],\n\t\t\t\t},\n\t\t\t},\n\t\t\trequired: ['location'],\n\t\t},\n\t},\n});\n\nexport default getCurrentWeatherTool;\n" }), ...{ title: './baseai/tools/get-current-weather.ts' } }), _jsx(_components.pre, { language: "ts", code: "import 'dotenv/config';\nimport {Pipe, getRunner} from '@baseai/core';\nimport pipeSummarizer from './baseai/pipes/summarizer';\n\nconst pipe = new Pipe(pipeSummarizer());\n\nconst userMsg = `\nLangbase studio is your playground to build, collaborate, and deploy AI. It allows you to experiment with your pipes in real-time, with real data, store messages, version your prompts, and truly helps you take your idea from building prototypes to deployed in production with LLMOps on usage, cost, and quality.\nA complete AI developers platform.\n- Collaborate: Invite all team members to collaborate on the pipe. Build AI together.\n- Developers & Stakeholders: All your R&D team, engineering, product, GTM (marketing and sales), literally invlove every stakeholder can collaborate on the same pipe. It's like a powerful version of GitHub x Google Docs for AI. A complete AI developers platform.\n`;\n\nasync function main() {\n\tconst {stream} = await pipe.run({\n\t\tmessages: [{role: 'user', content: userMsg}],\n\t\tstream: true\n\t});\n\n\tconst runner = getRunner(stream);\n\n\t// Method 1: Using event listeners\n\trunner.on('connect', () => {\n\t\tconsole.log('Stream started.\\n');\n\t});\n\n\trunner.on('content', content => {\n\t\tprocess.stdout.write(content);\n\t});\n\n\trunner.on('end', () => {\n\t\tconsole.log('\\nStream ended.');\n\t});\n\n\trunner.on('error', error => {\n\t\tconsole.error('Error:', error);\n\t});\n}\n\nmain();\n", children: _jsx(_components.code, { className: "language-ts", children: "import 'dotenv/config';\nimport {Pipe, getRunner} from '@baseai/core';\nimport pipeSummarizer from './baseai/pipes/summarizer'