Coding Drills & Take-Home Exercises

import random, time

class Transient(Exception): ...
class Permanent(Exception): ...

def call_with_retry(fn, *, attempts=4, base=0.5, cap=20.0, sleep=time.sleep):
    for attempt in range(1, attempts + 1):
        try:
            return fn()
        except Permanent:
            raise                                   # never retry bad arguments / auth / policy
        except Transient:
            if attempt == attempts:
                raise
            delay = random.uniform(0, min(cap, base * 2 ** attempt))   # full jitter
            sleep(delay)

# What interviewers look for: classify errors, bound attempts, jitter, injectable sleep for tests,
# and a note that non-idempotent side effects need idempotency keys before retrying.

import heapq, math

def normalize(v):
    n = math.sqrt(sum(x * x for x in v)) or 1.0
    return [x / n for x in v]

def top_k(query, docs, k=5):
    """docs: list of (doc_id, vector). Returns [(score, doc_id)] best first."""
    q = normalize(query)
    scored = ((sum(a * b for a, b in zip(q, normalize(vec))), doc_id) for doc_id, vec in docs)
    return heapq.nlargest(k, scored)

# Discuss: pre-normalize vectors at index time so cosine == dot product; ANN indexes for large corpora;
# filter by tenant BEFORE scoring; handle zero vectors; k larger than the corpus.

def rrf(rankings: list[list[str]], k: int = 60) -> list[str]:
    """rankings: several best-first lists of doc ids (e.g. keyword results, vector results)."""
    scores: dict[str, float] = {}
    for ranking in rankings:
        for rank, doc_id in enumerate(ranking, start=1):
            scores[doc_id] = scores.get(doc_id, 0.0) + 1.0 / (k + rank)
    return sorted(scores, key=scores.get, reverse=True)

# Why rank-based? BM25 and cosine scores aren't on comparable scales; ranks are.
assert rrf([["a", "b", "c"], ["c", "a", "d"]])[0] == "a"

import json

class BudgetExceeded(Exception): ...

class Dispatcher:
    def __init__(self, tools, max_calls=10):
        self.tools, self.max_calls, self.calls = tools, max_calls, 0   # tools: name -> (validate_fn, run_fn)

    def run(self, name: str, raw_args: dict, principal) -> str:
        self.calls += 1
        if self.calls > self.max_calls:
            raise BudgetExceeded("tool-call budget exhausted")
        if name not in self.tools:
            return json.dumps({"error": "unknown_tool", "available": sorted(self.tools)})
        validate, run = self.tools[name]
        try:
            args = validate(raw_args)                      # raises ValueError on bad input
        except ValueError as e:
            return json.dumps({"error": "invalid_arguments", "detail": str(e)})
        if not principal.can(name, args):                  # authorization is code, not prompt
            return json.dumps({"error": "forbidden"})
        try:
            return json.dumps(run(**args))[:8000]          # bound the result size
        except Exception as e:
            return json.dumps({"error": type(e).__name__})

# Talk through: errors returned as tool results so the model can adapt; idempotency keys for writes; logging.

import re

def chunk_markdown(text: str, max_chars=1200, overlap=100):
    sections, path, buf = [], [], []
    def flush():
        if buf:
            sections.append((" > ".join(path), "\n".join(buf).strip())); buf.clear()
    for line in text.splitlines():
        m = re.match(r"^(#{1,6})\s+(.*)", line)
        if m:
            flush()
            level = len(m.group(1))
            path[:] = path[: level - 1] + [m.group(2).strip()]
        else:
            buf.append(line)
    flush()
    chunks = []
    for heading, body in sections:
        start = 0
        while start < len(body):
            piece = body[start : start + max_chars]
            chunks.append({"heading": heading, "text": f"{heading}\n\n{piece}"})
            if start + max_chars >= len(body):
                break
            start += max_chars - overlap
    return chunks

# Discuss: tokens vs characters, never splitting code fences/tables, metadata (source, page, ACL), parent-child retrieval.

import time

class TokenBucket:
    def __init__(self, rate_per_sec: float, capacity: float, now=time.monotonic):
        self.rate, self.capacity, self.now = rate_per_sec, capacity, now
        self.tokens, self.last = capacity, now()

    def allow(self, cost: float = 1.0) -> bool:
        t = self.now()
        self.tokens = min(self.capacity, self.tokens + (t - self.last) * self.rate)
        self.last = t
        if self.tokens >= cost:
            self.tokens -= cost
            return True
        return False

buckets: dict[str, TokenBucket] = {}
def allow(tenant_id: str, tokens_requested: int) -> bool:
    b = buckets.setdefault(tenant_id, TokenBucket(rate_per_sec=1000, capacity=20000))
    return b.allow(tokens_requested)     # meter LLM tokens, not just requests

# Discuss: distributed limiting (Redis), burst vs sustained, graceful degradation, fairness between tenants.