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25
docs/en/sdks/JS-sdk/_index.md
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25
docs/en/sdks/JS-sdk/_index.md
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|||||||
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---
|
||||||
|
title: "JS SDK"
|
||||||
|
type: docs
|
||||||
|
weight: 7
|
||||||
|
description: >
|
||||||
|
JS SDKs to connect to the MCP Toolbox server.
|
||||||
|
---
|
||||||
|
|
||||||
|
|
||||||
|
## Overview
|
||||||
|
|
||||||
|
The MCP Toolbox service provides a centralized way to manage and expose tools
|
||||||
|
(like API connectors, database query tools, etc.) for use by GenAI applications.
|
||||||
|
|
||||||
|
These JS SDKs act as clients for that service. They handle the communication needed to:
|
||||||
|
|
||||||
|
* Fetch tool definitions from your running Toolbox instance.
|
||||||
|
* Provide convenient JS objects or functions representing those tools.
|
||||||
|
* Invoke the tools (calling the underlying APIs/services configured in Toolbox).
|
||||||
|
* Handle authentication and parameter binding as needed.
|
||||||
|
|
||||||
|
By using these SDKs, you can easily leverage your Toolbox-managed tools directly
|
||||||
|
within your JS applications or AI orchestration frameworks.
|
||||||
|
|
||||||
|
[Github](https://github.com/googleapis/mcp-toolbox-sdk-js)
|
||||||
@@ -1,15 +0,0 @@
|
|||||||
---
|
|
||||||
title: "Go SDK"
|
|
||||||
weight: 2
|
|
||||||
description: Go lang client SDK
|
|
||||||
icon: fa-brands fa-golang
|
|
||||||
manualLink: "https://github.com/googleapis/mcp-toolbox-sdk-go"
|
|
||||||
manualLinkTarget: _blank
|
|
||||||
---
|
|
||||||
|
|
||||||
<html>
|
|
||||||
<head>
|
|
||||||
<link rel="canonical" href="https://github.com/googleapis/mcp-toolbox-sdk-go"/>
|
|
||||||
<meta http-equiv="refresh" content="0;url=https://github.com/googleapis/mcp-toolbox-sdk-go"/>
|
|
||||||
</head>
|
|
||||||
</html>
|
|
||||||
25
docs/en/sdks/go-sdk/_index.md
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25
docs/en/sdks/go-sdk/_index.md
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|
|||||||
|
---
|
||||||
|
title: "Go SDK"
|
||||||
|
type: docs
|
||||||
|
weight: 7
|
||||||
|
description: >
|
||||||
|
Go SDKs to connect to the MCP Toolbox server.
|
||||||
|
---
|
||||||
|
|
||||||
|
|
||||||
|
## Overview
|
||||||
|
|
||||||
|
The MCP Toolbox service provides a centralized way to manage and expose tools
|
||||||
|
(like API connectors, database query tools, etc.) for use by GenAI applications.
|
||||||
|
|
||||||
|
The Go SDK act as clients for that service. They handle the communication needed to:
|
||||||
|
|
||||||
|
* Fetch tool definitions from your running Toolbox instance.
|
||||||
|
* Provide convenient Go structs representing those tools.
|
||||||
|
* Invoke the tools (calling the underlying APIs/services configured in Toolbox).
|
||||||
|
* Handle authentication and parameter binding as needed.
|
||||||
|
|
||||||
|
By using the SDK, you can easily leverage your Toolbox-managed tools directly
|
||||||
|
within your Go applications or AI orchestration frameworks.
