def get_index_config(model: BaseEmbeddingModel) -> dict[str, Any]:
        """Get Pinecone index configuration for the model."""
        base_config = {"dimension": model.vector_dimensions, "metric": "cosine"}

        # Add model-specific configurations
        if model.model_name == "openai-text2vec":
            base_config.update(
                {
                    "pod_type": "p1",  # Standard pod type
                    "metadata_config": {
                        "indexed": ["timestamp", "source"]  # Example metadata fields
                    },
                }
            )
        elif model.model_name == "local-text2vec-transformers":
            base_config.update(
                {
                    "pod_type": "s1",  # Starter pod type
                    "metadata_config": {"indexed": ["timestamp", "source"]},
                }
            )

        return base_config
Get Pinecone index configuration for the model.
backend/airweave/platform/embedding_models/_adapters.py