chain.py

import json
import os
import pathlib
from typing import Dict, List, Tuple

import weaviate
from langchain import OpenAI, PromptTemplate
from langchain.chains import LLMChain
from langchain.chains.base import Chain
from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
from langchain.chains.conversation.memory import ConversationBufferMemory
from langchain.chains.question_answering import load_qa_chain
from langchain.embeddings import OpenAIEmbeddings
from langchain.prompts import FewShotPromptTemplate, PromptTemplate
from langchain.prompts.example_selector import \
    SemanticSimilarityExampleSelector
from langchain.vectorstores import FAISS, Weaviate
from pydantic import BaseModel


class CustomChain(Chain, BaseModel):

    vstore: Weaviate
    chain: BaseCombineDocumentsChain
    key_word_extractor: Chain

    @property
    def input_keys(self) -> List[str]:
        return ["question"]

    @property
    def output_keys(self) -> List[str]:
        return ["answer"]

    def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
        question = inputs["question"]
        chat_history_str = _get_chat_history(inputs["chat_history"])
        if chat_history_str:
            new_question = self.key_word_extractor.run(
                question=question, chat_history=chat_history_str
            )
        else:
            new_question = question
        print(new_question)
        docs = self.vstore.similarity_search(new_question, k=4)
        new_inputs = inputs.copy()
        new_inputs["question"] = new_question
        new_inputs["chat_history"] = chat_history_str
        answer, _ = self.chain.combine_docs(docs, **new_inputs)
        return {"answer": answer}


def get_new_chain1(vectorstore) -> Chain:
    WEAVIATE_URL = os.environ["WEAVIATE_URL"]
    client = weaviate.Client(
        url=WEAVIATE_URL,
        additional_headers={"X-OpenAI-Api-Key": os.environ["OPENAI_API_KEY"]},
    )

    _eg_template = """## Example:

    Chat History:
    {chat_history}
    Follow Up Input: {question}
    Standalone question: {answer}"""
    _eg_prompt = PromptTemplate(
        template=_eg_template,
        input_variables=["chat_history", "question", "answer"],
    )

    _prefix = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question. You should assume that the question is related to marine biology."""
    _suffix = """## Example:

    Chat History:
    {chat_history}
    Follow Up Input: {question}
    Standalone question:"""
    eg_store = Weaviate(
        client,
        "Rephrase",
        "content",
        attributes=["question", "answer", "chat_history"],
    )
    example_selector = SemanticSimilarityExampleSelector(vectorstore=eg_store, k=4)
    prompt = FewShotPromptTemplate(
        prefix=_prefix,
        suffix=_suffix,
        example_selector=example_selector,
        example_prompt=_eg_prompt,
        input_variables=["question", "chat_history"],
    )
    llm = OpenAI(temperature=0, model_name="text-davinci-003")
    key_word_extractor = LLMChain(llm=llm, prompt=prompt)

    EXAMPLE_PROMPT = PromptTemplate(
        template=">Example:\nContent:\n---------\n{page_content}\n----------\nSource: {source}",
        input_variables=["page_content", "source"],
    )
    template = """You are an AI assistant for Wikipedia information about marine biology.
You are given the following extracted parts of a long document and a question. Provide a conversational answer with a hyperlink to the wikipedia page.
You should only use hyperlinks that are explicitly listed as a source in the context. Do NOT make up a hyperlink that is not listed.
If you don't know the answer, just say "Hmm, I'm not sure." Don't try to make up an answer.
If the question is not about marine biology, the oceans, or biology, politely inform them that you are tuned to only answer questions about marine biology.
Question: {question}
=========
{context}
=========
Answer in Markdown:"""
    PROMPT = PromptTemplate(template=template, input_variables=["question", "context"])
    doc_chain = load_qa_chain(
        OpenAI(temperature=0, model_name="text-davinci-003", max_tokens=-1),
        chain_type="stuff",
        prompt=PROMPT,
        document_prompt=EXAMPLE_PROMPT,
    )
    return CustomChain(
        chain=doc_chain, vstore=vectorstore, key_word_extractor=key_word_extractor
    )


def _get_chat_history(chat_history: List[Tuple[str, str]]):
    buffer = ""
    for human_s, ai_s in chat_history:
        human = f"Human: " + human_s
        ai = f"Assistant: " + ai_s
        buffer += "\n" + "\n".join([human, ai])
    return buffer