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3.2 KiB
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90 lines
3.2 KiB
Plaintext
---
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title: "Visualizing Haystack Pipelines"
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id: visualizing-pipelines
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slug: "/visualizing-pipelines"
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description: "You can visualize your Haystack AI pipelines as graphs to better understand how the components are connected."
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---
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import ClickableImage from "@site/src/components/ClickableImage";
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# Visualizing Haystack Pipelines
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You can visualize your pipelines as graphs to better understand how the components are connected.
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Haystack pipelines have `draw()` and `show()` methods that enable you to visualize the pipeline as a graph using Mermaid graphs.
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:::note Data Privacy Notice
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Exercise caution with sensitive data when using pipeline visualization.
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This feature is based on Mermaid graphs web service that doesn't have clear terms of data retention or privacy policy.
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:::
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## Prerequisites
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To use Mermaid graphs, you must have an internet connection to reach the Mermaid graph renderer at https://mermaid.ink.
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## Displaying a Graph
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Use the pipeline's `show()` method to display the diagram in Jupyter notebooks.
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```python
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my_pipeline.show()
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```
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## Saving a Graph
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Use the pipeline's `draw()` method passing the path where you want to save the diagram and the diagram format. Possible formats are: `mermaid-text` and `mermaid-image` (default).
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```python
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my_pipeline.draw(path=local_path)
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```
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## Visualizing SuperComponents
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To show the internal structure of [SuperComponents](../components/supercomponents.mdx) in your digram instead of a black box component, set the `super_component_expansion` parameter to `True`:
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```python
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my_pipeline.show(super_component_expansion=True)
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## or
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my_pipeline.draw(path=local_path,
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super_component_expansion=True)
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```
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## Visualizing Locally
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If you don't have an internet connection or don't want to send your pipeline data to the remote https://mermaid.ink, you can install a local mermaid.ink server and use it to render your pipeline.
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Let's run a local mermaid.ink server using their official Docker images from https://github.com/jihchi/mermaid.ink/pkgs/container/mermaid.ink.
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In this case, let's install one for a system running a MacOS M3 chip and expose it on port 3000:
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```dockerfile
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docker run --platform linux/amd64 --publish 3000:3000 --cap-add=SYS_ADMIN ghcr.io/jihchi/mermaid.ink
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```
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Check that the local mermaid.ink server is running by going to http://localhost:3000/.
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You should see a local server running, and now you can simply render the image using your local mermaid.ink server by specifying the URL when calling the`show()`or `draw()` method:
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```python
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my_pipeline.show(server_url="http://localhost:3000")
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## or
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my_pipeline.draw("my_pipeline.png", server_url="http://localhost:3000")
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```
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## Example
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This is an example of what a pipeline graph may look like:
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<ClickableImage src="/img/46a8989-Untitled.png" alt="RAG pipeline flowchart showing the data flow from query through retriever, prompt builder, language model, and answer builder components" size="large" />
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<br />
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## Importing a Pipeline to Haystack Enterprise Platform
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You can import your Haystack pipeline into Haystack Enterprise Platform and continue visually building your pipeline
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To do that, follow the steps described in Haystack Enterprise Platform [documentation](https://docs.cloud.deepset.ai/docs/import-a-pipeline#import-your-pipeline).
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