Agentic AI is an intelligent agent designed for hybrid search and information retrieval. It integrates PineconeDB for vector storage, Google's Gemini for embeddings, and the Groq model for AI-based responses. This project leverages website knowledge bases and search tools to provide comprehensive answers.

• Hybrid Search: Utilizes PineconeDB with Gemini embeddings.
• Knowledge Base: Extracts and processes information from specified websites.
• AI-Powered Responses: Uses the Groq model for intelligent answers.
• Search Tools: Incorporates DuckDuckGo search for additional sources.
• Agentic Capabilities: Structured response handling with streaming output.
• Latest Information Retrieval: Uses DuckDuckGoTools to fetch real-time data from the web.

1. Clone the repository:

   git clone <repository-url>
   cd agentic-ai

2. Install dependencies:

   pip install -r requirements.txt

3. Set up environment variables: Create a .env file and add your API keys:

   PINECONE_API=<your_pinecone_api_key>
   GROQ_API_KEY=<your_groq_api_key>
   GEMINI_API_KEY=<your_gemini_api_key>

Run the agent with the following command:

python agent.py

Example query:

agent.print_response("What is the history of Thai curry?", stream=True, markdown=True)

• Loading API Keys: Environment variables are loaded via dotenv.
• Embedding Model: GeminiEmbedder is used for text embeddings.
• Vector Database: PineconeDB stores embeddings and enables hybrid search.
• Knowledge Base: WebsiteKnowledgeBase fetches relevant information.
• Agent: Uses the Groq model for AI-based answers and integrates search tools.
• Real-Time Search: DuckDuckGoTools is used to retrieve the latest information from the web.

Feel free to contribute by opening a pull request or reporting issues!

This project is licensed under the MIT License.