import openai
import langsmith as ls
from langsmith.wrappers import wrap_openai
client = openai.Client()
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Hello!"}
]
# You can set metadata & tags **statically** when decorating a function
# Use the @traceable decorator with tags and metadata
# Ensure that the LANGSMITH_TRACING environment variables are set for @traceable to work
@ls.traceable(
run_type="llm",
name="OpenAI Call Decorator",
tags=["my-tag"],
metadata={"my-key": "my-value"}
)
def call_openai(
messages: list[dict], model: str = "gpt-4o-mini"
) -> str:
# You can also dynamically set metadata on the parent run:
rt = ls.get_current_run_tree()
rt.metadata["some-conditional-key"] = "some-val"
rt.tags.extend(["another-tag"])
return client.chat.completions.create(
model=model,
messages=messages,
).choices[0].message.content
call_openai(
messages,
# To add at **invocation time**, when calling the function.
# via the langsmith_extra parameter
langsmith_extra={"tags": ["my-other-tag"], "metadata": {"my-other-key": "my-value"}}
)
# Alternatively, you can use the context manager
with ls.trace(
name="OpenAI Call Trace",
run_type="llm",
inputs={"messages": messages},
tags=["my-tag"],
metadata={"my-key": "my-value"},
) as rt:
chat_completion = client.chat.completions.create(
model="gpt-4o-mini",
messages=messages,
)
rt.metadata["some-conditional-key"] = "some-val"
rt.end(outputs={"output": chat_completion})
# You can use the same techniques with the wrapped client
patched_client = wrap_openai(
client, tracing_extra={"metadata": {"my-key": "my-value"}, "tags": ["a-tag"]}
)
chat_completion = patched_client.chat.completions.create(
model="gpt-4o-mini",
messages=messages,
langsmith_extra={
"tags": ["my-other-tag"],
"metadata": {"my-other-key": "my-value"},
},
)
