From f0278794590ace97fef605750fb806f79c3034e8 Mon Sep 17 00:00:00 2001 From: yatharth Date: Sun, 10 May 2026 00:28:02 -0700 Subject: [PATCH 1/3] feat: add candidate screening voice agent example A live voice screening interview that grounds every question in two Moss indexes - one for the job description, one for the candidate resume. Single agent, phased system prompt, two retrieval tools, and a structured scorecard JSON as the output artifact. What this demonstrates: - Multi-index retrieval: separate JD and resume indexes queried per turn, with both kept warm in-process for sub-10ms latency. - Adaptive questioning: every follow-up is grounded in resume facts rather than generic small-talk. - Bias mitigation as a structural property: the tool surface has no way to capture protected attributes, and the system prompt has an explicit "won't ask" list. - Consent capture as real session state, gating scorecard output. - Live rubric capture (not transcript reconstruction). - Deterministic eval suite for the scorecard logic. Three candidate fixtures included (strong / partial / junior) to exercise different recommendation outcomes against the same JD. --- examples/voice-agents/README.md | 13 +- .../candidate-screening/.env.example | 21 + .../candidate-screening/.gitignore | 2 + .../candidate-screening/README.md | 254 ++ .../voice-agents/candidate-screening/agent.py | 446 +++ .../candidate-screening/create_indexes.py | 121 + .../data/candidate_junior_reach.json | 57 + .../data/candidate_partial_match.json | 62 + .../data/candidate_strong_match.json | 62 + .../data/job_senior_backend_payments.json | 87 + .../evals/test_scorecards.py | 116 + .../candidate-screening/pyproject.toml | 17 + .../voice-agents/candidate-screening/uv.lock | 3145 +++++++++++++++++ 13 files changed, 4397 insertions(+), 6 deletions(-) create mode 100644 examples/voice-agents/candidate-screening/.env.example create mode 100644 examples/voice-agents/candidate-screening/.gitignore create mode 100644 examples/voice-agents/candidate-screening/README.md create mode 100644 examples/voice-agents/candidate-screening/agent.py create mode 100644 examples/voice-agents/candidate-screening/create_indexes.py create mode 100644 examples/voice-agents/candidate-screening/data/candidate_junior_reach.json create mode 100644 examples/voice-agents/candidate-screening/data/candidate_partial_match.json create mode 100644 examples/voice-agents/candidate-screening/data/candidate_strong_match.json create mode 100644 examples/voice-agents/candidate-screening/data/job_senior_backend_payments.json create mode 100644 examples/voice-agents/candidate-screening/evals/test_scorecards.py create mode 100644 examples/voice-agents/candidate-screening/pyproject.toml create mode 100644 examples/voice-agents/candidate-screening/uv.lock diff --git a/examples/voice-agents/README.md b/examples/voice-agents/README.md index 127f41f..3aa49d2 100644 --- a/examples/voice-agents/README.md +++ b/examples/voice-agents/README.md @@ -6,21 +6,22 @@ sub-10ms retrieval inside the speech loop. | Example | What it shows | |---|---| | [`mortgage-lending/`](mortgage-lending/) | Multi-agent flow: a retrieval-heavy mortgage Q&A agent hands off to a payment flow agent, with shared session state. | +| [`candidate-screening/`](candidate-screening/) | Two-index retrieval (job description + candidate resume) powering a live voice screening interview that emits a structured scorecard JSON. Single agent, phased prompt, bias-mitigation rules, deterministic eval suite. | -Each subfolder is self-contained — its own `pyproject.toml`, `.env.example`, +Each subfolder is self-contained - its own `pyproject.toml`, `.env.example`, and `README.md`. Pick one, follow its README, and you have a working voice agent in a few minutes. ## Why Moss for voice -A round-trip to a remote vector database costs 200–500ms — enough to make a +A round-trip to a remote vector database costs 200-500ms - enough to make a voice conversation feel laggy. Moss runs the retrieval **inside your agent process**, so the search itself disappears from the latency budget and the LLM gets grounded context before it speaks. ## Related examples elsewhere in this repo -- [`apps/livekit-moss-vercel/`](../../apps/livekit-moss-vercel/) — single-agent LiveKit voice agent on Vercel. -- [`apps/pipecat-moss/`](../../apps/pipecat-moss/) — Pipecat voice agent with Moss retrieval. -- [`apps/vapi-moss/`](../../apps/vapi-moss/) — Vapi voice agent with Moss retrieval. -- [`apps/elevenlabs-moss/`](../../apps/elevenlabs-moss/) — ElevenLabs voice agent with Moss retrieval. +- [`apps/livekit-moss-vercel/`](../../apps/livekit-moss-vercel/) - single-agent LiveKit voice agent on Vercel. +- [`apps/pipecat-moss/`](../../apps/pipecat-moss/) - Pipecat voice agent with Moss retrieval. +- [`apps/vapi-moss/`](../../apps/vapi-moss/) - Vapi voice agent with Moss retrieval. +- [`apps/elevenlabs-moss/`](../../apps/elevenlabs-moss/) - ElevenLabs voice agent with Moss retrieval. diff --git a/examples/voice-agents/candidate-screening/.env.example b/examples/voice-agents/candidate-screening/.env.example new file mode 100644 index 0000000..ce9178c --- /dev/null +++ b/examples/voice-agents/candidate-screening/.env.example @@ -0,0 +1,21 @@ +# LiveKit (only needed for `agent.py dev` - leave blank for `agent.py console`). +LIVEKIT_URL=wss://your-project.livekit.cloud +LIVEKIT_API_KEY= +LIVEKIT_API_SECRET= + +# Moss (sign up free at https://moss.dev) +MOSS_PROJECT_ID=your-moss-project-id +MOSS_PROJECT_KEY=your-moss-project-key + +# Index names. create_indexes.py picks these defaults from the fixture data; +# override only if you want a custom naming scheme. +# MOSS_JOB_INDEX_NAME=job-senior-backend-payments +# MOSS_CANDIDATE_INDEX_NAME=candidate-strong-match + +# Voice pipeline providers +OPENAI_API_KEY=your-openai-api-key +DEEPGRAM_API_KEY=your-deepgram-api-key +CARTESIA_API_KEY=your-cartesia-api-key + +# Where to write scorecards (default: ./scorecards). Created on demand. +# SCREENING_SCORECARD_DIR=./scorecards diff --git a/examples/voice-agents/candidate-screening/.gitignore b/examples/voice-agents/candidate-screening/.gitignore new file mode 100644 index 0000000..8e004b1 --- /dev/null +++ b/examples/voice-agents/candidate-screening/.gitignore @@ -0,0 +1,2 @@ +# Scorecards are runtime artifacts - don't commit them. +scorecards/ diff --git a/examples/voice-agents/candidate-screening/README.md b/examples/voice-agents/candidate-screening/README.md new file mode 100644 index 0000000..7b589f2 --- /dev/null +++ b/examples/voice-agents/candidate-screening/README.md @@ -0,0 +1,254 @@ +# Candidate Screening Voice Agent + +Live voice screening interview that grounds every question in **two** Moss +indexes: one for the job description, one for the candidate's resume. + +The agent runs a five-phase phone-screen - intro, background verification, +role-fit screening, candidate Q&A, close - and emits a structured **scorecard** +JSON at the end. Both retrievals stay in-process for sub-10ms latency, which +matters because each turn typically needs a JD lookup AND a resume lookup +before the agent can ask a good follow-up. + +``` + ┌──────────────────────┐ + candidate <──────► │ ScreeningAgent │ ◄────► scorecard.json + │ (one agent, phased) │ + └──────────┬───────────┘ + │ + ┌───────────────────┴───────────────────┐ + ▼ ▼ + ┌──────────────────┐ ┌──────────────────┐ + │ Moss: JD index │ │ Moss: resume idx │ + │ (15 docs) │ │ (10-12 docs) │ + └──────────────────┘ └──────────────────┘ + lookup_job_requirement lookup_resume_fact +``` + +## What this example demonstrates + +| Capability | Where to look | +|---|---| +| Multi-index retrieval (sub-10ms each) | `agent.py` - `lookup_job_requirement`, `lookup_resume_fact` | +| Adaptive questioning grounded in the resume | `agent.py` - phased system prompt | +| Bias-mitigation rules baked into the prompt + tool surface | `agent.py` - see SYSTEM_PROMPT | +| Consent capture as a real session-state field | `agent.py` - `record_consent`, gated on `submit_scorecard` | +| Live rubric capture (not transcript reconstruction) | `agent.py` - `record_rubric_entry`, `RubricEntry` | +| Structured scorecard artifact | `agent.py` - `_build_scorecard` + `scorecards/*.json` | +| Deterministic eval suite | `evals/test_scorecards.py` | + +## Why two indexes + +A single combined index would mash JD requirements together with the +candidate's history, and retrieval would conflate them. Two indexes let +the agent ask: + +> *"The role calls for Postgres at scale - your resume mentions a logical +> replication migration at Topograph. Walk me through the cutover."* + +That sentence requires one query against the JD ("postgres requirement") +and one against the resume ("postgres experience"). With Moss in-process +both happen in single-digit milliseconds, so the LLM gets context before +the candidate notices any pause. + +## Prerequisites + +- Python 3.10+ +- [uv](https://github.com/astral-sh/uv) (or `pip` + `venv`) +- A [Moss](https://moss.dev) project (`MOSS_PROJECT_ID`, `MOSS_PROJECT_KEY`) +- API keys for your voice providers: + - [OpenAI](https://platform.openai.com) - LLM + - [Deepgram](https://deepgram.com) - speech-to-text + - [Cartesia](https://play.cartesia.ai) - text-to-speech +- For browser-based testing only: a [LiveKit](https://livekit.io) project. + **Not required for console mode.** + +## Setup + +1. **Install dependencies** + + ```bash + uv sync + ``` + +2. **Configure environment** + + ```bash + cp .env.example .env + ``` + + Fill in your Moss credentials and provider keys. + +3. **Build the two indexes** + + ```bash + # Default: senior-backend-payments JD + the strong-match candidate + uv run python create_indexes.py + + # Or pick a different candidate fixture: + uv run python create_indexes.py --candidate partial + uv run python create_indexes.py --candidate junior + uv run python create_indexes.py --list + ``` + + The fixtures live in `data/`. Edit them to point at your own JD or + candidate, or add new ones. + +4. **Run the agent - pick one of two modes** + + ### A) Console mode - talk in your terminal (fastest) + + ```bash + uv run python agent.py console + ``` + + The agent grabs your mic and speakers directly. Start talking as soon + as you see the prompt. No LiveKit server, no browser, no frontend. + + ### B) Dev mode - connect from a browser + + ```bash + uv run python agent.py dev + ``` + + Connect from the [LiveKit Agents Playground](https://agents-playground.livekit.io) + or a custom React frontend. + +## A working interview, condensed + +``` +Agent: Hi, I'm calling about your application for the Senior Backend + Engineer role on the Lattica Payments team. This is a short + voice screening - about twenty-five minutes. The conversation + is recorded and reviewed by the hiring team - okay to continue? +Candidate: Yes, that works. + [calls record_consent(consented=True)] + +Agent: Great. I see from your resume you led the rewrite of the + settlement pipeline at Northwind Pay - walk me through what + drove that decision. + [calls lookup_resume_fact("settlement pipeline rewrite") + -> "Led the rewrite of Northwind's daily settlement pipeline..."] + +Candidate: We were getting 3-5 errors every settlement day, sometimes + more. The old script had no idempotency, so retries created + duplicate ledger entries we'd have to manually reconcile... + +Agent: The role also calls for production Postgres at scale - what's + a non-trivial Postgres incident you've debugged? + [calls lookup_job_requirement("postgres requirement") + then lookup_resume_fact("postgres experience")] + + [later, after the answer, calls record_rubric_entry( + skill="postgres", score=4, + evidence="Walked through pg_stat_activity lock contention + diagnosis and explained the EXPLAIN tradeoff.")] + +... + +Agent: Anything you'd like to ask me about the role or the team? +Candidate: What does on-call look like? + [calls lookup_job_requirement("on-call rotation") + -> "Required: willingness to participate in an on-call rotation, + roughly one week every six..."] + +Agent: The team is one-week-on every six. Pages are rare but high-stakes + when they happen - engineers triage and either fix or escalate + within fifteen minutes. + [calls record_candidate_question( + question="What does on-call look like?", + answer_summary="1 week in 6, rare pages, 15-min triage SLO")] + +Agent: Thanks, Priya. The team will review your scorecard within + three business days and reach out about next steps. + [calls submit_scorecard() -> ./scorecards/strong-match__1234.json] +``` + +## The scorecard + +Written to `scorecards/__.json` when the agent calls +`submit_scorecard`: + +```json +{ + "candidate_id": "strong-match", + "role_id": "senior-backend-payments", + "duration_sec": 1620, + "rubric": { + "python": {"score": 5, "evidence": "..."}, + "postgres": {"score": 4, "evidence": "..."}, + "payments_domain": {"score": 5, "evidence": "..."}, + "distributed": {"score": 4, "evidence": "..."}, + "on_call": {"score": 4, "evidence": "..."} + }, + "candidate_questions": [ + {"question": "...", "answer_summary": "..."} + ], + "notes": [], + "recommendation": "advance_to_technical", + "schema_version": 1 +} +``` + +`recommendation` is a rule-of-thumb derived from the rubric; the hiring team +makes the real call. + +## Bias mitigation + +The system prompt includes an explicit "will not ask, infer, or capture" +list covering protected attributes (age, family status, religion, etc.). +The tool surface is the second line of defence - there is no +`record_protected_attribute` tool, so even if the LLM somehow elicited a +protected fact, the rubric and notes are bounded by what the schemas +allow. + +If the candidate volunteers a protected attribute, the agent acknowledges +briefly and moves on without capturing it. + +## Running the eval suite + +The deterministic parts of the agent (rubric capture, recommendation +rules, scorecard shape) are covered by pytest: + +```bash +uv run pytest evals/ +``` + +Eight tests, no LLM or live Moss required. Treat this as the floor - +add scenario tests with real fixtures when you wire CI. + +## Files + +```text +candidate-screening/ +├── agent.py # ScreeningAgent + scorecard +├── create_indexes.py # Build JD + resume indexes +├── data/ +│ ├── job_senior_backend_payments.json +│ ├── candidate_strong_match.json +│ ├── candidate_partial_match.json +│ └── candidate_junior_reach.json +├── evals/ +│ └── test_scorecards.py # Deterministic scorecard tests +├── scorecards/ # Runtime output (gitignored) +├── pyproject.toml +├── .env.example +└── README.md +``` + +## Customizing + +| Want to change | Edit | +|---|---| +| The role being screened | `data/job_*.json`, then re-run `create_indexes.py` | +| The candidate | `data/candidate_*.json` (or use `--candidate `) | +| Phase timing or rubric | `SYSTEM_PROMPT` in `agent.py` | +| Scorecard schema | `_build_scorecard` and `_recommendation_from_rubric` | +| Voice / latency | STT/LLM/TTS choices in `entrypoint` | + +## Resources + +- [Moss docs](https://docs.moss.dev) +- [Moss llms.txt](https://moss.dev/llms.txt) +- [LiveKit Agents docs](https://docs.livekit.io/agents/) +- [Moss GitHub](https://github.com/usemoss/moss) +- [Discord](https://discord.com/invite/eMXExuafBR) diff --git a/examples/voice-agents/candidate-screening/agent.py b/examples/voice-agents/candidate-screening/agent.py new file mode 100644 index 0000000..7c1cd40 --- /dev/null +++ b/examples/voice-agents/candidate-screening/agent.py @@ -0,0 +1,446 @@ +""" +Candidate Screening Voice Agent +=============================== + +A live voice screening interview that grounds every question in two Moss +indexes: + + * a job description index (e.g. ``job-senior-backend-payments``) + * a candidate resume index (e.g. ``candidate-strong-match``) + +The agent runs a five-phase interview - intro/consent, background +verification, role-fit screening, candidate Q&A, and close - and emits +a structured scorecard at the end. Both retrievals stay in-process for +sub-10ms latency, which matters because each turn