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* feat(view_logs.tsx): show model id + api base in request logs easier debugging * fix(index.tsx): fix length of api base easier viewing * refactor(leftnav.tsx): show models tab to team admin * feat(model_dashboard.tsx): add explainer for what the 'models' page is for team admin helps them understand how they can use it * feat(model_management_endpoints.py): restrict model add by team to just team admin allow team admin to add models via non-team keys (e.g. ui token) * test(test_add_update_models.py): update unit testing for new behaviour * fix(model_dashboard.tsx): show user the models * feat(proxy_server.py): add new query param 'user_models_only' to `/v2/model/info` Allows user to retrieve just the models they've added Used in UI to show internal users just the models they've added * feat(model_dashboard.tsx): allow team admins to view their own models * fix: allow ui user to fetch model cost map * feat(add_model_tab.tsx): require team admins to specify team when onboarding models * fix(_types.py): add `/v1/model/info` to info route `/model/info` was already there * fix(model_info_view.tsx): allow user to edit a model they created * fix(model_management_endpoints.py): allow team admin to update team model * feat(model_managament_endpoints.py): allow team admin to delete team models * fix(model_management_endpoints.py): don't require team id to be set when adding a model * fix(proxy_server.py): fix linting error * fix: fix ui linting error * fix(model_management_endpoints.py): ensure consistent auth checks on all model calls * test: remove old test - function no longer exists in same form * test: add updated mock testing |
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.. | ||
_experimental | ||
analytics_endpoints | ||
anthropic_endpoints | ||
auth | ||
batches_endpoints | ||
common_utils | ||
config_management_endpoints | ||
credential_endpoints | ||
db | ||
example_config_yaml | ||
fine_tuning_endpoints | ||
guardrails | ||
health_endpoints | ||
hooks | ||
management_endpoints | ||
management_helpers | ||
openai_files_endpoints | ||
pass_through_endpoints | ||
rerank_endpoints | ||
response_api_endpoints | ||
spend_tracking | ||
types_utils | ||
ui_crud_endpoints | ||
vertex_ai_endpoints | ||
.gitignore | ||
__init__.py | ||
_logging.py | ||
_new_new_secret_config.yaml | ||
_new_secret_config.yaml | ||
_super_secret_config.yaml | ||
_types.py | ||
cached_logo.jpg | ||
caching_routes.py | ||
common_request_processing.py | ||
custom_prompt_management.py | ||
custom_sso.py | ||
custom_validate.py | ||
enterprise | ||
health_check.py | ||
lambda.py | ||
litellm_pre_call_utils.py | ||
llamaguard_prompt.txt | ||
logo.jpg | ||
mcp_tools.py | ||
model_config.yaml | ||
openapi.json | ||
post_call_rules.py | ||
prisma_migration.py | ||
proxy_cli.py | ||
proxy_config.yaml | ||
proxy_server.py | ||
README.md | ||
route_llm_request.py | ||
schema.prisma | ||
start.sh | ||
utils.py |
litellm-proxy
A local, fast, and lightweight OpenAI-compatible server to call 100+ LLM APIs.
usage
$ pip install litellm
$ litellm --model ollama/codellama
#INFO: Ollama running on http://0.0.0.0:8000
replace openai base
import openai # openai v1.0.0+
client = openai.OpenAI(api_key="anything",base_url="http://0.0.0.0:8000") # set proxy to base_url
# request sent to model set on litellm proxy, `litellm --model`
response = client.chat.completions.create(model="gpt-3.5-turbo", messages = [
{
"role": "user",
"content": "this is a test request, write a short poem"
}
])
print(response)
See how to call Huggingface,Bedrock,TogetherAI,Anthropic, etc.
Folder Structure
Routes
proxy_server.py
- all openai-compatible routes -/v1/chat/completion
,/v1/embedding
+ model info routes -/v1/models
,/v1/model/info
,/v1/model_group_info
routes.health_endpoints/
-/health
,/health/liveliness
,/health/readiness
management_endpoints/key_management_endpoints.py
- all/key/*
routesmanagement_endpoints/team_endpoints.py
- all/team/*
routesmanagement_endpoints/internal_user_endpoints.py
- all/user/*
routesmanagement_endpoints/ui_sso.py
- all/sso/*
routes