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docs: sync Core Integrations API reference (datadog) on Docusaurus (#11651)
Co-authored-by: sjrl <10526848+sjrl@users.noreply.github.com>
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‎docs-website/reference/integrations-api/datadog.md‎

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---
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title: "Datadog"
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id: integrations-datadog
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description: "Datadog integration for Haystack"
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slug: "/integrations-datadog"
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---
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## haystack_integrations.components.connectors.datadog.datadog_connector
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### DatadogConnector
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DatadogConnector connects Haystack to [Datadog](https://www.datadoghq.com/) in order to enable the tracing of
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operations and data flow within the components of a pipeline.
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To use the DatadogConnector, add it to your pipeline without connecting it to any other component. It will
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automatically trace all pipeline operations when tracing is enabled.
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**Environment Configuration:**
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- `HAYSTACK_CONTENT_TRACING_ENABLED`: Must be set to `"true"` to trace the content (inputs and outputs) of the
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pipeline components.
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- Datadog is configured through the standard `ddtrace` mechanisms, e.g. the `DD_SERVICE`, `DD_ENV` and
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`DD_VERSION` environment variables or by running your application with the `ddtrace-run` command. See the
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[ddtrace documentation](https://ddtrace.readthedocs.io/en/stable/) for more details.
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Here is an example of how to use the DatadogConnector in a pipeline:
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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
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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.components.connectors.datadog import DatadogConnector
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pipe = Pipeline()
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pipe.add_component("tracer", DatadogConnector("Chat example"))
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pipe.add_component("prompt_builder", ChatPromptBuilder())
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pipe.add_component("llm", OpenAIChatGenerator(model="gpt-4o-mini"))
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pipe.connect("prompt_builder.prompt", "llm.messages")
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messages = [
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ChatMessage.from_system("Always respond in German even if some input data is in other languages."),
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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={"prompt_builder": {"template_variables": {"location": "Berlin"}, "template": messages}}
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)
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print(response["llm"]["replies"][0])
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```
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#### __init__
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```python
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__init__(name: str = 'datadog') -> None
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```
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Initialize the DatadogConnector component.
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**Parameters:**
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- **name** (<code>str</code>) – The name used to identify this tracing component. It is returned by the `run` method and can be
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used to mark traces produced by this connector.
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#### run
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```python
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run() -> dict[str, str]
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```
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Runs the DatadogConnector component.
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**Returns:**
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- <code>dict\[str, str\]</code> – A dictionary with the following keys:
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- `name`: The name of the tracing component.
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#### to_dict
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```python
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to_dict() -> dict[str, Any]
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```
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Serialize this component to a dictionary.
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**Returns:**
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- <code>dict\[str, Any\]</code> – The serialized component as a dictionary.
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#### from_dict
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```python
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from_dict(data: dict[str, Any]) -> DatadogConnector
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```
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Deserialize this component from a dictionary.
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**Parameters:**
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- **data** (<code>dict\[str, Any\]</code>) – The dictionary representation of this component.
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**Returns:**
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- <code>DatadogConnector</code> – The deserialized component instance.
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## haystack_integrations.tracing.datadog.tracer
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### DatadogSpan
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Bases: <code>Span</code>
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#### __init__
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```python
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__init__(span: ddSpan) -> None
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```
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Creates an instance of DatadogSpan.
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#### set_tag
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```python
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set_tag(key: str, value: Any) -> None
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```
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Set a single tag on the span.
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**Parameters:**
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- **key** (<code>str</code>) – the name of the tag.
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- **value** (<code>Any</code>) – the value of the tag.
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#### raw_span
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```python
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raw_span() -> Any
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```
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Provides access to the underlying span object of the tracer.
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**Returns:**
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- <code>Any</code> – The underlying span object.
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#### get_correlation_data_for_logs
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```python
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get_correlation_data_for_logs() -> dict[str, Any]
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```
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Return a dictionary with correlation data for logs.
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### DatadogTracer
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Bases: <code>Tracer</code>
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#### __init__
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```python
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__init__(tracer: ddTracer) -> None
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```
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Creates an instance of DatadogTracer.
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#### trace
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```python
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trace(
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operation_name: str,
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tags: dict[str, Any] | None = None,
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parent_span: Span | None = None,
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) -> Iterator[Span]
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```
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Activate and return a new span that inherits from the current active span.
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#### current_span
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```python
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current_span() -> Span | None
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```
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Return the current active span

