async def embed(
        self,
        text: str,
        model: Optional[str] = None,
        encoding_format: str = "float",
        dimensions: Optional[int] = None,
    ) -> List[float]:
        """Embed a single text string using the local text2vec model.

        Args:
            text: The text to embed
            model: Optional model override (defaults to self.model_name)
            encoding_format: Format of the embedding (default: float)
            dimensions: Vector dimensions (defaults to self.vector_dimensions)

        Returns:
            List of embedding values
        """
        if model:
            raise ValueError("Model override not supported for local text2vec")

        if dimensions:
            raise ValueError("Dimensions override not supported for local text2vec")

        if not text.strip():
            # Return zero vector for empty text
            return [0.0] * self.vector_dimensions

        async with httpx.AsyncClient() as client:
            response = await client.post(f"{self.inference_url}/vectors", json={"text": text})
            response.raise_for_status()
            return response.json()["vector"]
Embed a single text string using the local text2vec model.

        Args:
            text: The text to embed
            model: Optional model override (defaults to self.model_name)
            encoding_format: Format of the embedding (default: float)
            dimensions: Vector dimensions (defaults to self.vector_dimensions)

        Returns:
            List of embedding values
        
backend/airweave/platform/embedding_models/local_text2vec.py