Weights & Biases

If you want to seamlessly use Weights & Biases’ logging functionality, we’ve got you covered

Weave

Mirascope seamlessly integrates with Weave with just a few lines of code. You can use it with any BaseCall or BaseExtractor extension such as OpenAICall or AnthropicCall. Simply add the with_weave decorator to your class and the call, call_async, stream, stream_async, extract, and extract_async methods will be automatically logged to the Weave project you initialize.

The below examples show how to use the with_weave decorator to automatically log your runs to Weave. We've highlighted the lines that we've added to the original example to demonstrate how easy it is to use Weave with Mirascope.

Call Example

import weave

from mirascope.openai import OpenAICall
from mirascope.wandb import with_weave

weave.init("my-project")


@with_weave
class BookRecommender(OpenAICall):
    prompt_template = "Please recommend some {genre} books"

    genre: str


recommender = BookRecommender(genre="fantasy")
response = recommender.call()  # this will automatically get logged with weave
print(response.content)

Extract Example

from typing import Literal, Type

import weave
from pydantic import BaseModel

from mirascope.openai import OpenAIExtractor
from mirascope.wandb import with_weave

weave.init("scratch-test")


class TaskDetails(BaseModel):
    description: str
    due_date: str
    priority: Literal["low", "normal", "high"]


@with_weave
class TaskExtractor(OpenAIExtractor[TaskDetails]):
    extract_schema: Type[TaskDetails] = TaskDetails
    prompt_template = """
    Extract the task details from the following task:
    {task}
    """

    task: str


task = "Submit quarterly report by next Friday. Task is high priority."
task_details = TaskExtractor(task=task).extract()  # this will be logged automatically
assert isinstance(task_details, TaskDetails)
print(task_details)

Trace

WandbCallMixin is a mixin with creation methods that internally call W&B’s Trace() function so you can easily log your runs. For standard responses, you can use call_with_trace(), and for extractions, you can use WandbExtractorMixin's extract_with_trace method. These mixins are agnostic to the LLM provider, so you can use it with any BaseCall or BaseExtractor extension such as OpenAICall or AnthropicCall.

Generating Content with a W&B Trace

The call_with_trace() function internally calls both call() and wandb.Trace() and is configured to properly log both successful completions and errors.

Note that unlike a standard call, it requires the argument span_type to specify the type of Trace it initializes. Once called, it will return a tuple of the call response and the created span Trace.

import os
from mirascope.openai import OpenAICall, OpenAICallResponse
from mirascope.wandb import WandbCallMixin
import wandb

wandb.login(key="YOUR_WANDB_API_KEY")
wandb.init(project="wandb_logged_chain")

os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"


class Explainer(OpenAICall, WandbCallMixin[OpenAICallResponse]):
    prompt_template = "Tell me more about {topic} in detail."

    topic: str


explainer = Explainer(span_type="llm", topic="the Roman Empire")
response, span = explainer.call_with_trace()
span.log(name="my_trace")

In addition, call_with_trace can take an argument parent for chained calls, and the initialized Trace will be linked with its parent within W&B logs.

import os
from mirascope.openai import OpenAICall, OpenAICallResponse
from mirascope.wandb import WandbCallMixin
import wandb

wandb.login(key="YOUR_WANDB_API_KEY")
wandb.init(project="wandb_logged_chain")

os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"


class Explainer(OpenAICall, WandbCallMixin[OpenAICallResponse]):
    prompt_template = "Tell me more about {topic} in detail."

    topic: str


class Summarizer(OpenAICall, WandbCallMixin[OpenAICallResponse]):
    prompt_template = "Summarize the following: {text}"

    text: str


explainer = Explainer(span_type="llm", topic="the Roman Empire")
response, explain_span = explainer.call_with_trace()

summarizer = Summarizer(span_type="llm", text=explanation.content)
response, _ = summarizer.call_with_trace(explain_span)

explain_span.log(name="my_trace")

Since WandbCallMixin just adds a method to the call of your choice (e.g. OpenAICall as above), it will support function calling the same way you would a standard OpenAICall, as seen here

Extracting with a W&B Trace

When working with longer chains, it is often useful to use extractions so that data is passed along in a structured format. Just like call_with_trace() , you will need to pass in a span_type argument to the extractor and a parent to the extraction.

import os
from typing import Type

from mirascope.openai import OpenAIExtractor
from mirascope.wandb import WandbExtractorMixin
import wandb

wandb.login(key="YOUR_WANDB_API_KEY")
wandb.init(project="wandb_logged_chain")

os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"


class OceanCounter(OpenAIExtractor[int], WandbExtractorMixin[int]):
    extract_schema: Type[int] = int
    prompt_template = "There are 7 oceans on earth."


num_oceans, span = OceanCounter(span_type="tool").extract_with_trace()

span.log(name="mirascope_trace")