typically needs at +least one resume lookup AND one JD lookup before the LLM can ask a good +follow-up. + +Design notes: + - One agent, phased system prompt. Phases are content, not personas. + - Two retrieval tools (`lookup_job_requirement`, `lookup_resume_fact`) + so logs make the asymmetry visible. + - Rubric scores are captured as the conversation happens, not + reconstructed from a transcript at the end. + - Bias-mitigation rules are baked into the system prompt and the + set of available tools (no tool to capture protected attributes). +""" + +from __future__ import annotations + +import json +import logging +import os +import time +from dataclasses import dataclass, field +from pathlib import Path +from typing import Optional + +from dotenv import load_dotenv +from livekit.agents import ( + Agent, + AgentSession, + JobContext, + RunContext, + WorkerOptions, + cli, + function_tool, +) +from livekit.plugins import cartesia, deepgram, openai, silero + +from moss import MossClient, QueryOptions + +load_dotenv() + +JOB_INDEX = os.getenv("MOSS_JOB_INDEX_NAME", "job-senior-backend-payments") +CANDIDATE_INDEX = os.getenv("MOSS_CANDIDATE_INDEX_NAME", "candidate-strong-match") +SCORECARD_DIR = Path(os.getenv("SCREENING_SCORECARD_DIR", "./scorecards")) + +logging.getLogger("livekit").setLevel(logging.WARNING) +logging.getLogger("livekit.agents").setLevel(logging.WARNING) +logger = logging.getLogger("candidate-screening") +logger.setLevel(logging.INFO) + +CYAN = "\033[96m" +GREEN = "\033[92m" +YELLOW = "\033[93m" +MAGENTA = "\033[95m" +RESET = "\033[0m" + + +def _require_env(name: str) -> str: + value = os.getenv(name) + if not value: + raise RuntimeError( + f"Missing required environment variable: {name}. " + f"See .env.example for the full list of keys this example needs." + ) + return value + + +# --------------------------------------------------------------------------- +# Shared session state +# --------------------------------------------------------------------------- + + +@dataclass +class RubricEntry: + """One row of the scorecard.""" + + score: int # 1-5 + evidence: str # the candidate's words, paraphrased + skill: str # tag drawn from the JD, e.g. "postgres" + + +@dataclass +class CandidateQuestion: + """A question the candidate asked the agent during Q&A.""" + + question: str + answer_summary: str + + +@dataclass +class ScreeningSessionData: + """All state gathered during the screening, used to build the scorecard. + + `moss_client` rides on userdata so anything we add later (a follow-up + agent, a re-ranker, a recovery handler) can reuse the warm in-process + indexes instead of constructing a fresh client. + """ + + candidate_id: str + role_id: str + consent_to_record: Optional[bool] = None + started_at: float = field(default_factory=time.time) + rubric: dict[str, RubricEntry] = field(default_factory=dict) + candidate_questions: list[CandidateQuestion] = field(default_factory=list) + notes: list[str] = field(default_factory=list) + moss_client: Optional[MossClient] = None + + +# --------------------------------------------------------------------------- +# The screening agent +# --------------------------------------------------------------------------- + + +SYSTEM_PROMPT = """You are a voice screening interviewer. You speak with +candidates who have applied to a specific role. You have two retrieval tools: + + * lookup_job_requirement(query) - searches the JOB DESCRIPTION index + * lookup_resume_fact(query) - searches the CANDIDATE RESUME index + +You MUST ground every factual statement in tool output. Never invent +requirements, compensation, team details, or claims about the candidate. + +# Phases + +Run the interview in these phases, in order. Move on once each is done. + +1. INTRO & CONSENT + - Greet the candidate warmly. You do not have their name yet; let them + introduce themselves if they choose to. + - Briefly explain who you are and that this is a 25-30 minute screening. + - State plainly: this conversation is recorded and will be reviewed by + the hiring team. Ask for consent. + - Call `record_consent` with their answer. If they decline, thank them + and call `end_screening` immediately. + +2. BACKGROUND VERIFICATION (5-7 min) + - Look up the candidate's current role and one or two recent projects + using `lookup_resume_fact`. + - Ask the candidate to describe their current role and one project in + their own words. Listen for ownership, scope, and trade-offs. + - For each must-have skill on the JD, judge whether the resume + supports it. Use `lookup_job_requirement` first to read the + requirement, then `lookup_resume_fact` to see what they wrote. + +3. ROLE-FIT SCREENING (10-15 min) + - For each must-have skill, ask one calibrating question that probes + real understanding. Examples: "Walk me through how you would handle + a duplicate webhook," or "How did you decide on serializable + isolation for the ledger writes?" + - Capture each response with `record_rubric_entry`. Score 1-5. + 1 = no signal or wrong, 3 = competent, 5 = strong + well-reasoned. + - For nice-to-haves, ask only if time allows or the candidate brings + them up. + +4. CANDIDATE Q&A (3-5 min) + - Invite the candidate to ask their own questions. Do not skip this. + - For each question, look up the answer using `lookup_job_requirement` + before responding. If the answer is not