Integration: Weights & Biases Weave Tracer

Send Haystack traces to Weights & Biases for monitoring and visualization

Authors

deepset

Table of Contents

• Overview
• Installation
• Usage
• License

Overview

This integration allows you to use Weights & Biases Weave framework for tracing and monitoring Haystack pipeline components. It provides a connector that sends Haystack traces to Weights & Biases for monitoring and visualization.

Installation

pip install weights_biases-haystack

Usage

Components

This integration introduces one new component, a connector named WeaveConnector whose only responsibility is to send traces to Weights & Biases.

Note that you need to have the WANDB_API_KEY environment variable set to your Weights & Biases API key.

NOTE: If you don’t have a Weights & Biases account, it will interactively ask you to set one and your input will then be stored in ~/.netrc

In addition, you need to set the HAYSTACK_CONTENT_TRACING_ENABLED environment variable to true in order to enable Haystack tracing in your pipeline.

To use this connector, simply add it to your pipeline without any connections, and it will automatically start sending traces to Weights & Biases.

import os

from haystack import Pipeline
from haystack.components.builders import ChatPromptBuilder
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.dataclasses import ChatMessage

from haystack_integrations.components.connectors import WeaveConnector

os.environ["HAYSTACK_CONTENT_TRACING_ENABLED"] = "true"
messages = [
    ChatMessage.from_system(
        "Always respond in German even if some input data is in other languages."
    ),
    ChatMessage.from_user("Tell me about {{location}}"),
]

pipe = Pipeline()
pipe.add_component("prompt_builder", ChatPromptBuilder(template=messages))
pipe.add_component("llm", OpenAIChatGenerator(model="gpt-4o-mini"))
pipe.connect("prompt_builder.prompt", "llm.messages")

connector = WeaveConnector(pipeline_name="test_pipeline")
pipe.add_component("weave", connector)

response = pipe.run(
    data={
        "prompt_builder": {
            "location": "Berlin"
        }
    }
)
print(response["llm"]["replies"][0])

You should then head to https://wandb.ai/<user_name>/projects and see the complete trace for your pipeline under the pipeline name you specified, when creating the WeaveConnector.

License

weights_biases-haystack is distributed under the terms of the Apache-2.0 license.