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| """Identity persisted with an index; equal dimensions alone do not imply compatibility.""" | |
| class EmbeddingProfileError(ValueError): | |
| pass | |
| def index_profile(config, stored_dimension: int) -> dict: | |
| provider = config.embeddings_provider | |
| attribute = {"gcp_vertex": "gcp", "amazon_bedrock": "bedrock"}.get(provider, provider) | |
| provider_config = getattr(config, attribute, None) | |
| model = getattr(provider_config, "embedding_model", None) or config.embeddings.local_model | |
| gemma = provider == "ollama" and model == "embeddinggemma-2:270m" | |
| return { | |
| "profile": config.embeddings.profile or f"{provider}:{model}", | |
| "provider": provider, | |
| "model": model, | |
| "native_dimension": config.embeddings.native_dimension if gemma else stored_dimension, | |
| "stored_dimension": stored_dimension, | |
| "transform": "truncate-512-l2" if gemma else "identity", | |
| "instructions": "google-search-query-document-v1" if gemma else "none", | |
| } | |
| def require_matching_profile(existing: dict | None, expected: dict, *, legacy_index: bool = False) -> None: | |
| if (existing is None and legacy_index) or (existing is not None and existing != expected): | |
| raise EmbeddingProfileError( | |
| f"Embedding index/profile mismatch: existing={existing!r}, configured={expected!r}. " | |
| "Use a new empty brain/index or explicitly re-index its source texts; " | |
| "existing vectors will not be reused or deleted automatically." | |
| ) | |