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https://github.com/microsoft/AICentral
An AI Control Centre for monitoring, authenticating, and providing resilient access to multiple Open AI services.
https://github.com/microsoft/AICentral
Last synced: about 2 months ago
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An AI Control Centre for monitoring, authenticating, and providing resilient access to multiple Open AI services.
- Host: GitHub
- URL: https://github.com/microsoft/AICentral
- Owner: microsoft
- License: mit
- Created: 2023-10-21T02:45:04.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-07-29T11:51:23.000Z (about 2 months ago)
- Last Synced: 2024-07-30T09:13:52.468Z (about 2 months ago)
- Language: C#
- Homepage:
- Size: 1.74 MB
- Stars: 75
- Watchers: 5
- Forks: 29
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Security: SECURITY.md
- Support: SUPPORT.md
Awesome Lists containing this project
- awesome-azure-openai-llm - AI Central
README
# AI Central
[](https://opensource.org/licenses/mit)
[](https://www.nuget.org/packages/aicentral/)
## Take control of your AI services.
### Features
- Minimal overhead - written on Asp.Net Core, on dotnet 8. One of the fastest web-servers in the business.
- Enable advanced Azure APIm scenarios such as passing a Subscription Key, and a JWT from libraries like PromptFlow that don't support that out-of-the-box.
- PII Stripping logging to Cosmos DB
- Powered by ```graemefoster/aicentral.logging.piistripping```
- Lightweight out-the-box token metrics surfaced through Open Telemetry
- **Does not buffer and block streaming**
- Use for PTU Chargeback scenarios
- Gain quick insights into who's using what, how much, and how often
- Standard Open Telemetry format to surface Dashboards in you monitoring solution of choice
- Prompt and usage logging to Azure Monitor
- **Works for streaming endpoints as-well as non-streaming**
- Intelligent Routing
- Endpoint Selector that favours endpoints reporting higher available capacity
- Random endpoint selector
- Prioritised endpoint selector with fallback
- Lowest Latency endpoint selector
- Can proxy asynchronous requests such as Azure OpenAI DALLE2 Image Generation across fleets of servers
- Custom consumer OAuth2 authorisation
- Can mint JWT time-bound and consumer-bound JWT tokens to make it easy to run events like Hackathons without blowing your budget
- Circuit breakers and backoff-retry over downstream AI services
- Local token rate limiting
- By consumer / by endpoint
- By number of tokens (including streaming by estimated token count)
- Local request rate limiting
- By consumer / by endpoint
- Bulkhead support for buffering requests to backend
- Distributed token rate limiting (using Redis)
- Powered by an extension ```graemefoster/aicentral.ratelimiting.distributedredis```
- AI Search Vectorization endpoint
- Powered by an extension ```graemefoster/aicentral.azureaisearchvectorizer```
> Extensibility model makes it easy to build your own plugins
## Quickstarts
To make it easy to get up and running, we are creating QuickStart configurations. Simply pull the docker container, set a few environment variables, and you're away.
| Quickstart | Features |
|--------------------------------------------------------------------|-------------------------------------------------------------------------------------|
| [APImProxyWithCosmosLogging](./docs/apimproxywithcosmoslogging.md) | Run in-front of Azure APIm AI Gateway for easy PromptFlow and PII stripped logging. |
## Configuration
See [Configuration](./docs/configuration.md) for more details.
> The Azure OpenAI SDK retries by default. As AI Central does this for you you can turn it off in the client by passing ```new Azure.AI.OpenAI.OpenAIClientOptions() {
RetryPolicy = new RetryPolicy(0) }``` when you create an OpenAIClient
## Deployment
1. [Install Azure CLI](https://learn.microsoft.com/en-us/cli/azure/install-azure-cli) if you haven't done so already.
2. Install the Bicep CLI by running the following command in your terminal:
```bash
az bicep install
```
3. Compile your Bicep file to an ARM template with the following command:
```bash
az bicep build --file ./infra/main.bicep
```
This will create a file named `main.json` in the same directory as your [`main.bicep`](./infra/main.json) file.
4. Deploy the generated ARM template using the Azure CLI. You'll need to login to your Azure account and select the subscription where you want to deploy the resources:
```bash
az login
az account set --subscription "your-subscription-id"
az deployment sub create --template-file ./infra/main.json --location "your-location"
```
Replace `"your-subscription-id"` with your actual Azure subscription ID and `"your-location"` with the location where you want to deploy the resources (e.g., "westus2").
