litellm-mirror/litellm/proxy/auth/auth_checks.py
Krish Dholakia fe1da228f4 Litellm lm studio embedding params (#6746)
* fix(ollama.py): fix get model info request

Fixes https://github.com/BerriAI/litellm/issues/6703

* feat(anthropic/chat/transformation.py): support passing user id to anthropic via openai 'user' param

* docs(anthropic.md): document all supported openai params for anthropic

* test: fix tests

* fix: fix tests

* feat(jina_ai/): add rerank support

Closes https://github.com/BerriAI/litellm/issues/6691

* test: handle service unavailable error

* fix(handler.py): refactor together ai rerank call

* test: update test to handle overloaded error

* test: fix test

* Litellm router trace (#6742)

* feat(router.py): add trace_id to parent functions - allows tracking retry/fallbacks

* feat(router.py): log trace id across retry/fallback logic

allows grouping llm logs for the same request

* test: fix tests

* fix: fix test

* fix(transformation.py): only set non-none stop_sequences

* Litellm router disable fallbacks (#6743)

* bump: version 1.52.6 → 1.52.7

* feat(router.py): enable dynamically disabling fallbacks

Allows for enabling/disabling fallbacks per key

* feat(litellm_pre_call_utils.py): support setting 'disable_fallbacks' on litellm key

* test: fix test

* fix(exception_mapping_utils.py): map 'model is overloaded' to internal server error

* fix(lm_studio/embed): support translating lm studio optional params

'

* feat(auth_checks.py): fix auth check inside route - `/team/list`

Fixes regression where non-admin w/ user_id=None able to query all teams

* docs proxy_budget_rescheduler_min_time

* helm run DISABLE_SCHEMA_UPDATE

* docs helm pre sync hook

* fix migration job.yaml

* fix DATABASE_URL

* use existing spec for migrations job

* fix yaml on migrations job

* fix migration job

* update doc on pre sync hook

* fix migrations-job.yaml

* fix migration job

* fix prisma migration

* test - handle eol model claude-2, use claude-2.1 instead

* (docs) add instructions on how to contribute to docker image

* Update code blocks huggingface.md (#6737)

* Update prefix.md (#6734)

* fix test_supports_response_schema

* mark Helm PreSyn as BETA

* (Feat) Add support for storing virtual keys in AWS SecretManager  (#6728)

* add SecretManager to httpxSpecialProvider

* fix importing AWSSecretsManagerV2

* add unit testing for writing keys to AWS secret manager

* use KeyManagementEventHooks for key/generated events

* us event hooks for key management endpoints

* working AWSSecretsManagerV2

* fix write secret to AWS secret manager on /key/generate

* fix KeyManagementSettings

* use tasks for key management hooks

* add async_delete_secret

* add test for async_delete_secret

* use _delete_virtual_keys_from_secret_manager

* fix test secret manager

* test_key_generate_with_secret_manager_call

* fix check for key_management_settings

* sync_read_secret

* test_aws_secret_manager

* fix sync_read_secret

* use helper to check when _should_read_secret_from_secret_manager

* test_get_secret_with_access_mode

* test - handle eol model claude-2, use claude-2.1 instead

* docs AWS secret manager

* fix test_read_nonexistent_secret

* fix test_supports_response_schema

* ci/cd run again

* LiteLLM Minor Fixes & Improvement (11/14/2024)  (#6730)

* fix(ollama.py): fix get model info request

Fixes https://github.com/BerriAI/litellm/issues/6703

* feat(anthropic/chat/transformation.py): support passing user id to anthropic via openai 'user' param

* docs(anthropic.md): document all supported openai params for anthropic

* test: fix tests

* fix: fix tests

* feat(jina_ai/): add rerank support

Closes https://github.com/BerriAI/litellm/issues/6691

* test: handle service unavailable error

* fix(handler.py): refactor together ai rerank call

* test: update test to handle overloaded error

* test: fix test

* Litellm router trace (#6742)

* feat(router.py): add trace_id to parent functions - allows tracking retry/fallbacks

* feat(router.py): log trace id across retry/fallback logic

allows grouping llm logs for the same request

* test: fix tests

* fix: fix test

* fix(transformation.py): only set non-none stop_sequences

* Litellm router disable fallbacks (#6743)

* bump: version 1.52.6 → 1.52.7

* feat(router.py): enable dynamically disabling fallbacks

Allows for enabling/disabling fallbacks per key

* feat(litellm_pre_call_utils.py): support setting 'disable_fallbacks' on litellm key

* test: fix test

* fix(exception_mapping_utils.py): map 'model is overloaded' to internal server error

* test: handle gemini error

* test: fix test

* fix: new run

* bump: version 1.52.7 → 1.52.8

* docs: add docs on jina ai rerank support

* docs(reliability.md): add tutorial on disabling fallbacks per key

* docs(logging.md): add 'trace_id' param to standard logging payload

* (feat) add bedrock/stability.stable-image-ultra-v1:0 (#6723)

* add stability.stable-image-ultra-v1:0

* add pricing for stability.stable-image-ultra-v1:0

* fix test_supports_response_schema

* ci/cd run again

* [Feature]: Stop swallowing up AzureOpenAi exception responses in litellm's implementation for a BadRequestError (#6745)

