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94 lines
3.2 KiB
Plaintext
94 lines
3.2 KiB
Plaintext
---
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title: "Weights & Biases Weave"
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id: weave
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slug: "/tracing-weave"
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description: "Learn how to trace your Haystack pipelines with Weights & Biases Weave."
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---
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# Weights & Biases Weave
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Learn how to trace your Haystack pipelines with Weights & Biases Weave.
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<div className="key-value-table">
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| | |
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| --- | --- |
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| **Tracer class** | `WeaveTracer` |
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| **How to enable** | Enable the tracer with `tracing.enable_tracing(WeaveTracer(project_name="..."))`, or add the `WeaveConnector` component to your pipeline |
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| **Content tracing** | Required. Set `HAYSTACK_CONTENT_TRACING_ENABLED` to `true` |
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| **Package** | `weave-haystack` |
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| **API reference** | [Weave](/reference/integrations-weave) |
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| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/weave |
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</div>
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## Overview
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Trace and visualize your pipeline execution in [Weights & Biases](https://wandb.ai/site/). 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.
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## Installation
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Install the `weave-haystack` package:
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```shell
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pip install weave-haystack
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```
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## Prerequisites
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1. A Weave account. You can sign up for free on the [Weights & Biases website](https://wandb.ai/site).
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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](https://wandb.ai/home).
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3. Set the `HAYSTACK_CONTENT_TRACING_ENABLED` environment variable to `true`.
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## Usage
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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.
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```python
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import os
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os.environ["HAYSTACK_CONTENT_TRACING_ENABLED"] = "true"
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from haystack import Pipeline, tracing
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from haystack.components.builders import ChatPromptBuilder
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from haystack.components.generators.chat import OpenAIChatGenerator
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from haystack.dataclasses import ChatMessage
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from haystack_integrations.tracing.weave import WeaveTracer
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# Enable the Weave tracer
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tracing.enable_tracing(WeaveTracer(project_name="test_pipeline"))
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pipe = Pipeline()
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pipe.add_component("prompt_builder", ChatPromptBuilder())
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pipe.add_component("llm", OpenAIChatGenerator())
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pipe.connect("prompt_builder.prompt", "llm.messages")
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messages = [
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ChatMessage.from_system(
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"Always respond in German even if some input data is in other languages.",
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),
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ChatMessage.from_user("Tell me about {{location}}"),
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]
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response = pipe.run(
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data={
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"prompt_builder": {
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"template_variables": {"location": "Berlin"},
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"template": messages,
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},
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},
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)
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print(response["llm"]["replies"][0])
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```
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You can then see the complete trace for your pipeline at `https://wandb.ai/<user_name>/projects` under the project name you specified.
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## Alternative: the WeaveConnector component
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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.
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:::info
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See the [`WeaveConnector` documentation page](../../pipeline-components/connectors/weaveconnector.mdx) for full usage examples.
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:::
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