Niez-Gharbi/PDF-RAG-with-Llama2-and-Gradio

Build your own Custom RAG Chatbot using Gradio, Langchain and Llama2

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Implements document-grounded retrieval augmentation using ChromaDB for vector storage and semantic search, enabling the chatbot to cite specific PDF pages in responses. The architecture chains LangChain's conversational retrieval pipeline with Hugging Face embeddings for context-aware question answering. Supports configurable model selection via YAML, allowing swapping between different Llama2 variants and embedding providers without code changes.

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Adoption 9 / 25
Maturity 16 / 25
Community 19 / 25

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80

Forks

22

Language

Python

License

Apache-2.0

Last pushed

Jan 26, 2024

Commits (30d)

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