How to Build a Retrieval-Augmented Generation (RAG) System with Python, LangChain, and ChromaDB

Building a Retrieval-Augmented Generation (RAG) System with Python

Artificial Intelligence is transforming the way we interact with information. However, Large Language Models (LLMs) often struggle with up-to-date or private knowledge. Retrieval-Augmented Generation (RAG) solves this problem by combining information retrieval with generative AI.

RAG enables AI models to answer questions using custom documents, PDFs, websites, databases, and knowledge bases.

What is RAG?

Retrieval-Augmented Generation (RAG) is an AI architecture that retrieves relevant information from external sources before generating an answer.

User Question → Retriever → Relevant Documents → LLM → Accurate Answer

Why Use RAG?

• Access to custom knowledge
• Improved response accuracy
• Reduced hallucinations
• No model retraining required
• Enterprise-ready architecture
• Support for PDFs, websites, and databases

Core Components of a RAG System

Document Loader

Loads data from PDFs, websites, text files, Word documents, and other sources.

Text Chunking

Large documents are divided into smaller chunks for efficient retrieval.

Embeddings

Text is converted into vector representations that capture semantic meaning.

Vector Database

Stores embeddings and enables fast similarity search.

• ChromaDB
• FAISS
• Pinecone
• Weaviate

Retriever

Finds the most relevant content for a user's query.

Large Language Model

Generates responses using the retrieved context.

Recommended Technology Stack

Frontend: Streamlit
Backend: Python
Framework: LangChain
Vector Database: ChromaDB
Embeddings: Sentence Transformers
LLM: Groq Llama 3

Conclusion

Retrieval-Augmented Generation is one of the most important techniques in modern AI development. By combining retrieval systems with large language models, developers can build intelligent applications capable of providing accurate, context-aware responses from custom data sources.

Follow me on GitHub

---

Viraj Jadhav | AI Development | Python | LangChain | RAG