chore: import upstream snapshot with attribution
CF: Deploy Dev Docs / deploy (push) Has been cancelled
Sync Labels / build (push) Has been cancelled
tests / unit tests (macos-latest) (push) Has been cancelled
tests / unit tests (windows-latest) (push) Has been cancelled
tests / unit tests (ubuntu-latest) (push) Has been cancelled
CF: Deploy Dev Docs / deploy (push) Has been cancelled
Sync Labels / build (push) Has been cancelled
tests / unit tests (macos-latest) (push) Has been cancelled
tests / unit tests (windows-latest) (push) Has been cancelled
tests / unit tests (ubuntu-latest) (push) Has been cancelled
This commit is contained in:
@@ -0,0 +1,50 @@
|
||||
---
|
||||
title: "Python: Pre & Post Processing"
|
||||
type: docs
|
||||
weight: 1
|
||||
description: >
|
||||
How to add pre- and post- processing to your Agents using Python.
|
||||
sample_filters: ["Pre & Post Processing", "Python", "ADK", "LangChain"]
|
||||
is_sample: true
|
||||
---
|
||||
|
||||
## Prerequisites
|
||||
|
||||
This tutorial assumes that you have set up MCP Toolbox with a basic agent as described in the [local quickstart](../../../getting-started/local_quickstart.md).
|
||||
|
||||
This guide demonstrates how to implement these patterns in your Toolbox applications.
|
||||
|
||||
## Implementation
|
||||
|
||||
{{< tabpane persist=header >}}
|
||||
{{% tab header="ADK" text=true %}}
|
||||
The following example demonstrates how to use `ToolboxToolset` with ADK's pre and post processing hooks to implement pre and post processing for tool calls.
|
||||
|
||||
{{< include "adk/agent.py" "python">}}
|
||||
|
||||
You can also add model-level (`before_model_callback`, `after_model_callback`) and agent-level (`before_agent_callback`, `after_agent_callback`) hooks to intercept messages at different stages of the execution loop.
|
||||
|
||||
For more information, see the [ADK Callbacks documentation](https://google.github.io/adk-docs/callbacks/types-of-callbacks/).
|
||||
{{% /tab %}}
|
||||
{{% tab header="Langchain" text=true %}}
|
||||
The following example demonstrates how to use `ToolboxClient` with LangChain's middleware to implement pre- and post- processing for tool calls.
|
||||
|
||||
{{< include "langchain/agent.py" "python" >}}
|
||||
|
||||
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](https://docs.langchain.com/oss/python/langchain/middleware/custom#wrap-style-hooks) for details on these additional hook types.
|
||||
{{% /tab %}}
|
||||
{{< /tabpane >}}
|
||||
|
||||
## Results
|
||||
|
||||
The output should look similar to the following.
|
||||
|
||||
{{< notice note >}}
|
||||
The exact responses may vary due to the non-deterministic nature of LLMs and differences between orchestration frameworks.
|
||||
{{< /notice >}}
|
||||
|
||||
```
|
||||
AI: Booking Confirmed! You earned 500 Loyalty Points with this stay.
|
||||
|
||||
AI: Error: Maximum stay duration is 14 days.
|
||||
```
|
||||
Reference in New Issue
Block a user