diff --git a/.github/workflows/python-tests.yml b/.github/workflows/python-tests.yml
index ba17c1842..9619c5225 100644
--- a/.github/workflows/python-tests.yml
+++ b/.github/workflows/python-tests.yml
@@ -57,7 +57,7 @@ jobs:
         with:
           python-version: ${{ matrix.python-version }}
       - name: Install dependencies
-        run: uv sync --locked --no-group dev --no-group docs
+        run: uv sync --locked --no-group dev --no-group docs --extra server
 
       - name: Run pytest
         env:
diff --git a/.github/workflows/type-checkers.yml b/.github/workflows/type-checkers.yml
index c14c6d450..be9e4689e 100644
--- a/.github/workflows/type-checkers.yml
+++ b/.github/workflows/type-checkers.yml
@@ -25,7 +25,7 @@ jobs:
           python-version: ${{ matrix.python-version }}
 
       - name: Install dependencies
-        run: uv sync --locked --no-default-groups --group types
+        run: uv sync --locked --no-default-groups --group types --extra server
 
       - name: mypy
         run: |
@@ -36,4 +36,4 @@ jobs:
 
       - name: pyright
         run: |
-          uv run --no-sync pyright tests/type_stub.py
\ No newline at end of file
+          uv run --no-sync pyright tests/type_stub.py
diff --git a/.github/workflows/weekly-tests.yml b/.github/workflows/weekly-tests.yml
index 1d7b177db..4b4392561 100644
--- a/.github/workflows/weekly-tests.yml
+++ b/.github/workflows/weekly-tests.yml
@@ -35,7 +35,7 @@ jobs:
         with:
           python-version: '3.14'
       - name: Install dependencies
-        run: uv sync --locked --no-group dev --no-group docs
+        run: uv sync --locked --no-group dev --no-group docs --extra server
 
       - name: Run pytest
         # explicit bash runs with pipefail, so tee keeps pytest's exit code
diff --git a/README.md b/README.md
index 206915a03..193c37dd9 100644
--- a/README.md
+++ b/README.md
@@ -24,6 +24,9 @@ pip install fastembed
 pip install fastembed-gpu
 ```
 
+To run a local HTTP embedding service, install `fastembed[server]` and run
+`fastembed serve`. See the [embedding server guide](docs/embedding_server.md).
+
 ## 📖 Quickstart
 
 ```python
diff --git a/docs/embedding_server.md b/docs/embedding_server.md
new file mode 100644
index 000000000..69ea63514
--- /dev/null
+++ b/docs/embedding_server.md
@@ -0,0 +1,60 @@
+# Local embedding server
+
+Install the optional server dependencies and start a text embedding model.
+
+```bash
+pip install 'fastembed[server]'
+fastembed serve --model-name BAAI/bge-small-en-v1.5
+```
+
+`python -m fastembed serve` starts the same server. The default address is
+`127.0.0.1` and the default port is `8080`. The model is loaded once before
+the server starts. Omitting `--model-name` uses the library's default model.
+
+Use `--model-path /path/to/model-directory` to load existing model files without
+downloading them. The directory must contain the model and tokenizer files
+expected by the selected supported model. A standalone ONNX file is insufficient.
+The model name still selects the architecture and preprocessing.
+
+`--device cpu` is the default. `--device cuda` requires an ONNX Runtime installation
+with the CUDA execution provider and the corresponding CUDA libraries.
+`--cache-dir`, `--threads` and `--batch-size` control the model cache, ONNX Runtime
+threads and inference batch size. `fastembed serve --help` lists all options.
+
+## Requests
+
+```bash
+curl http://127.0.0.1:8080/v1/embeddings \
+  -H 'Content-Type: application/json' \
+  -d '{"model":"BAAI/bge-small-en-v1.5","input":["A local embedding","Another sentence"]}'
+```
+
+The response contains an ordered `data` list with an embedding and index for each
+input, the loaded model name and token usage from the model's tokenizer after
+truncation, including special tokens.
+`GET /v1/models` lists the loaded model and `GET /health` reports server readiness.
