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Merge pull request #37 from daydreamsai/updates
fix: Updates
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,234 @@ | ||
| /** | ||
| * Example demonstrating a Twitter bot using the Daydreams package. | ||
| * This bot can: | ||
| * - Monitor Twitter mentions and auto-reply | ||
| * - Generate autonomous thoughts and tweet them | ||
| * - Maintain conversation memory using ChromaDB | ||
| * - Process inputs through a character-based personality | ||
| */ | ||
|
|
||
| import { Orchestrator } from "../packages/core/src/core/orchestrator"; | ||
| import { HandlerRole } from "../packages/core/src/core/types"; | ||
| import { RoomManager } from "../packages/core/src/core/room-manager"; | ||
| import { ChromaVectorDB } from "../packages/core/src/core/vector-db"; | ||
| import { Processor } from "../packages/core/src/core/processor"; | ||
| import { LLMClient } from "../packages/core/src/core/llm-client"; | ||
| import { LogLevel } from "../packages/core/src/core/types"; | ||
| import chalk from "chalk"; | ||
| import { defaultCharacter } from "../packages/core/src/core/character"; | ||
| import { Consciousness } from "../packages/core/src/core/consciousness"; | ||
| import { z } from "zod"; | ||
| import readline from "readline"; | ||
|
|
||
| async function main() { | ||
| const loglevel = LogLevel.INFO; | ||
| // Initialize core dependencies | ||
| const vectorDb = new ChromaVectorDB("twitter_agent", { | ||
| chromaUrl: "http://localhost:8000", | ||
| logLevel: loglevel, | ||
| }); | ||
|
|
||
| await vectorDb.purge(); // Clear previous session data | ||
|
|
||
| const roomManager = new RoomManager(vectorDb); | ||
|
|
||
| const llmClient = new LLMClient({ | ||
| model: "anthropic/claude-3-5-sonnet-latest", // Using a known supported model | ||
| temperature: 0.3, | ||
| }); | ||
|
|
||
| // Initialize processor with default character personality | ||
| const processor = new Processor( | ||
| vectorDb, | ||
| llmClient, | ||
| defaultCharacter, | ||
| loglevel | ||
| ); | ||
|
|
||
| // Initialize core system | ||
| const orchestrator = new Orchestrator(roomManager, vectorDb, processor, { | ||
| level: loglevel, | ||
| enableColors: true, | ||
| enableTimestamp: true, | ||
| }); | ||
|
|
||
| // Initialize autonomous thought generation | ||
| const consciousness = new Consciousness(llmClient, roomManager, { | ||
| intervalMs: 300000, // Think every 5 minutes | ||
| minConfidence: 0.7, | ||
| logLevel: loglevel, | ||
| }); | ||
|
|
||
| orchestrator.registerIOHandler({ | ||
| name: "fetchGithubIssues", | ||
| role: HandlerRole.ACTION, | ||
| schema: z.object({ | ||
| repo: z.string(), | ||
| }), | ||
| handler: async (payload) => { | ||
| // 1. Fetch some info from GitHub | ||
| // 2. Return the fetched data so it can be processed as "new input" | ||
| // to the next step in the chain. | ||
| const { repo } = payload as { repo: string }; | ||
| const response = await fetch( | ||
| `https://api.github.com/repos/${repo}/issues` | ||
| ); | ||
| const issues = await response.json(); | ||
| // The data returned here is fed back into the Orchestrator's chain flow. | ||
| return issues; | ||
| }, | ||
| }); | ||
|
|
||
| orchestrator.registerIOHandler({ | ||
| name: "universalApiCall", | ||
| role: HandlerRole.ACTION, | ||
| // The agent must fill out these fields to make a valid request | ||
| schema: z | ||
| .object({ | ||
| method: z.enum(["GET", "POST", "PUT", "PATCH", "DELETE"]), | ||
| url: z.string().url(), | ||
| headers: z.record(z.string()).optional(), | ||
| body: z.union([z.string(), z.record(z.any())]).optional(), | ||
| }) | ||
| .describe( | ||
| "Use this to fetch data from an API. It should include the method, url, headers, and body." | ||
| ), | ||
| handler: async (payload) => { | ||
| const { method, url, headers, body } = payload as { | ||
| method: string; | ||
| url: string; | ||
| headers?: Record<string, string>; | ||
| body?: any; | ||
| }; | ||
|
|
||
| // Make the HTTP call | ||
