RyanCodrai/turbovec · error · FileNotFoundError

missing one of / under

Error message

missing one of {_STORE_FILENAME}/{_INDEX_FILENAME} under {folder}

What it means

Raised in TurboQuantVectorDb._load_from (via create()) when the target folder does not contain both required files: the docstore.json side-car and index.tvim index payload. Either the store was never saved to this folder, the folder path is wrong, or one of the two files was deleted.

Solutions

1. Point create()/load at the folder that actually contains the saved pair.
2. Re-run save() from the live store if the files were never written.
3. Catch the FileNotFoundError in bootstrap code to fall back to creating a fresh store when no saved store exists.

When it happens

Trigger: Thrown at turbovec-python/python/turbovec/agno.py:1209 when the library encounters an invalid state.

Common situations: See trigger scenarios.

AI-assisted analysis of RyanCodrai/turbovec@ccab9f325e (2026-09-06). Data as JSON: /api/errors/0a8fb8631567de2b. Report an issue: GitHub.

Appendix: source

Thrown at turbovec-python/python/turbovec/agno.py:1209

                "dimensions": self.dimensions,
                # Recorded so a later create()/load restores the mode the
                # vectors were written under (v2+).
                "distance": self.distance.value,
            }
            # Atomic: serializes in memory first, then temp-file + replace,
            # so a failed save can't destroy a previous store at this path.
            atomic_save(
                self._index,
                folder / _INDEX_FILENAME,
                payload,
                folder / _STORE_FILENAME,
            )

    def _load_from(self, folder: Path) -> None:
        side_car = folder / _STORE_FILENAME
        index_file = folder / _INDEX_FILENAME
        if not side_car.exists() or not index_file.exists():
            raise FileNotFoundError(
                f"missing one of {_STORE_FILENAME}/{_INDEX_FILENAME} under {folder}"
            )
        with open(side_car) as f:
            state = json.load(f)
        version = state.get("schema_version", 0)
        check_schema_version(
            version,
            _DOCSTORE_SCHEMA_COMPAT,
            prefix=f"{_STORE_FILENAME} has schema_version",
        )
        if state.get("dimensions") != self.dimensions:
            raise ValueError(
                f"persisted dimensions={state.get('dimensions')} does not "
                f"match this store's embedder dimensions={self.dimensions}"
            )
        # Similarity mode of the persisted vectors. The construct-then-
        # create() API shape means the store already has a mode when the
        # load runs, so a *recorded* mode that conflicts with it is an

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