Build a Production-Level AI RAG System with LangChain

Level: Real World

Yesterday, we built a simple RAG system which loads an entire dataset to the LLM prompt. This is slow and expensive because it eats up tokens. Today we build a production-level RAG (Retrieval Augmented Generation) system using LangChain 1.0 that efficiently retrieves and answers questions from your documents using vector embeddings and similarity search. This method saves considerable tokens compares to the simple method of loading all the documents to the LLM because it pulls only the part of documents that are needed for the user query.

Project Task

Create a RAG document assistant that:

• Loads documents from multiple sources (PDFs and text files)

• Uses OpenAI embeddings to create vector representations of documents

• Stores vectors in an efficient vector store for fast retrieval

• Implements similarity search to find relevant document chunks

• Answers questions using retrieved context with token-efficient prompting

• Handles multiple document types and formats

• Uses proper chunking strategies to optimize token usage

Production-Level Features:

• Efficient document chunking to reduce token consumption

• Metadata filtering capabilities for targeted retrieval

• Proper error handling and validation

• Environment variable management for API keys

• Optimized prompt engineering for token efficiency

• Support for multiple document formats

This project gives you hands-on practice with vector embeddings, semantic search, RAG architecture, LangChain 1.0, and production-level AI application development — essential skills for building intelligent document processing systems.

Expected Output

RAG Document Assistant
======================

Loading documents from: ./My Documents
Found 15 PDF files and 8 text files
Processing documents...
Documents chunked into 247 segments
Vector store initialized with OpenAI embeddings
Ready to answer questions!

Question: What are the land and building asset amounts of SparkTech in 2023

Retrieving relevant documents...
Found 2 relevant chunks

Answer: Based on the retrieved documents, the land and building asset amounts for SparkTech in 2023 are:
- Land assets: $2.5 million
- Building assets: $5.8 million
Total real estate assets: $8.3 million

Ask another question? (y/n): n

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