1.5 KiB
title, type, weight, description
| title | type | weight | description |
|---|---|---|---|
| Python | docs | 1 | How to add pre- and post- processing to your Python toolbox applications. |
Prerequisites
This tutorial assumes that you have set up a basic toolbox application as described in the local quickstart.
This guide demonstrates how to implement these patterns in your Toolbox applications.
Implementation
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Coming soon.
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The following example demonstrates how to use ToolboxClient with LangChain's middleware to implement pre- and post- processing for tool calls.
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For more information, see the LangChain Middleware documentation.
You can also add model-level (wrap_model) and agent-level (before_agent, after_agent) hooks to intercept messages at different stages of the execution loop. See the LangChain Middleware documentation for details on these additional hook types.
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Results
The output should look similar to the following. Note that exact responses may vary due to the non-deterministic nature of LLMs and differences between orchestration frameworks.
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