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fix chatvectordbchain to use pinecone namespace (#1139)

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In the similarity search, the pinecone namespace is not used, which
makes the bot return _I don't know_ where the embeddings are stored in
the pinecone namespace. Now we can query by passing the namespace
optionally.
```result = qa({"question": query, "chat_history": chat_history, "namespace":"01gshyhjcfgkq1q5wxjtm17gjh"})```
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kekayan 2023-02-19 00:28:48 +05:30 committed by GitHub
parent fb3c73d194
commit 9111f4ca8a
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1 changed files with 4 additions and 2 deletions
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6
langchain/chains/chat_vector_db/base.py
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@ -81,13 +81,14 @@ class ChatVectorDBChain(Chain, BaseModel):
def _call(self, inputs: Dict[str, Any]) -> Dict[str, Any]: def _call(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
question = inputs["question"] question = inputs["question"]
chat_history_str = _get_chat_history(inputs["chat_history"]) chat_history_str = _get_chat_history(inputs["chat_history"])
vectordbkwargs = inputs.get("vectordbkwargs", {})
if chat_history_str: if chat_history_str:
new_question = self.question_generator.run( new_question = self.question_generator.run(
question=question, chat_history=chat_history_str question=question, chat_history=chat_history_str
) )
else: else:
new_question = question new_question = question
docs = self.vectorstore.similarity_search(new_question, k=4) docs = self.vectorstore.similarity_search(new_question, k=4, **vectordbkwargs)
new_inputs = inputs.copy() new_inputs = inputs.copy()
new_inputs["question"] = new_question new_inputs["question"] = new_question
new_inputs["chat_history"] = chat_history_str new_inputs["chat_history"] = chat_history_str
@ -100,6 +101,7 @@ class ChatVectorDBChain(Chain, BaseModel):
async def _acall(self, inputs: Dict[str, Any]) -> Dict[str, str]: async def _acall(self, inputs: Dict[str, Any]) -> Dict[str, str]:
question = inputs["question"] question = inputs["question"]
chat_history_str = _get_chat_history(inputs["chat_history"]) chat_history_str = _get_chat_history(inputs["chat_history"])
vectordbkwargs = inputs.get("vectordbkwargs", {})
if chat_history_str: if chat_history_str:
new_question = await self.question_generator.arun( new_question = await self.question_generator.arun(
question=question, chat_history=chat_history_str question=question, chat_history=chat_history_str
@ -107,7 +109,7 @@ class ChatVectorDBChain(Chain, BaseModel):
else: else:
new_question = question new_question = question
# TODO: This blocks the event loop, but it's not clear how to avoid it. # TODO: This blocks the event loop, but it's not clear how to avoid it.
docs = self.vectorstore.similarity_search(new_question, k=4) docs = self.vectorstore.similarity_search(new_question, k=4, **vectordbkwargs)
new_inputs = inputs.copy() new_inputs = inputs.copy()
new_inputs["question"] = new_question new_inputs["question"] = new_question
new_inputs["chat_history"] = chat_history_str new_inputs["chat_history"] = chat_history_str