When to Use

User wants to convert text/images to vectors, build semantic search, or integrate embeddings into applications.

Quick Reference

Core Capabilities

1. Generate embeddings — Call provider APIs (OpenAI, Cohere, Voyage, local models)
2. Chunk content — Split documents with overlap, semantic boundaries, token limits
3. Store vectors — Insert into Pinecone, Weaviate, Qdrant, pgvector, Chroma
4. Similarity search — Query with top-k, filters, hybrid search
5. Batch processing — Handle large datasets with rate limiting and retries
6. Model comparison — Evaluate embedding quality for specific use cases

Decision Checklist

Before recommending approach, ask:

• What content type? (text, code, images, multimodal)
• Volume and update frequency?
• Latency requirements? (real-time vs batch)
• Budget constraints? (API costs vs self-hosted)
• Existing infrastructure? (cloud provider, database)

Critical Rules

• Same model everywhere — Query embeddings MUST use identical model as document embeddings
• Normalize before storage — Most similarity metrics assume unit vectors
• Chunk with overlap — 10-20% overlap prevents context loss at boundaries
• Batch API calls — Never embed one item at a time in production
• Cache embeddings — Regenerating is expensive; store with source hash
• Monitor dimensions — Higher isn't always better; 768-1536 is usually optimal

Provider Quick Selection

Common Patterns

# Batch embedding with retry
def embed_batch(texts, model="text-embedding-3-small"):
    results = []
    for chunk in batched(texts, 100):  # API limit
        response = client.embeddings.create(input=chunk, model=model)
        results.extend([e.embedding for e in response.data])
    return results

# Similarity search with filter
results = index.query(
    vector=query_embedding,
    top_k=10,
    filter={"category": "technical"},
    include_metadata=True
)