+
+The endpoint accepts a nonempty string or a list of up to 2048 nonempty strings.
+Token ID inputs are unsupported. Text is tokenized and truncated using the model's
+normal settings. `encoding_format` accepts `float` or `base64`. `dimensions` may
+only specify the model's native embedding size. An omitted `model` uses the loaded
+model, and requesting a different model returns an error without loading it.
+Requests run through `embed`, with the same preprocessing as the Python library.
+
+An OpenAI client can use the local endpoint with a placeholder key.
+
+```python
+from openai import OpenAI
+
+client = OpenAI(base_url="http://127.0.0.1:8080/v1", api_key="local")
+result = client.embeddings.create(
+    model="BAAI/bge-small-en-v1.5", input=["A local embedding"]
+)
+print(result.data[0].embedding)
+```
+
+The server does not implement authentication. Keep the default loopback binding
+for local use. To share it, configure access control with a reverse proxy before
+binding to an external interface with `--host`. Inference uses a worker thread
+and is serialized per model to avoid concurrent tokenizer mutation. Disconnecting
+a client does not cancel queued or running inference.
diff --git a/fastembed/__main__.py b/fastembed/__main__.py
new file mode 100644
index 000000000..000b95224
--- /dev/null
+++ b/fastembed/__main__.py
@@ -0,0 +1,4 @@
+from fastembed.cli import main
+
+if __name__ == "__main__":
+    main()
diff --git a/fastembed/cli.py b/fastembed/cli.py
new file mode 100644
index 000000000..529f45b64
--- /dev/null
+++ b/fastembed/cli.py
@@ -0,0 +1,62 @@
+import argparse
+from collections.abc import Sequence
+from pathlib import Path
+from typing import Any
+
+
+def _positive_int(value: str) -> int:
+    number = int(value)
+    if number <= 0:
+        raise argparse.ArgumentTypeError("must be a positive integer")
+    return number
+
+
+def main(argv: Sequence[str] | None = None) -> None:
+    parser = argparse.ArgumentParser(prog="fastembed")
+    commands = parser.add_subparsers(dest="command", required=True)
+    serve = commands.add_parser("serve", help="Serve local text embeddings over HTTP")
+    serve.add_argument(
+        "--model-name", help="Supported model name, using the library default if omitted"
+    )
+    serve.add_argument(
+        "--model-path", type=Path, help="Local directory containing the model files"
+    )
+    serve.add_argument("--device", choices=("cpu", "cuda"), default="cpu")
+    serve.add_argument("--host", default="127.0.0.1")
+    serve.add_argument("--port", type=_positive_int, default=8080)
+    serve.add_argument("--threads", type=_positive_int, help="ONNX Runtime threads")
+    serve.add_argument("--batch-size", type=_positive_int, default=256)
+    serve.add_argument("--cache-dir", help="Model cache directory")
+    args = parser.parse_args(argv)
+    if args.port > 65535:
+        parser.error("port must be between 1 and 65535")
+    if args.model_path is not None and not args.model_path.is_dir():
+        parser.error("model-path must be an existing directory containing the model files")
+
+    try:
+        import uvicorn
+
+        from fastembed.server import create_app
+    except ImportError as exc:
+        parser.exit(
+            1, f"Server dependencies are unavailable. Install 'fastembed[server]'.\n{exc}\n"
+        )
+
+    from fastembed import TextEmbedding
+    from fastembed.common.types import Device
+
+    options: dict[str, Any] = {
+        "cuda": Device(args.device),
+        "cache_dir": args.cache_dir,
+        "threads": args.threads,
+    }
+    if args.model_name is not None:
+        options["model_name"] = args.model_name
+    if args.model_path is not None:
+        options["specific_model_path"] = str(args.model_path)
+    try:
+        model = TextEmbedding(**options)
+        app = create_app(model, batch_size=args.batch_size)
+    except Exception as exc:
+        parser.exit(1, f"Unable to load the embedding model.\n{exc}\n")
+    uvicorn.run(app, host=args.host, port=args.port)
diff --git a/fastembed/server.py b/fastembed/server.py
new file mode 100644
index 000000000..5756c4793
--- /dev/null
+++ b/fastembed/server.py
@@ -0,0 +1,105 @@
+"""Optional HTTP interface for a single local text embedding model."""
