Build RAG with Your Own Data 🔥 | LangChain + ChromaDB + Gemini + Local Docker LLM (Full Demo)

Description

This video showcases a complete end-to-end Retrieval-Augmented Generation (RAG) pipeline built using real-world data and modern AI tooling. The implementation demonstrates how to design a scalable RAG system using LangChain, ChromaDB, Gemini embeddings, and a locally hosted Docker-based LLM, enabling secure and efficient querying over private datasets. 🚀 Key Highlights: Build RAG using your own text and PDF documents Chunking and preprocessing using LangChain Vector storage using ChromaDB (persistent local database) Semantic search with similarity retrieval Gemini embeddings for high-quality vector representation Switch from cloud LLM to local Docker-served model Fully functional question-answering system over private data 📦 Tech Stack: Python | LangChain | ChromaDB | Google Gemini | OpenAI-compatible API | Docker | PyPDF 📊 Project Workflow Covered: Text ingestion and chunking PDF ingestion pipeline Vector database creation and reuse Similarity search implementation Retrieval + generation pipeline Local LLM integration using Docker 💡 Use Cases: Enterprise knowledge base systems Document search engines Private AI assistants Offline AI applications 🔗 Run Locally: Docker-based LLM endpoint Local Chroma vector database Notebook-based implementation Github: https://github.com/swapniltake1/rag-implementation-for-own-data ⚠️ This is a no-voice demo video, focused on practical implementation and workflow visualization. Credit for track & artist(s) Fluidscape by Kevin MacLeod is licensed under a Creative Commons Attribution 4.0 licence. https://creativecommons.org/licenses/by/4.0/ #RAG #RetrievalAugmentedGeneration #LangChain #ChromaDB #GenerativeAI #LLM #AIProjects #MachineLearning #DataEngineering #Python #Docker #LocalLLM #GeminiAI #VectorDatabase #AIEngineering #OpenAI #AIApps #BuildInPublic #TechProjects #AIWorkflow #swapniltake1 #RAG #GenerativeAI #LocalLLM #Docker #LangChain #ChromaDB #GeminiAI #Python #MachineLearning #ArtificialIntelligence #Qwen3 #VectorDatabase #PrivateAI Thanks for watching