|
||||||
|
|
||||||
|
[Github](https://github.com/googleapis/mcp-toolbox-sdk-go)
|
||||||
@@ -1,15 +0,0 @@
|
|||||||
---
|
|
||||||
title: "JS SDK"
|
|
||||||
weight: 2
|
|
||||||
description: Javascript client SDK
|
|
||||||
icon: fa-brands fa-node-js
|
|
||||||
manualLink: "https://github.com/googleapis/mcp-toolbox-sdk-js"
|
|
||||||
manualLinkTarget: _blank
|
|
||||||
---
|
|
||||||
|
|
||||||
<html>
|
|
||||||
<head>
|
|
||||||
<link rel="canonical" href="https://github.com/googleapis/mcp-toolbox-sdk-js"/>
|
|
||||||
<meta http-equiv="refresh" content="0;url=https://github.com/googleapis/mcp-toolbox-sdk-js"/>
|
|
||||||
</head>
|
|
||||||
</html>
|
|
||||||
@@ -1,15 +0,0 @@
|
|||||||
---
|
|
||||||
title: "Python SDK"
|
|
||||||
weight: 2
|
|
||||||
description: Python client SDK
|
|
||||||
icon: fa-brands fa-python
|
|
||||||
manualLink: "https://github.com/googleapis/mcp-toolbox-sdk-python"
|
|
||||||
manualLinkTarget: _blank
|
|
||||||
---
|
|
||||||
|
|
||||||
<html>
|
|
||||||
<head>
|
|
||||||
<link rel="canonical" href="https://github.com/googleapis/mcp-toolbox-sdk-python"/>
|
|
||||||
<meta http-equiv="refresh" content="0;url=https://github.com/googleapis/mcp-toolbox-sdk-python"/>
|
|
||||||
</head>
|
|
||||||
</html>
|
|
||||||
57
docs/en/sdks/python-sdk/_index.md
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57
docs/en/sdks/python-sdk/_index.md
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|
|||||||
|
---
|
||||||
|
title: "Python SDK"
|
||||||
|
type: docs
|
||||||
|
weight: 7
|
||||||
|
description: >
|
||||||
|
Python SDKs to connect to the MCP Toolbox server.
|
||||||
|
---
|
||||||
|
|
||||||
|
|
||||||
|
## Overview
|
||||||
|
|
||||||
|
The MCP Toolbox service provides a centralized way to manage and expose tools
|
||||||
|
(like API connectors, database query tools, etc.) for use by GenAI applications.
|
||||||
|
|
||||||
|
These Python SDKs act as clients for that service. They handle the communication needed to:
|
||||||
|
|
||||||
|
* Fetch tool definitions from your running Toolbox instance.
|
||||||
|
* Provide convenient Python objects or functions representing those tools.
|
||||||
|
* Invoke the tools (calling the underlying APIs/services configured in Toolbox).
|
||||||
|
* Handle authentication and parameter binding as needed.
|
||||||
|
|
||||||
|
By using these SDKs, you can easily leverage your Toolbox-managed tools directly
|
||||||
|
within your Python applications or AI orchestration frameworks.
|
||||||
|
|
||||||
|
## Which Package Should I Use?
|
||||||
|
|
||||||
|
Choosing the right package depends on how you are building your application:
|
||||||
|
|
||||||
|
* [`toolbox-langchain`](langchain):
|
||||||
|
Use this package if you are building your application using the LangChain or
|
||||||
|
LangGraph frameworks. It provides tools that are directly compatible with the
|
||||||
|
LangChain ecosystem (`BaseTool` interface), simplifying integration.
|
||||||
|
* [`toolbox-llamaindex`](llamaindex):
|
||||||
|
Use this package if you are building your application using the LlamaIndex framework.
|
||||||
|
It provides tools that are directly compatible with the
|
||||||
|
LlamaIndex ecosystem (`BaseTool` interface), simplifying integration.
|
||||||
|
* [`toolbox-core`](core):
|
||||||
|
Use this package if you are not using LangChain/LangGraph or any other
|
||||||
|
orchestration framework, or if you need a framework-agnostic way to interact
|
||||||
|
with Toolbox tools (e.g., for custom orchestration logic or direct use in
|
||||||
|
Python scripts).
