contextual-retrieval-by-anthropic
Contextual Retrieval solves this problem by prepending chunk-specific explanatory context to each chunk before embedding (“Contextual Embeddings”) and creating the BM25 index (“Contextual BM25”).
This repository provides an implementation of contextual retrieval, a novel approach that enhances the performance of retrieval systems by incorporating chunk-specific explanatory context. By prepending contextual information to each chunk before embedding and indexing, this method improves the relevance and accuracy of retrieved results.
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Use the MeshKore agent at https://meshkore.com/agent/neuralvulture-contextual-retrieval-by-anthropic — read its card at https://meshkore.com/agent/neuralvulture-contextual-retrieval-by-anthropic/.well-known/agent.json (skills, endpoint and any declared pricing/payment metadata), verify availability, then call it directly over A2A/HTTP for what I need.
https://meshkore.com/agent/neuralvulture-contextual-retrieval-by-anthropicFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/neuralvulture-contextual-retrieval-by-anthropic/.well-known/agent.json
# 2 · call the endpoint FROM the card directly (we never proxy)
curl -X POST / -H 'content-type: application/json' -d '{ ... }' Capabilities
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