follow the YAML formatting conventions with an indent of 2 spaces.\\n-DO NOT mistaken the \"properties\" and \"type\" in the schema as the actual fields in the YAML output.\\n-Quote the string values properly.', requires_opt=False), PromptData(id='8640ed64-3658-445d-ad82-011d398499f2', name='llm.few_shot_demos', data=None, requires_opt=True)]\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "\n",
-            "\n",
-            "\n",
-            "Loading Data: 100%|██████████| 3/3 [00:00<00:00, 302.95it/s]\n",
-            "Predicting: step(0): 0.6667 across 3 samples, Max potential: 0.6667: 100%|██████████| 3/3 [00:00<00:00,  4.25it/s]\n",
-            "\n",
-            "\n",
-            "Proposing:  40%|████      | 2/5 [00:06<00:09,  3.24s/it]\u001b[A\u001b[A"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "completed_samples: 3, len: 3\n",
-            "Fail subset check, try next proposal: 0.6666666666666666 <= 0.6666666666666666\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "Proposing:  40%|████      | 2/5 [00:07<00:10,  3.55s/it]\n",
-            "Training Step: 1:   0%|          | 0/30 [00:18<?, ?it/s]\n",
-            "Epoch:   0%|          | 0/1 [00:18<?, ?it/s]\n"
-          ]
-        },
-        {
-          "output_type": "error",
-          "ename": "KeyboardInterrupt",
-          "evalue": "",
-          "traceback": [
-            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-            "\u001b[0;32m<ipython-input-31-a934b5e59252>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mgpt_3_model\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-            "\u001b[0;32m<ipython-input-30-d83a6f6f0e0a>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(model_client, model_kwargs, train_batch_size, raw_shots, bootstrap_shots, max_steps, num_workers, strategy, optimization_order, debug)\u001b[0m\n\u001b[1;32m     61\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Starting model training...\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m         trainer.fit(\n\u001b[0m\u001b[1;32m     64\u001b[0m             \u001b[0mtrain_dataset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain_dataset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m             \u001b[0mval_dataset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtest_dataset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/adalflow/optim/trainer/trainer.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, adaltask, train_loader, train_dataset, val_dataset, test_dataset, debug, save_traces, raw_shots, bootstrap_shots, resume_from_ckpt)\u001b[0m\n\u001b[1;32m    477\u001b[0m                     \u001b[0mstarting_step\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_steps\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    478\u001b[0m                 \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrategy\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"constrained\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 479\u001b[0;31m                     trainer_results = self._fit_text_grad_constraint(\n\u001b[0m\u001b[1;32m    480\u001b[0m                         \u001b[0mtrain_loader\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    481\u001b[0m                         \u001b[0mval_dataset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/adalflow/optim/trainer/trainer.py\u001b[0m in \u001b[0;36m_fit_text_grad_constraint\u001b[0;34m(self, train_loader, val_dataset, test_dataset, trainer_results, starting_step)\u001b[0m\n\u001b[1;32m   1779\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1780\u001b[0m                 all_samples, all_losses, all_y_preds = (\n\u001b[0;32m-> 1781\u001b[0;31m                     self._text_grad_constraint_propose_step(\n\u001b[0m\u001b[1;32m   1782\u001b[0m                         \u001b[0msteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1783\u001b[0m                         \u001b[0mall_samples\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mall_samples\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/adalflow/optim/trainer/trainer.py\u001b[0m in \u001b[0;36m_text_grad_constraint_propose_step\u001b[0;34m(self, steps, all_samples, all_losses, all_y_preds, include_demo_optimizers)\u001b[0m\n\u001b[1;32m   1657\u001b[0m             \u001b[0;31m# print(f\"Proposing step: {i}\")\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1658\u001b[0m             \u001b[0;31m# self.optimizer.propose()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1659\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_propose_text_optimizers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# new prompts\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1660\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0minclude_demo_optimizers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1661\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_demo_optimizers_propose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/adalflow/optim/trainer/trainer.py\u001b[0m in \u001b[0;36m_propose_text_optimizers\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    857\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_propose_text_optimizers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    858\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mtext_optimizer\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext_optimizers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 859\u001b[0;31m             \u001b[0mtext_optimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpropose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    860\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    861\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_trainable_text_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/adalflow/optim/text_grad/tgd_optimizer.py\u001b[0m in \u001b[0;36mpropose\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    323\u001b[0m             }\n\u001b[1;32m    324\u001b[0m             \u001b[0;31m# turn off cache\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 325\u001b[0;31m             response = self.llm_optimizer.call(\n\u001b[0m\u001b[1;32m    326\u001b[0m                 \u001b[0mprompt_kwargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprompt_kwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_cache\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mnot\u001b[0m \u001b[0mno_cache\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    327\u001b[0m             )\n",
-            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/adalflow/core/generator.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, prompt_kwargs, model_kwargs, use_cache, id)\u001b[0m\n\u001b[1;32m    771\u001b[0m         \u001b[0;31m# call the model client\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    772\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 773\u001b[0;31m         \u001b[0mcompletion\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    774\u001b[0m         \u001b[0muse_cache\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0muse_cache\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0muse_cache\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_use_cache\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    775\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/adalflow/core/generator.py\u001b[0m in \u001b[0;36m_model_client_call\u001b[0;34m(self, api_kwargs, use_cache)\u001b[0m\n\u001b[1;32m    345\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    346\u001b[0m                 \u001b[0mcached_completion\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex_content\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 347\u001b[0;31m                 \u001b[0;32mif\u001b[0m \u001b[0mcached_completion\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    348\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0mcached_completion\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    349\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/backoff/_sync.py\u001b[0m in \u001b[0;36mretry\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    104\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 105\u001b[0;31m                 \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    106\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mexception\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    107\u001b[0m                 \u001b[0mmax_tries_exceeded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtries\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mmax_tries_value\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/adalflow/components/model_client/openai_client.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, api_kwargs, model_type)\u001b[0m\n\u001b[1;32m    285\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchat_completion_parser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhandle_streaming_response\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    286\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msync_client\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompletions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mapi_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 287\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msync_client\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompletions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mapi_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    288\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    289\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"model_type {model_type} is not supported\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/openai/_utils/_utils.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    273\u001b[0m                         \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"Missing required argument: {quote(missing[0])}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    274\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 275\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    276\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    277\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m  \u001b[0;31m# type: ignore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/openai/resources/chat/completions.py\u001b[0m in \u001b[0;36mcreate\u001b[0;34m(self, messages, model, audio, frequency_penalty, function_call, functions, logit_bias, logprobs, max_completion_tokens, max_tokens, metadata, modalities, n, parallel_tool_calls, prediction, presence_penalty, response_format, seed, service_tier, stop, store, stream, stream_options, temperature, tool_choice, tools, top_logprobs, top_p, user, extra_headers, extra_query, extra_body, timeout)\u001b[0m\n\u001b[1;32m    827\u001b[0m     ) -> ChatCompletion | Stream[ChatCompletionChunk]:\n\u001b[1;32m    828\u001b[0m         \u001b[0mvalidate_response_format\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse_format\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 829\u001b[0;31m         return self._post(\n\u001b[0m\u001b[1;32m    830\u001b[0m             \u001b[0;34m\"/chat/completions\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    831\u001b[0m             body=maybe_transform(\n",
-            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/openai/_base_client.py\u001b[0m in \u001b[0;36mpost\u001b[0;34m(self, path, cast_to, body, options, files, stream, stream_cls)\u001b[0m\n\u001b[1;32m   1276\u001b[0m             \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"post\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjson_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfiles\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mto_httpx_files\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiles\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1277\u001b[0m         )\n\u001b[0;32m-> 1278\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mResponseT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcast_to\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstream\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstream\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstream_cls\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstream_cls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1279\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1280\u001b[0m     def patch(\n",
-            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/openai/_base_client.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001b[0m\n\u001b[1;32m    953\u001b[0m             \u001b[0mretries_taken\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    954\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 955\u001b[0;31m         return self._request(\n\u001b[0m\u001b[1;32m    956\u001b[0m             \u001b[0mcast_to\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcast_to\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    957\u001b[0m             \u001b[0moptions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/openai/_base_client.py\u001b[0m in \u001b[0;36m_request\u001b[0;34m(self, cast_to, options, retries_taken, stream, stream_cls)\u001b[0m\n\u001b[1;32m    989\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    990\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 991\u001b[0;31m             response = self._client.send(\n\u001b[0m\u001b[1;32m    992\u001b[0m                 \u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    993\u001b[0m                 \u001b[0mstream\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstream\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_should_stream_response_body\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpx/_client.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, auth, follow_redirects)\u001b[0m\n\u001b[1;32m    899\u001b[0m         \u001b[0mauth\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_build_request_auth\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mauth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    900\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 901\u001b[0;31m         response = self._send_handling_auth(\n\u001b[0m\u001b[1;32m    902\u001b[0m             \u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    903\u001b[0m             \u001b[0mauth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mauth\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpx/_client.py\u001b[0m in \u001b[0;36m_send_handling_auth\u001b[0;34m(self, request, auth, follow_redirects, history)\u001b[0m\n\u001b[1;32m    927\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    928\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 929\u001b[0;31m                 response = self._send_handling_redirects(\n\u001b[0m\u001b[1;32m    930\u001b[0m                     \u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    931\u001b[0m                     \u001b[0mfollow_redirects\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfollow_redirects\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpx/_client.py\u001b[0m in \u001b[0;36m_send_handling_redirects\u001b[0;34m(self, request, follow_redirects, history)\u001b[0m\n\u001b[1;32m    964\u001b[0m                 \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    965\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 966\u001b[0;31m             \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_single_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    967\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    968\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_event_hooks\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"response\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpx/_client.py\u001b[0m in \u001b[0;36m_send_single_request\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m   1000\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1001\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mrequest_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1002\u001b[0;31m             \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtransport\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandle_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1003\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1004\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstream\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSyncByteStream\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpx/_transports/default.py\u001b[0m in \u001b[0;36mhandle_request\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m    216\u001b[0m         )\n\u001b[1;32m    217\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mmap_httpcore_exceptions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 218\u001b[0;31m             \u001b[0mresp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pool\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandle_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    219\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    220\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstream\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtyping\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIterable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpcore/_sync/connection_pool.py\u001b[0m in \u001b[0;36mhandle_request\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m    260\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0mShieldCancellation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    261\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresponse_closed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstatus\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 262\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    263\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    264\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpcore/_sync/connection_pool.py\u001b[0m in \u001b[0;36mhandle_request\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m    243\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    244\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 245\u001b[0;31m                 \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconnection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandle_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    246\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mConnectionNotAvailable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    247\u001b[0m                 \u001b[0;31m# The ConnectionNotAvailable exception is a special case, that\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpcore/_sync/connection.py\u001b[0m in \u001b[0;36mhandle_request\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m     94\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mConnectionNotAvailable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     95\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 96\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_connection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandle_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     97\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_connect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mRequest\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mNetworkStream\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpcore/_sync/http11.py\u001b[0m in \u001b[0;36mhandle_request\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m    119\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0mTrace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"response_closed\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogger\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtrace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_response_closed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 121\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    123\u001b[0m     \u001b[0;31m# Sending the request...\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpcore/_sync/http11.py\u001b[0m in \u001b[0;36mhandle_request\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m     97\u001b[0m                     \u001b[0mreason_phrase\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m                     \u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 99\u001b[0;31m                 ) = self._receive_response_headers(**kwargs)\n\u001b[0m\u001b[1;32m    100\u001b[0m                 trace.return_value = (\n\u001b[1;32m    101\u001b[0m                     \u001b[0mhttp_version\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpcore/_sync/http11.py\u001b[0m in \u001b[0;36m_receive_response_headers\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m    162\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    163\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 164\u001b[0;31m             \u001b[0mevent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_receive_event\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    165\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh11\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mResponse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    166\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpcore/_sync/http11.py\u001b[0m in \u001b[0;36m_receive_event\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    198\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    199\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mevent\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mh11\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNEED_DATA\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 200\u001b[0;31m                 data = self._network_stream.read(\n\u001b[0m\u001b[1;32m    201\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mREAD_NUM_BYTES\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    202\u001b[0m                 )\n",
-            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpcore/_backends/sync.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, max_bytes, timeout)\u001b[0m\n\u001b[1;32m     26\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mmap_exceptions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexc_map\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msettimeout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax_bytes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     29\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtyping\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/lib/python3.10/ssl.py\u001b[0m in \u001b[0;36mrecv\u001b[0;34m(self, buflen, flags)\u001b[0m\n\u001b[1;32m   1286\u001b[0m                     \u001b[0;34m\"non-zero flags not allowed in calls to recv() on %s\"\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1287\u001b[0m                     self.__class__)\n\u001b[0;32m-> 1288\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuflen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1289\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1290\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuflen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;32m/usr/lib/python3.10/ssl.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, len, buffer)\u001b[0m\n\u001b[1;32m   1159\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1160\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1161\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1162\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mSSLError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1163\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mSSL_ERROR_EOF\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msuppress_ragged_eofs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-          ]
-        }
-      ]
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "Proposing:  40%|████      | 2/5 [00:07<00:10,  3.55s/it]\n",
+      "Training Step: 1:   0%|          | 0/30 [00:18<?, ?it/s]\n",
+      "Epoch:   0%|          | 0/1 [00:18<?, ?it/s]\n"
+     ]
     },
     {
-      "cell_type": "markdown",
-      "source": [
-        "# Issues and feedback\n",
-        "\n",
-        "If you encounter any issues, please report them here: [GitHub Issues](https://github.com/SylphAI-Inc/LightRAG/issues).\n",
-        "\n",
-        "For feedback, you can use either the [GitHub discussions](https://github.com/SylphAI-Inc/LightRAG/discussions) or [Discord](https://discord.gg/ezzszrRZvT)."
-      ],
-      "metadata": {
-        "id": "AmkbyxmuruUu"
-      }
+     "output_type": "error",
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-31-a934b5e59252>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mgpt_3_model\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-30-d83a6f6f0e0a>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(model_client, model_kwargs, train_batch_size, raw_shots, bootstrap_shots, max_steps, num_workers, strategy, optimization_order, debug)\u001b[0m\n\u001b[1;32m     61\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Starting model training...\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m         trainer.fit(\n\u001b[0m\u001b[1;32m     64\u001b[0m             \u001b[0mtrain_dataset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain_dataset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m             \u001b[0mval_dataset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtest_dataset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/adalflow/optim/trainer/trainer.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, adaltask, train_loader, train_dataset, val_dataset, test_dataset, debug, save_traces, raw_shots, bootstrap_shots, resume_from_ckpt)\u001b[0m\n\u001b[1;32m    477\u001b[0m                     \u001b[0mstarting_step\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_steps\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    478\u001b[0m                 \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrategy\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"constrained\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 479\u001b[0;31m                     trainer_results = self._fit_text_grad_constraint(\n\u001b[0m\u001b[1;32m    480\u001b[0m                         \u001b[0mtrain_loader\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    481\u001b[0m                         \u001b[0mval_dataset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/adalflow/optim/trainer/trainer.py\u001b[0m in \u001b[0;36m_fit_text_grad_constraint\u001b[0;34m(self, train_loader, val_dataset, test_dataset, trainer_results, starting_step)\u001b[0m\n\u001b[1;32m   1779\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1780\u001b[0m                 all_samples, all_losses, all_y_preds = (\n\u001b[0;32m-> 1781\u001b[0;31m                     self._text_grad_constraint_propose_step(\n\u001b[0m\u001b[1;32m   1782\u001b[0m                         \u001b[0msteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1783\u001b[0m                         \u001b[0mall_samples\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mall_samples\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/adalflow/optim/trainer/trainer.py\u001b[0m in \u001b[0;36m_text_grad_constraint_propose_step\u001b[0;34m(self, steps, all_samples, all_losses, all_y_preds, include_demo_optimizers)\u001b[0m\n\u001b[1;32m   1657\u001b[0m             \u001b[0;31m# print(f\"Proposing step: {i}\")\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1658\u001b[0m             \u001b[0;31m# self.optimizer.propose()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1659\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_propose_text_optimizers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# new prompts\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1660\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0minclude_demo_optimizers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1661\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_demo_optimizers_propose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/adalflow/optim/trainer/trainer.py\u001b[0m in \u001b[0;36m_propose_text_optimizers\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    857\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_propose_text_optimizers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    858\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mtext_optimizer\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext_optimizers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 859\u001b[0;31m             \u001b[0mtext_optimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpropose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    860\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    861\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_trainable_text_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/adalflow/optim/text_grad/tgd_optimizer.py\u001b[0m in \u001b[0;36mpropose\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    323\u001b[0m             }\n\u001b[1;32m    324\u001b[0m             \u001b[0;31m# turn off cache\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 325\u001b[0;31m             response = self.llm_optimizer.call(\n\u001b[0m\u001b[1;32m    326\u001b[0m                 \u001b[0mprompt_kwargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprompt_kwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_cache\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mnot\u001b[0m \u001b[0mno_cache\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    327\u001b[0m             )\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/adalflow/core/generator.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, prompt_kwargs, model_kwargs, use_cache, id)\u001b[0m\n\u001b[1;32m    771\u001b[0m         \u001b[0;31m# call the model client\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    772\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 773\u001b[0;31m         \u001b[0mcompletion\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    774\u001b[0m         \u001b[0muse_cache\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0muse_cache\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0muse_cache\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_use_cache\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    775\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/adalflow/core/generator.py\u001b[0m in \u001b[0;36m_model_client_call\u001b[0;34m(self, api_kwargs, use_cache)\u001b[0m\n\u001b[1;32m    345\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    346\u001b[0m                 \u001b[0mcached_completion\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex_content\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 347\u001b[0;31m                 \u001b[0;32mif\u001b[0m \u001b[0mcached_completion\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    348\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0mcached_completion\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    349\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/backoff/_sync.py\u001b[0m in \u001b[0;36mretry\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    104\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 105\u001b[0;31m                 \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    106\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mexception\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    107\u001b[0m                 \u001b[0mmax_tries_exceeded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtries\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mmax_tries_value\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/adalflow/components/model_client/openai_client.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, api_kwargs, model_type)\u001b[0m\n\u001b[1;32m    285\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchat_completion_parser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhandle_streaming_response\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    286\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msync_client\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompletions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mapi_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 287\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msync_client\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompletions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mapi_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    288\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    289\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"model_type {model_type} is not supported\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/openai/_utils/_utils.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    273\u001b[0m                         \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"Missing required argument: {quote(missing[0])}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    274\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 275\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    276\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    277\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m  \u001b[0;31m# type: ignore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/openai/resources/chat/completions.py\u001b[0m in \u001b[0;36mcreate\u001b[0;34m(self, messages, model, audio, frequency_penalty, function_call, functions, logit_bias, logprobs, max_completion_tokens, max_tokens, metadata, modalities, n, parallel_tool_calls, prediction, presence_penalty, response_format, seed, service_tier, stop, store, stream, stream_options, temperature, tool_choice, tools, top_logprobs, top_p, user, extra_headers, extra_query, extra_body, timeout)\u001b[0m\n\u001b[1;32m    827\u001b[0m     ) -> ChatCompletion | Stream[ChatCompletionChunk]:\n\u001b[1;32m    828\u001b[0m         \u001b[0mvalidate_response_format\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse_format\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 829\u001b[0;31m         return self._post(\n\u001b[0m\u001b[1;32m    830\u001b[0m             \u001b[0;34m\"/chat/completions\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    831\u001b[0m             body=maybe_transform(\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/openai/_base_client.py\u001b[0m in \u001b[0;36mpost\u001b[0;34m(self, path, cast_to, body, options, files, stream, stream_cls)\u001b[0m\n\u001b[1;32m   1276\u001b[0m             \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"post\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjson_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfiles\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mto_httpx_files\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiles\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1277\u001b[0m         )\n\u001b[0;32m-> 1278\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mResponseT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcast_to\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstream\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstream\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstream_cls\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstream_cls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1279\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1280\u001b[0m     def patch(\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/openai/_base_client.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001b[0m\n\u001b[1;32m    953\u001b[0m             \u001b[0mretries_taken\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    954\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 955\u001b[0;31m         return self._request(\n\u001b[0m\u001b[1;32m    956\u001b[0m             \u001b[0mcast_to\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcast_to\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    957\u001b[0m             \u001b[0moptions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/openai/_base_client.py\u001b[0m in \u001b[0;36m_request\u001b[0;34m(self, cast_to, options, retries_taken, stream, stream_cls)\u001b[0m\n\u001b[1;32m    989\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    990\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 991\u001b[0;31m             response = self._client.send(\n\u001b[0m\u001b[1;32m    992\u001b[0m                 \u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    993\u001b[0m                 \u001b[0mstream\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstream\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_should_stream_response_body\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpx/_client.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, auth, follow_redirects)\u001b[0m\n\u001b[1;32m    899\u001b[0m         \u001b[0mauth\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_build_request_auth\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mauth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    900\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 901\u001b[0;31m         response = self._send_handling_auth(\n\u001b[0m\u001b[1;32m    902\u001b[0m             \u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    903\u001b[0m             \u001b[0mauth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mauth\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpx/_client.py\u001b[0m in \u001b[0;36m_send_handling_auth\u001b[0;34m(self, request, auth, follow_redirects, history)\u001b[0m\n\u001b[1;32m    927\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    928\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 929\u001b[0;31m                 response = self._send_handling_redirects(\n\u001b[0m\u001b[1;32m    930\u001b[0m                     \u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    931\u001b[0m                     \u001b[0mfollow_redirects\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfollow_redirects\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpx/_client.py\u001b[0m in \u001b[0;36m_send_handling_redirects\u001b[0;34m(self, request, follow_redirects, history)\u001b[0m\n\u001b[1;32m    964\u001b[0m                 \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    965\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 966\u001b[0;31m             \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_single_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    967\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    968\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_event_hooks\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"response\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpx/_client.py\u001b[0m in \u001b[0;36m_send_single_request\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m   1000\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1001\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mrequest_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1002\u001b[0;31m             \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtransport\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandle_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1003\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1004\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstream\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSyncByteStream\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpx/_transports/default.py\u001b[0m in \u001b[0;36mhandle_request\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m    216\u001b[0m         )\n\u001b[1;32m    217\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mmap_httpcore_exceptions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 218\u001b[0;31m             \u001b[0mresp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pool\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandle_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    219\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    220\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstream\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtyping\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIterable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpcore/_sync/connection_pool.py\u001b[0m in \u001b[0;36mhandle_request\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m    260\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0mShieldCancellation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    261\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresponse_closed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstatus\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 262\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    263\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    264\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpcore/_sync/connection_pool.py\u001b[0m in \u001b[0;36mhandle_request\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m    243\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    244\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 245\u001b[0;31m                 \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconnection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandle_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    246\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mConnectionNotAvailable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    247\u001b[0m                 \u001b[0;31m# The ConnectionNotAvailable exception is a special case, that\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpcore/_sync/connection.py\u001b[0m in \u001b[0;36mhandle_request\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m     94\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mConnectionNotAvailable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     95\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 96\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_connection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandle_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     97\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_connect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mRequest\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mNetworkStream\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpcore/_sync/http11.py\u001b[0m in \u001b[0;36mhandle_request\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m    119\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0mTrace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"response_closed\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogger\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtrace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_response_closed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 121\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    123\u001b[0m     \u001b[0;31m# Sending the request...\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpcore/_sync/http11.py\u001b[0m in \u001b[0;36mhandle_request\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m     97\u001b[0m                     \u001b[0mreason_phrase\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m                     \u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 99\u001b[0;31m                 ) = self._receive_response_headers(**kwargs)\n\u001b[0m\u001b[1;32m    100\u001b[0m                 trace.return_value = (\n\u001b[1;32m    101\u001b[0m                     \u001b[0mhttp_version\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpcore/_sync/http11.py\u001b[0m in \u001b[0;36m_receive_response_headers\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m    162\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    163\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 164\u001b[0;31m             \u001b[0mevent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_receive_event\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    165\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh11\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mResponse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    166\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpcore/_sync/http11.py\u001b[0m in \u001b[0;36m_receive_event\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    198\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    199\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mevent\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mh11\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNEED_DATA\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 200\u001b[0;31m                 data = self._network_stream.read(\n\u001b[0m\u001b[1;32m    201\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mREAD_NUM_BYTES\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    202\u001b[0m                 )\n",
+      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpcore/_backends/sync.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, max_bytes, timeout)\u001b[0m\n\u001b[1;32m     26\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mmap_exceptions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexc_map\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msettimeout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax_bytes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     29\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtyping\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/lib/python3.10/ssl.py\u001b[0m in \u001b[0;36mrecv\u001b[0;34m(self, buflen, flags)\u001b[0m\n\u001b[1;32m   1286\u001b[0m                     \u001b[0;34m\"non-zero flags not allowed in calls to recv() on %s\"\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1287\u001b[0m                     self.__class__)\n\u001b[0;32m-> 1288\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuflen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1289\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1290\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuflen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/lib/python3.10/ssl.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, len, buffer)\u001b[0m\n\u001b[1;32m   1159\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1160\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1161\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1162\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mSSLError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1163\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mSSL_ERROR_EOF\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msuppress_ragged_eofs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
     }
-  ]
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Issues and feedback\n",
+    "\n",
+    "If you encounter any issues, please report them here: [GitHub Issues](https://github.com/SylphAI-Inc/LightRAG/issues).\n",
+    "\n",
+    "For feedback, you can use either the [GitHub discussions](https://github.com/SylphAI-Inc/LightRAG/discussions) or [Discord](https://discord.gg/ezzszrRZvT)."
+   ],
+   "metadata": {
+    "id": "AmkbyxmuruUu"
+   }
+  }
+ ]
 }
diff --git a/notebooks/tutorials/adalflow_embedder.ipynb b/notebooks/tutorials/adalflow_embedder.ipynb
index 8150bf07b..bdd20ba37 100644
--- a/notebooks/tutorials/adalflow_embedder.ipynb
+++ b/notebooks/tutorials/adalflow_embedder.ipynb
@@ -228,8 +228,9 @@
     "from adalflow.core.component import Component\n",
     "from copy import deepcopy\n",
     "\n",
+    "\n",
     "class DecreaseEmbeddingDim(Component):\n",
-    "    def __init__(self, old_dim: int, new_dim: int,  normalize: bool = True):\n",
+    "    def __init__(self, old_dim: int, new_dim: int, normalize: bool = True):\n",
     "        super().__init__()\n",
     "        self.old_dim = old_dim\n",
     "        self.new_dim = new_dim\n",
@@ -258,10 +259,10 @@
    "outputs": [],
    "source": [
     "local_embedder_256 = Embedder(\n",
-    "     model_client=TransformersClient(),\n",
-    "     model_kwargs=model_kwargs,\n",
-    "     output_processors=DecreaseEmbeddingDim(768, 256),\n",
-    " )\n",
+    "    model_client=TransformersClient(),\n",
+    "    model_kwargs=model_kwargs,\n",
+    "    output_processors=DecreaseEmbeddingDim(768, 256),\n",
+    ")\n",
     "print(local_embedder_256)"
    ]
   },
diff --git a/notebooks/tutorials/adalflow_modelclient.ipynb b/notebooks/tutorials/adalflow_modelclient.ipynb
index f1c89bee1..05ff5f3d0 100644
--- a/notebooks/tutorials/adalflow_modelclient.ipynb
+++ b/notebooks/tutorials/adalflow_modelclient.ipynb
@@ -2059,85 +2059,93 @@
     "def analyze_single_image():\n",
     "    \"\"\"Example of analyzing a single image with GPT-4 Vision\"\"\"\n",
     "    client = OpenAIClient()\n",
-    "    \n",
+    "\n",
     "    gen = Generator(\n",
     "        model_client=client,\n",
     "        model_kwargs={\n",
     "            \"model\": \"gpt-4o-mini\",\n",
     "            \"images\": \"https://raw.githubusercontent.com/openai/openai-cookbook/main/examples/images/happy_cat.jpg\",\n",
-    "            \"max_tokens\": 300\n",
-    "        }\n",
+    "            \"max_tokens\": 300,\n",
+    "        },\n",
+    "    )\n",
+    "\n",
+    "    response = gen(\n",
+    "        {\"input_str\": \"What do you see in this image? Be detailed but concise.\"}\n",
     "    )\n",
-    "    \n",
-    "    response = gen({\"input_str\": \"What do you see in this image? Be detailed but concise.\"})\n",
     "    print(\"\\n=== Single Image Analysis ===\")\n",
     "    print(f\"Description: {response.raw_response}\")\n",
     "\n",
+    "\n",
     "def analyze_multiple_images():\n",
     "    \"\"\"Example of analyzing multiple images in one prompt\"\"\"\n",
     "    client = OpenAIClient()\n",
-    "    \n",
+    "\n",
     "    # List of images to analyze together\n",
     "    images = [\n",
     "        \"https://raw.githubusercontent.com/openai/openai-cookbook/main/examples/images/happy_cat.jpg\",\n",
-    "        \"https://raw.githubusercontent.com/openai/openai-cookbook/main/examples/images/sad_cat.jpg\"\n",
+    "        \"https://raw.githubusercontent.com/openai/openai-cookbook/main/examples/images/sad_cat.jpg\",\n",
     "    ]\n",
-    "    \n",
+    "\n",
     "    gen = Generator(\n",
     "        model_client=client,\n",
-    "        model_kwargs={\n",
-    "            \"model\": \"gpt-4o-mini\",\n",
-    "            \"images\": images,\n",
-    "            \"max_tokens\": 300\n",
+    "        model_kwargs={\"model\": \"gpt-4o-mini\", \"images\": images, \"max_tokens\": 300},\n",
+    "    )\n",
+    "\n",
+    "    response = gen(\n",
+    "        {\n",
+    "            \"input_str\": \"Compare and contrast these two images. What are the main differences?\"\n",
     "        }\n",
     "    )\n",
-    "    \n",
-    "    response = gen({\"input_str\": \"Compare and contrast these two images. What are the main differences?\"})\n",
     "    print(\"\\n=== Multiple Images Analysis ===\")\n",
     "    print(f\"Comparison: {response.raw_response}\")\n",
     "\n",
+    "\n",
     "def generate_art_with_dalle():\n",
     "    \"\"\"Example of generating art using DALL-E 3\"\"\"\n",
     "    client = OpenAIClient()\n",
-    "    \n",
+    "\n",
     "    gen = Generator(\n",
     "        model_client=client,\n",
     "        model_kwargs={\n",
     "            \"model\": \"dall-e-3\",\n",
     "            \"size\": \"1024x1024\",\n",
     "            \"quality\": \"standard\",\n",
-    "            \"n\": 1\n",
+    "            \"n\": 1,\n",
+    "        },\n",
+    "    )\n",
+    "\n",
+    "    response = gen(\n",
+    "        {\n",
+    "            \"input_str\": \"A serene Japanese garden with a small bridge over a koi pond, cherry blossoms falling gently in the breeze\"\n",
     "        }\n",
     "    )\n",
-    "    \n",
-    "    response = gen({\n",
-    "        \"input_str\": \"A serene Japanese garden with a small bridge over a koi pond, cherry blossoms falling gently in the breeze\"\n",
-    "    })\n",
     "    print(\"\\n=== Art Generation with DALL-E 3 ===\")\n",
     "    print(f\"Generated Image URL: {response.data}\")\n",
     "\n",
+    "\n",
     "def create_image_variations(image_path=\"path/to/your/image.jpg\"):\n",
     "    \"\"\"Example of creating variations of an existing image\"\"\"\n",
     "    client = OpenAIClient()\n",
-    "    \n",
+    "\n",
     "    gen = Generator(\n",
     "        model_client=client,\n",
     "        model_kwargs={\n",
     "            \"model\": \"dall-e-2\",\n",
     "            \"image\": image_path,\n",
     "            \"n\": 2,  # Generate 2 variations\n",
-    "            \"size\": \"1024x1024\"\n",
-    "        }\n",
+    "            \"size\": \"1024x1024\",\n",
+    "        },\n",
     "    )\n",
-    "    \n",
+    "\n",
     "    response = gen({\"input_str\": \"\"})\n",
     "    print(\"\\n=== Image Variations ===\")\n",
     "    print(f\"Variation URLs: {response.data}\")\n",
     "\n",
+    "\n",
     "def edit_image_with_mask(image_path=\"path/to/image.jpg\", mask_path=\"path/to/mask.jpg\"):\n",
     "    \"\"\"Example of editing specific parts of an image using a mask\"\"\"\n",
     "    client = OpenAIClient()\n",
-    "    \n",
+    "\n",
     "    gen = Generator(\n",
     "        model_client=client,\n",
     "        model_kwargs={\n",
@@ -2145,40 +2153,37 @@
     "            \"image\": image_path,\n",
     "            \"mask\": mask_path,\n",
     "            \"n\": 1,\n",
-    "            \"size\": \"1024x1024\"\n",
-    "        }\n",
+    "            \"size\": \"1024x1024\",\n",
+    "        },\n",
     "    )\n",
-    "    \n",
-    "    response = gen({\n",
-    "        \"input_str\": \"Replace the masked area with a beautiful sunset\"\n",
-    "    })\n",
+    "\n",
+    "    response = gen({\"input_str\": \"Replace the masked area with a beautiful sunset\"})\n",
     "    print(\"\\n=== Image Editing ===\")\n",
     "    print(f\"Edited Image URL: {response.data}\")\n",
     "\n",
+    "\n",
     "def mixed_image_text_conversation():\n",
     "    \"\"\"Example of having a conversation that includes both images and text\"\"\"\n",
     "    client = OpenAIClient()\n",
-    "    \n",
+    "\n",
     "    gen = Generator(\n",
     "        model_client=client,\n",
     "        model_kwargs={\n",
     "            \"model\": \"gpt-4o-mini\",\n",
     "            \"images\": [\n",
     "                \"https://raw.githubusercontent.com/openai/openai-cookbook/main/examples/images/happy_cat.jpg\",\n",
-    "                \"https://path/to/local/image.jpg\"  # Replace with your local image path\n",
+    "                \"https://path/to/local/image.jpg\",  # Replace with your local image path\n",
     "            ],\n",
-    "            \"max_tokens\": 300\n",
-    "        }\n",
+    "            \"max_tokens\": 300,\n",
+    "        },\n",
     "    )\n",
-    "    \n",
+    "\n",
     "    conversation = \"\"\"<START_OF_SYSTEM_PROMPT>You are a helpful assistant skilled in analyzing images and providing detailed descriptions.</END_OF_SYSTEM_PROMPT>\n",
     "    <START_OF_USER_PROMPT>I'm showing you two images. Please analyze them and tell me what emotions they convey.</END_OF_USER_PROMPT>\"\"\"\n",
-    "    \n",
+    "\n",
     "    response = gen({\"input_str\": conversation})\n",
     "    print(\"\\n=== Mixed Image-Text Conversation ===\")\n",
-    "    print(f\"Assistant's Analysis: {response.raw_response}\")\n",
-    "\n",
-    "\n"
+    "    print(f\"Assistant's Analysis: {response.raw_response}\")"
    ]
   },
   {
@@ -2189,16 +2194,16 @@
    "source": [
     "if __name__ == \"__main__\":\n",
     "    print(\"OpenAI Image Processing Examples\\n\")\n",
-    "    \n",
+    "\n",
     "    # Basic image analysis\n",
     "    analyze_single_image()\n",
-    "    \n",
+    "\n",
     "    # Multiple image analysis\n",
     "    analyze_multiple_images()\n",
-    "    \n",
+    "\n",
     "    # Image generation\n",
     "    generate_art_with_dalle()\n",
-    "    \n",
+    "\n",
     "    # create_image_variations(<path_to_image>)\n",
     "    # edit_image_with_mask(<path_to_image>, <path_to_mask>)\n",
     "    # mixed_image_text_conversation(<conversation_prompt>)"
@@ -2230,23 +2235,27 @@
    "source": [
     "class ImageGenerator(Generator):\n",
     "    \"\"\"Generator subclass for image generation.\"\"\"\n",
+    "\n",
     "    model_type = ModelType.IMAGE_GENERATION\n",
     "\n",
+    "\n",
     "def test_vision_and_generation():\n",
     "    \"\"\"Test both vision analysis and image generation\"\"\"\n",
     "    client = OpenAIClient()\n",
-    "    \n",
+    "\n",
     "    # 1. Test Vision Analysis\n",
     "    vision_gen = Generator(\n",
     "        model_client=client,\n",
     "        model_kwargs={\n",
     "            \"model\": \"gpt-4o-mini\",\n",
     "            \"images\": \"https://upload.wikimedia.org/wikipedia/en/7/7d/Lenna_%28test_image%29.png\",\n",
-    "            \"max_tokens\": 300\n",
-    "        }\n",
+    "            \"max_tokens\": 300,\n",
+    "        },\n",
+    "    )\n",
+    "\n",
+    "    vision_response = vision_gen(\n",
+    "        {\"input_str\": \"What do you see in this image? Be detailed but concise.\"}\n",
     "    )\n",
-    "    \n",
-    "    vision_response = vision_gen({\"input_str\": \"What do you see in this image? Be detailed but concise.\"})\n",
     "    print(\"\\n=== Vision Analysis ===\")\n",
     "    print(f\"Description: {vision_response.raw_response}\")\n",
     "\n",
@@ -2257,12 +2266,14 @@
     "            \"model\": \"dall-e-3\",\n",
     "            \"size\": \"1024x1024\",\n",
     "            \"quality\": \"standard\",\n",
-    "            \"n\": 1\n",
-    "        }\n",
+    "            \"n\": 1,\n",
+    "        },\n",
     "    )\n",
-    "    \n",
+    "\n",
     "    # For image generation, input_str becomes the prompt\n",
-    "    response = dalle_gen({\"input_str\": \"A happy siamese cat playing with a red ball of yarn\"})\n",
+    "    response = dalle_gen(\n",
+    "        {\"input_str\": \"A happy siamese cat playing with a red ball of yarn\"}\n",
+    "    )\n",
     "    print(\"\\n=== DALL-E Generation ===\")\n",
     "    print(f\"Generated Image URL: {response.data}\")"
    ]
@@ -2288,10 +2299,10 @@
     "        model_kwargs={\n",
     "            \"model\": \"gpt-4o-mini\",\n",
     "            \"images\": \"https://invalid.url/nonexistent.jpg\",\n",
-    "            \"max_tokens\": 300\n",
-    "        }\n",
+    "            \"max_tokens\": 300,\n",
+    "        },\n",
     "    )\n",
-    "    \n",
+    "\n",
     "    print(\"\\n=== Testing Invalid Image URL ===\")\n",
     "    response = gen({\"input_str\": \"What do you see in this image?\"})\n",
     "    print(f\"Response with invalid image URL: {response}\")"
@@ -2306,7 +2317,7 @@
     "if __name__ == \"__main__\":\n",
     "    print(\"Starting OpenAI Vision and DALL-E test...\\n\")\n",
     "    test_invalid_image_url()\n",
-    "    test_vision_and_generation() "
+    "    test_vision_and_generation()"
    ]
   },
   {
diff --git a/poetry.lock b/poetry.lock
index 6116b1618..a934e82dc 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+# This file is automatically @generated by Poetry 2.0.1 and should not be changed by hand.
 
