gemini-embedding-001
Google 的向量模型，用于将输入内容转换为数值向量，支持语义检索和相似度计算。
- Starting price
- Input / Output · 1M
- Context
- 2K
- Maximum input window
- Modalities
- →
- Released
- Jul 2025
Pricing by Supplier
Capabilities / Supported modalities
Provider & data privacy
SentencePiece (Gemini)Performance
About gemini-embedding-001
Google 的向量模型，用于将输入内容转换为数值向量，支持语义检索和相似度计算。
Use cases and prompting
Starting points for evaluation; supported inputs and options are listed in API access.
Use cases to explore
- Evaluate semantic search with representative queries and documents.
- Compare retrieval quality on your own knowledge base before connecting a downstream assistant.
Practical tips
Check whether the endpoint returns embeddings or reranks documents. Keep indexing and query preprocessing consistent.
API access
Code samples
/v1beta/models/gemini-embedding-001:generateContent| Parameter | Type | Default / range | Description |
|---|---|---|---|
inputrequired | string | — | Text or array of texts to embed |
dimensions | integer | >= 1 | Truncate embeddings to this many dimensions |
encoding_format | enum | = float | Wire encoding for the embedding vectors |
user | string | — | End-user identifier for abuse monitoring |
Replace <YOUR_API_KEY> with the API key from your token settings.
Authentication
All requests must include Authorization: Bearer <TOKEN> header. Anthropic-formatted endpoints accept the x-api-key header instead.
Generate tokens from the Tokens page; you can scope them to specific models, groups, IPs, and rate-limits.
Supported parameters
| Parameter | Type | Default / range | Description |
|---|---|---|---|
inputrequired | string | — | Text or array of texts to embed |
dimensions | integer | >= 1 | Truncate embeddings to this many dimensions |
encoding_format | enum | = float | Wire encoding for the embedding vectors |
user | string | — | End-user identifier for abuse monitoring |
Rate limits
| Supplier | RPM | TPM | RPD |
|---|---|---|---|
| Google AI Studio | 3.4K | 685K | 69K |
| Google Vertex | 3.9K | 772K | 77K |
RPM = requests per minute, TPM = tokens per minute, RPD = requests per day. Limits apply per token group.
Frequently asked questions about gemini-embedding-001
What is gemini-embedding-001?
Google 的向量模型，用于将输入内容转换为数值向量，支持语义检索和相似度计算。
How do I call gemini-embedding-001?
Create an API key with access to gemini-embedding-001, then use the exact model ID and a supported endpoint from the API access section. Request fields depend on the selected endpoint.
How is gemini-embedding-001 priced?
Pricing depends on the selected provider group and the model billing unit. The current input, output, request, or media prices are shown on this page before sign-up.
What is the context window of gemini-embedding-001?
The model catalog lists a context window of 2048 tokens. Check the selected endpoint for request limits.
How should I evaluate gemini-embedding-001 for my project?
Check whether the endpoint returns embeddings or reranks documents. Keep indexing and query preprocessing consistent.