litellm-mirror/litellm/litellm_core_utils/rules.py
Krish Dholakia 1bef6457c7
Litellm dev 11 07 2024 (#6649)
* fix(streaming_handler.py): save finish_reasons which might show up mid-stream (store last received one)

Fixes https://github.com/BerriAI/litellm/issues/6104

* refactor: add readme to litellm_core_utils/

make it easier to navigate

* fix(team_endpoints.py): return team id + object for invalid team in `/team/list`

* fix(streaming_handler.py): remove import

* fix(pattern_match_deployments.py): default to user input if unable to map based on wildcards (#6646)

* fix(pattern_match_deployments.py): default to user input if unable to… (#6632)

* fix(pattern_match_deployments.py): default to user input if unable to map based on wildcards

* test: fix test

* test: reset test name

* test: update conftest to reload proxy server module between tests

* ci(config.yml): move langfuse out of local_testing

reduce ci/cd time

* ci(config.yml): cleanup langfuse ci/cd tests

* fix: update test to not use global proxy_server app module

* ci: move caching to a separate test pipeline

speed up ci pipeline

* test: update conftest to check if proxy_server attr exists before reloading

* build(conftest.py): don't block on inability to reload proxy_server

* ci(config.yml): update caching unit test filter to work on 'cache' keyword as well

* fix(encrypt_decrypt_utils.py): use function to get salt key

* test: mark flaky test

* test: handle anthropic overloaded errors

* refactor: create separate ci/cd pipeline for proxy unit tests

make ci/cd faster

* ci(config.yml): add litellm_proxy_unit_testing to build_and_test jobs

* ci(config.yml): generate prisma binaries for proxy unit tests

* test: readd vertex_key.json

* ci(config.yml): remove `-s` from proxy_unit_test cmd

speed up test

* ci: remove any 'debug' logging flag

speed up ci pipeline

* test: fix test

* test(test_braintrust.py): rerun

* test: add delay for braintrust test

* chore: comment for maritalk (#6607)

* Update gpt-4o-2024-08-06, and o1-preview, o1-mini models in model cost map  (#6654)

* Adding supports_response_schema to gpt-4o-2024-08-06 models

* o1 models do not support vision

---------

Co-authored-by: Emerson Gomes <emerson.gomes@thalesgroup.com>

* (QOL improvement) add unit testing for all static_methods in litellm_logging.py  (#6640)

* add unit testing for standard logging payload

* unit testing for static methods in litellm_logging

* add code coverage check for litellm_logging

* litellm_logging_code_coverage

* test_get_final_response_obj

* fix validate_redacted_message_span_attributes

* test validate_redacted_message_span_attributes

* (feat) log error class, function_name on prometheus service failure hook + only log DB related failures on DB service hook  (#6650)

* log error on prometheus service failure hook

* use a more accurate function name for wrapper that handles logging db metrics

* fix log_db_metrics

* test_log_db_metrics_failure_error_types

* fix linting

* fix auth checks

* Update several Azure AI models in model cost map (#6655)

* Adding Azure Phi 3/3.5 models to model cost map

* Update gpt-4o-mini models

* Adding missing Azure Mistral models to model cost map

* Adding Azure Llama3.2 models to model cost map

* Fix Gemini-1.5-flash pricing

* Fix Gemini-1.5-flash output pricing

* Fix Gemini-1.5-pro prices

* Fix Gemini-1.5-flash output prices

* Correct gemini-1.5-pro prices

* Correction on Vertex Llama3.2 entry

---------

Co-authored-by: Emerson Gomes <emerson.gomes@thalesgroup.com>

* fix(streaming_handler.py): fix linting error

* test: remove duplicate test

causes gemini ratelimit error

---------

Co-authored-by: nobuo kawasaki <nobu007@users.noreply.github.com>
Co-authored-by: Emerson Gomes <emerson.gomes@gmail.com>
Co-authored-by: Emerson Gomes <emerson.gomes@thalesgroup.com>
Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>
2024-11-08 19:34:22 +05:30

50 lines
2 KiB
Python

from typing import Optional
import litellm
class Rules:
"""
Fail calls based on the input or llm api output
Example usage:
import litellm
def my_custom_rule(input): # receives the model response
if "i don't think i can answer" in input: # trigger fallback if the model refuses to answer
return False
return True
litellm.post_call_rules = [my_custom_rule] # have these be functions that can be called to fail a call
response = litellm.completion(model="gpt-3.5-turbo", messages=[{"role": "user",
"content": "Hey, how's it going?"}], fallbacks=["openrouter/mythomax"])
"""
def __init__(self) -> None:
pass
def pre_call_rules(self, input: str, model: str):
for rule in litellm.pre_call_rules:
if callable(rule):
decision = rule(input)
if decision is False:
raise litellm.APIResponseValidationError(message="LLM Response failed post-call-rule check", llm_provider="", model=model) # type: ignore
return True
def post_call_rules(self, input: Optional[str], model: str) -> bool:
if input is None:
return True
for rule in litellm.post_call_rules:
if callable(rule):
decision = rule(input)
if isinstance(decision, bool):
if decision is False:
raise litellm.APIResponseValidationError(message="LLM Response failed post-call-rule check", llm_provider="", model=model) # type: ignore
elif isinstance(decision, dict):
decision_val = decision.get("decision", True)
decision_message = decision.get(
"message", "LLM Response failed post-call-rule check"
)
if decision_val is False:
raise litellm.APIResponseValidationError(message=decision_message, llm_provider="", model=model) # type: ignore
return True