ragxo 0.1.10

A RAG (Retrieval-Augmented Generation) toolkit with Milvus integration

RagXO

Export, version and reuse your RAG pipeline everywhere 🚀

RagXO extends the capabilities of traditional RAG (Retrieval-Augmented Generation) systems by providing a unified way to package, version, and deploy your entire RAG pipeline with LLM integration. Export your complete system—including embedding functions, preprocessing steps, vector store, and LLM configurations—into a single, portable artifact.

Features ✨

• Complete RAG Pipeline: Package your entire RAG system into a versioned artifact
• LLM Integration: Built-in support for OpenAI models
• Flexible Embedding: Compatible with any embedding function (Sentence Transformers, OpenAI, etc.)
• Custom Preprocessing: Chain multiple preprocessing steps
• Vector Store Integration: Built-in Milvus support
• System Prompts: Include and version your system prompts

Installation 🛠️

pip install ragxo

Quickstart 🚀

Build a RAG pipeline

from ragxo import Ragxo, Document



ragxo_client = Ragxo(dimension=1536)

def preprocess_text_remove_special_chars(text: str) -> str:
    return re.sub(r'[^a-zA-Z0-9\s]', '', text)

def preprocess_text_lower(text: str) -> str:
    return text.lower()

def get_embeddings(text: str) -> list[float]:
    return openai.embeddings.create(input=text, model="text-embedding-ada-002").data[0].embedding

ragxo_client.add_preprocess(preprocess_text_lower)
ragxo_client.add_preprocess(preprocess_text_remove_special_chars)
ragxo_client.add_embedding_fn(get_embeddings)

ragxo_client.add_system_prompt("You are a helpful assistant that can answer questions about the data provided.")
ragxo_client.add_model(
    "gpt-4o-mini",
    limit=10,
    temperature=0.5,
    max_tokens=1000,
    top_p=1.0,
    frequency_penalty=0.0,
    presence_penalty=0.0
)

ragxo_client.index([
    Document(text="Capital of France is Paris", metadata={"source": "example"}, id=1),
    Document(text="Capital of Germany is Berlin", metadata={"source": "example"}, id=2),
    Document(text="Capital of Italy is Rome", metadata={"source": "example"}, id=3),
])

ragxo_client.export("my_rag_v1.0.0")

# or export to s3
ragxo_client.export("my_rag_v1.0.0", s3_bucket="my_bucket")

Load a RAG pipeline

loaded_ragxo_client = Ragxo.load("my_rag_v1.0.0")

vector_search_results = loaded_ragxo_client.query("What is the capital of France?")

llm_response = loaded_ragxo_client.generate_llm_response(
    "What is the capital of France?")

print(llm_response.choices[0].message.content)

Usage Guide 📚

Import

from ragxo import Ragxo, Document

ragxo_client = Ragxo(dimension=768)

Adding Preprocessing Steps

import re

def remove_special_chars(text: str) -> str:
    return re.sub(r'[^a-zA-Z0-9\s]', '', text)

def lowercase(text: str) -> str:
    return text.lower()

ragxo_client.add_preprocess(remove_special_chars)
ragxo_client.add_preprocess(lowercase)

Custom Embedding Functions

# Using SentenceTransformers
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('all-MiniLM-L6-v2')

def get_embeddings(text: str) -> list[float]:
    return model.encode(text).tolist()

ragxo.add_embedding_fn(get_embeddings)

# Or using OpenAI
from openai import OpenAI
client = OpenAI()

def get_openai_embeddings(text: str) -> list[float]:
    response = client.embeddings.create(
        input=text,
        model="text-embedding-ada-002"
    )
    return response.data[0].embedding

ragxo.add_embedding_fn(get_openai_embeddings)

Creating Documents

from ragxo import Document

doc = Document(
    text="Your document content here",
    metadata={"source": "wiki", "category": "science"},
    id=1
)

ragxo_client.index([doc])

LLM Configuration

# Set system prompt
ragxo_client.add_system_prompt("""
You are a helpful assistant. Use the provided context to answer questions accurately.
If you're unsure about something, please say so.
""")

# Set LLM model
ragxo_client.add_model("gpt-4")

Export and Load

# Export your RAG pipeline
ragxo_client.export("rag_pipeline_v1")

# Load it elsewhere
loaded_ragxo_client = Ragxo.load("rag_pipeline_v1")

Best Practices 💡

1. Version Your Exports: Use semantic versioning for your exports:

ragxo.export("my_rag_v1.0.0")

2. S3: Use S3 to store your exports

export AWS_ACCESS_KEY_ID=your_access_key
export AWS_SECRET_ACCESS_KEY=your_secret_key

ragxo_client.export("my_rag_v1.0.0", s3_bucket="my_bucket")

License 📝

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing 🤝

Contributions are welcome! Please feel free to submit a Pull Request.