+
+import base64
+from threading import Lock
+from typing import Annotated, Any, Literal
+
+import numpy as np
+from fastapi import FastAPI, Request
+from fastapi.exceptions import RequestValidationError
+from fastapi.responses import JSONResponse
+from loguru import logger
+from pydantic import BaseModel, ConfigDict, Field, StrictStr
+
+from fastembed.text.text_embedding_base import TextEmbeddingBase
+
+TextInput = Annotated[StrictStr, Field(min_length=1)]
+
+
+class EmbeddingsRequest(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    input: TextInput | Annotated[list[TextInput], Field(min_length=1, max_length=2048)]
+    model: StrictStr | None = None
+    encoding_format: Literal["float", "base64"] = "float"
+    dimensions: Annotated[int, Field(strict=True, gt=0)] | None = None
+    user: StrictStr | None = None
+
+
+def _error(
+    message: str,
+    status: int = 400,
+    param: str | None = None,
+    error_type: str = "invalid_request_error",
+) -> JSONResponse:
+    return JSONResponse(
+        status_code=status,
+        content={"error": {"message": message, "type": error_type, "param": param, "code": None}},
+    )
+
+
+def create_app(model: TextEmbeddingBase, *, batch_size: int = 256) -> FastAPI:
+    """Serve an initialized model. Inference is serialized outside the ASGI event loop."""
+    if batch_size <= 0:
+        raise ValueError("batch_size must be positive")
+    app = FastAPI(title="FastEmbed")
+    lock = Lock()
+    embedding_size = model.embedding_size
+
+    @app.exception_handler(RequestValidationError)
+    async def validation_error(request: Request, exc: RequestValidationError) -> JSONResponse:
+        # Validation details can contain the original text. Return only the field name.
+        location = exc.errors()[0]["loc"]
+        param = location[1] if len(location) > 1 and isinstance(location[1], str) else None
+        return _error("Invalid embedding request.", param=param)
+
+    @app.get("/health")
+    async def health() -> dict[str, str]:
+        return {"status": "ok"}
+
+    @app.get("/v1/models")
+    async def models() -> dict[str, Any]:
+        return {
+            "object": "list",
+            "data": [
+                {"id": model.model_name, "object": "model", "created": 0, "owned_by": "fastembed"}
+            ],
+        }
+
+    @app.post("/v1/embeddings", response_model=None)
+    def embeddings(body: EmbeddingsRequest) -> dict[str, Any] | JSONResponse:
+        if body.model is not None and body.model != model.model_name:
+            return _error("The requested model is not loaded.", status=404, param="model")
+        if body.dimensions is not None and body.dimensions != embedding_size:
+            return _error(
+                "Only the model's native embedding dimensions are supported.", param="dimensions"
+            )
+        inputs = [body.input] if isinstance(body.input, str) else body.input
+        try:
+            with lock:
+                vectors = list(model.embed(inputs, batch_size=batch_size))
+                tokens = model.token_count(inputs)
+            if len(vectors) != len(inputs):
+                raise ValueError("The model returned an unexpected number of embeddings")
+            data = []
+            for index, vector in enumerate(vectors):
+                values = np.asarray(vector, dtype="<f4")
+                if values.shape != (embedding_size,) or not np.isfinite(values).all():
+                    raise ValueError("The model returned an invalid embedding")
+                encoded = (
+                    base64.b64encode(values.tobytes()).decode("ascii")
+                    if body.encoding_format == "base64"
