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# docs: added `additional_resources` folder The additional resource files were inside the doc top-level folder, which polluted the top-level folder. - added the `additional_resources` folder and moved correspondent files to this folder; - fixed a broken link to the "Model comparison" page (model_laboratory notebook) - fixed a broken link to one of the YouTube videos (sorry, it is not directly related to this PR) ## Who can review? @dev2049
58 lines
2.5 KiB
Markdown
58 lines
2.5 KiB
Markdown
# Tracing
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By enabling tracing in your LangChain runs, you’ll be able to more effectively visualize, step through, and debug your chains and agents.
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First, you should install tracing and set up your environment properly.
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You can use either a locally hosted version of this (uses Docker) or a cloud hosted version (in closed alpha).
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If you're interested in using the hosted platform, please fill out the form [here](https://forms.gle/tRCEMSeopZf6TE3b6).
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- [Locally Hosted Setup](../tracing/local_installation.md)
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- [Cloud Hosted Setup](../tracing/hosted_installation.md)
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## Tracing Walkthrough
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When you first access the UI, you should see a page with your tracing sessions.
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An initial one "default" should already be created for you.
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A session is just a way to group traces together.
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If you click on a session, it will take you to a page with no recorded traces that says "No Runs."
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You can create a new session with the new session form.
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If we click on the `default` session, we can see that to start we have no traces stored.
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If we now start running chains and agents with tracing enabled, we will see data show up here.
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To do so, we can run [this notebook](../tracing/agent_with_tracing.ipynb) as an example.
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After running it, we will see an initial trace show up.
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From here we can explore the trace at a high level by clicking on the arrow to show nested runs.
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We can keep on clicking further and further down to explore deeper and deeper.
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We can also click on the "Explore" button of the top level run to dive even deeper.
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Here, we can see the inputs and outputs in full, as well as all the nested traces.
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We can keep on exploring each of these nested traces in more detail.
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For example, here is the lowest level trace with the exact inputs/outputs to the LLM.
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## Changing Sessions
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1. To initially record traces to a session other than `"default"`, you can set the `LANGCHAIN_SESSION` environment variable to the name of the session you want to record to:
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```python
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import os
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os.environ["LANGCHAIN_TRACING"] = "true"
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os.environ["LANGCHAIN_SESSION"] = "my_session" # Make sure this session actually exists. You can create a new session in the UI.
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```
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2. To switch sessions mid-script or mid-notebook, do NOT set the `LANGCHAIN_SESSION` environment variable. Instead: `langchain.set_tracing_callback_manager(session_name="my_session")`
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