mirror of
https://github.com/tiennm99/litellm.git
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f42ffed2bd
* fix(vertex_ai): support pluggable (executable) credential_source for WIF auth (#24700) The WIF credential dispatch in load_auth() only handled identity_pool and aws credential types. When credential_source.executable was present (used for Azure Managed Identity via Workload Identity Federation), it fell through to identity_pool.Credentials which rejected it with MalformedError. Add dispatch to google.auth.pluggable.Credentials for executable-type credential sources, following the same pattern as the existing identity_pool and aws helpers. Fixes authentication for Azure Container Apps → GCP Vertex AI via WIF with executable credential sources. * feat(logging): add component and logger fields to JSON logs for 3rd p… (#24447) * feat(logging): add component and logger fields to JSON logs for 3rd party filtering * Let user-supplied extra fields win over auto-generated component/logger, tighten test assertions * Feat - Add organization into the metrics metadata for org_id & org_alias (#24440) * Add org_id and org_alias label names to Prometheus metric definitions * Add user_api_key_org_alias to StandardLoggingUserAPIKeyMetadata * Populate user_api_key_org_alias in pre-call metadata * Pass org_id and org_alias into per-request Prometheus metric labels * Add test for org labels on per-request Prometheus metrics * chore: resolve test mockdata * Address review: populate org_alias from DB view, add feature flag, use .get() for org metadata * Add org labels to failure path and verify flag behavior in test * Fix test: build flag-off enum_values without org fields * Gate org labels behind feature flag in get_labels() instead of static metric lists * Scope org label injection to metrics that carry team context, remove orphaned budget label defs, add test teardown * Use explicit metric allowlist for org label injection instead of team heuristic * Fix duplicate org label guard, move _org_label_metrics to class constant * Reset custom_prometheus_metadata_labels after duplicate label assertion * fix: emit org labels by default, remove flag, fix missing org_alias in all metadata paths * fix: emit org labels by default, no opt-in flag required * fix: write org_alias to metadata unconditionally in proxy_server.py * fix: 429s from batch creation being converted to 500 (#24703) * add us gov models (#24660) * add us gov models * added max tokens * Litellm dev 04 02 2026 p1 (#25052) * fix: replace hardcoded url * fix: Anthropic web search cost not tracked for Chat Completions The ModelResponse branch in response_object_includes_web_search_call() only checked url_citation annotations and prompt_tokens_details, missing Anthropic's server_tool_use.web_search_requests field. This caused _handle_web_search_cost() to never fire for Anthropic Claude models. Also routes vertex_ai/claude-* models to the Anthropic cost calculator instead of the Gemini one, since Claude on Vertex uses the same server_tool_use billing structure as the direct Anthropic API. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com> * fix(anthropic): pass logging_obj to client.post for litellm_overhead_time_ms (#24071) When LITELLM_DETAILED_TIMING=true, litellm_overhead_time_ms was null for Anthropic because the handler did not pass logging_obj to client.post(), so track_llm_api_timing could not set llm_api_duration_ms. Pass logging_obj=logging_obj at all four post() call sites (make_call, make_sync_call, acompletion, completion). Add test to ensure make_call passes logging_obj to client.post. Made-with: Cursor * sap - add additional parameters for grounding - additional parameter for grounding added for the sap provider * sap - fix models * (sap) add filtering, masking, translation SAP GEN AI Hub modules * (sap) add tests and docs for new SAP modules * (sap) add support of multiple modules config * (sap) code refactoring * (sap) rename file * test(): add safeguard tests * (sap) update tests * (sap) update docs, solve merge conflict in transformation.py * (sap) linter fix * (sap) Align embedding request transformation with current API * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) mock commit * (sap) run black formater * (sap) add literals to models, add negative tests, fix test for tool transformation * (sap) fix formating * (sap) fix models * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) commit for rerun bot review * (sap) minor improve * (sap) fix after bot review * (sap) lint fix * docs(sap): update documentation * fix(sap): change creds priority * fix(sap): change creds priority * fix(sap): fix sap creds unit test * fix(sap): linter fix * fix(sap): linter fix * linter fix * (sap) update logic of fetching creds, add additional tests * (sap) clean up code * (sap) fix after review * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) add a possibility to put the service key by both variants * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) update test * (sap) update service key resolve function * (sap) run black formater * (sap) fix validate credentials, add negative tests for credential fetching * (sap) fix validate credentials, add negative tests for credential fetching * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) lint fix * (sap) lint fix * feat: support service_tier in gemini * chore: add a service_tier field mapping from openai to gemini * fix: use x-gemini-service-tier header in response * docs: add service_tier to gemini docs * chore: add defaut/standard mapping, and some tests * chore: tidying up some case insensitivity * chore: remove unnecessary guard * fix: remove redundant test file * fix: handle 'auto' case-insensitively * fix: return service_tier on final steamed chunk * chore: black * feat: enable