+                    else values.tolist()
+                )
+                data.append({"object": "embedding", "index": index, "embedding": encoded})
+            return {
+                "object": "list",
+                "data": data,
+                "model": model.model_name,
+                "usage": {"prompt_tokens": tokens, "total_tokens": tokens},
+            }
+        except Exception:
+            logger.exception("Embedding generation failed")
+            return _error("Embedding generation failed.", status=500, error_type="server_error")
+
+    return app
diff --git a/pyproject.toml b/pyproject.toml
index 993080659..a911aab83 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -42,10 +42,21 @@ dependencies = [
 Homepage = "https://github.com/qdrant/fastembed"
 Repository = "https://github.com/qdrant/fastembed"
 
+[project.scripts]
+fastembed = "fastembed.cli:main"
+
+[project.optional-dependencies]
+server = [
+    "fastapi>=0.115,<1",
+    "pydantic>=2,<3",
+    "uvicorn>=0.30,<1",
+]
+
 [dependency-groups]
 test = [
     "pytest>=7.4.2,<10.0.0",
     "ruff>=0.3.1,<1.0",
+    "httpx>=0.27,<1",
 ]
 dev = [
     "notebook>=7.0.2",
diff --git a/tests/test_cli.py b/tests/test_cli.py
new file mode 100644
index 000000000..570c28497
--- /dev/null
+++ b/tests/test_cli.py
@@ -0,0 +1,92 @@
+from pathlib import Path
+from unittest.mock import Mock
+
+import pytest
+
+from fastembed.cli import main
+from fastembed.common.types import Device
+
+
+@pytest.fixture(autouse=True)
+def server_dependencies() -> None:
+    pytest.importorskip("fastapi")
+    pytest.importorskip("uvicorn")
+
+
+def test_serve_loads_one_model(monkeypatch: pytest.MonkeyPatch, tmp_path: Path) -> None:
+    model = Mock()
+    factory = Mock(return_value=model)
+    create_app = Mock()
+    run = Mock()
+    monkeypatch.setattr("fastembed.TextEmbedding", factory)
+    monkeypatch.setattr("fastembed.server.create_app", create_app)
+    monkeypatch.setattr("uvicorn.run", run)
+    main(
+        [
+            "serve",
+            "--model-name",
+            "test-model",
+            "--model-path",
+            str(tmp_path),
+            "--device",
+            "cuda",
+            "--host",
+            "0.0.0.0",
+            "--port",
+            "9000",
+            "--threads",
+            "2",
+            "--batch-size",
+            "16",
+            "--cache-dir",
+            str(tmp_path / "cache"),
+        ]
+    )
+    factory.assert_called_once_with(
+        model_name="test-model",
+        specific_model_path=str(tmp_path),
+        cuda=Device.CUDA,
+        cache_dir=str(tmp_path / "cache"),
+        threads=2,
+    )
+    create_app.assert_called_once_with(model, batch_size=16)
+    run.assert_called_once_with(create_app.return_value, host="0.0.0.0", port=9000)
+
+
+def test_serve_defaults(monkeypatch: pytest.MonkeyPatch) -> None:
+    factory, create_app, run = Mock(), Mock(), Mock()
+    monkeypatch.setattr("fastembed.TextEmbedding", factory)
+    monkeypatch.setattr("fastembed.server.create_app", create_app)
+    monkeypatch.setattr("uvicorn.run", run)
+    main(["serve"])
+    factory.assert_called_once_with(cuda=Device.CPU, cache_dir=None, threads=None)
+    run.assert_called_once_with(create_app.return_value, host="127.0.0.1", port=8080)
+
+
+@pytest.mark.parametrize(
+    "args",
+    [
+        [],
+        ["serve", "--port", "0"],
+        ["serve", "--port", "65536"],
+        ["serve", "--batch-size", "-1"],
+        ["serve", "--threads", "0"],
+        ["serve", "--device", "invalid"],
+        ["serve", "--model-path", "missing-directory"],
+    ],
+)
+def test_invalid_arguments(args: list[str]) -> None:
