🦉 AIProxy is a Python library that serves as a reverse proxy LLM APIs including ChatGPT and Claude2. It provides enhanced features like monitoring, logging, and filtering requests and responses. This library is especially useful for developers and administrators who need detailed oversight and control over the interaction with LLM APIs.
- ✅ Streaming support: Logs every bit of request and response data with token count – never miss a beat! 💓
- ✅ Custom monitoring: Tailor-made for logging any specific info you fancy. Make it your own! 🔍
- ✅ Custom filtering: Flexibly blocks access based on specific info or sends back your own responses. Be in control! 🛡️
- ✅ Multiple AI Services: Supports ChatGPT (OpenAI and Azure OpenAI Service), Claude2 on AWS Bedrock, and is extensible by yourself! 🤖
- ✅ Express dashboard: We provide template for Apache Superset that's ready to use right out of the box – get insights quickly and efficiently! 📊
Install.
$ pip install aiproxy-pythonRun.
$ python -m aiproxy [--host host] [--port port] [--openai_api_key OPENAI_API_KEY]Use.
import openai
client = openai.Client(base_url="http://127.0.0.1:8000/openai", api_key="YOUR_API_KEY")
resp = client.chat.completions.create(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "hello!"}])
print(resp)Enjoy😊🦉
You can use the official client libraries for each LLM by just changing API endpoint url.
Set http|https://your_host/openai as base_url to client.
import openai
client = openai.Client(
api_key="YOUR_API_KEY",
base_url="http://127.0.0.1:8000/openai"
)
resp = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": "hello!"}]
)
print(resp)Set http|https://your_host/anthropic as base_url to client.
from anthropic import Anthropic
client = Anthropic(
api_key="YOUR_API_KEY",
base_url="http://127.0.0.1:8000/anthropic"
)
resp = client.messages.create(
max_tokens=1024,
messages=[{"role": "user", "content": "Hello, Claude",}],
model="claude-3-haiku-20240307",
)
print(resp.content)API itself is compatible but client of google.generativeai doesn't support rewriting urls. Use httpx instead.
import httpx
resp = httpx.post(
url="http://127.0.0.1:8000/googleaistudio/v1beta/models/gemini-1.5-pro-latest:generateContent",
json={
"contents": [{"role": "user", "parts":[{"text": "Hello, Gemini!"}]}],
"generationConfig": {"temperature": 0.5, "maxOutputTokens": 1000}
}
)
print(resp.json())To customize 🦉AIProxy, make your custom entrypoint first. You can customize the metrics you want to monitor, add filters, change databases, etc.
from contextlib import asynccontextmanager
import threading
from fastapi import FastAPI
from aiproxy import AccessLogWorker
from aiproxy.chatgpt import ChatGPTProxy
from aiproxy.anthropic_claude import ClaudeProxy
from aiproxy.gemini import GeminiProxy
# Setup access log worker
worker = AccessLogWorker(connection_str="sqlite:///aiproxy.db")
# Setup server application
@asynccontextmanager
async def lifespan(app: FastAPI):
# Start access log worker
threading.Thread(target=worker.run, daemon=True).start()
yield
# Stop access log worker
worker.queue_client.put(None)
app = FastAPI(lifespan=lifespan, docs_url=None, redoc_url=None, openapi_url=None)
# Proxy for ChatGPT
chatgpt_proxy = ChatGPTProxy(
api_key=OPENAI_API_KEY,
access_logger_queue=worker.queue_client
)
chatgpt_proxy.add_route(app)
# Proxy for Anthropic Claude
claude_proxy = ClaudeProxy(
api_key=ANTHROPIC_API_KEY,
access_logger_queue=worker.queue_client
)
claude_proxy.add_route(app)
# Proxy for Gemini on Google AI Studio (not Vertex AI)
gemini_proxy = GeminiProxy(
api_key=GOOGLE_API_KEY,
access_logger_queue=worker.queue_client
)
gemini_proxy.add_route(app)Run with uvicorn with some params if you need.
$ uvicorn run:app --host 0.0.0.0 --port 8000By default, see accesslog table in aiproxy.db. If you want to use other RDBMS like PostgreSQL, set SQLAlchemy-formatted connection string as connection_str argument when instancing AccessLogWorker.
