Text Embeddings

In practice, generating a text embedding is a single API call — send text in, get a vector back. The complexity is mostly in what you do with that vector afterward, not in generating it.

Basic Usage (Conceptual)

# Conceptual — real syntax differs by provider
embedding = embedding_client.embed(
    model="embedding-model-name",
    text="How do I reset my password?"
)
print(embedding.vector)        # [0.021, -0.184, 0.093, ...]
print(len(embedding.vector))   # e.g. 1536 — the dimensionality

Embedding Multiple Pieces of Text (Batch)

texts = [
    "How do I reset my password?",
    "What's your refund policy?",
    "How do I cancel my subscription?"
]
embeddings = embedding_client.embed_batch(model="...", texts=texts)
# Returns one vector per input text — batching is typically more
# efficient than embedding one text at a time in a loop

What Text Embeddings Are Used For

• Semantic search — finding relevant documents by meaning (see Embedding Search)
• RAG retrieval — the core mechanism behind fetching relevant context (see RAG)
• Clustering/deduplication — grouping similar support tickets, articles, or feedback
• Recommendation — finding items similar to what a user engaged with before

Embedding Text Is Cheap and Fast Compared to Generation

Generating an embedding doesn't involve autoregressive token-by-token generation the way LLM text generation does (see LLM Inference) — it's typically a single forward pass producing one fixed-size vector, meaningfully faster and cheaper per call than a full generative response.

Practical Use Case

A support-documentation search feature embeds every article once (during ingestion), stores the vectors, then embeds each incoming user query at search time and compares against the stored vectors — the expensive part (embedding the whole document set) happens once, not on every search.

Common Mistakes

• Re-embedding the same static content on every request instead of embedding once and storing/reusing the result
• Mixing embeddings from two different models in the same comparison — vectors from different models aren't guaranteed to be comparable, even if they happen to have the same dimensionality

Interview Relevance

"Why is embedding a document once and storing the vector better than re-embedding it on every search?" — cost and latency: embedding is a real (if cheap) computation, and static content's meaning doesn't change between searches.

Practice Question

Design the ingestion flow (in plain steps) for embedding and storing 10,000 support articles for later semantic search.

Related Notes

• Embedding Models
• Embedding Search
• Vector Database