mirror of
https://github.com/BerriAI/litellm.git
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(security fix) - update base image for all docker images to python:3.13.1-slim (#7388)
* update base image for all docker files * remove unused files * fix sec vuln
This commit is contained in:
Ishaan Jaff
GitHub
parent
d883241b36
commit
564ecc728d
11 changed files with 5 additions and 328 deletions
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@ -1,8 +1,8 @@
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# Base image for building
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ARG LITELLM_BUILD_IMAGE=python:3.11.8-slim
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ARG LITELLM_BUILD_IMAGE=python:3.13.1-slim
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# Runtime image
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ARG LITELLM_RUNTIME_IMAGE=python:3.11.8-slim
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ARG LITELLM_RUNTIME_IMAGE=python:3.13.1-slim
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# Builder stage
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FROM $LITELLM_BUILD_IMAGE AS builder
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@ -1,8 +1,8 @@
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# Base image for building
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ARG LITELLM_BUILD_IMAGE=python:3.11.8-slim
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ARG LITELLM_BUILD_IMAGE=python:3.13.1-slim
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# Runtime image
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ARG LITELLM_RUNTIME_IMAGE=python:3.11.8-slim
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ARG LITELLM_RUNTIME_IMAGE=python:3.13.1-slim
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# Builder stage
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FROM $LITELLM_BUILD_IMAGE AS builder
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@ -1,5 +1,5 @@
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# Use the specific Node.js v20.11.0 image
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FROM node:20.18.1
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FROM node:20.18.1-alpine3.20
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# Set the working directory inside the container
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WORKDIR /app
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@ -1,6 +0,0 @@
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model_list:
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- model_name: gpt-3.5-turbo
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litellm_params:
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model: openai/my-fake-model
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api_key: my-fake-key
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api_base: http://0.0.0.0:8090
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@ -1,20 +0,0 @@
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# Use the official Python image as the base image
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FROM python:3.9-slim
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# Set the working directory in the container
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WORKDIR /app
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# Copy the Python requirements file
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COPY requirements.txt .
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# Install the Python dependencies
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RUN pip install --no-cache-dir -r requirements.txt
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# Copy the application code
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COPY . .
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# Expose the port the app will run on
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EXPOSE 8090
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# Start the application
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CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8090"]
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@ -1,57 +0,0 @@
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# import sys, os
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# sys.path.insert(
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# 0, os.path.abspath("../")
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# ) # Adds the parent directory to the system path
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from fastapi import FastAPI, Request
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from fastapi.middleware.cors import CORSMiddleware
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import litellm
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app = FastAPI()
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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litellm_router = litellm.Router(
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model_list=[
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{
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"model_name": "anything", # model alias -> loadbalance between models with same `model_name`
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"litellm_params": { # params for litellm completion/embedding call
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"model": "openai/anything", # actual model name
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"api_key": "sk-1234",
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"api_base": "https://exampleopenaiendpoint-production.up.railway.app/",
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},
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}
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]
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)
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# for completion
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@app.post("/chat/completions")
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@app.post("/v1/chat/completions")
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async def completion(request: Request):
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# this proxy uses the OpenAI SDK to call a fixed endpoint
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response = await litellm_router.acompletion(
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model="anything",
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messages=[
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{
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"role": "user",
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"content": "hello who are you",
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}
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],
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)
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return response
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if __name__ == "__main__":
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import uvicorn
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# run this on 8090, 8091, 8092 and 8093
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uvicorn.run(app, host="0.0.0.0", port=8090)
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@ -1,34 +0,0 @@
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import uuid
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from locust import HttpUser, between, task
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class MyUser(HttpUser):
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wait_time = between(1, 5)
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@task
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def chat_completion(self):
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headers = {
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"Content-Type": "application/json",
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"Authorization": "Bearer sk-1234",
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# Include any additional headers you may need for authentication, etc.
