diff --git a/frigate/embeddings/genai_embedding.py b/frigate/embeddings/genai_embedding.py
new file mode 100644
index 000000000..0059693f8
--- /dev/null
+++ b/frigate/embeddings/genai_embedding.py
@@ -0,0 +1,85 @@
+"""GenAI-backed embeddings for semantic search."""
+
+import io
+import logging
+from typing import TYPE_CHECKING
+
+import numpy as np
+from PIL import Image
+
+if TYPE_CHECKING:
+    from frigate.genai import GenAIClient
+
+logger = logging.getLogger(__name__)
+
+EMBEDDING_DIM = 768
+
+
+class GenAIEmbedding:
+    """Embedding adapter that delegates to a GenAI provider's embed API.
+
+    Provides the same interface as JinaV2Embedding for semantic search:
+    __call__(inputs, embedding_type) -> list[np.ndarray]. Output embeddings are
+    normalized to 768 dimensions for Frigate's sqlite-vec schema.
+    """
+
+    def __init__(self, client: "GenAIClient") -> None:
+        self.client = client
+
+    def __call__(
+        self,
+        inputs: list[str] | list[bytes] | list[Image.Image],
+        embedding_type: str = "text",
+    ) -> list[np.ndarray]:
+        """Generate embeddings for text or images.
+
+        Args:
+            inputs: List of strings (text) or bytes/PIL images (vision).
+            embedding_type: "text" or "vision".
+
+        Returns:
+            List of 768-dim numpy float32 arrays.
+        """
+        if not inputs:
+            return []
+
+        if embedding_type == "text":
+            texts = [str(x) for x in inputs]
+            embeddings = self.client.embed(texts=texts)
+        elif embedding_type == "vision":
+            images: list[bytes] = []
+            for inp in inputs:
+                if isinstance(inp, bytes):
+                    images.append(inp)
+                elif isinstance(inp, Image.Image):
+                    buf = io.BytesIO()
+                    inp.convert("RGB").save(buf, format="JPEG")
+                    images.append(buf.getvalue())
+                else:
+                    logger.warning(
+                        "GenAIEmbedding: skipping unsupported vision input type %s",
+                        type(inp).__name__,
+                    )
+            if not images:
+                return []
+            embeddings = self.client.embed(images=images)
+        else:
+            raise ValueError(
+                f"Invalid embedding_type '{embedding_type}'. Must be 'text' or 'vision'."
+            )
+
+        result = []
+        for emb in embeddings:
+            arr = np.asarray(emb, dtype=np.float32).flatten()
+            if arr.size != EMBEDDING_DIM:
+                if arr.size > EMBEDDING_DIM:
+                    arr = arr[:EMBEDDING_DIM]
+                else:
+                    arr = np.pad(
+                        arr,
+                        (0, EMBEDDING_DIM - arr.size),
+                        mode="constant",
+                        constant_values=0,
+                    )
+            result.append(arr)
+        return result