Embeddings API Guide: Vector Search, RAG, Recommendations

Embeddings turn text into vectors that software can compare. They are the foundation for semantic search, recommendations, clustering, deduplication, and many RAG systems.

Choosing an embeddings API is a product and infrastructure decision, not just a model choice.

Common use cases

Embeddings are useful for:

semantic search
FAQ matching
product recommendations
support ticket routing
duplicate detection
document clustering
RAG retrieval
personalization

They help systems find meaning, not just matching keywords.

Embeddings and RAG

In RAG, embeddings are used to find relevant chunks before sending context to an LLM.

The pipeline usually looks like:

1. Split documents into chunks. 2. Create embeddings for each chunk. 3. Store vectors in a database. 4. Embed the user query. 5. Retrieve similar chunks. 6. Send selected context to the model.

Retrieval quality strongly affects final answer quality.

Cost factors

Embedding cost depends on:

document volume
update frequency
query volume
chunk size
duplicate content
model price
vector database cost

Deduplicate content before embedding whenever possible.

Evaluation

Test embeddings with real search queries. Measure:

top-k relevance
recall
precision
click-through
answer quality in RAG
latency

Do not evaluate only with synthetic examples.

Final thoughts

Embeddings are one of the highest-leverage parts of AI infrastructure. Better embeddings and retrieval can reduce LLM cost, improve answer quality, and make AI features more reliable.