supports_service_tier to gemini models * Fix get_standard_logging_metadata tests * Fix test_get_model_info_bedrock_models * Fix test_get_model_info_bedrock_models * Fix remaining tests * Fix mypy issues * Fix tests * Fix merge conflicts * Fix code qa * Fix code qa * Fix code qa * Fix greptile review --------- Co-authored-by: michelligabriele <gabriele.michelli@icloud.com> Co-authored-by: Josh <36064836+J-Byron@users.noreply.github.com> Co-authored-by: mubashir1osmani <mubashir.osmani777@gmail.com> Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com> Co-authored-by: milan-berri <milan@berri.ai> Co-authored-by: Alperen Kömürcü <alperen.koemuercue@sap.com> Co-authored-by: Vasilisa Parshikova <vasilisa.parshikova@sap.com> Co-authored-by: Lin Xu <lin.xu03@sap.com> Co-authored-by: Mark McDonald <macd@google.com> Co-authored-by: Sameer Kankute <sameer@berri.ai>
331 lines
11 KiB
Python
331 lines
11 KiB
Python
import asyncio
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import json
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import os
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import sys
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from typing import List
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import pytest
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sys.path.insert(
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0, os.path.abspath("../../..")
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) # Adds the parent directory to the system-path
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import logging
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import sys
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import litellm
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from litellm._logging import (
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ALL_LOGGERS,
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JsonFormatter,
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_initialize_loggers_with_handler,
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_turn_on_json,
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verbose_logger,
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verbose_proxy_logger,
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verbose_router_logger,
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)
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from litellm.integrations.custom_logger import CustomLogger
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from litellm.types.utils import StandardLoggingPayload
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class CacheHitCustomLogger(CustomLogger):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.logged_standard_logging_payloads: List[StandardLoggingPayload] = []
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async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
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standard_logging_payload = kwargs.get("standard_logging_object", None)
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if standard_logging_payload:
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self.logged_standard_logging_payloads.append(standard_logging_payload)
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def test_json_mode_emits_one_record_per_logger(capfd):
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# Turn on JSON logging
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_turn_on_json()
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# Make sure our loggers will emit INFO-level records
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for lg in (verbose_logger, verbose_router_logger, verbose_proxy_logger):
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lg.setLevel(logging.INFO)
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# Log one message from each logger at different levels
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verbose_logger.info("first info")
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verbose_router_logger.info("second info from router")
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verbose_proxy_logger.info("third info from proxy")
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# Capture stdout
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out, err = capfd.readouterr()
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print("out", out)
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print("err", err)
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lines = [l for l in err.splitlines() if l.strip()]
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# Expect exactly three JSON lines
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assert len(lines) == 3, f"got {len(lines)} lines, want 3: {lines!r}"
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# Each line must be valid JSON with the required fields
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for line in lines:
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obj = json.loads(line)
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assert "message" in obj, "`message` key missing"
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assert "level" in obj, "`level` key missing"
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assert "timestamp" in obj, "`timestamp` key missing"
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def test_json_formatter_parses_embedded_json_message():
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"""
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Test that JsonFormatter parses embedded JSON in the message field and promotes
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sub-fields to first-class JSON properties for downstream querying.
