SchoginiAI 0.2.3

A sample AI toolkit by Schogini Systems with Retrieval-Augmented Generation (RAG).

SchoginiAI

SchoginiAI is a robust AI toolkit developed by Schogini Systems, providing advanced Retrieval-Augmented Generation (RAG) capabilities. It leverages the power of LangChain, OpenAI, and multiple vector storage options like FAISS, ChromaDB, and Pinecone. This makes it a versatile tool for building AI-driven solutions for various applications.

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🚀 Features

• PDF & Text Processing: Extracts knowledge from PDF files or raw text for vector storage.
• Flexible Vector Stores: Supports FAISS, ChromaDB, and Pinecone for vector management.
• Advanced Text Chunking: Includes configurable chunking strategies like RecursiveCharacterTextSplitter and TokenTextSplitter.
• Environment-Based Configurations: Manages API keys and settings securely using .env files.
• RAG Capabilities: Combines vector retrieval with OpenAI’s LLMs for generating relevant, context-aware responses.

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🛠 Installation

From PyPI

Install SchoginiAI directly from PyPI:

pip install SchoginiAI

From Source

Clone the repository and install locally:

git clone https://github.com/yourusername/SchoginiAI.git
cd SchoginiAI
pip install .

Replace yourusername with your GitHub username.

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🔧 Configuration

Environment Variables

Create a .env file to securely store your API keys:

OPENAI_API_KEY=your_openai_api_key
PINECONE_API_KEY=your_pinecone_api_key

Custom Settings

Edit the settings.py file to configure the following options:

• Vector Store Type: Choose between faiss, chroma, or pinecone.
• Text Splitter Strategy: Select chunking strategies like RecursiveCharacterTextSplitter or TokenTextSplitter.
• Chunk Size and Overlap: Control the size and overlap of text chunks.

Example settings.py snippet:

vector_store_type = "chroma"  # Options: 'faiss', 'chroma', 'pinecone'
text_splitter_strategy = "RecursiveCharacterTextSplitter"
chunk_size = 1000
chunk_overlap = 200

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📚 Usage

Build Vector Store from Text

from SchoginiAI import SchoginiAIRAG

rag_ai = SchoginiAIRAG()

sample_text = """
Schogini Systems is a pioneer in AI Chatbots.
We specialize in automation solutions for small businesses.
"""

rag_ai.build_vector_store(sample_text)
rag_ai.save_vector_store()

Query the Vector Store

from SchoginiAI import SchoginiAIRAG

rag_ai = SchoginiAIRAG()
rag_ai.load_vector_store()

response = rag_ai.ask_question("What services does Schogini Systems provide?")
print("Response:", response)

Build Vector Store from PDF

from SchoginiAI import SchoginiAIRAG

rag_ai = SchoginiAIRAG()
rag_ai.build_vector_store_from_pdf("path/to/your/pdf_file.pdf")
rag_ai.save_vector_store()

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🐳 Docker Usage

Build Docker Image

docker build --no-cache -t schoginiai .

Run Docker Commands

Create Vector Store

• Using FAISS:

  docker run --rm -e OPENAI_API_KEY="your_openai_api_key" -e PINECONE_API_KEY="your_pinecone_api_key" schoginiai create
  

• Using ChromaDB:

  docker run --rm -e OPENAI_API_KEY="your_openai_api_key" -e PINECONE_API_KEY="your_pinecone_api_key" schoginiai create
  

Query Vector Store

• Using FAISS:

  docker run --rm -e OPENAI_API_KEY="your_openai_api_key" -e PINECONE_API_KEY="your_pinecone_api_key" schoginiai query
  

• Using ChromaDB:

  docker run --rm -e OPENAI_API_KEY="your_openai_api_key" -e PINECONE_API_KEY="your_pinecone_api_key" schoginiai query
  

Replace "your_openai_api_key" and "your_pinecone_api_key" with your actual keys.

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🗃 Project Structure

SchoginiAI/
├── SchoginiAI/
│   ├── __init__.py
│   └── main.py
├── examples/
│   ├── usage_example.py
│   ├── knowledge_creation.py
│   └── .env
├── tests/
│   ├── test_faiss.py
│   ├── test_chroma.py
├── settings.py
├── README.md
├── requirements.txt
├── Dockerfile
├── doit.sh
└── setup.py

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📦 Dependencies

SchoginiAI relies on the following Python packages:

• langchain>=0.0.200
• openai>=0.28.1
• faiss-cpu>=1.7.6
• chromadb>=0.3.22
• python-dotenv>=0.21.0

Install these dependencies using:

pip install -r requirements.txt

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🛡 License

This project is licensed under the MIT License.

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📚 Additional Resources

• LangChain Documentation
• OpenAI API Documentation
• FAISS Documentation
• ChromaDB Documentation
• Python-Dotenv Documentation

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With this setup, SchoginiAI is ready to handle advanced AI-driven tasks with ease and flexibility. For more details, refer to the source code or open an issue in the repository.