What default retriever do I need to specify in this def...

ddkatz123

What default retriever do I need to specify in this definition?
rerank_query_engine = vector_query_engine.from_args(
similarity_top_k=10,
node_postprocessors=[postprocessor],
)

6 comments

kkapa.ai

Please stand by @dkatz123 as I search the knowledge base. I normally take about 20 seconds or so. :wave:

kkapa.ai

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The from_args method of the vector_query_engine object you're using to create rerank_query_engine does not directly specify a retriever. However, the vector_query_engine itself is typically created from a retriever.

In the provided knowledge sources, the VectorIndexRetriever is commonly used. Here's an example of how it's defined:

Plain Text

retriever = VectorIndexRetriever(
    index=index,
    similarity_top_k=10,
)

Then, a query engine like vector_query_engine can be created from the retriever:

Plain Text

query_engine = RetrieverQueryEngine(
    retriever=retriever, 
    response_synthesizer=get_response_synthesizer()
)

In your case, you would replace query_engine with vector_query_engine.

Please note that the from_args method you're using to create rerank_query_engine is not directly covered in the provided knowledge sources. You might need to refer to the LlamaIndex documentation or source code for the exact usage of this method.

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ddkatz123

@kapa.ai How can I then assign the rerank_query_engine to the newly defined vector_query_engine and include the node_postprocessors?

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

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Based on the provided knowledge sources, you can define a query engine and include node postprocessors as follows:

Plain Text

query_engine = index.as_query_engine(
    similarity_top_k=10,
    node_postprocessors=[postprocessor],
)

In this example, index is an instance of VectorStoreIndex which is created from your documents. The as_query_engine method is used to create a query engine from the index. The similarity_top_k parameter specifies the number of relevant nodes to retrieve, and node_postprocessors is a list of postprocessors to apply.

The postprocessor in the node_postprocessors list can be an instance of any postprocessor class. For example, it could be an instance of JinaRerank or LLMRerank:

Plain Text

from llama_index.postprocessor.jinaai_rerank import JinaRerank

jina_rerank = JinaRerank(api_key=api_key, top_n=2)

query_engine = index.as_query_engine(
    similarity_top_k=10, node_postprocessors=[jina_rerank]
)

Plain Text

query_engine = index.as_query_engine(
    similarity_top_k=10,
    node_postprocessors=[
        LLMRerank(
            choice_batch_size=5,
            top_n=2,
        )
    ],
    response_mode="tree_summarize",
)

Please note that the exact way to assign the rerank_query_engine to the vector_query_engine is not covered in the provided knowledge sources. You might need to refer to the LlamaIndex documentation or source code for the exact usage.

kkapa.ai

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What default retriever do I need to specify in this definition? rerank_query_engine = vec