* fix azure exceptions

* test_bad_request_error_contains_httpx_response

* test_bad_request_error_contains_httpx_response

* use safe access to get exception response

* fix get attr

* [Feature]: json_schema in response support for Anthropic  (#6748)

* _convert_tool_response_to_message

* fix ModelResponseIterator

* fix test_json_response_format

* test_json_response_format_stream

* fix _convert_tool_response_to_message

* use helper _handle_json_mode_chunk

* fix _process_response

* unit testing for test_convert_tool_response_to_message_no_arguments

* update doc for JSON mode

* fix: import audio check (#6740)

* fix imagegeneration output_cost_per_image on model cost map (#6752)

* (feat) Vertex AI - add support for fine tuned embedding models  (#6749)

* fix use fine tuned vertex embedding models

* test_vertex_embedding_url

* add _transform_openai_request_to_fine_tuned_embedding_request

* add _transform_openai_request_to_fine_tuned_embedding_request

* add transform_openai_request_to_vertex_embedding_request

* add _transform_vertex_response_to_openai_for_fine_tuned_models

* test_vertexai_embedding for ft models

* fix test_vertexai_embedding_finetuned

* doc fine tuned / custom embedding models

* fix test test_partner_models_httpx

* bump: version 1.52.8 → 1.52.9

* LiteLLM Minor Fixes & Improvements (11/13/2024)  (#6729)

* fix(utils.py): add logprobs support for together ai

Fixes

https://github.com/BerriAI/litellm/issues/6724

* feat(pass_through_endpoints/): add anthropic/ pass-through endpoint

adds new `anthropic/` pass-through endpoint + refactors docs

* feat(spend_management_endpoints.py): allow /global/spend/report to query team + customer id

enables seeing spend for a customer in a team

* Add integration with MLflow Tracing (#6147)

* Add MLflow logger

Signed-off-by: B-Step62 <yuki.watanabe@databricks.com>

* Streaming handling

Signed-off-by: B-Step62 <yuki.watanabe@databricks.com>

* lint

Signed-off-by: B-Step62 <yuki.watanabe@databricks.com>

* address comments and fix issues

Signed-off-by: B-Step62 <yuki.watanabe@databricks.com>

* address comments and fix issues

Signed-off-by: B-Step62 <yuki.watanabe@databricks.com>

* Move logger construction code

Signed-off-by: B-Step62 <yuki.watanabe@databricks.com>

* Add docs

Signed-off-by: B-Step62 <yuki.watanabe@databricks.com>

* async handlers

Signed-off-by: B-Step62 <yuki.watanabe@databricks.com>

* new picture

Signed-off-by: B-Step62 <yuki.watanabe@databricks.com>

---------

Signed-off-by: B-Step62 <yuki.watanabe@databricks.com>

* fix(mlflow.py): fix ruff linting errors

* ci(config.yml): add mlflow to ci testing

* fix: fix test

* test: fix test

* Litellm key update fix (#6710)

* fix(caching): convert arg to equivalent kwargs in llm caching handler

prevent unexpected errors

* fix(caching_handler.py): don't pass args to caching

* fix(caching): remove all *args from caching.py

* fix(caching): consistent function signatures + abc method

* test(caching_unit_tests.py): add unit tests for llm caching

ensures coverage for common caching scenarios across different implementations

* refactor(litellm_logging.py): move to using cache key from hidden params instead of regenerating one

* fix(router.py): drop redis password requirement

* fix(proxy_server.py): fix faulty slack alerting check

* fix(langfuse.py): avoid copying functions/thread lock objects in metadata

fixes metadata copy error when parent otel span in metadata

* test: update test

* fix(key_management_endpoints.py): fix /key/update with metadata update

* fix(key_management_endpoints.py): fix key_prepare_update helper

* fix(key_management_endpoints.py): reset value to none if set in key update

* fix: update test

'

* Litellm dev 11 11 2024 (#6693)

* fix(__init__.py): add 'watsonx_text' as mapped llm api route

Fixes https://github.com/BerriAI/litellm/issues/6663

* fix(opentelemetry.py): fix passing parallel tool calls to otel

Fixes https://github.com/BerriAI/litellm/issues/6677

* refactor(test_opentelemetry_unit_tests.py): create a base set of unit tests for all logging integrations - test for parallel tool call handling

reduces bugs in repo

* fix(__init__.py): update provider-model mapping to include all known provider-model mappings

Fixes https://github.com/BerriAI/litellm/issues/6669

* feat(anthropic): support passing document in llm api call

* docs(anthropic.md): add pdf anthropic call to docs + expose new 'supports_pdf_input' function

* fix(factory.py): fix linting error

* add clear doc string for GCS bucket logging

* Add docs to export logs to Laminar (#6674)

* Add docs to export logs to Laminar

* minor fix: newline at end of file

* place laminar after http and grpc

* (Feat) Add langsmith key based logging (#6682)

* add langsmith_api_key to StandardCallbackDynamicParams

* create a file for langsmith types

* langsmith add key / team based logging

* add key based logging for langsmith

* fix langsmith key based logging

* fix linting langsmith

* remove NOQA violation

* add unit test coverage for all helpers in test langsmith

* test_langsmith_key_based_logging

* docs langsmith key based logging

* run langsmith tests in logging callback tests

* fix logging testing

* test_langsmith_key_based_logging

* test_add_callback_via_key_litellm_pre_call_utils_langsmith

* add debug statement langsmith key based logging

* test_langsmith_key_based_logging

* (fix) OpenAI's optional messages[].name  does not work with Mistral API  (#6701)

