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Fix for recent breaking changes #73

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Fix for recent breaking changes #73

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73d3a66
Update query_data.py
gadkins May 3, 2023
8075602
Update README.md
gadkins May 3, 2023
528a227
Update README.md
gadkins May 3, 2023
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2 changes: 1 addition & 1 deletion README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -34,7 +34,7 @@ Ingestion has the following steps:
3. Split documents with LangChain's [TextSplitter](https://langchain.readthedocs.io/en/latest/reference/modules/text_splitter.html)
4. Create a vectorstore of embeddings, using LangChain's [vectorstore wrapper](https://python.langchain.com/en/latest/modules/indexes/vectorstores.html) (with OpenAI's embeddings and FAISS vectorstore).

Question-Answering has the following steps, all handled by [ChatVectorDBChain](https://langchain.readthedocs.io/en/latest/modules/indexes/chain_examples/chat_vector_db.html):
Question-Answering has the following steps, all handled by [ConversationalRetrievalChain](https://python.langchain.com/en/latest/reference/modules/chains.html?highlight=ConversationalRetrievalChain#langchain.chains.ConversationalRetrievalChain):

1. Given the chat history and new user input, determine what a standalone question would be (using GPT-3).
2. Given that standalone question, look up relevant documents from the vectorstore.
Expand Down
17 changes: 9 additions & 8 deletions query_data.py
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@@ -1,7 +1,7 @@
"""Create a ChatVectorDBChain for question/answering."""
from langchain.callbacks.base import AsyncCallbackManager
"""Create a ConversationalRetrievalChain for question/answering."""
from langchain.callbacks.manager import AsyncCallbackManager
from langchain.callbacks.tracers import LangChainTracer
from langchain.chains import ChatVectorDBChain
from langchain.chains import ConversationalRetrievalChain
from langchain.chains.chat_vector_db.prompts import (CONDENSE_QUESTION_PROMPT,
QA_PROMPT)
from langchain.chains.llm import LLMChain
Expand All @@ -12,9 +12,9 @@

def get_chain(
vectorstore: VectorStore, question_handler, stream_handler, tracing: bool = False
) -> ChatVectorDBChain:
"""Create a ChatVectorDBChain for question/answering."""
# Construct a ChatVectorDBChain with a streaming llm for combine docs
) -> ConversationalRetrievalChain:
"""Create a ConversationalRetrievalChain for question/answering."""
# Construct a ConversationalRetrievalChain with a streaming llm for combine docs
# and a separate, non-streaming llm for question generation
manager = AsyncCallbackManager([])
question_manager = AsyncCallbackManager([question_handler])
Expand Down Expand Up @@ -45,10 +45,11 @@ def get_chain(
streaming_llm, chain_type="stuff", prompt=QA_PROMPT, callback_manager=manager
)

qa = ChatVectorDBChain(
vectorstore=vectorstore,
qa = ConversationalRetrievalChain(
vectorstore=vectorstore.as_retriever(),
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Also needs to update to the parameter from vectorstore to retriever:

    qa = ConversationalRetrievalChain(
        retriever=vectorstore.as_retriever(),
        combine_docs_chain=doc_chain,
        question_generator=question_generator,
        callback_manager=manager,
        return_source_documents=True,
    )

combine_docs_chain=doc_chain,
question_generator=question_generator,
callback_manager=manager,
return_source_documents=True
)
return qa