 [[package]]
 name = "absl-py"
@@ -6,6 +6,8 @@ version = "2.1.0"
 description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py."
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "absl-py-2.1.0.tar.gz", hash = "sha256:7820790efbb316739cde8b4e19357243fc3608a152024288513dd968d7d959ff"},
     {file = "absl_py-2.1.0-py3-none-any.whl", hash = "sha256:526a04eadab8b4ee719ce68f204172ead1027549089702d99b9059f129ff1308"},
@@ -17,6 +19,8 @@ version = "0.33.0"
 description = "Accelerate"
 optional = false
 python-versions = ">=3.8.0"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "accelerate-0.33.0-py3-none-any.whl", hash = "sha256:0a7f33d60ba09afabd028d4f0856dd19c5a734b7a596d637d9dd6e3d0eadbaf3"},
     {file = "accelerate-0.33.0.tar.gz", hash = "sha256:11ba481ed6ea09191775df55ce464aeeba67a024bd0261a44b77b30fb439e26a"},
@@ -44,41 +48,43 @@ testing = ["bitsandbytes", "datasets", "diffusers", "evaluate", "parameterized",
 
 [[package]]
 name = "adalflow"
-version = "0.2.6"
+version = "0.2.7"
 description = "The Library to Build and Auto-optimize LLM Applications"
 optional = false
 python-versions = ">=3.9, <4.0"
+groups = ["main"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = []
 develop = true
 
 [package.dependencies]
 backoff = "^2.2.1"
-botocore = "^1.34.149"
 colorama = "^0.4.6"
 diskcache = "^5.6.3"
 jinja2 = "^3.1.3"
 jsonlines = "^4.0.0"
 nest-asyncio = "^1.6.0"
-numpy = "^1.26.4"
+numpy = "*"
 python-dotenv = "^1.0.1"
-pyyaml = "^6.0.1"
-tiktoken = "^0.7.0"
+PyYAML = ">=6.0.1"
+tiktoken = ">=0.3.3"
 tqdm = "^4.66.4"
 
 [package.extras]
-anthropic = ["anthropic (>=0.31.1,<0.32.0)"]
-azure = ["azure-core (>=1.24.0,<2.0.0)", "azure-identity (>=1.12.0,<2.0.0)"]
+anthropic = ["anthropic (>=0.31.1)"]
+azure = ["azure-core (>=1.24.0)", "azure-identity (>=1.12.0)"]
 bedrock = ["boto3 (>=1.35.19,<2.0.0)"]
-cohere = ["cohere (>=5.5.8,<6.0.0)"]
+cohere = ["cohere (>=5.5.8)"]
 datasets = []
-faiss-cpu = ["faiss-cpu (>=1.8.0,<2.0.0)"]
-google-generativeai = ["google-generativeai (>=0.7.2,<0.8.0)"]
-groq = ["groq (>=0.9.0,<0.10.0)"]
-ollama = ["ollama (>=0.2.1,<0.3.0)"]
-openai = ["openai (>=1.12.0,<2.0.0)"]
-pgvector = ["pgvector (>=0.3.1,<0.4.0)"]
-sqlalchemy = ["sqlalchemy (>=2.0.30,<3.0.0)"]
-torch = ["torch (>=2.3.1,<3.0.0)"]
+faiss-cpu = ["faiss-cpu (>=1.8.0)"]
+google-generativeai = ["google-generativeai (>=0.7.2)"]
+groq = ["groq (>=0.9.0)"]
+lancedb = ["lancedb (>=0.5.2)"]
+ollama = ["ollama (>=0.2.1)"]
+openai = ["openai (>=1.12.0)"]
+pgvector = ["pgvector (>=0.3.1)"]
+sqlalchemy = ["sqlalchemy (>=2.0.30)"]
+torch = ["torch (>=2.3.1)"]
 
