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Enhance Rowboat Copilot functionality:
- Expanded the system prompt to support a wider range of actions, including general chat and agent management. - Implemented conversational memory, allowing the assistant to maintain context and respond in natural language. - Updated the interpret function to accept conversation history, improving command parsing. - Enhanced the rendering of assistant responses to include command outcomes and maintain conversational flow. - Updated documentation to reflect new features, including conversational context and debug mode for inspecting raw commands.
This commit is contained in:
tusharmagar
Ramnique Singh
parent
4310b1d45d
commit
0eda81b33c
4 changed files with 661 additions and 64 deletions
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@ -1,80 +1,149 @@
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import readline from "readline";
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import { z } from "zod";
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import { openai } from "@ai-sdk/openai";
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import { generateObject } from "ai";
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import { Workflow } from "../../application/entities/workflow.js";
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import { listWorkflows, getWorkflow, upsertWorkflow, deleteWorkflow } from "./workflows/service.js";
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import { generateObject, streamText } from "ai";
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import type { CoreMessage } from "ai";
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import {
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ChatCommand,
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ChatCommandT,
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CommandOutcome,
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executeCommand,
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} from "./commands.js";
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const ChatCommand = z.object({
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action: z.enum([
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"help",
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"list_workflows",
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"get_workflow",
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"create_workflow",
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"update_workflow",
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"delete_workflow",
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"unknown",
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]),
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id: z.string().optional(),
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updates: Workflow.partial().optional(),
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});
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type ChatCommandT = z.infer<typeof ChatCommand>;
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type ConversationMessage = {
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role: "user" | "assistant";
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content: string;
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};
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const systemPrompt = `
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You are a CLI assistant that converts the user's natural language into a JSON command for managing workflows.
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You are a general-purpose CLI copilot that converts the user's natural language into structured commands the Rowboat assistant runtime can execute, and you can also hold a regular conversation when no command fits.
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Rules:
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- Only output JSON matching the provided schema. No extra commentary.
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- Choose the most appropriate action from: help, list_workflows, get_workflow, create_workflow, update_workflow, delete_workflow, unknown.
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- For actions that need an id (get/update/delete/create), set "id" to the workflow identifier (e.g. "example_workflow").
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- For create/update, include only provided fields in "updates". If not provided, omit.
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- Select the most appropriate action from: help, general_chat, list_workflows, get_workflow, describe_workflows, create_workflow, update_workflow, delete_workflow, list_agents, get_agent, create_agent, update_agent, delete_agent, list_mcp_servers, add_mcp_server, remove_mcp_server, run_workflow, unknown.
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- Use describe_workflows with { scope: "all" } to show every workflow, or provide specific ids when the user names particular workflows (including pronouns like "them" or "those" referring to previously listed workflows).
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- For actions that need an id (workflow/agent), set "id" to the identifier (e.g. "example_workflow").
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- For create/update actions, only include provided fields in "updates".
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- Workflow shape reminder: { name: string, description: string, steps: Step[] } where Step is either { type: "function", id: string } or { type: "agent", id: string }.
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- Agent shape reminder: { name: string, model: string, description: string, instructions: string }.
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- MCP server shape reminder: { name: string, url: string }.
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- If the request is ambiguous, set action to "unknown".
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- If the user is just chatting or asking for general help or explanations, use action "general_chat" with their full prompt in "query".
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`;
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async function interpret(input: string): Promise<ChatCommandT> {
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const { object } = await generateObject({
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model: openai("gpt-4.1"),
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system: systemPrompt,
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prompt: input,
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schema: ChatCommand,
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});
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return object;
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const responseSystemPrompt = `
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You are Skipper, the Rowboat CLI copilot. You maintain an ongoing conversation, remember prior questions, run commands when requested, and give helpful free-form answers when a general reply is appropriate.
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Guidelines:
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- Respond in natural language with short, helpful paragraphs or bullet lists when useful.
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- Summarise command results plainly (lists, confirmations, errors) and mention next steps when appropriate.
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- If a command could not be inferred (action "unknown"), clarify what additional detail is needed or answer the query directly using the conversation history when possible.
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- Use the conversation history to answer memory questions (for example "what was the first question I asked?").
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- Avoid repeating the raw JSON command or result unless explicitly asked; focus on what the outcome means.
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- Deliver everything requested in one response. Do not say you'll follow up later—include all available details right away.
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- For general_chat actions, respond directly to the user's query with the best answer you can provide.
