langflow-ai/langflow · error · HTTPException

Knowledge base missing embedding configuration. Please creat

Error message

Knowledge base missing embedding configuration. Please create a new KB or reconfigure it.

What it means

A 400 raised by the file-upload ingest endpoint when KBAnalysisHelper.get_metadata(kb_path, fast=False) returns nothing for the target knowledge base directory. It means the KB on disk has no embedding configuration metadata, which can happen for a KB created without an embedding model or one whose metadata file was lost/corrupted. The fast=False call also runs legacy-KB migration/detection before giving up.

Source

Thrown at src/backend/base/langflow/api/v1/knowledge_bases.py:1082

                column_config_parsed = json.loads(column_config)
                if isinstance(column_config_parsed, list):
                    # Update embedding_metadata.json
                    cc_metadata_path = kb_path / "embedding_metadata.json"
                    if cc_metadata_path.exists():
                        existing_meta = json.loads(cc_metadata_path.read_text())
                        existing_meta["column_config"] = column_config_parsed
                        cc_metadata_path.write_text(json.dumps(existing_meta, indent=2))
                    # Write schema.json for text-metric helpers
                    schema_data = [{**col, "data_type": "string"} for col in column_config_parsed]
                    schema_path = kb_path / "schema.json"
                    schema_path.write_text(json.dumps(schema_data, indent=2))
            except (json.JSONDecodeError, TypeError):
                await logger.awarning("Malformed column_config received, using existing schema")

        # 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

View on GitHub (pinned to 976ec789d2)

Solutions

  1. Recreate the knowledge base from the UI, explicitly selecting an embedding model and provider.
  2. Reconfigure the existing KB (update embedding settings) so a valid metadata payload with model_selection/embedding_model is written to disk.
  3. Inspect the KB directory on the server and confirm the metadata file exists and is valid JSON; restore it from backup if corrupted.
  4. Verify kb_name matches an actually-created KB (GET /api/v1/knowledge_bases) and is not a leftover directory.
Defensive patterns

Strategy: validation

Validate before calling

async def kb_ready_for_ingest(client, kb_name: str) -> bool:
    resp = await client.get(f"/api/v1/knowledge_bases/{kb_name}")
    if resp.status_code != 200:
        return False
    meta = resp.json().get("embedding_config") or {}
    return bool(meta)

Try / catch

try:
    resp = await client.post(upload_url, files=files)
except HTTPError as e:
    if e.response.status_code == 400 and "missing embedding configuration" in e.response.text:
        # recreate/reconfigure KB, then retry once
        ...

Prevention

When it happens

Trigger: POST /api/v1/knowledge_bases/{kb_name}/upload against a KB whose directory contains no readable embedding metadata (missing/corrupted metadata file, or a KB directory created without ever selecting an embedding model). Also possible if kb_name resolves to a directory the metadata reader cannot parse.

Common situations: Using a KB created before embedding config was mandatory, deleting or partially copying the KB folder on disk, failed prior KB creation that left an empty directory, or pointing at a KB name that maps to a stale directory.

Related errors

AI-assisted analysis of langflow-ai/langflow@976ec789d2 (2026-08-14). Data as JSON: /api/errors/5432ad7d3a135913. Report an issue: GitHub.