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Updated 8 months ago

Hello, I have an issue when using

Hello, I have an issue when using QueryFusionRetriever, I'm using similarity_top_k=8 but in chunks I see only 3. How I can configure more chunks ? Thanks
L
A
10 comments
Do you have a code sample?

It might be removing duplicate chunks
Oh, thats the wrong place to put the top-k

You probably meant to have something like this

Plain Text
    index = VectorStoreIndex.from_vector_store(
                vector_store=vector_store,
                embed_model=embed_model
            )

    indexes.append(index.as_retriever(similarity_top_k=12))

retriever = QueryFusionRetriever(
    retrievers=indexes,
    llm=llm,
    mode=FUSION_MODES.SIMPLE,
    similarity_top_k=12,
    num_queries=1,
    use_async=False,
    verbose=True
)

query_engine = RetrieverQueryEngine.from_args(
            retriever,
            llm=llm,
            qa_prompt_template=qa_prompt_template,
            refine_prompt_template=refine_prompt_template,
        )


chat_engine = CondenseQuestionChatEngine.from_defaults(
            query_engine=query_engine,
            verbose=True,
        )
response = chat_engine.chat(question)
Now, every retriever retruns 12 nodes, and then the overall fusion retriever takes those returns the top 12 across all indexes
@Logan M how can I print each index from indexes to see what chunks have been selected ?
like, chunks from every index? Or just the overall top-12
@Logan M if I want to combine my index vector and BM25 retrievers for reciprocal reranking fusion
Plain Text
bm25_retriever = BM25Retriever.from_defaults(nodes=nodes, similarity_top_k=12)
I get this warning "BM25Retriever does not support embeddings, skipping..." what can be the issue ?
Thats a benign print, it means the query bundle sent to the bm25 retriever had embeddings attached to it, and its just saying its not using those. Its still running retrieval on bm25
There is a way to remove this warnings, maybe sent in a different way ?
hmm, probably, a PR is needed to remove it lol
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