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* Update documentation and remove unused assets. Enhanced the 'agents' and 'components' sections with clearer descriptions and examples. Removed obsolete images and updated links for better navigation. Adjusted formatting for consistency across various documentation pages. * remove dependency * address comments * delete more empty pages * broken link * unduplicate headings * alphabetical components nav
99 lines
4.0 KiB
Plaintext
99 lines
4.0 KiB
Plaintext
---
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title: "Debugging Pipelines"
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id: debugging-pipelines
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slug: "/debugging-pipelines"
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description: "Learn how to debug and troubleshoot your Haystack pipelines."
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---
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import ClickableImage from "@site/src/components/ClickableImage";
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# Debugging Pipelines
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Learn how to debug and troubleshoot your Haystack pipelines.
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There are several options available to you to debug your pipelines:
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- [Inspect your components' outputs](#inspecting-component-outputs)
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- [Adjust logging](#logging)
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- [Set up tracing](#tracing)
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- [Try one of the monitoring tool integrations](#monitoring-tools)
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## Inspecting Component Outputs
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To view outputs from specific pipeline components, add the `include_outputs_from` parameter when executing your pipeline. Place it after the input dictionary and set it to the name of the component whose output you want included in the result.
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For example, here’s how you can print the output of `PromptBuilder` in this pipeline:
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```python
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from haystack import Pipeline, Document
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from haystack.utils import Secret
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from haystack.components.generators.chat import OpenAIChatGenerator
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from haystack.components.builders.chat_prompt_builder import ChatPromptBuilder
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from haystack.dataclasses import ChatMessage
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## Documents
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documents = [Document(content="Joe lives in Berlin"), Document(content="Joe is a software engineer")]
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## Define prompt template
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prompt_template = [
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ChatMessage.from_system("You are a helpful assistant."),
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ChatMessage.from_user(
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"Given these documents, answer the question.\nDocuments:\n"
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"{% for doc in documents %}{{ doc.content }}{% endfor %}\n"
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"Question: {{query}}\nAnswer:"
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)
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]
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## Define pipeline
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p = Pipeline()
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p.add_component(instance=ChatPromptBuilder(template=prompt_template, required_variables={"query", "documents"}), name="prompt_builder")
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p.add_component(instance=OpenAIChatGenerator(api_key=Secret.from_env_var("OPENAI_API_KEY")), name="llm")
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p.connect("prompt_builder", "llm.messages")
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## Define question
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question = "Where does Joe live?"
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## Execute pipeline
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result = p.run({"prompt_builder": {"documents": documents, "query": question}},
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include_outputs_from="prompt_builder")
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## Print result
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print(result)
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```
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## Logging
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Adjust the logging format according to your debugging needs. See our [Logging](../../development/logging.mdx) documentation for details.
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## Real-Time Pipeline Logging
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Use Haystack's [`LoggingTracer`](https://github.com/deepset-ai/haystack/blob/main/haystack/tracing/logging_tracer.py) logs to inspect the data that's flowing through your pipeline in real-time.
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This feature is particularly helpful during experimentation and prototyping, as you don’t need to set up any tracing backend beforehand.
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Here’s how you can enable this tracer. In this example, we are adding color tags (this is optional) to highlight the components' names and inputs:
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```python
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import logging
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from haystack import tracing
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from haystack.tracing.logging_tracer import LoggingTracer
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logging.basicConfig(format="%(levelname)s - %(name)s - %(message)s", level=logging.WARNING)
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logging.getLogger("haystack").setLevel(logging.DEBUG)
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tracing.tracer.is_content_tracing_enabled = True # to enable tracing/logging content (inputs/outputs)
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tracing.enable_tracing(LoggingTracer(tags_color_strings={"haystack.component.input": "\x1b[1;31m", "haystack.component.name": "\x1b[1;34m"}))
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```
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Here’s what the resulting log would look like when a pipeline is run:
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<ClickableImage src="/img/55c3d5c84282d726c95fb3350ec36be49a354edca8a6164f5dffdab7121cec58-image_2.png" alt="" />
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## Tracing
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To get a bigger picture of the pipeline’s performance, try tracing it with [Langfuse](../../development/tracing.mdx#langfuse).
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Our [Tracing](../../development/tracing.mdx) page has more about other tracing solutions for Haystack.
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## Monitoring Tools
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Take a look at available tracing and monitoring [integrations](https://haystack.deepset.ai/integrations?type=Monitoring+Tool&version=2.0) for Haystack pipelines, such as Arize AI or Arize Phoenix. |