‎docs-website/reference_versioned_docs/version-2.18/integrations-api/datadog.md‎

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---
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title: "Datadog"
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id: integrations-datadog
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description: "Datadog integration for Haystack"
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slug: "/integrations-datadog"
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---
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## haystack_integrations.components.connectors.datadog.datadog_connector
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### DatadogConnector
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DatadogConnector connects Haystack to [Datadog](https://www.datadoghq.com/) in order to enable the tracing of
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operations and data flow within the components of a pipeline.
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To use the DatadogConnector, add it to your pipeline without connecting it to any other component. It will
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automatically trace all pipeline operations when tracing is enabled.
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**Environment Configuration:**
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- `HAYSTACK_CONTENT_TRACING_ENABLED`: Must be set to `"true"` to trace the content (inputs and outputs) of the
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pipeline components.
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- Datadog is configured through the standard `ddtrace` mechanisms, e.g. the `DD_SERVICE`, `DD_ENV` and
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`DD_VERSION` environment variables or by running your application with the `ddtrace-run` command. See the
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[ddtrace documentation](https://ddtrace.readthedocs.io/en/stable/) for more details.
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Here is an example of how to use the DatadogConnector in a pipeline:
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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
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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.components.connectors.datadog import DatadogConnector
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pipe = Pipeline()
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pipe.add_component("tracer", DatadogConnector("Chat example"))
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pipe.add_component("prompt_builder", ChatPromptBuilder())
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pipe.add_component("llm", OpenAIChatGenerator(model="gpt-4o-mini"))
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pipe.connect("prompt_builder.prompt", "llm.messages")
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messages = [
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ChatMessage.from_system("Always respond in German even if some input data is in other languages."),
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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={"prompt_builder": {"template_variables": {"location": "Berlin"}, "template": messages}}
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)
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print(response["llm"]["replies"][0])
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```
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#### __init__
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```python
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__init__(name: str = 'datadog') -> None
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```
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Initialize the DatadogConnector component.
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**Parameters:**
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- **name** (<code>str</code>) – The name used to identify this tracing component. It is returned by the `run` method and can be
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used to mark traces produced by this connector.
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#### run
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```python
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run() -> dict[str, str]
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```
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Runs the DatadogConnector component.
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**Returns:**
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- <code>dict\[str, str\]</code> – A dictionary with the following keys:
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- `name`: The name of the tracing component.
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#### to_dict
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```python
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to_dict() -> dict[str, Any]
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```
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Serialize this component to a dictionary.
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**Returns:**
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- <code>dict\[str, Any\]</code> – The serialized component as a dictionary.
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#### from_dict
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```python
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from_dict(data: dict[str, Any]) -> DatadogConnector
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```
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Deserialize this component from a dictionary.
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**Parameters:**
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- **data** (<code>dict\[str, Any\]</code>) – The dictionary representation of this component.
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**Returns:**
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- <code>DatadogConnector</code> – The deserialized component instance.
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## haystack_integrations.tracing.datadog.tracer
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### DatadogSpan
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Bases: <code>Span</code>
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#### __init__
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```python
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__init__(span: ddSpan) -> None
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```
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Creates an instance of DatadogSpan.
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#### set_tag
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```python
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set_tag(key: str, value: Any) -> None
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```
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Set a single tag on the span.
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**Parameters:**
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- **key** (<code>str</code>) – the name of the tag.
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- **value** (<code>Any</code>) – the value of the tag.
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#### raw_span
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```python
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raw_span() -> Any
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```
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Provides access to the underlying span object of the tracer.
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**Returns:**
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- <code>Any</code> – The underlying span object.
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#### get_correlation_data_for_logs
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```python
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get_correlation_data_for_logs() -> dict[str, Any]
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```
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Return a dictionary with correlation data for logs.
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### DatadogTracer
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Bases: <code>Tracer</code>
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#### __init__
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```python
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__init__(tracer: ddTracer) -> None
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```
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Creates an instance of DatadogTracer.
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#### trace
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```python
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trace(
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operation_name: str,
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tags: dict[str, Any] | None = None,
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parent_span: Span | None = None,
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) -> Iterator[Span]
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```
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Activate and return a new span that inherits from the current active span.
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#### current_span
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```python
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current_span() -> Span | None
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```
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Return the current active span

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