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
# 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
# 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.
Demo
https://github.com/user-attachments/assets/173f7a1f-7c0b-4055-b667-e2cdcf70128b
Using the API
1. Install SDK & Setup
# Install SDK
pip install r2r # Python
# or
npm i r2r-js # JavaScript
2. Client Initialization
from r2r import R2RClient
client = R2RClient(base_url="http://localhost:7272")
const { r2rClient } = require('r2r-js');
const client = new r2rClient("http://localhost:7272");
3. Document Operations
# Ingest sample or your own document
client.documents.create(file_path="/path/to/file")
# List documents
client.documents.list()
Key Features
- 📁 Multimodal Ingestion: Parse
.txt,.pdf,.json,.png,.mp3, and more - 🔍 Hybrid Search: Semantic + keyword search with reciprocal rank fusion
- 🔗 Knowledge Graphs: Automatic entity & relationship extraction
- 🤖 Agentic RAG: Reasoning agent integrated with retrieval
- 🔐 User & Access Management: Complete authentication & collection system
Community & Contributing
- Join our Discord for support and discussion
- Submit feature requests or bug reports
- Open PRs for new features, improvements, or documentation
Our Contributors
1 maintainer
Metadata
1 maintainer
Release files for r2r 3.6.6
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| r2r-3.6.6.tar.gz | 396.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| r2r-3.6.6-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 910.7 kB
Release files / r2r-3.6.6.tar.gz
| Download URL | r2r-3.6.6.tar.gz |
|---|---|
| Size | 396.7 kB |
| Tags | Source |
|
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| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.12.11
|
Release files / r2r-3.6.6-py3-none-any.whl
| Download URL | r2r-3.6.6-py3-none-any.whl |
|---|---|
| Size | 514.0 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.12.11
|
