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

knowledgehub/embeddings/base.py

@@ -1,10 +1,6 @@
 from __future__ import annotations
 
 from abc import abstractmethod
-from typing import Type
-
-from langchain.schema.embeddings import Embeddings as LCEmbeddings
-from theflow import Param
 
 from kotaemon.base import BaseComponent, Document, DocumentWithEmbedding
 
@@ -15,52 +11,3 @@ class BaseEmbeddings(BaseComponent):
         self, text: str | list[str] | Document | list[Document]
     ) -> list[DocumentWithEmbedding]:
         ...
-
-
-class LangchainEmbeddings(BaseEmbeddings):
-    _lc_class: Type[LCEmbeddings]
-
-    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__:  # type: ignore
-                self._kwargs[param] = params.pop(param)
-        super().__init__(**params)
-
-    def __setattr__(self, name, value):
-        if name in self._lc_class.__fields__:
-            self._kwargs[name] = value
-        else:
-            super().__setattr__(name, value)
-
-    @Param.auto(cache=False)
-    def agent(self):
-        return self._lc_class(**self._kwargs)
-
-    def run(self, text):
-        input_: list[str] = []
-        if not isinstance(text, list):
-            text = [text]
-
-        for item in text:
-            if isinstance(item, str):
-                input_.append(item)
-            elif isinstance(item, Document):
-                input_.append(item.text)
-            else:
-                raise ValueError(
-                    f"Invalid input type {type(item)}, should be str or Document"
-                )
-
-        embeddings = self.agent.embed_documents(input_)
-
-        return [
-            DocumentWithEmbedding(text=each_text, embedding=each_embedding)
-            for each_text, each_embedding in zip(input_, embeddings)
-        ]