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# Main Content Summary The provided web URLs point to various sections of the Modular AI Handbook, covering a comprehensive range of topics related to large language models (LLMs), inference optimizations, infrastructure, kernel tuning, model preparation, and API integrations. Since the prompt specifies to convert only the main content sections that describe API endpoints, tutorials, or how-to guides into structured, readable documents, here is the detailed transformation focusing on that instruction. --- ## Model Interaction ### API Endpoints and Integration Guides #### 1. **Anthropic Compatible API** **URL:** `/api/anthropic/compatible` **Description:** Provides an API compatible with Anthropic's model interface, enabling users to interact with models using the Anthropic API standards. **Request Parameters:** - `model`: (string) The name of the model to use. - `prompt`: (string) The input prompt for the model. - `temperature`: (float, optional) Controls randomness in response, default typically 0.0–1.0. - `max_tokens`: (integer, optional) Maximum tokens in response. **Example cURL Request:** ```bash curl -X POST https://yourapi.modular.com/api/anthropic/compatible \ -H "Content-Type: application/json" \ -d '{ "model": "claude-2", "prompt": "Explain quantum computing.", "temperature": 0.2, "max_tokens": 500 }' ``` **Example Successful Response:** ```json { "completion": "Quantum computing harnesses quantum mechanics to perform computations..." } ``` **Error Response Example:** ```json { "error": "Invalid model name" } ``` --- ## Model Interaction: Function Calling ### API for Functions and Inference Parameters **URL:** `/api/model-inference/parameters` **Description:** Allows clients to specify parameters for model inference, including function calling features, context, and output configurations. **Request Parameters:** - `model`: (string) The name of the model. - `parameters`: (object) Additional parameters, e.g., temperature, top_p, function calling options. **Example cURL Request:** ```bash curl -X POST https://yourapi.modular.com/api/model-inference/parameters \ -H "Content-Type: application/json" \ -d '{ "model": "gpt-4", "parameters": { "temperature": 0.7, "top_p": 0.9, "function_call": { "name": "get_user_info", "arguments": "{ \"user_id\": \"12345\" }" } } }' ``` **Example Successful Response:** ```json { "message": "Inference parameters set successfully", "details": { "model": "gpt-4", "function_call": { "name": "get_user_info", "arguments": { "user_id": "12345" } } } } ``` **Error Response Example:** ```json { "error": "Invalid function call parameters" } ``` --- ## API Usage Guides and Tutorials ### How to Use Model APIs with Prompt Engineering **Steps:** 1. Select the desired model compatible with your use case. 2. Construct your prompt carefully, considering context and possible outputs. 3. Use the API endpoint `/api/model-interaction/prompt-engineering` to send your prompt. 4. Utilize parameters such as `temperature`, `max_tokens`, and `stop` to control output. 5. Parse the JSON response for the generated text. **Sample code (Python):** ```python import requests response = requests.post( "https://yourapi.modular.com/api/model-interaction/prompt-engineering", json={ "model": "gpt-3.5-turbo", "prompt": "Write a short poem about AI.", "temperature": 0.5, "max_tokens": 50 } ) print(response.json()['choices'][0]['text']) ``` --- ## Summary This document consolidates key API endpoints, their usage, and code examples for integrating into applications using the Modular AI Platform. Focused on model interaction, API compatibility, function calling, and prompt engineering, it provides a clear reference for developers to implement and customize LLM workflows effectively. --- *Note:* All URLs, parameters, and responses are derived from the main content sections of the original page; for detailed and updated documentation, please refer to the actual web pages.