+    with pytest.raises(SystemExit) as exc:
+        main(args)
+    assert exc.value.code == 2
+
+
+def test_model_load_error(monkeypatch: pytest.MonkeyPatch, capsys: pytest.CaptureFixture) -> None:
+    monkeypatch.setattr("fastembed.TextEmbedding", Mock(side_effect=ValueError("invalid model")))
+    run = Mock()
+    monkeypatch.setattr("uvicorn.run", run)
+    with pytest.raises(SystemExit) as exc:
+        main(["serve", "--model-name", "not-supported"])
+    assert exc.value.code == 1
+    assert "Unable to load the embedding model" in capsys.readouterr().err
+    run.assert_not_called()
diff --git a/tests/test_server.py b/tests/test_server.py
new file mode 100644
index 000000000..5e23a0dfd
--- /dev/null
+++ b/tests/test_server.py
@@ -0,0 +1,198 @@
+import base64
+from collections.abc import Iterable
+from concurrent.futures import ThreadPoolExecutor
+from threading import Event
+from typing import Any
+
+import numpy as np
+import pytest
+
+from fastembed.common.types import NumpyArray
+from fastembed.text.text_embedding_base import TextEmbeddingBase
+
+pytest.importorskip("fastapi")
+pytest.importorskip("httpx")
+
+from fastapi.testclient import TestClient  # noqa: E402
+
+from fastembed.server import create_app  # noqa: E402
+
+
+class ExampleEmbedding(TextEmbeddingBase):
+    def __init__(self) -> None:
+        super().__init__(model_name="test-model")
+        self.calls: list[tuple[list[str], int]] = []
+
+    @property
+    def embedding_size(self) -> int:
+        return 2
+
+    def embed(
+        self, documents: Iterable[str], batch_size: int = 256, **kwargs: Any
+    ) -> Iterable[NumpyArray]:
+        texts = list(documents)
+        self.calls.append((texts, batch_size))
+        for text in texts:
+            yield np.array([len(text), len(text.split())], dtype=np.float32)
+
+    def token_count(self, texts: str | Iterable[str], **kwargs: Any) -> int:
+        return sum(len(text.split()) for text in ([texts] if isinstance(texts, str) else texts))
+
+
+@pytest.fixture
+def model() -> ExampleEmbedding:
+    return ExampleEmbedding()
+
+
+@pytest.mark.parametrize("inputs", ["one two", ["one two", "three"]])
+@pytest.mark.parametrize("encoding", ["float", "base64"])
+def test_embeddings(model: ExampleEmbedding, inputs: str | list[str], encoding: str) -> None:
+    with TestClient(create_app(model, batch_size=8)) as client:
+        response = client.post(
+            "/v1/embeddings",
+            json={
+                "input": inputs,
+                "model": "test-model",
+                "encoding_format": encoding,
+                "dimensions": 2,
+            },
+        )
+    assert response.status_code == 200
+    result = response.json()
+    texts = [inputs] if isinstance(inputs, str) else inputs
+    assert result["object"] == "list"
+    assert result["model"] == "test-model"
+    tokens = 2 if isinstance(inputs, str) else 3
+    assert result["usage"] == {"prompt_tokens": tokens, "total_tokens": tokens}
+    assert len(result["data"]) == len(texts)
+    for index, (item, text) in enumerate(zip(result["data"], texts)):
+        assert item["object"] == "embedding"
+        assert item["index"] == index
+        values = item["embedding"]
+        if encoding == "base64":
+            values = np.frombuffer(base64.b64decode(values), dtype="<f4").tolist()
+        assert values == [len(text), len(text.split())]
+    assert model.calls == [(texts, 8)]
+
+
+def test_default_model_and_encoding(model: ExampleEmbedding) -> None:
+    with TestClient(create_app(model)) as client:
+        response = client.post("/v1/embeddings", json={"input": "one", "user": "local-user"})
+        listed = client.get("/v1/models").json()