|
||||||
|
|
||||||
|
## Available Packages
|
||||||
|
|
||||||
|
This repository hosts the following Python packages. See the package-specific
|
||||||
|
README for detailed installation and usage instructions:
|
||||||
|
|
||||||
|
| Package | Target Use Case | Integration | Path | Details (README) | PyPI Status |
|
||||||
|
| :------ | :---------- | :---------- | :---------------------- | :---------- | :---------
|
||||||
|
| `toolbox-core` | Framework-agnostic / Custom applications | Use directly / Custom | `packages/toolbox-core/` | 📄 [View README](https://github.com/googleapis/mcp-toolbox-sdk-python/blob/main/packages/toolbox-core/README.md) |  |
|
||||||
|
| `toolbox-langchain` | LangChain / LangGraph applications | LangChain / LangGraph | `packages/toolbox-langchain/` | 📄 [View README](https://github.com/googleapis/mcp-toolbox-sdk-python/blob/main/packages/toolbox-langchain/README.md) |  |
|
||||||
|
| `toolbox-llamaindex` | LlamaIndex applications | LlamaIndex | `packages/toolbox-llamaindex/` | 📄 [View README](https://github.com/googleapis/mcp-toolbox-sdk-python/blob/main/packages/toolbox-llamaindex/README.md) |  |
|
||||||
|
|
||||||
|
|
||||||
|
{{< notice note >}}
|
||||||
|
Source code for [python-sdk](https://github.com/googleapis/mcp-toolbox-sdk-python)
|
||||||
|
{{< /notice >}}
|
||||||
401
docs/en/sdks/python-sdk/langchain/index.md
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401
docs/en/sdks/python-sdk/langchain/index.md
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@@ -0,0 +1,401 @@
|
|||||||
|
---
|
||||||
|
title: "langchain"
|
||||||
|
type: docs
|
||||||
|
weight: 8
|
||||||
|
description: >
|
||||||
|
Toolbox-langchain SDK for connecting to the MCP Toolbox server and invoking tools programmatically.
|
||||||
|
---
|
||||||
|
|
||||||
|
## Overview
|
||||||
|
|
||||||
|
The `toolbox-langchain` package provides a Python interface to the MCP Toolbox service, enabling you to load and invoke tools from your own applications.
|
||||||
|
|
||||||
|
## Installation
|
||||||
|
|
||||||
|
```bash
|
||||||
|
pip install toolbox-langchain
|
||||||
|
```
|
||||||
|
## Quickstart
|
||||||
|
|
||||||
|
Here's a minimal example to get you started using
|
||||||
|
[LangGraph](https://langchain-ai.github.io/langgraph/reference/prebuilt/#langgraph.prebuilt.chat_agent_executor.create_react_agent):
|
||||||
|
|
||||||
|
```py
|
||||||
|
from toolbox_langchain import ToolboxClient
|
||||||
|
from langchain_google_vertexai import ChatVertexAI
|
||||||
|
from langgraph.prebuilt import create_react_agent
|
||||||
|
|
||||||
|
async with ToolboxClient("http://127.0.0.1:5000") as toolbox:
|
||||||
|
tools = toolbox.load_toolset()
|
||||||
|
|
||||||
|
model = ChatVertexAI(model="gemini-2.0-flash-001")
|
||||||
|
agent = create_react_agent(model, tools)
|
||||||
|
|
||||||
|
prompt = "How's the weather today?"
|
||||||
|
|
||||||
|
for s in agent.stream({"messages": [("user", prompt)]}, stream_mode="values"):
|
||||||
|
message = s["messages"][-1]
|
||||||
|
if isinstance(message, tuple):
|
||||||
|
print(message)
|
||||||
|
else:
|
||||||
|
message.pretty_print()
|
||||||
|
```
|
||||||
|
{{< notice tip >}}
|
||||||
|
For a complete, end-to-end example including setting up the service and using an SDK, see the full tutorial: [Toolbox Quickstart Tutorial](getting-started/local_quickstart)
|
||||||
|
{{< /notice >}}
|
||||||
|
|
||||||
|
## Usage
|
||||||
|
|
||||||
|
Import and initialize the toolbox client.
|
||||||
|
|
||||||
|
```py
|
||||||
|
from toolbox_langchain import ToolboxClient
|
||||||
|
|
||||||
|
# Replace with your Toolbox service's URL
|
||||||
|
async with ToolboxClient("http://127.0.0.1:5000") as toolbox:
|
||||||
|
```
|
||||||
|
|
||||||
|
## Loading Tools
|
||||||
|
|
||||||
|
### Load a toolset
|
||||||
|
|
||||||
|
A toolset is a collection of related tools. You can load all tools in a toolset
|
||||||
|
or a specific one:
|
||||||
|
|
||||||
|
```py
|
||||||
|
# Load all tools
|
||||||
|
tools = toolbox.load_toolset()
|
||||||
|
|
||||||
|
# Load a specific toolset
|
||||||
|
tools = toolbox.load_toolset("my-toolset")
|
||||||
|
```
|
||||||
|
|
||||||
|
### Load a single tool
|
||||||
|
|
||||||
|
```py
|
||||||
|
tool = toolbox.load_tool("my-tool")
|
||||||
|
```
|
||||||
|
|
||||||
|
Loading individual tools gives you finer-grained control over which tools are
|
||||||
|
available to your LLM agent.