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"""
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formatter = JsonFormatter()
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record = logging.LogRecord(
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name="LiteLLM",
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level=logging.DEBUG,
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pathname="",
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lineno=0,
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msg='{"event": "giveup", "exception": "Connection failed", "model_name": "gpt-4"}',
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args=(),
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exc_info=None,
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)
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output = formatter.format(record)
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obj = json.loads(output)
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# Standard fields preserved
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assert "message" in obj
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assert obj["level"] == "DEBUG"
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assert "timestamp" in obj
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# Embedded JSON fields promoted to top-level for querying
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assert obj["event"] == "giveup"
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assert obj["exception"] == "Connection failed"
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assert obj["model_name"] == "gpt-4"
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def test_json_formatter_includes_extra_attributes():
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"""
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Test that JsonFormatter includes extra attributes from logger.debug("msg", extra={...}).
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"""
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formatter = JsonFormatter()
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record = logging.LogRecord(
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name="LiteLLM",
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level=logging.DEBUG,
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pathname="",
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lineno=0,
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msg="POST Request Sent from LiteLLM",
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args=(),
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exc_info=None,
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)
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record.api_base = "https://api.openai.com"
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record.authorization = "Bearer sk-***"
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output = formatter.format(record)
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obj = json.loads(output)
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assert obj["message"] == "POST Request Sent from LiteLLM"
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assert obj["api_base"] == "https://api.openai.com"
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assert obj["authorization"] == "Bearer sk-***"
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def test_json_formatter_plain_message_unchanged():
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"""
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Test that non-JSON messages are passed through as-is in the message field.
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"""
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formatter = JsonFormatter()
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record = logging.LogRecord(
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name="LiteLLM",
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level=logging.INFO,
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pathname="",
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lineno=0,
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msg="Cache hit!",
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args=(),
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exc_info=None,
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)
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output = formatter.format(record)
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obj = json.loads(output)
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assert obj["message"] == "Cache hit!"
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assert "event" not in obj
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assert "exception" not in obj
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def test_json_formatter_parses_embedded_python_dict_repr():
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"""
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Test that JsonFormatter parses Python dict repr (str/deployment) embedded in
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plain text, e.g. from get_available_deployment logs.
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Reproduces Roni's reported case.
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"""
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formatter = JsonFormatter()
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msg = (
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"get_available_deployment for model: text-embedding-3-large, "
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"Selected deployment: {'model_name': 'text-embedding-3-large', "
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"'litellm_params': {'api_key': 'sk**********', 'tpm': 1000000, 'rpm': 2000, "
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"'use_in_pass_through': False, 'use_litellm_proxy': False, "
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"'merge_reasoning_content_in_choices': False, 'model': 'text-embedding-3-large'}, "
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"'model_info': {'id': 'a624b057aec64ada48311', 'db_model': False}} "
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"for model: text-embedding-3-large"
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)
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record = logging.LogRecord(
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name="LiteLLM Router",
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level=logging.INFO,
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pathname="",
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lineno=0,
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msg=msg,
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args=(),
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exc_info=None,
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)
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output = formatter.format(record)
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obj = json.loads(output)
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assert "message" in obj
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assert obj["level"] == "INFO"
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# Python dict parsed and promoted to first-class properties
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assert obj["model_name"] == "text-embedding-3-large"
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assert "litellm_params" in obj
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assert obj["litellm_params"]["api_key"] == "sk**********"
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assert obj["litellm_params"]["tpm"] == 1000000
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assert obj["litellm_params"]["use_in_pass_through"] is False
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assert "model_info" in obj
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assert obj["model_info"]["id"] == "a624b057aec64ada48311"
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assert obj["model_info"]["db_model"] is False
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def test_json_formatter_includes_component_field():
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"""
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Test that JsonFormatter always emits a 'component' field equal to the logger name.
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This allows filtering by component (e.g. "LiteLLM Proxy") in Datadog / third-party log services.
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"""
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formatter = JsonFormatter()
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for logger_name in ("LiteLLM Proxy", "LiteLLM Router", "LiteLLM"):
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record = logging.LogRecord(
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name=logger_name,
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level=logging.ERROR,
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pathname="proxy_server.py",
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lineno=42,
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msg="something went wrong",
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args=(),
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exc_info=None,
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)
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output = formatter.format(record)
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obj = json.loads(output)
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assert obj["component"] == logger_name, (
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f"Expected component={logger_name!r}, got {obj.get('component')!r}"
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)
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def test_json_formatter_includes_logger_field():
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"""
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Test that JsonFormatter always emits a 'logger' field with filename:lineno.
