llama-index-postprocessor-needlepath

Needlepath context selection as a LlamaIndex BaseNodePostprocessor: given the retrieved nodes and the query, keep the nodes that carry the answer and replace each one's text with the part of it that does.

pip install llama-index-postprocessor-needlepath

from llama_index.postprocessor.needlepath import NeedlepathPostprocessor

query_engine = index.as_query_engine(
    node_postprocessors=[NeedlepathPostprocessor(operating_point="np-2026-07-r2")],
)

The key comes from NEEDLEPATH_API_KEY. If the service is slow, down, or stands down, the query engine gets the nodes it was given, unchanged.

Scope, stated honestly

This is a RAG-retrieval seam. It never sees tool outputs, so it exercises the weakest of Needlepath's use cases — retrieved chunks are already short and already ranked. It exists because LlamaIndex is where a large number of developers already have a postprocessor slot, and an integration that does not exist cannot be evaluated. If you are choosing where to try Needlepath first and you have an agent with tool calls, start there instead.

Configuration

Any other keyword is forwarded to the core client (base_url, api_key, timeout, …).

Two LlamaIndex-specific decisions

Scores are not overwritten by default. A reranker replaces NodeWithScore.score with its own. Ours is not calibrated against your retriever's similarity, and silently substituting one for the other corrupts any downstream threshold or similarity_cutoff. The selection score is available as node metadata; set update_scores=True if you want reranker-like behaviour and know what your thresholds mean.

Provenance metadata is hidden from the model. Node metadata is interpolated into the prompt under MetadataMode.LLM and into embeddings under MetadataMode.EMBED. Every needlepath_* key this package writes is added to excluded_llm_metadata_keys and excluded_embed_metadata_keys on the copy, so debug data never reaches the model or an index.

Node identity survives

A rewritten node is a model_copy(deep=True) with set_content() applied, so id_, metadata, relationships and therefore ref_doc_id are carried over verbatim. Building a fresh TextNode would mint a new random id_ and drop relationships, severing ref_doc_id, dedup and PrevNextNodePostprocessor.

The nodes you pass in are never mutated.

Fail open

The no-query case is worth calling out: every shipped reranker raises ValueError("Missing query bundle in extra info.") there. This does not. A postprocessor that raises turns a missing query into a failed query for the whole engine, and the fail-open rule is binding at a framework boundary.

postprocessor.stats.as_dict() aggregates the counters.

Async

_apostprocess_nodes is overridden rather than inherited. The base class's default is asyncio.to_thread(self._postprocess_nodes, …), which is correct but burns a thread per query on what is a network call — and nine call sites in llama-index-core reach the async path, including RetrieverQueryEngine and every chat engine.

Packaging

Named llama-index-postprocessor-needlepath rather than needlepath-llamaindex. That is the one naming exception to Needlepath's vendor-first convention: a package outside the host convention is not discoverable on LlamaHub at all.

PEP 420 namespace layout — llama_index/ and llama_index/postprocessor/ carry no __init__.py, so this distribution coexists with llama-index-core and every other integration under the same directories.

Tested against

llama-index-core==0.14.23. Range >=0.13.0,<0.15, matching every shipped llama-index-postprocessor-* package. CI runs against the newest minor.