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To add a
node_postprocessor to a
RetrieverQueryEngine in LlamaIndex, you can follow these steps:
- First, you need to define your
node_postprocessor. This could be any of the built-in postprocessors like KeywordNodePostprocessor, SimilarityPostprocessor, PrevNextNodePostprocessor, etc., or a custom one that you have created. Here's an example of defining a KeywordNodePostprocessor:
node_postprocessors = [
KeywordNodePostprocessor(
required_keywords=["Combinator"], exclude_keywords=["Italy"]
)
] ()
- Then, you can add this
node_postprocessor to the RetrieverQueryEngine using the from_args method:
query_engine = RetrieverQueryEngine.from_args(
retriever, node_postprocessors=node_postprocessors
) ()
- Now, you can use this
query_engine to query your data:
response = query_engine.query("What did the author do growing up?") ()
Remember, the
node_postprocessor is applied within a query engine, after the node retrieval step and before the response synthesis step. It takes a set of nodes, and applies some kind of transformation or filtering before returning them. This can help reduce the time/number of LLM calls/cost or improve response quality.
Source ()
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