Weights & Biases Weave

Learn how to trace your Haystack pipelines with Weights & Biases Weave.

Tracer class	WeaveTracer
How to enable	Enable the tracer with tracing.enable_tracing(WeaveTracer(project_name="...")), or add the WeaveConnector component to your pipeline
Content tracing	Required. Set HAYSTACK_CONTENT_TRACING_ENABLED to true
Package	weave-haystack
API reference	Weave
GitHub link	https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/weave

Overview

Trace and visualize your pipeline execution in Weights & Biases. Information captured by the Haystack tracing tool, such as API calls, context data, and prompts, is sent to Weights & Biases, where you can see the complete trace of your pipeline execution.

Installation

Install the weave-haystack package:

shell

pip install weave-haystack

Prerequisites

1. A Weave account. You can sign up for free on the Weights & Biases website.
2. Set the WANDB_API_KEY environment variable with your Weights & Biases API key. Once logged in, you can find your API key on your home page.
3. Set the HAYSTACK_CONTENT_TRACING_ENABLED environment variable to true.

Usage

Enable the WeaveTracer directly to trace any Haystack pipeline, without adding a component to it. The project_name is the name that will appear in your Weave project.

python

import os

os.environ["HAYSTACK_CONTENT_TRACING_ENABLED"] = "true"

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

from haystack_integrations.tracing.weave import WeaveTracer

# Enable the Weave tracer
tracing.enable_tracing(WeaveTracer(project_name="test_pipeline"))

pipe = Pipeline()
pipe.add_component("prompt_builder", ChatPromptBuilder())
pipe.add_component("llm", OpenAIChatGenerator())
pipe.connect("prompt_builder.prompt", "llm.messages")

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

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

You can then see the complete trace for your pipeline at https://wandb.ai/<user_name>/projects under the project name you specified.

Alternative: the WeaveConnector component

If you prefer to manage tracing as part of your pipeline definition, you can add the WeaveConnector component instead. It enables the same Weave tracing as soon as it runs.

info

See the WeaveConnector documentation page for full usage examples.