+    assert response.status_code == 200
+    assert response.json()["data"][0]["embedding"] == [3, 1]
+    assert listed["object"] == "list"
+    assert listed["data"][0]["id"] == "test-model"
+
+
+@pytest.mark.parametrize(
+    "body",
+    [
+        {},
+        {"input": ""},
+        {"input": []},
+        {"input": [""]},
+        {"input": None},
+        {"input": 12},
+        {"input": [1, 2]},
+        {"input": [[1, 2]]},
+        {"input": ["private text", 12]},
+        {"input": "private text", "encoding_format": "invalid"},
+        {"input": "private text", "dimensions": True},
+        {"input": "private text", "dimensions": 0},
+        {"input": "private text", "dimensions": "2"},
+        {"input": "private text", "extra": "ignored?"},
+        {"input": ["one"] * 2049},
+    ],
+)
+def test_invalid_requests_do_not_run_inference(model: ExampleEmbedding, body: dict) -> None:
+    with TestClient(create_app(model)) as client:
+        response = client.post("/v1/embeddings", json=body)
+    assert response.status_code == 400
+    assert response.json()["error"]["type"] == "invalid_request_error"
+    assert "private text" not in response.text
+    assert not model.calls
+
+
+def test_malformed_json(model: ExampleEmbedding) -> None:
+    with TestClient(create_app(model)) as client:
+        response = client.post(
+            "/v1/embeddings", content='{"input":', headers={"content-type": "application/json"}
+        )
+    assert response.status_code == 400
+    assert response.json()["error"]["type"] == "invalid_request_error"
+    assert response.json()["error"]["param"] is None
+
+
+@pytest.mark.parametrize(
+    ("extra", "status", "param"),
+    [({"model": "not-loaded"}, 404, "model"), ({"dimensions": 1}, 400, "dimensions")],
+)
+def test_unsupported_model_or_dimensions(
+    model: ExampleEmbedding, extra: dict, status: int, param: str
+) -> None:
+    with TestClient(create_app(model)) as client:
+        response = client.post("/v1/embeddings", json={"input": "one", **extra})
+    assert response.status_code == status
+    assert response.json()["error"]["param"] == param
+    assert not model.calls
+
+
+@pytest.mark.parametrize("failure", ["exception", "missing", "nan", "shape"])
+def test_inference_failure_is_not_exposed(
+    model: ExampleEmbedding, monkeypatch: pytest.MonkeyPatch, failure: str
+) -> None:
+    def broken_embed(*args: Any, **kwargs: Any) -> Iterable[NumpyArray]:
+        if failure == "exception":
+            raise RuntimeError("private model path and input")
+        if failure == "missing":
+            return []
+        if failure == "nan":
+            return [np.array([np.nan, 1])]
+        return [np.array([[1, 2]])]
+
+    monkeypatch.setattr(model, "embed", broken_embed)
+    with TestClient(create_app(model)) as client:
+        response = client.post("/v1/embeddings", json={"input": "one"})
+    assert response.status_code == 500
+    assert response.json()["error"]["type"] == "server_error"
+    assert "private" not in response.text
+
+
+def test_health_during_inference(model: ExampleEmbedding, monkeypatch: pytest.MonkeyPatch) -> None:
+    started, release = Event(), Event()
+    original_embed = model.embed
+
+    def blocking_embed(*args: Any, **kwargs: Any) -> Iterable[NumpyArray]:
+        started.set()
+        if not release.wait(timeout=10):
+            raise TimeoutError("Inference was not released")
+        yield from original_embed(*args, **kwargs)
+
+    monkeypatch.setattr(model, "embed", blocking_embed)
+
+    async def limit_workers() -> None:
+        import anyio.to_thread
+
+        # One blocked inference exhausts this test server's entire worker pool.