|
||||||
|
|
||||||
|
## Use with LangChain
|
||||||
|
|
||||||
|
LangChain's agents can dynamically choose and execute tools based on the user
|
||||||
|
input. Include tools loaded from the Toolbox SDK in the agent's toolkit:
|
||||||
|
|
||||||
|
```py
|
||||||
|
from langchain_google_vertexai import ChatVertexAI
|
||||||
|
|
||||||
|
model = ChatVertexAI(model="gemini-2.0-flash-001")
|
||||||
|
|
||||||
|
# Initialize agent with tools
|
||||||
|
agent = model.bind_tools(tools)
|
||||||
|
|
||||||
|
# Run the agent
|
||||||
|
result = agent.invoke("Do something with the tools")
|
||||||
|
```
|
||||||
|
|
||||||
|
## Use with LangGraph
|
||||||
|
|
||||||
|
Integrate the Toolbox SDK with LangGraph to use Toolbox service tools within a
|
||||||
|
graph-based workflow. Follow the [official
|
||||||
|
guide](https://langchain-ai.github.io/langgraph/) with minimal changes.
|
||||||
|
|
||||||
|
### Represent Tools as Nodes
|
||||||
|
|
||||||
|
Represent each tool as a LangGraph node, encapsulating the tool's execution within the node's functionality:
|
||||||
|
|
||||||
|
```py
|
||||||
|
from toolbox_langchain import ToolboxClient
|
||||||
|
from langgraph.graph import StateGraph, MessagesState
|
||||||
|
from langgraph.prebuilt import ToolNode
|
||||||
|
|
||||||
|
# Define the function that calls the model
|
||||||
|
def call_model(state: MessagesState):
|
||||||
|
messages = state['messages']
|
||||||
|
response = model.invoke(messages)
|
||||||
|
return {"messages": [response]} # Return a list to add to existing messages
|
||||||
|
|
||||||
|
model = ChatVertexAI(model="gemini-2.0-flash-001")
|
||||||
|
builder = StateGraph(MessagesState)
|
||||||
|
tool_node = ToolNode(tools)
|
||||||
|
|
||||||
|
builder.add_node("agent", call_model)
|
||||||
|
builder.add_node("tools", tool_node)
|
||||||
|
```
|
||||||
|
|
||||||
|
### Connect Tools with LLM
|
||||||
|
|
||||||
|
Connect tool nodes with LLM nodes. The LLM decides which tool to use based on
|
||||||
|
input or context. Tool output can be fed back into the LLM:
|
||||||
|
|
||||||
|
```py
|
||||||
|
from typing import Literal
|
||||||
|
from langgraph.graph import END, START
|
||||||
|
from langchain_core.messages import HumanMessage
|
||||||
|
|
||||||
|
# Define the function that determines whether to continue or not
|
||||||
|
def should_continue(state: MessagesState) -> Literal["tools", END]:
|
||||||
|
messages = state['messages']
|
||||||
|
last_message = messages[-1]
|
||||||
|
if last_message.tool_calls:
|
||||||
|
return "tools" # Route to "tools" node if LLM makes a tool call
|
||||||
|
return END # Otherwise, stop
|
||||||
|
|
||||||
|
builder.add_edge(START, "agent")
|
||||||
|
builder.add_conditional_edges("agent", should_continue)
|
||||||
|
builder.add_edge("tools", 'agent')
|
||||||
|
|
||||||
|
graph = builder.compile()
|
||||||
|
|
||||||
|
graph.invoke({"messages": [HumanMessage(content="Do something with the tools")]})
|
||||||
|
```
|
||||||
|
|
||||||
|
## Manual usage
|
||||||
|
|
||||||
|
Execute a tool manually using the `invoke` method:
|
||||||
|
|
||||||
|
```py
|
||||||
|
result = tools[0].invoke({"name": "Alice", "age": 30})
|
||||||
|
```
|
||||||
|
|
||||||
|
This is useful for testing tools or when you need precise control over tool
|
||||||
|
execution outside of an agent framework.
