chroma-core/chroma · error · ValueError

Schema is missing keys[{embedding_key}].float_list.vector_in

Error message

Schema is missing keys[{embedding_key}].float_list.vector_index

What it means

This ValueError from update_schema_from_collection_configuration fires when the schema's '#embedding' key exists but has no float_list.vector_index attached (embedding_value_types.float_list is None or its vector_index is None). The update loop needs the concrete vector-index object to mutate hnsw/spann settings, so a #embedding column that exists but is unindexed cannot be updated and the call is rejected.

Source

Thrown at chromadb/api/collection_configuration.py:843

    """

    # Get the vector index from defaults and #embedding key
    if (
        schema.defaults.float_list is None
        or schema.defaults.float_list.vector_index is None
    ):
        raise ValueError("Schema is missing defaults.float_list.vector_index")

    embedding_key = "#embedding"
    if embedding_key not in schema.keys:
        raise ValueError(f"Schema is missing keys[{embedding_key}]")

    embedding_value_types = schema.keys[embedding_key]
    if (
        embedding_value_types.float_list is None
        or embedding_value_types.float_list.vector_index is None
    ):
        raise ValueError(
            f"Schema is missing keys[{embedding_key}].float_list.vector_index"
        )

    # Update vector index config in both locations
    for vector_index in [
        schema.defaults.float_list.vector_index,
        embedding_value_types.float_list.vector_index,
    ]:
        if "hnsw" in configuration and configuration["hnsw"] is not None:
            # Update HNSW config
            if vector_index.config.hnsw is None:
                raise ValueError("Trying to update HNSW config but schema has SPANN")

            hnsw_config = vector_index.config.hnsw
            update_hnsw = configuration["hnsw"]

            # Only update fields that are present in the update
            if "ef_search" in update_hnsw:

View on GitHub (pinned to aecdd12c8a)

Solutions

Recreate the collection with a full configuration (including the desired vector index) and re-ingest
Ensure collection creation in your code/version always assigns a vector index to '#embedding' before later modify calls
Keep client and server versions in sync so schema invariants hold

Defensive patterns

Strategy: try-catch

Validate before calling

def collection_has_indexed_embeddings(collection) -> bool:
    cfg = collection.configuration or {}
    return cfg.get('hnsw') is not None or cfg.get('spann') is not None

if collection_has_indexed_embeddings(collection):
    collection.modify(configuration=update)

Try / catch

try:
    collection.modify(configuration=update)
except ValueError as e:
    if 'float_list.vector_index' in str(e):
        # vector index missing from schema: rebuild collection with full config
        migrate_to_new_collection(collection)
    else:
        raise

Prevention

Always create collections with an explicit vector index configuration
Do not fabricate Schema objects outside chromadb's creation path
Test modify flows against collections created by the same Chroma version

When it happens

Trigger: Calling collection.modify with an hnsw or spann configuration update on a collection whose '#embedding' key lacks a float_list.vector_index — e.g. an embedding column declared but never assigned an index, a schema built partially in tests, or a collection created by a version/fork that skips index assignment.

Common situations: Schemas from partial migrations or hand-constructed test fixtures; collections whose embeddings were added through non-standard paths; version skew between the client sending updates and the server owning the schema.

Related errors

AI-assisted analysis of chroma-core/chroma@aecdd12c8a (2026-08-16). Data as JSON: /api/errors/e253e81c0c5eb41f. Report an issue: GitHub.