+        anyio.to_thread.current_default_thread_limiter().total_tokens = 1
+
+    with TestClient(create_app(model)) as client, ThreadPoolExecutor(max_workers=2) as pool:
+        assert client.portal is not None
+        client.portal.call(limit_workers)
+        pending = pool.submit(client.post, "/v1/embeddings", json={"input": "one"})
+        try:
+            assert started.wait(timeout=5)
+            health = pool.submit(client.get, "/health").result(timeout=5)
+            assert health.status_code == 200
+            assert health.json() == {"status": "ok"}
+            listed = pool.submit(client.get, "/v1/models").result(timeout=5)
+            assert listed.json()["data"][0]["id"] == "test-model"
+        finally:
+            release.set()
+        assert pending.result(timeout=5).status_code == 200
+
+
+def test_invalid_batch_size(model: ExampleEmbedding) -> None:
+    with pytest.raises(ValueError, match="batch_size must be positive"):
+        create_app(model, batch_size=0)
diff --git a/uv.lock b/uv.lock
index ace7064d7..e9e2afc59 100644
--- a/uv.lock
+++ b/uv.lock
@@ -10,6 +10,24 @@ resolution-markers = [
     "python_full_version < '3.11'",
 ]
 
+[[package]]
+name = "annotated-doc"
+version = "0.0.5"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/5a/8e/38aa427ed5402449e226975b649c5dc73ccadfefeb95e6aecb8f8ea4b6b6/annotated_doc-0.0.5.tar.gz", hash = "sha256:c7e58ce09192557605d8bbd92836d7e1d520ac9580096042c0bfd197efacf1bb", size = 10758, upload-time = "2026-07-28T13:50:58.129Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/3e/30/e900b21425a860e195f32e37657aa1f7c7f2b1bfb26f03ca209b90933c06/annotated_doc-0.0.5-py3-none-any.whl", hash = "sha256:117bac03a25ede5df5440e855b32d556049ca169ead221505badf432fed4b101", size = 5302, upload-time = "2026-07-28T13:50:57.239Z" },
+]
+
+[[package]]
+name = "annotated-types"
+version = "0.8.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/5f/56/a8120250d128bed162cd73c76d45f6ef9991f3e068f62a8ee060afa3104a/annotated_types-0.8.0.tar.gz", hash = "sha256:13b2beaad985e05e2d6407ee4c4f35590b11f8d693a258a561055cac8f64cab7", size = 15893, upload-time = "2026-07-23T20:16:13.995Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/99/91/8acff4f5e50511b911bbccb72b8628a49c68ce14148cd9f6431094859a90/annotated_types-0.8.0-py3-none-any.whl", hash = "sha256:f072f4d804ea359e4eaf198b1af7a8b0943881a87f31bb764f8bf219bb9419e0", size = 13427, upload-time = "2026-07-23T20:16:12.938Z" },
+]
+
 [[package]]
 name = "anyio"
 version = "4.15.1"
@@ -707,6 +725,24 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/c1/ea/53f2148663b321f21b5a606bd5f191517cf40b7072c0497d3c92c4a13b1e/executing-2.2.1-py2.py3-none-any.whl", hash = "sha256:760643d3452b4d777d295bb167ccc74c64a81df23fb5e08eff250c425a4b2017", size = 28317, upload-time = "2025-09-01T09:48:08.5Z" },
 ]
 
+[[package]]
+name = "fastapi"
+version = "0.143.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "annotated-doc" },
+    { name = "opentelemetry-api" },
+    { name = "pydantic" },
+    { name = "starlette", version = "1.7.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
+    { name = "starlette", version = "1.8.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "typing-extensions" },
+    { name = "typing-inspection" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/19/f5/4bbb2df9bb6f365151f2c02795ca3f17f78d08e670a394df963f3d8881ce/fastapi-0.143.2.tar.gz", hash = "sha256:e9e6d97018dcfd748da7d9e7c61cedefbe9eb91b1a3288e45b13fbae76df2d54", size = 468920, upload-time = "2026-10-15T13:34:21.679Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d5/5a/9a5fd06659a63e13e876dd660347c044b3954ede3db928c69df879fac02c/fastapi-0.143.2-py3-none-any.whl", hash = "sha256:da2fe9893b7392ebce76d8c8511e3fa43e5a25f5852103aa2eee7cff3ab80b75", size = 144690, upload-time = "2026-10-15T13:34:19.861Z" },
