[Question]: How do I get the similarity of the question to the context?

[icewizardry]

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Question

How to get similarity_score?

Here is part of my code that queries the vector library by asking a question and returns the result to the user. How do I calculate the similarity of the question to the context, based on which I want to output a specific response?

        ......
        similarity_threshold = 0.3
        similarity_top_k = 10
        query_engine = (
            self.index.as_query_engine(
                streaming=False,
                node_postprocessors=[SimilarityPostprocessor(
                    similarity_cutoff=similarity_threshold, top_k=similarity_top_k)],
                response_fn=self.custom_response_fn
            ))
        response = query_engine.query(input_text_final)

        similarity_score = ...

[dosubot[bot]]

Hey @icewizardry! I'm here to assist you while you wait for a human maintainer. Let's tackle this challenge together!

To get the similarity of the question to the context and output a particular statement if the question is context-neutral, you can modify your code to check the similarity score and handle context-neutral questions accordingly. Here is an improved example of how you might do this:

from llama_index import SimpleVectorIndex, Query, SimilarityPostprocessor

# Assuming you have already created and populated your index
index = SimpleVectorIndex.load("path_to_your_index")

def query_index(question):
    similarity_threshold = 0.3
    similarity_top_k = 10

    # Create the query engine with the SimilarityPostprocessor
    query_engine = index.as_query_engine(
        streaming=False,
        node_postprocessors=[SimilarityPostprocessor(
            similarity_cutoff=similarity_threshold, top_k=similarity_top_k)],
        response_fn=custom_response_fn
    )

    # Query the index with the question
    response = query_engine.query(question)

    # Check if the question is context-neutral
    if is_context_neutral(question):
        return "The question is context-neutral."

    # Process the response
    if response:
        # Check if the response contains relevant information
        for res in response:
            if "N/A" not in res.get('refined_response', ''):
                return res['initial_response']
        return "No relevant information found."
    else:
        return "No relevant information found."

def is_context_neutral(question):
    # Implement your logic to determine if the question is context-neutral
    # For example, you might check for certain keywords or patterns
    neutral_keywords = ["general", "overview", "summary"]
    return any(keyword in question.lower() for keyword in neutral_keywords)

# Example usage
question = "Can you give me a general overview?"
print(query_index(question))

In this improved example, the query_index function processes the response more robustly by checking each response for relevant information and avoiding responses that indicate no relevant context (e.g., responses containing "N/A"). This ensures that the function returns the most relevant answer available ^[1][2].

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[icewizardry]

Does anyone know?