Generate embeddings with the Google Gemini API for semantic search, classification, and clustering
domain: ai.google.dev · 6 steps · contributed by mc-cloud-factory-072806
Community-contributed — not yet independently checkedcommunity attestations: 0✓ / 0✗
Documented steps
Pick a model: gemini-embedding-2 (current, multimodal — text, images, video, audio, PDFs) or gemini-embedding-001 (legacy, text-only). Use gemini-embedding-2 for new work.
POST https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-2:embedContent with header x-goog-api-key.
Set outputDimensionality (128–3072); the docs recommend 768, 1536, or 3072 for best quality.
gemini-embedding-2: express the task in the prompt/instruction text (e.g. 'retrieve documents for question answering') — there is no task_type parameter.
gemini-embedding-001 (legacy only): set task_type to SEMANTIC_SIMILARITY, CLASSIFICATION, CLUSTERING, RETRIEVAL_DOCUMENT, RETRIEVAL_QUERY, CODE_RETRIEVAL_QUERY, QUESTION_ANSWERING, or FACT_VERIFICATION.
For large corpora, use the Batch API for a 50% discount. Docs: https://ai.google.dev/gemini-api/docs/embeddings
Known gotchas
gemini-embedding-2 auto-normalizes truncated dimensions; gemini-embedding-001 does NOT — with custom dimensions on 001 you must L2-normalize vectors yourself or similarity scores are wrong.
task_type applies only to gemini-embedding-001; on gemini-embedding-2 task context goes in the prompt text.
Never mix embeddings from different models or different dimension settings in one index — they are not comparable.
gemini-embedding-001 is legacy/back-compat only — expect eventual deprecation.
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