fix(genai): drop twelvelabs SDK dep, call Marengo via REST

docker/main/requirements-wheels.txt

@@ -50,7 +50,6 @@ transformers == 4.45.*
 google-genai == 1.58.*
 ollama == 0.6.*
 openai == 1.65.*
-twelvelabs == 1.2.*
 # push notifications
 py-vapid == 1.9.*
 pywebpush == 2.0.*

frigate/genai/plugins/twelvelabs.py

@@ -20,6 +20,7 @@ upstream and is consistent for both text and image inputs.
 import logging
 
 import numpy as np
+import requests
 
 from frigate.config import GenAIProviderEnum
 from frigate.genai import GenAIClient, register_genai_provider
@@ -29,26 +30,28 @@ logger = logging.getLogger(__name__)
 # Default Marengo model. Overridable via the `model` config field.
 DEFAULT_MODEL = "marengo3.0"
 
+# Marengo embed REST endpoint. No SDK is needed — this is a plain multipart POST
+# made through Frigate's existing `requests` dependency.
+EMBED_URL = "https://api.twelvelabs.io/v1.3/embed"
+
 
 @register_genai_provider(GenAIProviderEnum.twelvelabs)
 class TwelveLabsClient(GenAIClient):
     """GenAI client for Frigate using TwelveLabs Marengo embeddings."""
 
     def _init_provider(self):
-        """Initialize the TwelveLabs SDK client."""
-        try:
-            from twelvelabs import TwelveLabs
-        except ImportError:
-            logger.error(
-                "The twelvelabs package is required for the TwelveLabs provider."
-            )
-            return None
+        """Validate config for the TwelveLabs REST provider.
 
+        The provider is just an HTTPS API, so there is no client object to
+        build — the API key is the only thing required. A non-None sentinel is
+        returned so the shared ``ensure_provider``/initialization machinery
+        treats the provider as available.
+        """
         if not self.genai_config.api_key:
             logger.error("TwelveLabs provider requires an api_key.")
             return None
 
-        return TwelveLabs(api_key=self.genai_config.api_key)
+        return self.genai_config.api_key
 
     @property
     def _model(self) -> str:
@@ -69,6 +72,8 @@ class TwelveLabsClient(GenAIClient):
         sent one at a time. Returns one 512-dim float32 vector per input, in
         order (texts first, then images). The shared GenAIEmbedding adapter
         pads these to Frigate's 768-dim search schema.
+
+        Calls the Marengo REST endpoint directly via ``requests`` — no SDK.
         """
         if self.provider is None:
             logger.warning(
@@ -93,28 +98,42 @@ class TwelveLabsClient(GenAIClient):
     def _embed_one(
         self, text: str | None = None, image: bytes | None = None
     ) -> np.ndarray | None:
-        """Embed a single text or image input, returning a float32 vector."""
-        try:
-            if text is not None:
-                response = self.provider.embed.create(
-                    model_name=self._model,
-                    text=text,
-                    request_options={"timeout_in_seconds": self.timeout},
-                )
-                result = response.text_embedding
-            else:
-                response = self.provider.embed.create(
-                    model_name=self._model,
-                    image_file=image,
-                    request_options={"timeout_in_seconds": self.timeout},
-                )
-                result = response.image_embedding
+        """Embed a single text or image input, returning a float32 vector.
 
-            if result is None or not result.segments:
+        Posts a multipart form to the Marengo embed endpoint (``model_name`` plus
+        either a ``text`` or an ``image_file`` part). The endpoint requires
+        multipart/form-data, so every field — including text — is passed via
+        ``files`` (the ``(None, value)`` form makes requests emit a multipart
+        text part). ``self.provider`` holds the validated API key. The 512-dim
+        vector is at ``<text|image>_embedding.segments[0].float`` in the JSON
+        response.
+        """
+        headers = {"x-api-key": self.provider}
+        files: dict = {"model_name": (None, self._model)}
+
+        if text is not None:
+            files["text"] = (None, text)
+            result_key = "text_embedding"
+        else:
+            files["image_file"] = ("image.jpg", image, "image/jpeg")
+            result_key = "image_embedding"
+
+        try:
+            response = requests.post(
+                EMBED_URL,
+                headers=headers,
+                files=files,
+                timeout=self.timeout,
+            )
+            response.raise_for_status()
+            result = response.json().get(result_key) or {}
+            segments = result.get("segments") or []
+
+            if not segments:
                 logger.warning("TwelveLabs returned no embedding for input.")
                 return None
 
-            return np.array(result.segments[0].float_, dtype=np.float32)
+            return np.array(segments[0]["float"], dtype=np.float32)
         except Exception as e:
             logger.warning("TwelveLabs returned an error: %s", e)
             return None

frigate/test/test_genai_twelvelabs.py

@@ -3,7 +3,7 @@
 import io
 import os
 import unittest
-from unittest.mock import MagicMock
+from unittest.mock import MagicMock, patch
 
 import numpy as np
 
@@ -24,91 +24,85 @@ def _make_config(model: str = "") -> GenAIConfig:
     )
 
 
-def _segment(values):
-    """Mimic the SDK BaseSegment shape (a `float_` list per segment)."""
-    seg = MagicMock()
-    seg.float_ = values
-    return seg
+def _response(key: str, values):
+    """Mimic the Marengo REST JSON: ``{<key>: {segments: [{float: [...]}]}}``."""
+    resp = MagicMock()
+    resp.raise_for_status.return_value = None
+    resp.json.return_value = {key: {"segments": [{"float": values}]}}
+    return resp
 
 
 class TestTwelveLabsEmbedNoNetwork(unittest.TestCase):
-    """Unit tests with the SDK client mocked — no network access."""
+    """Unit tests with ``requests`` mocked — no network access, no SDK."""
 
