docs: add reference to docs.ollama.com (#12800)

docs/capabilities/embeddings.mdx

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+---
+title: Embeddings
+description: Generate text embeddings for semantic search, retrieval, and RAG.
+---
+
+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).
+
+## Recommended models
+
+- [embeddinggemma](https://ollama.com/library/embeddinggemma)
+- [qwen3-embedding](https://ollama.com/library/qwen3-embedding)
+- [all-minilm](https://ollama.com/library/all-minilm)
+
+## Generate embeddings
+
+Use `/api/embed` with a single string.
+
+<Tabs>
+  <Tab title="cURL">
+    ```shell
+    curl -X POST http://localhost:11434/api/embed \
+      -H "Content-Type: application/json" \
+      -d '{
+        "model": "embeddinggemma",
+        "input": "The quick brown fox jumps over the lazy dog."
+      }'
+    ```
+  </Tab>
+  <Tab title="Python">
+    ```python
+    import ollama
+
+    single = ollama.embed(
+      model='embeddinggemma',
+      input='The quick brown fox jumps over the lazy dog.'
+    )
+    print(len(single['embeddings'][0]))  # vector length
+    ```
+  </Tab>
+  <Tab title="JavaScript">
+    ```javascript
+    import ollama from 'ollama'
+
+    const single = await ollama.embed({
+      model: 'embeddinggemma',
+      input: 'The quick brown fox jumps over the lazy dog.',
+    })
+    console.log(single.embeddings[0].length) // vector length
+    ```
+  </Tab>
+</Tabs>
+
+<Note>
+  The `/api/embed` endpoint returns L2‑normalized (unit‑length) vectors.
+</Note>
+
+## Generate a batch of embeddings
+
+Pass an array of strings to `input`.
+
+<Tabs>
+  <Tab title="cURL">
+    ```shell
+    curl -X POST http://localhost:11434/api/embed \
+      -H "Content-Type: application/json" \
+      -d '{
+        "model": "embeddinggemma",
+        "input": [
+          "First sentence",
+          "Second sentence",
+          "Third sentence"
+        ]
+      }'
+    ```
+  </Tab>
+  <Tab title="Python">
+    ```python
+    import ollama
+
+    batch = ollama.embed(
+      model='embeddinggemma',
+      input=[
+        'The quick brown fox jumps over the lazy dog.',
+        'The five boxing wizards jump quickly.',
+        'Jackdaws love my big sphinx of quartz.',
+      ]
+    )
+    print(len(batch['embeddings']))  # number of vectors
+    ```
+  </Tab>
+  <Tab title="JavaScript">
+    ```javascript
+    import ollama from 'ollama'
+
+    const batch = await ollama.embed({
+      model: 'embeddinggemma',
+      input: [
+        'The quick brown fox jumps over the lazy dog.',
+        'The five boxing wizards jump quickly.',
+        'Jackdaws love my big sphinx of quartz.',
+      ],
+    })
+    console.log(batch.embeddings.length) // number of vectors
+    ```
+  </Tab>
+</Tabs>
+
+## Tips
+
+- Use cosine similarity for most semantic search use cases.
+- Use the same embedding model for both indexing and querying.
+
+