getting error while using retreival chain

[deepak-hl]

def retrieval_qa_chain(isStream=None, updated_query=None):
    prompt = create_prompt_template_old()

    embedding = OpenAIEmbeddings()
    entity = extract_entities(updated_query)
    llm = ChatXAI(
        xai_api_key=XAI_API_KEY,
        model="grok-beta",
    )

    vector_store = PGVector(
        connection_string=PGVECTOR_CONNECTION_STRING,
        collection_name=persist_directory,
        embedding_function=embedding,
        use_jsonb=True
    )
    retriever = vector_store.as_retriever(
        search_kwargs={
            "k": 8,
            "filter": {"username": "inversebrah"}}
        )
    chain = (
        {
            "context": retriever,
            "question": RunnablePassthrough()
        }
        | prompt
        | llm
        | StrOutputParser()
    )

    return chain
    
    
    
    async def tweets_data_qna(user_query: str):
    updated_query = generate_query_with_dates(user_query)
    chain = retrieval_qa_chain(updated_query=updated_query)
    result = chain.invoke({"query": updated_query})
    print(result)

    return result["result"]

getting below error:
Error occurred: argument 'text': 'dict' object cannot be converted to 'PyString'
in tweets_data_qna in result

[dosubot[bot]]

I found a similar open issue related to the error you're encountering: "TypeError: argument 'text': 'dict' object cannot be converted to 'PyString'" ^[1]. The suggested solution for this type of error is to ensure that the input being passed to the function expecting a string is indeed a string and not a dictionary.

In your code, the error occurs when invoking the chain with {"query": updated_query}. It seems like the invoke method or one of the components in the chain is expecting a string input rather than a dictionary. You might want to check the expected input format for each component in the chain, especially the StrOutputParser, which expects a string input.

To resolve this, ensure that the input to the invoke method matches the expected format. If updated_query is a dictionary, you may need to extract the relevant string value from it before passing it to the chain.

To continue talking to Dosu, mention @dosu.

---

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[dosubot[bot]]

To view the retrieved content using the retriever in the retrieval chain, you can invoke the retriever and print the results. Here's an example of how you can do it:

# Assuming 'retriever' is already created
docs = retriever.invoke("Your query here")
print(docs)

This code will display the documents that the retriever has fetched, which can be used as context for further processing or answering questions ^[1].

To continue talking to Dosu, mention @dosu.

---

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[deepak-hl]

@dosu
is get_relevant_document is applicable to see all the 8 chunks retreived

[dosubot[bot]]

Yes, the get_relevant_document method can be used to retrieve a specified number of chunks, such as 8, by configuring the retriever accordingly. However, it's important to note that get_relevant_document is deprecated in favor of the invoke method. You can achieve the same result by using the invoke method with the k parameter set to 8, which specifies the number of chunks to retrieve.

Here's how you can configure it:

# Assuming 'retriever' is already created
result = retriever.invoke("Your query here", k=8)
print(result)

This will ensure that the retriever returns exactly 8 chunks ^[1][2].

To continue talking to Dosu, mention @dosu.

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[deepak-hl]

@dosu
print(retriever.get_relevant_document(k=8))
Error occurred: 'VectorStoreRetriever' object has no attribute 'get_relevant_document'

[dosubot[bot]]

The error you're encountering indicates that the VectorStoreRetriever object does not have a method named get_relevant_document. This method is deprecated, and you should use the invoke method instead to retrieve documents. Here's how you can retrieve 8 document chunks using the invoke method:

# Assuming 'retriever' is already created
result = retriever.invoke("Your query here", k=8)
print(result)

This will retrieve and print 8 document chunks that are most relevant to your query.

To continue talking to Dosu, mention @dosu.

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