* use helper for _transform_messages mistral

* add test_message_with_name to base LLMChat test

* fix linting

* add xAI on Admin UI (#6680)

* (docs) add benchmarks on 1K RPS  (#6704)

* docs litellm proxy benchmarks

* docs GCS bucket

* doc fix - reduce clutter on logging doc title

* (feat) add cost tracking stable diffusion 3 on Bedrock  (#6676)

* add cost tracking for sd3

* test_image_generation_bedrock

* fix get model info for image cost

* add cost_calculator for stability 1 models

* add unit testing for bedrock image cost calc

* test_cost_calculator_with_no_optional_params

* add test_cost_calculator_basic

* correctly allow size Optional

* fix cost_calculator

* sd3 unit tests cost calc

* fix raise correct error 404 when /key/info is called on non-existent key  (#6653)

* fix raise correct error on /key/info

* add not_found_error error

* fix key not found in DB error

* use 1 helper for checking token hash

* fix error code on key info

* fix test key gen prisma

* test_generate_and_call_key_info

* test fix test_call_with_valid_model_using_all_models

* fix key info tests

* bump: version 1.52.4 → 1.52.5

* add defaults used for GCS logging

* LiteLLM Minor Fixes & Improvements (11/12/2024)  (#6705)

* fix(caching): convert arg to equivalent kwargs in llm caching handler

prevent unexpected errors

* fix(caching_handler.py): don't pass args to caching

* fix(caching): remove all *args from caching.py

* fix(caching): consistent function signatures + abc method

* test(caching_unit_tests.py): add unit tests for llm caching

ensures coverage for common caching scenarios across different implementations

* refactor(litellm_logging.py): move to using cache key from hidden params instead of regenerating one

* fix(router.py): drop redis password requirement

* fix(proxy_server.py): fix faulty slack alerting check

* fix(langfuse.py): avoid copying functions/thread lock objects in metadata

fixes metadata copy error when parent otel span in metadata

* test: update test

* bump: version 1.52.5 → 1.52.6

* (feat) helm hook to sync db schema  (#6715)

* v0 migration job

* fix job

* fix migrations job.yml

* handle standalone DB on helm hook

* fix argo cd annotations

* fix db migration helm hook

* fix migration job

* doc fix Using Http/2 with Hypercorn

* (fix proxy redis) Add redis sentinel support  (#6154)

* add sentinel_password support

* add doc for setting redis sentinel password

* fix redis sentinel - use sentinel password

* Fix: Update gpt-4o costs to that of gpt-4o-2024-08-06 (#6714)

Fixes #6713

* (fix) using Anthropic `response_format={"type": "json_object"}`  (#6721)

* add support for response_format=json anthropic

* add test_json_response_format to baseLLM ChatTest

* fix test_litellm_anthropic_prompt_caching_tools

* fix test_anthropic_function_call_with_no_schema

* test test_create_json_tool_call_for_response_format

* (feat) Add cost tracking for Azure Dall-e-3 Image Generation  + use base class to ensure basic image generation tests pass  (#6716)

* add BaseImageGenTest

* use 1 class for unit testing

* add debugging to BaseImageGenTest

* TestAzureOpenAIDalle3

* fix response_cost_calculator

* test_basic_image_generation

* fix img gen basic test

* fix _select_model_name_for_cost_calc

* fix test_aimage_generation_bedrock_with_optional_params

* fix undo changes cost tracking

* fix response_cost_calculator

* fix test_cost_azure_gpt_35

* fix remove dup test (#6718)

* (build) update db helm hook

* (build) helm db pre sync hook

* (build) helm db sync hook

* test: run test_team_logging firdst

---------

Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>
Co-authored-by: Dinmukhamed Mailibay <47117969+dinmukhamedm@users.noreply.github.com>
Co-authored-by: Kilian Lieret <kilian.lieret@posteo.de>

* test: update test

* test: skip anthropic overloaded error

* test: cleanup test

* test: update tests

* test: fix test

* test: handle gemini overloaded model error

* test: handle internal server error

* test: handle anthropic overloaded error

* test: handle claude instability

---------

Signed-off-by: B-Step62 <yuki.watanabe@databricks.com>
Co-authored-by: Yuki Watanabe <31463517+B-Step62@users.noreply.github.com>
Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>
Co-authored-by: Dinmukhamed Mailibay <47117969+dinmukhamedm@users.noreply.github.com>
Co-authored-by: Kilian Lieret <kilian.lieret@posteo.de>

---------

Signed-off-by: B-Step62 <yuki.watanabe@databricks.com>
Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>
Co-authored-by: Jongseob Jeon <aiden.jongseob@gmail.com>
Co-authored-by: Camden Clark <camdenaws@gmail.com>
Co-authored-by: Rasswanth <61219215+IamRash-7@users.noreply.github.com>
Co-authored-by: Yuki Watanabe <31463517+B-Step62@users.noreply.github.com>
Co-authored-by: Dinmukhamed Mailibay <47117969+dinmukhamedm@users.noreply.github.com>
Co-authored-by: Kilian Lieret <kilian.lieret@posteo.de>
2024-11-19 09:54:50 +05:30

