The most advanced AI retrieval system.
Agentic Retrieval-Augmented Generation (RAG) with a RESTful API.
# About
R2R is an advanced AI retrieval system supporting Retrieval-Augmented Generation (RAG) with production-ready features. Built around a RESTful API, R2R offers multimodal content ingestion, hybrid search, knowledge graphs, and comprehensive document management.
R2R also includes a **Deep Research API**, a multi-step reasoning system that fetches relevant data from your knowledgebase and/or the internet to deliver richer, context-aware answers for complex queries.
# Usage
```python
# Basic search
results = client.retrieval.search(query="What is DeepSeek R1?")
# RAG with citations
response = client.retrieval.rag(query="What is DeepSeek R1?")
# Deep Research RAG Agent
response = client.retrieval.agent(
message={"role":"user", "content": "What does deepseek r1 imply? Think about market, societal implications, and more."},
rag_generation_config={
"model": "anthropic/claude-3-7-sonnet-20250219",
"extended_thinking": True,
"thinking_budget": 4096,
"temperature": 1,
"top_p": None,
"max_tokens_to_sample": 16000,
},
)
```
## Getting Started
```bash
# Quick install and run in light mode
pip install r2r
export OPENAI_API_KEY=sk-...
python -m r2r.serve
# Or run in full mode with Docker
# git clone git@github.com:SciPhi-AI/R2R.git && cd R2R
# export R2R_CONFIG_NAME=full OPENAI_API_KEY=sk-...
# docker compose -f compose.full.yaml --profile postgres up -d
```
For detailed self-hosting instructions, see the [self-hosting docs](https://r2r-docs.sciphi.ai/self-hosting/installation/overview).
## Demo
https://github.com/user-attachments/assets/173f7a1f-7c0b-4055-b667-e2cdcf70128b
## Using the API
### 1. Install SDK & Setup
```bash
# Install SDK
pip install r2r # Python
# or
npm i r2r-js # JavaScript
```
### 2. Client Initialization
```python
from r2r import R2RClient
client = R2RClient(base_url="http://localhost:7272")
```
```javascript
const { r2rClient } = require('r2r-js');
const client = new r2rClient("http://localhost:7272");
```
### 3. Document Operations
```python
# Ingest sample or your own document
client.documents.create(file_path="/path/to/file")
# List documents
client.documents.list()
```
## Key Features
- **๐ Multimodal Ingestion**: Parse `.txt`, `.pdf`, `.json`, `.png`, `.mp3`, and more
- **๐ Hybrid Search**: Semantic + keyword search with reciprocal rank fusion
- **๐ Knowledge Graphs**: Automatic entity & relationship extraction
- **๐ค Agentic RAG**: Reasoning agent integrated with retrieval
- **๐ User & Access Management**: Complete authentication & collection system
## Community & Contributing
- [Join our Discord](https://discord.gg/p6KqD2kjtB) for support and discussion
- Submit [feature requests](https://github.com/SciPhi-AI/R2R/issues/new?assignees=&labels=&projects=&template=feature_request.md&title=) or [bug reports](https://github.com/SciPhi-AI/R2R/issues/new?assignees=&labels=&projects=&template=bug_report.md&title=)
- Open PRs for new features, improvements, or documentation
### Our Contributors
