Pass context from previous steps.

[ignaceHelsen]

Greetings,

I'm trying to access the retrieved context from 'chain_with_context' in 'chain'. However, the only things that are being passed are:
{
"context": [],
"question": "..."
"chat_history": [...],
}

Although I see a filled context in my logs, the context in the later steps is empty. What am I doing wrong here?

Any help is greatly appreciated!

My code so far:

chain_with_context = (RunnablePassthrough.assign(
                        question=lambda input: input.get('question', ''),
                        context=history_aware_retriever,
                        chat_history=lambda input: input.get("chat_history", [])
            ) | RunnablePassthrough.assign(
                        question=lambda input: input.get('question', ''),
                        context=perform_reranking,
                        chat_history=lambda input: input.get("chat_history", []))
                    | prompt
                    | self.answer_generation_llm
                    | StrOutputParser()
            )

chain = RunnableParallel(
                context=lambda input: input.get('context', []),
                chat_history=lambda input: input.get("chat_history", []),
                question=lambda input: input.get("question", '')
             ).assign(baseline_response=rag_chain
        ) | RunnablePassthrough.assign(
            verification_question_template=verification_question_template_chain
        ) | RunnablePassthrough.assign(
            verification_answers=verification_chain
        ) | final_answer_chain

[dosubot[bot]]

Hello, @ignaceHelsen! I'm here to assist you with any bugs, questions, or contributions. Let's tackle this issue together!

To ensure that the context retrieved in chain_with_context is correctly passed to chain, you should verify that the context is being properly assigned and passed through each step. Here are a few suggestions to help you troubleshoot and resolve the issue:

1. Consistent Context Handling: Make sure that the context is consistently handled and passed through each step in your RunnablePassthrough assignments. Each step should correctly assign and pass the context to the next step.

2. Use of RunnablePassthrough.assign(): Ensure that the assign() method is used correctly to add the context to the output dictionary. This method allows you to add new key-value pairs to the output, which can then be used as input for subsequent steps.

3. Check the Logic in history_aware_retriever and perform_reranking: Verify that these functions are correctly retrieving and processing the context. If they are not returning the expected context, it will not be passed to the next steps.

4. Debugging: Add logging or print statements to check the values of the context at each step. This can help you identify where the context is being lost or not passed correctly.

5. Ensure Correct Input and Output Structure: Make sure that the input and output structures match the expected format for each step. The input should be a dictionary with keys that match the expected input variables, and the output should be a dictionary with keys that match the expected output variables.

If you continue to face issues, you might find it helpful to refer to similar discussions, such as this one on how to pass retrieved context to later steps in the chain ^[1].

To continue talking to Dosu, mention @dosu.

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[ignaceHelsen]

@dosu

Can you solve the problem in my code?

chain_with_context = (RunnablePassthrough.assign(
                        question=lambda input: input.get('question', ''),
                        context=history_aware_retriever,
                        chat_history=lambda input: input.get("chat_history", [])
            ) | RunnablePassthrough.assign(
                        question=lambda input: input.get('question', ''),
                        context=perform_reranking,
                        chat_history=lambda input: input.get("chat_history", []))
                    | prompt
                    | self.answer_generation_llm
                    | StrOutputParser()
            )

chain = RunnableParallel(
                context=lambda input: input.get('context', []),
                chat_history=lambda input: input.get("chat_history", []),
                question=lambda input: input.get("question", '')
             ).assign(baseline_response=rag_chain
        ) | RunnablePassthrough.assign(
            verification_question_template=verification_question_template_chain
        ) | RunnablePassthrough.assign(
            verification_answers=verification_chain
        ) | final_answer_chain

[dosubot[bot]]

To solve the problem in your code where the context is not being passed correctly, consider the following adjustments:

1. Ensure Proper Context Assignment: Make sure that the context is being correctly assigned and passed through each step. The RunnablePassthrough.assign() method should be used to add the context to the output dictionary, which can then be used as input for subsequent steps.

2. Order of Operations: Verify that the context is set before it is retrieved. The context setter should be placed before any context getter in the sequence.

3. Check history_aware_retriever and perform_reranking: Ensure these functions are correctly retrieving and processing the context. If they are not returning the expected context, it will not be passed to the next steps.

4. Debugging: Add logging or print statements to check the values of the context at each step. This can help you identify where the context is being lost or not passed correctly.

Here's a refined version of your code with these considerations:

chain_with_context = (
    RunnablePassthrough.assign(
        question=lambda input: input.get('question', ''),
        context=history_aware_retriever,
        chat_history=lambda input: input.get("chat_history", [])
    )
    | RunnablePassthrough.assign(
        question=lambda input: input.get('question', ''),
        context=perform_reranking,
        chat_history=lambda input: input.get("chat_history", [])
    )
    | prompt
    | self.answer_generation_llm
    | StrOutputParser()
)

chain = (
    RunnableParallel(
        context=lambda input: input.get('context', []),
        chat_history=lambda input: input.get("chat_history", []),
        question=lambda input: input.get("question", '')
    )
    .assign(baseline_response=rag_chain)
    | RunnablePassthrough.assign(
        verification_question_template=verification_question_template_chain
    )
    | RunnablePassthrough.assign(
        verification_answers=verification_chain
    )
    | final_answer_chain
)

Ensure that each step correctly processes and passes the context. If the issue persists, focus on the logic within history_aware_retriever and perform_reranking to ensure they are functioning as expected ^[1][2].

To continue talking to Dosu, mention @dosu.

---

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