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95 lines
3.6 KiB
Plaintext
95 lines
3.6 KiB
Plaintext
---
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title: "Datadog"
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id: datadog
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slug: "/tracing-datadog"
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description: "Learn how to trace your Haystack pipelines with Datadog."
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---
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# Datadog
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Learn how to trace your Haystack pipelines with Datadog.
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<div className="key-value-table">
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| --- | --- |
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| **Tracer class** | `DatadogTracer` |
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| **How to enable** | Enable the tracer with `tracing.enable_tracing(DatadogTracer(ddtrace.tracer))`, or add the `DatadogConnector` component to your pipeline |
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| **Content tracing** | Set `HAYSTACK_CONTENT_TRACING_ENABLED` to `true` to trace component inputs and outputs |
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| **Package** | `datadog-haystack` |
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| **API reference** | [datadog](/reference/integrations-datadog) |
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| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/datadog |
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</div>
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## Overview
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Trace your Haystack pipelines with [Datadog](https://www.datadoghq.com/) through [Datadog's tracing library `ddtrace`](https://ddtrace.readthedocs.io/en/stable/). Haystack captures detailed information about pipeline runs, like API calls, context data, and prompts, so you can see the complete trace of your pipeline execution in Datadog.
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## Installation
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Install the `datadog-haystack` package:
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```shell
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pip install datadog-haystack
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```
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## Prerequisites
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1. A way to receive traces, such as a running [Datadog Agent](https://docs.datadoghq.com/agent/). `ddtrace` sends traces to the Datadog Agent at `localhost:8126` by default.
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2. Configure `ddtrace` through the standard mechanisms, for example the `DD_SERVICE`, `DD_ENV`, and `DD_VERSION` environment variables, or by running your application with the `ddtrace-run` command. See the [ddtrace documentation](https://ddtrace.readthedocs.io/en/stable/) for more details.
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## Usage
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Enable the `DatadogTracer` directly to trace any Haystack pipeline, without adding a component to it. Make sure to set the `HAYSTACK_CONTENT_TRACING_ENABLED` environment variable before importing any Haystack components.
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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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import ddtrace
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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.datadog import DatadogTracer
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# Enable the Datadog tracer
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tracing.enable_tracing(DatadogTracer(ddtrace.tracer))
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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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Each pipeline run produces a trace that includes the entire execution context, including prompts, completions, and metadata. You can then view the traces in your Datadog dashboard.
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## Alternative: the DatadogConnector component
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If you prefer to manage tracing as part of your pipeline definition (for example, so it serializes to YAML), you can add the `DatadogConnector` component instead. It enables the same Datadog tracing as soon as it is initialized.
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:::info
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See the [`DatadogConnector` documentation page](../../pipeline-components/connectors/datadogconnector.mdx) for full usage examples, or check out the [integration page](https://haystack.deepset.ai/integrations/datadog).
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:::
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