|
||||||
|
|
||||||
|
## Client to Server Authentication
|
||||||
|
|
||||||
|
This section describes how to authenticate the ToolboxClient itself when
|
||||||
|
connecting to a Toolbox server instance that requires authentication. This is
|
||||||
|
crucial for securing your Toolbox server endpoint, especially when deployed on
|
||||||
|
platforms like Cloud Run, GKE, or any environment where unauthenticated access
|
||||||
|
is restricted.
|
||||||
|
|
||||||
|
This client-to-server authentication ensures that the Toolbox server can verify
|
||||||
|
the identity of the client making the request before any tool is loaded or
|
||||||
|
called. It is different from [Authenticating Tools](#authenticating-tools),
|
||||||
|
which deals with providing credentials for specific tools within an already
|
||||||
|
connected Toolbox session.
|
||||||
|
|
||||||
|
### When is Client-to-Server Authentication Needed?
|
||||||
|
|
||||||
|
You'll need this type of authentication if your Toolbox server is configured to
|
||||||
|
deny unauthenticated requests. For example:
|
||||||
|
|
||||||
|
- Your Toolbox server is deployed on Cloud Run and configured to "Require authentication."
|
||||||
|
- Your server is behind an Identity-Aware Proxy (IAP) or a similar
|
||||||
|
authentication layer.
|
||||||
|
- You have custom authentication middleware on your self-hosted Toolbox server.
|
||||||
|
|
||||||
|
Without proper client authentication in these scenarios, attempts to connect or
|
||||||
|
make calls (like `load_tool`) will likely fail with `Unauthorized` errors.
|
||||||
|
|
||||||
|
### How it works
|
||||||
|
|
||||||
|
The `ToolboxClient` allows you to specify functions (or coroutines for the async
|
||||||
|
client) that dynamically generate HTTP headers for every request sent to the
|
||||||
|
Toolbox server. The most common use case is to add an Authorization header with
|
||||||
|
a bearer token (e.g., a Google ID token).
|
||||||
|
|
||||||
|
These header-generating functions are called just before each request, ensuring
|
||||||
|
that fresh credentials or header values can be used.
|
||||||
|
|
||||||
|
### Configuration
|
||||||
|
|
||||||
|
You can configure these dynamic headers as follows:
|
||||||
|
|
||||||
|
```python
|
||||||
|
from toolbox_langchain import ToolboxClient
|
||||||
|
|
||||||
|
async with ToolboxClient(
|
||||||
|
"toolbox-url",
|
||||||
|
client_headers={"header1": header1_getter, "header2": header2_getter, ...}
|
||||||
|
) as client:
|
||||||
|
```
|
||||||
|
|
||||||
|
### Authenticating with Google Cloud Servers
|
||||||
|
|
||||||
|
For Toolbox servers hosted on Google Cloud (e.g., Cloud Run) and requiring
|
||||||
|
`Google ID token` authentication, the helper module
|
||||||
|
[auth_methods](https://github.com/googleapis/mcp-toolbox-sdk-python/blob/main/packages/toolbox-core/src/toolbox_core/auth_methods.py) provides utility functions.
|
||||||
|
|
||||||
|
### Step by Step Guide for Cloud Run
|
||||||
|
|
||||||
|
1. **Configure Permissions**:
|
||||||
|
[Grant](https://cloud.google.com/run/docs/securing/managing-access#service-add-principals)
|
||||||
|
the `roles/run.invoker` IAM role on the Cloud
|
||||||
|
Run service to the principal. This could be your `user account email` or a
|
||||||
|
`service account`.
|
||||||
|
2. **Configure Credentials**
|
||||||
|
- Local Development: Set up
|
||||||
|
[ADC](https://cloud.google.com/docs/authentication/set-up-adc-local-dev-environment).
|
||||||
|
- Google Cloud Environments: When running within Google Cloud (e.g., Compute
|
||||||
|
Engine, GKE, another Cloud Run service, Cloud Functions), ADC is typically
|
||||||
|
configured automatically, using the environment's default service account.
