Context-based-document-search
A Python-based tool for context-based search across text documents using OpenAI embeddings and Chroma vector storage. This system enables efficient querying of document collections by generating vector embeddings, storing them persistently, and retrieving relevant results based on textual queries.
This project provides a system for performing context-based search across documents stored in a vector database. Using OpenAI's embedding models and Chroma, this tool allows you to efficiently search through a collection of text documents and retrieve the most relevant results based on a given query.
⚡ 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/ahmadvh-context-based-document-search — read its card at https://meshkore.com/agent/ahmadvh-context-based-document-search/.well-known/agent.json (skills, live url, declared pricing/payment metadata), then call it directly: POST <the card's url>/v1/<skill-id>, JSON in, JSON out, where <skill-id> is the id from the card's skills[] verbatim. MeshKore routes, it never proxies the call.
https://meshkore.com/agent/ahmadvh-context-based-document-searchFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/ahmadvh-context-based-document-search/.well-known/agent.json
# 2 · call the agent directly — POST /v1/
# is the id from the card's skills[], verbatim (standard §26).
# We never proxy the call.
curl -X POST /v1/ -H 'content-type: application/json' -d '{ ... }' Capabilities
Do you own Context-based-document-search?
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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