Added docstring for get_langchain_context, lint

chatsky/llm/filters.py

@@ -29,7 +29,9 @@ class BaseHistoryFilter(BaseModel, abc.ABC):
     """
 
     @abc.abstractmethod
-    def call(self, ctx: Context, request: Optional[Message], response: Optional[Message], llm_model_name: str) -> Union[Return, int]:
+    def call(
+        self, ctx: Context, request: Optional[Message], response: Optional[Message], llm_model_name: str
+    ) -> Union[Return, int]:
         """
         :param ctx: Context object.
         :param request: Request message.

chatsky/llm/langchain_context.py

@@ -7,17 +7,16 @@
 import re
 import logging
 from typing import Literal, Union
-from pydantic import validate_call
 
-from chatsky.core import AnyResponse, Context, Message
-from chatsky.core.script_function import ConstResponse
+from chatsky.core import Context, Message
 from chatsky.llm._langchain_imports import HumanMessage, SystemMessage, AIMessage, check_langchain_available
 from chatsky.llm.filters import BaseHistoryFilter
 from chatsky.llm.prompt import Prompt, PositionConfig
 
 logger = logging.getLogger(__name__)
 logger.debug("Loaded LLM Utils logger.")
 
+
 async def message_to_langchain(
     message: Message, ctx: Context, source: Literal["human", "ai", "system"] = "human", max_size: int = 1000
 ) -> Union[HumanMessage, AIMessage, SystemMessage]:
@@ -64,22 +63,7 @@ async def context_to_history(
     :return: List of Langchain message objects.
     """
     history = []
-
-    # pairs = zip(
-    #     [ctx.requests[x] for x in range(1, len(ctx.requests) + 1)],
-    #     [ctx.responses[x] for x in range(1, len(ctx.responses) + 1)],
-    # )
-    # pairs_list = list(pairs)
-    # filtered_pairs = filter(
-    #     lambda x: filter_func(ctx, x[0], x[1], llm_model_name), pairs_list[-length:] if length != -1 else pairs_list
-    # )
-
-    # for req, resp in filtered_pairs:
-    #     logger.debug(f"This pair is valid: {req, resp}")
-    #     history.append(await message_to_langchain(req, ctx=ctx, max_size=max_size))
-    #     history.append(await message_to_langchain(resp, ctx=ctx, source="ai", max_size=max_size))
-
-    indices = range(1, min(max([*ctx.requests.keys(), 0]), max([*ctx.responses.keys(), 0]))+1)
+    indices = range(1, min(max([*ctx.requests.keys(), 0]), max([*ctx.responses.keys(), 0])) + 1)
 
     if length == 0:
         return []
@@ -112,6 +96,17 @@ async def get_langchain_context(
 ) -> list[HumanMessage | AIMessage | SystemMessage]:
     """
     Get a list of Langchain messages using the context and prompts.
+
+    :param system_prompt: System message to be included in the context.
+    :param ctx: Current dialog context.
+    :param call_prompt: Prompt to be used for the current call.
+    :param prompt_misc_filter: Regex pattern to filter miscellaneous prompts from context.
+        Defaults to r"prompt".
+    :param position_config: Configuration for positioning different parts of the context.
+        Defaults to default PositionConfig().
+    :param history_args: Additional arguments to be passed to context_to_history function.
+
+    :return: List of Langchain message objects ordered by their position values.
     """
     logger.debug(f"History args: {history_args}")
 

chatsky/llm/llm_api.py

@@ -4,7 +4,7 @@
 Wrapper around langchain.
 """
 
-from typing import Union, Type, Optional
+from typing import Union, Type
 from pydantic import BaseModel, TypeAdapter
 import logging
 from chatsky.core.message import Message
@@ -26,7 +26,7 @@ def __init__(
         self,
         model: BaseChatModel,
         system_prompt: Union[AnyResponse, MessageInitTypes] = "",
-        position_config: PositionConfig = None
+        position_config: PositionConfig = None,
     ) -> None:
         """
         :param model: Model object

chatsky/responses/llm.py

@@ -11,11 +11,11 @@
 
 from chatsky.core.message import Message
 from chatsky.core.context import Context
-from chatsky.llm.langchain_context import message_to_langchain, context_to_history, get_langchain_context
+from chatsky.llm.langchain_context import get_langchain_context
 from chatsky.llm._langchain_imports import check_langchain_available
 from chatsky.llm.filters import BaseHistoryFilter, DefaultFilter
 from chatsky.llm.prompt import Prompt, PositionConfig
-from chatsky.core.script_function import BaseResponse, AnyResponse
+from chatsky.core.script_function import BaseResponse
 
 
 class LLMResponse(BaseResponse):
@@ -77,7 +77,7 @@ async def call(self, ctx: Context) -> Message:
                 max_size=self.max_size,
             )
         )
-        
+
         logging.debug(f"History: {history_messages}")
         result = await model.respond(history_messages, message_schema=self.message_schema)
 

chatsky/slots/llm.py

@@ -26,6 +26,7 @@ class LLMSlot(ValueSlot, frozen=True):
     LLMSlot is a slot type that extract information described in
     `caption` parameter using LLM.
     """
+
     # TODO:
     # add history (and overall update the class)