ragtime

ragtime

Self-Hosted RAG with Mistral and Milvus

This project provides a complete, local RAG (Retrieval-Augmented Generation) environment using Mistral 7B LLM and Milvus vector database, all running in Docker containers. It’s designed for developers who want to experiment with RAG without relying on external APIs or cloud services.

Why Local RAG?

Running RAG locally offers several advantages:

Privacy: Your data never leaves your machine—critical for sensitive documents Cost: No per-token API charges from commercial LLM providers Customization: Full control over the model and retrieval pipeline Learning: Understand how RAG actually works under the hood Offline Capability: No internet dependency once configured

The Stack

Mistral 7B

• Open-source LLM with strong performance
• Efficient enough to run on consumer hardware
• Competitive with larger proprietary models

Milvus Vector Database

• Purpose-built for similarity search at scale
• High-performance vector indexing
• Flexible filtering and querying capabilities

Docker Containerization

• Reproducible environment
• Easy setup and teardown
• Isolated from host system

RAG Fundamentals

This project demonstrates the core RAG workflow:

1. Document Ingestion: Load and chunk source documents
2. Embedding Generation: Convert text chunks to vector representations
3. Vector Storage: Index embeddings in Milvus for fast retrieval
4. Query Processing: Convert user questions to vector embeddings
5. Similarity Search: Retrieve relevant document chunks
6. Context Augmentation: Feed retrieved context to Mistral 7B
7. Response Generation: Generate grounded, contextual answers

Use Cases

Personal Knowledge Base

• Query your own documents, notes, research papers
• Build a searchable archive of technical documentation
• Create a personal assistant for your data

Prototype Development

• Test RAG architectures before committing to cloud infrastructure
• Experiment with chunking strategies and retrieval parameters
• Benchmark embedding models and generation quality

Educational Projects

• Learn RAG implementation details
• Understand vector similarity search
• Practice LLM application development

Technical Learning Path

Working with this project teaches:

• Docker multi-container orchestration
• Vector database operations
• Embedding model integration
• LLM inference and prompt engineering
• RAG pipeline architecture

The Value of Self-Hosting

As RAG becomes a standard pattern for LLM applications, understanding how to build and deploy these systems independently—without reliance on commercial APIs—becomes an increasingly valuable skill.

This project provides a complete, working reference implementation that you can modify, extend, and deploy according to your specific needs.

Perfect for developers who want to truly understand RAG rather than just consume it as a service.