mirror of
https://github.com/lfnovo/open-notebook.git
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5d84ab0768
- Add batching to generate_embeddings() (50 texts per batch with per-batch retry) to prevent 413 Payload Too Large errors on large documents - Add 413 error classification rule for user-friendly error messages - Fix misleading "Created 0 embedded chunks" log in process_source_command by removing premature get_embedded_chunks() call (embedding is fire-and-forget) Closes #594
103 lines
3.6 KiB
Python
103 lines
3.6 KiB
Python
"""
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Error classification utility for LLM provider errors.
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Maps raw exceptions from AI providers/Esperanto/LangChain to user-friendly
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error messages and appropriate exception types.
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"""
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from loguru import logger
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from open_notebook.exceptions import (
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AuthenticationError,
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ConfigurationError,
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ExternalServiceError,
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NetworkError,
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OpenNotebookError,
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RateLimitError,
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)
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# Classification rules: (keywords, exception_class, user_message or None to pass through)
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_CLASSIFICATION_RULES: list[tuple[list[str], type[OpenNotebookError], str | None]] = [
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# Authentication errors
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(
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["authentication", "unauthorized", "invalid api key", "invalid_api_key", "401"],
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AuthenticationError,
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"Authentication failed. Please check your API key in Settings -> Credentials.",
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),
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# Rate limit errors
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(
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["rate limit", "rate_limit", "429", "too many requests", "quota exceeded"],
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RateLimitError,
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"Rate limit exceeded. Please wait a moment and try again.",
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),
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# Model not found (pass through original message)
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(
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["model not found", "does not exist", "model_not_found"],
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ConfigurationError,
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None,
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),
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# Configuration errors from provision.py (pass through)
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(
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["no model configured", "please go to settings"],
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ConfigurationError,
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None,
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),
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# Network errors
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(
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["connecterror", "timeoutexception", "connection refused", "connection error", "timed out", "timeout"],
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NetworkError,
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"Could not connect to the AI provider. Please check your network connection and provider URL.",
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),
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# Context length errors
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(
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["context length", "token limit", "maximum context", "context_length_exceeded", "max_tokens"],
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ExternalServiceError,
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"Content too large for the selected model. Try using a smaller selection or a model with a larger context window.",
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),
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# Payload too large errors
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(
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["413", "payload too large", "request entity too large"],
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ExternalServiceError,
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"The request payload is too large for the AI provider. Try reducing the content size or using a different model.",
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),
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# Provider availability errors
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(
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["500", "502", "503", "service unavailable", "overloaded", "internal server error"],
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ExternalServiceError,
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"The AI provider is temporarily unavailable. Please try again in a few minutes.",
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),
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]
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def classify_error(exception: BaseException) -> tuple[type[OpenNotebookError], str]:
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"""
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Classify a raw exception into a user-friendly error type and message.
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Args:
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exception: Any exception from LLM providers/Esperanto/LangChain
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Returns:
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Tuple of (exception_class, user_friendly_message)
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"""
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error_str = str(exception).lower()
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error_type_name = type(exception).__name__.lower()
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combined = f"{error_type_name}: {error_str}"
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for keywords, exc_class, message in _CLASSIFICATION_RULES:
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for keyword in keywords:
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if keyword in combined:
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user_message = message if message is not None else _truncate(str(exception))
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return exc_class, user_message
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# Unclassified error - log for future improvement
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logger.warning(
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f"Unclassified LLM error ({type(exception).__name__}): {exception}"
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)
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return ExternalServiceError, f"AI service error: {_truncate(str(exception))}"
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def _truncate(text: str, max_length: int = 200) -> str:
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"""Truncate text to max_length to avoid leaking verbose internal details."""
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if len(text) <= max_length:
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return text
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return text[:max_length] + "..."
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