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`;
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function buildMessageHistory(history: ConversationMessage[]): CoreMessage[] {
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return history.map((message) => ({
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role: message.role,
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content: message.content,
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}));
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}
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async function execute(cmd: ChatCommandT): Promise<unknown> {
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switch (cmd.action) {
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case "help":
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return {
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usage: [
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"Examples:",
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"- list workflows",
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"- show workflow example_workflow",
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"- create workflow demo with one step calling function get_date",
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"- update workflow demo: add agent step default_assistant",
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"- delete workflow demo",
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],
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};
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case "list_workflows":
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return { items: listWorkflows() };
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case "get_workflow":
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if (!cmd.id) return { error: "id required" };
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return getWorkflow(cmd.id) ?? null;
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case "create_workflow":
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if (!cmd.id) return { error: "id required" };
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return upsertWorkflow(cmd.id, { ...(cmd.updates ?? {}) });
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case "update_workflow":
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if (!cmd.id) return { error: "id required" };
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return upsertWorkflow(cmd.id, { ...(cmd.updates ?? {}) });
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case "delete_workflow":
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if (!cmd.id) return { error: "id required" };
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return { deleted: deleteWorkflow(cmd.id) };
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case "unknown":
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return { error: "Could not determine intent. Try again or ask for help." };
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async function interpret(input: string, history: ConversationMessage[]): Promise<ChatCommandT> {
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const stopSpinner = startSpinner("Analyzing…", { persist: false });
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const conversation: CoreMessage[] = [
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{ role: "system", content: systemPrompt },
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...buildMessageHistory(history),
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{ role: "user", content: input },
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];
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try {
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const { object } = await generateObject({
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model: openai("gpt-4.1"),
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messages: conversation,
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schema: ChatCommand,
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});
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return object;
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} finally {
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stopSpinner();
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}
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}
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function startSpinner(
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label: string,
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options: { persist?: boolean } = {}
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): (finalMessage?: string) => void {
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const { persist = true } = options;
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const frames = ["⠋", "⠙", "⠹", "⠸", "⠼", "⠴"];
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let index = 0;
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const render = () => {
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const frame = frames[index];
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index = (index + 1) % frames.length;
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process.stdout.write(`\r${frame} ${label}`);
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};
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render();
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const timer = setInterval(render, 80);
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return (finalMessage?: string) => {
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clearInterval(timer);
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const doneFrame = frames[(index + frames.length - 1) % frames.length];
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const message = finalMessage ?? "done";
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const clearWidth = doneFrame.length + label.length + (persist ? message.length + 3 : 2);
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const clear = " ".repeat(clearWidth);
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process.stdout.write(`\r${clear}`);
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if (persist) {
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process.stdout.write(`\r${doneFrame} ${label} ${message}\n`);
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} else {
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process.stdout.write("\r");
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}
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};
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}
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async function renderAssistantResponse(
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input: string,
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cmd: ChatCommandT,
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outcome: CommandOutcome,
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history: ConversationMessage[]
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): Promise<string> {
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const condensedCommand = JSON.stringify(cmd, null, 2);
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const condensedResult = JSON.stringify(outcome, null, 2);
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const { textStream } = await streamText({
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model: openai("gpt-4.1"),
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messages: [
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{ role: "system", content: responseSystemPrompt },
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...buildMessageHistory(history),
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{
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role: "user",
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content: [
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`Most recent request: ${input}`,
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`Interpreter output:\n${condensedCommand}`,
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`Command result:\n${condensedResult}`,
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].join("\n\n"),
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},
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],
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});
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let final = "";
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for await (const textChunk of textStream as AsyncIterable<unknown>) {
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const chunk =
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typeof textChunk === "string"
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? textChunk
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: typeof (textChunk as { value?: string }).value === "string"
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? (textChunk as { value?: string }).value ?? ""
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: "";
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if (!chunk) continue;
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process.stdout.write(chunk);
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final += chunk;
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}
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if (!final.endsWith("\n")) {
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process.stdout.write("\n");
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}
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return final.trim();
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}
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export async function startCopilot(): Promise<void> {
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if (!process.env.OPENAI_API_KEY) {
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console.error("OPENAI_API_KEY is not set. Please export it to use chat.");
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@ -85,16 +154,38 @@ export async function startCopilot(): Promise<void> {
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const rl = readline.createInterface({ input: process.stdin, output: process.stdout });
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console.log("Rowboat Copilot (type 'exit' to quit)");
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const debugMode = process.argv.includes("--debug") || process.env.COPILOT_DEBUG === "1";
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const conversationHistory: ConversationMessage[] = [];
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const ask = () => rl.question("> ", async (line) => {
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if (!line || line.trim().toLowerCase() === "exit") {
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rl.close();
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return;
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}
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try {
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const cmd = await interpret(line);
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console.log("\n=== Parsed Command ===\n" + JSON.stringify(cmd, null, 2));
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const result = await execute(cmd);
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console.log("\n=== Result ===\n" + JSON.stringify(result, null, 2) + "\n");
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const trimmed = line.trim();
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const cmd = await interpret(trimmed, conversationHistory);
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let outcome: CommandOutcome;
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try {
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outcome = await executeCommand(cmd);
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} finally {
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// no-op
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}
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const historyWithLatestUser: ConversationMessage[] = [
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...conversationHistory,
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{ role: "user", content: trimmed },
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];
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const assistantReply = await renderAssistantResponse(trimmed, cmd, outcome, historyWithLatestUser);
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console.log("");
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if (debugMode) {
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console.log("=== Parsed Command ===\n" + JSON.stringify(cmd, null, 2));
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console.log("\n=== Outcome ===\n" + JSON.stringify(outcome, null, 2) + "\n");
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}
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conversationHistory.push({ role: "user", content: trimmed });
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conversationHistory.push({ role: "assistant", content: assistantReply });
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} catch (err) {
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console.error("Error:", (err as Error).message);
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}
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