How do you create combability with ConversationalRetrievalChain about lanarky HOT 4 CLOSED

from dotenv import load_dotenv
from fastapi import FastAPI
from fastapi.templating import Jinja2Templates
from langchain.chains import ConversationalRetrievalChain, LLMChain
from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT
from langchain.chains.question_answering import load_qa_chain
from langchain.chat_models import ChatOpenAI
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.vectorstores.weaviate import Weaviate
from lanarky import LangchainRouter
import weaviate
from langchain.prompts import (
    ChatPromptTemplate,
    HumanMessagePromptTemplate,
    MessagesPlaceholder,
    SystemMessagePromptTemplate,
    PromptTemplate,
)
from langchain.memory import ConversationBufferMemory
from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT


load_dotenv()

app = FastAPI(title="ConversationalRetrievalChainDemo")


chatTemplate = """
Answer the question based on the chat history(delimited by <hs></hs>) and context(delimited by <ctx> </ctx>) below.
-----------
<ctx>
{context}
</ctx>
-----------
<hs>
{chat_history}
</hs>
-----------
Question: {question}
Answer:
"""

PROMPT = PromptTemplate(
    input_variables=["context", "question", "chat_history"], template=chatTemplate
)


def create_chain():
    weaviate_client = weaviate.Client("http://localhost:8080")

    vectorstore: Any = Weaviate(weaviate_client, "Idx_664773d4e6", "text")

    question_generator = LLMChain(
        llm=ChatOpenAI(
            temperature=0,
            streaming=True,
        ),
        prompt=PROMPT,
    )

    doc_chain = load_qa_chain(
        llm=ChatOpenAI(
            temperature=0,
            streaming=True,
        ),
        chain_type="stuff",
    )

    memory = ConversationBufferMemory(
        return_messages=True,
        memory_key="chat_history",
        max_token_limit=20,
        prompt=PROMPT,
    )

    chain = ConversationalRetrievalChain.from_llm(
        llm=ChatOpenAI(
            temperature=0,
            streaming=True,
        ),
        retriever=vectorstore.as_retriever(),
        memory=memory,
        combine_docs_chain_kwargs={"prompt": PROMPT},
        verbose=True,
    )

    return chain


chain = create_chain()


langchain_router = LangchainRouter(
    langchain_url="/chat", langchain_object=chain, streaming_mode=0
)


app.include_router(langchain_router)

if __name__ == "__main__":
    import uvicorn

    uvicorn.run(host="0.0.0.0", port=8000, app=app)