r2r 0.1.1

SciPhi R2R

R2R

R2R (RAG to Riches) is a Python framework designed for the rapid construction and deployment of production-ready Retrieval-Augmented Generation (RAG) systems. This semi-opinionated framework accelerates the transition from experimental stages to production-grade RAG systems.

Quick Install:

Install R2R directly using pip:

pip install r2r

Full Install:

Follow these steps to ensure a smooth setup:

1. Install Poetry:

   • Before installing the project, make sure you have Poetry on your system. If not, visit the official Poetry website for installation instructions.

2. Clone and Install Dependencies:

   • Clone the project repository and navigate to the project directory:

     git clone [e-mail]:SciPhi-AI/r2r.git
     cd r2r
     

   • Install the project dependencies with Poetry:

     # See pyproject.toml for available extras
     # use "all" to include every optional dependency
     poetry install --extras "parsing"
     

3. Configure Environment Variables:

   • You need to set up cloud provider secrets in your .env. At a minimum, you will need an OpenAI key.
   • The framework currently supports pgvector and Qdrant with plans to extend coverage.
   • If starting from the example, copy .env.example to .env to apply your configurations:

     cp .env.example .env
     

Basic Examples

The project includes several basic examples that demonstrate application deployment and standalone usage of the embedding and RAG pipelines:

1. app.py: This example runs the main application, which includes the ingestion, embedding, and RAG pipelines served via FastAPI.

   poetry run uvicorn examples.basic.app:app
   

2. run_client.py: This example should be run after starting the main application. It demonstrates uploading text entries as well as a PDF with the python client. Further, it shows document and user-level management with built-in features.

   poetry run python -m examples.client.test_client
   

3. run_pdf_chat.py: A more comprehensive example demonstrating upload and chat with a more realistic pdf.

   # Ingest pdf
   poetry run python -m examples.pdf_chat.run_demo ingest
   
   # Ask a question
   poetry run python -m examples.pdf_chat.run_demo search "What are the key themes of Meditations?"
   

4. web: A web application which is meant to accompany the framework to provide visual intelligence.

   cd web && pnpm install
   # Serve the web app
   pnpm dev
   

Demonstration

https://github.com/SciPhi-AI/r2r/assets/68796651/c648ab67-973a-416a-985e-2eafb0a41ef0

Community

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Core Abstractions

The framework primarily revolves around three core abstractions:

• The Ingestion Pipeline: Facilitates the preparation of embeddable 'Documents' from various data formats (json, txt, pdf, html, etc.). The abstraction can be found in ingestion.py.

• The Embedding Pipeline: Manages the transformation of text into stored vector embeddings, interacting with embedding and vector database providers through a series of steps (e.g., extract_text, transform_text, chunk_text, embed_chunks, etc.). The abstraction can be found in embedding.py.

• The RAG Pipeline: Works similarly to the embedding pipeline but incorporates an LLM provider to produce text completions. The abstraction can be found in rag.py.

Each pipeline incorporates a logging database for operation tracking and observability.