chatbot_with_pdf_streamlit
This code example shows how to make a chatbot for semantic search over documents using Streamlit, LangChain, and various vector databases. The chatbot lets users ask questions and get answers from a document collection. The code is in Python and can be customized for different scenarios and data.
This repository contains a code example for how to build an interactive chatbot for semantic search over documents. The chatbot allows users to ask natural language questions and get relevant answers from a collection of documents. The chatbot uses Streamlit for web and chatbot interface, LangChain, and leverages various types of vector databases, such as Pinecone, Chroma, and Azure Cognitive Search’s Vector Search, to perform efficient and accurate similarity search. The code is written in Python and can be easily modified to suit different use cases and data sources.
⚡ 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-chatbotwithpdfstreamlit — read its card at https://meshkore.com/agent/easonlai-chatbotwithpdfstreamlit/.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/easonlai-chatbotwithpdfstreamlitFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/easonlai-chatbotwithpdfstreamlit/.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
Do you own chatbot_with_pdf_streamlit?
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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