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# What does this PR do? This PR adds support for Conversations in Responses. <!-- If resolving an issue, uncomment and update the line below --> <!-- Closes #[issue-number] --> ## Test Plan Unit tests Integration tests <Details> <Summary>Manual testing with this script: (click to expand)</Summary> ```python from openai import OpenAI client = OpenAI() client = OpenAI(base_url="http://localhost:8321/v1/", api_key="none") def test_conversation_create(): print("Testing conversation create...") conversation = client.conversations.create( metadata={"topic": "demo"}, items=[ {"type": "message", "role": "user", "content": "Hello!"} ] ) print(f"Created: {conversation}") return conversation def test_conversation_retrieve(conv_id): print(f"Testing conversation retrieve for {conv_id}...") retrieved = client.conversations.retrieve(conv_id) print(f"Retrieved: {retrieved}") return retrieved def test_conversation_update(conv_id): print(f"Testing conversation update for {conv_id}...") updated = client.conversations.update( conv_id, metadata={"topic": "project-x"} ) print(f"Updated: {updated}") return updated def test_conversation_delete(conv_id): print(f"Testing conversation delete for {conv_id}...") deleted = client.conversations.delete(conv_id) print(f"Deleted: {deleted}") return deleted def test_conversation_items_create(conv_id): print(f"Testing conversation items create for {conv_id}...") items = client.conversations.items.create( conv_id, items=[ { "type": "message", "role": "user", "content": [{"type": "input_text", "text": "Hello!"}] }, { "type": "message", "role": "user", "content": [{"type": "input_text", "text": "How are you?"}] } ] ) print(f"Items created: {items}") return items def test_conversation_items_list(conv_id): print(f"Testing conversation items list for {conv_id}...") items = client.conversations.items.list(conv_id, limit=10) print(f"Items list: {items}") return items def test_conversation_item_retrieve(conv_id, item_id): print(f"Testing conversation item retrieve for {conv_id}/{item_id}...") item = client.conversations.items.retrieve(conversation_id=conv_id, item_id=item_id) print(f"Item retrieved: {item}") return item def test_conversation_item_delete(conv_id, item_id): print(f"Testing conversation item delete for {conv_id}/{item_id}...") deleted = client.conversations.items.delete(conversation_id=conv_id, item_id=item_id) print(f"Item deleted: {deleted}") return deleted def test_conversation_responses_create(): print("\nTesting conversation create for a responses example...") conversation = client.conversations.create() print(f"Created: {conversation}") response = client.responses.create( model="gpt-4.1", input=[{"role": "user", "content": "What are the 5 Ds of dodgeball?"}], conversation=conversation.id, ) print(f"Created response: {response} for conversation {conversation.id}") return response, conversation def test_conversations_responses_create_followup( conversation, content="Repeat what you just said but add 'this is my second time saying this'", ): print(f"Using: {conversation.id}") response = client.responses.create( model="gpt-4.1", input=[{"role": "user", "content": content}], conversation=conversation.id, ) print(f"Created response: {response} for conversation {conversation.id}") conv_items = client.conversations.items.list(conversation.id) print(f"\nRetrieving list of items for conversation {conversation.id}:") print(conv_items.model_dump_json(indent=2)) def test_response_with_fake_conv_id(): fake_conv_id = "conv_zzzzzzzzz5dc81908289d62779d2ac510a2b0b602ef00a44" print(f"Using {fake_conv_id}") try: response = client.responses.create( model="gpt-4.1", input=[{"role": "user", "content": "say hello"}], conversation=fake_conv_id, ) print(f"Created response: {response} for conversation {fake_conv_id}") except Exception as e: print(f"failed to create response for conversation {fake_conv_id} with error {e}") def main(): print("Testing OpenAI Conversations API...") # Create conversation conversation = test_conversation_create() conv_id = conversation.id # Retrieve conversation test_conversation_retrieve(conv_id) # Update conversation test_conversation_update(conv_id) # Create items items = test_conversation_items_create(conv_id) # List items items_list = test_conversation_items_list(conv_id) # Retrieve specific item if items_list.data: item_id = items_list.data[0].id test_conversation_item_retrieve(conv_id, item_id) # Delete item test_conversation_item_delete(conv_id, item_id) # Delete conversation test_conversation_delete(conv_id) response, conversation2 = test_conversation_responses_create() print('\ntesting reseponse retrieval') test_conversation_retrieve(conversation2.id) print('\ntesting responses follow up') test_conversations_responses_create_followup(conversation2) print('\ntesting responses follow up x2!') test_conversations_responses_create_followup( conversation2, content="Repeat what you just said but add 'this is my third time saying this'", ) test_response_with_fake_conv_id() print("All tests completed!") if __name__ == "__main__": main() ``` </Details> --------- Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
289 lines
9.4 KiB
Python
289 lines
9.4 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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import logging # allow-direct-logging
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import os
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import re
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from logging.config import dictConfig # allow-direct-logging
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from rich.console import Console
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from rich.errors import MarkupError
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from rich.logging import RichHandler
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from llama_stack.core.datatypes import LoggingConfig
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# Default log level
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DEFAULT_LOG_LEVEL = logging.INFO
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# Predefined categories
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CATEGORIES = [
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"core",
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"server",
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"router",
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"inference",
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"agents",
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"safety",
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"eval",
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"tools",
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"client",
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"telemetry",
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"openai",
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"openai_responses",
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"openai_conversations",
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"testing",
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"providers",
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"models",
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"files",
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"vector_io",
