Building Privacy-First Local AI Systems

In an era where data privacy is paramount, building AI systems that run entirely on your local machine offers both security and control. This post explores the architecture behind Project Aeon, my local AI assistant that never sends your data to external servers.

The Privacy Problem

Traditional AI assistants like ChatGPT, Claude, and Gemini are powerful but come with privacy trade-offs:

• Your conversations are sent to external servers
• Data might be used for training (unless explicitly opted out)
• Requires internet connection
• Subject to service outages and rate limits

The Solution: Local-First Architecture

Project Aeon uses a completely local stack:

┌─────────────┐
│  Vue 3 UI   │
└──────┬──────┘
       │
┌──────▼──────┐
│   FastAPI   │ ◄─── Local API Server
└──────┬──────┘
       │
   ┌───┴────┐
   │        │
┌──▼──┐  ┌─▼────┐
│Ollama│  │ChromaDB│ ◄─── Vector Database
└──────┘  └───────┘

Core Components

1. FastAPI Backend

FastAPI provides a modern, async Python framework perfect for AI applications:

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import chromadb
from sentence_transformers import SentenceTransformer

app = FastAPI()
chroma_client = chromadb.Client()
embedding_model = SentenceTransformer('all-MiniLM-L6-v2')

class QueryRequest(BaseModel):
    query: str
    n_results: int = 5

@app.post("/semantic-search")
async def search(request: QueryRequest):
    try:
        # Generate query embedding
        query_embedding = embedding_model.encode([request.query])

        # Search vector database
        collection = chroma_client.get_collection("knowledge_base")
        results = collection.query(
            query_embeddings=query_embedding.tolist(),
            n_results=request.n_results
        )

        return {"results": results}
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

2. ChromaDB for Semantic Search

ChromaDB enables Retrieval-Augmented Generation (RAG) by storing and retrieving relevant context:

import chromadb
from chromadb.config import Settings

# Initialize ChromaDB with persistent storage
client = chromadb.Client(Settings(
    chroma_db_impl="duckdb+parquet",
    persist_directory="./chroma_data"
))

# Create collection
collection = client.create_collection(
    name="knowledge_base",
    metadata={"description": "Personal knowledge base"}
)

# Add documents
collection.add(
    documents=["Document content here..."],
    metadatas=[{"source": "notes.md", "date": "2025-10-25"}],
    ids=["doc1"]
)

3. Local LLM with Ollama

Ollama makes running LLMs locally simple:

import requests

def generate_response(prompt: str, context: list[str]) -> str:
    # Construct prompt with retrieved context
    full_prompt = f"""Context:
{chr(10).join(context)}

Question: {prompt}

Answer based on the context above:"""

    # Call local Ollama instance
    response = requests.post(
        "http://localhost:11434/api/generate",
        json={
            "model": "llama2",
            "prompt": full_prompt,
            "stream": False
        }
    )

    return response.json()["response"]

Benefits of This Approach

Complete Privacy

• Zero data leaves your machine
• No telemetry or tracking
• Full control over your information

Offline Capability

• Works without internet
• No dependency on external services
• Consistent performance

Customization

• Fine-tune models on your data
• Customize system prompts
• Integrate with local tools and workflows

Cost-Effective

• No API costs
• One-time hardware investment
• Unlimited usage

Performance Considerations

Running AI locally requires adequate hardware:

Minimum Requirements

• CPU: Modern multi-core processor (8+ cores recommended)
• RAM: 16GB minimum, 32GB recommended
• Storage: NVMe SSD for fast embeddings retrieval
• GPU: Optional but highly recommended (8GB+ VRAM)

Optimization Techniques

1. Model Quantization: Use 4-bit or 8-bit quantized models
2. Batch Processing: Process multiple queries together
3. Caching: Cache frequently accessed embeddings
4. Streaming: Stream responses for better UX

Real-World Use Cases

I use Project Aeon for:

• Code review and refactoring suggestions
• Research assistance with my notes and papers
• Learning new technologies with personalized explanations
• Documentation search across my projects

Next Steps

Future enhancements planned:

• Multi-modal support (images, audio)
• Integration with development tools
• Automated knowledge base updates
• Fine-tuning on domain-specific data

Conclusion

Building local AI systems is more accessible than ever. With tools like FastAPI, ChromaDB, and Ollama, you can create powerful, privacy-preserving AI assistants tailored to your needs.

The future of AI doesn’t have to mean sacrificing privacy. By running models locally, we can have both powerful AI capabilities and complete control over our data.

---

Want to learn more? The Project Aeon repository is coming soon. Feel free to reach out on Twitter for updates.