ollama--ollama
wehub-resource-sync
128 行
3.0 KiB
Plaintext
128 行
3.0 KiB
Plaintext
---
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title: Embeddings
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description: Generate text embeddings for semantic search, retrieval, and RAG.
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---
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Embeddings turn text into numeric vectors you can store in a vector database, search with cosine similarity, or use in RAG pipelines. The vector length depends on the model (typically 384–1024 dimensions).
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## Recommended models
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- [embeddinggemma](https://ollama.com/library/embeddinggemma)
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- [qwen3-embedding](https://ollama.com/library/qwen3-embedding)
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- [all-minilm](https://ollama.com/library/all-minilm)
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## Generate embeddings
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<Tabs>
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<Tab title="CLI">
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Generate embeddings directly from the command line:
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```shell
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ollama run embeddinggemma "Hello world"
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```
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You can also pipe text to generate embeddings:
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```shell
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echo "Hello world" | ollama run embeddinggemma
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```
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Output is a JSON array.
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</Tab>
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<Tab title="cURL">
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```shell
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curl -X POST http://localhost:11434/api/embed \
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-H "Content-Type: application/json" \
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-d '{
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"model": "embeddinggemma",
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"input": "The quick brown fox jumps over the lazy dog."
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}'
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```
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</Tab>
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<Tab title="Python">
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```python
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import ollama
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single = ollama.embed(
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model='embeddinggemma',
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input='The quick brown fox jumps over the lazy dog.'
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)
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print(len(single['embeddings'][0])) # vector length
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```
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</Tab>
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<Tab title="JavaScript">
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```javascript
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import ollama from 'ollama'
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const single = await ollama.embed({
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model: 'embeddinggemma',
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input: 'The quick brown fox jumps over the lazy dog.',
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})
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console.log(single.embeddings[0].length) // vector length
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```
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</Tab>
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</Tabs>
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<Note>
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The `/api/embed` endpoint returns L2‑normalized (unit‑length) vectors.
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</Note>
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## Generate a batch of embeddings
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Pass an array of strings to `input`.
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<Tabs>
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<Tab title="cURL">
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```shell
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curl -X POST http://localhost:11434/api/embed \
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-H "Content-Type: application/json" \
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-d '{
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"model": "embeddinggemma",
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"input": [
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"First sentence",
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"Second sentence",
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"Third sentence"
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]
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}'
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```
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</Tab>
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<Tab title="Python">
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```python
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import ollama
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batch = ollama.embed(
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model='embeddinggemma',
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input=[
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'The quick brown fox jumps over the lazy dog.',
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'The five boxing wizards jump quickly.',
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'Jackdaws love my big sphinx of quartz.',
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]
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)
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print(len(batch['embeddings'])) # number of vectors
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```
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</Tab>
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<Tab title="JavaScript">
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```javascript
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import ollama from 'ollama'
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const batch = await ollama.embed({
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model: 'embeddinggemma',
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input: [
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'The quick brown fox jumps over the lazy dog.',
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'The five boxing wizards jump quickly.',
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'Jackdaws love my big sphinx of quartz.',
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],
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})
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console.log(batch.embeddings.length) // number of vectors
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```
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</Tab>
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</Tabs>
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## Tips
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- Use cosine similarity for most semantic search use cases.
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- Use the same embedding model for both indexing and querying.
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