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}
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# Customize the payload with "model" and "messages" keys
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payload = {
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"model": "fake-openai-endpoint",
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"messages": [
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{
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"role": "system",
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"content": f"{uuid.uuid4()} this is a very sweet test message from ishaan"
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* 100,
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},
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{"role": "user", "content": "Hello, how are you?"},
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],
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# Add more data as necessary
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}
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# Make a POST request to the "chat/completions" endpoint
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self.client.post("chat/completions", json=payload, headers=headers)
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# Print or log the response if needed
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@ -1,50 +0,0 @@
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# import sys, os
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# sys.path.insert(
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# 0, os.path.abspath("../")
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# ) # Adds the parent directory to the system path
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import uuid
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from fastapi import FastAPI, Request
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from fastapi.middleware.cors import CORSMiddleware
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app = FastAPI()
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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# for completion
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@app.post("/chat/completions")
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@app.post("/v1/chat/completions")
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async def completion(request: Request):
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return {
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"id": f"chatcmpl-{uuid.uuid4().hex}",
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"object": "chat.completion",
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"created": 1677652288,
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"model": "gpt-3.5-turbo-0125",
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"system_fingerprint": "fp_44709d6fcb",
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"choices": [
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{
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"index": 0,
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"message": {
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"role": "assistant",
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"content": "\n\nHello there, how may I assist you today?",
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},
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"logprobs": None,
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"finish_reason": "stop",
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}
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],
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"usage": {"prompt_tokens": 9, "completion_tokens": 12, "total_tokens": 21},
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}
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if __name__ == "__main__":
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import uvicorn
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# run this on 8090, 8091, 8092 and 8093
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uvicorn.run(app, host="0.0.0.0", port=8090)
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@ -1,51 +0,0 @@
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# import sys, os
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# sys.path.insert(
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# 0, os.path.abspath("../")
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# ) # Adds the parent directory to the system path
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from fastapi import FastAPI, Request
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from fastapi.middleware.cors import CORSMiddleware
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from openai import AsyncOpenAI
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import litellm
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app = FastAPI()
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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litellm_client = AsyncOpenAI(
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base_url="https://exampleopenaiendpoint-production.up.railway.app/",
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api_key="sk-1234",
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)
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# for completion
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@app.post("/chat/completions")
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@app.post("/v1/chat/completions")
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async def completion(request: Request):
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# this proxy uses the OpenAI SDK to call a fixed endpoint
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response = await litellm.acompletion(
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model="openai/anything",
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messages=[
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{
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"role": "user",
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"content": "hello who are you",
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}
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],
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client=litellm_client,
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)
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return response
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if __name__ == "__main__":
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import uvicorn
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# run this on 8090, 8091, 8092 and 8093
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uvicorn.run(app, host="0.0.0.0", port=8090)
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@ -1,57 +0,0 @@
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# import sys, os
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# sys.path.insert(
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# 0, os.path.abspath("../")
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# ) # Adds the parent directory to the system path
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from fastapi import FastAPI, Request
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from fastapi.middleware.cors import CORSMiddleware
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import litellm
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app = FastAPI()
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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litellm_router = litellm.Router(
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model_list=[
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{
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"model_name": "anything", # model alias -> loadbalance between models with same `model_name`
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"litellm_params": { # params for litellm completion/embedding call
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"model": "openai/anything", # actual model name
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"api_key": "sk-1234",
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"api_base": "https://exampleopenaiendpoint-production.up.railway.app/",
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},
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}
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]
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)
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# for completion
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@app.post("/chat/completions")
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@app.post("/v1/chat/completions")
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async def completion(request: Request):
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# this proxy uses the OpenAI SDK to call a fixed endpoint
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response = await litellm_router.acompletion(
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model="anything",
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messages=[
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{
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"role": "user",
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"content": "hello who are you",
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}
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],
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)
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return response
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if __name__ == "__main__":
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import uvicorn
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# run this on 8090, 8091, 8092 and 8093
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uvicorn.run(app, host="0.0.0.0", port=8090)
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@ -1,48 +0,0 @@
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# import sys, os
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# sys.path.insert(
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# 0, os.path.abspath("../")
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# ) # Adds the parent directory to the system path
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from fastapi import FastAPI, Request
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from fastapi.middleware.cors import CORSMiddleware
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from openai import AsyncOpenAI
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app = FastAPI()
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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litellm_client = AsyncOpenAI(
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base_url="https://exampleopenaiendpoint-production.up.railway.app/",
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api_key="sk-1234",
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)
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# for completion
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@app.post("/chat/completions")
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@app.post("/v1/chat/completions")
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async def completion(request: Request):
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# this proxy uses the OpenAI SDK to call a fixed endpoint
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response = await litellm_client.chat.completions.create(
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model="anything",
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messages=[
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{
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"role": "user",
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"content": "hello who are you",
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}
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],
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)
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return response
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if __name__ == "__main__":
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import uvicorn
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# run this on 8090, 8091, 8092 and 8093
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uvicorn.run(app, host="0.0.0.0", port=8090)
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