The ultimate open source RAG answer engine

About

R2R was designed to bridge the gap between local LLM experimentation and scalable, production-ready Retrieval-Augmented Generation (RAG). R2R provides a comprehensive and SOTA RAG system for developers, built around a RESTful API for ease of use.

For a more complete view of R2R, check out the full documentation.

Key Features

• 📁 Multimodal Support: Ingest files ranging from .txt, .pdf, .json to .png, .mp3, and more.
• 🔍 Hybrid Search: Combine semantic and keyword search with reciprocal rank fusion for enhanced relevancy.
• 🔗 Graph RAG: Automatically extract relationships and build knowledge graphs.
• 🗂️ App Management: Efficiently manage documents and users with rich observability and analytics.
• 🌐 Client-Server: RESTful API support out of the box.
• 🧩 Configurable: Provision your application using intuitive configuration files.
• 🔌 Extensible: Develop your application further with easy builder + factory pattern.
• 🖥️ Dashboard: Use the R2R Dashboard, an open-source React+Next.js app for a user-friendly interaction with R2R.

Table of Contents

1. Install
2. R2R Quickstart
3. R2R Dashboard
4. Community and Support
5. Contributing

Install

[!NOTE] Windows users are advised to use Docker to run R2R.

Updates

Star R2R on GitHub by clicking "Star" in the upper right hand corner of the page to be instantly notified of new releases.

R2R Quickstart

Start the R2R server

Ingest a file

python -m r2r.examples.quickstart ingest --client-server-mode
# can be called with additional argument,
# e.g. `python -m r2r...  --client-server-mode /path/to/your_file`

{'results': {'processed_documents': ["File '.../aristotle.txt' processed successfully."], 'skipped_documents': []}}

Perform a search

python -m r2r.examples.quickstart search --query="who was aristotle?" --client-server-mode

{'results': {'vector_search_results': [
    {
        'id': '7ed3a01c-88dc-5a58-a68b-6e5d9f292df2',
        'score': 0.780314067545999,
        'metadata': {
            'text': 'Aristotle[A] (Greek: Ἀριστοτέλης Aristotélēs, pronounced [aristotélɛːs]; 384–322 BC) was an Ancient Greek philosopher and polymath. His writings cover a broad range of subjects spanning the natural sciences, philosophy, linguistics, economics, politics, psychology, and the arts. As the founder of the Peripatetic school of philosophy in the Lyceum in Athens, he began the wider Aristotelian tradition that followed, which set the groundwork for the development of modern science.',
            'title': 'aristotle.txt',
            'version': 'v0',
            'chunk_order': 0,
            'document_id': 'c9bdbac7-0ea3-5c9e-b590-018bd09b127b',
            'extraction_id': '472d6921-b4cd-5514-bf62-90b05c9102cb',
            ...

Perform RAG

python -m r2r.examples.quickstart rag --query="who was aristotle?" --client-server-mode

Search Results:
{'vector_search_results': [
    {'id': '7ed3a01c-88dc-5a58-a68b-6e5d9f292df2',
    'score': 0.7802911996841491,
    'metadata': {'text': 'Aristotle[A] (Greek: Ἀριστοτέλης Aristotélēs, pronounced [aristotélɛːs]; 384–322 BC) was an Ancient Greek philosopher and polymath. His writings cover a broad range of subjects spanning the natural sciences, philosophy, linguistics, economics, politics, psychology, and the arts. As the founder of the Peripatetic schoo
    ...
Completion:
{'results': [
    {
        'id': 'chatcmpl-9eXL6sKWlUkP3f6QBnXvEiKkWKBK4',
        'choices': [
            {
                'finish_reason': 'stop',
                'index': 0,
                'logprobs': None,
                'message': {
                    'content': "Aristotle (384–322 BC) was an Ancient Greek philosopher and polymath whose writings covered a broad range of subjects including the natural sciences,
                    ...

Stream a RAG Response

python -m r2r.examples.quickstart rag --query="who was aristotle?" --client-server-mode --stream

<search>"{\"id\":\"004ae2e3-c042-50f2-8c03-d4c282651fba\",\"score\":0.7803140675 ...</search>
<completion>Aristotle was an Ancient Greek philosopher and polymath who lived from 384 to 322 BC [1]. He was born in Stagira, Chalcidi....</completion>

Hello r2r

Building with R2R is easy - see the hello_r2r example below:

from r2r import Document, GenerationConfig, R2R

app = R2R() # You may pass a custom configuration to `R2R`

app.ingest_documents(
    [
        Document(
            type="txt",
            data="John is a person that works at Google.",
            metadata={},
        )
    ]
)

rag_results = app.rag(
    "Who is john", GenerationConfig(model="gpt-3.5-turbo", temperature=0.0)
)
print(f"Search Results:\n{rag_results.search_results}")
print(f"Completion:\n{rag_results.completion}")

# RAG Results:
# Search Results:
# AggregateSearchResult(vector_search_results=[VectorSearchResult(id=2d71e689-0a0e-5491-a50b-4ecb9494c832, score=0.6848798582029441, metadata={'text': 'John is a person that works at Google.', 'version': 'v0', 'chunk_order': 0, 'document_id': 'ed76b6ee-dd80-5172-9263-919d493b439a', 'extraction_id': '1ba494d7-cb2f-5f0e-9f64-76c31da11381', 'associatedQuery': 'Who is john'})], kg_search_results=None)
# Completion:
# ChatCompletion(id='chatcmpl-9g0HnjGjyWDLADe7E2EvLWa35cMkB', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='John is a person that works at Google [1].', role='assistant', function_call=None, tool_calls=None))], created=1719797903, model='gpt-3.5-turbo-0125', object='chat.completion', service_tier=None, system_fingerprint=None, usage=CompletionUsage(completion_tokens=11, prompt_tokens=145, total_tokens=156))

R2R Dashboard

Interact with R2R using our open-source React+Next.js dashboard. Check out the Dashboard Cookbook to get started!

Community and Support

• Discord: Chat live with maintainers and community members
• Github Issues: Report bugs and request features

Explore our R2R Docs for tutorials and cookbooks on various R2R features and integrations, including:

RAG Cookbooks

• Multiple LLMs: A simple cookbook showing how R2R supports multiple LLMs.
• Hybrid Search: A brief introduction to running hybrid search with R2R.
• Multimodal RAG: A cookbook on multimodal RAG with R2R.
• Knowledge Graphs: A walkthrough of automatic knowledge graph generation with R2R.
• Local RAG: A quick cookbook demonstration of how to run R2R with local LLMs.
• Reranking: A short guide on how to apply reranking to R2R results.

App Features

• Client-Server: An extension of the basic R2R Quickstart with client-server interactions.
• Document Management: A cookbook showing how to manage your documents with R2R.
• Analytics & Observability: A cookbook showing R2Rs end to end logging and analytics.
• Dashboard: A how-to guide on connecting with the R2R Dashboard.

Contributing

We welcome contributions of all sizes! Here's how you can help:

• Open a PR for new features, improvements, or better documentation.
• Submit a feature request or bug report

Our Contributors