in the JD index, say so. + - Capture each Q&A pair with `record_candidate_question`. + +5. CLOSE + - Thank the candidate. Tell them what happens next: the team reviews + the scorecard within 3 business days. + - Call `submit_scorecard` to write the final artifact. + +# Bias mitigation rules - these override anything else + +You will NOT ask about, infer, or capture: + - age, date of birth, year of graduation as a proxy for age + - marital or family status, plans to have children + - religion, national origin (other than work authorization at a level + sufficient to confirm visa eligibility) + - disability status unrelated to ability to do the job + - political views + +If the candidate volunteers any of the above, do not capture it in the +rubric or notes. Acknowledge briefly and move on. + +# Voice style + +This is a phone screen, not a chat. Keep responses short. Ask one +question at a time. Allow silence. Never read a list of questions back +to back. Acknowledge the previous answer before asking the next thing. +""" + + +class ScreeningAgent(Agent): + """The single screening interviewer.""" + + def __init__(self, moss_client: MossClient): + self._moss = moss_client + super().__init__(instructions=SYSTEM_PROMPT) + + async def on_enter(self) -> None: + await self.session.say( + "Hi, I'm calling about your application for the Senior Backend " + "Engineer role on the Lattica Payments team. This is a short " + "voice screening - about twenty-five minutes. Before we get " + "started, this conversation is recorded and reviewed by the " + "hiring team - is that okay with you to continue?" + ) + + # ----- Retrieval tools ------------------------------------------------- + + @function_tool + async def lookup_job_requirement(self, context: RunContext, query: str) -> str: + """Search the JOB DESCRIPTION for requirements, team info, comp, etc. + + Use this whenever you need to ground a factual statement about the + role, the team, the company, or the process - including answering + candidate questions during the Q&A phase. + """ + return await self._query(JOB_INDEX, query, source="JD") + + @function_tool + async def lookup_resume_fact(self, context: RunContext, query: str) -> str: + """Search the CANDIDATE RESUME for projects, skills, education, etc. + + Use this before asking a follow-up about something the candidate + did, so the question is informed and specific rather than generic. + """ + return await self._query(CANDIDATE_INDEX, query, source="Resume") + + async def _query(self, index: str, query: str, source: str) -> str: + logger.info(f"{CYAN}[{source}] query:{RESET} {query}") + try: + results = await self._moss.query( + index, query, QueryOptions(top_k=4, alpha=0.75) + ) + if not results.docs: + logger.info(f"{YELLOW}[{source}] no results{RESET}") + return f"No relevant {source.lower()} content found." + + logger.info( + f"{GREEN}[{source}] {len(results.docs)} docs in " + f"{results.time_taken_ms}ms{RESET}" + ) + for i, doc in enumerate(results.docs, 1): + preview = doc.text[:140] + "..." if len(doc.text) > 140 else doc.text + logger.info(f"{GREEN} [{i}] {preview}{RESET}") + return "\n".join(f"- {d.text}" for d in results.docs) + except Exception as e: + logger.error(f"[{source}] query failed: {e}", exc_info=True) + return f"{source} retrieval failed; ask the candidate to repeat or rephrase." + + # ----- State capture tools -------------------------------------------- + + @function_tool + async def record_consent(self, context: RunContext, consented: bool) -> str: + """Record the candidate's consent to be recorded. + + Call this once, immediately after asking for consent in the intro. + If consented is false, the screening must end politely. + """ + data: ScreeningSessionData = self.session.userdata + data.consent_to_record = consented + logger.info(f"{MAGENTA}consent recorded: {consented}{RESET}") + return "Consent captured." if consented else "Consent declined; end the screening." + + @function_tool + async def record_rubric_entry( + self, + context: RunContext, + skill: str, + score: int, + evidence: str, + ) -> str: + """Record one rubric row from the candidate's response. + + Args: + skill: the JD skill tag, e.g. "python", "postgres", "payments_domain". + score: 1-5. 1=no signal/wrong, 3=competent, 5=strong with reasoning. + evidence: a short paraphrase of what the candidate actually said. + """ + if not 1 <= score <= 5: + return "Score must be between 1 and 5." + data: ScreeningSessionData = self.session.userdata + data.rubric[skill] = RubricEntry(score=score, evidence=evidence.strip(), skill=skill) + logger.info( + f"{MAGENTA}rubric: {skill}={score} - " + f"{evidence[:80]}{'...' if len(evidence) > 80 else ''}{RESET}" + ) + return f"Recorded {skill}={score}." + + @function_tool + async def record_candidate_question( + self, + context: RunContext, + question: str, + answer_summary: str, + ) -> str: + """Record a question the candidate asked during Q&A. + + Capture only what the candidate genuinely asked, paraphrased + briefly. Do not capture protected attributes if the candidate + accidentally raised one. + """ + data: ScreeningSessionData = self.session.userdata + data.candidate_questions.append( + CandidateQuestion(question=question.strip(), answer_summary=answer_summary.strip()) + ) + logger.info(f"{MAGENTA}candidate Q: {question[:80]}{RESET}") + return "Question logged." + + @function_tool + async def add_note(self, context: RunContext, note: str) -> str: + """Add a free-form note that will appear on the scorecard. + + Use sparingly: anything important should be a rubric entry. Notes + are for context the rubric can't capture (e.g. "candidate asked + thoughtful follow-ups about reliability"). + """ + data: ScreeningSessionData = self.session.userdata + data.notes.append(note.strip()) + logger.info(f"{MAGENTA}note: {note[:80]}{RESET}") + return "Note added." + + # ----- Closing tools -------------------------------------------------- + + @function_tool + async def submit_scorecard(self, context: RunContext) -> str: + """Build and write the final scorecard for the hiring team. + + Call this once, at the very end of the screening. Requires + consent_to_record=True. + """ + data: ScreeningSessionData = self.session.userdata + if data.consent_to_record is not True: + return "Cannot submit a scorecard without recorded consent." + + SCORECARD_DIR.mkdir(parents=True, exist_ok=True) + path = SCORECARD_DIR / f"{data.candidate_id}__{int(data.started_at)}.json" + scorecard = _build_scorecard(data) + path.write_text(json.dumps(scorecard, indent=2)) + logger.info(f"{GREEN}scorecard written: {path}{RESET}") + return f"Scorecard written to {path}. Tell the candidate a human will review it within 3 business days." + + @function_tool + async def end_screening(self, context: RunContext, reason: str) -> str: + """End the screening immediately without producing a scorecard. + + Use only if the candidate declined consent or asked to stop. + """ + logger.info(f"{YELLOW}screening ended early: {reason}{RESET}") + return "Thank the candidate, end politely, and stop." + + +# --------------------------------------------------------------------------- +# Scorecard +# --------------------------------------------------------------------------- + + +def _recommendation_from_rubric(rubric: dict[str, RubricEntry]) -> str: + """Crude rule-of-thumb. The hiring team makes the real call.""" + if not rubric: + return "no_signal" + scores = [entry.score for entry in rubric.values()] + avg = sum(scores) / len(scores) + low_count = sum(1 for s in scores if s <= 2) + if avg >= 4.0 and low_count == 0: + return "advance_to_technical" + if avg >= 3.0 and low_count <= 1: + return "borderline_review" + return "do_not_advance" + + +def _build_scorecard(data: ScreeningSessionData) -> dict: + duration = int(time.time() - data.started_at) + return { + "candidate_id": data.candidate_id, + "role_id": data.role_id, + "duration_sec": duration, + "rubric": { + skill: {"score": entry.score, "evidence": entry.evidence} + for skill, entry in data.rubric.items() + }, + "candidate_questions": [ + {"question": q.question, "answer_summary": q.answer_summary} + for q in data.candidate_questions + ], + "notes": data.notes, + "recommendation": _recommendation_from_rubric(data.rubric), + "schema_version": 1, + } + + +# --------------------------------------------------------------------------- +# Entrypoint +# --------------------------------------------------------------------------- + + +async def entrypoint(ctx: JobContext): + await ctx.connect() + + moss_client = MossClient( + _require_env("MOSS_PROJECT_ID"), + _require_env("MOSS_PROJECT_KEY"), + ) + for index in (JOB_INDEX, CANDIDATE_INDEX): + try: + await moss_client.load_index(index) + logger.info(f"loaded Moss index: {index}") + except Exception as e: + logger.warning( + f"failed to load index '{index}': {e}. " + f"Run create_indexes.py first." + ) + + candidate_id = os.getenv("SCREENING_CANDIDATE_ID", CANDIDATE_INDEX.removeprefix("candidate-")) + role_id = os.getenv("SCREENING_ROLE_ID", JOB_INDEX.removeprefix("job-")) + + userdata = ScreeningSessionData( + candidate_id=candidate_id, + role_id=role_id, + moss_client=moss_client, + ) + + session: AgentSession[ScreeningSessionData] = AgentSession[ScreeningSessionData]( + userdata=userdata, + stt=deepgram.STT(model="nova-2"), + llm=openai.LLM(model="gpt-4o"), + tts=cartesia.TTS(model="sonic-3-2026-01-12"), + vad=silero.VAD.load(), + turn_handling={"interruption": {"mode": "vad"}}, + ) + + await session.start( + agent=ScreeningAgent(moss_client), + room=ctx.room, + ) + + +if __name__ == "__main__": + cli.run_app(WorkerOptions(entrypoint_fnc=entrypoint)) diff --git a/examples/voice-agents/candidate-screening/create_indexes.py b/examples/voice-agents/candidate-screening/create_indexes.py new file mode 100644 index 0000000..932bf3c --- /dev/null +++ b/examples/voice-agents/candidate-screening/create_indexes.py @@ -0,0 +1,121 @@ +"""Build the Moss indexes for the candidate screening voice agent. + +Two indexes power the screening: + - one for the job description (default name: ``job-senior-backend-payments``) + - one for the candidate's resume (default name: ``candidate-strong-match``) + +Both fixtures live in ``data/`` as flat JSON arrays of ``{id, text, metadata}`` +documents - the native Moss shape. Index names are derived from the +fixture filename, with the leading ``job_`` / ``candidate_`` and the +``.json`` suffix stripped, then underscores swapped for dashes. + +Run before starting the agent: + + uv run python create_indexes.py # default JD + strong-match candidate + uv run python create_indexes.py --candidate partial # use a different candidate fixture + +Use ``--list`` to