Tags

Tags allow you to label your queries and completions.

This is useful to further segment your data. For example, you can label all the queries that are related to a specific feature or a specific company.

Later on, this can be useful for creating fine-tune datasets.

Setup the SDK

Python

Learn how to install the Python SDK

JavaScript

Learn how to install the JS SDk

Simplest: Identify OpenAI calls

The easiest way to get started adding tags is to send them when doing your OpenAI API calls.

const res = await openai.chat.completions.create({
  model: "gpt-3.5-turbo",
  messages: [{ role: "user", content: "Hello" }],
  tags: ["some-tag"]
})

chat_completion = client.chat.completions.create(
    messages=[{"role": "user", "content": "Say this is a test"}],
    model="gpt-3.5-turbo",
    tags=["some-tag"],
)

If you're using LangChain, you can similarly pass the tags on any LangChain object.

const chat = new ChatOpenAI({
  callbacks: [new LLMonitorHandler()],
});

const res = await chat.call([new HumanMessage("Hello!")], {
  tags: ["some-tag"],
});

handler = LLMonitorCallbackHandler()

chat = ChatOpenAI(
  callbacks=[handler],
  tags=["some-tag"],
)

Advanced: Inject tag into context

You can also inject tags into the context of your code. This is useful if you want to tag all the queries that are related to a specific feature or a specific company.

Coming soon

from llmonitor import monitor, tags, tags_ctx
# Method 2: everything inside the with statement will have tags2 and tags3
with tags(["tag2", "tag3"]):
    my_agent()

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