 [package.source]
 type = "directory"
@@ -90,6 +96,8 @@ version = "2.3.5"
 description = "Happy Eyeballs for asyncio"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "aiohappyeyeballs-2.3.5-py3-none-any.whl", hash = "sha256:4d6dea59215537dbc746e93e779caea8178c866856a721c9c660d7a5a7b8be03"},
     {file = "aiohappyeyeballs-2.3.5.tar.gz", hash = "sha256:6fa48b9f1317254f122a07a131a86b71ca6946ca989ce6326fff54a99a920105"},
@@ -101,6 +109,8 @@ version = "3.10.2"
 description = "Async http client/server framework (asyncio)"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "aiohttp-3.10.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:95213b3d79c7e387144e9cb7b9d2809092d6ff2c044cb59033aedc612f38fb6d"},
     {file = "aiohttp-3.10.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:1aa005f060aff7124cfadaa2493f00a4e28ed41b232add5869e129a2e395935a"},
@@ -197,6 +207,8 @@ version = "1.3.1"
 description = "aiosignal: a list of registered asynchronous callbacks"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "aiosignal-1.3.1-py3-none-any.whl", hash = "sha256:f8376fb07dd1e86a584e4fcdec80b36b7f81aac666ebc724e2c090300dd83b17"},
     {file = "aiosignal-1.3.1.tar.gz", hash = "sha256:54cd96e15e1649b75d6c87526a6ff0b6c1b0dd3459f43d9ca11d48c339b68cfc"},
@@ -211,6 +223,8 @@ version = "1.13.2"
 description = "A database migration tool for SQLAlchemy."
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "alembic-1.13.2-py3-none-any.whl", hash = "sha256:6b8733129a6224a9a711e17c99b08462dbf7cc9670ba8f2e2ae9af860ceb1953"},
     {file = "alembic-1.13.2.tar.gz", hash = "sha256:1ff0ae32975f4fd96028c39ed9bb3c867fe3af956bd7bb37343b54c9fe7445ef"},
@@ -230,6 +244,8 @@ version = "0.7.0"
 description = "Reusable constraint types to use with typing.Annotated"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
     {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
@@ -241,6 +257,8 @@ version = "0.26.1"
 description = "The official Python library for the anthropic API"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "anthropic-0.26.1-py3-none-any.whl", hash = "sha256:2812b9b250b551ed8a1f0a7e6ae3f005654098994f45ebca5b5808bd154c9628"},
     {file = "anthropic-0.26.1.tar.gz", hash = "sha256:26680ff781a6f678a30a1dccd0743631e602b23a47719439ffdef5335fa167d8"},
@@ -266,6 +284,8 @@ version = "4.4.0"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "anyio-4.4.0-py3-none-any.whl", hash = "sha256:c1b2d8f46a8a812513012e1107cb0e68c17159a7a594208005a57dc776e1bdc7"},
     {file = "anyio-4.4.0.tar.gz", hash = "sha256:5aadc6a1bbb7cdb0bede386cac5e2940f5e2ff3aa20277e991cf028e0585ce94"},
@@ -286,6 +306,8 @@ version = "1.4.4"
 description = "A small Python module for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
 optional = false
 python-versions = "*"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "appdirs-1.4.4-py2.py3-none-any.whl", hash = "sha256:a841dacd6b99318a741b166adb07e19ee71a274450e68237b4650ca1055ab128"},
     {file = "appdirs-1.4.4.tar.gz", hash = "sha256:7d5d0167b2b1ba821647616af46a749d1c653740dd0d2415100fe26e27afdf41"},
@@ -297,6 +319,8 @@ version = "0.1.4"
 description = "Disable App Nap on macOS >= 10.9"
 optional = false
 python-versions = ">=3.6"
+groups = ["dev"]
+markers = "(python_version >= \"3.12\" or python_version == \"3.11\") and platform_system == \"Darwin\""
 files = [
     {file = "appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c"},
     {file = "appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee"},
@@ -308,6 +332,8 @@ version = "23.1.0"
 description = "Argon2 for Python"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "argon2_cffi-23.1.0-py3-none-any.whl", hash = "sha256:c670642b78ba29641818ab2e68bd4e6a78ba53b7eff7b4c3815ae16abf91c7ea"},
     {file = "argon2_cffi-23.1.0.tar.gz", hash = "sha256:879c3e79a2729ce768ebb7d36d4609e3a78a4ca2ec3a9f12286ca057e3d0db08"},
@@ -328,6 +354,8 @@ version = "21.2.0"
 description = "Low-level CFFI bindings for Argon2"
 optional = false
 python-versions = ">=3.6"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "argon2-cffi-bindings-21.2.0.tar.gz", hash = "sha256:bb89ceffa6c791807d1305ceb77dbfacc5aa499891d2c55661c6459651fc39e3"},
     {file = "argon2_cffi_bindings-21.2.0-cp36-abi3-macosx_10_9_x86_64.whl", hash = "sha256:ccb949252cb2ab3a08c02024acb77cfb179492d5701c7cbdbfd776124d4d2367"},
@@ -365,6 +393,8 @@ version = "1.3.0"
 description = "Better dates & times for Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "arrow-1.3.0-py3-none-any.whl", hash = "sha256:c728b120ebc00eb84e01882a6f5e7927a53960aa990ce7dd2b10f39005a67f80"},
     {file = "arrow-1.3.0.tar.gz", hash = "sha256:d4540617648cb5f895730f1ad8c82a65f2dad0166f57b75f3ca54759c4d67a85"},
@@ -384,6 +414,8 @@ version = "2.4.1"
 description = "Annotate AST trees with source code positions"
 optional = false
 python-versions = "*"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "asttokens-2.4.1-py2.py3-none-any.whl", hash = "sha256:051ed49c3dcae8913ea7cd08e46a606dba30b79993209636c4875bc1d637bc24"},
     {file = "asttokens-2.4.1.tar.gz", hash = "sha256:b03869718ba9a6eb027e134bfdf69f38a236d681c83c160d510768af11254ba0"},
@@ -402,6 +434,8 @@ version = "2.0.4"
 description = "Simple LRU cache for asyncio"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "async-lru-2.0.4.tar.gz", hash = "sha256:b8a59a5df60805ff63220b2a0c5b5393da5521b113cd5465a44eb037d81a5627"},
     {file = "async_lru-2.0.4-py3-none-any.whl", hash = "sha256:ff02944ce3c288c5be660c42dbcca0742b32c3b279d6dceda655190240b99224"},
@@ -413,6 +447,8 @@ version = "24.2.0"
 description = "Classes Without Boilerplate"
 optional = false
 python-versions = ">=3.7"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "attrs-24.2.0-py3-none-any.whl", hash = "sha256:81921eb96de3191c8258c199618104dd27ac608d9366f5e35d011eae1867ede2"},
     {file = "attrs-24.2.0.tar.gz", hash = "sha256:5cfb1b9148b5b086569baec03f20d7b6bf3bcacc9a42bebf87ffaaca362f6346"},
@@ -432,6 +468,8 @@ version = "2.16.0"
 description = "Internationalization utilities"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "babel-2.16.0-py3-none-any.whl", hash = "sha256:368b5b98b37c06b7daf6696391c3240c938b37767d4584413e8438c5c435fa8b"},
     {file = "babel-2.16.0.tar.gz", hash = "sha256:d1f3554ca26605fe173f3de0c65f750f5a42f924499bf134de6423582298e316"},
@@ -446,6 +484,8 @@ version = "2.2.1"
 description = "Function decoration for backoff and retry"
 optional = false
 python-versions = ">=3.7,<4.0"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "backoff-2.2.1-py3-none-any.whl", hash = "sha256:63579f9a0628e06278f7e47b7d7d5b6ce20dc65c5e96a6f3ca99a6adca0396e8"},
     {file = "backoff-2.2.1.tar.gz", hash = "sha256:03f829f5bb1923180821643f8753b0502c3b682293992485b0eef2807afa5cba"},
@@ -457,6 +497,8 @@ version = "4.12.3"
 description = "Screen-scraping library"
 optional = false
 python-versions = ">=3.6.0"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "beautifulsoup4-4.12.3-py3-none-any.whl", hash = "sha256:b80878c9f40111313e55da8ba20bdba06d8fa3969fc68304167741bbf9e082ed"},
     {file = "beautifulsoup4-4.12.3.tar.gz", hash = "sha256:74e3d1928edc070d21748185c46e3fb33490f22f52a3addee9aee0f4f7781051"},
@@ -478,6 +520,8 @@ version = "24.10.0"
 description = "The uncompromising code formatter."
 optional = false
 python-versions = ">=3.9"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "black-24.10.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:e6668650ea4b685440857138e5fe40cde4d652633b1bdffc62933d0db4ed9812"},
     {file = "black-24.10.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:1c536fcf674217e87b8cc3657b81809d3c085d7bf3ef262ead700da345bfa6ea"},
@@ -524,6 +568,8 @@ version = "6.1.0"
 description = "An easy safelist-based HTML-sanitizing tool."
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "bleach-6.1.0-py3-none-any.whl", hash = "sha256:3225f354cfc436b9789c66c4ee030194bee0568fbf9cbdad3bc8b5c26c5f12b6"},
     {file = "bleach-6.1.0.tar.gz", hash = "sha256:0a31f1837963c41d46bbf1331b8778e1308ea0791db03cc4e7357b97cf42a8fe"},
@@ -542,6 +588,8 @@ version = "1.34.157"
 description = "The AWS SDK for Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "boto3-1.34.157-py3-none-any.whl", hash = "sha256:3cc357156df5482154a016f138d1953061a181b4c594f8b6302c9d6c024bd950"},
     {file = "boto3-1.34.157.tar.gz", hash = "sha256:7ef19ed38cba9863b58430fb4a66a72a5c250304f234bd1c16b860f9bf25677b"},
@@ -561,6 +609,8 @@ version = "1.34.157"
 description = "Low-level, data-driven core of boto 3."
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "botocore-1.34.157-py3-none-any.whl", hash = "sha256:c6cba6de8eb86ca4d2f934e009b37adbe1e7fdcfa52fbab74783f4c30676e07d"},
     {file = "botocore-1.34.157.tar.gz", hash = "sha256:5628a36cec123cdc8c1158d05a7b06aa5e53649ad73796c50ef3fb51199785fb"},
@@ -580,6 +630,8 @@ version = "5.5.0"
 description = "Extensible memoizing collections and decorators"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "cachetools-5.5.0-py3-none-any.whl", hash = "sha256:02134e8439cdc2ffb62023ce1debca2944c3f289d66bb17ead3ab3dede74b292"},
     {file = "cachetools-5.5.0.tar.gz", hash = "sha256:2cc24fb4cbe39633fb7badd9db9ca6295d766d9c2995f245725a46715d050f2a"},
@@ -591,6 +643,8 @@ version = "2024.7.4"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
 python-versions = ">=3.6"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "certifi-2024.7.4-py3-none-any.whl", hash = "sha256:c198e21b1289c2ab85ee4e67bb4b4ef3ead0892059901a8d5b622f24a1101e90"},
     {file = "certifi-2024.7.4.tar.gz", hash = "sha256:5a1e7645bc0ec61a09e26c36f6106dd4cf40c6db3a1fb6352b0244e7fb057c7b"},
@@ -602,6 +656,8 @@ version = "1.17.0"
 description = "Foreign Function Interface for Python calling C code."
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "cffi-1.17.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:f9338cc05451f1942d0d8203ec2c346c830f8e86469903d5126c1f0a13a2bcbb"},
     {file = "cffi-1.17.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:a0ce71725cacc9ebf839630772b07eeec220cbb5f03be1399e0457a1464f8e1a"},
@@ -681,6 +737,8 @@ version = "3.4.0"
 description = "Validate configuration and produce human readable error messages."
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "cfgv-3.4.0-py2.py3-none-any.whl", hash = "sha256:b7265b1f29fd3316bfcd2b330d63d024f2bfd8bcb8b0272f8e19a504856c48f9"},
     {file = "cfgv-3.4.0.tar.gz", hash = "sha256:e52591d4c5f5dead8e0f673fb16db7949d2cfb3f7da4582893288f0ded8fe560"},
@@ -692,6 +750,8 @@ version = "3.3.2"
 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
 optional = false
 python-versions = ">=3.7.0"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "charset-normalizer-3.3.2.tar.gz", hash = "sha256:f30c3cb33b24454a82faecaf01b19c18562b1e89558fb6c56de4d9118a032fd5"},
     {file = "charset_normalizer-3.3.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:25baf083bf6f6b341f4121c2f3c548875ee6f5339300e08be3f2b2ba1721cdd3"},
@@ -791,6 +851,8 @@ version = "8.1.7"
 description = "Composable command line interface toolkit"
 optional = false
 python-versions = ">=3.7"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "click-8.1.7-py3-none-any.whl", hash = "sha256:ae74fb96c20a0277a1d615f1e4d73c8414f5a98db8b799a7931d1582f3390c28"},
     {file = "click-8.1.7.tar.gz", hash = "sha256:ca9853ad459e787e2192211578cc907e7594e294c7ccc834310722b41b9ca6de"},
@@ -805,6 +867,8 @@ version = "5.8.0"
 description = ""
 optional = false
 python-versions = "<4.0,>=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "cohere-5.8.0-py3-none-any.whl", hash = "sha256:f87f709be6dfe3dce57bef0dd5e90924e8828fb8d334c96fc27663b6a7298c6b"},
     {file = "cohere-5.8.0.tar.gz", hash = "sha256:c4e1ab064d66cc0170091f614b4ea22f55e079f2c7fe9e0de8752fd46f8d2a70"},
@@ -829,6 +893,8 @@ version = "0.4.6"
 description = "Cross-platform colored terminal text."
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"},
     {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
@@ -840,6 +906,8 @@ version = "6.8.2"
 description = "Add colours to the output of Python's logging module."
 optional = false
 python-versions = ">=3.6"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "colorlog-6.8.2-py3-none-any.whl", hash = "sha256:4dcbb62368e2800cb3c5abd348da7e53f6c362dda502ec27c560b2e58a66bd33"},
     {file = "colorlog-6.8.2.tar.gz", hash = "sha256:3e3e079a41feb5a1b64f978b5ea4f46040a94f11f0e8bbb8261e3dbbeca64d44"},
@@ -857,6 +925,8 @@ version = "0.2.2"
 description = "Jupyter Python Comm implementation, for usage in ipykernel, xeus-python etc."
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "comm-0.2.2-py3-none-any.whl", hash = "sha256:e6fb86cb70ff661ee8c9c14e7d36d6de3b4066f1441be4063df9c5009f0a64d3"},
     {file = "comm-0.2.2.tar.gz", hash = "sha256:3fd7a84065306e07bea1773df6eb8282de51ba82f77c72f9c85716ab11fe980e"},
@@ -874,6 +944,8 @@ version = "1.2.1"
 description = "Python library for calculating contours of 2D quadrilateral grids"
 optional = false
 python-versions = ">=3.9"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "contourpy-1.2.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:bd7c23df857d488f418439686d3b10ae2fbf9bc256cd045b37a8c16575ea1040"},
     {file = "contourpy-1.2.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:5b9eb0ca724a241683c9685a484da9d35c872fd42756574a7cfbf58af26677fd"},
@@ -937,6 +1009,8 @@ version = "0.12.1"
 description = "Composable style cycles"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "cycler-0.12.1-py3-none-any.whl", hash = "sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30"},
     {file = "cycler-0.12.1.tar.gz", hash = "sha256:88bb128f02ba341da8ef447245a9e138fae777f6a23943da4540077d3601eb1c"},
@@ -952,6 +1026,8 @@ version = "0.6.7"
 description = "Easily serialize dataclasses to and from JSON."
 optional = false
 python-versions = "<4.0,>=3.7"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "dataclasses_json-0.6.7-py3-none-any.whl", hash = "sha256:0dbf33f26c8d5305befd61b39d2b3414e8a407bedc2834dea9b8d642666fb40a"},
     {file = "dataclasses_json-0.6.7.tar.gz", hash = "sha256:b6b3e528266ea45b9535223bc53ca645f5208833c29229e847b3f26a1cc55fc0"},
@@ -967,6 +1043,8 @@ version = "2.16.0"
 description = "JSON schema generation from dataclasses"
 optional = false
 python-versions = "*"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "dataclasses-jsonschema-2.16.0.tar.gz", hash = "sha256:effb0c73db30a537a962da75c4f35b94a5b1b7c1b17806b1ef74aed8e0aa2768"},
     {file = "dataclasses_jsonschema-2.16.0-py3-none-any.whl", hash = "sha256:d203d6a16c990f7d09eae58c97ffaaea1e45ecb7a033d312e61e4c7836a741bf"},
@@ -989,6 +1067,8 @@ version = "2.20.0"
 description = "HuggingFace community-driven open-source library of datasets"
 optional = false
 python-versions = ">=3.8.0"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "datasets-2.20.0-py3-none-any.whl", hash = "sha256:76ac02e3bdfff824492e20678f0b6b1b6d080515957fe834b00c2ba8d6b18e5e"},
     {file = "datasets-2.20.0.tar.gz", hash = "sha256:3c4dbcd27e0f642b9d41d20ff2efa721a5e04b32b2ca4009e0fc9139e324553f"},
@@ -1033,6 +1113,8 @@ version = "1.8.5"
 description = "An implementation of the Debug Adapter Protocol for Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "debugpy-1.8.5-cp310-cp310-macosx_12_0_x86_64.whl", hash = "sha256:7e4d594367d6407a120b76bdaa03886e9eb652c05ba7f87e37418426ad2079f7"},
     {file = "debugpy-1.8.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4413b7a3ede757dc33a273a17d685ea2b0c09dbd312cc03f5534a0fd4d40750a"},
@@ -1064,6 +1146,8 @@ version = "5.1.1"
 description = "Decorators for Humans"
 optional = false
 python-versions = ">=3.5"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "decorator-5.1.1-py3-none-any.whl", hash = "sha256:b8c3f85900b9dc423225913c5aace94729fe1fa9763b38939a95226f02d37186"},
     {file = "decorator-5.1.1.tar.gz", hash = "sha256:637996211036b6385ef91435e4fae22989472f9d571faba8927ba8253acbc330"},
@@ -1075,6 +1159,8 @@ version = "0.7.1"
 description = "XML bomb protection for Python stdlib modules"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "defusedxml-0.7.1-py2.py3-none-any.whl", hash = "sha256:a352e7e428770286cc899e2542b6cdaedb2b4953ff269a210103ec58f6198a61"},
     {file = "defusedxml-0.7.1.tar.gz", hash = "sha256:1bb3032db185915b62d7c6209c5a8792be6a32ab2fedacc84e01b52c51aa3e69"},
@@ -1086,6 +1172,8 @@ version = "0.3.8"
 description = "serialize all of Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "dill-0.3.8-py3-none-any.whl", hash = "sha256:c36ca9ffb54365bdd2f8eb3eff7d2a21237f8452b57ace88b1ac615b7e815bd7"},
     {file = "dill-0.3.8.tar.gz", hash = "sha256:3ebe3c479ad625c4553aca177444d89b486b1d84982eeacded644afc0cf797ca"},
@@ -1101,6 +1189,8 @@ version = "5.6.3"
 description = "Disk Cache -- Disk and file backed persistent cache."
 optional = false
 python-versions = ">=3"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "diskcache-5.6.3-py3-none-any.whl", hash = "sha256:5e31b2d5fbad117cc363ebaf6b689474db18a1f6438bc82358b024abd4c2ca19"},
     {file = "diskcache-5.6.3.tar.gz", hash = "sha256:2c3a3fa2743d8535d832ec61c2054a1641f41775aa7c556758a109941e33e4fc"},
@@ -1112,6 +1202,8 @@ version = "0.3.8"
 description = "Distribution utilities"
 optional = false
 python-versions = "*"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "distlib-0.3.8-py2.py3-none-any.whl", hash = "sha256:034db59a0b96f8ca18035f36290806a9a6e6bd9d1ff91e45a7f172eb17e51784"},
     {file = "distlib-0.3.8.tar.gz", hash = "sha256:1530ea13e350031b6312d8580ddb6b27a104275a31106523b8f123787f494f64"},
@@ -1123,6 +1215,8 @@ version = "1.9.0"
 description = "Distro - an OS platform information API"
 optional = false
 python-versions = ">=3.6"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "distro-1.9.0-py3-none-any.whl", hash = "sha256:7bffd925d65168f85027d8da9af6bddab658135b840670a223589bc0c8ef02b2"},
     {file = "distro-1.9.0.tar.gz", hash = "sha256:2fa77c6fd8940f116ee1d6b94a2f90b13b5ea8d019b98bc8bafdcabcdd9bdbed"},
@@ -1134,6 +1228,8 @@ version = "2.4.13"
 description = "DSPy"
 optional = false
 python-versions = ">=3.9"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "dspy-ai-2.4.13.tar.gz", hash = "sha256:0ed5648d8267b6a4ebe5b72ec5dbcca9fa194d800885a0182cad93c312cd3166"},
     {file = "dspy_ai-2.4.13-py3-none-any.whl", hash = "sha256:b43aa117b4b6fcb009274f61adcfb0a1dbe1cbb4a370da3bd14cd4d230f17665"},
@@ -1174,6 +1270,8 @@ version = "2.0.1"
 description = "Get the currently executing AST node of a frame, and other information"
 optional = false
 python-versions = ">=3.5"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "executing-2.0.1-py2.py3-none-any.whl", hash = "sha256:eac49ca94516ccc753f9fb5ce82603156e590b27525a8bc32cce8ae302eb61bc"},
     {file = "executing-2.0.1.tar.gz", hash = "sha256:35afe2ce3affba8ee97f2d69927fa823b08b472b7b994e36a52a964b93d16147"},
@@ -1188,6 +1286,8 @@ version = "1.8.0.post1"
 description = "A library for efficient similarity search and clustering of dense vectors."
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "faiss_cpu-1.8.0.post1-cp310-cp310-macosx_10_14_x86_64.whl", hash = "sha256:fd84721eb599aa1da19b1b36345bb8705a60bb1d2887bbbc395a29e3d36a1a62"},
     {file = "faiss_cpu-1.8.0.post1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:b78ff9079d15fd0f156bf5dd8a2975a8abffac1854a86ece263eec1500a2e836"},
@@ -1227,6 +1327,8 @@ version = "1.9.5"
 description = "Fast read/write of AVRO files"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "fastavro-1.9.5-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:61253148e95dd2b6457247b441b7555074a55de17aef85f5165bfd5facf600fc"},
     {file = "fastavro-1.9.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b604935d671ad47d888efc92a106f98e9440874108b444ac10e28d643109c937"},
@@ -1273,6 +1375,8 @@ version = "2.20.0"
 description = "Fastest Python implementation of JSON schema"
 optional = false
 python-versions = "*"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "fastjsonschema-2.20.0-py3-none-any.whl", hash = "sha256:5875f0b0fa7a0043a91e93a9b8f793bcbbba9691e7fd83dca95c28ba26d21f0a"},
     {file = "fastjsonschema-2.20.0.tar.gz", hash = "sha256:3d48fc5300ee96f5d116f10fe6f28d938e6008f59a6a025c2649475b87f76a23"},
@@ -1287,6 +1391,8 @@ version = "3.15.4"
 description = "A platform independent file lock."
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "filelock-3.15.4-py3-none-any.whl", hash = "sha256:6ca1fffae96225dab4c6eaf1c4f4f28cd2568d3ec2a44e15a08520504de468e7"},
     {file = "filelock-3.15.4.tar.gz", hash = "sha256:2207938cbc1844345cb01a5a95524dae30f0ce089eba5b00378295a17e3e90cb"},
@@ -1303,6 +1409,8 @@ version = "4.53.1"
 description = "Tools to manipulate font files"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "fonttools-4.53.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:0679a30b59d74b6242909945429dbddb08496935b82f91ea9bf6ad240ec23397"},
     {file = "fonttools-4.53.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:e8bf06b94694251861ba7fdeea15c8ec0967f84c3d4143ae9daf42bbc7717fe3"},
@@ -1368,6 +1476,8 @@ version = "1.5.1"
 description = "Validates fully-qualified domain names against RFC 1123, so that they are acceptable to modern bowsers"
 optional = false
 python-versions = ">=2.7, !=3.0, !=3.1, !=3.2, !=3.3, !=3.4, <4"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "fqdn-1.5.1-py3-none-any.whl", hash = "sha256:3a179af3761e4df6eb2e026ff9e1a3033d3587bf980a0b1b2e1e5d08d7358014"},
     {file = "fqdn-1.5.1.tar.gz", hash = "sha256:105ed3677e767fb5ca086a0c1f4bb66ebc3c100be518f0e0d755d9eae164d89f"},
@@ -1379,6 +1489,8 @@ version = "1.4.1"
 description = "A list-like structure which implements collections.abc.MutableSequence"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "frozenlist-1.4.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:f9aa1878d1083b276b0196f2dfbe00c9b7e752475ed3b682025ff20c1c1f51ac"},
     {file = "frozenlist-1.4.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:29acab3f66f0f24674b7dc4736477bcd4bc3ad4b896f5f45379a67bce8b96868"},
@@ -1465,6 +1577,8 @@ version = "2024.5.0"
 description = "File-system specification"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "fsspec-2024.5.0-py3-none-any.whl", hash = "sha256:e0fdbc446d67e182f49a70b82cf7889028a63588fde6b222521f10937b2b670c"},
     {file = "fsspec-2024.5.0.tar.gz", hash = "sha256:1d021b0b0f933e3b3029ed808eb400c08ba101ca2de4b3483fbc9ca23fcee94a"},
@@ -1506,6 +1620,8 @@ version = "5.2.0"
 description = "Google Drive Public File/Folder Downloader"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "gdown-5.2.0-py3-none-any.whl", hash = "sha256:33083832d82b1101bdd0e9df3edd0fbc0e1c5f14c9d8c38d2a35bf1683b526d6"},
     {file = "gdown-5.2.0.tar.gz", hash = "sha256:2145165062d85520a3cd98b356c9ed522c5e7984d408535409fd46f94defc787"},
@@ -1526,6 +1642,8 @@ version = "0.6.10"
 description = "Google Ai Generativelanguage API client library"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "google_ai_generativelanguage-0.6.10-py3-none-any.whl", hash = "sha256:854a2bf833d18be05ad5ef13c755567b66a4f4a870f099b62c61fe11bddabcf4"},
     {file = "google_ai_generativelanguage-0.6.10.tar.gz", hash = "sha256:6fa642c964d8728006fe7e8771026fc0b599ae0ebeaf83caf550941e8e693455"},
@@ -1543,6 +1661,8 @@ version = "2.23.0"
 description = "Google API client core library"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "google_api_core-2.23.0-py3-none-any.whl", hash = "sha256:c20100d4c4c41070cf365f1d8ddf5365915291b5eb11b83829fbd1c999b5122f"},
     {file = "google_api_core-2.23.0.tar.gz", hash = "sha256:2ceb087315e6af43f256704b871d99326b1f12a9d6ce99beaedec99ba26a0ace"},
@@ -1572,6 +1692,8 @@ version = "2.154.0"
 description = "Google API Client Library for Python"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "google_api_python_client-2.154.0-py2.py3-none-any.whl", hash = "sha256:a521bbbb2ec0ba9d6f307cdd64ed6e21eeac372d1bd7493a4ab5022941f784ad"},
     {file = "google_api_python_client-2.154.0.tar.gz", hash = "sha256:1b420062e03bfcaa1c79e2e00a612d29a6a934151ceb3d272fe150a656dc8f17"},
@@ -1590,6 +1712,8 @@ version = "2.36.0"
 description = "Google Authentication Library"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "google_auth-2.36.0-py2.py3-none-any.whl", hash = "sha256:51a15d47028b66fd36e5c64a82d2d57480075bccc7da37cde257fc94177a61fb"},
     {file = "google_auth-2.36.0.tar.gz", hash = "sha256:545e9618f2df0bcbb7dcbc45a546485b1212624716975a1ea5ae8149ce769ab1"},
@@ -1613,6 +1737,8 @@ version = "0.2.0"
 description = "Google Authentication Library: httplib2 transport"
 optional = false
 python-versions = "*"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "google-auth-httplib2-0.2.0.tar.gz", hash = "sha256:38aa7badf48f974f1eb9861794e9c0cb2a0511a4ec0679b1f886d108f5640e05"},
     {file = "google_auth_httplib2-0.2.0-py2.py3-none-any.whl", hash = "sha256:b65a0a2123300dd71281a7bf6e64d65a0759287df52729bdd1ae2e47dc311a3d"},
@@ -1628,6 +1754,8 @@ version = "0.8.3"
 description = "Google Generative AI High level API client library and tools."
 optional = false
 python-versions = ">=3.9"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "google_generativeai-0.8.3-py3-none-any.whl", hash = "sha256:1108ff89d5b8e59f51e63d1a8bf84701cd84656e17ca28d73aeed745e736d9b7"},
 ]
@@ -1651,6 +1779,8 @@ version = "1.66.0"
 description = "Common protobufs used in Google APIs"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "googleapis_common_protos-1.66.0-py2.py3-none-any.whl", hash = "sha256:d7abcd75fabb2e0ec9f74466401f6c119a0b498e27370e9be4c94cb7e382b8ed"},
     {file = "googleapis_common_protos-1.66.0.tar.gz", hash = "sha256:c3e7b33d15fdca5374cc0a7346dd92ffa847425cc4ea941d970f13680052ec8c"},
@@ -1668,6 +1798,8 @@ version = "0.20.3"
 description = "Simple Python interface for Graphviz"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "graphviz-0.20.3-py3-none-any.whl", hash = "sha256:81f848f2904515d8cd359cc611faba817598d2feaac4027b266aa3eda7b3dde5"},
     {file = "graphviz-0.20.3.zip", hash = "sha256:09d6bc81e6a9fa392e7ba52135a9d49f1ed62526f96499325930e87ca1b5925d"},
@@ -1684,6 +1816,8 @@ version = "3.0.3"
 description = "Lightweight in-process concurrent programming"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
+markers = "python_version == \"3.11\" and (platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\") or python_version < \"3.13\" and python_version >= \"3.12\" and (platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\")"
 files = [
     {file = "greenlet-3.0.3-cp310-cp310-macosx_11_0_universal2.whl", hash = "sha256:9da2bd29ed9e4f15955dd1595ad7bc9320308a3b766ef7f837e23ad4b4aac31a"},
     {file = "greenlet-3.0.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d353cadd6083fdb056bb46ed07e4340b0869c305c8ca54ef9da3421acbdf6881"},
@@ -1755,6 +1889,8 @@ version = "0.9.0"
 description = "The official Python library for the groq API"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "groq-0.9.0-py3-none-any.whl", hash = "sha256:d0e46f4ad645504672bb09c8100af3ced3a7db0d5119dc13e4aca535fc455874"},
     {file = "groq-0.9.0.tar.gz", hash = "sha256:130ed5e35d3acfaab46b9e7a078eeaebf91052f4a9d71f86f87fb319b5fec332"},
@@ -1774,6 +1910,8 @@ version = "1.63.2"
 description = "HTTP/2-based RPC framework"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "grpcio-1.63.2-cp310-cp310-linux_armv7l.whl", hash = "sha256:bfb7443a525a0ccc8ae89d29d5257a895fe33af23ba8be21609138cef42deb79"},
     {file = "grpcio-1.63.2-cp310-cp310-macosx_12_0_universal2.whl", hash = "sha256:4410f179181961c043c58454ee9cb28474ab38080a1f12e56bac45dc1cf21491"},
@@ -1832,6 +1970,8 @@ version = "1.62.3"
 description = "Status proto mapping for gRPC"
 optional = false
 python-versions = ">=3.6"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "grpcio-status-1.62.3.tar.gz", hash = "sha256:289bdd7b2459794a12cf95dc0cb727bd4a1742c37bd823f760236c937e53a485"},
     {file = "grpcio_status-1.62.3-py3-none-any.whl", hash = "sha256:f9049b762ba8de6b1086789d8315846e094edac2c50beaf462338b301a8fd4b8"},
@@ -1848,6 +1988,8 @@ version = "0.14.0"
 description = "A pure-Python, bring-your-own-I/O implementation of HTTP/1.1"
 optional = false
 python-versions = ">=3.7"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "h11-0.14.0-py3-none-any.whl", hash = "sha256:e3fe4ac4b851c468cc8363d500db52c2ead036020723024a109d37346efaa761"},
     {file = "h11-0.14.0.tar.gz", hash = "sha256:8f19fbbe99e72420ff35c00b27a34cb9937e902a8b810e2c88300c6f0a3b699d"},
@@ -1859,6 +2001,8 @@ version = "1.0.5"
 description = "A minimal low-level HTTP client."
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "httpcore-1.0.5-py3-none-any.whl", hash = "sha256:421f18bac248b25d310f3cacd198d55b8e6125c107797b609ff9b7a6ba7991b5"},
     {file = "httpcore-1.0.5.tar.gz", hash = "sha256:34a38e2f9291467ee3b44e89dd52615370e152954ba21721378a87b2960f7a61"},
@@ -1880,6 +2024,8 @@ version = "0.22.0"
 description = "A comprehensive HTTP client library."
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "httplib2-0.22.0-py3-none-any.whl", hash = "sha256:14ae0a53c1ba8f3d37e9e27cf37eabb0fb9980f435ba405d546948b009dd64dc"},
     {file = "httplib2-0.22.0.tar.gz", hash = "sha256:d7a10bc5ef5ab08322488bde8c726eeee5c8618723fdb399597ec58f3d82df81"},
@@ -1894,6 +2040,8 @@ version = "0.27.0"
 description = "The next generation HTTP client."
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "httpx-0.27.0-py3-none-any.whl", hash = "sha256:71d5465162c13681bff01ad59b2cc68dd838ea1f10e51574bac27103f00c91a5"},
     {file = "httpx-0.27.0.tar.gz", hash = "sha256:a0cb88a46f32dc874e04ee956e4c2764aba2aa228f650b06788ba6bda2962ab5"},
@@ -1918,6 +2066,8 @@ version = "0.4.0"
 description = "Consume Server-Sent Event (SSE) messages with HTTPX."
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "httpx-sse-0.4.0.tar.gz", hash = "sha256:1e81a3a3070ce322add1d3529ed42eb5f70817f45ed6ec915ab753f961139721"},
     {file = "httpx_sse-0.4.0-py3-none-any.whl", hash = "sha256:f329af6eae57eaa2bdfd962b42524764af68075ea87370a2de920af5341e318f"},
@@ -1929,6 +2079,8 @@ version = "0.24.5"
 description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
 optional = false
 python-versions = ">=3.8.0"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "huggingface_hub-0.24.5-py3-none-any.whl", hash = "sha256:d93fb63b1f1a919a22ce91a14518974e81fc4610bf344dfe7572343ce8d3aced"},
     {file = "huggingface_hub-0.24.5.tar.gz", hash = "sha256:7b45d6744dd53ce9cbf9880957de00e9d10a9ae837f1c9b7255fc8fa4e8264f3"},
@@ -1963,6 +2115,8 @@ version = "2.6.0"
 description = "File identification library for Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "identify-2.6.0-py2.py3-none-any.whl", hash = "sha256:e79ae4406387a9d300332b5fd366d8994f1525e8414984e1a59e058b2eda2dd0"},
     {file = "identify-2.6.0.tar.gz", hash = "sha256:cb171c685bdc31bcc4c1734698736a7d5b6c8bf2e0c15117f4d469c8640ae5cf"},
@@ -1977,6 +2131,8 @@ version = "3.7"
 description = "Internationalized Domain Names in Applications (IDNA)"
 optional = false
 python-versions = ">=3.5"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "idna-3.7-py3-none-any.whl", hash = "sha256:82fee1fc78add43492d3a1898bfa6d8a904cc97d8427f683ed8e798d07761aa0"},
     {file = "idna-3.7.tar.gz", hash = "sha256:028ff3aadf0609c1fd278d8ea3089299412a7a8b9bd005dd08b9f8285bcb5cfc"},
@@ -1988,6 +2144,8 @@ version = "6.29.5"
 description = "IPython Kernel for Jupyter"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "ipykernel-6.29.5-py3-none-any.whl", hash = "sha256:afdb66ba5aa354b09b91379bac28ae4afebbb30e8b39510c9690afb7a10421b5"},
     {file = "ipykernel-6.29.5.tar.gz", hash = "sha256:f093a22c4a40f8828f8e330a9c297cb93dcab13bd9678ded6de8e5cf81c56215"},
@@ -2021,6 +2179,8 @@ version = "8.26.0"
 description = "IPython: Productive Interactive Computing"
 optional = false
 python-versions = ">=3.10"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "ipython-8.26.0-py3-none-any.whl", hash = "sha256:e6b347c27bdf9c32ee9d31ae85defc525755a1869f14057e900675b9e8d6e6ff"},
     {file = "ipython-8.26.0.tar.gz", hash = "sha256:1cec0fbba8404af13facebe83d04436a7434c7400e59f47acf467c64abd0956c"},
@@ -2058,6 +2218,8 @@ version = "8.1.3"
 description = "Jupyter interactive widgets"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "ipywidgets-8.1.3-py3-none-any.whl", hash = "sha256:efafd18f7a142248f7cb0ba890a68b96abd4d6e88ddbda483c9130d12667eaf2"},
     {file = "ipywidgets-8.1.3.tar.gz", hash = "sha256:f5f9eeaae082b1823ce9eac2575272952f40d748893972956dc09700a6392d9c"},
@@ -2079,6 +2241,8 @@ version = "20.11.0"
 description = "Operations with ISO 8601 durations"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "isoduration-20.11.0-py3-none-any.whl", hash = "sha256:b2904c2a4228c3d44f409c8ae8e2370eb21a26f7ac2ec5446df141dde3452042"},
     {file = "isoduration-20.11.0.tar.gz", hash = "sha256:ac2f9015137935279eac671f94f89eb00584f940f5dc49462a0c4ee692ba1bd9"},
@@ -2093,6 +2257,8 @@ version = "0.19.1"
 description = "An autocompletion tool for Python that can be used for text editors."
 optional = false
 python-versions = ">=3.6"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "jedi-0.19.1-py2.py3-none-any.whl", hash = "sha256:e983c654fe5c02867aef4cdfce5a2fbb4a50adc0af145f70504238f18ef5e7e0"},
     {file = "jedi-0.19.1.tar.gz", hash = "sha256:cf0496f3651bc65d7174ac1b7d043eff454892c708a87d1b683e57b569927ffd"},
@@ -2112,6 +2278,8 @@ version = "3.1.4"
 description = "A very fast and expressive template engine."
 optional = false
 python-versions = ">=3.7"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "jinja2-3.1.4-py3-none-any.whl", hash = "sha256:bc5dd2abb727a5319567b7a813e6a2e7318c39f4f487cfe6c89c6f9c7d25197d"},
     {file = "jinja2-3.1.4.tar.gz", hash = "sha256:4a3aee7acbbe7303aede8e9648d13b8bf88a429282aa6122a993f0ac800cb369"},
@@ -2129,6 +2297,8 @@ version = "0.5.0"
 description = "Fast iterable JSON parser."
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "jiter-0.5.0-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:b599f4e89b3def9a94091e6ee52e1d7ad7bc33e238ebb9c4c63f211d74822c3f"},
     {file = "jiter-0.5.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:2a063f71c4b06225543dddadbe09d203dc0c95ba352d8b85f1221173480a71d5"},
@@ -2199,6 +2369,8 @@ version = "1.0.1"
 description = "JSON Matching Expressions"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "jmespath-1.0.1-py3-none-any.whl", hash = "sha256:02e2e4cc71b5bcab88332eebf907519190dd9e6e82107fa7f83b1003a6252980"},
     {file = "jmespath-1.0.1.tar.gz", hash = "sha256:90261b206d6defd58fdd5e85f478bf633a2901798906be2ad389150c5c60edbe"},
@@ -2210,6 +2382,8 @@ version = "1.4.2"
 description = "Lightweight pipelining with Python functions"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "joblib-1.4.2-py3-none-any.whl", hash = "sha256:06d478d5674cbc267e7496a410ee875abd68e4340feff4490bcb7afb88060ae6"},
     {file = "joblib-1.4.2.tar.gz", hash = "sha256:2382c5816b2636fbd20a09e0f4e9dad4736765fdfb7dca582943b9c1366b3f0e"},
@@ -2221,6 +2395,8 @@ version = "0.9.25"
 description = "A Python implementation of the JSON5 data format."
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "json5-0.9.25-py3-none-any.whl", hash = "sha256:34ed7d834b1341a86987ed52f3f76cd8ee184394906b6e22a1e0deb9ab294e8f"},
     {file = "json5-0.9.25.tar.gz", hash = "sha256:548e41b9be043f9426776f05df8635a00fe06104ea51ed24b67f908856e151ae"},
@@ -2232,6 +2408,8 @@ version = "4.0.0"
 description = "Library with helpers for the jsonlines file format"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "jsonlines-4.0.0-py3-none-any.whl", hash = "sha256:185b334ff2ca5a91362993f42e83588a360cf95ce4b71a73548502bda52a7c55"},
     {file = "jsonlines-4.0.0.tar.gz", hash = "sha256:0c6d2c09117550c089995247f605ae4cf77dd1533041d366351f6f298822ea74"},
@@ -2246,6 +2424,8 @@ version = "1.33"
