Adaptive RAG Project by Bestine OkindaAdaptive RAG Project by Bestine Okinda

Adaptive RAG Project

Bestine Okinda

Completed work

AI Agent Engineer

Data Scientist

ML Engineer

Google Gemini

LangChain

Python

IT Infrastructure

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Completed work

Posted Sep 8, 2025

Refactored LangChain's Cookbook for Adaptive RAG.

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Jul 5, 2025 - Sep 8, 2025

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 🦜🕸️