Merge pull request #252 from ag2ai/interop-blog-polishing

Interop blog polishing
ag2ai · Dec 20, 2024 · 3c0b4e3 · 3c0b4e3
2 parents 16515f3 + 5b62def
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diff --git a/notebook/tools_interoperability.ipynb b/notebook/tools_interoperability.ipynb
@@ -38,7 +38,8 @@
     "### Imports\n",
     "\n",
     "Import necessary modules and tools.\n",
-    "- `WikipediaQueryRun` and `WikipediaAPIWrapper`: Tools for querying Wikipedia.\n",
+    "\n",
+    "- [WikipediaQueryRun](https://api.python.langchain.com/en/latest/tools/langchain_community.tools.wikipedia.tool.WikipediaQueryRun.html) and [WikipediaAPIWrapper](https://python.langchain.com/api_reference/community/utilities/langchain_community.utilities.wikipedia.WikipediaAPIWrapper.html): Tools for querying Wikipedia.\n",
     "- `AssistantAgent` and `UserProxyAgent`: Agents that facilitate communication in the AG2 framework.\n",
     "- `Interoperability`: This module acts as a bridge, making it easier to integrate LangChain tools with AG2’s architecture."
    ]
@@ -76,6 +77,7 @@
     "### Agent Configuration\n",
     "\n",
     "Configure the agents for the interaction.\n",
+    "\n",
     "- `config_list` defines the LLM configurations, including the model and API key.\n",
     "- `UserProxyAgent` simulates user inputs without requiring actual human interaction (set to `NEVER`).\n",
     "- `AssistantAgent` represents the AI agent, configured with the LLM settings."
@@ -106,9 +108,9 @@
     "### Tool Integration\n",
     "\n",
     "- Initialize and register the LangChain tool with AG2.\n",
-    "- `WikipediaAPIWrapper`: Configured to fetch the top 1 result from Wikipedia with a maximum of 1000 characters per document.\n",
-    "- `WikipediaQueryRun`: A LangChain tool that executes Wikipedia queries.\n",
-    "- `LangchainInteroperability`: Converts the LangChain tool into a format compatible with the AG2 framework.\n",
+    "- [WikipediaAPIWrapper](https://python.langchain.com/api_reference/community/utilities/langchain_community.utilities.wikipedia.WikipediaAPIWrapper.html): Configured to fetch the top 1 result from Wikipedia with a maximum of 1000 characters per document.\n",
+    "- [WikipediaQueryRun](https://api.python.langchain.com/en/latest/tools/langchain_community.tools.wikipedia.tool.WikipediaQueryRun.html): A LangChain tool that executes Wikipedia queries.\n",
+    "- `Interoperability`: Converts the LangChain tool into a format compatible with the AG2 framework.\n",
     "- `ag2_tool.register_for_execution(user_proxy)`: Registers the tool for use by the user_proxy agent.\n",
     "- `ag2_tool.register_for_llm(chatbot)`: Registers the tool for integration with the chatbot agent.\n"
    ]
@@ -215,7 +217,8 @@
     "### Imports\n",
     "\n",
     "Import necessary modules and tools.\n",
-    "- `ScrapeWebsiteTool` are the CrewAI tools for web scraping\n",
+    "\n",
+    "- [ScrapeWebsiteTool](https://docs.crewai.com/tools/scrapewebsitetool) are the CrewAI tools for web scraping\n",
     "- `AssistantAgent` and `UserProxyAgent` are core AG2 classes.\n",
     "- `Interoperability`: This module acts as a bridge, making it easier to integrate CrewAI tools with AG2’s architecture."
    ]
@@ -241,6 +244,7 @@
     "### Agent Configuration\n",
     "\n",
     "Configure the agents for the interaction.\n",
+    "\n",
     "- `config_list` defines the LLM configurations, including the model and API key.\n",
     "- `UserProxyAgent` simulates user inputs without requiring actual human interaction (set to `NEVER`).\n",
     "- `AssistantAgent` represents the AI agent, configured with the LLM settings."
