MemPalace/mempalace · error · ValueError

metadatas length {len(metadatas)} does not match ids length

Error message

metadatas length {len(metadatas)} does not match ids length {n}

What it means

_validate_write_batch requires that when metadatas is provided its length matches len(ids). The check fires before any network call, raising ValueError so a truncated or extended metadata list cannot be paired with the wrong documents.

Source

Thrown at mempalace/backends/qdrant.py:231

            if not any(_matches_where_document(document, clause) for clause in value or []):
                return False
            continue
        raise UnsupportedFilterError(f"where_document operator {key!r} not supported")
    return True


def _validate_write_batch(
    *,
    documents: list[str],
    ids: list[str],
    metadatas: Optional[list[dict]],
    embeddings: Optional[list[list[float]]],
) -> None:
    n = len(ids)
    if len(documents) != n:
        raise ValueError(f"documents length {len(documents)} does not match ids length {n}")
    if metadatas is not None and len(metadatas) != n:
        raise ValueError(f"metadatas length {len(metadatas)} does not match ids length {n}")
    if embeddings is not None and len(embeddings) != n:
        raise ValueError(f"embeddings length {len(embeddings)} does not match ids length {n}")


def _as_vector_array(vector: list[float]) -> np.ndarray:
    arr = np.asarray(vector, dtype=np.float32)
    if arr.ndim != 1 or arr.size == 0:
        raise ValueError("embedding must be a non-empty 1D vector")
    return arr


def _normalize_vectors(embeddings: list[list[float]]) -> tuple[list[list[float]], int]:
    vectors = []
    dims = set()
    for embedding in embeddings:
        arr = _as_vector_array(embedding)
        vectors.append(arr.astype(float).tolist())
        dims.add(int(arr.size))

View on GitHub (pinned to 06cb6987f0)

Solutions

1. Ensure one metadata entry per id; use [{}] as a placeholder when a row has no metadata (or pass None entirely).
2. Construct the batch as records and derive all arrays together.
3. Assert equal lengths in your ingest pipeline before calling add/upsert.

Example fix

# before
metas = [m for m in raw_metas if m]  # may drop entries
col.add(ids=ids, documents=docs, metadatas=metas)

# after
metas = [m if m else {} for m in raw_metas]
col.add(ids=ids, documents=docs, metadatas=metas)

metas = [m or {} for m in metas]  # one per id, never fewer
assert len(metas) == len(ids)
col.add(ids=ids, documents=docs, metadatas=metas)

def metadata_aligned(ids, metas) -> bool:
    return metas is None or len(metas) == len(ids)

try:
    col.add(ids=ids, documents=docs, metadatas=metas)
except ValueError as e:
    if "metadatas length" in str(e):
        metas = (metas or []) + [{}] * (len(ids) - len(metas or []))
        col.add(ids=ids, documents=docs, metadatas=metas)

When it happens

Trigger: add(ids=["1","2"], documents=[d1,d2], metadatas=[m1]) — metadata built per-document but one entry dropped/skipped by a conditional.

Common situations: Metadata dicts appended inside try/except blocks that swallow failures; defaulting some rows to no metadata and building a shorter list.

Related errors

• documents length {len(documents)} does not match ids length
• embeddings length {len(embeddings)} does not match ids lengt
• metadata key {key!r} clashes with a reserved Milvus field
• {label} length {len(value)} does not match ids length {n}
• embedding must be a non-empty 1D vector

AI-assisted analysis of MemPalace/mempalace@06cb6987f0 (2026-08-15). Data as JSON: /api/errors/badd170934f16739. Report an issue: GitHub.