async def embed_many(
        self,
        texts: List[str],
        model: Optional[str] = None,
        encoding_format: str = "float",
        dimensions: Optional[int] = None,
    ) -> List[List[float]]:
        """Embed multiple text strings using the local text2vec model.

        Args:
            texts: List of texts 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 vectors
        """
        if not texts:
            return []

        if model:
            raise ValueError("Model override not supported for local text2vec")

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

        # Create result list with same order as input
        result = []

        # Process each text individually since batch endpoint isn't available
        async with httpx.AsyncClient() as client:
            for text in texts:
                if text.strip():
                    try:
                        response = await client.post(
                            f"{self.inference_url}/vectors/", json={"text": text}
                        )
                        response.raise_for_status()
                        result.append(response.json()["vector"])
                    except Exception as e:
                        logger.error(f"Error embedding text: {e}")
                        # Return zero vector for failed embedding
                        result.append([0.0] * self.vector_dimensions)
                else:
                    # Return zero vector for empty text
                    result.append([0.0] * self.vector_dimensions)

        return result
Embed multiple text strings using the local text2vec model.

        Args:
            texts: List of texts 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 vectors
        
backend/airweave/platform/embedding_models/local_text2vec.py