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DeepSeek Chatbot - Technical Overview

This implementation combines modern AI infrastructure with containerized services to create a self-contained chatbot system. At its core lies DeepSeek-R1, an open-source large language model family developed by DeepSeek AI, specifically optimized for conversational tasks. The 7B parameter variant (7 billion neural connections) offers sophisticated reasoning capabilities at the cost of higher hardware requirements, while the 1.5B version provides faster inference on resource-constrained devices. Both models leverage advanced training techniques including grouped-query attention and rotary positional embeddings, achieving performance comparable to commercial alternatives while remaining fully localizable.

The system utilizes Ollama as the model serving framework - a lightweight abstraction layer that handles GPU-accelerated inference through llama.cpp, model version management, and REST API exposure. Unlike cloud-based alternatives, Ollama operates entirely offline after initial setup, ensuring data never leaves the local environment. Its modular architecture allows hot-swapping of models without service interruption, crucial for comparing the 1.5B and 7B variants.

Infrastructure components are orchestrated through Docker Compose, creating isolated environments for each service:

• MongoDB persists chat histories in JSON format

• Ollama serves model inferences via OpenAI-compatible API endpoints

• Custom Chat UI (Node.js/React frontend) provides browser access

• Nginx reverse proxy manages secure inter-container communication

Key architectural advantages include:

1. Zero external dependencies - All components run locally via Docker

2. Hardware optimization - Automatic CUDA detection for GPU acceleration

3. Persistent memory - Conversation continuity across sessions

4. Model version control - Pin specific model iterations for reproducibility

The hybrid web-native approach enables access from any device on the local network while maintaining enterprise-grade security. Memory management implements context window optimization, dynamically adjusting token allocation based on conversation length to prevent performance degradation.

This stack demonstrates how cutting-edge AI can be democratized through containerization, providing researchers and developers with a privacy-preserving alternative to cloud-based chatbots without sacrificing functionality. The modular design allows straightforward integration of future models from the DeepSeek family or other open-source LLMs supported by Ollama's growing ecosystem.