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generative-ai-project

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NxtGenCodeBase / pdf-rag-assistant

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Build a Production-Ready RAG system for intelligent Q&A over PDFs (policies, contracts, resumes). Powered by OpenAI Structured Outputs, Pydantic for schema enforcement, ChromaDB for local vector storage, and a Streamlit UI. Ideal for enterprise AI in legal, banking, and HR. Scalable, deterministic, and easy to deploy locally.

document-ai streamlit-chatbot openai-embeddings pdf-question-answering compliance-ai rag-pdf-chatbot generative-ai-project semantic-search-pdfs policy-document-chatbot python-rag-system chromadb-vector-search embeddings-search nlp-document-search ai-document-assistant enterprise-ai-chatbot pdf-semantic-search ai-engineering-project structured-output-chatbot legal-document-ai openai-rag
  • Updated Mar 21, 2026
  • Python

NxtGenCodeBase / structured-rag-chatbot

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Build a scalable, production-ready Q&A chatbot using OpenAI APIs, Retrieval-Augmented Generation (RAG), ChromaDB vector database, and Streamlit UI. This project demonstrates structured JSON outputs using Pydantic, semantic search with embeddings, and a clean modular Python architecture suitable for real-world AI systems.

embeddings semantic-search structured-output vector-database ai-chatbot nlp-chatbot ai-engineering openai-api streamlit-app prompt-engineering openai-python chromadb retrieval-augmented-generation rag-pipeline ai-architecture generative-ai-project pydantic-schema local-ai-app
  • Updated Mar 21, 2026
  • Python

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