887 lines
30 KiB
Python

# What is this?
## Common auth checks between jwt + key based auth
"""
Got Valid Token from Cache, DB
Run checks for:
1. If user can call model
2. If user is in budget
3. If end_user ('user' passed to /chat/completions, /embeddings endpoint) is in budget
"""
import time
import traceback
from datetime import datetime
from typing import TYPE_CHECKING, Any, List, Literal, Optional
import httpx
from pydantic import BaseModel
import litellm
from litellm._logging import verbose_proxy_logger
from litellm.caching.caching import DualCache
from litellm.caching.dual_cache import LimitedSizeOrderedDict
from litellm.proxy._types import (
LiteLLM_EndUserTable,
LiteLLM_JWTAuth,
LiteLLM_OrganizationTable,
LiteLLM_TeamTable,
LiteLLM_TeamTableCachedObj,
LiteLLM_UserTable,
LiteLLMRoutes,
LitellmUserRoles,
UserAPIKeyAuth,
)
from litellm.proxy.auth.route_checks import RouteChecks
from litellm.proxy.utils import PrismaClient, ProxyLogging, log_db_metrics
from litellm.types.services import ServiceLoggerPayload, ServiceTypes
from .auth_checks_organization import organization_role_based_access_check
if TYPE_CHECKING:
from opentelemetry.trace import Span as _Span
Span = _Span
else:
Span = Any
last_db_access_time = LimitedSizeOrderedDict(max_size=100)
db_cache_expiry = 5 # refresh every 5s
all_routes = LiteLLMRoutes.openai_routes.value + LiteLLMRoutes.management_routes.value
def common_checks( # noqa: PLR0915
request_body: dict,
team_object: Optional[LiteLLM_TeamTable],
user_object: Optional[LiteLLM_UserTable],
end_user_object: Optional[LiteLLM_EndUserTable],
global_proxy_spend: Optional[float],
general_settings: dict,
route: str,
) -> bool:
"""
Common checks across jwt + key-based auth.
1. If team is blocked
2. If team can call model
3. If team is in budget
4. If user passed in (JWT or key.user_id) - is in budget
5. If end_user (either via JWT or 'user' passed to /chat/completions, /embeddings endpoint) is in budget
6. [OPTIONAL] If 'enforce_end_user' enabled - did developer pass in 'user' param for openai endpoints
7. [OPTIONAL] If 'litellm.max_budget' is set (>0), is proxy under budget
8. [OPTIONAL] If guardrails modified - is request allowed to change this
9. Check if request body is safe
10. [OPTIONAL] Organization checks - is user_object.organization_id is set, run these checks
"""
_model = request_body.get("model", None)
if team_object is not None and team_object.blocked is True:
raise Exception(
f"Team={team_object.team_id} is blocked. Update via `/team/unblock` if your admin."
)
# 2. If team can call model
if (
_model is not None
and team_object is not None
and team_object.models is not None
and len(team_object.models) > 0
and _model not in team_object.models
):
# this means the team has access to all models on the proxy
if (
"all-proxy-models" in team_object.models
or "*" in team_object.models
or "openai/*" in team_object.models
):
# this means the team has access to all models on the proxy
pass
# check if the team model is an access_group
elif model_in_access_group(_model, team_object.models) is True:
pass
elif _model and "*" in _model:
pass
else:
raise Exception(
f"Team={team_object.team_id} not allowed to call model={_model}. Allowed team models = {team_object.models}"
)
# 3. If team is in budget
if (
team_object is not None
and team_object.max_budget is not None
and team_object.spend is not None
and team_object.spend > team_object.max_budget
):
raise litellm.BudgetExceededError(
current_cost=team_object.spend,
max_budget=team_object.max_budget,
message=f"Team={team_object.team_id} over budget. Spend={team_object.spend}, Budget={team_object.max_budget}",
)
# 4. If user is in budget
## 4.1 check personal budget, if personal key
if (
(team_object is None or team_object.team_id is None)
and user_object is not None
and user_object.max_budget is not None
):
user_budget = user_object.max_budget
if user_budget < user_object.spend:
raise litellm.BudgetExceededError(
current_cost=user_object.spend,
max_budget=user_budget,
message=f"ExceededBudget: User={user_object.user_id} over budget. Spend={user_object.spend}, Budget={user_budget}",
)
## 4.2 check team member budget, if team key
# 5. If end_user ('user' passed to /chat/completions, /embeddings endpoint) is in budget
if end_user_object is not None and end_user_object.litellm_budget_table is not None:
end_user_budget = end_user_object.litellm_budget_table.max_budget
if end_user_budget is not None and end_user_object.spend > end_user_budget:
raise litellm.BudgetExceededError(
current_cost=end_user_object.spend,
max_budget=end_user_budget,
message=f"ExceededBudget: End User={end_user_object.user_id} over budget. Spend={end_user_object.spend}, Budget={end_user_budget}",
)
# 6. [OPTIONAL] If 'enforce_user_param' enabled - did developer pass in 'user' param for openai endpoints
if (
general_settings.get("enforce_user_param", None) is not None
and general_settings["enforce_user_param"] is True
):
if RouteChecks.is_llm_api_route(route=route) and "user" not in request_body:
raise Exception(