To test deployment retrieve url for the webapp and update the following `curl` command:
```bash
curl -X POST \
-H "Content-Type: application/json" \
-H "api-key: {your-customer-key}" \
-d '{
"messages": [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "what is .net core"}
]
}' \
"https://{your-web-url}/openai/deployments/Gpt35Turbo0613/chat/completions?api-version=2024-02-01"
```
To test if everything works by running some code of your choice, e.g., this code with OpenAI Python SDK:
```python
import json
import httpx
from openai import AzureOpenAI
api_key = ""
def event_hook(req: httpx.Request) -> None:
print(json.dumps(dict(req.headers), indent=2))
client = AzureOpenAI(
azure_endpoint="https://app-[a]-[b].azurewebsites.net", #if you deployed to Azure Web App app-[a]-[b].azurewebsites.net
api_key=api_key,
api_version="2023-05-15",
http_client=httpx.Client(event_hooks={"request": [event_hook]})
)
response = client.chat.completions.create(
model="Gpt35Turbo0613",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "What is the first letter of the alphabet?"}
]
)
print(response)
```
> Note: delete create resources az deployment group list --resource-group "your-resource-group-name" --query "[].{Name:name, Timestamp:properties.timestamp, State:properties.provisioningState}" --output table
## Minimal
This sample produces a AI-Central proxy that
- Listens on a hostname of your choosing
- Proxies directly through to a back-end Open AI server
- Can be accessed using standard SDKs
### Installation
#### Docker
```bash
# Run container in Docker, referencing a local configuration file
docker run -p 8080:8080 -v .\appsettings.Development.json:/app/appsettings.Development.json -e ASPNETCORE_ENVIRONMENT=Development graemefoster/aicentral:latest
```
#### Asp.Net CORE
```bash
#Create new project and bootstrap the AICentral nuget package
dotnet new web -o MyAICentral
cd MyAICentral
dotnet add package AICentral
#dotnet add package AICentral.Logging.AzureMonitor
```
#### Program.cs
```csharp
//Minimal API to configure AI Central
var builder = WebApplication.CreateBuilder(args);
builder.Services.AddAICentral(builder.Configuration);
app.UseAICentral(
builder.Configuration,
//if using logging extension
additionalComponentAssemblies: [ typeof(AzureMonitorLoggerFactory).Assembly ]
);
var app = builder.Build();
app.Run();
```
### appsettings.<environment>.json
```json
{
"AICentral": {
"Endpoints": [
{
"Type": "AzureOpenAIEndpoint",
"Name": "openai-1",
"Properties": {
"LanguageEndpoint": "https://.openai.azure.com",
"AuthenticationType": "Entra"
}
}
],
"AuthProviders": [
{
"Type": "Entra",
"Name": "aad-role-auth",
"Properties": {
"Entra": {
"ClientId": "",
"TenantId": "",
"Instance": "https://login.microsoftonline.com/"
},
"Requirements" : {
"Roles": ["required-roles"]
}
}
}
],
"EndpointSelectors": [
{
"Type": "SingleEndpoint",
"Name": "default",
"Properties": {
"Endpoint": "openai-1"
}
}
],
"Pipelines": [
{
"Name": "AzureOpenAIPipeline",
"Host": "mypipeline.mydomain.com",
"AuthProvider": "aad-role-auth",
"EndpointSelector": "default"
}
]
}
}
```
## Enriched telemetry and logging
Out of the box AI Central emits Open Telemetry metrics with the following dimensions:
- Consumer
- Endpoint
- Pipeline
- Prompt Tokens
- Response Tokens **including streaming**
Allowing insightful dashboards to be built using your monitoring tool of choice.
AI Central also allows fine-grained logging. We ship an extension that logs to Azure Monitor, but it's easy to build your own.
See [advanced-otel](./docs/advanced-otel.md) for dashboard inspiration!
## Full example
This pipeline will:
- Present an Azure OpenAI, and an Open AI downstream as a single upstream endpoint
- maps the incoming deployment Name "GPT35Turbo0613" to the downstream Azure OpenAI deployment "MyGptModel"
- maps incoming Azure OpenAI deployments to Open AI models
- Present it as an Azure OpenAI style endpoint
- Protect the front-end by requiring an AAD token issued for your own AAD application
- Put a local Asp.Net core rate-limiting policy over the endpoint
- Add logging to Azure monitor
- Logs quota, client caller information, and in this case the Prompt but not the response.
```json
{
"AICentral": {
"Endpoints": [
{
"Type": "AzureOpenAIEndpoint",
"Name": "openai-priority",
"Properties": {
"LanguageEndpoint": "https://.openai.azure.com",
"AuthenticationType": "Entra|EntraPassThrough|ApiKey",
"ModelMappings": {
"Gpt35Turbo0613": "MyGptModel"
}
}
},
{
"Type": "OpenAIEndpoint",
"Name": "openai-fallback",
"Properties": {
"LanguageEndpoint": "https://api.openai.com",
"ModelMappings": {
"Gpt35Turbo0613": "gpt-3.5-turbo",
"Ada002Embedding": "text-embedding-ada-002"
},
"ApiKey": "",
"Organization": ""
}
}
],
"AuthProviders": [
{
"Type": "Entra",
"Name": "simple-aad",
"Properties": {
"Entra": {
"ClientId": "",
"TenantId": "",
"Instance": "https://login.microsoftonline.com/",
"Audience": ""
},
"Requirements" : {
"Roles": ["required-roles"]
}
}
}
],
"EndpointSelectors": [
{
"Type": "Prioritised",
"Name": "my-endpoint-selector",
"Properties": {
"PriorityEndpoints": ["openai-1"],
"FallbackEndpoints": ["openai-fallback"]
}
}
],
"GenericSteps": [
{
"Type": "AspNetCoreFixedWindowRateLimiting",
"Name": "window-rate-limiter",
"Properties": {
"LimitType": "PerConsumer|PerAICentralEndpoint",
"MetricType": "Requests",
"Options": {
"Window": "00:00:10",
"PermitLimit": 100
}
}
},
{
"Type": "AzureMonitorLogger",
"Name": "azure-monitor-logger",
"Properties": {
"WorkspaceId": "",
"Key": "",
"LogPrompt": true,
"LogResponse": false,
"LogClient": true
}
}
],
"Pipelines": [
{
"Name": "MyPipeline",
"Host": "prioritypipeline.mydomain.com",
"EndpointSelector": "my-endpoint-selector",
"AuthProvider": "simple-aad",
"Steps": [
"window-rate-limiter",
"azure-monitor-logger"
],
"OpenTelemetryConfig": {
"AddClientNameTag": true,
"Transmit": true
}
}
]
}
}
```