 description = "Apply JSON-Patches (RFC 6902)"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "jsonpatch-1.33-py2.py3-none-any.whl", hash = "sha256:0ae28c0cd062bbd8b8ecc26d7d164fbbea9652a1a3693f3b956c1eae5145dade"},
     {file = "jsonpatch-1.33.tar.gz", hash = "sha256:9fcd4009c41e6d12348b4a0ff2563ba56a2923a7dfee731d004e212e1ee5030c"},
@@ -2260,6 +2440,8 @@ version = "3.0.0"
 description = "Identify specific nodes in a JSON document (RFC 6901)"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "jsonpointer-3.0.0-py2.py3-none-any.whl", hash = "sha256:13e088adc14fca8b6aa8177c044e12701e6ad4b28ff10e65f2267a90109c9942"},
     {file = "jsonpointer-3.0.0.tar.gz", hash = "sha256:2b2d729f2091522d61c3b31f82e11870f60b68f43fbc705cb76bf4b832af59ef"},
@@ -2271,6 +2453,8 @@ version = "4.23.0"
 description = "An implementation of JSON Schema validation for Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "jsonschema-4.23.0-py3-none-any.whl", hash = "sha256:fbadb6f8b144a8f8cf9f0b89ba94501d143e50411a1278633f56a7acf7fd5566"},
     {file = "jsonschema-4.23.0.tar.gz", hash = "sha256:d71497fef26351a33265337fa77ffeb82423f3ea21283cd9467bb03999266bc4"},
@@ -2300,6 +2484,8 @@ version = "2023.12.1"
 description = "The JSON Schema meta-schemas and vocabularies, exposed as a Registry"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "jsonschema_specifications-2023.12.1-py3-none-any.whl", hash = "sha256:87e4fdf3a94858b8a2ba2778d9ba57d8a9cafca7c7489c46ba0d30a8bc6a9c3c"},
     {file = "jsonschema_specifications-2023.12.1.tar.gz", hash = "sha256:48a76787b3e70f5ed53f1160d2b81f586e4ca6d1548c5de7085d1682674764cc"},
@@ -2314,6 +2500,8 @@ version = "1.0.0"
 description = "Jupyter metapackage. Install all the Jupyter components in one go."
 optional = false
 python-versions = "*"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "jupyter-1.0.0-py2.py3-none-any.whl", hash = "sha256:5b290f93b98ffbc21c0c7e749f054b3267782166d72fa5e3ed1ed4eaf34a2b78"},
     {file = "jupyter-1.0.0.tar.gz", hash = "sha256:d9dc4b3318f310e34c82951ea5d6683f67bed7def4b259fafbfe4f1beb1d8e5f"},
@@ -2334,6 +2522,8 @@ version = "8.6.2"
 description = "Jupyter protocol implementation and client libraries"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "jupyter_client-8.6.2-py3-none-any.whl", hash = "sha256:50cbc5c66fd1b8f65ecb66bc490ab73217993632809b6e505687de18e9dea39f"},
     {file = "jupyter_client-8.6.2.tar.gz", hash = "sha256:2bda14d55ee5ba58552a8c53ae43d215ad9868853489213f37da060ced54d8df"},
@@ -2356,6 +2546,8 @@ version = "6.6.3"
 description = "Jupyter terminal console"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "jupyter_console-6.6.3-py3-none-any.whl", hash = "sha256:309d33409fcc92ffdad25f0bcdf9a4a9daa61b6f341177570fdac03de5352485"},
     {file = "jupyter_console-6.6.3.tar.gz", hash = "sha256:566a4bf31c87adbfadf22cdf846e3069b59a71ed5da71d6ba4d8aaad14a53539"},
@@ -2380,6 +2572,8 @@ version = "5.7.2"
 description = "Jupyter core package. A base package on which Jupyter projects rely."
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "jupyter_core-5.7.2-py3-none-any.whl", hash = "sha256:4f7315d2f6b4bcf2e3e7cb6e46772eba760ae459cd1f59d29eb57b0a01bd7409"},
     {file = "jupyter_core-5.7.2.tar.gz", hash = "sha256:aa5f8d32bbf6b431ac830496da7392035d6f61b4f54872f15c4bd2a9c3f536d9"},
@@ -2400,6 +2594,8 @@ version = "0.10.0"
 description = "Jupyter Event System library"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "jupyter_events-0.10.0-py3-none-any.whl", hash = "sha256:4b72130875e59d57716d327ea70d3ebc3af1944d3717e5a498b8a06c6c159960"},
     {file = "jupyter_events-0.10.0.tar.gz", hash = "sha256:670b8229d3cc882ec782144ed22e0d29e1c2d639263f92ca8383e66682845e22"},
@@ -2425,6 +2621,8 @@ version = "2.2.5"
 description = "Multi-Language Server WebSocket proxy for Jupyter Notebook/Lab server"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "jupyter-lsp-2.2.5.tar.gz", hash = "sha256:793147a05ad446f809fd53ef1cd19a9f5256fd0a2d6b7ce943a982cb4f545001"},
     {file = "jupyter_lsp-2.2.5-py3-none-any.whl", hash = "sha256:45fbddbd505f3fbfb0b6cb2f1bc5e15e83ab7c79cd6e89416b248cb3c00c11da"},
@@ -2439,6 +2637,8 @@ version = "2.14.2"
 description = "The backend—i.e. core services, APIs, and REST endpoints—to Jupyter web applications."
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "jupyter_server-2.14.2-py3-none-any.whl", hash = "sha256:47ff506127c2f7851a17bf4713434208fc490955d0e8632e95014a9a9afbeefd"},
     {file = "jupyter_server-2.14.2.tar.gz", hash = "sha256:66095021aa9638ced276c248b1d81862e4c50f292d575920bbe960de1c56b12b"},
@@ -2475,6 +2675,8 @@ version = "0.5.3"
 description = "A Jupyter Server Extension Providing Terminals."
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "jupyter_server_terminals-0.5.3-py3-none-any.whl", hash = "sha256:41ee0d7dc0ebf2809c668e0fc726dfaf258fcd3e769568996ca731b6194ae9aa"},
     {file = "jupyter_server_terminals-0.5.3.tar.gz", hash = "sha256:5ae0295167220e9ace0edcfdb212afd2b01ee8d179fe6f23c899590e9b8a5269"},
@@ -2494,6 +2696,8 @@ version = "4.2.4"
 description = "JupyterLab computational environment"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "jupyterlab-4.2.4-py3-none-any.whl", hash = "sha256:807a7ec73637744f879e112060d4b9d9ebe028033b7a429b2d1f4fc523d00245"},
     {file = "jupyterlab-4.2.4.tar.gz", hash = "sha256:343a979fb9582fd08c8511823e320703281cd072a0049bcdafdc7afeda7f2537"},
@@ -2527,6 +2731,8 @@ version = "0.3.0"
 description = "Pygments theme using JupyterLab CSS variables"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "jupyterlab_pygments-0.3.0-py3-none-any.whl", hash = "sha256:841a89020971da1d8693f1a99997aefc5dc424bb1b251fd6322462a1b8842780"},
     {file = "jupyterlab_pygments-0.3.0.tar.gz", hash = "sha256:721aca4d9029252b11cfa9d185e5b5af4d54772bb8072f9b7036f4170054d35d"},
@@ -2538,6 +2744,8 @@ version = "2.27.3"
 description = "A set of server components for JupyterLab and JupyterLab like applications."
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "jupyterlab_server-2.27.3-py3-none-any.whl", hash = "sha256:e697488f66c3db49df675158a77b3b017520d772c6e1548c7d9bcc5df7944ee4"},
     {file = "jupyterlab_server-2.27.3.tar.gz", hash = "sha256:eb36caca59e74471988f0ae25c77945610b887f777255aa21f8065def9e51ed4"},
@@ -2563,6 +2771,8 @@ version = "3.0.11"
 description = "Jupyter interactive widgets for JupyterLab"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "jupyterlab_widgets-3.0.11-py3-none-any.whl", hash = "sha256:78287fd86d20744ace330a61625024cf5521e1c012a352ddc0a3cdc2348becd0"},
     {file = "jupyterlab_widgets-3.0.11.tar.gz", hash = "sha256:dd5ac679593c969af29c9bed054c24f26842baa51352114736756bc035deee27"},
@@ -2574,6 +2784,8 @@ version = "1.4.5"
 description = "A fast implementation of the Cassowary constraint solver"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "kiwisolver-1.4.5-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:05703cf211d585109fcd72207a31bb170a0f22144d68298dc5e61b3c946518af"},
     {file = "kiwisolver-1.4.5-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:146d14bebb7f1dc4d5fbf74f8a6cb15ac42baadee8912eb84ac0b3b2a3dc6ac3"},
@@ -2687,6 +2899,8 @@ version = "0.2.16"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = "<4.0,>=3.8.1"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "langchain-0.2.16-py3-none-any.whl", hash = "sha256:8f59ee8b45f268df4b924ea3b9c63e49286efa756d16b3f6a9de5c6e502c36e1"},
     {file = "langchain-0.2.16.tar.gz", hash = "sha256:ffb426a76a703b73ac69abad77cd16eaf03dda76b42cff55572f592d74944166"},
@@ -2713,6 +2927,8 @@ version = "0.2.16"
 description = "Community contributed LangChain integrations."
 optional = false
 python-versions = "<4.0,>=3.8.1"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "langchain_community-0.2.16-py3-none-any.whl", hash = "sha256:115e1419c176091d4e00240cb5a38612a249e70f213516b6cacae61a8794a868"},
     {file = "langchain_community-0.2.16.tar.gz", hash = "sha256:ab416b793a7aed1fa46ebaffd29993296b02286a99819eabc43be2ea8e41ae78"},
@@ -2739,6 +2955,8 @@ version = "0.2.38"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = "<4.0,>=3.8.1"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "langchain_core-0.2.38-py3-none-any.whl", hash = "sha256:8a5729bc7e68b4af089af20eff44fe4e7ca21d0e0c87ec21cef7621981fd1a4a"},
     {file = "langchain_core-0.2.38.tar.gz", hash = "sha256:eb69dbedd344f2ee1f15bcea6c71a05884b867588fadc42d04632e727c1238f3"},
@@ -2762,6 +2980,8 @@ version = "0.1.23"
 description = "An integration package connecting OpenAI and LangChain"
 optional = false
 python-versions = "<4.0,>=3.8.1"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "langchain_openai-0.1.23-py3-none-any.whl", hash = "sha256:8e3d215803e157f26480c6108eb4333629832b1a0e746723060c24f93b8b78f4"},
     {file = "langchain_openai-0.1.23.tar.gz", hash = "sha256:ed7f16671ea0af177ac5f82d5645a746c5097c56f97b31798e5c07b5c84f0eed"},
@@ -2778,6 +2998,8 @@ version = "0.2.4"
 description = "LangChain text splitting utilities"
 optional = false
 python-versions = "<4.0,>=3.8.1"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "langchain_text_splitters-0.2.4-py3-none-any.whl", hash = "sha256:2702dee5b7cbdd595ccbe43b8d38d01a34aa8583f4d6a5a68ad2305ae3e7b645"},
     {file = "langchain_text_splitters-0.2.4.tar.gz", hash = "sha256:f7daa7a3b0aa8309ce248e2e2b6fc8115be01118d336c7f7f7dfacda0e89bf29"},
@@ -2792,6 +3014,8 @@ version = "0.1.114"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "langsmith-0.1.114-py3-none-any.whl", hash = "sha256:2b6b6b49ddb1cea75f465da107ddc21e60d3c7242813dcc0de90f914e4957249"},
     {file = "langsmith-0.1.114.tar.gz", hash = "sha256:1683e1505d034d1bf7c960067c1357fd0d294172dd20540f913093e4b86857a2"},
@@ -2812,6 +3036,8 @@ version = "0.11.6"
 description = "Lightning toolbox for across the our ecosystem."
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "lightning_utilities-0.11.6-py3-none-any.whl", hash = "sha256:ecd9953c316cbaf56ad820fbe7bd062187b9973c4a23d47b076cd59dc080a310"},
     {file = "lightning_utilities-0.11.6.tar.gz", hash = "sha256:79fc27ef8ec8b8d55a537920f2c7610270c0c9e037fa6efc78f1aa34ec8cdf04"},
@@ -2833,6 +3059,8 @@ version = "1.3.5"
 description = "A super-fast templating language that borrows the best ideas from the existing templating languages."
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "Mako-1.3.5-py3-none-any.whl", hash = "sha256:260f1dbc3a519453a9c856dedfe4beb4e50bd5a26d96386cb6c80856556bb91a"},
     {file = "Mako-1.3.5.tar.gz", hash = "sha256:48dbc20568c1d276a2698b36d968fa76161bf127194907ea6fc594fa81f943bc"},
@@ -2852,6 +3080,8 @@ version = "3.6"
 description = "Python implementation of John Gruber's Markdown."
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "Markdown-3.6-py3-none-any.whl", hash = "sha256:48f276f4d8cfb8ce6527c8f79e2ee29708508bf4d40aa410fbc3b4ee832c850f"},
     {file = "Markdown-3.6.tar.gz", hash = "sha256:ed4f41f6daecbeeb96e576ce414c41d2d876daa9a16cb35fa8ed8c2ddfad0224"},
@@ -2867,6 +3097,8 @@ version = "2.1.5"
 description = "Safely add untrusted strings to HTML/XML markup."
 optional = false
 python-versions = ">=3.7"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "MarkupSafe-2.1.5-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:a17a92de5231666cfbe003f0e4b9b3a7ae3afb1ec2845aadc2bacc93ff85febc"},
     {file = "MarkupSafe-2.1.5-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:72b6be590cc35924b02c78ef34b467da4ba07e4e0f0454a2c5907f473fc50ce5"},
@@ -2936,6 +3168,8 @@ version = "3.22.0"
 description = "A lightweight library for converting complex datatypes to and from native Python datatypes."
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "marshmallow-3.22.0-py3-none-any.whl", hash = "sha256:71a2dce49ef901c3f97ed296ae5051135fd3febd2bf43afe0ae9a82143a494d9"},
     {file = "marshmallow-3.22.0.tar.gz", hash = "sha256:4972f529104a220bb8637d595aa4c9762afbe7f7a77d82dc58c1615d70c5823e"},
@@ -2955,6 +3189,8 @@ version = "3.9.1.post1"
 description = "Python plotting package"
 optional = false
 python-versions = ">=3.9"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "matplotlib-3.9.1.post1-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:3779ad3e8b72df22b8a622c5796bbcfabfa0069b835412e3c1dec8ee3de92d0c"},
     {file = "matplotlib-3.9.1.post1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:ec400340f8628e8e2260d679078d4e9b478699f386e5cc8094e80a1cb0039c7c"},
@@ -3007,6 +3243,8 @@ version = "0.1.7"
 description = "Inline Matplotlib backend for Jupyter"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "matplotlib_inline-0.1.7-py3-none-any.whl", hash = "sha256:df192d39a4ff8f21b1895d72e6a13f5fcc5099f00fa84384e0ea28c2cc0653ca"},
     {file = "matplotlib_inline-0.1.7.tar.gz", hash = "sha256:8423b23ec666be3d16e16b60bdd8ac4e86e840ebd1dd11a30b9f117f2fa0ab90"},
@@ -3021,6 +3259,8 @@ version = "3.0.2"
 description = "A sane and fast Markdown parser with useful plugins and renderers"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "mistune-3.0.2-py3-none-any.whl", hash = "sha256:71481854c30fdbc938963d3605b72501f5c10a9320ecd412c121c163a1c7d205"},
     {file = "mistune-3.0.2.tar.gz", hash = "sha256:fc7f93ded930c92394ef2cb6f04a8aabab4117a91449e72dcc8dfa646a508be8"},
@@ -3032,6 +3272,8 @@ version = "1.3.0"
 description = "Python library for arbitrary-precision floating-point arithmetic"
 optional = false
 python-versions = "*"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "mpmath-1.3.0-py3-none-any.whl", hash = "sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c"},
     {file = "mpmath-1.3.0.tar.gz", hash = "sha256:7a28eb2a9774d00c7bc92411c19a89209d5da7c4c9a9e227be8330a23a25b91f"},
@@ -3049,6 +3291,8 @@ version = "6.0.5"
 description = "multidict implementation"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "multidict-6.0.5-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:228b644ae063c10e7f324ab1ab6b548bdf6f8b47f3ec234fef1093bc2735e5f9"},
     {file = "multidict-6.0.5-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:896ebdcf62683551312c30e20614305f53125750803b614e9e6ce74a96232604"},
@@ -3148,6 +3392,8 @@ version = "0.70.16"
 description = "better multiprocessing and multithreading in Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "multiprocess-0.70.16-pp310-pypy310_pp73-macosx_10_13_x86_64.whl", hash = "sha256:476887be10e2f59ff183c006af746cb6f1fd0eadcfd4ef49e605cbe2659920ee"},
     {file = "multiprocess-0.70.16-pp310-pypy310_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:d951bed82c8f73929ac82c61f01a7b5ce8f3e5ef40f5b52553b4f547ce2b08ec"},
@@ -3172,6 +3418,8 @@ version = "1.0.0"
 description = "Type system extensions for programs checked with the mypy type checker."
 optional = false
 python-versions = ">=3.5"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "mypy_extensions-1.0.0-py3-none-any.whl", hash = "sha256:4392f6c0eb8a5668a69e23d168ffa70f0be9ccfd32b5cc2d26a34ae5b844552d"},
     {file = "mypy_extensions-1.0.0.tar.gz", hash = "sha256:75dbf8955dc00442a438fc4d0666508a9a97b6bd41aa2f0ffe9d2f2725af0782"},
@@ -3183,6 +3431,8 @@ version = "0.10.0"
 description = "A client library for executing notebooks. Formerly nbconvert's ExecutePreprocessor."
 optional = false
 python-versions = ">=3.8.0"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "nbclient-0.10.0-py3-none-any.whl", hash = "sha256:f13e3529332a1f1f81d82a53210322476a168bb7090a0289c795fe9cc11c9d3f"},
     {file = "nbclient-0.10.0.tar.gz", hash = "sha256:4b3f1b7dba531e498449c4db4f53da339c91d449dc11e9af3a43b4eb5c5abb09"},
@@ -3205,6 +3455,8 @@ version = "7.16.4"
 description = "Converting Jupyter Notebooks (.ipynb files) to other formats.  Output formats include asciidoc, html, latex, markdown, pdf, py, rst, script.  nbconvert can be used both as a Python library (`import nbconvert`) or as a command line tool (invoked as `jupyter nbconvert ...`)."
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "nbconvert-7.16.4-py3-none-any.whl", hash = "sha256:05873c620fe520b6322bf8a5ad562692343fe3452abda5765c7a34b7d1aa3eb3"},
     {file = "nbconvert-7.16.4.tar.gz", hash = "sha256:86ca91ba266b0a448dc96fa6c5b9d98affabde2867b363258703536807f9f7f4"},
@@ -3242,6 +3494,8 @@ version = "5.10.4"
 description = "The Jupyter Notebook format"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "nbformat-5.10.4-py3-none-any.whl", hash = "sha256:3b48d6c8fbca4b299bf3982ea7db1af21580e4fec269ad087b9e81588891200b"},
     {file = "nbformat-5.10.4.tar.gz", hash = "sha256:322168b14f937a5d11362988ecac2a4952d3d8e3a2cbeb2319584631226d5b3a"},
@@ -3263,6 +3517,8 @@ version = "1.6.0"
 description = "Patch asyncio to allow nested event loops"
 optional = false
 python-versions = ">=3.5"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "nest_asyncio-1.6.0-py3-none-any.whl", hash = "sha256:87af6efd6b5e897c81050477ef65c62e2b2f35d51703cae01aff2905b1852e1c"},
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@@ -3274,6 +3530,8 @@ version = "3.3"
 description = "Python package for creating and manipulating graphs and networks"
 optional = false
 python-versions = ">=3.10"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "networkx-3.3-py3-none-any.whl", hash = "sha256:28575580c6ebdaf4505b22c6256a2b9de86b316dc63ba9e93abde3d78dfdbcf2"},
     {file = "networkx-3.3.tar.gz", hash = "sha256:0c127d8b2f4865f59ae9cb8aafcd60b5c70f3241ebd66f7defad7c4ab90126c9"},
@@ -3292,6 +3550,8 @@ version = "3.9.1"
 description = "Natural Language Toolkit"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "nltk-3.9.1-py3-none-any.whl", hash = "sha256:4fa26829c5b00715afe3061398a8989dc643b92ce7dd93fb4585a70930d168a1"},
     {file = "nltk-3.9.1.tar.gz", hash = "sha256:87d127bd3de4bd89a4f81265e5fa59cb1b199b27440175370f7417d2bc7ae868"},
@@ -3317,6 +3577,8 @@ version = "1.9.1"
 description = "Node.js virtual environment builder"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "nodeenv-1.9.1-py2.py3-none-any.whl", hash = "sha256:ba11c9782d29c27c70ffbdda2d7415098754709be8a7056d79a737cd901155c9"},
     {file = "nodeenv-1.9.1.tar.gz", hash = "sha256:6ec12890a2dab7946721edbfbcd91f3319c6ccc9aec47be7c7e6b7011ee6645f"},
@@ -3328,6 +3590,8 @@ version = "7.2.1"
 description = "Jupyter Notebook - A web-based notebook environment for interactive computing"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "notebook-7.2.1-py3-none-any.whl", hash = "sha256:f45489a3995746f2195a137e0773e2130960b51c9ac3ce257dbc2705aab3a6ca"},
     {file = "notebook-7.2.1.tar.gz", hash = "sha256:4287b6da59740b32173d01d641f763d292f49c30e7a51b89c46ba8473126341e"},
@@ -3351,6 +3615,8 @@ version = "0.2.4"
 description = "A shim layer for notebook traits and config"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "notebook_shim-0.2.4-py3-none-any.whl", hash = "sha256:411a5be4e9dc882a074ccbcae671eda64cceb068767e9a3419096986560e1cef"},
     {file = "notebook_shim-0.2.4.tar.gz", hash = "sha256:b4b2cfa1b65d98307ca24361f5b30fe785b53c3fd07b7a47e89acb5e6ac638cb"},
@@ -3368,6 +3634,8 @@ version = "1.26.4"
 description = "Fundamental package for array computing in Python"
 optional = false
 python-versions = ">=3.9"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "numpy-1.26.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:9ff0f4f29c51e2803569d7a51c2304de5554655a60c5d776e35b4a41413830d0"},
     {file = "numpy-1.26.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:2e4ee3380d6de9c9ec04745830fd9e2eccb3e6cf790d39d7b98ffd19b0dd754a"},
@@ -3413,6 +3681,8 @@ version = "12.1.3.1"
 description = "CUBLAS native runtime libraries"
 optional = false
 python-versions = ">=3"
+groups = ["dev"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (python_version >= \"3.12\" or python_version == \"3.11\")"
 files = [
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     {file = "nvidia_cublas_cu12-12.1.3.1-py3-none-win_amd64.whl", hash = "sha256:2b964d60e8cf11b5e1073d179d85fa340c120e99b3067558f3cf98dd69d02906"},
@@ -3424,6 +3694,8 @@ version = "12.1.105"
 description = "CUDA profiling tools runtime libs."
 optional = false
 python-versions = ">=3"
+groups = ["dev"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (python_version >= \"3.12\" or python_version == \"3.11\")"
 files = [
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     {file = "nvidia_cuda_cupti_cu12-12.1.105-py3-none-win_amd64.whl", hash = "sha256:bea8236d13a0ac7190bd2919c3e8e6ce1e402104276e6f9694479e48bb0eb2a4"},
@@ -3435,6 +3707,8 @@ version = "12.1.105"
 description = "NVRTC native runtime libraries"
 optional = false
 python-versions = ">=3"
+groups = ["dev"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (python_version >= \"3.12\" or python_version == \"3.11\")"
 files = [
     {file = "nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:339b385f50c309763ca65456ec75e17bbefcbbf2893f462cb8b90584cd27a1c2"},
     {file = "nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-win_amd64.whl", hash = "sha256:0a98a522d9ff138b96c010a65e145dc1b4850e9ecb75a0172371793752fd46ed"},
@@ -3446,6 +3720,8 @@ version = "12.1.105"
 description = "CUDA Runtime native Libraries"
 optional = false
 python-versions = ">=3"
+groups = ["dev"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (python_version >= \"3.12\" or python_version == \"3.11\")"
 files = [
     {file = "nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:6e258468ddf5796e25f1dc591a31029fa317d97a0a94ed93468fc86301d61e40"},
     {file = "nvidia_cuda_runtime_cu12-12.1.105-py3-none-win_amd64.whl", hash = "sha256:dfb46ef84d73fababab44cf03e3b83f80700d27ca300e537f85f636fac474344"},
@@ -3457,6 +3733,8 @@ version = "9.1.0.70"
 description = "cuDNN runtime libraries"
 optional = false
 python-versions = ">=3"
+groups = ["dev"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (python_version >= \"3.12\" or python_version == \"3.11\")"
 files = [
     {file = "nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl", hash = "sha256:165764f44ef8c61fcdfdfdbe769d687e06374059fbb388b6c89ecb0e28793a6f"},
     {file = "nvidia_cudnn_cu12-9.1.0.70-py3-none-win_amd64.whl", hash = "sha256:6278562929433d68365a07a4a1546c237ba2849852c0d4b2262a486e805b977a"},
@@ -3471,6 +3749,8 @@ version = "11.0.2.54"
 description = "CUFFT native runtime libraries"
 optional = false
 python-versions = ">=3"
+groups = ["dev"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (python_version >= \"3.12\" or python_version == \"3.11\")"
 files = [
     {file = "nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl", hash = "sha256:794e3948a1aa71fd817c3775866943936774d1c14e7628c74f6f7417224cdf56"},
     {file = "nvidia_cufft_cu12-11.0.2.54-py3-none-win_amd64.whl", hash = "sha256:d9ac353f78ff89951da4af698f80870b1534ed69993f10a4cf1d96f21357e253"},
@@ -3482,6 +3762,8 @@ version = "10.3.2.106"
 description = "CURAND native runtime libraries"
 optional = false
 python-versions = ">=3"
+groups = ["dev"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (python_version >= \"3.12\" or python_version == \"3.11\")"
 files = [
     {file = "nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl", hash = "sha256:9d264c5036dde4e64f1de8c50ae753237c12e0b1348738169cd0f8a536c0e1e0"},
     {file = "nvidia_curand_cu12-10.3.2.106-py3-none-win_amd64.whl", hash = "sha256:75b6b0c574c0037839121317e17fd01f8a69fd2ef8e25853d826fec30bdba74a"},
@@ -3493,6 +3775,8 @@ version = "11.4.5.107"
 description = "CUDA solver native runtime libraries"
 optional = false
 python-versions = ">=3"
+groups = ["dev"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (python_version >= \"3.12\" or python_version == \"3.11\")"
 files = [
     {file = "nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl", hash = "sha256:8a7ec542f0412294b15072fa7dab71d31334014a69f953004ea7a118206fe0dd"},
     {file = "nvidia_cusolver_cu12-11.4.5.107-py3-none-win_amd64.whl", hash = "sha256:74e0c3a24c78612192a74fcd90dd117f1cf21dea4822e66d89e8ea80e3cd2da5"},
@@ -3509,6 +3793,8 @@ version = "12.1.0.106"
 description = "CUSPARSE native runtime libraries"
 optional = false
 python-versions = ">=3"
+groups = ["dev"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (python_version >= \"3.12\" or python_version == \"3.11\")"
 files = [
     {file = "nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl", hash = "sha256:f3b50f42cf363f86ab21f720998517a659a48131e8d538dc02f8768237bd884c"},
     {file = "nvidia_cusparse_cu12-12.1.0.106-py3-none-win_amd64.whl", hash = "sha256:b798237e81b9719373e8fae8d4f091b70a0cf09d9d85c95a557e11df2d8e9a5a"},
@@ -3523,6 +3809,8 @@ version = "2.20.5"
 description = "NVIDIA Collective Communication Library (NCCL) Runtime"
 optional = false
 python-versions = ">=3"
+groups = ["dev"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (python_version >= \"3.12\" or python_version == \"3.11\")"
 files = [
     {file = "nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_aarch64.whl", hash = "sha256:1fc150d5c3250b170b29410ba682384b14581db722b2531b0d8d33c595f33d01"},
     {file = "nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl", hash = "sha256:057f6bf9685f75215d0c53bf3ac4a10b3e6578351de307abad9e18a99182af56"},
@@ -3534,6 +3822,8 @@ version = "12.6.20"
 description = "Nvidia JIT LTO Library"
 optional = false
 python-versions = ">=3"
+groups = ["dev"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (python_version >= \"3.12\" or python_version == \"3.11\")"
 files = [
     {file = "nvidia_nvjitlink_cu12-12.6.20-py3-none-manylinux2014_aarch64.whl", hash = "sha256:84fb38465a5bc7c70cbc320cfd0963eb302ee25a5e939e9f512bbba55b6072fb"},
     {file = "nvidia_nvjitlink_cu12-12.6.20-py3-none-manylinux2014_x86_64.whl", hash = "sha256:562ab97ea2c23164823b2a89cb328d01d45cb99634b8c65fe7cd60d14562bd79"},
@@ -3546,6 +3836,8 @@ version = "12.1.105"
 description = "NVIDIA Tools Extension"
 optional = false
 python-versions = ">=3"
+groups = ["dev"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (python_version >= \"3.12\" or python_version == \"3.11\")"
 files = [
     {file = "nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:dc21cf308ca5691e7c04d962e213f8a4aa9bbfa23d95412f452254c2caeb09e5"},
     {file = "nvidia_nvtx_cu12-12.1.105-py3-none-win_amd64.whl", hash = "sha256:65f4d98982b31b60026e0e6de73fbdfc09d08a96f4656dd3665ca616a11e1e82"},
@@ -3557,6 +3849,8 @@ version = "0.2.1"
 description = "The official Python client for Ollama."
 optional = false
 python-versions = "<4.0,>=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "ollama-0.2.1-py3-none-any.whl", hash = "sha256:b6e2414921c94f573a903d1069d682ba2fb2607070ea9e19ca4a7872f2a460ec"},
     {file = "ollama-0.2.1.tar.gz", hash = "sha256:fa316baa9a81eac3beb4affb0a17deb3008fdd6ed05b123c26306cfbe4c349b6"},
@@ -3571,6 +3865,8 @@ version = "1.40.2"
 description = "The official Python library for the openai API"
 optional = false
 python-versions = ">=3.7.1"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "openai-1.40.2-py3-none-any.whl", hash = "sha256:38068f858f310b4fd4b0ea8734c3efcfde3c15a2978311e1453bd84817231b96"},
     {file = "openai-1.40.2.tar.gz", hash = "sha256:2180e9070bd36084328248b3ce668964e8ddd2e9019e1d426e31dc54cc117bb5"},
@@ -3595,6 +3891,8 @@ version = "3.6.1"
 description = "A hyperparameter optimization framework"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "optuna-3.6.1-py3-none-any.whl", hash = "sha256:b32e0490bd6552790b70ec94de77dd2855057c9e229cd9f4da48fe8a31c7f1cc"},
     {file = "optuna-3.6.1.tar.gz", hash = "sha256:146e530b57b4b9afd7526b3e642fbe65491f7e292b405913355f8e438e361ecf"},
@@ -3622,6 +3920,8 @@ version = "3.10.7"
 description = "Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "orjson-3.10.7-cp310-cp310-macosx_10_15_x86_64.macosx_11_0_arm64.macosx_10_15_universal2.whl", hash = "sha256:74f4544f5a6405b90da8ea724d15ac9c36da4d72a738c64685003337401f5c12"},
     {file = "orjson-3.10.7-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:34a566f22c28222b08875b18b0dfbf8a947e69df21a9ed5c51a6bf91cfb944ac"},
@@ -3688,6 +3988,8 @@ version = "7.7.0"
 description = "A decorator to automatically detect mismatch when overriding a method."
 optional = false
 python-versions = ">=3.6"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "overrides-7.7.0-py3-none-any.whl", hash = "sha256:c7ed9d062f78b8e4c1a7b70bd8796b35ead4d9f510227ef9c5dc7626c60d7e49"},
     {file = "overrides-7.7.0.tar.gz", hash = "sha256:55158fa3d93b98cc75299b1e67078ad9003ca27945c76162c1c0766d6f91820a"},
@@ -3699,6 +4001,8 @@ version = "24.1"
 description = "Core utilities for Python packages"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "packaging-24.1-py3-none-any.whl", hash = "sha256:5b8f2217dbdbd2f7f384c41c628544e6d52f2d0f53c6d0c3ea61aa5d1d7ff124"},
     {file = "packaging-24.1.tar.gz", hash = "sha256:026ed72c8ed3fcce5bf8950572258698927fd1dbda10a5e981cdf0ac37f4f002"},
@@ -3710,6 +4014,8 @@ version = "2.2.2"
 description = "Powerful data structures for data analysis, time series, and statistics"
 optional = false
 python-versions = ">=3.9"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "pandas-2.2.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:90c6fca2acf139569e74e8781709dccb6fe25940488755716d1d354d6bc58bce"},
     {file = "pandas-2.2.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:c7adfc142dac335d8c1e0dcbd37eb8617eac386596eb9e1a1b77791cf2498238"},
@@ -3782,6 +4088,8 @@ version = "1.5.1"
 description = "Utilities for writing pandoc filters in python"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "pandocfilters-1.5.1-py2.py3-none-any.whl", hash = "sha256:93be382804a9cdb0a7267585f157e5d1731bbe5545a85b268d6f5fe6232de2bc"},
     {file = "pandocfilters-1.5.1.tar.gz", hash = "sha256:002b4a555ee4ebc03f8b66307e287fa492e4a77b4ea14d3f934328297bb4939e"},
@@ -3793,6 +4101,8 @@ version = "0.9.0"
 description = "Parameterized testing with any Python test framework"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "parameterized-0.9.0-py2.py3-none-any.whl", hash = "sha256:4e0758e3d41bea3bbd05ec14fc2c24736723f243b28d702081aef438c9372b1b"},
     {file = "parameterized-0.9.0.tar.gz", hash = "sha256:7fc905272cefa4f364c1a3429cbbe9c0f98b793988efb5bf90aac80f08db09b1"},
@@ -3807,6 +4117,8 @@ version = "0.8.4"
 description = "A Python Parser"
 optional = false
 python-versions = ">=3.6"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "parso-0.8.4-py2.py3-none-any.whl", hash = "sha256:a418670a20291dacd2dddc80c377c5c3791378ee1e8d12bffc35420643d43f18"},
     {file = "parso-0.8.4.tar.gz", hash = "sha256:eb3a7b58240fb99099a345571deecc0f9540ea5f4dd2fe14c2a99d6b281ab92d"},
@@ -3822,6 +4134,8 @@ version = "0.12.1"
 description = "Utility library for gitignore style pattern matching of file paths."
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "pathspec-0.12.1-py3-none-any.whl", hash = "sha256:a0d503e138a4c123b27490a4f7beda6a01c6f288df0e4a8b79c7eb0dc7b4cc08"},
     {file = "pathspec-0.12.1.tar.gz", hash = "sha256:a482d51503a1ab33b1c67a6c3813a26953dbdc71c31dacaef9a838c4e29f5712"},
@@ -3833,6 +4147,8 @@ version = "4.9.0"
 description = "Pexpect allows easy control of interactive console applications."
 optional = false
 python-versions = "*"
+groups = ["main", "dev"]
+markers = "(python_version >= \"3.12\" or python_version == \"3.11\") and (sys_platform != \"win32\" and sys_platform != \"emscripten\")"
 files = [
     {file = "pexpect-4.9.0-py2.py3-none-any.whl", hash = "sha256:7236d1e080e4936be2dc3e326cec0af72acf9212a7e1d060210e70a47e253523"},
     {file = "pexpect-4.9.0.tar.gz", hash = "sha256:ee7d41123f3c9911050ea2c2dac107568dc43b2d3b0c7557a33212c398ead30f"},
@@ -3847,6 +4163,8 @@ version = "0.2.5"
 description = "pgvector support for Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "pgvector-0.2.5-py2.py3-none-any.whl", hash = "sha256:5e5e93ec4d3c45ab1fa388729d56c602f6966296e19deee8878928c6d567e41b"},
 ]
@@ -3860,6 +4178,8 @@ version = "10.4.0"
 description = "Python Imaging Library (Fork)"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "pillow-10.4.0-cp310-cp310-macosx_10_10_x86_64.whl", hash = "sha256:4d9667937cfa347525b319ae34375c37b9ee6b525440f3ef48542fcf66f2731e"},
     {file = "pillow-10.4.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:543f3dc61c18dafb755773efc89aae60d06b6596a63914107f75459cf984164d"},
@@ -3957,6 +4277,8 @@ version = "4.2.2"
 description = "A small Python package for determining appropriate platform-specific dirs, e.g. a `user data dir`."
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "platformdirs-4.2.2-py3-none-any.whl", hash = "sha256:2d7a1657e36a80ea911db832a8a6ece5ee53d8de21edd5cc5879af6530b1bfee"},
     {file = "platformdirs-4.2.2.tar.gz", hash = "sha256:38b7b51f512eed9e84a22788b4bce1de17c0adb134d6becb09836e37d8654cd3"},
@@ -3973,6 +4295,8 @@ version = "3.8.0"
 description = "A framework for managing and maintaining multi-language pre-commit hooks."
 optional = false
 python-versions = ">=3.9"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "pre_commit-3.8.0-py2.py3-none-any.whl", hash = "sha256:9a90a53bf82fdd8778d58085faf8d83df56e40dfe18f45b19446e26bf1b3a63f"},
     {file = "pre_commit-3.8.0.tar.gz", hash = "sha256:8bb6494d4a20423842e198980c9ecf9f96607a07ea29549e180eef9ae80fe7af"},
@@ -3991,6 +4315,8 @@ version = "0.20.0"
 description = "Python client for the Prometheus monitoring system."
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "prometheus_client-0.20.0-py3-none-any.whl", hash = "sha256:cde524a85bce83ca359cc837f28b8c0db5cac7aa653a588fd7e84ba061c329e7"},
     {file = "prometheus_client-0.20.0.tar.gz", hash = "sha256:287629d00b147a32dcb2be0b9df905da599b2d82f80377083ec8463309a4bb89"},
@@ -4005,6 +4331,8 @@ version = "3.0.47"
 description = "Library for building powerful interactive command lines in Python"
 optional = false
 python-versions = ">=3.7.0"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "prompt_toolkit-3.0.47-py3-none-any.whl", hash = "sha256:0d7bfa67001d5e39d02c224b663abc33687405033a8c422d0d675a5a13361d10"},
     {file = "prompt_toolkit-3.0.47.tar.gz", hash = "sha256:1e1b29cb58080b1e69f207c893a1a7bf16d127a5c30c9d17a25a5d77792e5360"},
@@ -4019,6 +4347,8 @@ version = "1.25.0"
 description = "Beautiful, Pythonic protocol buffers."
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "proto_plus-1.25.0-py3-none-any.whl", hash = "sha256:c91fc4a65074ade8e458e95ef8bac34d4008daa7cce4a12d6707066fca648961"},
     {file = "proto_plus-1.25.0.tar.gz", hash = "sha256:fbb17f57f7bd05a68b7707e745e26528b0b3c34e378db91eef93912c54982d91"},
@@ -4036,6 +4366,8 @@ version = "4.25.4"
 description = ""
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "protobuf-4.25.4-cp310-abi3-win32.whl", hash = "sha256:db9fd45183e1a67722cafa5c1da3e85c6492a5383f127c86c4c4aa4845867dc4"},
     {file = "protobuf-4.25.4-cp310-abi3-win_amd64.whl", hash = "sha256:ba3d8504116a921af46499471c63a85260c1a5fc23333154a427a310e015d26d"},
@@ -4056,6 +4388,8 @@ version = "6.0.0"
 description = "Cross-platform lib for process and system monitoring in Python."
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "psutil-6.0.0-cp27-cp27m-macosx_10_9_x86_64.whl", hash = "sha256:a021da3e881cd935e64a3d0a20983bda0bb4cf80e4f74fa9bfcb1bc5785360c6"},
     {file = "psutil-6.0.0-cp27-cp27m-manylinux2010_i686.whl", hash = "sha256:1287c2b95f1c0a364d23bc6f2ea2365a8d4d9b726a3be7294296ff7ba97c17f0"},
@@ -4085,10 +4419,12 @@ version = "0.7.0"
 description = "Run a subprocess in a pseudo terminal"
 optional = false
 python-versions = "*"
+groups = ["main", "dev"]
 files = [
     {file = "ptyprocess-0.7.0-py2.py3-none-any.whl", hash = "sha256:4b41f3967fce3af57cc7e94b888626c18bf37a083e3651ca8feeb66d492fef35"},
     {file = "ptyprocess-0.7.0.tar.gz", hash = "sha256:5c5d0a3b48ceee0b48485e0c26037c0acd7d29765ca3fbb5cb3831d347423220"},
 ]
+markers = {main = "(python_version >= \"3.12\" or python_version == \"3.11\") and (sys_platform != \"win32\" and sys_platform != \"emscripten\")", dev = "(sys_platform != \"win32\" and sys_platform != \"emscripten\" or os_name != \"nt\") and (python_version >= \"3.12\" or python_version == \"3.11\")"}
 
