Chat With Your Documents Data Ingestion & RAG Pipeline(Beginner Friendly)

ChethanAIChronicles · Beginner ·🔍 RAG & Vector Search ·5mo ago

Skills: RAG Basics95%

Key Takeaways

Building a complete data ingestion and RAG pipeline to enable chatbots to read and provide accurate answers from Markdown, PDF, DOCX, CSV, and TXT files using ChromaDB and vector database storage

Original Description

In this video, we build the complete data ingestion and RAG pipeline so your chatbot can read Markdown files, PDF files,docs and CSV files— with accurate answers and sources. In this video, we build the core of a RAG chatbot — the data ingestion and retrieval pipeline. You’ll learn how to: ✅ Load PDFs, TXT, DOCX, CSV, and Markdown files ✅ Split documents into chunks ✅ Create embeddings ✅ Store data in ChromaDB (vector database) ✅ Retrieve relevant chunks ✅ Use them in a RAG pipeline to answer questions accurately This is a step-by-step beginner tutorial using: Groq (LLM) LangChain FastAPI ChromaDB Streamlit 👉 This video is part of the Customer Support RAG Chatbot series.

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