Self RAG Pipeline | LangGraph | Groq LLM | ChromaDB | HuggingFace | NLP Project | English

🎯 End-to-End RAG Pipeline | Retrieval-Augmented Generation with LangChain + Groq LLM

💻 My Kaggle Profile:
🔗 https://www.kaggle.com/codingloading

💻 My GitHub Repository:
🔗 https://github.com/DataScientist00

📁 Knowledge Base Sources:
🔗 https://www.geeksforgeeks.org/dsa/dis...
🔗 https://en.wikipedia.org/wiki/Data_sc...

💻 Explore the Full Project Code:
🔗 https://github.com/DataScientist00/Yo...

📌 In this video, I walk you through a complete Retrieval-Augmented Generation (RAG) workflow built using LangChain, ChromaDB, HuggingFace embeddings, and Groq LLMs. The pipeline connects document retrieval with answer generation and integrates grading steps to reduce hallucinations and validate responses.

We start by creating a vector database from external sources, then retrieve relevant chunks for a given user query. Next, we generate responses using Groq’s LLMs, filter irrelevant documents, detect hallucinations, and validate answers before presenting the final output.

📊 Whether you're exploring RAG basics, experimenting with Groq LLMs, or looking to build a production-ready retrieval pipeline, this video covers everything from document loading to answer validation.

🧠 What You’ll Learn:
✅ Loading and splitting documents with LangChain
✅ Creating a vector store with HuggingFace embeddings + Chroma
✅ Retrieving relevant documents for user queries
✅ Generating answers using Groq’s Llama 3.3 model
✅ Filtering irrelevant content and grading outputs
✅ Detecting hallucinations and validating answers

🚀 Ideal For:
📚 NLP & RAG Enthusiasts
🧠 LLM Developers
💡 AI & ML Learners
📈 Data Science Students
💻 Anyone exploring Retrieval-Augmented Generation

📌 Keywords for SEO:
#RAG #LangChain #Groq #VectorDatabase #ChromaDB #LLM #RetrievalAugmentedGeneration #AIProjects #NLPProject #MachineLearning #DeepLearning #HuggingFace #LangGraph #PythonCode #DataScience #CodeWithMe #GroqLLM #LangChainProjects #RAGPipeline #OpenAIAlternatives