R2R (Rag to Riches) is the ultimate open-source framework for building and deploying high-quality Retrieval-Augmented Generation (RAG) systems. Designed to bridge the gap between local LLM experimentation and scalable, production-ready applications, R2R provides a comprehensive, feature-rich environment for developers.

• 📁 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.

R2R ships with a fully configurable and customizable REST API that includes user-level and document-level management as well as advanced RAG features. This framework ensures adequate performance and feature support for most RAG use cases, making it an ideal solution for developers looking to create observable user-facing RAG applications.

1. Quick Install
2. Links
3. R2R Demo
4. R2R Client-Server Demo
5. Core Abstractions
6. Summary

# use the `'r2r[all]'` to download all required deps
pip install r2r

# setup env 
export OPENAI_API_KEY=sk-...

python -m r2r.examples.servers.configurable_pipeline --host 0.0.0.0 --port 8000

docker pull emrgntcmplxty/r2r:latest
docker run -d --name r2r -p 8000:8000 emrgntcmplxty/r2r:latest -e OPENAI_API_KEY=...

Join the Discord server

R2R Docs Quickstart

• R2R Demo: A basic demo script designed to get you started with an R2R RAG application.
• R2R Client-Server: An extension of the basic R2R Demo with client-server interactions.
• Local RAG: A quick cookbook demonstration of how to run R2R with local LLMs.
• Hybrid Search: A brief introduction to running hybrid search with R2R.
• Reranking: A short guide on how to apply reranking to R2R results.
• Dashboard: A how-to guide on connecting with the R2R Dashboard.
• SciPhi Cloud Docs: SciPhi Cloud documentation.

The R2R demo offers a step-by-step guide on running the default R2R Retrieval-Augmented Generation (RAG) pipeline. The demo ingests a list of provided provided documents and demonstrates search, RAG, and advanced functionality. The script at r2r/examples/demo.py, which powers the demo, can be configured and extended with sufficient developer familiarity.

To comprehensively demonstrate the RAG functionalities of the R2R framework, we must start by ingesting a realistic set of documents. Running the command below will parse, chunk, embed, and store a preset list of files. The included file types cover HTML, PDF, PNG, and TXT examples:

python -m r2r.examples.demo ingest_as_files

Demo Output:

...
r2r.main.r2r_config - INFO - Loading configuration from <YOUR_WORKDIR>/config.json - 2024-05-20 22:08:48,025
r2r.core.providers.llm_provider - INFO - Initializing LLM provider with config: extra_fields={} provider='litellm' - 2024-05-20 22:08:48,562
r2r.core.providers.vector_db_provider - INFO - Initializing VectorDBProvider with config extra_fields={} provider='local' collection_name='demo_vecs'. - 2024-05-20 22:08:48,765
r2r.providers.embeddings.openai.openai_base - INFO - Initializing `OpenAIEmbeddingProvider` to provide embeddings. - 2024-05-20 22:08:48,774
...
r2r.pipes.parsing_pipe - INFO - Parsed document with metadata={'title': 'pg_essay_5.html', 'user_id': '063edaf8-3e63-4cb9-a4d6-a855f36376c3'} and id=ef66e5dd-2130-5fd5-9bdd-aa7eff59fda5 in t=0.00 seconds. - 2024-05-21 08:40:32,317
r2r.pipes.embedding_pipe - INFO - Fragmented the input document ids into counts as shown: {UUID('4a4fb848-fc03-5487-a7e5-33c9fdfb73cc'): 31, UUID('c5abc0b7-b9e5-54d9-b3d3-fdb14af4d065'): 2094, UUID('f0c63aff-af59-50c9-81fc-2fe55004c771'): 17, UUID('c996e617-88a4-5c65-ab1e-948344b18d27'): 3108, UUID('1a9d4d3b-bbe9-53b9-8149-67806bdf60f2'): 18, UUID('c9bdbac7-0ea3-5c9e-b590-018bd09b127b'): 233, UUID('b722f1ec-b90e-5ed8-b7c8-c768e8b323cb'): 5, UUID('74f1506a-9a37-59d7-b288-5ef3683dca8f'): 10, UUID('ef66e5dd-2130-5fd5-9bdd-aa7eff59fda5'): 11} - 2024-06-04 13:34:40,885
{'results': ["File 'aristotle.txt' processed successfully.", "File 'screen_shot.png' processed successfully.", "File 'pg_essay_1.html' processed successfully.", "File 'pg_essay_2.html' processed successfully.", "File 'pg_essay_3.html' processed successfully.", "File 'pg_essay_4.html' processed successfully.", "File 'pg_essay_5.html' processed successfully.", "File 'lyft_2021.pdf' processed successfully.", "File 'uber_2021.pdf' processed successfully.", "File sample.mp3 processed successfully.", "File sample2.mp3 processed successfully."]}
...

