What causes "Turbopuffer vectors only support float32 or float16, got {dt}." in cocoindex-io/cocoindex?

Cast the vector schema's dtype to float32 (or float16): np.asarray(embedding, dtype=np.float32) at the point vectors are produced.

cocoindex-io/cocoindex · error · ValueError

Turbopuffer vectors only support float32 or float16, got

Error message

Turbopuffer vectors only support float32 or float16, got {dt}.

What it means

Turbopuffer's ANN index supports only float32 (`f32`) and float16 (`f16`) vector element types. `_vector_type_str` renders the schema's dtype into turbopuffer's `[N]fXX` type string, and raises this ValueError if the VectorSchema's dtype is anything else (e.g. float64, bfloat16, int8), because no valid wire type string exists for it.

Solutions

Cast the vector schema's dtype to float32 (or float16): np.asarray(embedding, dtype=np.float32) at the point vectors are produced.
Set dtype=np.float32 when constructing the numpy array backing the VectorDef schema.
If you need another precision (e.g. bfloat16 or int8 quantization), use a backend that supports it instead of turbopuffer.

Example fix

// before
vec = np.array(model.encode(text))  # float64

// after
vec = np.asarray(model.encode(text), dtype=np.float32)

Defensive patterns

Strategy: validation

Validate before calling

import numpy as _np
dt = _np.dtype(vec_schema.dtype)
assert dt in (_np.float32, _np.float16), f"Cast {dt} to float32/float16 for turbopuffer"

Type guard

def is_tp_supported_dtype(dt: _np.dtype) -> bool:
    return dt in (_np.float32, _np.float16)

Try / catch

try:
    target = await NamespaceSchema.create(vectors=vdef, ...)
except ValueError as e:
    if "only support float32 or float16" in str(e):
        raise ConfigError("Recast vector schema dtype to float32") from e
    raise

Prevention

Always construct embedding arrays with dtype=np.float32 explicitly
Never rely on np.array() defaults — float64 is the default for Python float lists
Validate vector schema dtype at app startup, before any write path runs

When it happens

Trigger: Declaring a VectorDef whose schema produces vectors with dtype float64 (numpy's default `np.array([...])` without dtype=), or any non-float dtype, so that `_resolve_vector_def`/`_build_write_schema` calls `_vector_type_str` and fails.

Common situations: NumPy defaults: embeddings loaded via np.array(list) come out as float64; models/ONNX pipelines outputting bfloat16; int8-quantized embeddings being passed to turbopuffer directly.

Related errors

AI-assisted analysis of cocoindex-io/cocoindex@e84aa99b32 (2026-09-08). Data as JSON: /api/errors/55c7baecb7c563c4. Report an issue: GitHub.

Appendix: source

Thrown at python/cocoindex/connectors/turbopuffer/_target.py:242

    reserved = {"id"} | vector_field_names
    if row.attributes:
        for k, v in row.attributes.items():
            if k in reserved:
                raise ValueError(f"Row {row.id!r}: attribute name {k!r} is reserved.")
            out[k] = v

    return out


def _vector_type_str(vs: res_schema.VectorSchema) -> str:
    """Render a VectorSchema as turbopuffer's ``[N]fXX`` type string."""
    dt = np.dtype(vs.dtype)
    if dt == np.float32:
        suffix = "f32"
    elif dt == np.float16:
        suffix = "f16"
    else:
        raise ValueError(
            f"Turbopuffer vectors only support float32 or float16, got {dt}."
        )
    return f"[{vs.size}]{suffix}"


def _build_write_schema(schema: NamespaceSchema) -> dict[str, Any]:
    """Build the explicit ``schema`` payload passed to ``namespace.write()``."""
    out: dict[str, Any] = {}
    if isinstance(schema.vectors, _ResolvedNamedVectorsDef):
        for name, vd in schema.vectors.vectors.items():
            out[name] = {"type": _vector_type_str(vd.schema), "ann": True}
    else:
        out[_DEFAULT_VECTOR_FIELD] = {
            "type": _vector_type_str(schema.vectors.schema),
            "ann": True,
        }
    return out

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