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Haystack is an open-source framework for building search and LLM applications. Deepset maintains a WeaveConnector component that forwards Haystack pipeline traces to W&B Weave so you can inspect component runs, prompts, and outputs in the Weave UI. This guide is for Haystack developers who want to add observability to their pipelines. For full API details and additional examples, see these Deepset resources:

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Prerequisites

Before you begin, you must complete the following:
  • Set WANDB_API_KEY in your environment using your W&B API key. This authenticates the connector with W&B Weave.
  • Set HAYSTACK_CONTENT_TRACING_ENABLED to true before you run a pipeline so Haystack emits tracing data the connector can forward.

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Install

Install the required dependencies using pip: 
pip install weave-haystack
The package declares compatible versions of haystack-ai and weave as dependencies.

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Trace a Haystack pipeline with Weave

The following example adds Haystack’s WeaveConnector to a Haystack Pipeline and integrates with W&B Weave to trace and monitor your pipeline components. The pipeline_name you pass is used as the Weave project name for traces from that pipeline. In your Haystack pipeline, don’t connect WeaveConnector to other components. 
import os

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

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.weave import WeaveConnector

pipe = Pipeline()
pipe.add_component("prompt_builder", ChatPromptBuilder())
pipe.add_component("llm", OpenAIChatGenerator(model="gpt-3.5-turbo"))
pipe.connect("prompt_builder.prompt", "llm.messages")

# pipeline_name becomes your W&B project name.
connector = WeaveConnector(pipeline_name="haystack_demo")
# Add connector to pipeline but don't connect it.
pipe.add_component("weave", connector)

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])
After the pipeline runs, open your W&B workspace, select the project named with pipeline_name, and go to Traces to review the completed trace.