Provide type hints for pass-through Langchain and Llama-index objects (#95)

knowledgehub/llms/chats/base.py

@@ -1,12 +1,8 @@
 from __future__ import annotations
 
 import logging
-from typing import Type
 
-from langchain.chat_models.base import BaseChatModel
-from theflow.base import Param
-
-from kotaemon.base import BaseComponent, BaseMessage, HumanMessage, LLMInterface
+from kotaemon.base import BaseComponent
 
 logger = logging.getLogger(__name__)
 
@@ -23,83 +19,3 @@ class ChatLLM(BaseComponent):
 
         text = self.inflow.flow().text
         return self.__call__(text)
-
-
-class LangchainChatLLM(ChatLLM):
-    _lc_class: Type[BaseChatModel]
-
-    def __init__(self, **params):
-        if self._lc_class is None:
-            raise AttributeError(
-                "Should set _lc_class attribute to the LLM class from Langchain "
-                "if using LLM from Langchain"
-            )
-
-        self._kwargs: dict = {}
-        for param in list(params.keys()):
-            if param in self._lc_class.__fields__:
-                self._kwargs[param] = params.pop(param)
-        super().__init__(**params)
-
-    @Param.auto(cache=False)
-    def agent(self) -> BaseChatModel:
-        return self._lc_class(**self._kwargs)
-
-    def run(
-        self, messages: str | BaseMessage | list[BaseMessage], **kwargs
-    ) -> LLMInterface:
-        """Generate response from messages
-
-        Args:
-            messages: history of messages to generate response from
-            **kwargs: additional arguments to pass to the langchain chat model
-
-        Returns:
-            LLMInterface: generated response
-        """
-        input_: list[BaseMessage] = []
-
-        if isinstance(messages, str):
-            input_ = [HumanMessage(content=messages)]
-        elif isinstance(messages, BaseMessage):
-            input_ = [messages]
-        else:
-            input_ = messages
-
-        pred = self.agent.generate(messages=[input_], **kwargs)
-        all_text = [each.text for each in pred.generations[0]]
-        all_messages = [each.message for each in pred.generations[0]]
-
-        completion_tokens, total_tokens, prompt_tokens = 0, 0, 0
-        try:
-            if pred.llm_output is not None:
-                completion_tokens = pred.llm_output["token_usage"]["completion_tokens"]
-                total_tokens = pred.llm_output["token_usage"]["total_tokens"]
-                prompt_tokens = pred.llm_output["token_usage"]["prompt_tokens"]
-        except Exception:
-            logger.warning(
-                f"Cannot get token usage from LLM output for {self._lc_class.__name__}"
-            )
-
-        return LLMInterface(
-            text=all_text[0] if len(all_text) > 0 else "",
-            candidates=all_text,
-            completion_tokens=completion_tokens,
-            total_tokens=total_tokens,
-            prompt_tokens=prompt_tokens,
-            messages=all_messages,
-            logits=[],
-        )
-
-    def __setattr__(self, name, value):
-        if name in self._lc_class.__fields__:
-            self._kwargs[name] = value
-            setattr(self.agent, name, value)
-        else:
-            super().__setattr__(name, value)
-
-    def __getattr__(self, name):
-        if name in self._lc_class.__fields__:
-            return getattr(self.agent, name)
-
-        return super().__getattr__(name)  # type: ignore