And, you can customize log format as below:
This is an example to add user column to request log. In this case, the customized log are stored into table named customaccesslog, the lower case of your custom access log class.
from sqlalchemy import Column, String
from aiproxy.accesslog import AccessLogBase, AccessLogWorker
# Make custom schema for database
class CustomAccessLog(AccessLogBase):
user = Column(String)
# Make data mapping logic from HTTP headar/body to log
class CustomGPTRequestItem(ChatGPTRequestItem):
def to_accesslog(self, accesslog_cls: _AccessLogBase) -> _AccessLogBase:
accesslog = super().to_accesslog(accesslog_cls)
# In this case, set value of "x-user-id" in request header to newly added colmun "user"
accesslog.user = self.request_headers.get("x-user-id")
return accesslog
# Make worker with custom log schema
worker = AccessLogWorker(accesslog_cls=CustomAccessLog)
# Make proxy with your custom request item
proxy = ChatGPTProxy(
api_key=YOUR_API_KEY,
access_logger_queue=worker.queue_client,
request_item_class=CustomGPTRequestItem
)NOTE: By default AccessLog, OpenAI API Key in the request headers is masked.
The filter receives all requests and responses, allowing you to view and modify their content. For example:
- Detect and protect from misuse: From unknown apps, unauthorized users, etc.
- Trigger custom actions: Doing something triggered by a request.
This is an example for custom request filter that protects the service from banned user. uezo will receive "you can't use this service" as the ChatGPT response.
from typing import Union
from aiproxy import RequestFilterBase
class BannedUserFilter(RequestFilterBase):
async def filter(self, request_id: str, request_json: dict, request_headers: dict) -> Union[str, None]:
banned_user = ["uezo"]
user = request_json.get("user")
# Return string message to return response right after this filter ends (not to call ChatGPT)
if not user:
return "user is required"
elif user in banned_user:
return "you can't use this service"
# Enable this filter
proxy.add_filter(BannedUserFilter())Try it.
resp = client.chat.completions.create(model="gpt-3.5-turbo", messages=messages, user="uezo")
print(resp)ChatCompletion(id='-', choices=[Choice(finish_reason='stop', index=0, message=ChatCompletionMessage(content="you can't use this service", role='assistant', function_call=None, tool_calls=None))], created=0, model='request_filter', object='chat.completion', system_fingerprint=None, usage=CompletionUsage(completion_tokens=0, prompt_tokens=0, total_tokens=0))Another example is the model overwriter that forces the user to use GPT-3.5-Turbo.
class ModelOverwriteFilter(RequestFilterBase):
async def filter(self, request_id: str, request_json: dict, request_headers: dict) -> Union[str, None]:
request_model = request_json["model"]
if not request_model.startswith("gpt-3.5"):
print(f"Change model from {request_model} -> gpt-3.5-turbo")
# Overwrite request_json
request_json["model"] = "gpt-3.5-turbo"Lastly, ReplayFilter that retrieves content for a specific request_id from the histories. This is an exceptionally cool feature for developers to test AI-based applications.
class ReplayFilter(RequestFilterBase):
async def filter(self, request_id: str, request_json: dict, request_headers: dict) -> Union[str, None]:
# Get request_id to replay from request header
request_id = request_headers.get("x-aiproxy-replay")
if not request_id:
return
db = worker.get_session()
try:
# Get and return the response content from histories
r = db.query(AccessLog).where(AccessLog.request_id == request_id, AccessLog.direction == "response").first()
if r:
return r.content
else:
return "Record not found for {request_id}"
except Exception as ex:
logger.error(f"Error at ReplayFilter: {str(ex)}\n{traceback.format_exc()}")
return "Error at getting response for {request_id}"
finally:
db.close()request_id is included in HTTP response headers as x-aiproxy-request-id.
NOTE: Response filter doesn't work when stream=True.
We provide an Apache Superset template as our express dashboard. Please follow the steps below to set up.
Install Superset.
