rago 0.14.5

RAG framework

Rago

Rago is a lightweight framework for RAG.

• Software License: BSD 3 Clause
• Documentation: https://osl-incubator.github.io/rago

Features

• Vector Database support
  • FAISS
• Retrieval features
  • Support PDF extraction via Langchain
• Augmentation (Embedding + Vector Database Search)
  • Support for Sentence Transformer (Hugging Face)
  • Support for Open AI
  • Support for SpaCy
• Generation (LLM)
  • Support for Hugging Face
  • Support for Llama (Hugging Face)
  • Support for OpenAI
  • Support for Gemini

Roadmap

1. Add new Backends

As noted in several GitHub issues, our initial goal is to support as many backends as possible. This approach will provide valuable insights into user needs and inform the structure for the next phase.

2. Declarative API for Rago

Objective

To simplify and streamline the user experience in configuring RAG by introducing a declarative, composable API—similar to how Plotnine or Altair allows users to build visualizations.

Overview

The current procedural approach in Rago requires users to instantiate and connect individual components (retrieval, augmentation, generation, etc.) manually. This can become cumbersome as support for multiple backends grows. We propose a new declarative interface that lets users define their entire RAG steps in a single, fluent expression using operator overloading.

Proposed Syntax Example

from pathlib import Path

from rago import Rago, Retrieval, Augmented, Generation, DB, Cache

datasource = ...

rag = (
    Rago()
    | DB(backend="faiss")
    | Cache(backend="file", target_dir=Path(".rago-cache"))
    | Retrieval(backend="string")
    | Augmented(
        backend="openai",
        model_name="text-embedding-3-small",
        top_k=5,
    )
    | Generation(
        backend="openai",
        model_name="gpt-4o-mini",
        prompt_template="Question: {query}\nContext: {context}\nAnswer:"
    )
)

result = rag.run(query="What is the capital of France?", source=datasource)
print(result.result)

Key Benefits

• Intuitive Composition: Users can build complex pipelines by simply adding layers together.
• Modularity: Each component is encapsulated, making it easy to swap or extend backends without altering the overall architecture.
• Reduced Boilerplate: The declarative syntax minimizes the need for repetitive setup code, focusing on the "what" rather than the "how."
• Enhanced Readability: The pipeline’s structure becomes immediately clear, promoting easier maintenance and collaboration.

Implementation Plan

1. Define Base Classes: Develop abstract base classes for each component (DB, Cache, Retrieval, Augmented, Generation) to standardize interfaces and facilitate future extensions.
2. Operator Overloading: Implement the __or__ method in the main Rago class to allow chaining of components, effectively building the pipeline through a fluent interface.
3. Configuration and Defaults: Integrate sensible defaults and validation (using tools like Pydantic) so that users can override only when necessary.
4. Documentation and Examples: Provide comprehensive documentation and examples to illustrate the new declarative syntax and usage scenarios.

Installation

If you want to install it for cpu only, you can run:

$ pip install rago[cpu]

But, if you want to install it for gpu (cuda), you can run:

$ pip install rago[gpu]

Setup

Llama 3

In order to use a Llama model, visit its page on Hugging Face and request access via its form, for example: https://huggingface.co/meta-llama/Llama-3.2-1B.

After you are granted access to the desired model, you will be able to use it with Rago.

You will also need to provide a Hugging Face token in order to download the models locally, for example:

from rago import Augmented, Generation, Rago, Retrieval

# For Gated LLMs
HF_TOKEN = 'YOUR_HUGGING_FACE_TOKEN'

animals_data = [
    "The Blue Whale is the largest animal ever known to have existed, even "
    "bigger than the largest dinosaurs.",
    "The Peregrine Falcon is renowned as the fastest animal on the planet, "
    "capable of reaching speeds over 240 miles per hour.",
    "The Giant Panda is a bear species endemic to China, easily recognized by "
    "its distinctive black-and-white coat.",
    "The Cheetah is the world's fastest land animal, capable of sprinting at "
    "speeds up to 70 miles per hour in short bursts covering distances up to "
    "500 meters.",
    "The Komodo Dragon is the largest living species of lizard, found on "
    "several Indonesian islands, including its namesake, Komodo.",
]

rag = (
    Rago()
    | Retrieval(backend='string')
    | Augmented(
        backend='sentence_transformers',
        model_name='paraphrase-MiniLM-L12-v2',
        top_k=2,
    )
    | Generation(
        backend='llama',
        model_name='meta-llama/Llama-3.2-1B',
        api_key=HF_TOKEN,
    )
)

rag.prompt('What is the fastest animal on Earth?', source=animals_data)

Ollama

For testing the generation with Ollama, run first the following commands:

$ ollama pull llama3.2:1b
$ ollama serve