document-qa-rag-system and Agentic_RAG
About document-qa-rag-system
ZohaibCodez/document-qa-rag-system
A simple Retrieval-Augmented Generation (RAG) project built with LangChain and Streamlit. Upload documents (PDF/TXT) and interact with them using natural language questions powered by embeddings and vector search.
This tool helps you quickly get answers from your documents by turning any PDF or plain text file into an interactive Q&A experience. You upload your document, and then you can ask questions about its content in everyday language, getting direct answers back. It's ideal for professionals, researchers, or students who need to extract specific information or summarize key points from reports, articles, or books without manually sifting through pages.
About Agentic_RAG
rajveersinghcse/Agentic_RAG
✌️ A dynamic Retrieval-Augmented Generation (RAG) system with support for PDF indexing, website crawling, and semantic Q&A powered by OpenAI, Qdrant, and Streamlit.
This tool helps you quickly get answers from large documents and websites without manually sifting through information. You provide PDF files or website URLs, and it intelligently retrieves answers to your questions, even performing an online search if the information isn't found in your sources. Anyone needing to extract specific information from a collection of documents or web pages, like researchers, analysts, or content strategists, would find this useful.
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