Agentic RAG

A modular and extensible system of Retrieval-Augmented Generation (RAG) - with vanilla RAG and some prototype agentic RAG algorithms. Our key features include:

• undefinedResearch Prototype - A minimal viable implementation for experimentation and academic exploration
• undefinedModular Architecture - Clean, decoupled codebase designed for easy customization and extension
• undefinedExtensible - Easily add new RAG algorithms to the framework

Installation and Configuration

• Install dependencies:

pip install -r requirements.txt

• Set your OpenAI API key:

# Create a .env file in the root directory with:
OPENAI_API_KEY=your_api_key_here

• Other configuration options can be modified in config/config.py

Quick start

Running Evaluation on a Dataset

python run.py --model MODEL_NAME --dataset path/to/dataset.json --corpus path/to/corpus.json

Options:

• --max-rounds: Maximum number of agent retrieval rounds (default: 3)
• --top-k: Number of top contexts to retrieve (default: 5)
• --limit: Number of questions to evaluate (default: 20)

Running a Single Question

python run.py --model MODEL_NAME --question "Your question here" --corpus path/to/corpus.json

Using the Script

For convenience, you can use the provided script to run evaluations with specific RAG models on all datasets:

# Run evaluations with vanilla RAG on all datasets
./scripts/run.sh

# Run evaluations with LightAgenticRAG on all datasets 
./scripts/run.sh --model light

Components

Component	Features/Description
undefinedBaseRAGundefined	• Loading and processing document corpus • Computing and caching document embeddings • Basic retrieval functionality
undefinedVanillaRAGundefined	• Single retrieval step for relevant contexts • Direct answer generation from retrieved contexts
undefinedAgenticRAGundefined	• Multiple retrieval rounds with iterative refinement • Reflection on retrieved information to identify missing details • Generation of focused sub-queries for additional retrieval
undefinedLightAgenticRAGundefined	• Memory-efficient implementation of AgenticRAG • Optimized for running on systems with limited resources
undefinedEvaluationundefined	• Answer accuracy (LLM evaluated) • Retrieval metrics • Performance efficiency • String-based evaluation metrics

Adding a New RAG Model

To add a new RAG algorithm:

1. Create a new class that extends BaseRAG in the src/models directory
2. Implement the required methods (answer_question at minimum)
3. Add your model to the RAG_MODELS dictionary in src/main.py

# Example of a new RAG model
from src.models.base_rag import BaseRAG

class MyNewRAG(BaseRAG):
    def answer_question(self, question: str):
        # Implementation here
        return answer, contexts

Example Usage

from src.models.agentic_rag import AgenticRAG

# Initialize RAG system
rag = AgenticRAG('path/to/corpus.json')
rag.set_max_rounds(3)
rag.set_top_k(5)

# Ask a question
answer, contexts, rounds = rag.answer_question("What is the capital of France?")
print(f"Answer: {answer}")
print(f"Retrieved in {rounds} rounds")

Citation

If you find this work helpful, please cite our recent paper:

@article{zhang2025survey,
  title={A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models},
  author={Zhang, Qinggang and Chen, Shengyuan and Bei, Yuanchen and Yuan, Zheng and Zhou, Huachi and Hong, Zijin and Dong, Junnan and Chen, Hao and Chang, Yi and Huang, Xiao},
  journal={arXiv preprint arXiv:2501.13958},
  year={2025}
}