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"tool_runtime",
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"cli",
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"post_training",
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"scoring",
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"tests",
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]
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UNCATEGORIZED = "uncategorized"
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# Initialize category levels with default level
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_category_levels: dict[str, int] = dict.fromkeys(CATEGORIES, DEFAULT_LOG_LEVEL)
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def config_to_category_levels(category: str, level: str):
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"""
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Helper function to be called either by environment parsing or yaml parsing to go from a list of categories and levels to a dictionary ready to be
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used by the logger dictConfig.
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Parameters:
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category (str): logging category to apply the level to
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level (str): logging level to be used in the category
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Returns:
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Dict[str, int]: A dictionary mapping categories to their log levels.
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"""
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category_levels: dict[str, int] = {}
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level_value = logging._nameToLevel.get(str(level).upper())
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if level_value is None:
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logging.warning(f"Unknown log level '{level}' for category '{category}'. Falling back to default 'INFO'.")
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return category_levels
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if category == "all":
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# Apply the log level to all categories and the root logger
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for cat in CATEGORIES:
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category_levels[cat] = level_value
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# Set the root logger's level to the specified level
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category_levels["root"] = level_value
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elif category in CATEGORIES:
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category_levels[category] = level_value
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else:
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logging.warning(f"Unknown logging category: {category}. No changes made.")
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return category_levels
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def parse_yaml_config(yaml_config: LoggingConfig) -> dict[str, int]:
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"""
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Helper function to parse a yaml logging configuration found in the run.yaml
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Parameters:
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yaml_config (Logging): the logger config object found in the run.yaml
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Returns:
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Dict[str, int]: A dictionary mapping categories to their log levels.
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"""
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category_levels = {}
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for category, level in yaml_config.category_levels.items():
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category_levels.update(config_to_category_levels(category=category, level=level))
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return category_levels
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def parse_environment_config(env_config: str) -> dict[str, int]:
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"""
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Parse the LLAMA_STACK_LOGGING environment variable and return a dictionary of category log levels.
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Parameters:
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env_config (str): The value of the LLAMA_STACK_LOGGING environment variable.
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Returns:
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Dict[str, int]: A dictionary mapping categories to their log levels.
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"""
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category_levels = {}
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delimiter = ","
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for pair in env_config.split(delimiter):
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if not pair.strip():
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continue
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try:
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category, level = pair.split("=", 1)
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category = category.strip().lower()
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level = level.strip().upper() # Convert to uppercase for logging._nameToLevel
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category_levels.update(config_to_category_levels(category=category, level=level))
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except ValueError:
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logging.warning(f"Invalid logging configuration: '{pair}'. Expected format: 'category=level'.")
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return category_levels
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def strip_rich_markup(text):
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"""Remove Rich markup tags like [dim], [bold magenta], etc."""
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return re.sub(r"\[/?[a-zA-Z0-9 _#=,]+\]", "", text)
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class CustomRichHandler(RichHandler):
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def __init__(self, *args, **kwargs):
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# Set a reasonable default width for console output, especially when redirected to files
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console_width = int(os.environ.get("LLAMA_STACK_LOG_WIDTH", "120"))
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# Don't force terminal codes to avoid ANSI escape codes in log files
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kwargs["console"] = Console(width=console_width)
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super().__init__(*args, **kwargs)
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def emit(self, record):
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"""Override emit to handle markup errors gracefully."""
