From 911d21f82b01fccf89059c079fa055c8f8f122a8 Mon Sep 17 00:00:00 2001 From: Reza Rahemtola Date: Thu, 5 Dec 2024 01:00:00 +0900 Subject: [PATCH] feat(tools): Basic langchain integration --- libertai_agents/libertai_agents/agents.py | 140 +- .../libertai_agents/interfaces/messages.py | 8 +- .../libertai_agents/interfaces/tools.py | 55 + .../libertai_agents/models/base.py | 47 +- .../libertai_agents/models/hermes.py | 6 +- .../libertai_agents/models/mistral.py | 11 +- .../libertai_agents/models/models.py | 23 +- libertai_agents/poetry.lock | 1314 ++++++++++++----- libertai_agents/pyproject.toml | 6 +- 9 files changed, 1163 insertions(+), 447 deletions(-) create mode 100644 libertai_agents/libertai_agents/interfaces/tools.py diff --git a/libertai_agents/libertai_agents/agents.py b/libertai_agents/libertai_agents/agents.py index 6997761..27876d7 100644 --- a/libertai_agents/libertai_agents/agents.py +++ b/libertai_agents/libertai_agents/agents.py @@ -1,17 +1,28 @@ import asyncio +import inspect import json from http import HTTPStatus -from typing import Callable, Awaitable, Any, AsyncIterable +from typing import Awaitable, Any, AsyncIterable import aiohttp from aiohttp import ClientSession from fastapi import APIRouter, FastAPI from starlette.responses import StreamingResponse -from libertai_agents.interfaces.llamacpp import CustomizableLlamaCppParams, LlamaCppParams -from libertai_agents.interfaces.messages import Message, MessageRoleEnum, MessageToolCall, ToolCallFunction, \ - ToolCallMessage, ToolResponseMessage +from libertai_agents.interfaces.llamacpp import ( + CustomizableLlamaCppParams, + LlamaCppParams, +) +from libertai_agents.interfaces.messages import ( + Message, + MessageRoleEnum, + MessageToolCall, + ToolCallFunction, + ToolCallMessage, + ToolResponseMessage, +) from libertai_agents.interfaces.models import ModelInformation +from libertai_agents.interfaces.tools import Tool from libertai_agents.models import Model from libertai_agents.utils import find @@ -21,14 +32,18 @@ class ChatAgent: model: Model system_prompt: str | None - tools: list[Callable[..., Awaitable[Any]]] + tools: list[Tool] llamacpp_params: CustomizableLlamaCppParams app: FastAPI | None - def __init__(self, model: Model, system_prompt: str | None = None, - tools: list[Callable[..., Awaitable[Any]]] | None = None, - llamacpp_params: CustomizableLlamaCppParams = CustomizableLlamaCppParams(), - expose_api: bool = True): + def __init__( + self, + model: Model, + system_prompt: str | None = None, + tools: list[Tool] | None = None, + llamacpp_params: CustomizableLlamaCppParams = CustomizableLlamaCppParams(), + expose_api: bool = True, + ): """ Create a LibertAI chatbot agent that can answer to messages from users @@ -41,7 +56,7 @@ def __init__(self, model: Model, system_prompt: str | None = None, if tools is None: tools = [] - if len(set(map(lambda x: x.__name__, tools))) != len(tools): + if len(set(map(lambda x: x.name, tools))) != len(tools): raise ValueError("Tool functions must have different names") self.model = model self.system_prompt = system_prompt @@ -51,8 +66,12 @@ def __init__(self, model: Model, system_prompt: str | None = None, if expose_api: # Define API routes router = APIRouter() - router.add_api_route("/generate-answer", self.__api_generate_answer, methods=["POST"], - summary="Generate Answer") + router.add_api_route( + "/generate-answer", + self.