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This allows pinpointing the exact source of a log line in third-party services.
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"""
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formatter = JsonFormatter()
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record = logging.LogRecord(
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name="LiteLLM Proxy",
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level=logging.INFO,
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pathname="/app/litellm/proxy/proxy_server.py",
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lineno=123,
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msg="request received",
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args=(),
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exc_info=None,
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)
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output = formatter.format(record)
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obj = json.loads(output)
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assert obj["logger"] == "proxy_server.py:123", (
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f"Expected logger='proxy_server.py:123', got {obj['logger']!r}"
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)
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def test_json_formatter_extra_component_not_overwritten():
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"""
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User-supplied extra={"component": "..."} must not be silently dropped.
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"""
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formatter = JsonFormatter()
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record = logging.LogRecord(
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name="LiteLLM Proxy",
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level=logging.INFO,
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pathname="proxy_server.py",
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lineno=1,
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msg="event",
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args=(),
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exc_info=None,
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)
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record.component = "auth-service"
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obj = json.loads(formatter.format(record))
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assert obj["component"] == "auth-service", (
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f"User-supplied component was overwritten, got {obj['component']!r}"
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)
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def test_initialize_loggers_with_handler_sets_propagate_false():
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"""
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Test that the initialize_loggers_with_handler function sets propagate to False for all loggers
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"""
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# Initialize loggers with the test handler
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_initialize_loggers_with_handler(logging.StreamHandler())
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# Check that propagate is set to False for all loggers
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for logger in ALL_LOGGERS:
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assert (
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logger.propagate is False
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), f"Logger {logger.name} has propagate set to {logger.propagate}, expected False"
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@pytest.mark.asyncio
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async def test_cache_hit_includes_custom_llm_provider():
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"""
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Test that when there's a cache hit, the standard logging payload includes the custom_llm_provider
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"""
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# Set up caching and custom logger
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litellm.cache = litellm.Cache()
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test_custom_logger = CacheHitCustomLogger()
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original_callbacks = litellm.callbacks.copy() if litellm.callbacks else []
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litellm.callbacks = [test_custom_logger]
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try:
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# First call - should be a cache miss
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response1 = await litellm.acompletion(
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model="gpt-3.5-turbo",
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messages=[{"role": "user", "content": "test cache hit message"}],
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mock_response="test response",
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caching=True,
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)
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# Wait for logging to complete
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await asyncio.sleep(0.5)
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# Second identical call - should be a cache hit
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response2 = await litellm.acompletion(
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model="gpt-3.5-turbo",
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messages=[{"role": "user", "content": "test cache hit message"}],
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mock_response="test response",
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caching=True,
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)
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# Wait for logging to complete
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await asyncio.sleep(0.5)
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# Verify we have logged events
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assert (
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len(test_custom_logger.logged_standard_logging_payloads) >= 2
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), f"Expected at least 2 logged events, got {len(test_custom_logger.logged_standard_logging_payloads)}"
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# Find the cache hit event (should be the second call)
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cache_hit_payload = None
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for payload in test_custom_logger.logged_standard_logging_payloads:
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if payload.get("cache_hit") is True:
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cache_hit_payload = payload
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break
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# Verify cache hit event was found
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assert (
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cache_hit_payload is not None
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), "No cache hit event found in logged payloads"
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# Verify custom_llm_provider is included in the cache hit payload
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assert (
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"custom_llm_provider" in cache_hit_payload
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), "custom_llm_provider missing from cache hit standard logging payload"
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# Verify custom_llm_provider has a valid value (should be "openai" for gpt-3.5-turbo)
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custom_llm_provider = cache_hit_payload["custom_llm_provider"]
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assert (
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custom_llm_provider is not None and custom_llm_provider != ""
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), f"custom_llm_provider should not be None or empty, got: {custom_llm_provider}"
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print(
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f"Cache hit standard logging payload with custom_llm_provider: {custom_llm_provider}",
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json.dumps(cache_hit_payload, indent=2),
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)
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finally:
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# Clean up
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litellm.callbacks = original_callbacks
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litellm.cache = None
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