 [[package]]
 name = "pure-eval"
@@ -4096,6 +4432,8 @@ version = "0.2.3"
 description = "Safely evaluate AST nodes without side effects"
 optional = false
 python-versions = "*"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "pure_eval-0.2.3-py3-none-any.whl", hash = "sha256:1db8e35b67b3d218d818ae653e27f06c3aa420901fa7b081ca98cbedc874e0d0"},
     {file = "pure_eval-0.2.3.tar.gz", hash = "sha256:5f4e983f40564c576c7c8635ae88db5956bb2229d7e9237d03b3c0b0190eaf42"},
@@ -4110,6 +4448,8 @@ version = "17.0.0"
 description = "Python library for Apache Arrow"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "pyarrow-17.0.0-cp310-cp310-macosx_10_15_x86_64.whl", hash = "sha256:a5c8b238d47e48812ee577ee20c9a2779e6a5904f1708ae240f53ecbee7c9f07"},
     {file = "pyarrow-17.0.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:db023dc4c6cae1015de9e198d41250688383c3f9af8f565370ab2b4cb5f62655"},
@@ -4161,6 +4501,8 @@ version = "0.6"
 description = ""
 optional = false
 python-versions = ">=3.5"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "pyarrow_hotfix-0.6-py3-none-any.whl", hash = "sha256:dcc9ae2d220dff0083be6a9aa8e0cdee5182ad358d4931fce825c545e5c89178"},
     {file = "pyarrow_hotfix-0.6.tar.gz", hash = "sha256:79d3e030f7ff890d408a100ac16d6f00b14d44a502d7897cd9fc3e3a534e9945"},
@@ -4172,6 +4514,8 @@ version = "0.6.1"
 description = "Pure-Python implementation of ASN.1 types and DER/BER/CER codecs (X.208)"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "pyasn1-0.6.1-py3-none-any.whl", hash = "sha256:0d632f46f2ba09143da3a8afe9e33fb6f92fa2320ab7e886e2d0f7672af84629"},
     {file = "pyasn1-0.6.1.tar.gz", hash = "sha256:6f580d2bdd84365380830acf45550f2511469f673cb4a5ae3857a3170128b034"},
@@ -4183,6 +4527,8 @@ version = "0.4.1"
 description = "A collection of ASN.1-based protocols modules"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "pyasn1_modules-0.4.1-py3-none-any.whl", hash = "sha256:49bfa96b45a292b711e986f222502c1c9a5e1f4e568fc30e2574a6c7d07838fd"},
     {file = "pyasn1_modules-0.4.1.tar.gz", hash = "sha256:c28e2dbf9c06ad61c71a075c7e0f9fd0f1b0bb2d2ad4377f240d33ac2ab60a7c"},
@@ -4197,6 +4543,8 @@ version = "2.22"
 description = "C parser in Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "pycparser-2.22-py3-none-any.whl", hash = "sha256:c3702b6d3dd8c7abc1afa565d7e63d53a1d0bd86cdc24edd75470f4de499cfcc"},
     {file = "pycparser-2.22.tar.gz", hash = "sha256:491c8be9c040f5390f5bf44a5b07752bd07f56edf992381b05c701439eec10f6"},
@@ -4208,6 +4556,8 @@ version = "2.8.2"
 description = "Data validation using Python type hints"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "pydantic-2.8.2-py3-none-any.whl", hash = "sha256:73ee9fddd406dc318b885c7a2eab8a6472b68b8fb5ba8150949fc3db939f23c8"},
     {file = "pydantic-2.8.2.tar.gz", hash = "sha256:6f62c13d067b0755ad1c21a34bdd06c0c12625a22b0fc09c6b149816604f7c2a"},
@@ -4230,6 +4580,8 @@ version = "2.20.1"
 description = "Core functionality for Pydantic validation and serialization"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "pydantic_core-2.20.1-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:3acae97ffd19bf091c72df4d726d552c473f3576409b2a7ca36b2f535ffff4a3"},
     {file = "pydantic_core-2.20.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:41f4c96227a67a013e7de5ff8f20fb496ce573893b7f4f2707d065907bffdbd6"},
@@ -4331,6 +4683,8 @@ version = "2.0.0"
 description = "Python interface to Graphviz's Dot"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "pydot-2.0.0-py3-none-any.whl", hash = "sha256:408a47913ea7bd5d2d34b274144880c1310c4aee901f353cf21fe2e526a4ea28"},
     {file = "pydot-2.0.0.tar.gz", hash = "sha256:60246af215123fa062f21cd791be67dda23a6f280df09f68919e637a1e4f3235"},
@@ -4350,6 +4704,8 @@ version = "2.18.0"
 description = "Pygments is a syntax highlighting package written in Python."
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "pygments-2.18.0-py3-none-any.whl", hash = "sha256:b8e6aca0523f3ab76fee51799c488e38782ac06eafcf95e7ba832985c8e7b13a"},
     {file = "pygments-2.18.0.tar.gz", hash = "sha256:786ff802f32e91311bff3889f6e9a86e81505fe99f2735bb6d60ae0c5004f199"},
@@ -4364,6 +4720,8 @@ version = "3.1.2"
 description = "pyparsing module - Classes and methods to define and execute parsing grammars"
 optional = false
 python-versions = ">=3.6.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "pyparsing-3.1.2-py3-none-any.whl", hash = "sha256:f9db75911801ed778fe61bb643079ff86601aca99fcae6345aa67292038fb742"},
     {file = "pyparsing-3.1.2.tar.gz", hash = "sha256:a1bac0ce561155ecc3ed78ca94d3c9378656ad4c94c1270de543f621420f94ad"},
@@ -4378,6 +4736,8 @@ version = "0.3.4"
 description = "pysbd (Python Sentence Boundary Disambiguation) is a rule-based sentence boundary detection that works out-of-the-box across many languages."
 optional = false
 python-versions = ">=3"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "pysbd-0.3.4-py3-none-any.whl", hash = "sha256:cd838939b7b0b185fcf86b0baf6636667dfb6e474743beeff878e9f42e022953"},
 ]
@@ -4388,6 +4748,8 @@ version = "1.7.1"
 description = "A Python SOCKS client module. See https://github.com/Anorov/PySocks for more information."
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "PySocks-1.7.1-py27-none-any.whl", hash = "sha256:08e69f092cc6dbe92a0fdd16eeb9b9ffbc13cadfe5ca4c7bd92ffb078b293299"},
     {file = "PySocks-1.7.1-py3-none-any.whl", hash = "sha256:2725bd0a9925919b9b51739eea5f9e2bae91e83288108a9ad338b2e3a4435ee5"},
@@ -4400,6 +4762,8 @@ version = "2.9.0.post0"
 description = "Extensions to the standard Python datetime module"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "python-dateutil-2.9.0.post0.tar.gz", hash = "sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3"},
     {file = "python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427"},
@@ -4414,6 +4778,8 @@ version = "1.0.1"
 description = "Read key-value pairs from a .env file and set them as environment variables"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "python-dotenv-1.0.1.tar.gz", hash = "sha256:e324ee90a023d808f1959c46bcbc04446a10ced277783dc6ee09987c37ec10ca"},
     {file = "python_dotenv-1.0.1-py3-none-any.whl", hash = "sha256:f7b63ef50f1b690dddf550d03497b66d609393b40b564ed0d674909a68ebf16a"},
@@ -4428,6 +4794,8 @@ version = "2.0.7"
 description = "A python library adding a json log formatter"
 optional = false
 python-versions = ">=3.6"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "python-json-logger-2.0.7.tar.gz", hash = "sha256:23e7ec02d34237c5aa1e29a070193a4ea87583bb4e7f8fd06d3de8264c4b2e1c"},
     {file = "python_json_logger-2.0.7-py3-none-any.whl", hash = "sha256:f380b826a991ebbe3de4d897aeec42760035ac760345e57b812938dc8b35e2bd"},
@@ -4439,6 +4807,8 @@ version = "2024.1"
 description = "World timezone definitions, modern and historical"
 optional = false
 python-versions = "*"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "pytz-2024.1-py2.py3-none-any.whl", hash = "sha256:328171f4e3623139da4983451950b28e95ac706e13f3f2630a879749e7a8b319"},
     {file = "pytz-2024.1.tar.gz", hash = "sha256:2a29735ea9c18baf14b448846bde5a48030ed267578472d8955cd0e7443a9812"},
@@ -4450,6 +4820,8 @@ version = "306"
 description = "Python for Window Extensions"
 optional = false
 python-versions = "*"
+groups = ["dev"]
+markers = "sys_platform == \"win32\" and platform_python_implementation != \"PyPy\" and (python_version >= \"3.12\" or python_version == \"3.11\")"
 files = [
     {file = "pywin32-306-cp310-cp310-win32.whl", hash = "sha256:06d3420a5155ba65f0b72f2699b5bacf3109f36acbe8923765c22938a69dfc8d"},
     {file = "pywin32-306-cp310-cp310-win_amd64.whl", hash = "sha256:84f4471dbca1887ea3803d8848a1616429ac94a4a8d05f4bc9c5dcfd42ca99c8"},
@@ -4473,6 +4845,8 @@ version = "2.0.13"
 description = "Pseudo terminal support for Windows from Python."
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "(python_version >= \"3.12\" or python_version == \"3.11\") and os_name == \"nt\""
 files = [
     {file = "pywinpty-2.0.13-cp310-none-win_amd64.whl", hash = "sha256:697bff211fb5a6508fee2dc6ff174ce03f34a9a233df9d8b5fe9c8ce4d5eaf56"},
     {file = "pywinpty-2.0.13-cp311-none-win_amd64.whl", hash = "sha256:b96fb14698db1284db84ca38c79f15b4cfdc3172065b5137383910567591fa99"},
@@ -4488,6 +4862,8 @@ version = "6.0.2"
 description = "YAML parser and emitter for Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "PyYAML-6.0.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:0a9a2848a5b7feac301353437eb7d5957887edbf81d56e903999a75a3d743086"},
     {file = "PyYAML-6.0.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:29717114e51c84ddfba879543fb232a6ed60086602313ca38cce623c1d62cfbf"},
@@ -4550,6 +4926,8 @@ version = "26.1.0"
 description = "Python bindings for 0MQ"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "pyzmq-26.1.0-cp310-cp310-macosx_10_15_universal2.whl", hash = "sha256:263cf1e36862310bf5becfbc488e18d5d698941858860c5a8c079d1511b3b18e"},
     {file = "pyzmq-26.1.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:d5c8b17f6e8f29138678834cf8518049e740385eb2dbf736e8f07fc6587ec682"},
@@ -4671,6 +5049,8 @@ version = "5.5.2"
 description = "Jupyter Qt console"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "qtconsole-5.5.2-py3-none-any.whl", hash = "sha256:42d745f3d05d36240244a04e1e1ec2a86d5d9b6edb16dbdef582ccb629e87e0b"},
     {file = "qtconsole-5.5.2.tar.gz", hash = "sha256:6b5fb11274b297463706af84dcbbd5c92273b1f619e6d25d08874b0a88516989"},
@@ -4696,6 +5076,8 @@ version = "2.4.1"
 description = "Provides an abstraction layer on top of the various Qt bindings (PyQt5/6 and PySide2/6)."
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "QtPy-2.4.1-py3-none-any.whl", hash = "sha256:1c1d8c4fa2c884ae742b069151b0abe15b3f70491f3972698c683b8e38de839b"},
     {file = "QtPy-2.4.1.tar.gz", hash = "sha256:a5a15ffd519550a1361bdc56ffc07fda56a6af7292f17c7b395d4083af632987"},
@@ -4713,6 +5095,8 @@ version = "0.1.16"
 description = ""
 optional = false
 python-versions = "*"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "ragas-0.1.16-py3-none-any.whl", hash = "sha256:371378dda700fb7cf7ba5e473d280887e51a6248574bc474e5952a05b8312a80"},
     {file = "ragas-0.1.16.tar.gz", hash = "sha256:23d0d27272fd47d5e6687f1f05461098650c09ad20337e93a0cd7cbfcc2f65b8"},
@@ -4740,6 +5124,8 @@ version = "0.35.1"
 description = "JSON Referencing + Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "referencing-0.35.1-py3-none-any.whl", hash = "sha256:eda6d3234d62814d1c64e305c1331c9a3a6132da475ab6382eaa997b21ee75de"},
     {file = "referencing-0.35.1.tar.gz", hash = "sha256:25b42124a6c8b632a425174f24087783efb348a6f1e0008e63cd4466fedf703c"},
@@ -4755,6 +5141,8 @@ version = "2024.7.24"
 description = "Alternative regular expression module, to replace re."
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "regex-2024.7.24-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:228b0d3f567fafa0633aee87f08b9276c7062da9616931382993c03808bb68ce"},
     {file = "regex-2024.7.24-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:3426de3b91d1bc73249042742f45c2148803c111d1175b283270177fdf669024"},
@@ -4843,6 +5231,8 @@ version = "2.32.3"
 description = "Python HTTP for Humans."
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "requests-2.32.3-py3-none-any.whl", hash = "sha256:70761cfe03c773ceb22aa2f671b4757976145175cdfca038c02654d061d6dcc6"},
     {file = "requests-2.32.3.tar.gz", hash = "sha256:55365417734eb18255590a9ff9eb97e9e1da868d4ccd6402399eaf68af20a760"},
@@ -4865,6 +5255,8 @@ version = "0.1.4"
 description = "A pure python RFC3339 validator"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "rfc3339_validator-0.1.4-py2.py3-none-any.whl", hash = "sha256:24f6ec1eda14ef823da9e36ec7113124b39c04d50a4d3d3a3c2859577e7791fa"},
     {file = "rfc3339_validator-0.1.4.tar.gz", hash = "sha256:138a2abdf93304ad60530167e51d2dfb9549521a836871b88d7f4695d0022f6b"},
@@ -4879,6 +5271,8 @@ version = "0.1.1"
 description = "Pure python rfc3986 validator"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "rfc3986_validator-0.1.1-py2.py3-none-any.whl", hash = "sha256:2f235c432ef459970b4306369336b9d5dbdda31b510ca1e327636e01f528bfa9"},
     {file = "rfc3986_validator-0.1.1.tar.gz", hash = "sha256:3d44bde7921b3b9ec3ae4e3adca370438eccebc676456449b145d533b240d055"},
@@ -4890,6 +5284,8 @@ version = "0.20.0"
 description = "Python bindings to Rust's persistent data structures (rpds)"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "rpds_py-0.20.0-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:3ad0fda1635f8439cde85c700f964b23ed5fc2d28016b32b9ee5fe30da5c84e2"},
     {file = "rpds_py-0.20.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:9bb4a0d90fdb03437c109a17eade42dfbf6190408f29b2744114d11586611d6f"},
@@ -5002,6 +5398,8 @@ version = "4.9"
 description = "Pure-Python RSA implementation"
 optional = false
 python-versions = ">=3.6,<4"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "rsa-4.9-py3-none-any.whl", hash = "sha256:90260d9058e514786967344d0ef75fa8727eed8a7d2e43ce9f4bcf1b536174f7"},
     {file = "rsa-4.9.tar.gz", hash = "sha256:e38464a49c6c85d7f1351b0126661487a7e0a14a50f1675ec50eb34d4f20ef21"},
@@ -5016,6 +5414,8 @@ version = "0.8.0"
 description = "An extremely fast Python linter and code formatter, written in Rust."
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "ruff-0.8.0-py3-none-linux_armv6l.whl", hash = "sha256:fcb1bf2cc6706adae9d79c8d86478677e3bbd4ced796ccad106fd4776d395fea"},
     {file = "ruff-0.8.0-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:295bb4c02d58ff2ef4378a1870c20af30723013f441c9d1637a008baaf928c8b"},
@@ -5043,6 +5443,8 @@ version = "0.10.2"
 description = "An Amazon S3 Transfer Manager"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "s3transfer-0.10.2-py3-none-any.whl", hash = "sha256:eca1c20de70a39daee580aef4986996620f365c4e0fda6a86100231d62f1bf69"},
     {file = "s3transfer-0.10.2.tar.gz", hash = "sha256:0711534e9356d3cc692fdde846b4a1e4b0cb6519971860796e6bc4c7aea00ef6"},
@@ -5060,6 +5462,8 @@ version = "0.4.4"
 description = ""
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "safetensors-0.4.4-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:2adb497ada13097f30e386e88c959c0fda855a5f6f98845710f5bb2c57e14f12"},
     {file = "safetensors-0.4.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:7db7fdc2d71fd1444d85ca3f3d682ba2df7d61a637dfc6d80793f439eae264ab"},
@@ -5192,6 +5596,8 @@ version = "1.8.3"
 description = "Send file to trash natively under Mac OS X, Windows and Linux"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "Send2Trash-1.8.3-py3-none-any.whl", hash = "sha256:0c31227e0bd08961c7665474a3d1ef7193929fedda4233843689baa056be46c9"},
     {file = "Send2Trash-1.8.3.tar.gz", hash = "sha256:b18e7a3966d99871aefeb00cfbcfdced55ce4871194810fc71f4aa484b953abf"},
@@ -5208,6 +5614,8 @@ version = "72.1.0"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "setuptools-72.1.0-py3-none-any.whl", hash = "sha256:5a03e1860cf56bb6ef48ce186b0e557fdba433237481a9a625176c2831be15d1"},
     {file = "setuptools-72.1.0.tar.gz", hash = "sha256:8d243eff56d095e5817f796ede6ae32941278f542e0f941867cc05ae52b162ec"},
@@ -5224,6 +5632,8 @@ version = "1.16.0"
 description = "Python 2 and 3 compatibility utilities"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "six-1.16.0-py2.py3-none-any.whl", hash = "sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254"},
     {file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
@@ -5235,6 +5645,8 @@ version = "1.3.1"
 description = "Sniff out which async library your code is running under"
 optional = false
 python-versions = ">=3.7"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "sniffio-1.3.1-py3-none-any.whl", hash = "sha256:2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2"},
     {file = "sniffio-1.3.1.tar.gz", hash = "sha256:f4324edc670a0f49750a81b895f35c3adb843cca46f0530f79fc1babb23789dc"},
@@ -5246,6 +5658,8 @@ version = "2.5"
 description = "A modern CSS selector implementation for Beautiful Soup."
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "soupsieve-2.5-py3-none-any.whl", hash = "sha256:eaa337ff55a1579b6549dc679565eac1e3d000563bcb1c8ab0d0fefbc0c2cdc7"},
     {file = "soupsieve-2.5.tar.gz", hash = "sha256:5663d5a7b3bfaeee0bc4372e7fc48f9cff4940b3eec54a6451cc5299f1097690"},
@@ -5257,6 +5671,8 @@ version = "2.0.32"
 description = "Database Abstraction Library"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "SQLAlchemy-2.0.32-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:0c9045ecc2e4db59bfc97b20516dfdf8e41d910ac6fb667ebd3a79ea54084619"},
     {file = "SQLAlchemy-2.0.32-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:1467940318e4a860afd546ef61fefb98a14d935cd6817ed07a228c7f7c62f389"},
@@ -5344,6 +5760,8 @@ version = "0.6.3"
 description = "Extract data from python stack frames and tracebacks for informative displays"
 optional = false
 python-versions = "*"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "stack_data-0.6.3-py3-none-any.whl", hash = "sha256:d5558e0c25a4cb0853cddad3d77da9891a08cb85dd9f9f91b9f8cd66e511e695"},
     {file = "stack_data-0.6.3.tar.gz", hash = "sha256:836a778de4fec4dcd1dcd89ed8abff8a221f58308462e1c4aa2a3cf30148f0b9"},
@@ -5363,6 +5781,8 @@ version = "24.4.0"
 description = "Structured Logging for Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "structlog-24.4.0-py3-none-any.whl", hash = "sha256:597f61e80a91cc0749a9fd2a098ed76715a1c8a01f73e336b746504d1aad7610"},
     {file = "structlog-24.4.0.tar.gz", hash = "sha256:b27bfecede327a6d2da5fbc96bd859f114ecc398a6389d664f62085ee7ae6fc4"},
@@ -5380,6 +5800,8 @@ version = "1.13.1"
 description = "Computer algebra system (CAS) in Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "sympy-1.13.1-py3-none-any.whl", hash = "sha256:db36cdc64bf61b9b24578b6f7bab1ecdd2452cf008f34faa33776680c26d66f8"},
     {file = "sympy-1.13.1.tar.gz", hash = "sha256:9cebf7e04ff162015ce31c9c6c9144daa34a93bd082f54fd8f12deca4f47515f"},
@@ -5397,6 +5819,8 @@ version = "8.4.2"
 description = "Retry code until it succeeds"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "tenacity-8.4.2-py3-none-any.whl", hash = "sha256:9e6f7cf7da729125c7437222f8a522279751cdfbe6b67bfe64f75d3a348661b2"},
     {file = "tenacity-8.4.2.tar.gz", hash = "sha256:cd80a53a79336edba8489e767f729e4f391c896956b57140b5d7511a64bbd3ef"},
@@ -5412,6 +5836,8 @@ version = "2.17.0"
 description = "TensorBoard lets you watch Tensors Flow"
 optional = false
 python-versions = ">=3.9"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "tensorboard-2.17.0-py3-none-any.whl", hash = "sha256:859a499a9b1fb68a058858964486627100b71fcb21646861c61d31846a6478fb"},
 ]
@@ -5433,6 +5859,8 @@ version = "0.7.2"
 description = "Fast data loading for TensorBoard"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "tensorboard_data_server-0.7.2-py3-none-any.whl", hash = "sha256:7e0610d205889588983836ec05dc098e80f97b7e7bbff7e994ebb78f578d0ddb"},
     {file = "tensorboard_data_server-0.7.2-py3-none-macosx_10_9_x86_64.whl", hash = "sha256:9fe5d24221b29625dbc7328b0436ca7fc1c23de4acf4d272f1180856e32f9f60"},
@@ -5445,6 +5873,8 @@ version = "2.6.2.2"
 description = "TensorBoardX lets you watch Tensors Flow without Tensorflow"
 optional = false
 python-versions = "*"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "tensorboardX-2.6.2.2-py2.py3-none-any.whl", hash = "sha256:160025acbf759ede23fd3526ae9d9bfbfd8b68eb16c38a010ebe326dc6395db8"},
     {file = "tensorboardX-2.6.2.2.tar.gz", hash = "sha256:c6476d7cd0d529b0b72f4acadb1269f9ed8b22f441e87a84f2a3b940bb87b666"},
@@ -5461,6 +5891,8 @@ version = "0.18.1"
 description = "Tornado websocket backend for the Xterm.js Javascript terminal emulator library."
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "terminado-0.18.1-py3-none-any.whl", hash = "sha256:a4468e1b37bb318f8a86514f65814e1afc977cf29b3992a4500d9dd305dcceb0"},
     {file = "terminado-0.18.1.tar.gz", hash = "sha256:de09f2c4b85de4765f7714688fff57d3e75bad1f909b589fde880460c753fd2e"},
@@ -5482,6 +5914,8 @@ version = "0.1.5"
 description = ""
 optional = false
 python-versions = ">=3.9"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "textgrad-0.1.5.tar.gz", hash = "sha256:5f283aa21b70575f50018b7b6545402c61109292ea628f273416699515ebc009"},
 ]
@@ -5508,6 +5942,8 @@ version = "0.7.0"
 description = "tiktoken is a fast BPE tokeniser for use with OpenAI's models"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "tiktoken-0.7.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:485f3cc6aba7c6b6ce388ba634fbba656d9ee27f766216f45146beb4ac18b25f"},
     {file = "tiktoken-0.7.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:e54be9a2cd2f6d6ffa3517b064983fb695c9a9d8aa7d574d1ef3c3f931a99225"},
@@ -5560,6 +5996,8 @@ version = "1.3.0"
 description = "A tiny CSS parser"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "tinycss2-1.3.0-py3-none-any.whl", hash = "sha256:54a8dbdffb334d536851be0226030e9505965bb2f30f21a4a82c55fb2a80fae7"},
     {file = "tinycss2-1.3.0.tar.gz", hash = "sha256:152f9acabd296a8375fbca5b84c961ff95971fcfc32e79550c8df8e29118c54d"},
@@ -5578,6 +6016,8 @@ version = "6.1.0"
 description = "A wrapper around the stdlib `tokenize` which roundtrips."
 optional = false
 python-versions = ">=3.9"
+groups = ["main"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "tokenize_rt-6.1.0-py2.py3-none-any.whl", hash = "sha256:d706141cdec4aa5f358945abe36b911b8cbdc844545da99e811250c0cee9b6fc"},
     {file = "tokenize_rt-6.1.0.tar.gz", hash = "sha256:e8ee836616c0877ab7c7b54776d2fefcc3bde714449a206762425ae114b53c86"},
@@ -5589,6 +6029,8 @@ version = "0.19.1"
 description = ""
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "tokenizers-0.19.1-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:952078130b3d101e05ecfc7fc3640282d74ed26bcf691400f872563fca15ac97"},
     {file = "tokenizers-0.19.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:82c8b8063de6c0468f08e82c4e198763e7b97aabfe573fd4cf7b33930ca4df77"},
@@ -5706,6 +6148,8 @@ version = "2.4.0"
 description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
 optional = false
 python-versions = ">=3.8.0"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "torch-2.4.0-cp310-cp310-manylinux1_x86_64.whl", hash = "sha256:4ed94583e244af51d6a8d28701ca5a9e02d1219e782f5a01dd401f90af17d8ac"},
     {file = "torch-2.4.0-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:c4ca297b7bd58b506bfd6e78ffd14eb97c0e7797dcd7965df62f50bb575d8954"},
@@ -5759,6 +6203,8 @@ version = "1.4.1"
 description = "PyTorch native Metrics"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "torchmetrics-1.4.1-py3-none-any.whl", hash = "sha256:c2e7cd56dd8bdc60ae63d712f3bdce649f23bd174d9180bdd0b746e0230b865a"},
 ]
@@ -5786,6 +6232,8 @@ version = "6.4.1"
 description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed."
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "tornado-6.4.1-cp38-abi3-macosx_10_9_universal2.whl", hash = "sha256:163b0aafc8e23d8cdc3c9dfb24c5368af84a81e3364745ccb4427669bf84aec8"},
     {file = "tornado-6.4.1-cp38-abi3-macosx_10_9_x86_64.whl", hash = "sha256:6d5ce3437e18a2b66fbadb183c1d3364fb03f2be71299e7d10dbeeb69f4b2a14"},
@@ -5806,6 +6254,8 @@ version = "4.66.5"
 description = "Fast, Extensible Progress Meter"
 optional = false
 python-versions = ">=3.7"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "tqdm-4.66.5-py3-none-any.whl", hash = "sha256:90279a3770753eafc9194a0364852159802111925aa30eb3f9d85b0e805ac7cd"},
     {file = "tqdm-4.66.5.tar.gz", hash = "sha256:e1020aef2e5096702d8a025ac7d16b1577279c9d63f8375b63083e9a5f0fcbad"},
@@ -5826,6 +6276,8 @@ version = "5.14.3"
 description = "Traitlets Python configuration system"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "traitlets-5.14.3-py3-none-any.whl", hash = "sha256:b74e89e397b1ed28cc831db7aea759ba6640cb3de13090ca145426688ff1ac4f"},
     {file = "traitlets-5.14.3.tar.gz", hash = "sha256:9ed0579d3502c94b4b3732ac120375cda96f923114522847de4b3bb98b96b6b7"},
@@ -5841,6 +6293,8 @@ version = "4.44.0"
 description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
 optional = false
 python-versions = ">=3.8.0"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "transformers-4.44.0-py3-none-any.whl", hash = "sha256:ea0ff72def71e9f4812d9414d4803b22681b1617aa6f511bd51cfff2b44a6fca"},
     {file = "transformers-4.44.0.tar.gz", hash = "sha256:75699495e30b7635ca444d8d372e138c687ab51a875b387e33f1fb759c37f196"},
@@ -5909,17 +6363,14 @@ version = "3.0.0"
 description = "A language and compiler for custom Deep Learning operations"
 optional = false
 python-versions = "*"
+groups = ["dev"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version == \"3.11\" or platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.13\" and python_version >= \"3.12\""
 files = [
     {file = "triton-3.0.0-1-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:e1efef76935b2febc365bfadf74bcb65a6f959a9872e5bddf44cc9e0adce1e1a"},
     {file = "triton-3.0.0-1-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:5ce8520437c602fb633f1324cc3871c47bee3b67acf9756c1a66309b60e3216c"},
     {file = "triton-3.0.0-1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:34e509deb77f1c067d8640725ef00c5cbfcb2052a1a3cb6a6d343841f92624eb"},
     {file = "triton-3.0.0-1-cp38-cp38-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:bcbf3b1c48af6a28011a5c40a5b3b9b5330530c3827716b5fbf6d7adcc1e53e9"},
     {file = "triton-3.0.0-1-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:6e5727202f7078c56f91ff13ad0c1abab14a0e7f2c87e91b12b6f64f3e8ae609"},
-    {file = "triton-3.0.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:39b052da883351fdf6be3d93cedae6db3b8e3988d3b09ed221bccecfa9612230"},
-    {file = "triton-3.0.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cd34f19a8582af96e6291d4afce25dac08cb2a5d218c599163761e8e0827208e"},
-    {file = "triton-3.0.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0d5e10de8c011adeb7c878c6ce0dd6073b14367749e34467f1cff2bde1b78253"},
-    {file = "triton-3.0.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e8903767951bf86ec960b4fe4e21bc970055afc65e9d57e916d79ae3c93665e3"},
-    {file = "triton-3.0.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:41004fb1ae9a53fcb3e970745feb87f0e3c94c6ce1ba86e95fa3b8537894bef7"},
 ]
 