To verify the successful ingestion of the demo documents, you can fetch the metadata for the uploaded documents associated with the default demo user ID:

python -m r2r.examples.demo documents_info

Demo Output:

[
    DocumentInfo(
        document_id=UUID('c9bdbac7-0ea3-5c9e-b590-018bd09b127b'), 
        version='v0', 
        size_in_bytes=73353, 
        metadata={'title': 'aristotle.txt', 'user_id': '063edaf8-3e63-4cb9-a4d6-a855f36376c3'}, 
        title='aristotle.txt'
    ), 
    ... 
]

python -m r2r.examples.demo users_stats

[
    UserStats(
        user_id=UUID('063edaf8-3e63-4cb9-a4d6-a855f36376c3'), 
        num_files=9,
        total_size_in_bytes=4809510, 
        document_ids=[UUID('c9bdbac7-0ea3-5c9e-b590-018bd09b127b'), ...]
    ), 
]

Documents are stored by default in a local vector database. The vector database provider and settings can be specified via an input config.json. To perform a search query on the ingested user documents, use the following command:

python -m r2r.examples.demo search --query="Who was Aristotle?"

Demo Output:

{
    'id': UUID('93c44e73-8e95-50c2-84af-6a42f070b552'), 
    'score': 0.7739712385010018, 
    'metadata': 
    {
        'document_id': '15255e98-e245-5b58-a57f-6c51babf72dd', 
        'extraction_id': '5c61f9b9-b468-5fd7-8eb1-5d797a15c484', 
        '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',
        'user_id': '063edaf8-3e63-4cb9-a4d6-a855f36376c3', 
        'associatedQuery': 'Who was Aristotle?'
    }
},
...

To generate a response for a query using RAG, execute the following command:

python -m r2r.examples.demo rag --query="What was Uber's profit in 2020?"

Demo Output:

...
Time taken to run RAG: 2.29 seconds
{'results': 
   [
      ChatCompletion(
         id='chatcmpl-9RCB5xUbDuI1f0vPw3RUO7BWQImBN', 
         choices=[
            Choice(
               finish_reason='stop', 
               index=0, 
               logprobs=None, 
               message=ChatCompletionMessage(
                  content="Uber's profit in 2020 was a net loss of $6,768 million [10].", 
                  role='assistant', 
                  function_call=None, 
                  tool_calls=None
                  )
               )
            ], 
         created=1716268695, 
         model='gpt-3.5-turbo-0125', 
         object='chat.completion', 
         system_fingerprint=None, 
         usage=CompletionUsage(
            completion_tokens=20, 
            prompt_tokens=1470, 
            total_tokens=1490
            )
         )
   ]
}

For streaming results from a RAG query, use the following command:

python -m r2r.examples.demo rag --query="What was Lyft's profit in 2020?" --streaming=true

Demo Output:

r2r.main.r2r_config - INFO - Loading configuration from <YOUR_WORKDIR>/config.json - 2024-05-20 22:27:31,890
...
<search>["{\"id\":\"808c47c5-ebef-504a-a230-aa9ddcfbd87 .... </search>
<completion>Lyft reported a net loss of $1,752,857,000 in 2020 according to [2]. Therefore, Lyft did not make a profit in 2020.</completion>                                                      
Time taken to stream RAG response: 2.79 seconds

To update document(s) we may use the update_as_files or update_as_documents endpoints. Running the demo with update_as_files overwrites the data associated with 'aristotle.txt' with new data corresponding to 'aristotle_v2.txt' and increments the file version.