f"'user' param not passed in. 'enforce_user_param'={general_settings['enforce_user_param']}"
)
if general_settings.get("enforced_params", None) is not None:
# Enterprise ONLY Feature
# we already validate if user is premium_user when reading the config
# Add an extra premium_usercheck here too, just incase
from litellm.proxy.proxy_server import CommonProxyErrors, premium_user
if premium_user is not True:
raise ValueError(
"Trying to use `enforced_params`"
+ CommonProxyErrors.not_premium_user.value
)
if RouteChecks.is_llm_api_route(route=route):
# loop through each enforced param
# example enforced_params ['user', 'metadata', 'metadata.generation_name']
for enforced_param in general_settings["enforced_params"]:
_enforced_params = enforced_param.split(".")
if len(_enforced_params) == 1:
if _enforced_params[0] not in request_body:
raise ValueError(
f"BadRequest please pass param={_enforced_params[0]} in request body. This is a required param"
)
elif len(_enforced_params) == 2:
# this is a scenario where user requires request['metadata']['generation_name'] to exist
if _enforced_params[0] not in request_body:
raise ValueError(
f"BadRequest please pass param={_enforced_params[0]} in request body. This is a required param"
)
if _enforced_params[1] not in request_body[_enforced_params[0]]:
raise ValueError(
f"BadRequest please pass param=[{_enforced_params[0]}][{_enforced_params[1]}] in request body. This is a required param"
)
pass
# 7. [OPTIONAL] If 'litellm.max_budget' is set (>0), is proxy under budget
if (
litellm.max_budget > 0
and global_proxy_spend is not None
# only run global budget checks for OpenAI routes
# Reason - the Admin UI should continue working if the proxy crosses it's global budget
and RouteChecks.is_llm_api_route(route=route)
and route != "/v1/models"
and route != "/models"
):
if global_proxy_spend > litellm.max_budget:
raise litellm.BudgetExceededError(
current_cost=global_proxy_spend, max_budget=litellm.max_budget
)
_request_metadata: dict = request_body.get("metadata", {}) or {}
if _request_metadata.get("guardrails"):
# check if team allowed to modify guardrails
from litellm.proxy.guardrails.guardrail_helpers import can_modify_guardrails
can_modify: bool = can_modify_guardrails(team_object)
if can_modify is False:
from fastapi import HTTPException
raise HTTPException(
status_code=403,
detail={
"error": "Your team does not have permission to modify guardrails."
},
)
# 10 [OPTIONAL] Organization RBAC checks
organization_role_based_access_check(
user_object=user_object, route=route, request_body=request_body
)
return True
def _allowed_routes_check(user_route: str, allowed_routes: list) -> bool:
"""
Return if a user is allowed to access route. Helper function for `allowed_routes_check`.
Parameters:
- user_route: str - the route the user is trying to call
- allowed_routes: List[str|LiteLLMRoutes] - the list of allowed routes for the user.
"""
for allowed_route in allowed_routes:
if (
allowed_route in LiteLLMRoutes.__members__
and user_route in LiteLLMRoutes[allowed_route].value
):
return True
elif allowed_route == user_route:
return True
return False
def allowed_routes_check(
user_role: Literal[
LitellmUserRoles.PROXY_ADMIN,
LitellmUserRoles.TEAM,
LitellmUserRoles.INTERNAL_USER,
],
user_route: str,
litellm_proxy_roles: LiteLLM_JWTAuth,
) -> bool:
"""
Check if user -> not admin - allowed to access these routes
"""
if user_role == LitellmUserRoles.PROXY_ADMIN:
is_allowed = _allowed_routes_check(
user_route=user_route,
allowed_routes=litellm_proxy_roles.admin_allowed_routes,
)
return is_allowed
elif user_role == LitellmUserRoles.TEAM:
if litellm_proxy_roles.team_allowed_routes is None:
"""
By default allow a team to call openai + info routes
"""
is_allowed = _allowed_routes_check(
user_route=user_route, allowed_routes=["openai_routes", "info_routes"]
)
return is_allowed
elif litellm_proxy_roles.team_allowed_routes is not None:
is_allowed = _allowed_routes_check(
user_route=user_route,
allowed_routes=litellm_proxy_roles.team_allowed_routes,
)
return is_allowed
return False
def allowed_route_check_inside_route(
user_api_key_dict: UserAPIKeyAuth,
requested_user_id: Optional[str],
) -> bool:
ret_val = True
if (
user_api_key_dict.user_role != LitellmUserRoles.PROXY_ADMIN
and user_api_key_dict.user_role != LitellmUserRoles.PROXY_ADMIN_VIEW_ONLY
):
ret_val = False
if requested_user_id is not None and user_api_key_dict.user_id is not None:
if user_api_key_dict.user_id == requested_user_id:
ret_val = True
return ret_val
def get_actual_routes(allowed_routes: list) -> list:
actual_routes: list = []
for route_name in allowed_routes:
try:
route_value = LiteLLMRoutes[route_name].value
actual_routes = actual_routes + route_value
except KeyError:
actual_routes.append(route_name)
return actual_routes
@log_db_metrics
async def get_end_user_object(
end_user_id: Optional[str],
prisma_client: Optional[PrismaClient],
user_api_key_cache: DualCache,
parent_otel_span: Optional[Span] = None,
proxy_logging_obj: Optional[ProxyLogging] = None,
) -> Optional[LiteLLM_EndUserTable]:
"""
Returns end user object, if in db.
