Usage Pattern

Most commonly, node-postprocessors will be used in a query engine, where they are applied to the nodes returned from a retriever, and before the response synthesis step.

Using with a Query Engine

from llama_index import VectorStoreIndex, SimpleDirectoryReader
from llama_index.indices.postprocessor import TimeWeightedPostprocessor

documents = SimpleDirectoryReader("./data").load_data()

index = VectorStoreIndex.from_documents(documents)

query_engine = index.as_query_engine(
  node_postprocessors=[
    TimeWeightedPostprocessor(
        time_decay=0.5, time_access_refresh=False, top_k=1
    )
  ]
)

# all node post-processors will be applied during each query
response = query_engine.query("query string")

Using with Retrieved Nodes

Or used as a standalone object for filtering retrieved nodes:

from llama_index.indices.postprocessor import SimilarityPostprocessor

nodes = index.as_retriever().retrieve("test query str")

# filter nodes below 0.75 similarity score
processor = SimilarityPostprocessor(similarity_cutoff=0.75)
filtered_nodes = processor.postprocess_nodes(nodes)

Using with your own nodes

As you may have noticed, the postprocessors take NodeWithScore objects as inputs, which is just a wrapper class with a Node and a score value.

from llama_index.indices.postprocessor import SimilarityPostprocessor
from llama_index.schema import Node, NodeWithScore

nodes = [
  NodeWithScore(node=Node(text="text"), score=0.7),
  NodeWithScore(node=Node(text="text"), score=0.8)
]

# filter nodes below 0.75 similarity score
processor = SimilarityPostprocessor(similarity_cutoff=0.75)
filtered_nodes = processor.postprocess_nodes(nodes)

Custom Node PostProcessor

The base class is BaseNodePostprocessor, and the API interface is very simple:

class BaseNodePostprocessor:
    """Node postprocessor."""

    @abstractmethod
    def postprocess_nodes(
        self, nodes: List[NodeWithScore], query_bundle: Optional[QueryBundle]
    ) -> List[NodeWithScore]:
        """Postprocess nodes."""

A dummy node-postprocessor can be implemented in just a few lines of code:

from llama_index import QueryBundle
from llama_index.indices.postprocessor.base import BaseNodePostprocessor
from llama_index.schema import NodeWithScore

class DummyNodePostprocessor:

    def postprocess_nodes(
        self, nodes: List[NodeWithScore], query_bundle: Optional[QueryBundle]
    ) -> List[NodeWithScore]:

        # subtracts 1 from the score
        for n in nodes:
            n.score -= 1

        return nodes