PrivateGPT

Overview

PrivateGPT is a production-ready AI project that allows you to ask questions about your documents using Large Language Models (LLMs), even without Internet connectivity. 100% private - no data leaves your execution environment.

Core Architecture

PrivateGPT is a service that wraps AI RAG primitives in a comprehensive set of APIs, built on:

• FastAPI: RESTful API framework
• LlamaIndex: RAG orchestration framework
• Local LLMs: Offline language models
• Local Vector Stores: Private embedding storage

Privacy Guarantees

100% Private

• No Internet Required: Can run completely offline
• No Data Leakage: Data never leaves your infrastructure
• Local Processing: All computation happens locally
• Self-Hosted: Full control over deployment

Data Security

• Documents remain on your servers
• Embeddings stored locally
• No third-party API calls
• Compliance-ready for regulated industries

Local LLM Support

Ollama Integration

• Connect to local Ollama instance
• Simplifies local LLM installation
• Wide model selection
• Easy model management

LlamaCPP

• Direct LlamaCPP integration
• GGUF model format support
• CPU and GPU acceleration
• Memory-efficient inference

Vector Database Support

All supported vector stores run locally by default:

• Qdrant: High-performance vector search
• Milvus: Scalable vector database
• ChromaDB: Simple embedded vector store
• PostgreSQL: With pgvector extension

Document Processing

Chunking Strategy

• 500-token chunks (default with LangChain)
• Overlapping chunks for context preservation
• Configurable chunk size and overlap

Embedding Generation

• SentenceTransformers for embeddings
• Local embedding models
• No external API calls
• Multiple embedding model options

Vector Storage

• DuckDB integration (some configurations)
• Persistent vector storage
• Fast retrieval

RAG Pipeline

Ingestion Phase

1. Document loading and parsing
2. Text chunking
3. Local embedding generation
4. Vector database storage

Query Phase

1. Query embedding (local)
2. Vector similarity search
3. Context retrieval
4. LLM response generation (local)
5. Citation and source tracking

Key Features

• Production Ready: Battle-tested framework
• API-First: RESTful APIs for integration
• Customizable: Flexible configuration options
• Framework Support: Built on proven libraries
• Multiple Models: Support for various LLMs
• Document Types: PDF, TXT, DOCX, and more

Use Cases

• Enterprise Document Search: Private corporate knowledge bases
• Healthcare: HIPAA-compliant medical records search
• Legal: Confidential legal document analysis
• Financial Services: Sensitive financial data Q&A
• Government: Classified or restricted information
• Research: Private research document analysis
• Offline Environments: Air-gapped or disconnected systems

Deployment Options

Local Development

• Run on laptop or desktop
• Quick setup for testing
• Full feature access

On-Premise Servers

• Deploy on internal servers
• Scalable infrastructure
• Enterprise-grade deployment

Air-Gapped Environments

• Complete offline operation
• No internet dependency
• Secure isolated networks

Comparison

vs PrivateGPT vs LocalGPT

Both are excellent examples of running complete RAG pipelines locally:

• PrivateGPT: More production-ready, FastAPI-based
• LocalGPT: Similar concept, different implementation

vs Cloud-Based RAG

• Privacy: No data sent to external services
• Cost: No API fees for LLM calls
• Latency: Network latency eliminated
• Compliance: Easier regulatory compliance
• Offline: Works without internet

Technical Stack

• API Framework: FastAPI
• RAG Orchestration: LlamaIndex
• Embeddings: SentenceTransformers, local models
• LLMs: Ollama, LlamaCPP, local models
• Vector DBs: Qdrant, Milvus, ChromaDB, PostgreSQL
• Document Parsing: Unstructured, custom parsers

Integration Points

• RESTful API endpoints
• Python SDK
• Custom integrations via API
• Document upload endpoints
• Query endpoints
• Configuration management

Performance Considerations

• Hardware Requirements: CPU/GPU for local LLM inference
• Memory: Depends on model size and document volume
• Storage: Vector database and document storage
• Scalability: Horizontal scaling possible

Pricing

Free and open-source:

• GitHub: zylon-ai/private-gpt
• No licensing fees
• Community support
• Self-hosted infrastructure costs only