|
||||||
|
3. **Connect to the Toolbox Server**
|
||||||
|
|
||||||
|
```python
|
||||||
|
from toolbox_langchain import ToolboxClient
|
||||||
|
from toolbox_core import auth_methods
|
||||||
|
|
||||||
|
auth_token_provider = auth_methods.aget_google_id_token(URL) # can also use sync method
|
||||||
|
async with ToolboxClient(
|
||||||
|
URL,
|
||||||
|
client_headers={"Authorization": auth_token_provider},
|
||||||
|
) as client:
|
||||||
|
tools = client.load_toolset()
|
||||||
|
|
||||||
|
# Now, you can use the client as usual.
|
||||||
|
```
|
||||||
|
|
||||||
|
|
||||||
|
## Authenticating Tools
|
||||||
|
|
||||||
|
{{< notice info >}}
|
||||||
|
Always use HTTPS to connect your application with the Toolbox service, especially when using tools with authentication configured. Using HTTP exposes your application to serious security risks.
|
||||||
|
{{< /notice >}}
|
||||||
|
|
||||||
|
Some tools require user authentication to access sensitive data.
|
||||||
|
|
||||||
|
### Supported Authentication Mechanisms
|
||||||
|
Toolbox currently supports authentication using the [OIDC
|
||||||
|
protocol](https://openid.net/specs/openid-connect-core-1_0.html) with [ID
|
||||||
|
tokens](https://openid.net/specs/openid-connect-core-1_0.html#IDToken) (not
|
||||||
|
access tokens) for [Google OAuth
|
||||||
|
2.0](https://cloud.google.com/apigee/docs/api-platform/security/oauth/oauth-home).
|
||||||
|
|
||||||
|
### Configure Tools
|
||||||
|
|
||||||
|
Refer to [these
|
||||||
|
instructions](https://googleapis.github.io/genai-toolbox/resources/tools/#authenticated-parameters) on
|
||||||
|
configuring tools for authenticated parameters.
|
||||||
|
|
||||||
|
### Configure SDK
|
||||||
|
|
||||||
|
You need a method to retrieve an ID token from your authentication service:
|
||||||
|
|
||||||
|
```py
|
||||||
|
async def get_auth_token():
|
||||||
|
# ... Logic to retrieve ID token (e.g., from local storage, OAuth flow)
|
||||||
|
# This example just returns a placeholder. Replace with your actual token retrieval.
|
||||||
|
return "YOUR_ID_TOKEN" # Placeholder
|
||||||
|
```
|
||||||
|
|
||||||
|
#### Add Authentication to a Tool
|
||||||
|
|
||||||
|
```py
|
||||||
|
async with ToolboxClient("http://127.0.0.1:5000") as toolbox:
|
||||||
|
tools = toolbox.load_toolset()
|
||||||
|
|
||||||
|
auth_tool = tools[0].add_auth_token_getter("my_auth", get_auth_token) # Single token
|
||||||
|
|
||||||
|
multi_auth_tool = tools[0].add_auth_token_getters({"auth_1": get_auth_1}, {"auth_2": get_auth_2}) # Multiple tokens
|
||||||
|
|
||||||
|
# OR
|
||||||
|
|
||||||
|
auth_tools = [tool.add_auth_token_getter("my_auth", get_auth_token) for tool in tools]
|
||||||
|
```
|
||||||
|
|
||||||
|
#### Add Authentication While Loading
|
||||||
|
|
||||||
|
```py
|
||||||
|
auth_tool = toolbox.load_tool(auth_token_getters={"my_auth": get_auth_token})
|
||||||
|
|
||||||
|
auth_tools = toolbox.load_toolset(auth_token_getters={"my_auth": get_auth_token})
|
||||||
|
```
|
||||||
|
{{< notice note >}}
|
||||||
|
Adding auth tokens during loading only affect the tools loaded within that call.
