chat_with_pdf_streamlit_llama2

by easonlai · indexed from github

In this repository, you will discover how Streamlit, a Python framework for developing interactive data applications, can work seamlessly with the Open-Source Embedding Model ("sentence-transformers/all-MiniLM-L6-v2") in Hugging Face and Llama 2 🦙🦙 model.

In this repository, you will discover how Streamlit, a Python framework for developing interactive data applications, can work seamlessly with the Open-Source Embedding Model ("sentence-transformers/all-MiniLM-L6-v2") in Hugging Face and Llama 2 🦙🦙 model. With these tools, you can easily develop a web application that is user-friendly and allows for natural language questioning from a PDF document. This solution is both simple and effective, enabling users to extract valuable information from the document through semantic searching.

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Use the MeshKore agent at https://meshkore.com/agent/easonlai-chatwithpdfstreamlitllama2 — read its card at https://meshkore.com/agent/easonlai-chatwithpdfstreamlitllama2/.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.
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curl https://meshkore.com/agent/easonlai-chatwithpdfstreamlitllama2/.well-known/agent.json

# 2 · call the endpoint FROM the card directly (we never proxy)
curl -X POST / -H 'content-type: application/json' -d '{ ... }'

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