Talk to PDF with AI: RAG, LangChain & ChromaDB Guide

1 What is RAG (Retrieval-Augmented Generation)?

Article Updated: February 2026

This article has been refreshed with the latest RAG best practices, updated embedding models (text-embedding-3), and current LangChain/ChromaDB features. Originally published December 2023.

Quick Answer: RAG (Retrieval-Augmented Generation) is a technique that enhances AI responses by first retrieving relevant information from a knowledge base, then using that context to generate accurate, fact-grounded answers.

This tutorial is part of the AI series where we explore building practical AI applications. In this post, I share the source code and video tutorial for using LangChain with ChromaDB to create a conversational AI that can talk to PDF documents.

The concept is known as RAG - Retrieval-Augmented Generation. We use the ChromaDB vector database to store embedding vectors locally, which significantly reduces API costs from OpenAI while enabling fast semantic search.

Why RAG? Instead of fine-tuning a model on your data (expensive and slow), RAG retrieves relevant context at query time and passes it to the LLM. This gives you up-to-date, accurate answers grounded in your documents.

2 How RAG Works

The RAG workflow consists of two main phases: indexing (preparing your documents) and retrieval (answering questions).

Load Documents

PDF, text, or other files

Split into Chunks

Semantic chunking

Create Embeddings

OpenAI text-embedding-3

Store in ChromaDB

Persistent vector store

Chat with Docs

Conversational retrieval

Key Components

LangChain: Framework for building LLM-powered applications with composable components
OpenAI Embeddings: Converts text into vector representations for semantic search
ChromaDB: Open-source vector database optimized for AI applications
GPT-3.5/GPT-4: Language model for generating natural language responses

3 Source Code

The complete source code for this project is available on GitHub. The script loads PDF documents, creates embeddings, stores them in ChromaDB, and enables conversational interaction:

Quick Start

# Install dependencies
pip install langchain openai chromadb pypdf

# Set your OpenAI API key
export OPENAI_API_KEY="your-api-key"

# Run the script
python talk_to_pdf.py

Pro Tip: Enable persistent storage in ChromaDB to cache embeddings locally. This dramatically reduces API costs since you only need to create embeddings once per document.

4 Video Tutorial

Watch the complete video walkthrough where I demonstrate how to build and use this RAG application:

5 What's New in 2026

The RAG landscape has evolved significantly since this tutorial was first published. Here are the key updates:

OpenAI Embedding Models

Model	Dimensions	Performance	Cost
text-embedding-3-large	Up to 3072	Best accuracy (MTEB: 64.6%)	$0.00013 / 1k tokens
text-embedding-3-small	Up to 1536	Great for most tasks	$0.00002 / 1k tokens (5x cheaper)
text-embedding-ada-002 (legacy)	1536	Previous generation	$0.0001 / 1k tokens

ChromaDB Improvements (2025-2026)

BM25 & SPLADE Support: Native hybrid search combining keyword and semantic retrieval
Regex Search: Search using regular expressions (added July 2025)
Base64 Encoding: Performance boost for vector operations
Multi-modal: Support for images and other data types

RAG Best Practices (2026)

Semantic Chunking: Achieves 0.79-0.82 faithfulness scores vs 0.47-0.51 for naive chunking
Hybrid Retrieval: Combine BM25 (keyword) + vector search for best results
Agentic RAG: 35-50% better complex query handling (with 200-400ms latency trade-off)
Hierarchical Chunking: Preserving table structure reduces retrieval calls by 30-40%

6 Related Reading

Continue your AI learning journey with these related guides:

Talk to Salesforce Data Using OpenAI, LangChain & Chroma - Apply RAG to query your Salesforce lead data conversationally
Converting Salesforce Data into Embeddings with OpenAI and AWS Lambda - Create embeddings from Salesforce data using serverless functions
Salesforce Integration with ChatGPT - Security best practices for AI integrations

7 Frequently Asked Questions

RAG is a technique that enhances AI responses by first retrieving relevant information from a knowledge base (like PDFs), then using that context to generate accurate answers. It combines vector search with language models for better, fact-grounded responses.

ChromaDB is an open-source vector database optimized for AI applications. It supports vector search, full-text search, BM25 ranking, and metadata filtering. It's lightweight, easy to set up, and can persist embeddings locally to reduce API costs.

For most use cases, text-embedding-3-small offers excellent performance at 5x lower cost than previous models. For maximum accuracy on complex multilingual tasks, use text-embedding-3-large with up to 3072 dimensions.

LangChain provides pre-built components for document loading, text splitting, embedding creation, vector storage, and conversational retrieval chains. It abstracts complex AI workflows into composable modules that work together seamlessly.

Talk to PDF Documents Using AI

1 What is RAG (Retrieval-Augmented Generation)?

2 How RAG Works

Key Components

3 Source Code

Quick Start

4 Video Tutorial

5 What's New in 2026

OpenAI Embedding Models

ChromaDB Improvements (2025-2026)

RAG Best Practices (2026)

6 Related Reading

7 Frequently Asked Questions

Leave a ReplyCancel reply

Talk to PDF Documents Using AI

1 What is RAG (Retrieval-Augmented Generation)?

2 How RAG Works

Key Components

3 Source Code

Quick Start

4 Video Tutorial

5 What's New in 2026

OpenAI Embedding Models

ChromaDB Improvements (2025-2026)

RAG Best Practices (2026)

6 Related Reading

7 Frequently Asked Questions

Leave a ReplyCancel reply

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