keep original readme for now

Author: twishabansal

README.md

@@ -1,4 +1,341 @@
-# Libraries
-| Package           | Version |
-|-------------------|---------|
-| toolbox-langchain | 0.1.0   |
+# GenAI Toolbox LangChain SDK
+
+This SDK allows you to seamlessly integrate the functionalities of
+[Toolbox](https://github.com/googleapis/genai-toolbox) into your LangChain LLM
+applications, enabling advanced orchestration and interaction with GenAI models.
+
+<!-- TOC ignore:true -->
+## Table of Contents
+<!-- TOC -->
+
+- [Installation](#installation)
+- [Quickstart](#quickstart)
+- [Usage](#usage)
+- [Loading Tools](#loading-tools)
+    - [Load a toolset](#load-a-toolset)
+    - [Load a single tool](#load-a-single-tool)
+- [Use with LangChain](#use-with-langchain)
+- [Use with LangGraph](#use-with-langgraph)
+    - [Represent Tools as Nodes](#represent-tools-as-nodes)
+    - [Connect Tools with LLM](#connect-tools-with-llm)
+- [Manual usage](#manual-usage)
+- [Authenticating Tools](#authenticating-tools)
+    - [Supported Authentication Mechanisms](#supported-authentication-mechanisms)
+    - [Configure Tools](#configure-tools)
+    - [Configure SDK](#configure-sdk)
+        - [Add Authentication to a Tool](#add-authentication-to-a-tool)
+        - [Add Authentication While Loading](#add-authentication-while-loading)
+    - [Complete Example](#complete-example)
+- [Binding Parameter Values](#binding-parameter-values)
+    - [Binding Parameters to a Tool](#binding-parameters-to-a-tool)
+    - [Binding Parameters While Loading](#binding-parameters-while-loading)
+    - [Binding Dynamic Values](#binding-dynamic-values)
+- [Asynchronous Usage](#asynchronous-usage)
+
+<!-- /TOC -->
+
+## 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
+
+toolbox = ToolboxClient("http://127.0.0.1:5000")
+tools = toolbox.load_toolset()
+
+model = ChatVertexAI(model="gemini-1.5-pro-002")
+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()
+```
+
+## Usage
+
+Import and initialize the toolbox client.
+
+```py
+from toolbox_langchain import ToolboxClient
+
+# Replace with your Toolbox service's URL
+toolbox = ToolboxClient("http://127.0.0.1:5000")
+```
+
+## 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-1.5-pro-002")
+
+# 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-1.5-pro-002")
+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.
+
+## Authenticating Tools
+
+> [!WARNING]
+> 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.
+
+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
+toolbox = ToolboxClient("http://127.0.0.1:5000")
+tools = toolbox.load_toolset()
+
+auth_tool = tools[0].add_auth_token("my_auth", get_auth_token) # Single token
+
+multi_auth_tool = tools[0].add_auth_tokens({"my_auth", get_auth_token}) # Multiple tokens
+
+# OR
+
+auth_tools = [tool.add_auth_token("my_auth", get_auth_token) for tool in tools]
+```
+
+#### Add Authentication While Loading
+
+```py
+auth_tool = toolbox.load_tool(auth_tokens={"my_auth": get_auth_token})
+
+auth_tools = toolbox.load_toolset(auth_tokens={"my_auth": get_auth_token})
+```
+
+> [!NOTE]
+> Adding auth tokens during loading only affect the tools loaded within
+> that call.
+
+### 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
+
+toolbox = ToolboxClient("http://127.0.0.1:5000")
+tool = toolbox.load_tool("my-tool")
+
+auth_tool = tool.add_auth_token("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
+toolbox = ToolboxClient("http://127.0.0.1:5000")
+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(bound_params={"param": "value"})
+
+bound_tools = toolbox.load_toolset(bound_params={"param": "value"})
+```
+
+> [!NOTE]
+> Bound values during loading only affect the tools loaded in that call.
+
+### 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)
+```
+
+> [!IMPORTANT]
+> You don't need to modify tool configurations to bind parameter values.
+
+## Asynchronous Usage
+
+For better performance through [cooperative
+multitasking](https://en.wikipedia.org/wiki/Cooperative_multitasking), you can
+use the asynchronous interfaces of the `ToolboxClient`.
+
+> [!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).
+
+```py
+import asyncio
+from toolbox_langchain import ToolboxClient
+
+async def main():
+    toolbox = ToolboxClient("http://127.0.0.1:5000")
+    tool = await client.aload_tool("my-tool")
+    tools = await client.aload_toolset()
+    response = await tool.ainvoke()
+
+if __name__ == "__main__":
+    asyncio.run(main())
+```