fabricatio-rag 0.1.1.dev2

A Python library for Retrieval-Augmented Generation (RAG) capabilities in LLM applications.

fabricatio-rag

A Python library for Retrieval-Augmented Generation (RAG) capabilities in LLM applications.

📦 Installation

This package is part of the fabricatio monorepo and can be installed as an optional dependency:

pip install fabricatio[rag]

Or install all components:

pip install fabricatio[full]

🔍 Overview

Provides tools for:

• Document embedding and vector storage using Milvus
• Semantic search and context retrieval
• Integration with TEI (Text Embeddings Inference) services
• Database injection workflows
• Asynchronous RAG execution patterns

Built on top of Fabricatio's agent framework with support for asynchronous execution and Rust extensions.

🧩 Usage Example

from fabricatio_rag.capabilities.rag import RAG
from fabricatio_rag.models.rag import MilvusDataBase


async def search_knowledge():
    # Initialize database connection
    db = MilvusDataBase(collection_name="science_papers")

    # Initialize RAG capability
    rag = RAG(db)

    # Search for relevant information
    results = await rag.retrieve("climate change impact on coral reefs", limit=3)

    print("Top 3 relevant documents:")
    for result in results:
        print(f"- {result['title']}")
        print(f"  Relevance: {result['score']:.2f}")
        print(f"  Snippet: {result['text'][:150]}...")

📁 Structure

fabricatio-rag/
├── actions/          - Data injection workflows
├── capabilities/     - Core RAG functionality
├── models/           - Database and query models
├── proto/            - TEI service definitions
└── rust.pyi          - Rust extension interfaces

🔗 Dependencies

Core dependencies:

• pymilvus>=2.5.4 - Vector database integration
• fabricatio-core - Core interfaces and utilities

Rust extensions:

• TEI client bindings
• Protobuf definitions for gRPC communication

📄 License

MIT – see LICENSE

GitHub: github.com/Whth/fabricatio