ragify-lib 0.1.5

A simple, clean Python library for Retrieval-Augmented Generation (RAG)

ragify-lib: Effortless Retrieval-Augmented Generation (RAG) Workflows in Python

ragify-lib is a modern, production-ready Python library that makes Retrieval-Augmented Generation (RAG) simple, fast, and flexible. With just a few lines of code, you can chunk, embed, store, and retrieve text using state-of-the-art embedding models and vector databases. Whether you’re building chatbots, search engines, or knowledge assistants, ragify-lib helps you unlock the power of RAG with minimal setup.

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🚀 Why Choose ragify-lib?

• Minimal Setup: Go from raw text to powerful retrieval in minutes.
• Flexible: Easily configure your embedding model, chunking strategy, and vector database (supports Quadrant and mock mode).
• Human-Readable Results: Retrieve relevant text chunks with similarity scores and metadata—no need to handle raw embeddings.
• CLI Included: Use the command-line tool for quick experiments and automation.
• Open Source: Free to use for research and commercial projects.

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👤 About the Developer

Rahul Wale
AI Developer & Researcher
Rahul specializes in building practical, scalable AI solutions for real-world problems, with a focus on natural language processing and information retrieval.

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📦 Installation

pip install ragify-lib

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📝 Example 1: Local RAG Workflow in Python

from ragify import KaliRAG

# 1. Configure your database and embedding model (optional, uses sensible defaults)
rag = KaliRAG()
rag.configure_database(api_key="mock_key", host="localhost", port=6333, collection="my_collection")
rag.configure_embedding_model("all-MiniLM-L6-v2")
rag.configure_chunking(chunk_size=256, chunk_overlap=32)

# 2. Store your documents
documents = [
    "Retrieval-Augmented Generation (RAG) combines retrieval and generation for better answers.",
    "ragify-lib makes it easy to build RAG pipelines in Python.",
    "You can use Quadrant or mock mode for vector storage."
]
for doc in documents:
    rag.create_store_embedding(doc)

# 3. Retrieve relevant chunks for a query
results = rag.retrieve_embedding("How does RAG work?")
for chunk in results["results"]:
    print(f"Text: {chunk['text']}\nScore: {chunk['score']}\n")

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📝 Example 2: File-Based Workflow & CLI Usage

Create embeddings from a file and query them using the CLI:

# Store embeddings from a text file
ragify create --input knowledge.txt --output embeddings.json --api-key mock_key

# Query your knowledge base
ragify query "What is retrieval-augmented generation?" --top-k 3

Or configure everything via the CLI:

ragify config --api-key mock_key --host "localhost" --port 6333 --collection "my_collection" --model "all-MiniLM-L6-v2" --chunk-size 256 --chunk-overlap 32

Note: Use --api-key mock_key for local/mock mode. For production, use your real Quadrant API key.

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🌟 Features

• Plug-and-play with Quadrant vector database or use built-in mock mode
• Customizable chunking and embedding for any use case
• Returns human-readable results with scores and metadata
• Designed for both developers and researchers
• Robust CLI for automation and scripting
• Easy integration with existing Python projects

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🛠️ Advanced Usage

• Recursive Chunking: Handles very long documents with automatic recursion.
• Similarity Thresholds: Filter results by similarity score.
• Comprehensive Logging: Built-in logging for debugging and monitoring.
• Error Handling: Robust error handling with detailed error messages.

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📚 Use Cases

• AI-powered chatbots and assistants
• Semantic search engines
• Knowledge base augmentation
• Research and prototyping in NLP

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📖 Documentation

For full documentation, visit the official docs or see the CLI help:

ragify --help

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📄 License

This project is licensed under the MIT License.

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ragify-lib: The easiest way to add Retrieval-Augmented Generation to your Python projects.