langflow-ai/langflow · error · HTTPException

Invalid embedding configuration

Error message

Invalid embedding configuration

What it means

A 400 raised after metadata is loaded: the embedding config payload (model_selection, or the legacy embedding_model/embedding_provider fields) lacks a name or a provider. The KB exists and has metadata, but the embedding configuration recorded on it is incomplete, so ingestion cannot proceed because embeddings cannot be resolved.

Source

Thrown at src/backend/base/langflow/api/v1/knowledge_bases.py:1097

        # Read embedding metadata (Pass fast=False to ensure legacy KBs are migrated/detected)
        metadata = KBAnalysisHelper.get_metadata(kb_path, fast=False)
        if not metadata:
            raise HTTPException(
                status_code=400,
                detail="Knowledge base missing embedding configuration. Please create a new KB or reconfigure it.",
            )

        # ``model_selection`` is the canonical embedding-config payload.
        # Synthesize it from the legacy flat metadata fields when older
        # KBs only carry those (``record_to_metadata_dict`` writes both
        # forms for new KBs, so this branch is mainly for disk-only
        # ones that haven't been backfilled yet).
        model_selection = metadata.get("model_selection") or {
            "name": metadata.get("embedding_model"),
            "provider": metadata.get("embedding_provider"),
        }
        if not model_selection.get("name") or not model_selection.get("provider"):
            raise HTTPException(status_code=400, detail="Invalid embedding configuration")

        # Use ``KnowledgeBaseRecord.id`` (when present) as the Job's
        # ``asset_id`` so the read path can hit the indexed
        # ``Job.asset_id`` column instead of doing a JSON-extract on
        # ``Job.job_metadata.kb_name``. Falls back to legacy
        # ``metadata['id']`` for KBs that exist on disk only.
        asset_id = await _resolve_kb_asset_id(
            kb_name=kb_name,
            current_user=current_user,
            metadata=metadata,
        )

        # Get services and create job before async/sync split
        job_service = get_job_service()
        job_id = uuid.uuid4()

        # Create job record in database for both async and sync paths
        await job_service.create_job(

View on GitHub (pinned to 976ec789d2)

Solutions

1. Reconfigure the KB's embedding settings via the UI/API so both model name and provider are persisted.
2. Recreate the KB and re-ingest its files.
3. Fix the metadata file on disk directly: ensure model_selection = {"name": ..., "provider": ...} is complete.
4. If this repros for newly created KBs, check for a version mismatch between frontend creation flow and backend expectations and update Langflow.

Example fix

# repair on-disk metadata
import json, pathlib
p = pathlib.Path(kb_dir) / "metadata"  # location of KB metadata
meta = json.loads(p.read_text())
meta["model_selection"] = {"name": "openai/text-embedding-3-small", "provider": "openai"}
p.write_text(json.dumps(meta))

def has_complete_embedding_config(metadata: dict) -> bool:
    sel = metadata.get("model_selection") or {
        "name": metadata.get("embedding_model"),
        "provider": metadata.get("embedding_provider"),
    }
    return bool(sel.get("name") and sel.get("provider"))

from typing import TypedDict

class ModelSelection(TypedDict, total=False):
    name: str
    provider: str

def is_valid_model_selection(ms: dict) -> bool:
    return isinstance(ms, dict) and bool(ms.get("name")) and bool(ms.get("provider"))

try:
    await ingest(files)
except HTTPStatusError as e:
    if e.response.status_code == 400:
        detail = e.response.json()["detail"]
        if detail == "Invalid embedding configuration":
            await reconfigure_kb_embedding(kb, model, provider)

When it happens

Trigger: POST /api/v1/knowledge_bases/{kb_name}/upload where the KB metadata exists but model_selection.name or model_selection.provider is empty/missing AND the legacy embedding_model/embedding_provider fallbacks are also empty. Typically hand-edited metadata or a partially written KB config.

Common situations: Manually edited or migrated KB metadata files, a KB created against an older version whose creation flow did not persist provider, or a metadata write interrupted midway.

Related errors

• Knowledge base missing embedding configuration. Please creat
• No model provider is configured. Please configure at least o
• Unknown provider: {provider}
• Missing required configuration for {provider}: {', '.join(mi
• Cannot use deployment_provider_id: the wxo_deployments featu

AI-assisted analysis of langflow-ai/langflow@976ec789d2 (2026-08-14). Data as JSON: /api/errors/697d57d9d3e66a51. Report an issue: GitHub.