What's included

• OpenAI LLM Integration

  Integrate OpenAI’s large language models (e.g., GPT-4) to enable natural language understanding and generation for chatbots, customer support automations, or knowledge base Q&A. Includes prompt engineering and performance tuning.

• Flask API for Chatbot & Microservices

  Develop a lightweight Flask backend that exposes RESTful endpoints for AI-based services. This API will handle incoming requests, coordinate with various ML/LLM modules, and return responses to frontend or third-party systems.

• Gemini Integration for Advanced AI Features

  Leverage Google’s Gemini or similarly advanced AI platforms for enhanced reasoning, multi-modal features, and complex analytics. Integrate Gemini’s APIs into your existing pipeline for superior accuracy and performance.

• Vector Database Implementation

  Deploy and configure a vector database (e.g., Milvus, Pinecone, or similar) to store and retrieve embeddings. This database supports semantic search and allows your AI agents to quickly identify relevant information from large corpuses.