__api_generate_answer, + methods=["POST"], + summary="Generate Answer", + ) router.add_api_route("/model", self.get_model_information, methods=["GET"]) self.app = FastAPI(title="LibertAI ChatAgent") @@ -62,9 +81,13 @@ def get_model_information(self) -> ModelInformation: """ Get information about the model powering this agent """ - return ModelInformation(id=self.model.model_id, context_length=self.model.context_length) + return ModelInformation( + id=self.model.model_id, context_length=self.model.context_length + ) - async def generate_answer(self, messages: list[Message], only_final_answer: bool = True) -> AsyncIterable[Message]: + async def generate_answer( + self, messages: list[Message], only_final_answer: bool = True + ) -> AsyncIterable[Message]: """ Generate an answer based on a conversation @@ -78,7 +101,9 @@ async def generate_answer(self, messages: list[Message], only_final_answer: bool raise ValueError("Last message is not from the user or a tool response") for _ in range(MAX_TOOL_CALLS_DEPTH): - prompt = self.model.generate_prompt(messages, self.tools, system_prompt=self.system_prompt) + prompt = self.model.generate_prompt( + messages, self.tools, system_prompt=self.system_prompt + ) async with aiohttp.ClientSession() as session: response = await self.__call_model(session, prompt) @@ -97,36 +122,53 @@ async def generate_answer(self, messages: list[Message], only_final_answer: bool if not only_final_answer: yield tool_calls_message - executed_calls = self.__execute_tool_calls(tool_calls_message.tool_calls) + executed_calls = self.__execute_tool_calls( + tool_calls_message.tool_calls + ) results = await asyncio.gather(*executed_calls) tool_results_messages: list[Message] = [ - ToolResponseMessage(role=MessageRoleEnum.tool, name=call.function.name, tool_call_id=call.id, - content=str(results[i])) for i, call in - enumerate(tool_calls_message.tool_calls)] + ToolResponseMessage( + role=MessageRoleEnum.tool, + name=call.function.name, + tool_call_id=call.id, + content=str(results[i]), + ) + for i, call in enumerate(tool_calls_message.tool_calls) + ] if not only_final_answer: for tool_result_message in tool_results_messages: yield tool_result_message # Doing the next iteration of the loop with the results to make other tool calls or to answer messages = messages + tool_results_messages - async def __api_generate_answer(self, messages: list[Message], stream: bool = False, - only_final_answer: bool = True): + async def __api_generate_answer( + self, + messages: list[Message], + stream: bool = False, + only_final_answer: bool = True, + ): """ Generate an answer based on an existing conversation. The response messages can be streamed or sent in a single block. """ if stream: return StreamingResponse( - self.__dump_api_generate_streamed_answer(messages, only_final_answer=only_final_answer), - media_type='text/event-stream') + self.__dump_api_generate_streamed_answer( + messages, only_final_answer=only_final_answer + ), + media_type="text/event-stream", + ) response_messages: list[Message] = [] - async for message in self.generate_answer(messages, only_final_answer=only_final_answer): + async for message in self.generate_answer( + messages, only_final_answer=only_final_answer + ): response_messages.append(message) return response_messages - async def __dump_api_generate_streamed_answer(self, messages: list[Message], only_final_answer: bool) -> \ - AsyncIterable[str]: + async def __dump_api_generate_streamed_answer( + self, messages: list[Message], only_final_answer: bool + ) -> AsyncIterable[str]: """ Dump to JSON the generate_answer iterable @@ -134,7 +176,9 @@ async def __dump_api_generate_streamed_answer(self, messages: list[Message], onl :param only_final_answer: Param to pass to generate_answer :return: Iterable of each messages from generate_answer dumped to JSON """ - async for message in self.generate_answer(messages, only_final_answer=only_final_answer): + async for message in self.generate_answer( + messages, only_final_answer=only_final_answer + ): yield json.dumps(message.dict(), indent=4) async def __call_model(self, session: ClientSession, prompt: str) -> str | None: @@ -154,7 +198,9 @@ async def __call_model(self, session: ClientSession, prompt: str) -> str | None: return response_data["content"] return None - def __execute_tool_calls(self, tool_calls: list[MessageToolCall]) -> list[Awaitable[Any]]: + def __execute_tool_calls( + self, tool_calls: list[MessageToolCall] + ) -> list[Awaitable[Any]]: """ Execute the given tool calls (without waiting for completion) @@ -164,26 +210,42 @@ def __execute_tool_calls(self, tool_calls: list[MessageToolCall]) -> list[Awaita executed_calls: list[Awaitable[Any]] = [] for call in tool_calls: function_name = call.function.name - function_to_call = find(lambda x: x.