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* fix(anthropic/chat/transformation.py): support anthropic disable_parallel_tool_use param Fixes https://github.com/BerriAI/litellm/issues/6456 * feat(anthropic/chat/transformation.py): support anthropic computer tool use Closes https://github.com/BerriAI/litellm/issues/6427 * fix(vertex_ai/common_utils.py): parse out '$schema' when calling vertex ai Fixes issue when trying to call vertex from vercel sdk * fix(main.py): add 'extra_headers' support for azure on all translation endpoints Fixes https://github.com/BerriAI/litellm/issues/6465 * fix: fix linting errors * fix(transformation.py): handle no beta headers for anthropic * test: cleanup test * fix: fix linting error * fix: fix linting errors * fix: fix linting errors * fix(transformation.py): handle dummy tool call * fix(main.py): fix linting error * fix(azure.py): pass required param * LiteLLM Minor Fixes & Improvements (10/24/2024) (#6441) * fix(azure.py): handle /openai/deployment in azure api base * fix(factory.py): fix faulty anthropic tool result translation check Fixes https://github.com/BerriAI/litellm/issues/6422 * fix(gpt_transformation.py): add support for parallel_tool_calls to azure Fixes https://github.com/BerriAI/litellm/issues/6440 * fix(factory.py): support anthropic prompt caching for tool results * fix(vertex_ai/common_utils): don't pop non-null required field Fixes https://github.com/BerriAI/litellm/issues/6426 * feat(vertex_ai.py): support code_execution tool call for vertex ai + gemini Closes https://github.com/BerriAI/litellm/issues/6434 * build(model_prices_and_context_window.json): Add 'supports_assistant_prefill' for bedrock claude-3-5-sonnet v2 models Closes https://github.com/BerriAI/litellm/issues/6437 * fix(types/utils.py): fix linting * test: update test to include required fields * test: fix test * test: handle flaky test * test: remove e2e test - hitting gemini rate limits * Litellm dev 10 26 2024 (#6472) * docs(exception_mapping.md): add missing exception types Fixes https://github.com/Aider-AI/aider/issues/2120#issuecomment-2438971183 * fix(main.py): register custom model pricing with specific key Ensure custom model pricing is registered to the specific model+provider key combination * test: make testing more robust for custom pricing * fix(redis_cache.py): instrument otel logging for sync redis calls ensures complete coverage for all redis cache calls * (Testing) Add unit testing for DualCache - ensure in memory cache is used when expected (#6471) * test test_dual_cache_get_set * unit testing for dual cache * fix async_set_cache_sadd * test_dual_cache_local_only * redis otel tracing + async support for latency routing (#6452) * docs(exception_mapping.md): add missing exception types Fixes https://github.com/Aider-AI/aider/issues/2120#issuecomment-2438971183 * fix(main.py): register custom model pricing with specific key Ensure custom model pricing is registered to the specific model+provider key combination * test: make testing more robust for custom pricing * fix(redis_cache.py): instrument otel logging for sync redis calls ensures complete coverage for all redis cache calls * refactor: pass parent_otel_span for redis caching calls in router allows for more observability into what calls are causing latency issues * test: update tests with new params * refactor: ensure e2e otel tracing for router * refactor(router.py): add more otel tracing acrosss router catch all latency issues for router requests * fix: fix linting error * fix(router.py): fix linting error * fix: fix test * test: fix tests * fix(dual_cache.py): pass ttl to redis cache * fix: fix param * fix(dual_cache.py): set default value for parent_otel_span * fix(transformation.py): support 'response_format' for anthropic calls * fix(transformation.py): check for cache_control inside 'function' block * fix: fix linting error * fix: fix linting errors --------- Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>
89 lines
2.9 KiB
Python
89 lines
2.9 KiB
Python
import asyncio
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import traceback
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from typing import TYPE_CHECKING, Any, Optional
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from litellm._logging import verbose_router_logger
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from litellm.router_utils.cooldown_handlers import _async_get_cooldown_deployments
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from litellm.types.integrations.slack_alerting import AlertType
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from litellm.types.router import RouterRateLimitError
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if TYPE_CHECKING:
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from opentelemetry.trace import Span as _Span
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from litellm.router import Router as _Router
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LitellmRouter = _Router
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Span = _Span
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else:
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LitellmRouter = Any
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Span = Any
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async def send_llm_exception_alert(
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litellm_router_instance: LitellmRouter,
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request_kwargs: dict,
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error_traceback_str: str,
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original_exception,
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):
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"""
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Only runs if router.slack_alerting_logger is set
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Sends a Slack / MS Teams alert for the LLM API call failure. Only if router.slack_alerting_logger is set.
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Parameters:
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litellm_router_instance (_Router): The LitellmRouter instance.
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original_exception (Any): The original exception that occurred.
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Returns:
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None
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"""
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if litellm_router_instance is None:
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return
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if not hasattr(litellm_router_instance, "slack_alerting_logger"):
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return
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if litellm_router_instance.slack_alerting_logger is None:
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return
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if "proxy_server_request" in request_kwargs:
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# Do not send any alert if it's a request from litellm proxy server request
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# the proxy is already instrumented to send LLM API call failures
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return
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litellm_debug_info = getattr(original_exception, "litellm_debug_info", None)
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exception_str = str(original_exception)
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if litellm_debug_info is not None:
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exception_str += litellm_debug_info
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exception_str += f"\n\n{error_traceback_str[:2000]}"
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await litellm_router_instance.slack_alerting_logger.send_alert(
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message=f"LLM API call failed: `{exception_str}`",
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level="High",
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alert_type=AlertType.llm_exceptions,
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alerting_metadata={},
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)
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async def async_raise_no_deployment_exception(
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litellm_router_instance: LitellmRouter, model: str, parent_otel_span: Optional[Span]
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):
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"""
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Raises a RouterRateLimitError if no deployment is found for the given model.
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"""
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verbose_router_logger.info(
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f"get_available_deployment for model: {model}, No deployment available"
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)
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model_ids = litellm_router_instance.get_model_ids(model_name=model)
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_cooldown_time = litellm_router_instance.cooldown_cache.get_min_cooldown(
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model_ids=model_ids, parent_otel_span=parent_otel_span
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)
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_cooldown_list = await _async_get_cooldown_deployments(
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litellm_router_instance=litellm_router_instance,
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parent_otel_span=parent_otel_span,
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)
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return RouterRateLimitError(
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model=model,
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cooldown_time=_cooldown_time,
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enable_pre_call_checks=litellm_router_instance.enable_pre_call_checks,
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cooldown_list=_cooldown_list,
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)
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