流式输出与事件

import { OpenAIProvider, createLocalAgent, tool } from 'astorlm'
import { z } from 'zod'

// The stage's functions, wrapped as tools.
const crowdMood = tool({
  name: 'crowd_mood',
  description: 'Look at the crowd from the front of the stage: how they feel, and what they came to hear.',
  schema: z.object({}),
  execute: async () => stage.readCrowd(), // your code
})

const setLights = tool({
  name: 'set_lights',
  description: 'Set the color of every light on the stage.',
  schema: z.object({ color: z.enum(['amber', 'red', 'blue', 'white']) }),
  execute: async ({ color }) => stage.lights(color), // your code
})

const pyro = tool({
  name: 'pyro',
  description: 'Fire the pyrotechnics on both sides of the stage. Once per show.',
  schema: z.object({}),
  execute: async () => stage.firePyro(), // your code
})

const agent = await createLocalAgent({
  // Any OpenAI-compatible endpoint: OpenAI, Ollama, LM Studio, vLLM, a proxy…
  provider: new OpenAIProvider({
    baseURL: 'http://localhost:11434/v1', // e.g. Ollama's default address
    model: 'your-model', // e.g. 'llama3.1', 'gpt-4o-mini'
    apiKey: 'YOUR_API_KEY', // local servers usually ignore it
  }),
  tools: [crowdMood, setLights, pyro],
  maxTurns: 10,
})

// Streaming is always on: subscribe before run(). Each listener only hears what it asked for.
// The lyrics screen: every piece of text, the moment it arrives.
agent.on('text', (piece) => lyrics.append(piece))

// The tape deck: every event the loop emits, in order.
agent.on('event', (event) => tape.write(event))

// The meter: the tokens each turn reported.
agent.on('event', (event) => {
  if (event.type === 'turn_end' && event.usage) meter.add(event.usage.inputTokens + event.usage.outputTokens)
})

// Each on() returns a function that unsubscribes.
const stopTape = agent.on('tool-start', ({ name }) => console.log('→', name))

const last = await agent.run('Open the show: read the crowd, set the mood, and greet them.')
stopTape()
console.log(last.content) // the same text, whole, once the run is over

// Streaming and events from scratch. Plain fetch, no SDK.

// Any OpenAI-compatible endpoint: OpenAI, Ollama, LM Studio, vLLM, a proxy…
const LLM = {
  baseURL: 'http://localhost:11434/v1', // e.g. Ollama's default address
  model: 'your-model', // e.g. 'llama3.1', 'gpt-4o-mini'
  apiKey: 'YOUR_API_KEY', // local servers usually ignore it
}

type ToolCall = { id: string; type: 'function'; function: { name: string; arguments: string } }
type Message =
  | { role: 'user'; content: string }
  | { role: 'assistant'; content: string | null; tool_calls?: ToolCall[] }
  | { role: 'tool'; tool_call_id: string; content: string }

const tools: Record<string, (args: Record<string, string>) => Promise<string>> = { crowd_mood: crowdMood, set_lights: setLights, pyro }
const toolSchemas = [/* one JSON Schema per tool */]

// 1. The bus: a list of listeners. The loop emits; it never knows who is listening.
type AgentEvent =
  | { type: 'turn_start'; turn: number }
  | { type: 'text_delta'; text: string }
  | { type: 'tool_execution_start'; name: string }
  | { type: 'tool_execution_end'; name: string; output: string }
  | { type: 'turn_end'; turn: number; usage?: { prompt_tokens: number; completion_tokens: number } }
const listeners: ((event: AgentEvent) => void)[] = []
export const on = (listener: (event: AgentEvent) => void) => {
  listeners.push(listener)
  return () => listeners.splice(listeners.indexOf(listener), 1)
}
const emit = (event: AgentEvent) => listeners.forEach((listener) => listener(event))

// 2. One streamed model call: read the server-sent events and rebuild the message piece by piece.
async function streamReply(messages: Message[]) {
  const res = await fetch(`${LLM.baseURL}/chat/completions`, {
    method: 'POST',
    headers: { 'content-type': 'application/json', authorization: `Bearer ${LLM.apiKey}` },
    body: JSON.stringify({ model: LLM.model, messages, tools: toolSchemas, stream: true, stream_options: { include_usage: true } }),
  })
  const reader = res.body!.pipeThrough(new TextDecoderStream()).getReader()
  let text = ''
  let buffer = ''
  let usage
  const calls: ToolCall[] = []
  for (let read = await reader.read(); !read.done; read = await reader.read()) {
    buffer += read.value
    const lines = buffer.split('\n')
    buffer = lines.pop()! // a line can arrive cut in half: keep it for the next chunk
    for (const line of lines) {
      if (!line.startsWith('data: ') || line === 'data: [DONE]') continue
      const chunk = JSON.parse(line.slice(6))
      usage = chunk.usage ?? usage
      const delta = chunk.choices?.[0]?.delta
      if (delta?.content) {
        text += delta.content
        emit({ type: 'text_delta', text: delta.content }) // show it now, not at the end
      }
      // Tool calls arrive in pieces too: glue each one's arguments together by index.
      for (const part of delta?.tool_calls ?? []) {
        const call = (calls[part.index] ??= { id: '', type: 'function', function: { name: '', arguments: '' } })
        call.id ||= part.id ?? ''
        call.function.name += part.function?.name ?? ''
        call.function.arguments += part.function?.arguments ?? ''
      }
    }
  }
  const reply: Message = { role: 'assistant', content: text || null, ...(calls.length ? { tool_calls: calls } : {}) }
  return { reply, usage }
}