Do a isolated check for end user in table vs. doing a combined key + team + user + end-user check, as key might come in frequently for different end-users. Larger call will slowdown query time. This way we get to cache the constant (key/team/user info) and only update based on the changing value (end-user).
"""
if prisma_client is None:
raise Exception("No db connected")
if end_user_id is None:
return None
_key = "end_user_id:{}".format(end_user_id)
def check_in_budget(end_user_obj: LiteLLM_EndUserTable):
if end_user_obj.litellm_budget_table is None:
return
end_user_budget = end_user_obj.litellm_budget_table.max_budget
if end_user_budget is not None and end_user_obj.spend > end_user_budget:
raise litellm.BudgetExceededError(
current_cost=end_user_obj.spend, max_budget=end_user_budget
)
# check if in cache
cached_user_obj = await user_api_key_cache.async_get_cache(key=_key)
if cached_user_obj is not None:
if isinstance(cached_user_obj, dict):
return_obj = LiteLLM_EndUserTable(**cached_user_obj)
check_in_budget(end_user_obj=return_obj)
return return_obj
elif isinstance(cached_user_obj, LiteLLM_EndUserTable):
return_obj = cached_user_obj
check_in_budget(end_user_obj=return_obj)
return return_obj
# else, check db
try:
response = await prisma_client.db.litellm_endusertable.find_unique(
where={"user_id": end_user_id},
include={"litellm_budget_table": True},
)
if response is None:
raise Exception
# save the end-user object to cache
await user_api_key_cache.async_set_cache(
key="end_user_id:{}".format(end_user_id), value=response
)
_response = LiteLLM_EndUserTable(**response.dict())
check_in_budget(end_user_obj=_response)
return _response
except Exception as e: # if end-user not in db
if isinstance(e, litellm.BudgetExceededError):
raise e
return None
def model_in_access_group(model: str, team_models: Optional[List[str]]) -> bool:
from collections import defaultdict
from litellm.proxy.proxy_server import llm_router
if team_models is None:
return True
if model in team_models:
return True
access_groups = defaultdict(list)
if llm_router:
access_groups = llm_router.get_model_access_groups()
models_in_current_access_groups = []
if len(access_groups) > 0: # check if token contains any model access groups
for idx, m in enumerate(
team_models
): # loop token models, if any of them are an access group add the access group
if m in access_groups:
# if it is an access group we need to remove it from valid_token.models
models_in_group = access_groups[m]
models_in_current_access_groups.extend(models_in_group)
# Filter out models that are access_groups
filtered_models = [m for m in team_models if m not in access_groups]
filtered_models += models_in_current_access_groups
if model in filtered_models:
return True
return False
def _should_check_db(
key: str, last_db_access_time: LimitedSizeOrderedDict, db_cache_expiry: int
) -> bool:
"""
Prevent calling db repeatedly for items that don't exist in the db.
"""
current_time = time.time()
# if key doesn't exist in last_db_access_time -> check db
if key not in last_db_access_time:
return True
elif (
last_db_access_time[key][0] is not None
): # check db for non-null values (for refresh operations)
return True
elif last_db_access_time[key][0] is None:
if current_time - last_db_access_time[key] >= db_cache_expiry:
return True
return False
def _update_last_db_access_time(
key: str, value: Optional[Any], last_db_access_time: LimitedSizeOrderedDict
):
last_db_access_time[key] = (value, time.time())
@log_db_metrics
async def get_user_object(
user_id: str,
prisma_client: Optional[PrismaClient],
user_api_key_cache: DualCache,
user_id_upsert: bool,
parent_otel_span: Optional[Span] = None,
proxy_logging_obj: Optional[ProxyLogging] = None,
) -> Optional[LiteLLM_UserTable]:
"""
- Check if user id in proxy User Table
- if valid, return LiteLLM_UserTable object with defined limits
- if not, then raise an error
"""
if user_id is None:
return None
# check if in cache
cached_user_obj = await user_api_key_cache.async_get_cache(key=user_id)
if cached_user_obj is not None:
if isinstance(cached_user_obj, dict):
return LiteLLM_UserTable(**cached_user_obj)
elif isinstance(cached_user_obj, LiteLLM_UserTable):
return cached_user_obj
# else, check db
if prisma_client is None:
raise Exception("No db connected")
try:
db_access_time_key = "user_id:{}".format(user_id)
should_check_db = _should_check_db(
key=db_access_time_key,
last_db_access_time=last_db_access_time,
db_cache_expiry=db_cache_expiry,
)
if should_check_db:
response = await prisma_client.db.litellm_usertable.find_unique(
where={"user_id": user_id}, include={"organization_memberships": True}
)
else:
response = None
if response is None:
if user_id_upsert:
response = await prisma_client.db.litellm_usertable.create(
data={"user_id": user_id},
include={"organization_memberships": True},
)
else:
raise Exception
if (
response.organization_memberships is not None
and len(response.organization_memberships) > 0
):
# dump each organization membership to type LiteLLM_OrganizationMembershipTable
_dumped_memberships = [
