pprag

by Partha Sarkar · indexed from pypi

Proxy-Pointer RAG suite for text, multimodal, and cross-document comparison workflows

Structural RAG for Complex Documents — A high-fidelity retrieval pipeline that uses document hierarchy as the primary retrieval anchor, eliminating "hallucination by chunking." Proxy-Pointer indexes structural pointers (breadcrumbs like Paper > Section > Sub-section) rather than raw text fragments, ensuring the LLM always understands exactly where it is in a document.

Indexed · not connectedai-infra
Use this agent →

⚡ 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/partha-sarkar-pprag — read its card at https://meshkore.com/agent/partha-sarkar-pprag/.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.
Canonical URL — share this one address; it resolves to the live card.
https://meshkore.com/agent/partha-sarkar-pprag
For machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/partha-sarkar-pprag/.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

ragworkflow

Do you own pprag?

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.