__name__ == function_name, self.tools) - if function_to_call is None: + tool = find(lambda x: x.name == function_name, self.tools) + if tool is None: # TODO: handle error continue - function_response = function_to_call(*call.function.arguments.values()) + + function_to_call = tool.function + if inspect.iscoroutinefunction(function_to_call): + # Call async function directly + function_response = function_to_call(*call.function.arguments.values()) + else: + # Wrap sync function in asyncio.to_thread to make it awaitable + function_response = asyncio.to_thread( + function_to_call, *call.function.arguments.values() + ) + executed_calls.append(function_response) return executed_calls - def __create_tool_calls_message(self, tool_calls: list[ToolCallFunction]) -> ToolCallMessage: + def __create_tool_calls_message( + self, tool_calls: list[ToolCallFunction] + ) -> ToolCallMessage: """ Craft a tool call message :param tool_calls: Tool calls to include in the message :return: Crafted tool call message """ - return ToolCallMessage(role=MessageRoleEnum.assistant, - tool_calls=[MessageToolCall(type="function", - id=self.model.generate_tool_call_id(), - function=ToolCallFunction(name=call.name, - arguments=call.arguments)) for - call in - tool_calls]) + return ToolCallMessage( + role=MessageRoleEnum.assistant, + tool_calls=[ + MessageToolCall( + type="function", + id=self.model.generate_tool_call_id(), + function=ToolCallFunction(name=call.name, arguments=call.arguments), + ) + for call in tool_calls + ], + ) diff --git a/libertai_agents/libertai_agents/interfaces/messages.py b/libertai_agents/libertai_agents/interfaces/messages.py index dfdddce..7abce6c 100644 --- a/libertai_agents/libertai_agents/interfaces/messages.py +++ b/libertai_agents/libertai_agents/interfaces/messages.py @@ -5,10 +5,10 @@ class MessageRoleEnum(str, Enum): - user = 'user' - assistant = 'assistant' - system = 'system' - tool = 'tool' + user = "user" + assistant = "assistant" + system = "system" + tool = "tool" class ToolCallFunction(BaseModel): diff --git a/libertai_agents/libertai_agents/interfaces/tools.py b/libertai_agents/libertai_agents/interfaces/tools.py new file mode 100644 index 0000000..9ed803e --- /dev/null +++ b/libertai_agents/libertai_agents/interfaces/tools.py @@ -0,0 +1,55 @@ +from typing import Callable, Any, TYPE_CHECKING + +from pydantic.v1 import BaseModel +from transformers.utils import get_json_schema +from transformers.utils.chat_template_utils import _convert_type_hints_to_json_schema + +if TYPE_CHECKING: + # Importing only for type hinting purposes. + from langchain_core.tools import BaseTool + + +class Tool(BaseModel): + name: str + function: Callable[..., Any] + args_schema: dict + + @classmethod + def from_function(cls, function: Callable[..., Any]): + return cls( + name=function.