 [package.dependencies]
@@ -5936,6 +6387,8 @@ version = "2.9.0.20240316"
 description = "Typing stubs for python-dateutil"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "types-python-dateutil-2.9.0.20240316.tar.gz", hash = "sha256:5d2f2e240b86905e40944dd787db6da9263f0deabef1076ddaed797351ec0202"},
     {file = "types_python_dateutil-2.9.0.20240316-py3-none-any.whl", hash = "sha256:6b8cb66d960771ce5ff974e9dd45e38facb81718cc1e208b10b1baccbfdbee3b"},
@@ -5947,6 +6400,8 @@ version = "2.32.0.20240712"
 description = "Typing stubs for requests"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "types-requests-2.32.0.20240712.tar.gz", hash = "sha256:90c079ff05e549f6bf50e02e910210b98b8ff1ebdd18e19c873cd237737c1358"},
     {file = "types_requests-2.32.0.20240712-py3-none-any.whl", hash = "sha256:f754283e152c752e46e70942fa2a146b5bc70393522257bb85bd1ef7e019dcc3"},
@@ -5961,6 +6416,8 @@ version = "4.12.2"
 description = "Backported and Experimental Type Hints for Python 3.8+"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "typing_extensions-4.12.2-py3-none-any.whl", hash = "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d"},
     {file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
@@ -5972,6 +6429,8 @@ version = "0.9.0"
 description = "Runtime inspection utilities for typing module."
 optional = false
 python-versions = "*"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "typing_inspect-0.9.0-py3-none-any.whl", hash = "sha256:9ee6fc59062311ef8547596ab6b955e1b8aa46242d854bfc78f4f6b0eff35f9f"},
     {file = "typing_inspect-0.9.0.tar.gz", hash = "sha256:b23fc42ff6f6ef6954e4852c1fb512cdd18dbea03134f91f856a95ccc9461f78"},
@@ -5987,6 +6446,8 @@ version = "2024.1"
 description = "Provider of IANA time zone data"
 optional = false
 python-versions = ">=2"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "tzdata-2024.1-py2.py3-none-any.whl", hash = "sha256:9068bc196136463f5245e51efda838afa15aaeca9903f49050dfa2679db4d252"},
     {file = "tzdata-2024.1.tar.gz", hash = "sha256:2674120f8d891909751c38abcdfd386ac0a5a1127954fbc332af6b5ceae07efd"},
@@ -5998,6 +6459,8 @@ version = "5.10.0"
 description = "Ultra fast JSON encoder and decoder for Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "ujson-5.10.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:2601aa9ecdbee1118a1c2065323bda35e2c5a2cf0797ef4522d485f9d3ef65bd"},
     {file = "ujson-5.10.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:348898dd702fc1c4f1051bc3aacbf894caa0927fe2c53e68679c073375f732cf"},
@@ -6085,6 +6548,8 @@ version = "1.3.0"
 description = "RFC 6570 URI Template Processor"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "uri-template-1.3.0.tar.gz", hash = "sha256:0e00f8eb65e18c7de20d595a14336e9f337ead580c70934141624b6d1ffdacc7"},
     {file = "uri_template-1.3.0-py3-none-any.whl", hash = "sha256:a44a133ea12d44a0c0f06d7d42a52d71282e77e2f937d8abd5655b8d56fc1363"},
@@ -6099,6 +6564,8 @@ version = "4.1.1"
 description = "Implementation of RFC 6570 URI Templates"
 optional = false
 python-versions = ">=3.6"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "uritemplate-4.1.1-py2.py3-none-any.whl", hash = "sha256:830c08b8d99bdd312ea4ead05994a38e8936266f84b9a7878232db50b044e02e"},
     {file = "uritemplate-4.1.1.tar.gz", hash = "sha256:4346edfc5c3b79f694bccd6d6099a322bbeb628dbf2cd86eea55a456ce5124f0"},
@@ -6110,6 +6577,8 @@ version = "2.2.2"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "urllib3-2.2.2-py3-none-any.whl", hash = "sha256:a448b2f64d686155468037e1ace9f2d2199776e17f0a46610480d311f73e3472"},
     {file = "urllib3-2.2.2.tar.gz", hash = "sha256:dd505485549a7a552833da5e6063639d0d177c04f23bc3864e41e5dc5f612168"},
@@ -6127,6 +6596,8 @@ version = "20.26.3"
 description = "Virtual Python Environment builder"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "virtualenv-20.26.3-py3-none-any.whl", hash = "sha256:8cc4a31139e796e9a7de2cd5cf2489de1217193116a8fd42328f1bd65f434589"},
     {file = "virtualenv-20.26.3.tar.gz", hash = "sha256:4c43a2a236279d9ea36a0d76f98d84bd6ca94ac4e0f4a3b9d46d05e10fea542a"},
@@ -6147,6 +6618,8 @@ version = "0.2.13"
 description = "Measures the displayed width of unicode strings in a terminal"
 optional = false
 python-versions = "*"
+groups = ["main", "dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "wcwidth-0.2.13-py2.py3-none-any.whl", hash = "sha256:3da69048e4540d84af32131829ff948f1e022c1c6bdb8d6102117aac784f6859"},
     {file = "wcwidth-0.2.13.tar.gz", hash = "sha256:72ea0c06399eb286d978fdedb6923a9eb47e1c486ce63e9b4e64fc18303972b5"},
@@ -6158,6 +6631,8 @@ version = "24.6.0"
 description = "A library for working with the color formats defined by HTML and CSS."
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "webcolors-24.6.0-py3-none-any.whl", hash = "sha256:8cf5bc7e28defd1d48b9e83d5fc30741328305a8195c29a8e668fa45586568a1"},
     {file = "webcolors-24.6.0.tar.gz", hash = "sha256:1d160d1de46b3e81e58d0a280d0c78b467dc80f47294b91b1ad8029d2cedb55b"},
@@ -6173,6 +6648,8 @@ version = "0.5.1"
 description = "Character encoding aliases for legacy web content"
 optional = false
 python-versions = "*"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "webencodings-0.5.1-py2.py3-none-any.whl", hash = "sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78"},
     {file = "webencodings-0.5.1.tar.gz", hash = "sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923"},
@@ -6184,6 +6661,8 @@ version = "1.8.0"
 description = "WebSocket client for Python with low level API options"
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "websocket_client-1.8.0-py3-none-any.whl", hash = "sha256:17b44cc997f5c498e809b22cdf2d9c7a9e71c02c8cc2b6c56e7c2d1239bfa526"},
     {file = "websocket_client-1.8.0.tar.gz", hash = "sha256:3239df9f44da632f96012472805d40a23281a991027ce11d2f45a6f24ac4c3da"},
@@ -6200,6 +6679,8 @@ version = "3.0.3"
 description = "The comprehensive WSGI web application library."
 optional = false
 python-versions = ">=3.8"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "werkzeug-3.0.3-py3-none-any.whl", hash = "sha256:fc9645dc43e03e4d630d23143a04a7f947a9a3b5727cd535fdfe155a17cc48c8"},
     {file = "werkzeug-3.0.3.tar.gz", hash = "sha256:097e5bfda9f0aba8da6b8545146def481d06aa7d3266e7448e2cccf67dd8bd18"},
@@ -6217,6 +6698,8 @@ version = "4.0.11"
 description = "Jupyter interactive widgets for Jupyter Notebook"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "widgetsnbextension-4.0.11-py3-none-any.whl", hash = "sha256:55d4d6949d100e0d08b94948a42efc3ed6dfdc0e9468b2c4b128c9a2ce3a7a36"},
     {file = "widgetsnbextension-4.0.11.tar.gz", hash = "sha256:8b22a8f1910bfd188e596fe7fc05dcbd87e810c8a4ba010bdb3da86637398474"},
@@ -6228,6 +6711,8 @@ version = "3.4.1"
 description = "Python binding for xxHash"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "xxhash-3.4.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:91dbfa55346ad3e18e738742236554531a621042e419b70ad8f3c1d9c7a16e7f"},
     {file = "xxhash-3.4.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:665a65c2a48a72068fcc4d21721510df5f51f1142541c890491afc80451636d2"},
@@ -6345,6 +6830,8 @@ version = "1.9.4"
 description = "Yet another URL library"
 optional = false
 python-versions = ">=3.7"
+groups = ["dev"]
+markers = "python_version >= \"3.12\" or python_version == \"3.11\""
 files = [
     {file = "yarl-1.9.4-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:a8c1df72eb746f4136fe9a2e72b0c9dc1da1cbd23b5372f94b5820ff8ae30e0e"},
     {file = "yarl-1.9.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:a3a6ed1d525bfb91b3fc9b690c5a21bb52de28c018530ad85093cc488bee2dd2"},
@@ -6443,6 +6930,6 @@ idna = ">=2.0"
 multidict = ">=4.0"
 
