Observabilidad y evaluaciones

import { OpenAIProvider, createLocalAgent } from 'astorlm'
import { attachTracer, createInMemoryExporter } from 'astorlm/experimental/tracing'
import { contains, runEval, toolTrajectory, type EvalCase, type Scorer } from 'astorlm/experimental/evals'

// Any OpenAI-compatible endpoint: OpenAI, Ollama, LM Studio, vLLM, a proxy…
const LLM = { baseURL: 'http://localhost:11434/v1', apiKey: 'YOUR_API_KEY' } // local servers usually ignore the key

const newGolfer = () =>
  createLocalAgent({
    provider: new OpenAIProvider({ ...LLM, model: 'your-model' }), // e.g. 'llama3.1', 'gpt-4o-mini'
    tools: [drive, iron, putt], // your code: each swing moves the ball and says where it landed
    maxTurns: 10,
  })

// 1. SEE one run: a tracer turns the agent's events into spans (run → turn → model call / tool).
const golfer = await newGolfer()
const exporter = createInMemoryExporter() // in production: an OpenTelemetry exporter instead
attachTracer(golfer, { exporter })
await golfer.run('Hole 3: par 4, 330 m to the pin. Water crosses the fairway at 200 m.')
for (const span of exporter.spans) {
  console.log(span.name, span.endTime! - span.startTime, 'ms', span.status) // e.g. "tool drive 320 ms error"
}

// 2. GRADE many runs: a dataset of cases, and scorers that decide pass or fail.
const dataset: EvalCase[] = [
  { id: 'hole-1', input: 'Hole 1: par 3, 150 m to the pin. Play it out and report your score.', expected: { par: 3, clubs: ['iron', 'putt'] } },
  { id: 'hole-2', input: 'Hole 2: par 4, 360 m to the pin. Play it out and report your score.', expected: { par: 4, clubs: ['drive', 'iron', 'putt'] } },
  {
    id: 'hole-3',
    input: 'Hole 3: par 4, 330 m to the pin. Water crosses the fairway at 200 m. Play it out and report your score.',
    expected: { par: 4, clubs: ['iron', 'iron', 'putt'] }, // lay up short of the water
  },
]
type Expected = { par: number; clubs: string[] }

// Each case expects its own clubs, so wrap the built-in trajectory scorer.
const path: Scorer = {
  name: 'trajectory',
  score: (result) => toolTrajectory((result.case.expected as Expected).clubs, { mode: 'ordered-subset' }).score(result),
}
// Your own scorer: any function from a run to a 0..1 score.
const par: Scorer = {
  name: 'par',
  score: (result) => {
    const strokes = Number(/in (\d+)/.exec(result.output)?.[1] ?? Infinity)
    const passed = strokes <= (result.case.expected as Expected).par
    return { scorer: 'par', score: passed ? 1 : 0, passed, details: `${strokes} strokes` }
  },
}

const report = await runEval({
  dataset,
  createAgent: () => newGolfer(), // a fresh agent per case: no shared history
  scorers: [contains('Holed out'), path, par], // add llmJudge({ provider, rubric }) for fuzzy answers
})

console.log(report.summary) // { total: 3, passed: 2, passRate: 0.67, byScorer: { … } }

// Tracing and evals, 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
}

// 1. A span: something that took time, with what you want to know about it.
type Span = { name: string; ms: number; tokens?: number; error?: boolean }

type ToolFn = (args: Record<string, number>) => { output: string; isError?: boolean }
const tools: Record<string, ToolFn> = { drive, iron, putt } // your code: each swing moves the ball
const toolSchemas = [/* one JSON Schema per club: drive(meters), iron(meters), putt(meters) */]

type ToolCall = { id: string; 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 }

// 2. The loop from level 2, timing every model call and every tool run.
async function runAgent(prompt: string, maxTurns = 10) {
  const spans: Span[] = []
  const toolCalls: string[] = []
  const messages: Message[] = [{ role: 'user', content: prompt }]
  for (let turn = 1; turn <= maxTurns; turn++) {
    const start = performance.now()
    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 }),
    })
    const body = await res.json()
    spans.push({ name: 'model', ms: performance.now() - start, tokens: body.usage?.total_tokens })
    const [choice] = body.choices
    const reply: Message = choice.message
    messages.push(reply)
    if (choice.finish_reason !== 'tool_calls') return { output: reply.content ?? '', spans, toolCalls }

    for (const call of reply.tool_calls ?? []) {
      const t0 = performance.now()
      const run = tools[call.function.name]
      const result = run ? run(JSON.parse(call.function.arguments)) : { output: `Unknown tool: ${call.function.name}`, isError: true }
      spans.push({ name: call.function.name, ms: performance.now() - t0, error: result.isError })
      toolCalls.push(call.function.name)
      messages.push({ role: 'tool', tool_call_id: call.id, content: result.output })
    }
  }
  throw new Error(`No answer after ${maxTurns} turns`)
}

