What causes "File embedding failed." in danny-avila/LibreChat?

Check the RAG API service logs for the specific embedding failure that occurred.

File embedding failed.

Error message

File embedding failed.

What it means

Thrown by uploadVectors when the RAG API's /embed endpoint responds with HTTP 200 but the response body contains status: false. This indicates the RAG API received the file and recognized its type (known_type was not false), but the embedding process itself failed — the server could not generate or store the vector embeddings. This is a server-side processing failure, distinct from an unsupported file type.

Source

Thrown at api/server/services/Files/VectorDB/crud.js:104

    const formHeaders = formData.getHeaders();

    const response = await axios.post(`${process.env.RAG_API_URL}/embed`, formData, {
      headers: {
        Authorization: `Bearer ${jwtToken}`,
        accept: 'application/json',
        ...formHeaders,
      },
    });

    const responseData = response.data;
    logger.debug('Response from embedding file', responseData);

    if (responseData.known_type === false) {
      throw new Error(`File embedding failed. The filetype ${file.mimetype} is not supported`);
    }

    if (!responseData.status) {
      throw new Error('File embedding failed.');
    }

    return {
      bytes: file.size,
      filename: file.originalname,
      filepath: FileSources.vectordb,
      embedded: Boolean(responseData.known_type),
    };
  } catch (error) {
    logAxiosError({
      error,
      message: 'Error uploading vectors',
    });
    throw new Error(error.message || 'An error occurred during file upload.');
  }
}

module.exports = {

View on GitHub (pinned to 5ff282f900)

Solutions

Check the RAG API service logs for the specific embedding failure that occurred.
Verify the RAG API's embedding model and vector database are operational.
Retry the upload after a brief delay — transient RAG API failures often resolve.
If the failure is persistent, test with a known-good file (e.g., a small plaintext .txt) to isolate whether the issue is file-specific or systemic.

Defensive patterns

Strategy: retry

Try / catch

try {
  await uploadVectors({ req, file, file_id });
} catch (error) {
  if (error.message === 'File embedding failed.') {
    // RAG API processing failure — retry with backoff
    await retryWithBackoff(() => uploadVectors({ req, file, file_id }), { retries: 3 });
  }
  throw error;
}

Prevention

Monitor RAG API embedding model availability and vector database health.
Implement retry logic for transient embedding failures.
Test with a known-good file to distinguish file-specific issues from systemic failures.
Log RAG API responses for debugging embedding pipeline issues.

When it happens

Trigger: Calling uploadVectors({ req, file, file_id }) where the RAG API returns { status: false, known_type: true }. The RAG API's embedding pipeline encountered an error: database write failure, embedding model unavailable, document parsing error, or resource exhaustion.

Common situations: The RAG API's embedding model (e.g., an OpenAI embeddings endpoint or local model) is unavailable or rate-limited. Or the RAG API's vector database is down or full. Or the file content triggered a parsing error (e.g., a corrupted PDF). Or the RAG API has a transient internal error.

Related errors

AI-assisted analysis of danny-avila/LibreChat@5ff282f900 (2026-08-12). Data as JSON: /api/errors/e758047217b42e5e. Report an issue: GitHub.