Adaptive RAG Project

Bestine Okinda

Completed work

AI Agent Engineer

Data Scientist

ML Engineer

Google Gemini

LangChain

Python

IT Infrastructure

Adaptive RAG

Adaptive RAG represents the most sophisticated evolution, addressing a fundamental insight: not all queries are created equal. The research reveals that real-world queries exhibit vastly different complexity levels:

Simple queries: "Paris is the capital of what?" - Can be answered directly by LLMs

Multi-hop queries: "When did the people who captured Malakoff come to the region where Philipsburg is located?" - Requires four reasoning steps

This repository contains a refactored version of the original LangChain's Cookbook.

Project Structure

adaptive-RAG/
├── graph/
│   ├── chains/
│   │   ├── tests/
│   │   │   ├── __init__.py
│   │   │   └── test_chains.py
│   │   ├── __init__.py
│   │   ├── answer_grader.py
│   │   ├── generation.py
│   │   ├── hallucination_grader.py
│   │   ├── retrieval_grader.py
│   │   └── router.py
│   ├── nodes/
│   │   ├── __init__.py
│   │   ├── generate.py
│   │   ├── grade_documents.py
│   │   ├── retrieve.py
│   │   └── web_search.py
│   ├── __init__.py
│   ├── consts.py
│   ├── graph.py
│   └── state.py
├── static/
│   ├── LangChain-logo.png
│   ├── Langgraph Adaptive Rag.png
│   └── graph.png
├── .env
├── .gitignore
├── ingestion.py
├── main.py
├── model.py
├── README.md
└── requirements.txt

Getting Started

Prerequisites

Install uv (if not already installed):

curl -LsSf https://astral.sh/uv/install.sh | sh

Installation

Clone the repository

git clone https://github.com/piyushagni5/langgraph-ai.git

Navigate to the project directory

cd agentic-rag/agentic-rag-systems/building-adaptive-rag/

Create and activate virtual environment in the project directory

uv venv --python 3.10
source .venv/bin/activate

Install dependencies

uv pip install -r requirements.txt

Environment Variables

To run this project, you will need to add the following environment variables to your .env file:

GOOGLE_API_KEY=your_tavily_api_key_here
TAVILY_API_KEY=your_tavily_api_key_here  # For web search capabilities
LANGCHAIN_API_KEY=your_langchain_api_key_here  # Optional, for tracing
LANGCHAIN_TRACING_V2=true                      # Optional
LANGCHAIN_ENDPOINT=https://api.smith.langchain.com # Optional
LANGCHAIN_PROJECT=agentic-rag                  # Optional

Important Note: If you enable tracing by setting LANGCHAIN_TRACING_V2=true, you must have a valid LangSmith API key set in LANGCHAIN_API_KEY. Without a valid API key, the application will throw an error.

Usage

Start the Agentic RAG flow

uv run main.py

Running Tests

To run tests, execute the following command:

uv run pytest . -s -v

Features

Adaptive RAG: Dynamically routes queries to the most appropriate processing method

Self-RAG: Implements self-reflection mechanisms for improved answer quality

Reflective RAG: Incorporates reflection and grading for enhanced retrieval

Web Search Integration: Fallback to web search when local knowledge is insufficient

Document Grading: Evaluates relevance of retrieved documents

Hallucination Detection: Identifies and handles potential hallucinations in generated responses

Architecture

The system implements a sophisticated RAG pipeline with the following components:

Router: Intelligently routes queries between vectorstore retrieval and web search

Retrieval Grader: Evaluates the relevance of retrieved documents

Generation Chain: Produces answers based on retrieved context

Hallucination Grader: Detects potential hallucinations in generated responses

Answer Grader: Evaluates the quality and relevance of final answers

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Acknowledgements

Built with LangGraph 🦜🕸️

Like this project

Completed work

Posted Sep 8, 2025

Refactored LangChain's Cookbook for Adaptive RAG.

Likes

Views

Timeline

Jul 5, 2025 - Sep 8, 2025