Tags

Tags allow you to label 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 also be useful for creating fine-tune datasets.

1

Setup the SDK

2

Simplest: Identify OpenAI calls

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

chat_completion = client.chat.completions.create(
  messages=[{"role": "user", "content": "Say this is a test"}],
  model="gpt-4o",
  tags=["some-tag"],
)

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

handler = LunaryCallbackHandler()

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

3

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.

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

Questions? We're here to help.

Email