This page will show you how to log requests in Helicone when using OpenAI. This does not use the Helicone Proxy.
For more information on Async Logging, see the Proxy vs Async page.
1 line integration
Add HELICONE_API_KEY to your environment variables.
export HELICONE_API_KEY=sk-<your-api-key>
# You can also set it in your code (See below)
Replace
from openai import openai
with
from helicone.openai_async import openai
More complex example
from helicone.openai_async import openai, Meta
# export HELICONE_API_KEY=sk-<your-api-key>
# or ...
# from helicone.globals import helicone_global
# helicone_global.api_key = "sk-<your-api-key>"
x = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[{
"role": "system",
"content": "This will be logged"
}],
max_tokens=512,
helicone_meta=Meta(
custom_properties={
"age": 25
}
)
)
1 line integration
Add HELICONE_API_KEY to your environment variables.
export HELICONE_API_KEY=sk-<your-api-key>
# You can also set it in your code (See below)
Replace
from openai import openai
with
from helicone.openai_async import openai
More complex example
from helicone.openai_async import openai, Meta
# export HELICONE_API_KEY=sk-<your-api-key>
# or ...
# from helicone.globals import helicone_global
# helicone_global.api_key = "sk-<your-api-key>"
x = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[{
"role": "system",
"content": "This will be logged"
}],
max_tokens=512,
helicone_meta=Meta(
custom_properties={
"age": 25
}
)
)
Installation and Setup
To get started, install the `helicone-openai` package
npm install @helicone/helicone@2.1.19
Set `HELICONE_API_KEY` as an environment variable
export HELICONE_API_KEY=sk-<your-api-key>
You can also set the Helicone API Key in your code (See below)
Replace
const { ClientOptions, OpenAI } = require("openai");
with
const { HeliconeAsyncOpenAI as OpenAI,
IHeliconeAsyncClientOptions as ClientOptions } = require("helicone");
Make a request
Chat, Completion, Embedding, etc usage is equivalent to OpenAI package.
const openai = new OpenAI({
apiKey: process.env.OPENAI_API_KEY,
heliconeMeta: {
apiKey: process.env.HELICONE_API_KEY, // Can be set as env variable
// ... additional helicone meta fields
},
});
const chatCompletion = await openai.chat.completion.create({
model: "gpt-3.5-turbo",
messages: [{ role: "user", content: "Hello world" }],
});
console.log(chatCompletion.data.choices[0].message);
Send feedback
With Async logging, you must retrieve the helicone-id header from the log response (not LLM response).
const openai = new OpenAI({
apiKey: process.env.OPENAI_API_KEY,
heliconeMeta: {
apiKey: process.env.HELICONE_API_KEY,
onLog: async (response: Response) => {
const heliconeId = response.headers.get("helicone-id");
await openai.helicone.logFeedback(
heliconeId,
HeliconeFeedbackRating.Positive
);
},
},
});
Async logging loses some additional features such as cache, rate limits, and retries
interface IHeliconeMeta {
apiKey?: string;
properties?: { [key: string]: any };
user?: string;
baseUrl?: string;
onLog?: OnHeliconeLog;
onFeedback?: OnHeliconeFeedback;
}
type OnHeliconeLog = (response: Response) => Promise<void>;
type OnHeliconeFeedback = (result: Response) => Promise<void>;
The Helicone Async Log Request API is used for logging requests and responses that
go through an endpoint. This is highly useful for auditing, debugging and observing
the behavior of your interactions with the system.
Request Structure
A typical request will have the following structure:
Endpoint
POST https://api.helicone.ai/oai/v1/log
| Name | Value |
|---|
| Authorization | Bearer {API_KEY} |
Replace {API_KEY} with your actual API Key.
Body
The body of the request should follow the HeliconeAyncLogRequest structure:
export type HeliconeAyncLogRequest = {
providerRequest: ProviderRequest;
providerResponse: ProviderResponse;
timing: Timing;
};
export type ProviderRequest = {
url: string;
json: {
[key: string]: any;
};
meta: Record<string, string>;
};
export type ProviderResponse = {
json: {
[key: string]: any;
};
status: number;
headers: Record<string, string>;
};
export type Timing = {
// From Unix epoch in Milliseconds
startTime: {
seconds: number;
milliseconds: number;
};
endTime: {
seconds: number;
milliseconds: number;
};
};
Example Usage
Here’s an example using curl:
curl -X POST https://api.helicone.ai/oai/v1/log \
-H 'Authorization: Bearer your_api_key' \
-H 'Content-Type: application/json' \
-d '{
"providerRequest": {
"url": "https://example.com",
"json": {
"key1": "value1",
"key2": "value2"
},
"meta": {
"metaKey1": "metaValue1",
"metaKey2": "metaValue2"
}
},
"providerResponse": {
"json": {
"responseKey1": "responseValue1",
"responseKey2": "responseValue2"
},
"status": 200,
"headers": {
"headerKey1": "headerValue1",
"headerKey2": "headerValue2"
}
},
"timing": {
"startTime": {
"seconds": 1625686222,
"milliseconds": 500
},
"endTime": {
"seconds": 1625686244,
"milliseconds": 750
}
}
}'
In the curl command above, replace your_api_key with your actual API key, and adjust the values in the JSON to fit your actual request and response data and timing.
The response body is a JSON object of the entire response returned by OpenAI, unless it is a streamed request. In that case, it is a JSON object with a key called “streamed_data”, which is an array of every single chunk.
