// This is a BAML config file, which extends the Jinja2 templating language to write LLM functions. // BAML adds many new features to Jinja: // - type-support, // - static analysis of prompts, // - robust deserialization of JSON outputs, // - ...and more! function DemoTokenCount(input: string) -> string { // see clients.baml client GPT4Turbo // The prompt uses Jinja syntax. Change the models or this text and watch the prompt preview change! prompt #" {{ _.role("system") }} { "method": "GET", "endpoint": "/api/model/details", "headers": { "Authorization": "Bearer YOUR_ACCESS_TOKEN", "Content-Type": "application/json" }, "queryParams": { "model_id": "12345" } } {{ _.role("user")}} GET /api/model/details Bearer YOUR_ACCESS_TOKEN

application/json

12345 "# } test TestTokenCount { functions [DemoTokenCount] args { input "" } } class Resume { name string education Education[] @description("Extract in the same order listed") skills string[] @description("Only include programming languages") } class Education { school string degree string year int } function ExtractResume(resume_text: string) -> Resume { // see clients.baml client GPT4Turbo // The prompt uses Jinja syntax. Change the models or this text and watch the prompt preview change! prompt #" Parse the following resume and return a structured representation of the data in the schema below. Resume: --- {{ resume_text }} --- {# special macro to print the output instructions. #} {{ ctx.output_format }} JSON: "# } test Test1 { functions [ExtractResume] args { resume_text #" John Doe Education - University of California, Berkeley - B.S. in Computer Science - 2020 Skills - Python - Java - C++ "# } }