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https://github.com/Significant-Gravitas/AutoGPT.git
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refactor(backend): remove unused log_once_per_task function
The function was defined but never actually used in the codebase. With the logging improvements from other PRs, this is no longer needed.
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@@ -6,7 +6,6 @@ Handles generation and storage of OpenAI embeddings for all content types
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"""
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import asyncio
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import contextvars
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import logging
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import time
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from typing import Any
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@@ -22,12 +21,6 @@ from backend.util.json import dumps
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logger = logging.getLogger(__name__)
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# Context variable to track errors logged in the current task/operation
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# This prevents spamming the same error multiple times when processing batches
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_logged_errors: contextvars.ContextVar[set[str]] = contextvars.ContextVar(
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"_logged_errors"
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)
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# OpenAI embedding model configuration
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EMBEDDING_MODEL = "text-embedding-3-small"
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# Embedding dimension for the model above
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@@ -37,42 +30,6 @@ EMBEDDING_DIM = 1536
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EMBEDDING_MAX_TOKENS = 8191
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def log_once_per_task(error_key: str, log_fn, message: str, **kwargs) -> bool:
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"""
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Log an error/warning only once per task/operation to avoid log spam.
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Uses contextvars to track what has been logged in the current async context.
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Useful when processing batches where the same error might occur for many items.
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Args:
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error_key: Unique identifier for this error type
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log_fn: Logger function to call (e.g., logger.error, logger.warning)
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message: Message to log
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**kwargs: Additional arguments to pass to log_fn
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Returns:
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True if the message was logged, False if it was suppressed (already logged)
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Example:
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log_once_per_task("missing_api_key", logger.error, "API key not set")
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"""
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# Get current logged errors, or create a new set if this is the first call in this context
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logged = _logged_errors.get(None)
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if logged is None:
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logged = set()
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_logged_errors.set(logged)
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if error_key in logged:
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return False
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# Log the message with a note that it will only appear once
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log_fn(f"{message} (This message will only be shown once per task.)", **kwargs)
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# Mark as logged
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logged.add(error_key)
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return True
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def build_searchable_text(
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name: str,
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description: str,
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