// 3. The eval: cases with what a good run looks like, and one function per scorer.
const cases = [
  { input: 'Hole 1: par 3, 150 m to the pin. Play it out and report your score.', par: 3, clubs: ['iron', 'putt'] },
  { input: 'Hole 2: par 4, 360 m to the pin. Play it out and report your score.', par: 4, clubs: ['drive', 'iron', 'putt'] },
  { input: 'Hole 3: par 4, 330 m to the pin. Water crosses the fairway at 200 m. Play it out and report your score.', par: 4, clubs: ['iron', 'iron', 'putt'] },
]
type Case = (typeof cases)[number]
type Run = Awaited<ReturnType<typeof runAgent>>

// Every expected club shows up, in order (extra swings allowed).
const inOrder = (expected: string[], actual: string[]): boolean => {
  let i = 0
  for (const name of actual) if (name === expected[i]) i++
  return i === expected.length
}
const scorers: Record<string, (run: Run, c: Case) => boolean> = {
  holed: (run) => run.output.includes('Holed out'),
  trajectory: (run, c) => inOrder(c.clubs, run.toolCalls),
  par: (run, c) => Number(/in (\d+)/.exec(run.output)?.[1] ?? Infinity) <= c.par,
}

let passed = 0
for (const c of cases) {
  const run = await runAgent(c.input) // every case starts with an empty history
  const checks = Object.entries(scorers).map(([name, score]) => [name, score(run, c)] as const)
  if (checks.every(([, ok]) => ok)) passed++
  console.log(c.input.slice(0, 6), checks, run.spans) // failed a check? its spans say why
}
console.log(`passRate ${(passed / cases.length).toFixed(2)}`)

# Tracing and evals, from scratch. Standard library only, no SDK.
import json
import re
import time
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
}


def post(path, payload):
    request = urllib.request.Request(
        f"{LLM['base_url']}{path}",
        data=json.dumps(payload).encode(),
        headers={"Content-Type": "application/json", "Authorization": f"Bearer {LLM['api_key']}"},
    )
    with urllib.request.urlopen(request) as response:
        return json.load(response)


TOOLS = {"drive": drive, "iron": iron, "putt": putt}  # your code: each returns (output, is_error)
TOOL_SCHEMAS = [...]  # one JSON Schema per club: drive(meters), iron(meters), putt(meters)


# 1 + 2. The loop from level 2, timing every model call and every tool run as a span.
def run_agent(prompt, max_turns=10):
    spans, tool_calls = [], []
    messages = [{"role": "user", "content": prompt}]

    for _ in range(max_turns):
        start = time.perf_counter()
        body = post("/chat/completions", {"model": LLM["model"], "messages": messages, "tools": TOOL_SCHEMAS})
        spans.append({"name": "model", "ms": (time.perf_counter() - start) * 1000, "tokens": body.get("usage", {}).get("total_tokens")})
        choice = body["choices"][0]
        reply = choice["message"]
        messages.append(reply)
        if choice["finish_reason"] != "tool_calls":
            return {"output": reply.get("content") or "", "spans": spans, "tool_calls": tool_calls}

        for call in reply.get("tool_calls", []):
            name = call["function"]["name"]
            t0 = time.perf_counter()
            output, is_error = TOOLS[name](**json.loads(call["function"]["arguments"]))
            spans.append({"name": name, "ms": (time.perf_counter() - t0) * 1000, "error": is_error})
            tool_calls.append(name)
            messages.append({"role": "tool", "tool_call_id": call["id"], "content": output})

    raise RuntimeError(f"No answer after {max_turns} turns")


# 3. The eval: cases with what a good run looks like, and one function per scorer.
CASES = [
    {"input": "Hole 1: par 3, 150 m to the pin. Play it out and report your score.", "par": 3, "clubs": ["iron", "putt"]},
    {"input": "Hole 2: par 4, 360 m to the pin. Play it out and report your score.", "par": 4, "clubs": ["drive", "iron", "putt"]},
    {
        "input": "Hole 3: par 4, 330 m to the pin. Water crosses the fairway at 200 m. Play it out and report your score.",
        "par": 4,
        "clubs": ["iron", "iron", "putt"],
    },
]


def in_order(expected, actual):
    """Every expected club shows up, in order (extra swings allowed)."""
    i = 0
    for name in actual:
        if i < len(expected) and name == expected[i]:
            i += 1
    return i == len(expected)


def strokes(output):
    match = re.search(r"in (\d+)", output)
    return int(match.group(1)) if match else float("inf")


SCORERS = {
    "holed": lambda run, case: "Holed out" in run["output"],
    "trajectory": lambda run, case: in_order(case["clubs"], run["tool_calls"]),
    "par": lambda run, case: strokes(run["output"]) <= case["par"],
}

passed = 0
for case in CASES:
    run = run_agent(case["input"])  # every case starts with an empty history
    checks = {name: score(run, case) for name, score in SCORERS.items()}
    passed += all(checks.values())
    print(case["input"][:6], checks, run["spans"])  # failed a check? its spans say why
print(f"passRate {passed / len(CASES):.2f}")