RecallAIsh 0.1.0

RecallAI is a cutting-edge Retrieval-Augmented Generation (RAG) framework designed for Large Language Models (LLMs). It enhances LLM responses by integrating real-time knowledge retrieval from structured and unstructured data sources.

RecallAI

Overview

RecallAI is a powerful Retrieval-Augmented Generation (RAG) framework designed to enhance the capabilities of Large Language Models (LLMs). It integrates real-time knowledge retrieval from structured and unstructured data sources, enabling more informed and accurate responses.

Features

• Retrieval-Augmented Generation (RAG): Enhances LLM responses with real-time information retrieval.
• Integration with OpenAI Models: Supports GPT-based models for improved text generation.
• Qdrant Vector Store: Efficient vector storage and retrieval for document embeddings.
• Document & Web Ingestion: Supports ingesting PDFs and web pages for knowledge retrieval.

Installation

Prerequisites

• Python 3.10+
• pip package manager
• Qdrant or Pinecone vector database
• OpenAI API Key

Install via PyPI

You can install RecallAI directly from PyPI:

pip install recall-ai

Manual Installation

1. Clone the repository:

   git clone https://github.com/AshishChandpa/RecallAI.git
   cd RecallAI
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Set up environment variables:
   • Create a .env file in the project root.
   • Add your OpenAI API key:

     OPENAI_API_KEY=your_openai_api_key
     

4. Ensure Qdrant is running:

   docker run -p 6333:6333 -p 6334:6334 qdrant/qdrant
   

Usage

Running the Example Script

Execute the script to see the RAG system in action:

python examples/example.py

Using RecallAI in Your Project

Import and use RecallAI in your Python scripts:

from recallai import RAGSystem, QdrantVectorStore, PromptManager

# Configure Qdrant connection
qdrant_store = QdrantVectorStore(
    url="http://localhost:6333",
    collection_name="my_rag_collection",
    vector_size=1536,  # adjust to your embedding dimension
)

# Initialize the RAG system
rag_system = RAGSystem(
    vector_store=qdrant_store,
    vector_namespace="<NameSpaceName>",
    openai_api_key=os.getenv("OPENAI_API_KEY"),
)

# Retrieve documents and generate responses
user_query = "Explain the main concepts from the documents."
context = rag_system.retrieve_documents(user_query, "pdf")

instructions = "You are an expert assistant tasked with answering questions using the provided context."
prompt_manager = PromptManager(instructions=instructions)
full_prompt = prompt_manager.create_prompt(context, user_query)

answer = rag_system.chat(full_prompt, "gpt-4o-mini")
print("Answer:", answer)

Configuration

• Adjust the QdrantVectorStore parameters for different vector dimensions or collection names.
• Modify the ingestion pipeline to include additional document sources.

Contributing

Contributions are welcome! Please open an issue or submit a pull request.

License

This project is licensed under the MIT License.

Contact

For questions or suggestions, reach out via email.