membership.model_dump()
for membership in response.organization_memberships
if membership is not None
]
response.organization_memberships = _dumped_memberships
_response = LiteLLM_UserTable(**dict(response))
response_dict = _response.model_dump()
# save the user object to cache
await user_api_key_cache.async_set_cache(key=user_id, value=response_dict)
# save to db access time
_update_last_db_access_time(
key=db_access_time_key,
value=response_dict,
last_db_access_time=last_db_access_time,
)
return _response
except Exception as e: # if user not in db
raise ValueError(
f"User doesn't exist in db. 'user_id'={user_id}. Create user via `/user/new` call. Got error - {e}"
)
async def _cache_management_object(
key: str,
value: BaseModel,
user_api_key_cache: DualCache,
proxy_logging_obj: Optional[ProxyLogging],
):
await user_api_key_cache.async_set_cache(key=key, value=value)
async def _cache_team_object(
team_id: str,
team_table: LiteLLM_TeamTableCachedObj,
user_api_key_cache: DualCache,
proxy_logging_obj: Optional[ProxyLogging],
):
key = "team_id:{}".format(team_id)
## CACHE REFRESH TIME!
team_table.last_refreshed_at = time.time()
await _cache_management_object(
key=key,
value=team_table,
user_api_key_cache=user_api_key_cache,
proxy_logging_obj=proxy_logging_obj,
)
async def _cache_key_object(
hashed_token: str,
user_api_key_obj: UserAPIKeyAuth,
user_api_key_cache: DualCache,
proxy_logging_obj: Optional[ProxyLogging],
):
key = hashed_token
## CACHE REFRESH TIME
user_api_key_obj.last_refreshed_at = time.time()
await _cache_management_object(
key=key,
value=user_api_key_obj,
user_api_key_cache=user_api_key_cache,
proxy_logging_obj=proxy_logging_obj,
)
async def _delete_cache_key_object(
hashed_token: str,
user_api_key_cache: DualCache,
proxy_logging_obj: Optional[ProxyLogging],
):
key = hashed_token
user_api_key_cache.delete_cache(key=key)
## UPDATE REDIS CACHE ##
if proxy_logging_obj is not None:
await proxy_logging_obj.internal_usage_cache.dual_cache.async_delete_cache(
key=key
)
@log_db_metrics
async def _get_team_db_check(team_id: str, prisma_client: PrismaClient):
return await prisma_client.db.litellm_teamtable.find_unique(
where={"team_id": team_id}
)
async def get_team_object(
team_id: str,
prisma_client: Optional[PrismaClient],
user_api_key_cache: DualCache,
parent_otel_span: Optional[Span] = None,
proxy_logging_obj: Optional[ProxyLogging] = None,
check_cache_only: Optional[bool] = None,
) -> LiteLLM_TeamTableCachedObj:
"""
- Check if team id in proxy Team Table
- if valid, return LiteLLM_TeamTable object with defined limits
- if not, then raise an error
"""
if prisma_client is None:
raise Exception(
"No DB Connected. See - https://docs.litellm.ai/docs/proxy/virtual_keys"
)
# check if in cache
key = "team_id:{}".format(team_id)
cached_team_obj: Optional[LiteLLM_TeamTableCachedObj] = None
## CHECK REDIS CACHE ##
if (
proxy_logging_obj is not None
and proxy_logging_obj.internal_usage_cache.dual_cache
):
cached_team_obj = (
await proxy_logging_obj.internal_usage_cache.dual_cache.async_get_cache(
key=key, parent_otel_span=parent_otel_span
)
)
if cached_team_obj is None:
cached_team_obj = await user_api_key_cache.async_get_cache(key=key)
if cached_team_obj is not None:
if isinstance(cached_team_obj, dict):
return LiteLLM_TeamTableCachedObj(**cached_team_obj)
elif isinstance(cached_team_obj, LiteLLM_TeamTableCachedObj):
return cached_team_obj
if check_cache_only:
raise Exception(
f"Team doesn't exist in cache + check_cache_only=True. Team={team_id}."
)
# else, check db
try:
db_access_time_key = "team_id:{}".format(team_id)
should_check_db = _should_check_db(
key=db_access_time_key,
last_db_access_time=last_db_access_time,
db_cache_expiry=db_cache_expiry,
)
if should_check_db:
response = await _get_team_db_check(
team_id=team_id, prisma_client=prisma_client
)
else:
response = None
if response is None:
raise Exception
_response = LiteLLM_TeamTableCachedObj(**response.dict())
# save the team object to cache
await _cache_team_object(
team_id=team_id,
team_table=_response,
user_api_key_cache=user_api_key_cache,
proxy_logging_obj=proxy_logging_obj,
)
# save to db access time
# save to db access time
_update_last_db_access_time(
key=db_access_time_key,
value=_response,
last_db_access_time=last_db_access_time,
)
return _response
except Exception:
raise Exception(
f"Team doesn't exist in db. Team={team_id}. Create team via `/team/new` call."
)
@log_db_metrics
async def get_key_object(
hashed_token: str,
prisma_client: Optional[PrismaClient],
user_api_key_cache: DualCache,
parent_otel_span: Optional[Span] = None,
proxy_logging_obj: Optional[ProxyLogging] = None,
check_cache_only: Optional[bool] = None,
) -> UserAPIKeyAuth:
"""
- Check if team id in proxy Team Table
- if valid, return LiteLLM_TeamTable object with defined limits
- if not, then raise an error
"""
if prisma_client is None:
raise Exception(
"No DB Connected. See - https://docs.litellm.ai/docs/proxy/virtual_keys"
)
# check if in cache
key = hashed_token
cached_key_obj: Optional[UserAPIKeyAuth] = await user_api_key_cache.async_get_cache(
