👌 IMPROVE: BaseAI readme (#107)

* 📖 DOC: Cookbook

* 📖 DOC: Directory structure

Author: msaaddev

README.md

@@ -17,6 +17,190 @@
 
 BaseAI is the AI framework for building declarative and composable AI-powered LLM products. It allows you to develop AI agent pipes on your local machine with integrated agentic tools and memory (RAG). Visit our [learn](https://baseai.dev/learn) guide to get started with BaseAI.
 
+### 1. Initialize a new BaseAI project
+
+BaseAI is a TypeScript-first framework. To create a new BaseAI project, run the following command in your project:
+
+```bash
+npx baseai@latest init
+```
+
+This command will create a `baseai` directory in your project. This is what the directory structure looks like:
+
+```
+ROOT (of your app)
+├── baseai
+|  ├── baseai.config.ts
+|  ├── memory
+|  ├── pipes
+|  └── tools
+├── .env (your env file)
+└── package.json
+```
+
+### 2. Add API keys
+
+Copy the following in your  `.env` file and add appropriate LLM API keys:
+
+```
+# !! SERVER SIDE ONLY !!
+# Keep all your API keys secret — use only on the server side.
+
+# TODO: ADD: Both in your production and local env files.
+# Langbase API key for your User or Org account.
+# How to get this API key https://langbase.com/docs/api-reference/api-keys
+LANGBASE_API_KEY=
+
+# TODO: ADD: LOCAL ONLY. Add only to local env files.
+# Following keys are needed for local pipe runs. For providers you are using.
+# For Langbase, please add the key to your LLM keysets.
+# Read more: Langbase LLM Keysets https://langbase.com/docs/features/keysets
+OPENAI_API_KEY=
+ANTHROPIC_API_KEY=
+COHERE_API_KEY=
+FIREWORKS_API_KEY=
+GOOGLE_API_KEY=
+GROQ_API_KEY=
+MISTRAL_API_KEY=
+PERPLEXITY_API_KEY=
+TOGETHER_API_KEY=
+XAI_API_KEY=
+```
+
+### 3. Create a new AI agent
+
+Pipe is your custom-built AI agent as an API. It's the fastest way to ship AI features/apps. Let's create a new pipe:
+
+```bash
+npx baseai@latest pipe
+```
+
+It will ask you for the name, description, and other details of the pipe step-by-step. Once done, a pipe will be created inside the `/baseai/pipes` directory. You can now edit the system prompt, change model params, and more. Here is what a pipe code looks like:
+
+```ts
+import { PipeI } from '@baseai/core';
+
+const pipeSummary = (): PipeI => ({
+	// Replace with your API key https://langbase.com/docs/api-reference/api-keys
+	apiKey: process.env.LANGBASE_API_KEY!,
+	name: 'summary',
+	description: 'AI Summary agent',
+	status: 'public',
+	model: 'openai:gpt-4o-mini',
+	stream: true,
+	json: false,
+	store: true,
+	moderate: true,
+	top_p: 1,
+	max_tokens: 1000,
+	temperature: 0.7,
+	presence_penalty: 1,
+	frequency_penalty: 1,
+	stop: [],
+	tool_choice: 'auto',
+	parallel_tool_calls: true,
+	messages: [
+		{
+			role: 'system',
+			content: `You are a helpful AI agent. Make everything Less wordy.`
+		}
+	],
+	variables: [],
+	memory: [],
+	tools: []
+});
+
+export default pipeSummary;
+```
+
+### 4. Integrate pipe in your app
+
+Let's create a new `index.ts` file in your project root. Now we need to do the following:
+
+1. Import the pipe config we created.
+2. Create a new pipe instance with the pipe config.
+3. Run the pipe with a user message.
+4. Listen to the stream events.
+
+Here is what the code looks like:
+
+```ts
+import { Pipe, getRunner } from '@baseai/core';
+import pipeSummarizer from './baseai/pipes/summary';
+
+const pipe = new Pipe(pipeSummarizer());
+
+const userMsg = `
+Langbase 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.
+A complete AI developers platform.
+- Collaborate: Invite all team members to collaborate on the pipe. Build AI together.
+- 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.
+`;
+
+async function main() {
+	const { stream } = await pipe.run({
+		messages: [{ role: 'user', content: userMsg }],
+		stream: true,
+	});
+
+	const runner = getRunner(stream);
+
+	// Method 1: Using event listeners
+	runner.on('connect', () => {
+		console.log('Stream started.\n');
+	});
+
+	runner.on('content', content => {
+		process.stdout.write(content);
+	});
+
+	runner.on('end', () => {
+		console.log('\nStream ended.');
+	});
+
+	runner.on('error', error => {
+		console.error('Error:', error);
+	});
+}
+
+main();
+```
+
+Make sure to install and import `dotenv` at the top if you are using Node.js:
+
+```ts
+import 'dotenv/config';
+```
+
+### 5. Run the AI agent
+
+To run the pipe locally, you need to start the BaseAI server. Run the following command in your terminal:
+
+```bash
+npx baseai@latest dev
+```
+
+Now, run the `index.ts` file in your terminal:
+
+```bash
+npx tsx index.ts
+```
+
+You should see the following output in your terminal:
+
+```md
+Stream started.
+
+Langbase Studio is your AI development playground. Experiment in real-time with real data, store messages, and version prompts to move from prototype to production seamlessly.
+
+Key Features:
+- **Collaborate**: Invite team members to build AI together.
+- **Inclusive Teams**: Engage all stakeholders—R&D, engineering, product, and marketing—in a shared space. It’s like GitHub combined with Google Docs for AI development.
+Stream ended.
+```
+> [!TIP]
+> You can also run RAG locally with BaseAI. Check out memory quickstart [guide](https://baseai.dev/docs/memory/quickstart) for more details.
+
 ## Documentation
 
 Visit [baseai.dev/docs](https://baseai.dev/docs) for the full documentation.