| const response = await fetch(url, { | ||
| method, | ||
| headers: { | ||
| "Content-Type": "application/json", | ||
| ...headers, | ||
| }, | ||
| body: body ? JSON.stringify(body) : undefined, | ||
| }); | ||
|
|
||
| // Return JSON or text | ||
| let responseData; | ||
| try { | ||
| responseData = await response.json(); | ||
| } catch { | ||
| responseData = await response.text(); | ||
| } | ||
|
|
||
| // Return the result so the agent can process it further | ||
| return { | ||
| status: response.status, | ||
| statusText: response.statusText, | ||
| data: responseData, | ||
| }; | ||
| }, | ||
| }); | ||
|
|
||
| orchestrator.registerIOHandler({ | ||
| name: "user_chat", | ||
| role: HandlerRole.INPUT, | ||
| // This schema describes what a user message looks like | ||
| schema: z.object({ | ||
| content: z.string(), | ||
| userId: z.string().optional(), | ||
| }), | ||
| // For "on-demand" input handlers, the `handler()` can be a no-op. | ||
| // We'll call it manually with data, so we don't need an interval. | ||
| handler: async (payload) => { | ||
| // We simply return the payload so the Orchestrator can process it | ||
| return payload; | ||
| }, | ||
| }); | ||
|
|
||
| orchestrator.registerIOHandler({ | ||
| name: "ui_chat_reply", | ||
| role: HandlerRole.OUTPUT, | ||
| schema: z.object({ | ||
| userId: z.string().optional(), | ||
| message: z.string(), | ||
| }), | ||
| handler: async (payload) => { | ||
| const { userId, message } = payload as { | ||
| userId?: string; | ||
| message: string; | ||
| }; | ||
|
|
||
| // In a real app, you might push this to a WebSocket, or store it in a DB, | ||
| // or just log it to the console: | ||
| console.log(`Reply to user ${userId ?? "??"}: ${message}`); | ||
|
|
||
| // No need to return anything if it's a final "output" | ||
| }, | ||
| }); | ||
|
|
||
| // Set up readline interface | ||
| const rl = readline.createInterface({ | ||
| input: process.stdin, | ||
| output: process.stdout, | ||
| }); | ||
|
|
||
| // Function to prompt for user input | ||
| const promptUser = () => { | ||
| rl.question( | ||
| 'Enter your message (or "exit" to quit): ', | ||
| async (userMessage) => { | ||
| if (userMessage.toLowerCase() === "exit") { | ||
| rl.close(); | ||
| process.exit(0); | ||
| } | ||
|
|
||
| // Dispatch the message | ||
| const userId = "console-user"; | ||
| const outputs: any = await orchestrator.dispatchToInput("user_chat", { | ||
| content: userMessage, | ||
| userId, | ||
| }); | ||
|
|
||
| // Now `outputs` is an array of suggestions with role=output that got triggered | ||
| if (outputs && outputs.length > 0) { | ||
| for (const out of outputs) { | ||
| if (out.name === "ui_chat_reply") { | ||
| // Our "ui_chat_reply" handler data has { userId, message } | ||
| console.log(chalk.green(`AI says: ${out.data.message}`)); | ||
| } | ||
| } | ||
| } | ||
|
|
||
| // Continue prompting | ||
| promptUser(); | ||
| } | ||
| ); | ||
| }; | ||
|
|
||
| // Start the prompt loop | ||
| console.log(chalk.cyan("🤖 Bot is now running and monitoring Twitter...")); | ||
| console.log(chalk.cyan("You can type messages in the console.")); | ||
| console.log(chalk.cyan('Type "exit" to quit')); | ||
| promptUser(); | ||
|
|
||
| // Handle graceful shutdown | ||
| process.on("SIGINT", async () => { | ||
| console.log(chalk.yellow("\n\nShutting down...")); | ||
|
|
||
| // Clean up resources | ||
| await consciousness.stop(); | ||
| orchestrator.removeIOHandler("twitter_mentions"); | ||
| orchestrator.removeIOHandler("consciousness_thoughts"); | ||
| orchestrator.removeIOHandler("twitter_reply"); | ||
| orchestrator.removeIOHandler("twitter_thought"); | ||
| rl.close(); | ||
|
|
||
| console.log(chalk.green("✅ Shutdown complete")); | ||
| process.exit(0); | ||
| }); | ||
| } | ||
|
|
||
| // Run the example | ||
| main().catch((error) => { | ||
| console.error(chalk.red("Fatal error:"), error); | ||
| process.exit(1); | ||
| }); |
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