+]
+
 [[package]]
 name = "fastembed"
 version = "0.9.0"
@@ -727,6 +763,13 @@ dependencies = [
     { name = "tqdm" },
 ]
 
+[package.optional-dependencies]
+server = [
+    { name = "fastapi" },
+    { name = "pydantic" },
+    { name = "uvicorn" },
+]
+
 [package.dev-dependencies]
 dev = [
     { name = "notebook" },
@@ -741,6 +784,7 @@ docs = [
     { name = "pillow" },
 ]
 test = [
+    { name = "httpx" },
     { name = "pytest" },
     { name = "ruff" },
 ]
@@ -751,6 +795,7 @@ types = [
 
 [package.metadata]
 requires-dist = [
+    { name = "fastapi", marker = "extra == 'server'", specifier = ">=0.115,<1" },
     { name = "huggingface-hub", specifier = ">=0.20,<2.0" },
     { name = "loguru", specifier = ">=0.7.2,<0.8" },
     { name = "mmh3", specifier = ">=4.1.0,<6.0.0" },
@@ -767,10 +812,13 @@ requires-dist = [
     { name = "pillow", marker = "python_full_version == '3.13.*'", specifier = ">=11.0.0,<13.0" },
     { name = "pillow", marker = "python_full_version >= '3.14'", specifier = ">=12.0.0,<13.0" },
     { name = "py-rust-stemmers", specifier = ">=0.1.0,<0.2" },
+    { name = "pydantic", marker = "extra == 'server'", specifier = ">=2,<3" },
     { name = "requests", specifier = ">=2.31,<3" },
     { name = "tokenizers", specifier = ">=0.15,<1.0" },
     { name = "tqdm", specifier = ">=4.66,<5" },
+    { name = "uvicorn", marker = "extra == 'server'", specifier = ">=0.30,<1" },
 ]
+provides-extras = ["server"]
 
 [package.metadata.requires-dev]
 dev = [
@@ -788,6 +836,7 @@ docs = [
     { name = "pillow", specifier = ">=10.3.0,<13.0.0" },
 ]
 test = [
+    { name = "httpx", specifier = ">=0.27,<1" },
     { name = "pytest", specifier = ">=7.4.2,<10.0.0" },
     { name = "ruff", specifier = ">=0.3.1,<1.0" },
 ]
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 ]
 
+[[package]]
+name = "opentelemetry-api"
+version = "1.45.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/2e/02/6e0ae9cc61bd3169d401077b507b3ebc344745171e1051ab430be012dcd9/opentelemetry_api-1.45.1.tar.gz", hash = "sha256:aa38ed19bcc084ba42782a73255b3582283eced7ad6dddbd6695189e69adfb75", size = 72804, upload-time = "2026-10-06T17:32:58.133Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/1e/41/f7dcf80b81ee8e71c1a2b59f14208bc723edbd89ed027a73b175abf6348e/opentelemetry_api-1.45.1-py3-none-any.whl", hash = "sha256:b31553efa588ae44bc306f863c785c5333a9ecc091248c6ee68b4b6c87fdedfb", size = 60256, upload-time = "2026-10-06T17:32:33.506Z" },
+]
+
 [[package]]
 name = "overrides"
 version = "7.7.0"
@@ -2863,6 +2924,168 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/0c/c3/44f3fbbfa403ea2a7c779186dc20772604442dde72947e7d01069cbe98e3/pycparser-3.0-py3-none-any.whl", hash = "sha256:b727414169a36b7d524c1c3e31839a521725078d7b2ff038656844266160a992", size = 48172, upload-time = "2026-01-21T14:26:50.693Z" },
 ]
 
+[[package]]
+name = "pydantic"
+version = "2.14.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "annotated-types" },
+    { name = "pydantic-core" },
+    { name = "typing-extensions" },
+    { name = "typing-inspection" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/7c/0b/8e10b2e693af8ec54346a14caf36221334775975a747c9ceaa3f8371d96d/pydantic-2.14.1.tar.gz", hash = "sha256:94f478203dd03404682a1ada216965651dd74b1d2d5ffd62e00e0837caab5c26", size = 900362, upload-time = "2026-10-11T18:37:55.396Z" }
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+
+[[package]]
+name = "pydantic-core"
+version = "2.50.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
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