__name__, + function=function, + args_schema=get_json_schema(function), + ) + + @classmethod + def from_langchain(cls, langchain_tool: "BaseTool"): + try: + from langchain_core.tools import StructuredTool + except ImportError: + raise RuntimeError( + "langchain_core is required for this functionality. Install with: libertai-agents[langchain]" + ) + + if isinstance(langchain_tool, StructuredTool): + # TODO: handle this case + raise NotImplementedError("Langchain StructuredTool aren't supported yet") + + # Extracting function parameters to JSON schema + function_parameters = _convert_type_hints_to_json_schema(langchain_tool._run) + # Removing langchain-specific parameters + function_parameters["properties"].pop("run_manager", None) + function_parameters["properties"].pop("return", None) + + return cls( + name=langchain_tool.name, + function=langchain_tool._run, + args_schema={ + "type": "function", + "function": { + "name": langchain_tool.name, + "description": langchain_tool.description, + "parameters": function_parameters, + }, + }, + ) diff --git a/libertai_agents/libertai_agents/models/base.py b/libertai_agents/libertai_agents/models/base.py index 8081994..f3c9f76 100644 --- a/libertai_agents/libertai_agents/models/base.py +++ b/libertai_agents/libertai_agents/models/base.py @@ -2,14 +2,18 @@ from abc import ABC, abstractmethod from typing import Literal -from libertai_agents.interfaces.messages import Message, ToolCallFunction, MessageRoleEnum +from libertai_agents.interfaces.messages import ( + Message, + ToolCallFunction, + MessageRoleEnum, +) +from libertai_agents.interfaces.tools import Tool # Disables the error about models not available logging.getLogger("transformers").disabled = True ModelId = Literal[ - "NousResearch/Hermes-3-Llama-3.1-8B", - "mistralai/Mistral-Nemo-Instruct-2407" + "NousResearch/Hermes-3-Llama-3.1-8B", "mistralai/Mistral-Nemo-Instruct-2407" ] @@ -22,7 +26,13 @@ class Model(ABC): context_length: int include_system_message: bool - def __init__(self, model_id: ModelId, vm_url: str, context_length: int, include_system_message: bool = True): + def __init__( + self, + model_id: ModelId, + vm_url: str, + context_length: int, + include_system_message: bool = True, + ): """ Creates a new instance of a model @@ -49,7 +59,12 @@ def __count_tokens(self, content: str) -> int: tokens = self.tokenizer.tokenize(content) return len(tokens) - def generate_prompt(self, messages: list[Message], tools: list, system_prompt: str | None = None) -> str: + def generate_prompt( + self, + messages: list[Message], + tools: list[Tool], + system_prompt: str | None = None, + ) -> str: """ Generate the whole chat prompt @@ -58,25 +73,33 @@ def generate_prompt(self, messages: list[Message], tools: list, system_prompt: s :param tools: Available tools :return: Prompt string """ - system_messages = [Message(role=MessageRoleEnum.system, - content=system_prompt)] if self.include_system_message and system_prompt is not None else [] + system_messages = ( + [Message(role=MessageRoleEnum.system, content=system_prompt)] + if self.include_system_message and system_prompt is not None + else [] + ) raw_messages = list(map(lambda x: x.dict(), messages)) for i in range(len(raw_messages)): included_messages: list = system_messages + raw_messages[i:] - prompt = self.tokenizer.apply_chat_template(conversation=included_messages, tools=tools, - tokenize=False, - add_generation_prompt=True) + prompt = self.tokenizer.apply_chat_template( + conversation=included_messages, + tools=list(map(lambda x: x.args_schema, tools)), + tokenize=False, + add_generation_prompt=True, + ) if not isinstance(prompt, str): raise TypeError("Generated prompt isn't a string") if self.__count_tokens(prompt) <= self.context_length: return prompt - raise ValueError(f"Can't fit messages into the available context length ({self.context_length} tokens)") + raise ValueError( + f"Can't fit messages into the available context length ({self.context_length} tokens)" + ) def generate_tool_call_id(self) -> str | None: """ Generate a random ID for a tool call - + :return: A string, or None if this model doesn't require a tool call ID """ return None diff --git a/libertai_agents/libertai_agents/models/hermes.py b/libertai_agents/libertai_agents/models/hermes.py index 8e85c6a..2eb233f 100644 --- a/libertai_agents/libertai_agents/models/hermes.py +++ b/libertai_agents/libertai_agents/models/hermes.py @@ -7,12 +7,14 @@ class HermesModel(Model): def __init__(self, model_id: ModelId, vm_url: str, context_length: int): - super().