$ pip install apache-supersetGet dashboard.zip from release page and extract it to the same directory as aiproxy.db.
https://github.com/uezo/aiproxy/releases/tag/v0.3.0
Set required environment variables.
$ export SUPERSET_CONFIG_PATH=$(pwd)/dashboard/superset_config.py
$ export FLASK_APP=supersetMake database.
$ superset db upgradeCreate admin user. Change username and password as you like.
$ superset fab create-admin --username admin --firstname AIProxyAdmin --lastname AIProxyAdmin --email admin@localhost --password adminInitialize Superset.
$ superset initImport 🦉AIProxy dashboard template. Execute this command in the same directory as aiproxy.db. If you execute from a different location, open the Database connections page in the Superset after completing these steps and modify the database connection string to the absolute path.
$ superset import-directory dashboard/resourcesStart Superset.
$ superset run -p 8088Open and customize the dashboard to your liking, including the metrics you want to monitor and their conditions.👍
📕 Superset official docs: https://superset.apache.org/docs/intro
Configure CORS if you call API from web apps. https://fastapi.tiangolo.com/tutorial/cors/
You can use other RDBMS that is supported by SQLAlchemy. You can use them by just changing connection string. (and, install client libraries required.)
$ pip install psycopg2-binary# connection_str = "sqlite:///aiproxy.db"
connection_str = f"postgresql://{USER}:{PASSWORD}@{HOST}:{PORT}/{DATABASE}"
worker = AccessLogWorker(connection_str=connection_str)This is a temporary workaroud from AIProxy >= 0.3.6. Set AIPROXY_USE_NVARCHAR=1 to use NVARCHAR internally.
$ export AIPROXY_USE_NVARCHAR=1Install ODBC driver (version 18 in this example) and pyodbc then set connection string as follows:
# connection_str = "sqlite:///aiproxy.db"
connection_str = f"mssql+pyodbc:///?odbc_connect=DRIVER={ODBC Driver 18 for SQL Server};SERVER=YOUR_SERVER;PORT=1433;DATABASE=YOUR_DB;UID=YOUR_UID;PWD=YOUR_PWD"
worker = AccessLogWorker(connection_str=connection_str)To use Azure OpenAI, use AzureOpenAIProxy instead of ChatGPTProxy.
from aiproxy.chatgpt import AzureOpenAIProxy
aoai_proxy = AzureOpenAIProxy(
api_key="YOUR_API_KEY",
resource_name="YOUR_RESOURCE_NAME",
deployment_id="YOUR_DEPLOYMENT_ID",
api_version="2024-02-01", # https://learn.microsoft.com/ja-jp/azure/ai-services/openai/reference#chat-completions
access_logger_queue=worker.queue_client
)
aoai_proxy.add_route(app)Clients do not need to be aware that it is Azure OpenAI; use the same code for ChatGPT API.
To use Claude on Amazon Bedrock, use BedrockClaudeProxy instead of ClaudeProxy.
from aiproxy.bedrock_claude import BedrockClaudeProxy
bedrock_claude_proxy = BedrockClaudeProxy(
aws_access_key_id="YOUR_AWS_ACCESS_KEY_ID",
aws_secret_access_key="YOUR_AWS_SECRET_ACCESS_KEY",
region_name="YOUR_REGION",
access_logger_queue=worker.queue_client
)
bedrock_claude_proxy.add_route(app)Client side. We test API with AnthropicBedrock.
# Make client with `base_url`
client = anthropic.AnthropicBedrock(
aws_secret_key="dummy_aws_secret_access_key",
aws_access_key="dummy_aws_access_key_id",
aws_region="dummy_region_name",
base_url="http://127.0.0.1:8000/bedrock-claude"
)
resp = client.messages.create(
model="anthropic.claude-3-haiku-20240307-v1:0",
messages=[{"role": "user", "content": [{"type": "text", "text": "こんにちは！"}]}],
max_tokens=512,
stream=True
)
for r in resp:
print(r)For support, questions, or contributions, please open an issue in the GitHub repository. Please contact me directly when you need an enterprise or business support😊.
🦉AIProxy is released under the Apache License v2.
Made with ❤️ by Uezo, the representive of Unagiken.