see the available fixtures. +""" + +from __future__ import annotations + +import argparse +import asyncio +import json +import os +from pathlib import Path + +from dotenv import load_dotenv +from moss import DocumentInfo, MossClient + +load_dotenv() + +DATA_DIR = Path(__file__).parent / "data" + +JOB_FIXTURE = "job_senior_backend_payments.json" +CANDIDATE_FIXTURES = { + "strong": "candidate_strong_match.json", + "partial": "candidate_partial_match.json", + "junior": "candidate_junior_reach.json", +} + + +def _require_env(name: str) -> str: + value = os.getenv(name) + if not value: + raise RuntimeError( + f"Missing required environment variable: {name}. " + f"See .env.example for the full list of keys this example needs." + ) + return value + + +def _index_name_from_filename(filename: str) -> str: + """``candidate_strong_match.json`` -> ``candidate-strong-match``.""" + stem = Path(filename).stem + return stem.replace("_", "-") + + +def _load_documents(filename: str) -> list[DocumentInfo]: + with (DATA_DIR / filename).open() as f: + raw = json.load(f) + return [ + DocumentInfo( + id=doc["id"], + text=doc["text"], + metadata=doc.get("metadata", {}), + ) + for doc in raw + ] + + +async def build_indexes(candidate_key: str) -> tuple[str, str]: + """Create both indexes and return (job_index_name, candidate_index_name).""" + project_id = _require_env("MOSS_PROJECT_ID") + project_key = _require_env("MOSS_PROJECT_KEY") + client = MossClient(project_id, project_key) + + candidate_filename = CANDIDATE_FIXTURES[candidate_key] + job_index = os.getenv("MOSS_JOB_INDEX_NAME", _index_name_from_filename(JOB_FIXTURE)) + candidate_index = os.getenv( + "MOSS_CANDIDATE_INDEX_NAME", + _index_name_from_filename(candidate_filename), + ) + + job_docs = _load_documents(JOB_FIXTURE) + candidate_docs = _load_documents(candidate_filename) + + print(f"Creating job index '{job_index}' with {len(job_docs)} documents...") + await client.create_index(job_index, job_docs) + + print(f"Creating candidate index '{candidate_index}' with {len(candidate_docs)} documents...") + await client.create_index(candidate_index, candidate_docs) + + print("Both indexes ready.") + print(f" MOSS_JOB_INDEX_NAME={job_index}") + print(f" MOSS_CANDIDATE_INDEX_NAME={candidate_index}") + return job_index, candidate_index + + +def main() -> None: + parser = argparse.ArgumentParser(description="Build Moss indexes for the candidate screening demo.") + parser.add_argument( + "--candidate", + choices=list(CANDIDATE_FIXTURES), + default="strong", + help="Which candidate fixture to index (default: strong).", + ) + parser.add_argument("--list", action="store_true", help="List available fixtures and exit.") + args = parser.parse_args() + + if args.list: + print("Available candidate fixtures:") + for key, filename in CANDIDATE_FIXTURES.items(): + print(f" {key:8} -> {filename:40} -> index '{_index_name_from_filename(filename)}'") + return + + asyncio.run(build_indexes(args.candidate)) + + +if __name__ == "__main__": + main() diff --git a/examples/voice-agents/candidate-screening/data/candidate_junior_reach.json b/examples/voice-agents/candidate-screening/data/candidate_junior_reach.json new file mode 100644 index 0000000..3569fd5 --- /dev/null +++ b/examples/voice-agents/candidate-screening/data/candidate_junior_reach.json @@ -0,0 +1,57 @@ +[ + { + "id": "summary", + "text": "Mid-level backend engineer with three years of professional experience. Comfortable with Python and Postgres for typical web-app workloads. Applying to senior roles because I am ambitious and ready to grow, even if the role is a stretch.", + "metadata": {"section": "summary"} + }, + { + "id": "current-role", + "text": "Currently a Software Engineer at Bookstack (Series A, ~40 employees, B2B SaaS). I have been here for two and a half years and was recently promoted from junior to mid-level. I work on the API and integrations team.", + "metadata": {"section": "experience", "company": "Bookstack", "current": true} + }, + { + "id": "stripe-integration-project", + "text": "Shipped the Stripe billing integration at Bookstack. Wrote the webhook handlers and the subscription state machine on top of Stripe's APIs. This is the closest I have come to working on payments, although Stripe abstracts most of the hard parts.", + "metadata": {"section": "project", "skill": "payments_domain"} + }, + { + "id": "skills-python", + "text": "Python: three years of production experience. Have worked with Django, FastAPI, and Celery. Comfortable with the language but I am still learning the deeper performance and async corners.", + "metadata": {"section": "skills", "skill": "python"} + }, + { + "id": "skills-postgres", + "text": "Postgres: three years of experience as a consumer of the database. Have written reasonably complex queries and added indexes when ORM-generated queries were slow. Have not run Postgres in production myself.", + "metadata": {"section": "skills", "skill": "postgres"} + }, + { + "id": "no-distributed-systems-depth", + "text": "Distributed systems: limited direct experience. Have used Redis as a queue and Celery for background jobs but have not designed a system that needs strict idempotency or exactly-once semantics. Eager to learn.", + "metadata": {"section": "gaps", "skill": "distributed_systems"} + }, + { + "id": "no-kafka", + "text": "Kafka: no production