prp-e / Maux-RAG-API

a complete RAG-Ready fast api app for your Rag Systems

🚀 MAUX INC RAG API

Welcome to the MAUX INC RAG API! This powerful API enables you to create a Retrieval-Augmented Generation (RAG) system using OpenAI's chat completions and embeddings, along with ChromaDB for efficient document storage and retrieval.

🌟 Features

• 📚 Document embedding and storage
• 🔍 Semantic search for relevant documents
• 🤖 AI-powered question answering
• 🔗 Easy integration with OpenAI and ChromaDB

🛠️ Installation

1. Clone the repository
2. Install dependencies:

pip install -r requirements.txt 

3. Set up your environment variables in a .env file

OPENAI_API_KEY=your_openai_api_key
CHROMA_PERSIST_DIRECTORY=./chroma_db
OPENAI_EMBEDDING_MODEL=text-embedding-3-small
CHAT_MODEL=gpt-4o-mini
RAG_SEARCH_LIMIT=3
SYSTEM_PROMPT="You are a helpful assistant. Use the provided context to answer the user's question. If the context is not relevant, just say 'I don't know'"

🚀 Usage

start the api with the following command:

uvicorn app.main:app --host 0.0.0.0 --port 8000

🛣️ Routes

1. initialize the collection in the vector database Before using the API, you need to initialize the collection:

import requests

BASE_URL = "http://localhost:8000/v1"

response = requests.post(f"{BASE_URL}/vector_db/initialize_collection")
print(response.json())

2. Add Documents to the collection

import requests

BASE_URL = "http://localhost:8000/v1"

response = requests.post(f"{BASE_URL}/vector_db/add_document", json={
    "text": "MAUX INC is a leading AI platform for innovative solutions.",
    "metadata": {"source": "company_info"}
})
print(response.json())

3. Search Documents Search for similar documents:

import requests

BASE_URL = "http://localhost:8000/v1"

response = requests.post(f"{BASE_URL}/vector_db/search_documents", json={
    "text": "ماکس چیه؟"
})
print(response.json())

4. Chat Completions

import requests

BASE_URL = "http://localhost:8000/v1"

response = requests.post(f"{BASE_URL}/chat/completions", json={"prompt": "ماکس چیه؟"})
response_data = response.json()
    
# Extract and print only the content
content = response_data['choices'][0]['message']['content']
print(content)

📚 Examples

examples/chat.py is a simple example of how to use the chat completions route.

📚 License

This project is licensed under the MIT License

📚 Contributing

Contributions are welcome! Please feel free to submit a PR.