__init__(model_id=model_id, vm_url=vm_url, context_length=context_length) + super().__init__( + model_id=model_id, vm_url=vm_url, context_length=context_length + ) @staticmethod def extract_tool_calls_from_response(response: str) -> list[ToolCallFunction]: try: - tool_calls = re.findall(r'\s*(.*)\s*', response) + tool_calls = re.findall(r"\s*(.*)\s*", response) return [ToolCallFunction(**json.loads(call)) for call in tool_calls] except Exception: return [] diff --git a/libertai_agents/libertai_agents/models/mistral.py b/libertai_agents/libertai_agents/models/mistral.py index a24b317..56673b4 100644 --- a/libertai_agents/libertai_agents/models/mistral.py +++ b/libertai_agents/libertai_agents/models/mistral.py @@ -8,7 +8,12 @@ class MistralModel(Model): def __init__(self, model_id: ModelId, vm_url: str, context_length: int): - super().__init__(model_id=model_id, vm_url=vm_url, context_length=context_length, include_system_message=False) + super().__init__( + model_id=model_id, + vm_url=vm_url, + context_length=context_length, + include_system_message=False, + ) @staticmethod def extract_tool_calls_from_response(response: str) -> list[ToolCallFunction]: @@ -19,4 +24,6 @@ def extract_tool_calls_from_response(response: str) -> list[ToolCallFunction]: return [] def generate_tool_call_id(self) -> str: - return ''.join(random.choice(string.ascii_letters + string.digits) for _ in range(9)) + return "".join( + random.choice(string.ascii_letters + string.digits) for _ in range(9) + ) diff --git a/libertai_agents/libertai_agents/models/models.py b/libertai_agents/libertai_agents/models/models.py index 0684c2c..97bfbfa 100644 --- a/libertai_agents/libertai_agents/models/models.py +++ b/libertai_agents/libertai_agents/models/models.py @@ -23,16 +23,21 @@ class FullModelConfiguration(ModelConfiguration): "NousResearch/Hermes-3-Llama-3.1-8B": FullModelConfiguration( vm_url="https://curated.aleph.cloud/vm/84df52ac4466d121ef3bb409bb14f315de7be4ce600e8948d71df6485aa5bcc3/completion", context_length=4096, - constructor=HermesModel), + constructor=HermesModel, + ), "mistralai/Mistral-Nemo-Instruct-2407": FullModelConfiguration( vm_url="https://curated.aleph.cloud/vm/2c4ad0bf343fb12924936cbc801732d95ce90f84cd895aa8bee82c0a062815c2/completion", context_length=4096, - constructor=MistralModel) + constructor=MistralModel, + ), } -def get_model(model_id: ModelId, hf_token: str | None = None, - custom_configuration: ModelConfiguration | None = None) -> Model: +def get_model( + model_id: ModelId, + hf_token: str | None = None, + custom_configuration: ModelConfiguration | None = None, +) -> Model: """ Get one of the available models @@ -46,12 +51,16 @@ def get_model(model_id: ModelId, hf_token: str | None = None, full_config = MODELS_CONFIG.get(model_id) if full_config is None: - raise ValueError(f'model_id must be one of {MODEL_IDS}') + raise ValueError(f"model_id must be one of {MODEL_IDS}") if hf_token is not None: login(hf_token) # Using our configuration if the user didn't pass a custom model config - configuration = custom_configuration if custom_configuration is not None else full_config + configuration = ( + custom_configuration if custom_configuration is not None else full_config + ) - return full_config.constructor(model_id=model_id, **configuration.dict(exclude={'constructor'})) + return full_config.constructor( + model_id=model_id, **configuration.dict(exclude={"constructor"}) + ) diff --git a/libertai_agents/poetry.lock b/libertai_agents/poetry.lock index 0ef1075..b94cbfd 100644 --- a/libertai_agents/poetry.lock +++ b/libertai_agents/poetry.lock @@ -2,98 +2,98 @@ [[package]] name = "aiohappyeyeballs" -version = "2.4.3" +version = "2.4.4" 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