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
- Document loading and parsing
- Text chunking
- Local embedding generation
- Vector database storage
Query Phase
- Query embedding (local)
- Vector similarity search
- Context retrieval
- LLM response generation (local)
- 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
- 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