 [metadata]
-lock-version = "2.0"
+lock-version = "2.1"
 python-versions = ">=3.11, <4.0"
 content-hash = "1fdfe039ade0d41d28cab1d52f8f9fddcb23599178d433e31702386b2b1c5b2e"
diff --git a/tutorials/deepseek_note.py b/tutorials/deepseek_note.py
new file mode 100644
index 000000000..ac83cd861
--- /dev/null
+++ b/tutorials/deepseek_note.py
@@ -0,0 +1,66 @@
+from adalflow.components.model_client import DeepSeekClient
+from adalflow.core.types import ModelType
+
+from adalflow.utils import setup_env
+
+# Initialize the DeepSeekClient
+setup_env() 
+deepseek_client = DeepSeekClient()
+
+# Example query for the DeepSeek reasoning model
+query = "What is the capital of France?"
+
+# === Example 1: Using DeepSeek LLM Model ===
+print("=== Example 1: Using DeepSeek LLM Model ===")
+
+# Set the model type for LLM
+model_type = ModelType.LLM
+
+# Define the system prompt and user query
+system_prompt = "You are a helpful assistant."
+prompt = f"<START_OF_SYSTEM_PROMPT>\n{system_prompt}\n<END_OF_SYSTEM_PROMPT>\n<START_OF_USER_PROMPT>\n{query}\n<END_OF_USER_PROMPT>"
+
+# Define model-specific parameters
+model_kwargs = {"model": "deepseek-reasoner", "temperature": 0.7, "max_tokens": 100, "stream": False}
+
+# Convert the inputs into API-compatible arguments
+api_kwargs = deepseek_client.convert_inputs_to_api_kwargs(
+    input=prompt, model_kwargs=model_kwargs, model_type=model_type
+)
+print(f"api_kwargs: {api_kwargs}")
+
+# Call the DeepSeek reasoning model
+response = deepseek_client.call(api_kwargs=api_kwargs, model_type=model_type)
+
+# Parse the response
+response_text = deepseek_client.parse_chat_completion(response)
+print(f"response_text: {response_text}")
+
+# === Example 2: Using DeepSeek Embedder Model ===
+print("\n=== Example 2: Using DeepSeek Embedder Model ===")
+
+# Set the model type for embedding
+model_type = ModelType.EMBEDDER
+
+# Define the input for embedding
+input = [query] * 2  # Batch embedding
+
+# Define model-specific parameters for embedding
+model_kwargs = {
+    "model": "deepseek-embedder",  # Replace with the actual embedding model name from DeepSeek
+    "dimensions": 512,  # Example dimension size
+    "encoding_format": "float",
+}
+
+# Convert the inputs into API-compatible arguments
+api_kwargs = deepseek_client.convert_inputs_to_api_kwargs(
+    input=input, model_kwargs=model_kwargs, model_type=model_type
+)
+print(f"api_kwargs: {api_kwargs}")
+
+# Call the DeepSeek embedding model
+response = deepseek_client.call(api_kwargs=api_kwargs, model_type=model_type)
+
+# Parse the embedding response
+response_embedder_output = deepseek_client.parse_embedding_response(response)
+print(f"response_embedder_output: {response_embedder_output}")
diff --git a/tutorials/model_client.ipynb b/tutorials/model_client.ipynb
index b61e7ec7a..93766c35e 100644
--- a/tutorials/model_client.ipynb
+++ b/tutorials/model_client.ipynb
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -17,9 +17,9 @@
      "output_type": "stream",
      "text": [
       "api_kwargs: {'model': 'gpt-3.5-turbo', 'temperature': 0.5, 'max_tokens': 100, 'messages': [{'role': 'system', 'content': 'User: What is the capital of France?\\n'}]}\n",
-      "response_text: The capital of France is Paris.\n",
+      "response_text: GeneratorOutput(id=None, data=None, error=None, usage=CompletionUsage(completion_tokens=8, prompt_tokens=16, total_tokens=24), raw_response='The capital of France is Paris.', metadata=None)\n",
       "api_kwargs: {'model': 'text-embedding-3-small', 'dimensions': 8, 'encoding_format': 'float', 'input': ['What is the capital of France?', 'What is the capital of France?']}\n",
-      "reponse_embedder_output: EmbedderOutput(data=[Embedding(embedding=[0.6175549, 0.24047995, 0.4509756, 0.37041178, -0.33437008, -0.050995983, -0.24366009, 0.21549304], index=0), Embedding(embedding=[0.6175549, 0.24047995, 0.4509756, 0.37041178, -0.33437008, -0.050995983, -0.24366009, 0.21549304], index=1)], model='text-embedding-3-small', usage=Usage(prompt_tokens=14, total_tokens=14), error=None, raw_response=None)\n"
+      "reponse_embedder_output: EmbedderOutput(data=[Embedding(embedding=[0.63402575, 0.24025092, 0.42818537, 0.37026355, -0.3518905, -0.041650757, -0.21627253, 0.21798527], index=0), Embedding(embedding=[0.63402575, 0.24025092, 0.42818537, 0.37026355, -0.3518905, -0.041650757, -0.21627253, 0.21798527], index=1)], model='text-embedding-3-small', usage=Usage(prompt_tokens=14, total_tokens=14), error=None, raw_response=None, input=None)\n"
      ]
     }
    ],
@@ -73,9 +73,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "my-project-kernel",
+   "display_name": "AdalFlow",
    "language": "python",
-   "name": "my-project-kernel"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -87,7 +87,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.6"
+   "version": "3.12.0"
   }
  },
  "nbformat": 4,
diff --git a/tutorials/multimodal_client_testing_examples.py b/tutorials/multimodal_client_testing_examples.py
index 44bdbc69e..ee3a171dd 100644
--- a/tutorials/multimodal_client_testing_examples.py
+++ b/tutorials/multimodal_client_testing_examples.py
@@ -17,31 +17,26 @@
 from adalflow.core import Generator
 from adalflow.components.model_client.openai_client import OpenAIClient
 from adalflow.core.types import ModelType
-import asyncio
-import numpy as np
-from dataclasses import dataclass
-from typing import List
-from numpy.linalg import norm
+
 
