> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.privategpt.dev/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.privategpt.dev/_mcp/server.

# Embeddings

> Generate vector embeddings from text for semantic search, clustering, and similarity.

The Embeddings API (`POST /v1/embeddings`) converts text into high-dimensional vectors. Use them to build your own semantic search, clustering pipelines, or similarity scoring outside of PrivateGPT's built-in retrieval.

---

## Single input

```bash
curl -X POST http://localhost:8080/v1/embeddings \
  -H "Content-Type: application/json" \
  -d '{
    "model": "mxbai-embed-large",
    "input": "The quick brown fox jumps over the lazy dog."
  }'
```

Response:

```json
{
  "data": [
    {"index": 0, "embedding": [0.021, -0.013, ...], "object": "embedding"}
  ],
  "model": "mxbai-embed-large",
  "usage": {"input_tokens": 12, "total_tokens": 12}
}
```

---

## Batch input

Pass an array of strings to embed multiple texts in one request. The response preserves input order — `data[i]` corresponds to `input[i]`:

```bash
curl -X POST http://localhost:8080/v1/embeddings \
  -H "Content-Type: application/json" \
  -d '{
    "model": "mxbai-embed-large",
    "input": [
      "First document text",
      "Second document text",
      "Third document text"
    ]
  }'
```

---

## Choosing a model

The `model` field must match the name of an embedding model registered in your PrivateGPT instance. Use `GET /v1/models` to list available models and their types.

For consistent similarity results, always use the same model to embed both your corpus and your queries. Mixing models produces incomparable vectors.