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* feat(cohere/chat.py): return citations in model response Closes https://github.com/BerriAI/litellm/issues/6814 * fix(cohere/chat.py): fix linting errors * fix(langsmith.py): support 'run_id' for langsmith Fixes https://github.com/BerriAI/litellm/issues/6862 * fix(langsmith.py): fix langsmith quickstart Fixes https://github.com/BerriAI/litellm/issues/6861 * fix: suppress linting error * LiteLLM Minor Fixes & Improvements (11/29/2024) (#6965) * fix(factory.py): ensure tool call converts image url Fixes https://github.com/BerriAI/litellm/issues/6953 * fix(transformation.py): support mp4 + pdf url's for vertex ai Fixes https://github.com/BerriAI/litellm/issues/6936 * fix(http_handler.py): mask gemini api key in error logs Fixes https://github.com/BerriAI/litellm/issues/6963 * docs(prometheus.md): update prometheus FAQs * feat(auth_checks.py): ensure specific model access > wildcard model access if wildcard model is in access group, but specific model is not - deny access * fix(auth_checks.py): handle auth checks for team based model access groups handles scenario where model access group used for wildcard models * fix(internal_user_endpoints.py): support adding guardrails on `/user/update` Fixes https://github.com/BerriAI/litellm/issues/6942 * fix(key_management_endpoints.py): fix prepare_metadata_fields helper * fix: fix tests * build(requirements.txt): bump openai dep version fixes proxies argument * test: fix tests * fix(http_handler.py): fix error message masking * fix(bedrock_guardrails.py): pass in prepped data * test: fix test * test: fix nvidia nim test * fix(http_handler.py): return original response headers * fix: revert maskedhttpstatuserror * test: update tests * test: cleanup test * fix(key_management_endpoints.py): fix metadata field update logic * fix(key_management_endpoints.py): maintain initial order of guardrails in key update * fix(key_management_endpoints.py): handle prepare metadata * fix: fix linting errors * fix: fix linting errors * fix: fix linting errors * fix: fix key management errors * fix(key_management_endpoints.py): update metadata * test: update test * refactor: add more debug statements * test: skip flaky test * test: fix test * fix: fix test * fix: fix update metadata logic * fix: fix test * ci(config.yml): change db url for e2e ui testing * test: add more debug logs to langsmith * fix: test change * build(config.yml): fix db url '
127 lines
4 KiB
Python
127 lines
4 KiB
Python
import io
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import os
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import sys
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sys.path.insert(0, os.path.abspath("../.."))
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import asyncio
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import logging
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import uuid
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import pytest
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import litellm
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from litellm import completion
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from litellm._logging import verbose_logger
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from litellm.integrations.langsmith import LangsmithLogger
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from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
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verbose_logger.setLevel(logging.DEBUG)
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litellm.set_verbose = True
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import time
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# test_langsmith_logging()
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@pytest.mark.skip(reason="Flaky test. covered by unit tests on custom logger.")
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def test_async_langsmith_logging_with_metadata():
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try:
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litellm.success_callback = ["langsmith"]
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litellm.set_verbose = True
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response = completion(
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model="gpt-3.5-turbo",
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messages=[{"role": "user", "content": "what llm are u"}],
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max_tokens=10,
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temperature=0.2,
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)
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print(response)
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time.sleep(3)
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for cb in litellm.callbacks:
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if isinstance(cb, LangsmithLogger):
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cb.async_httpx_client.close()
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except Exception as e:
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pytest.fail(f"Error occurred: {e}")
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print(e)
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@pytest.mark.skip(reason="Flaky test. covered by unit tests on custom logger.")
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@pytest.mark.parametrize("sync_mode", [False, True])
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@pytest.mark.asyncio
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async def test_async_langsmith_logging_with_streaming_and_metadata(sync_mode):
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try:
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litellm.DEFAULT_BATCH_SIZE = 1
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litellm.DEFAULT_FLUSH_INTERVAL_SECONDS = 1
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test_langsmith_logger = LangsmithLogger()
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litellm.success_callback = ["langsmith"]
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litellm.set_verbose = True
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run_id = "497f6eca-6276-4993-bfeb-53cbbbba6f08"
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run_name = "litellmRUN"
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test_metadata = {
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"run_name": run_name, # langsmith run name
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"run_id": run_id, # langsmith run id
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}
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messages = [{"role": "user", "content": "what llm are u"}]
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if sync_mode is True:
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response = completion(
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model="gpt-3.5-turbo",
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messages=messages,
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max_tokens=10,
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temperature=0.2,
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stream=True,
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metadata=test_metadata,
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)
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for cb in litellm.callbacks:
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if isinstance(cb, LangsmithLogger):
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cb.async_httpx_client = AsyncHTTPHandler()
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for chunk in response:
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continue
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time.sleep(3)
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else:
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response = await litellm.acompletion(
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model="gpt-3.5-turbo",
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messages=messages,
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max_tokens=10,
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temperature=0.2,
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mock_response="This is a mock request",
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stream=True,
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metadata=test_metadata,
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)
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for cb in litellm.callbacks:
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if isinstance(cb, LangsmithLogger):
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cb.async_httpx_client = AsyncHTTPHandler()
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async for chunk in response:
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continue
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await asyncio.sleep(3)
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print("run_id", run_id)
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logged_run_on_langsmith = test_langsmith_logger.get_run_by_id(run_id=run_id)
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print("logged_run_on_langsmith", logged_run_on_langsmith)
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print("fields in logged_run_on_langsmith", logged_run_on_langsmith.keys())
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input_fields_on_langsmith = logged_run_on_langsmith.get("inputs")
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extra_fields_on_langsmith = logged_run_on_langsmith.get("extra", {}).get(
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"invocation_params"
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)
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assert (
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logged_run_on_langsmith.get("run_type") == "llm"
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), f"run_type should be llm. Got: {logged_run_on_langsmith.get('run_type')}"
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assert (
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logged_run_on_langsmith.get("name") == run_name
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), f"run_type should be llm. Got: {logged_run_on_langsmith.get('run_type')}"
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print("\nLogged INPUT ON LANGSMITH", input_fields_on_langsmith)
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print("\nextra fields on langsmith", extra_fields_on_langsmith)
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assert isinstance(input_fields_on_langsmith, dict)
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except Exception as e:
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pytest.fail(f"Error occurred: {e}")
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print(e)
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