Offline_Agent__Ollama_Deepseek — DeepSeek AI Agent
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    Offline_Agent__Ollama_Deepseek

    Offline_Agent__Ollama_Deepseek

    aqeeljanjua February 5, 2025
    1 copies 0 downloads

    An Offline intelligent inventory management system that uses LLMs (Large Language Models) to process natural language queries and manage inventory data through MongoDB.

    Offline AI Agent with Ollama & DeepSeek

    An offline-capable AI agent that leverages DeepSeek and Llama2 models through Ollama for natural language processing and intelligent inventory management, with MongoDB integration for data persistence.

    Overview

    This project demonstrates an intelligent system that can:

    • Process natural language queries offline using local LLM models
    • Manage inventory data through MongoDB
    • Handle complex business logic without internet connectivity
    • Maintain conversation context and chat history
    • Generate dynamic database queries from natural language input

    Key Components

    • Ollama Integration: Local model management and inference
    • DeepSeek Model: Primary language model for query processing
    • MongoDB Backend: Persistent data storage and retrieval
    • Query Processing: Natural language to database query conversion
    • Session Management: Maintains context across conversations

    Developer

    Muhammad Aqeel Yasin
    Shadow Analytics

    Features

    • Natural language query processing using Deepseek and Llama2 models
    • MongoDB integration for data persistence
    • Intelligent query parsing and response generation
    • Real-time inventory tracking
    • Supplier management
    • Chat history tracking
    • Session-based interactions

    Prerequisites

    • Python 3.8+
    • MongoDB
    • Ollama

    Installation

    1. Clone the repository
    git clone [repository-url]
    
    1. Install required packages
    pip install -r requirements.txt
    
    1. Install and start MongoDB

    2. Install Ollama and pull required models

    ollama pull deepseek-r1:14b
    

    Usage

    1. Start the application:
    python main.py
    
    1. Enter natural language queries, for example:
    • "What is the current stock level of laptops?"
    • "Who is the supplier for item ID 1?"
    • "Update stock level for laptops"

    Project Structure

    • main.py - Application entry point
    • query_agent.py - Main query processing agent
    • database_setup.py - MongoDB database initialization an

    Tags

    agentic-aideepseek-aideepseek-r1offline-agentoffline-ai-agentoffline-ai-solutionsollama-deepseekollama-python

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