# PrivateGPT

> Production-ready AI project for private, local document Q&A using RAG. 100% private with no data leaving your environment, supporting offline operation with local LLMs and vector databases.

- **URL:** https://docs.privategpt.dev/
- **Category:** LLM Frameworks
- **Tags:** privacy, Local, Rag
- **Updated:** 2026-03-11 12:14
- **Canonical page:** https://vectordb.ever.works/items/privategpt

## Details

## 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

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