From 73d3a668c99b83f19a2cdf2580e0a4c2654bbef0 Mon Sep 17 00:00:00 2001
From: Grayson Adkins <grayson@idyl.tech>
Date: Tue, 2 May 2023 23:44:48 -0500
Subject: [PATCH 1/3] Update query_data.py

Use ConversationalRetrievalChain instead of ChatVectorDBChain;
Update AsyncCallbackManager after refactor
---
 query_data.py | 17 +++++++++--------
 1 file changed, 9 insertions(+), 8 deletions(-)

diff --git a/query_data.py b/query_data.py
index c0028317f..99a48bee9 100644
--- a/query_data.py
+++ b/query_data.py
@@ -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
@@ -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])
@@ -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(),
         combine_docs_chain=doc_chain,
         question_generator=question_generator,
         callback_manager=manager,
+        return_source_documents=True
     )
     return qa

From 80756027bca22c3bbd6ef12f25943b6c3cf3b676 Mon Sep 17 00:00:00 2001
From: Grayson Adkins <grayson@idyl.tech>
Date: Wed, 3 May 2023 00:39:43 -0500
Subject: [PATCH 2/3] Update README.md

Link to ConversationRetrievalChain example docs
---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index fd2de7e20..3ed2a4b7f 100644
--- a/README.md
+++ b/README.md
@@ -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/use_cases/code.html):
 
 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.

From 528a2279e68082be164060fb47d3cf7553f6c7e0 Mon Sep 17 00:00:00 2001
From: Grayson Adkins <grayson@idyl.tech>
Date: Wed, 3 May 2023 00:44:07 -0500
Subject: [PATCH 3/3] Update README.md

Better link
---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 3ed2a4b7f..bbac6fbe1 100644
--- a/README.md
+++ b/README.md
@@ -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 [ConversationalRetrievalChain](https://python.langchain.com/en/latest/use_cases/code.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.