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try:
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super().emit(record)
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except MarkupError:
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original_markup = self.markup
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self.markup = False
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try:
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super().emit(record)
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finally:
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self.markup = original_markup
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class CustomFileHandler(logging.FileHandler):
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def __init__(self, filename, mode="a", encoding=None, delay=False):
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super().__init__(filename, mode, encoding, delay)
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# Default formatter to match console output
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self.default_formatter = logging.Formatter("%(asctime)s %(name)s:%(lineno)d %(category)s: %(message)s")
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self.setFormatter(self.default_formatter)
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def emit(self, record):
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if hasattr(record, "msg"):
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record.msg = strip_rich_markup(str(record.msg))
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super().emit(record)
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def setup_logging(category_levels: dict[str, int], log_file: str | None) -> None:
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"""
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Configure logging based on the provided category log levels and an optional log file.
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Parameters:
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category_levels (Dict[str, int]): A dictionary mapping categories to their log levels.
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log_file (str): Path to a log file to additionally pipe the logs into
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"""
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log_format = "%(asctime)s %(name)s:%(lineno)d %(category)s: %(message)s"
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class CategoryFilter(logging.Filter):
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"""Ensure category is always present in log records."""
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def filter(self, record):
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if not hasattr(record, "category"):
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record.category = UNCATEGORIZED # Default to 'uncategorized' if no category found
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return True
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# Determine the root logger's level (default to WARNING if not specified)
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root_level = category_levels.get("root", logging.WARNING)
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handlers = {
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"console": {
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"()": CustomRichHandler, # Use custom console handler
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"formatter": "rich",
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"rich_tracebacks": True,
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"show_time": False,
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"show_path": False,
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"markup": True,
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"filters": ["category_filter"],
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}
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}
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# Add a file handler if log_file is set
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if log_file:
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handlers["file"] = {
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"()": CustomFileHandler,
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"filename": log_file,
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"mode": "a",
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"encoding": "utf-8",
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}
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logging_config = {
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"version": 1,
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"disable_existing_loggers": False,
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"formatters": {
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"rich": {
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"()": logging.Formatter,
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"format": log_format,
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}
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},
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"handlers": handlers,
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"filters": {
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"category_filter": {
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"()": CategoryFilter,
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}
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},
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"loggers": {
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category: {
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"handlers": list(handlers.keys()), # Apply all handlers
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"level": category_levels.get(category, DEFAULT_LOG_LEVEL),
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"propagate": False, # Disable propagation to root logger
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}
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for category in CATEGORIES
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},
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"root": {
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"handlers": list(handlers.keys()),
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"level": root_level, # Set root logger's level dynamically
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},
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}
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dictConfig(logging_config)
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# Ensure third-party libraries follow the root log level
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for _, logger in logging.root.manager.loggerDict.items():
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if isinstance(logger, logging.Logger):
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logger.setLevel(root_level)
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def get_logger(
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name: str, category: str = "uncategorized", config: LoggingConfig | None | None = None
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) -> logging.LoggerAdapter:
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"""
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Returns a logger with the specified name and category.
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If no category is provided, defaults to 'uncategorized'.
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Parameters:
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name (str): The name of the logger (e.g., module or filename).
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category (str): The category of the logger (default 'uncategorized').
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config (Logging): optional yaml config to override the existing logger configuration
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Returns:
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logging.LoggerAdapter: Configured logger with category support.
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"""
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if config:
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_category_levels.update(parse_yaml_config(config))
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logger = logging.getLogger(name)
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if category in _category_levels:
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log_level = _category_levels[category]
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else:
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root_category = category.split("::")[0]
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if root_category in _category_levels:
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log_level = _category_levels[root_category]
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else:
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if category != UNCATEGORIZED:
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raise ValueError(
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f"Unknown logging category: {category}. To resolve, choose a valid category from the CATEGORIES list "
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f"or add it to the CATEGORIES list. Available categories: {CATEGORIES}"
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)
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log_level = _category_levels.get("root", DEFAULT_LOG_LEVEL)
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logger.setLevel(log_level)
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return logging.LoggerAdapter(logger, {"category": category})
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env_config = os.environ.get("LLAMA_STACK_LOGGING", "")
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if env_config:
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_category_levels.update(parse_environment_config(env_config))
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log_file = os.environ.get("LLAMA_STACK_LOG_FILE")
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setup_logging(_category_levels, log_file)
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