-    def _client_with_provider(self, provider) -> TwelveLabsClient:
+    def _client(self) -> TwelveLabsClient:
         client = TwelveLabsClient.__new__(TwelveLabsClient)
         client.genai_config = _make_config()
         client.timeout = 120
-        client.provider = provider
+        client.provider = "test-key"
         return client
 
-    def test_text_embedding_returns_vector(self):
-        provider = MagicMock()
-        response = MagicMock()
-        response.text_embedding.segments = [_segment([0.1, 0.2, 0.3])]
-        provider.embed.create.return_value = response
+    @patch("frigate.genai.plugins.twelvelabs.requests.post")
+    def test_text_embedding_returns_vector(self, post):
+        post.return_value = _response("text_embedding", [0.1, 0.2, 0.3])
 
-        client = self._client_with_provider(provider)
-        out = client.embed(texts=["a person walking a dog"])
+        out = self._client().embed(texts=["a person walking a dog"])
 
         self.assertEqual(len(out), 1)
         self.assertIsInstance(out[0], np.ndarray)
         self.assertEqual(out[0].dtype, np.float32)
         np.testing.assert_allclose(out[0], [0.1, 0.2, 0.3], rtol=1e-6)
 
-        _, kwargs = provider.embed.create.call_args
-        self.assertEqual(kwargs["model_name"], DEFAULT_MODEL)
-        self.assertEqual(kwargs["text"], "a person walking a dog")
+        _, kwargs = post.call_args
+        self.assertEqual(kwargs["files"]["model_name"][1], DEFAULT_MODEL)
+        self.assertEqual(kwargs["files"]["text"][1], "a person walking a dog")
+        self.assertEqual(kwargs["headers"]["x-api-key"], "test-key")
+        self.assertNotIn("image_file", kwargs["files"])
 
-    def test_image_embedding_uses_image_file(self):
-        provider = MagicMock()
-        response = MagicMock()
-        response.image_embedding.segments = [_segment([1.0, 2.0])]
-        provider.embed.create.return_value = response
+    @patch("frigate.genai.plugins.twelvelabs.requests.post")
+    def test_image_embedding_uses_image_file(self, post):
+        post.return_value = _response("image_embedding", [1.0, 2.0])
 
-        client = self._client_with_provider(provider)
-        out = client.embed(images=[b"\xff\xd8\xff jpeg bytes"])
+        out = self._client().embed(images=[b"\xff\xd8\xff jpeg bytes"])
 
         self.assertEqual(len(out), 1)
-        _, kwargs = provider.embed.create.call_args
-        self.assertEqual(kwargs["image_file"], b"\xff\xd8\xff jpeg bytes")
-        self.assertNotIn("text", kwargs)
+        _, kwargs = post.call_args
+        self.assertEqual(kwargs["files"]["image_file"][1], b"\xff\xd8\xff jpeg bytes")
+        self.assertNotIn("text", kwargs["files"])
 
-    def test_custom_model_name_is_used(self):
-        provider = MagicMock()
-        response = MagicMock()
-        response.text_embedding.segments = [_segment([0.0])]
-        provider.embed.create.return_value = response
+    @patch("frigate.genai.plugins.twelvelabs.requests.post")
+    def test_custom_model_name_is_used(self, post):
+        post.return_value = _response("text_embedding", [0.0])
 
-        client = self._client_with_provider(provider)
+        client = self._client()
         client.genai_config = _make_config(model="marengo-custom")
         client.embed(texts=["x"])
 
-        _, kwargs = provider.embed.create.call_args
-        self.assertEqual(kwargs["model_name"], "marengo-custom")
+        _, kwargs = post.call_args
+        self.assertEqual(kwargs["files"]["model_name"][1], "marengo-custom")
 
-    def test_empty_segments_are_skipped(self):
-        provider = MagicMock()
-        response = MagicMock()
-        response.text_embedding = None
-        provider.embed.create.return_value = response
+    @patch("frigate.genai.plugins.twelvelabs.requests.post")
+    def test_empty_segments_are_skipped(self, post):
+        resp = MagicMock()
+        resp.raise_for_status.return_value = None
+        resp.json.return_value = {"text_embedding": {"segments": []}}
+        post.return_value = resp
 
-        client = self._client_with_provider(provider)
-        self.assertEqual(client.embed(texts=["x"]), [])
+        self.assertEqual(self._client().embed(texts=["x"]), [])
 
-    def test_api_error_is_swallowed(self):
-        provider = MagicMock()
-        provider.embed.create.side_effect = RuntimeError("boom")
+    @patch("frigate.genai.plugins.twelvelabs.requests.post")
+    def test_api_error_is_swallowed(self, post):
+        post.side_effect = RuntimeError("boom")
 
-        client = self._client_with_provider(provider)
-        self.assertEqual(client.embed(texts=["x"]), [])
+        self.assertEqual(self._client().embed(texts=["x"]), [])
 
     def test_no_provider_returns_empty(self):
-        client = self._client_with_provider(None)
+        client = self._client()
+        client.provider = None
         self.assertEqual(client.embed(texts=["x"]), [])
 
     def test_no_inputs_returns_empty(self):
-        client = self._client_with_provider(MagicMock())
-        self.assertEqual(client.embed(), [])
+        self.assertEqual(self._client().embed(), [])
 
 
 @unittest.skipUnless(