Code Examples - Real-World Implementations

AI / ML

RAG Backend

Build a complete Retrieval-Augmented Generation system with HeliosDB as the vector store. Chunk documents, generate embeddings, store them with HNSW + Product Quantization, and retrieve context for LLM generation.

HNSW index with Product Quantization for 8-16x compression
Hybrid queries: combine vector similarity with SQL filters
Works with OpenAI, Cohere, or any embedding model
Sub-millisecond retrieval at scale

Python

class RAGBackend:
    def search(self, query: str, limit=5):
        embedding = self.embed(query)
        return requests.post(
            f"{self.url}/api/v1/vectors/search",
            json={
                "table": "documents",
                "column": "embedding",
                "query_vector": embedding,
                "limit": limit,
                "metric": "cosine"
            }
        ).json()["data"]["results"]

    def answer(self, question: str):
        context = self.search(question)
        return openai.chat.completions.create(
            model="gpt-4",
            messages=[
                {"role": "system",
                 "content": f"Context:\n{context}"},
                {"role": "user",
                 "content": question}
            ]
        )

AI Agents

AI Agent Memory

Implement persistent memory for AI agents with automatic context summarization. Store conversation history, track token counts, and auto-summarize long sessions to stay within context windows.

Session-scoped conversation history
Automatic token counting per message
Summarize when context exceeds thresholds
Built-in REST API — no custom tables needed

TypeScript

const memory = new AgentMemory(url, token);

// Start conversation
const session = await memory.createSession(
    "support-bot", "user-123"
);

// Store messages
await memory.addMessage(session, "user",
    "How do I reset my password?");
await memory.addMessage(session, "assistant",
    "To reset your password...");

// Get context for next response
const history = await memory.getHistory(session);

// Auto-summarize long conversations
await memory.summarizeIfNeeded(session,
    4000 /* max tokens */);

SaaS

Multi-Tenant SaaS

Build a SaaS platform with automatic tenant isolation using Row-Level Security. Each tenant sees only their own data, enforced at the database level — no application-level filtering required.

Row-Level Security policies enforce isolation automatically
Tenant-scoped JWT tokens via the auth API
Per-tenant resource quotas and connection limits
Zero risk of cross-tenant data leakage

SQL + Python

-- Row-Level Security Policy
CREATE POLICY tenant_isolation
ON projects
USING (tenant_id = current_setting(
    'app.tenant_id')::uuid);
ALTER TABLE projects
    ENABLE ROW LEVEL SECURITY;

# Each tenant only sees their own data
acme_token = db.get_tenant_token(acme_id)
db.query_as_tenant(acme_token,
    "SELECT * FROM projects")
# Returns: only Acme's projects

startup_token = db.get_tenant_token(startup_id)
db.query_as_tenant(startup_token,
    "SELECT * FROM projects")
# Returns: only Startup's projects

Search

Semantic Product Search

Implement natural language product search by combining vector similarity with traditional SQL filters. Users search by intent, not keywords — "headphones for working from home" finds noise-canceling headphones even without exact keyword matches.

Hybrid queries: vector similarity + price/category filters
Works with sentence-transformers, OpenAI, or any embedding model
HNSW index for sub-millisecond similarity search
Product Quantization reduces vector storage 8-16x

SQL

-- Semantic search with filters
SELECT name, price,
       embedding <=> $query_vec AS distance
FROM products
WHERE category = 'Electronics'
  AND price < 100
ORDER BY embedding <=> $query_vec
LIMIT 10;

-- Create optimized index
CREATE INDEX products_emb_idx
ON products
USING hnsw (embedding vector_cosine_ops)
WITH (m = 16, ef_construction = 200);

Analytics

Event Analytics

Track user behavior with time-series event storage and aggregate with SQL. JSONB properties support flexible event schemas, and time-travel queries let you analyze historical state without separate audit tables.

JSONB properties for flexible event payloads
Time-series aggregation with date_trunc and window functions
Time-travel queries: AS OF TIMESTAMP for historical analysis
Indexed by user + timestamp for efficient range scans

TypeScript

// Track events
await tracker.track("user-123", "page_view",
    { page: "/products", referrer: "google" });
await tracker.track("user-123", "purchase",
    { order_id: "ord-789", total: 29.99 });

// Daily aggregation
const metrics = await query(`
  SELECT date_trunc('day', timestamp) AS period,
         COUNT(*) AS events,
         COUNT(DISTINCT user_id) AS users
  FROM events
  WHERE event_type = 'purchase'
    AND timestamp >= NOW() - INTERVAL '30 days'
  GROUP BY period
  ORDER BY period DESC
`);

DevOps

Feature Flags

Implement database-backed feature flags with percentage-based rollouts, user targeting, and plan-based access control. Deterministic bucket assignment ensures consistent flag evaluation per user.

Boolean flags with percentage-based rollouts
Target specific users or subscription plans
Deterministic hashing for consistent evaluation
Cacheable with instant invalidation on change