|
||||||
|
{{< /notice >}}
|
||||||
|
|
||||||
|
### Complete Example
|
||||||
|
|
||||||
|
```py
|
||||||
|
import asyncio
|
||||||
|
from toolbox_langchain import ToolboxClient
|
||||||
|
|
||||||
|
async def get_auth_token():
|
||||||
|
# ... Logic to retrieve ID token (e.g., from local storage, OAuth flow)
|
||||||
|
# This example just returns a placeholder. Replace with your actual token retrieval.
|
||||||
|
return "YOUR_ID_TOKEN" # Placeholder
|
||||||
|
|
||||||
|
async with ToolboxClient("http://127.0.0.1:5000") as toolbox:
|
||||||
|
tool = toolbox.load_tool("my-tool")
|
||||||
|
|
||||||
|
auth_tool = tool.add_auth_token_getter("my_auth", get_auth_token)
|
||||||
|
result = auth_tool.invoke({"input": "some input"})
|
||||||
|
print(result)
|
||||||
|
```
|
||||||
|
|
||||||
|
## Binding Parameter Values
|
||||||
|
|
||||||
|
Predetermine values for tool parameters using the SDK. These values won't be
|
||||||
|
modified by the LLM. This is useful for:
|
||||||
|
|
||||||
|
* **Protecting sensitive information:** API keys, secrets, etc.
|
||||||
|
* **Enforcing consistency:** Ensuring specific values for certain parameters.
|
||||||
|
* **Pre-filling known data:** Providing defaults or context.
|
||||||
|
|
||||||
|
### Binding Parameters to a Tool
|
||||||
|
|
||||||
|
```py
|
||||||
|
async with ToolboxClient("http://127.0.0.1:5000") as toolbox:
|
||||||
|
tools = toolbox.load_toolset()
|
||||||
|
|
||||||
|
bound_tool = tool[0].bind_param("param", "value") # Single param
|
||||||
|
|
||||||
|
multi_bound_tool = tools[0].bind_params({"param1": "value1", "param2": "value2"}) # Multiple params
|
||||||
|
|
||||||
|
# OR
|
||||||
|
|
||||||
|
bound_tools = [tool.bind_param("param", "value") for tool in tools]
|
||||||
|
```
|
||||||
|
|
||||||
|
### Binding Parameters While Loading
|
||||||
|
|
||||||
|
```py
|
||||||
|
bound_tool = toolbox.load_tool("my-tool", bound_params={"param": "value"})
|
||||||
|
|
||||||
|
bound_tools = toolbox.load_toolset(bound_params={"param": "value"})
|
||||||
|
```
|
||||||
|
{{< notice note >}}
|
||||||
|
Bound values during loading only affect the tools loaded in that call.
|
||||||
|
{{< /notice >}}
|
||||||
|
|
||||||
|
### Binding Dynamic Values
|
||||||
|
|
||||||
|
Use a function to bind dynamic values:
|
||||||
|
|
||||||
|
```py
|
||||||
|
def get_dynamic_value():
|
||||||
|
# Logic to determine the value
|
||||||
|
return "dynamic_value"
|
||||||
|
|
||||||
|
dynamic_bound_tool = tool.bind_param("param", get_dynamic_value)
|
||||||
|
```
|
||||||
|
{{< notice note >}}
|
||||||
|
You don’t need to modify tool configurations to bind parameter values.
|
||||||
|
{{< /notice >}}
|
||||||
|
|
||||||
|
## Asynchronous Usage
|
||||||
|
|
||||||
|
For better performance through [cooperative
|
||||||
|
multitasking](https://en.wikipedia.org/wiki/Cooperative_multitasking), you can
|
||||||
|
use the asynchronous interfaces of the `ToolboxClient`.
|
||||||
|
|
||||||
|
{{< notice note >}}
|
||||||
|
Asynchronous interfaces like `aload_tool` and `aload_toolset` require an asynchronous environment. For guidance on running asynchronous Python programs, see [asyncio documentation](https://docs.python.org/3/library/asyncio-runner.html#running-an-asyncio-program).
|
||||||
|
{{< /notice >}}
|
||||||
|
|
||||||
|
```py
|
||||||
|
import asyncio
|
||||||
|
from toolbox_langchain import ToolboxClient
|
||||||
|
|
||||||
|
async def main():
|
||||||
|
async with ToolboxClient("http://127.0.0.1:5000") as toolbox:
|
||||||
|
tool = await client.aload_tool("my-tool")
|
||||||
|
tools = await client.aload_toolset()
|
||||||
|
response = await tool.ainvoke()
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
asyncio.run(main())
|
||||||
|
```
|
||||||
Reference in New Issue
Block a user