@@ -271,7 +275,8 @@
     "### Tool Integration\n",
     "\n",
     "Initialize and register the CrewAI tool with AG2.\n",
-    "- `crewai_tool` is an instance of the `ScrapeWebsiteTool` from CrewAI.\n",
+    "\n",
+    "- `crewai_tool` is an instance of the [ScrapeWebsiteTool](https://docs.crewai.com/tools/scrapewebsitetool) from CrewAI.\n",
     "- `Interoperability` converts the CrewAI tool to make it usable in AG2.\n",
     "- `register_for_execution` and `register_for_llm` allow the tool to work with the UserProxyAgent and AssistantAgent."
    ]
@@ -413,12 +418,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Imports\n",
+    "### Imports\n",
     "\n",
     "Import necessary modules and tools.\n",
-    "- `BaseModel`: Used to define data structures for tool inputs and outputs.\n",
-    "- `RunContext`: Provides context during the execution of tools.\n",
-    "- `PydanticAITool`: Represents a tool in the PydanticAI framework.\n",
+    "\n",
+    "- [BaseModel](https://docs.pydantic.dev/latest/api/base_model/): Used to define data structures for tool inputs and outputs.\n",
+    "- [RunContext](https://ai.pydantic.dev/api/tools/#pydantic_ai.tools.RunContext): Provides context during the execution of tools.\n",
+    "- [PydanticAITool](https://ai.pydantic.dev/api/tools/#pydantic_ai.tools.Tool): Represents a tool in the PydanticAI framework.\n",
     "- `AssistantAgent` and `UserProxyAgent`: Agents that facilitate communication in the AG2 framework.\n",
     "- `Interoperability`: This module acts as a bridge, making it easier to integrate PydanticAI tools with AG2’s architecture."
    ]
@@ -447,6 +453,7 @@
     "### Agent Configuration\n",
     "\n",
     "Configure the agents for the interaction.\n",
+    "\n",
     "- `config_list` defines the LLM configurations, including the model and API key.\n",
     "- `UserProxyAgent` simulates user inputs without requiring actual human interaction (set to `NEVER`).\n",
     "- `AssistantAgent` represents the AI agent, configured with the LLM settings."
@@ -478,8 +485,8 @@
     "\n",
     "Integrate the PydanticAI tool with AG2.\n",
     "\n",
-    "- Define a `Player` model using `BaseModel` to structure the input data.\n",
-    "- Use `RunContext` to securely inject dependencies (like the `Player` instance) into the tool function without exposing them to the LLM.\n",
+    "- Define a `Player` model using [BaseModel](https://docs.pydantic.dev/latest/api/base_model/) to structure the input data.\n",
+    "- Use [RunContext](https://ai.pydantic.dev/api/tools/#pydantic_ai.tools.RunContext) to securely inject dependencies (like the `Player` instance) into the tool function without exposing them to the LLM.\n",
     "- Implement `get_player` to define the tool's functionality, accessing `ctx.deps` for injected data.\n",
     "- Convert the tool to an AG2-compatible format with `Interoperability` and register it for execution and LLM communication.\n",
     "- Convert the PydanticAI tool into an AG2-compatible format using `convert_tool`.\n",
@@ -525,7 +532,7 @@
     "\n",
     "- Use the `initiate_chat` method to send a message from the `user_proxy` to the `chatbot`.\n",
     "- In this example, the user requests the chatbot to retrieve player information, providing \"goal keeper\" as additional context.\n",
-    "- The `Player` instance is securely injected into the tool using `RunContext`, ensuring the chatbot can retrieve and use this data during the interaction."
+    "- The `Player` instance is securely injected into the tool using [RunContext](https://ai.pydantic.dev/api/tools/#pydantic_ai.tools.RunContext), ensuring the chatbot can retrieve and use this data during the interaction."
    ]
   },
   {
@@ -595,6 +602,15 @@
   }
  ],
  "metadata": {
+  "front_matter": {
+   "description": "Cross-Framework LLM Tool Integration with AG2",
+   "tags": [
+    "tools",
+    "langchain",
+    "crewai",
+    "pydanticai"
+   ]
+  },
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",