python -m r2r.examples.demo update_as_files

To delete a document by its ID, or any other metadata field, use the delete command. For example, to delete all chunks corresponding to the uploaded file aristotle.txt, we can call delete on the metadata field document_id with the value 15255e98-e245-5b58-a57f-6c51babf72dd:

python -m r2r.examples.demo delete --keys="['document_id']" --values="['c9bdbac7-0ea3-5c9e-b590-018bd09b127b']"

To delete all documents associated with a given user, run the delete command on the user_id:

# run the following command with care, as it will erase all ingested user data for `063edaf8-3e63-4cb9-a4d6-a855f36376c3`
python -m r2r.examples.demo delete --keys="['user_id']" --values="['063edaf8-3e63-4cb9-a4d6-a855f36376c3']"

This section extends the previous demo by showing how to set up and use the R2R framework with a Client-Server architecture. The R2R server can be stood up to handle requests, while the client can communicate with the server to perform various operations.

Use the following command to start the server:

python -m r2r.examples.demo serve

This command starts the R2R server on the default host 0.0.0.0 and port 8000.

1. Ingest Documents as Files:

   python -m r2r.examples.demo ingest_as_files --client_server_mode

   This command will send the ingestion request to the server running at http:https://localhost:8000.

2. Perform a Search:

   python -m r2r.examples.demo search --query="Who was Aristotle?" --client_server_mode

   This command sends the search query to the server and retrieves the results.

3. Run a RAG Completion:

   python -m r2r.examples.demo rag --query="What was Uber's profit in 2020?" --client_server_mode

   This command sends the RAG query to the server and retrieves the generated response.

4. Run a RAG Stream:

   python -m r2r.examples.demo rag --query="What was Lyft's profit in 2020?" --streaming=true --client_server_mode

   This command streams the RAG query results from the server.

By using the Client-Server model, you can extend the basic R2R demo to support more scalable and modular deployments. The server handles requests and performs heavy computations, while clients can communicate with the server to perform ingestion, search, RAG, and other operations, as shown in the examples above. For detailed setup and basic functionality, refer back to the R2R Demo.

R2R ships with an open-source React+Next.js dashboard to facilitate managing and interacting with deployed pipelines. You can download the dashboard here.

The framework revolves around three core abstractions: Providers, Pipes, and Pipelines.

Providers supply the necessary resources and capabilities to the pipes and pipelines. Key provider types include:

• Vector Database Provider: Manages the storage and retrieval of vector embeddings. Examples include PGVector, and SQLite.
• Embedding Provider: Converts text into vector embeddings. Supported providers include OpenAI, SentenceTransformers, and DummyEmbeddingProvider.
• LLM Provider: Interfaces with large language models for text generation. Supported providers include OpenAI, and LiteLLM.
• Prompt Provider: Manages prompts for various tasks.
• Eval Provider: Evaluates the quality of generated responses.

Pipes represent individual steps in the data processing workflow. Each pipe performs a specific task, such as parsing, embedding, searching, or generating text. Pipes are designed to be composable and reusable within different pipelines.

• Parsing Pipe: Extracts and structures data from various formats.
• Embedding Pipe: Generates embeddings from text and stores them in a vector database.
• Vector Storage Pipe: Handles the storage of embeddings in a vector database.
• Search Pipe: Performs vector-based searches.
• RAG Pipe: Integrates search results with language generation to produce responses.
• Streaming RAG Pipe: Extends RAG functionality to support streaming responses.
• Eval Pipe: Evaluates the quality of generated responses.

Pipelines are composed of multiple pipes arranged in a sequence. They manage the flow of data through the pipes, ensuring that each step is executed in the correct order. R2R supports several types of pipelines:

• Ingestion Pipeline: Prepares and ingests documents, converting them into embeddings.
• Embedding Pipeline: Manages the transformation of text into vector embeddings.
• RAG Pipeline: Combines search and language generation to produce detailed responses.
• Eval Pipeline: Evaluates the quality of generated responses using LLM-powered evaluations.

R2R (RAG to Riches) is a comprehensive framework designed to streamline the development, deployment, and optimization of Retrieval-Augmented Generation (RAG) systems. With its robust core abstractions—Providers, Pipes, and Pipelines—R2R offers a modular and flexible approach to building high-quality RAG pipelines.