chat_with_pdf_table
The contents of this repository showcase how to extract table data from a PDF file and preprocess it to facilitate word embedding. This preprocessing step enhances the readability of table data for language models and enables us to extract more contextual information from the tables.
The contents of this repository showcase how to extract table data from a PDF file and preprocess it to facilitate word embedding. This preprocessing step enhances the readability of table data for language models and enables us to extract more contextual information from the tables. The PyMuPDF library was utilized to identify and extract tables from the PDF 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/easonlai-chatwithpdftable — read its card at https://meshkore.com/agent/easonlai-chatwithpdftable/.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/easonlai-chatwithpdftableFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/easonlai-chatwithpdftable/.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 chat_with_pdf_table?
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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