 class ImageGenerator(Generator):
     """Generator subclass for image generation."""
+
     model_type = ModelType.IMAGE_GENERATION
 
+
 def test_basic_generation():
     """Test basic text generation"""
     client = OpenAIClient()
     gen = Generator(
-        model_client=client,
-        model_kwargs={
-            "model": "gpt-4o-mini",
-            "max_tokens": 100
-        }
+        model_client=client, model_kwargs={"model": "gpt-4o-mini", "max_tokens": 100}
     )
-    
+
     print("\n=== Testing Basic Generation ===")
     response = gen({"input_str": "Hello, world!"})
     print(f"Response: {response}")
 
+
 def test_invalid_image_url():
     """Test Generator output with invalid image URL"""
     client = OpenAIClient()
@@ -50,14 +45,15 @@ def test_invalid_image_url():
         model_kwargs={
             "model": "gpt-4o-mini",
             "images": "https://invalid.url/nonexistent.jpg",
-            "max_tokens": 300
-        }
+            "max_tokens": 300,
+        },
     )
-    
+
     print("\n=== Testing Invalid Image URL ===")
     response = gen({"input_str": "What do you see in this image?"})
     print(f"Response with invalid image URL: {response}")
 
+
 def test_invalid_image_generation():
     """Test DALL-E generation with invalid parameters"""
     client = OpenAIClient()
@@ -67,29 +63,32 @@ def test_invalid_image_generation():
             "model": "dall-e-3",
             "size": "invalid_size",  # Invalid size parameter
             "quality": "standard",
-            "n": 1
-        }
+            "n": 1,
+        },
     )
-    
+
     print("\n=== Testing Invalid DALL-E Parameters ===")
     response = gen({"input_str": "A cat"})
     print(f"Response with invalid DALL-E parameters: {response}")
 
+
 def test_vision_and_generation():
     """Test both vision analysis and image generation"""
     client = OpenAIClient()
-    
+
     # 1. Test Vision Analysis
     vision_gen = Generator(
         model_client=client,
         model_kwargs={
             "model": "gpt-4o-mini",
             "images": "https://upload.wikimedia.org/wikipedia/en/7/7d/Lenna_%28test_image%29.png",
-            "max_tokens": 300
-        }
+            "max_tokens": 300,
+        },
+    )
+
+    vision_response = vision_gen(
+        {"input_str": "What do you see in this image? Be detailed but concise."}
     )
-    
-    vision_response = vision_gen({"input_str": "What do you see in this image? Be detailed but concise."})
     print("\n=== Vision Analysis ===")
     print(f"Description: {vision_response.raw_response}")
 
@@ -100,20 +99,23 @@ def test_vision_and_generation():
             "model": "dall-e-3",
             "size": "1024x1024",
             "quality": "standard",
-            "n": 1
-        }
+            "n": 1,
+        },
     )
-    
+
     # For image generation, input_str becomes the prompt
-    response = dalle_gen({"input_str": "A happy siamese cat playing with a red ball of yarn"})
+    response = dalle_gen(
+        {"input_str": "A happy siamese cat playing with a red ball of yarn"}
+    )
     print("\n=== DALL-E Generation ===")
     print(f"Generated Image URL: {response.data}")
 
+
 if __name__ == "__main__":
     print("Starting OpenAI Vision and DALL-E test...\n")
-    
+
     # Run all tests - they will show errors if API key is invalid/empty
     test_basic_generation()
     test_invalid_image_url()
     test_invalid_image_generation()
-    test_vision_and_generation() 
\ No newline at end of file
+    test_vision_and_generation()