key=key
)
if cached_key_obj is not None:
if isinstance(cached_key_obj, dict):
return UserAPIKeyAuth(**cached_key_obj)
elif isinstance(cached_key_obj, UserAPIKeyAuth):
return cached_key_obj
if check_cache_only:
raise Exception(
f"Key doesn't exist in cache + check_cache_only=True. key={key}."
)
# else, check db
try:
_valid_token: Optional[BaseModel] = await prisma_client.get_data(
token=hashed_token,
table_name="combined_view",
parent_otel_span=parent_otel_span,
proxy_logging_obj=proxy_logging_obj,
)
if _valid_token is None:
raise Exception
_response = UserAPIKeyAuth(**_valid_token.model_dump(exclude_none=True))
# save the key object to cache
await _cache_key_object(
hashed_token=hashed_token,
user_api_key_obj=_response,
user_api_key_cache=user_api_key_cache,
proxy_logging_obj=proxy_logging_obj,
)
return _response
except httpx.ConnectError as e:
return await _handle_failed_db_connection_for_get_key_object(e=e)
except Exception:
raise Exception(
f"Key doesn't exist in db. key={hashed_token}. Create key via `/key/generate` call."
)
async def _handle_failed_db_connection_for_get_key_object(
e: Exception,
) -> UserAPIKeyAuth:
"""
Handles httpx.ConnectError when reading a Virtual Key from LiteLLM DB
Use this if you don't want failed DB queries to block LLM API reqiests
Returns:
- UserAPIKeyAuth: If general_settings.allow_requests_on_db_unavailable is True
Raises:
- Orignal Exception in all other cases
"""
from litellm.proxy.proxy_server import (
general_settings,
litellm_proxy_admin_name,
proxy_logging_obj,
)
# If this flag is on, requests failing to connect to the DB will be allowed
if general_settings.get("allow_requests_on_db_unavailable", False) is True:
# log this as a DB failure on prometheus
proxy_logging_obj.service_logging_obj.service_failure_hook(
service=ServiceTypes.DB,
call_type="get_key_object",
error=e,
duration=0.0,
)
return UserAPIKeyAuth(
key_name="failed-to-connect-to-db",
token="failed-to-connect-to-db",
user_id=litellm_proxy_admin_name,
)
else:
# raise the original exception, the wrapper on `get_key_object` handles logging db failure to prometheus
raise e
@log_db_metrics
async def get_org_object(
org_id: str,
prisma_client: Optional[PrismaClient],
user_api_key_cache: DualCache,
parent_otel_span: Optional[Span] = None,
proxy_logging_obj: Optional[ProxyLogging] = None,
):
"""
- Check if org id in proxy Org Table
- if valid, return LiteLLM_OrganizationTable object
- if not, then raise an error
"""
if prisma_client is None:
raise Exception(
"No DB Connected. See - https://docs.litellm.ai/docs/proxy/virtual_keys"
)
# check if in cache
cached_org_obj = user_api_key_cache.async_get_cache(key="org_id:{}".format(org_id))
if cached_org_obj is not None:
if isinstance(cached_org_obj, dict):
return cached_org_obj
elif isinstance(cached_org_obj, LiteLLM_OrganizationTable):
return cached_org_obj
# else, check db
try:
response = await prisma_client.db.litellm_organizationtable.find_unique(
where={"organization_id": org_id}
)
if response is None:
raise Exception
return response
except Exception:
raise Exception(
f"Organization doesn't exist in db. Organization={org_id}. Create organization via `/organization/new` call."
)
async def can_key_call_model(
model: str, llm_model_list: Optional[list], valid_token: UserAPIKeyAuth
) -> Literal[True]:
"""
Checks if token can call a given model
Returns:
- True: if token allowed to call model
Raises:
- Exception: If token not allowed to call model
"""
if model in litellm.model_alias_map:
model = litellm.model_alias_map[model]
## check if model in allowed model names
verbose_proxy_logger.debug(
f"LLM Model List pre access group check: {llm_model_list}"
)
from collections import defaultdict
from litellm.proxy.proxy_server import llm_router
access_groups = defaultdict(list)
if llm_router:
access_groups = llm_router.get_model_access_groups()
models_in_current_access_groups = []
if len(access_groups) > 0: # check if token contains any model access groups
for idx, m in enumerate(
valid_token.models
): # loop token models, if any of them are an access group add the access group
if m in access_groups:
# if it is an access group we need to remove it from valid_token.models
models_in_group = access_groups[m]
models_in_current_access_groups.extend(models_in_group)
# Filter out models that are access_groups
filtered_models = [m for m in valid_token.models if m not in access_groups]
filtered_models += models_in_current_access_groups
verbose_proxy_logger.debug(f"model: {model}; allowed_models: {filtered_models}")
all_model_access: bool = False
if (
len(filtered_models) == 0
or "*" in filtered_models
or "openai/*" in filtered_models
):
all_model_access = True
if model is not None and model not in filtered_models and all_model_access is False:
raise ValueError(
f"API Key not allowed to access model. This token can only access models={valid_token.models}. Tried to access {model}"
)
valid_token.models = filtered_models
verbose_proxy_logger.debug(
f"filtered allowed_models: {filtered_models}; valid_token.models: {valid_token.models}"
)
return True