By their nature, large language models (LLMs) have certain limitations. For example, most LLMs are not good at performing mathematical calculations or accessing up-to-date information beyond their training cutoff date. To address these limitations, LLMs can be given the ability to use external tools or perform function calls to implement specific functionalities.

The flow of function calling works as follows:

1. When a user query is received, the LLM first determines whether it needs to use an external tool or not.
2. If the LLM decides to use a tool, it needs to select the appropriate tool from the available options based on the query.
3. The LLM then makes a call to the selected tool, which could be an API or an external function.
4. The response from the tool is then passed back to the LLM, which uses it along with the initial user query to generate the final response.

This approach allows the LLM to leverage external capabilities and resources to provide more comprehensive and accurate responses to user queries.

By their nature, large language models (LLMs) have certain limitations. For example, most LLMs are not good at performing mathematical calculations or accessing up-to-date information beyond their training cutoff date. To address these limitations, LLMs can be given the ability to use external tools or perform function calls to implement specific functionalities.

The function calling flow works as follows:

1. Tool Determination: When a user query is received, the LLM will first evaluate whether it needs to use an external tool to generate a response. If no tool is required, the LLM will use its internal training data to generate a response.

2. Tool Selection: If the LLM determines that a tool is needed, it will select the appropriate tool from the available options. For example, if the query requires a calculation, the LLM will select a calculator tool; if the query requires weather information, the LLM will select a web search tool.

3. Tool Invocation: Once the tool is selected, the LLM will make a call to the external function or API that implements the tool's functionality. The input parameters for the tool are determined by the tool's input schema.

4. Response Generation: The response from the external tool or function call is then passed back to the LLM, which will use this information, along with the original user query, to generate a final response.

This flow allows the LLM to leverage external capabilities and resources to provide more comprehensive and accurate responses to user queries.

To define tools in the Cloud 3 family, there are two key components:

1. Description: This is a detailed description of the tool, which the Cloud model uses to determine which tool to use for a given query. The description should provide as much detail as possible, including what the tool does, when it should be used, any parameters it requires, and any important caveats or limitations.

2. Implementation: This is the actual implementation of the tool, which can be an external API or function. The tool definition specifies the input schema for the tool, which determines what inputs the user query needs to provide.

When the user provides a query, the Cloud model first determines which tool to use based on the tool descriptions. It then makes a call to the corresponding tool implementation, passing in the required inputs. The tool's response is then fed back into the Cloud model, which generates the final response to the user.

Some best practices for defining tools include:

• Provide a highly detailed description, covering all the key aspects of the tool.
• Ensure the tool name is clear and descriptive.
• Carefully define the input schema to match the user's query.
• Consider chaining multiple tools together for more complex use cases.
• Test the tool definitions and implementations thoroughly to ensure they work as expected.

By following these guidelines, you can effectively leverage the tool usage capabilities of the Cloud 3 family to enhance the capabilities of your language models.

When defining tools for use with the Anthropic Cloud 3 family of models, it is important to follow these best practices for the tool descriptions:

1. Provide Detailed Descriptions: Ensure that the description of each tool is highly detailed. Include information about what the tool does, when it should be used, and how it affects the tool's behavior.

2. Explain Parameters: Clearly explain the meaning and impact of each parameter required by the tool. This helps the language model understand how to use the tool effectively.

3. Highlight Limitations: Mention any important caveats or limitations of the tool, such as the type of information it does not return.

4. Ensure Clarity: Make sure the tool name is clear and unambiguous. The language model will use the description to determine which tool to use, so a clear and concise name is crucial.

5. Prioritize Usefulness: Focus on providing tools that are genuinely useful and relevant to the task at hand. Avoid including unnecessary or redundant tools.

6. Consider Tool Chaining: If your use case requires a sequence of tool calls, consider using the Opus model, which is better equipped to handle serialized tool usage.

7. Test Thoroughly: Thoroughly test your tool definitions and implementations to ensure they work as expected and provide the desired functionality.

By following these best practices, you can create high-quality tool definitions that enable the Anthropic Cloud 3 models to effectively leverage external functionality and enhance their capabilities.

To build a customer service chatbot using the Cloud 3 family of models, we'll follow these steps:

1. Install the Anthropic package: We'll start by installing the Anthropic Python client package.

2. Set up the Anthropic API key: We'll set up the Anthropic API key, which is required to use the Cloud 3 models.

3. Choose the Cloud 3 model: For this example, we'll use the CLA 3 Opus model, as it supports more complex tool usage and chaining.

4. Define the client-side tools: We'll define three tools for our customer service chatbot:

   • Get customer info
   • Get order details
   • Cancel order

   Each tool has a detailed description, input schema, and implementation through external functions.

5. Implement the main loop: We'll create a main loop that handles the user's input, determines which tool to use, calls the appropriate function, and feeds the response back into the language model to generate the final output.

6. Test the chatbot: We'll test the chatbot by providing different user queries, such as retrieving a customer's email address, checking the status of an order, and canceling an order.

By following this process, we can build a customer service chatbot that leverages the capabilities of the Cloud 3 family of models and the ability to call external tools or functions to enhance its functionality.

In this video, we have explored the concept of function calling and external tool usage with the Anthropic Cloud 3 family of models. We have learned the following key points:

1. Motivation for Function Calling: LLMs have certain limitations, such as the inability to perform complex calculations or access up-to-date information. Function calling allows the LLM to leverage external tools and APIs to overcome these limitations.

2. Function Calling Flow: The LLM first determines whether it needs to use an external tool, then selects the appropriate tool based on the provided descriptions, and finally makes a call to the tool's implementation to get the necessary information.

3. Defining Tools: Tools are defined with a name, a detailed description, and an input schema. The description is crucial as it helps the LLM decide which tool to use.

4. Best Practices: Provide clear and comprehensive descriptions for the tools, including details about their functionality, parameters, and limitations. This ensures the LLM can make informed decisions about which tool to use.

5. Example Implementation: We walked through an example of building a customer service chatbot using Anthropic's Cloud 3 models and client-side tools. The example demonstrated how to define tools, implement their functionality, and integrate them into the LLM's decision-making process.

6. Comparison between Opus and Haiku: While both Opus and Haiku can be used for function calling, Opus is better suited for more complex scenarios that involve serialized or chained tool usage.

By understanding these concepts, you can effectively leverage the power of function calling and external tool usage to enhance the capabilities of your Anthropic Cloud 3-based applications.