feat(qa): improve code (#886)

* feat(qa): improve code * feat: 🎸 customprompt now in system
QuivrHQ · Aug 7, 2023 · 7028505 · 7028505
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diff --git a/backend/core/llm/base.py b/backend/core/llm/base.py
@@ -2,8 +2,6 @@
 from typing import AsyncIterable, List
 
 from langchain.callbacks.streaming_aiter import AsyncIteratorCallbackHandler
-from langchain.chains import ConversationalRetrievalChain, LLMChain
-from langchain.llms.base import LLM
 from logger import get_logger
 from models.settings import BrainSettings  # Importing settings related to the 'brain'
 from pydantic import BaseModel  # For data validation and settings management
@@ -73,75 +71,6 @@ class Config:
         arbitrary_types_allowed = True
 
         # the below methods define the names, arguments and return types for the most useful functions for the child classes. These should be overwritten if they are used.
-        @abstractmethod
-        def _create_llm(self, model, temperature=0, streaming=False, callbacks=None) -> LLM:
-            """
-            Determine and construct the language model.
-            :param model: Language model name to be used.
-            :return: Language model instance
-
-            This method should take into account the following:
-            - Whether the model is streaming compatible
-            - Whether the model is private
-            - Whether the model should use an openai api key and use the _determine_api_key method
-            """
-
-        @abstractmethod
-        def _create_question_chain(self, model) -> LLMChain:
-            """
-            Determine and construct the question chain.
-            :param model: Language model name to be used.
-            :return: Question chain instance
-
-            This method should take into account the following:
-            - Which prompt to use (normally CONDENSE_QUESTION_PROMPT)
-            """
-
-        @abstractmethod
-        def _create_doc_chain(self, model) -> LLMChain:
-            """
-            Determine and construct the document chain.
-            :param model Language model name to be used.
-            :return: Document chain instance
-
-            This method should take into account the following:
-            - chain_type (normally "stuff")
-            - Whether the model is streaming compatible and/or streaming is set (determine_streaming).
-            """
-
-        @abstractmethod
-        def _create_qa(
-            self, question_chain, document_chain
-        ) -> ConversationalRetrievalChain:
-            """
-            Constructs a conversational retrieval chain .
-            :param question_chain
-            :param document_chain
-            :return: ConversationalRetrievalChain instance
-            """
-
-        @abstractmethod
-        def _call_chain(self, chain, question, history) -> str:
-            """
-            Call a chain with a given question and history.
-            :param chain: The chain eg QA (ConversationalRetrievalChain)
-            :param question: The user prompt
-            :param history: The chat history from DB
-            :return: The answer.
-            """
-
-        async def _acall_chain(self, chain, question, history) -> str:
-            """
-            Call a chain with a given question and history.
-            :param chain: The chain eg qa (ConversationalRetrievalChain)
-            :param question: The user prompt
-            :param history: The chat history from DB
-            :return: The answer.
-            """
-            raise NotImplementedError(
-                "Async generation not implemented for this BrainPicking Class."
-            )
-
         @abstractmethod
         def generate_answer(self, question: str) -> str:
             """
@@ -153,7 +82,7 @@ def generate_answer(self, question: str) -> str:
             It should also update the chat_history in the DB.
             """
 
-
+        @abstractmethod
         async def generate_stream(self, question: str) -> AsyncIterable:
             """
             Generate a streaming answer to a given question using QA Chain.

diff --git a/backend/core/llm/openai.py b/backend/core/llm/openai.py
@@ -1,6 +1,4 @@
-from langchain.chat_models import ChatOpenAI
 from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain.llms.base import BaseLLM
 from llm.qa_base import QABaseBrainPicking
 from logger import get_logger
 
@@ -46,19 +44,4 @@ def embeddings(self) -> OpenAIEmbeddings:
             openai_api_key=self.openai_api_key
         )  # pyright: ignore reportPrivateUsage=none
 
-    def _create_llm(self, model, temperature=0, streaming=False, callbacks=None) -> BaseLLM:
-        """
-        Determine the language model to be used.
-        :param model: Language model name to be used.
-        :param streaming: Whether to enable streaming of the model
-        :param callbacks: Callbacks to be used for streaming
-        :return: Language model instance
-        """
-        return ChatOpenAI(
-            temperature=temperature,
-            model=model,
-            streaming=streaming,
-            verbose=True,
-            callbacks=callbacks,
-            openai_api_key=self.openai_api_key,
-        )  # pyright: ignore reportPrivateUsage=none
+