// 3. The loop from level 2, emitting as it goes.
export async function runAgent(prompt: string, maxTurns = 10): Promise<string> {
  const messages: Message[] = [{ role: 'user', content: prompt }]
  for (let turn = 1; turn <= maxTurns; turn++) {
    emit({ type: 'turn_start', turn })
    const { reply, usage } = await streamReply(messages)
    messages.push(reply)
    if (reply.role !== 'assistant' || !reply.tool_calls) {
      emit({ type: 'turn_end', turn, usage })
      return reply.content ?? ''
    }
    for (const call of reply.tool_calls) {
      emit({ type: 'tool_execution_start', name: call.function.name })
      const output = await tools[call.function.name]!(JSON.parse(call.function.arguments || '{}'))
      emit({ type: 'tool_execution_end', name: call.function.name, output })
      messages.push({ role: 'tool', tool_call_id: call.id, content: output })
    }
    emit({ type: 'turn_end', turn, usage })
  }
  throw new Error(`No answer after ${maxTurns} turns`)
}

// 4. Your app subscribes. The loop above doesn't change to add one.
on((e) => e.type === 'text_delta' && lyrics.append(e.text))
on((e) => tape.write(e))
on((e) => e.type === 'turn_end' && e.usage && meter.add(e.usage.prompt_tokens + e.usage.completion_tokens))

# Streaming and events from scratch. Standard library only, no SDK.
import json
import urllib.request

# Any OpenAI-compatible endpoint: OpenAI, Ollama, LM Studio, vLLM, a proxy...
LLM = {
    "base_url": "http://localhost:11434/v1",  # e.g. Ollama's default address
    "model": "your-model",  # e.g. "llama3.1", "gpt-4o-mini"
    "api_key": "YOUR_API_KEY",  # local servers usually ignore it
}

TOOLS = {"crowd_mood": crowd_mood, "set_lights": set_lights, "pyro": pyro}
TOOL_SCHEMAS = [...]  # one JSON Schema per tool

# 1. The bus: a list of listeners. The loop emits; it never knows who is listening.
listeners = []


def on(listener):
    listeners.append(listener)
    return lambda: listeners.remove(listener)


def emit(event):
    for listener in listeners:
        listener(event)


# 2. One streamed model call: read the server-sent events and rebuild the message piece by piece.
def stream_reply(messages):
    body = {
        "model": LLM["model"],
        "messages": messages,
        "tools": TOOL_SCHEMAS,
        "stream": True,
        "stream_options": {"include_usage": True},
    }
    request = urllib.request.Request(
        f"{LLM['base_url']}/chat/completions",
        data=json.dumps(body).encode(),
        headers={"Content-Type": "application/json", "Authorization": f"Bearer {LLM['api_key']}"},
    )
    text, calls, usage = "", {}, None
    with urllib.request.urlopen(request) as response:
        for raw in response:  # one server-sent event per line
            line = raw.decode().strip()
            if not line.startswith("data: ") or line == "data: [DONE]":
                continue
            chunk = json.loads(line[6:])
            usage = chunk.get("usage") or usage
            delta = (chunk.get("choices") or [{}])[0].get("delta", {})
            if delta.get("content"):
                text += delta["content"]
                emit({"type": "text_delta", "text": delta["content"]})  # show it now, not at the end
            # Tool calls arrive in pieces too: glue each one's arguments together by index.
            for part in delta.get("tool_calls") or []:
                call = calls.setdefault(part["index"], {"id": "", "type": "function", "function": {"name": "", "arguments": ""}})
                call["id"] = call["id"] or part.get("id") or ""
                call["function"]["name"] += part.get("function", {}).get("name") or ""
                call["function"]["arguments"] += part.get("function", {}).get("arguments") or ""
    reply = {"role": "assistant", "content": text or None}
    if calls:
        reply["tool_calls"] = [calls[i] for i in sorted(calls)]
    return reply, usage


# 3. The loop from level 2, emitting as it goes.
def run_agent(prompt, max_turns=10):
    messages = [{"role": "user", "content": prompt}]
    for turn in range(1, max_turns + 1):
        emit({"type": "turn_start", "turn": turn})
        reply, usage = stream_reply(messages)
        messages.append(reply)
        if not reply.get("tool_calls"):
            emit({"type": "turn_end", "turn": turn, "usage": usage})
            return reply["content"] or ""
        for call in reply["tool_calls"]:
            name = call["function"]["name"]
            emit({"type": "tool_execution_start", "name": name})
            output = TOOLS[name](**json.loads(call["function"]["arguments"] or "{}"))
            emit({"type": "tool_execution_end", "name": name, "output": output})
            messages.append({"role": "tool", "tool_call_id": call["id"], "content": output})
        emit({"type": "turn_end", "turn": turn, "usage": usage})
    raise RuntimeError(f"No answer after {max_turns} turns")


# 4. Your app subscribes. The loop above doesn't change to add one.
on(lambda e: e["type"] == "text_delta" and print(e["text"], end="", flush=True))
on(lambda e: tape.write(e))
on(lambda e: e["type"] == "turn_end" and e["usage"] and meter.add(e["usage"]["total_tokens"]))