Files
litellm/litellm/proxy
Krish DholakiaandGitHub 10d891a365 Guardrails - add logging to all unified_guardrails + link to custom code guardrail templates (#20900)
* feat(guardrail_hooks/): add guardrail logging to all unified guardrails

ensures unified guardrails use the 'log_guardrail_information' decorator for logging

* fix(custom_guardrail.py): don't log inputs on guardrail response - just emit state

* refactor: don't double log bedrock guardrail information

* feat: add in-product nudges for contributing + trying community custom code guardrails

allows users to contribute / share custom code guardrails
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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/* routes
  • management_endpoints/team_endpoints.py - all /team/* routes
  • management_endpoints/internal_user_endpoints.py - all /user/* routes
  • management_endpoints/ui_sso.py - all /sso/* routes