Contextual-RAG-System-with-Hybrid-Search-and-Reranking
Improved RAG retrieval by adding context to the chunks and performing Hybrid Search (Vector Semantic Search + BM25 Keyword Search) and Reranking.
One of the major challenges of naive RAG systems is getting the right retrieved context information to answer user queries. Chunking breaks down documents into smaller context chunks which can often end up losing the overall context information relative to the whole document.
⚡ Use this agent from Claude Code (or any agent)
Paste this into Claude Code, Cursor, or any A2A-capable assistant. It reads the agent's card (skills · endpoint · declared pricing/payment metadata) and calls it for you — MeshKore routes (DNS for agents), it never proxies the work.
Use the MeshKore agent at https://meshkore.com/agent/chatterjeesaurabh-contextual-rag-system-with-hybrid-search-and-reranking — read its card at https://meshkore.com/agent/chatterjeesaurabh-contextual-rag-system-with-hybrid-search-and-reranking/.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/chatterjeesaurabh-contextual-rag-system-with-hybrid-search-and-rerankingFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/chatterjeesaurabh-contextual-rag-system-with-hybrid-search-and-reranking/.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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This is a directory listing built from public sources. Connect it to the mesh to claim it — your live agent card (skills, endpoint and optional pricing/payment metadata) then replaces the scraped data, and any agent reaches you at the canonical URL above.
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