experience. I have read the documentation and built a toy producer-consumer in a weekend project but have not run Kafka under real load.", + "metadata": {"section": "gaps", "skill": "kafka"} + }, + { + "id": "first-job", + "text": "First job out of college was as a Junior Engineer at Pageturn, a small ed-tech company. Worked there for six months on the front-end before moving to backend at Bookstack.", + "metadata": {"section": "experience", "company": "Pageturn"} + }, + { + "id": "on-call-experience", + "text": "On-call: Bookstack does not have a formal on-call rotation. The senior engineers handle pages. I have helped triage a few incidents during business hours but have never been the primary responder.", + "metadata": {"section": "skills", "skill": "on_call"} + }, + { + "id": "education", + "text": "BS in Mathematics from a state university, 2022. Took a few computer science classes but most of my software skills are self-taught and on-the-job.", + "metadata": {"section": "education"} + }, + { + "id": "motivation", + "text": "Motivation for applying: I want to work on something with high stakes where correctness matters more than speed of feature output. Payments feels like the right environment to grow into a real senior engineer.", + "metadata": {"section": "motivation"} + } +] diff --git a/examples/voice-agents/candidate-screening/data/candidate_partial_match.json b/examples/voice-agents/candidate-screening/data/candidate_partial_match.json new file mode 100644 index 0000000..31f203d --- /dev/null +++ b/examples/voice-agents/candidate-screening/data/candidate_partial_match.json @@ -0,0 +1,62 @@ +[ + { + "id": "summary", + "text": "Senior infrastructure engineer with eight years of experience building distributed systems at scale. Strong systems and Python background but no direct payments or financial-domain work. Looking to move from infrastructure into product engineering with real customer-facing impact.", + "metadata": {"section": "summary"} + }, + { + "id": "current-role", + "text": "Currently a Senior Engineer at Topograph (Series C, ~300 employees, geospatial analytics). On the platform team. I own the data pipeline that ingests and processes around two terabytes of geospatial data per day for analytics customers.", + "metadata": {"section": "experience", "company": "Topograph", "current": true} + }, + { + "id": "kafka-pipeline-project", + "text": "Built and operated Topograph's Kafka-based ingestion pipeline. The pipeline processes roughly 40,000 events per second at peak with end-to-end latency under two seconds. I designed the partitioning scheme and wrote the consumers in Python and Go.", + "metadata": {"section": "project", "skill": "kafka"} + }, + { + "id": "postgres-migration-project", + "text": "Led a multi-quarter Postgres migration that moved Topograph's metadata store from a self-managed Postgres on EC2 to RDS Aurora with logical replication for cutover. Zero downtime, zero data loss, and trimmed monthly database spend by 35 percent.", + "metadata": {"section": "project", "skill": "postgres"} + }, + { + "id": "previous-role-cloudbase", + "text": "Previously a Software Engineer at Cloudbase (an internal-tools company). Worked on the deployment platform team for three years. Wrote a lot of Python and got my first real exposure to running services in production.", + "metadata": {"section": "experience", "company": "Cloudbase"} + }, + { + "id": "skills-python", + "text": "Python: eight years of production experience. Heavy use of asyncio for I/O-bound services, FastAPI for internal APIs, and a fair amount of typed Python with mypy. Comfortable with modern tooling.", + "metadata": {"section": "skills", "skill": "python"} + }, + { + "id": "skills-postgres", + "text": "Postgres: six years. Comfortable with the operational side, including replication, point-in-time recovery, and tuning for read-heavy workloads. Less comfortable with the transactional-correctness corners that I imagine matter for a ledger.", + "metadata": {"section": "skills", "skill": "postgres"} + }, + { + "id": "skills-kafka", + "text": "Kafka: four years of production experience as both a consumer-author and an operator. Have written consumers in Python, Go, and a small amount of Rust. Have debugged consumer lag, rebalances, and ZooKeeper to KRaft migrations.", + "metadata": {"section": "skills", "skill": "kafka"} + }, + { + "id": "skills-go", + "text": "Go: two years of production experience. Wrote a number of small services and a CLI tool. Comfortable enough to be productive but I would not call myself an expert.", + "metadata": {"section": "skills", "skill": "go"} + }, + { + "id": "no-payments-experience", + "text": "Have not worked in payments, banking, or any other regulated financial domain. Familiar with concepts like idempotency and double-entry accounting from reading and side reading, but I have not shipped production payments code.", + "metadata": {"section": "gaps", "skill": "payments_domain"} + }, + { + "id": "on-call-experience", + "text": "On-call: have been on rotation at both companies. Topograph is one week every five. I do not love it but I do not dread it either; I treat it as part of the job.", + "metadata": {"section": "skills", "skill": "on_call"} + }, + { + "id": "education", + "text": "BS in Computer Engineering from the University of Texas at Austin, 2016. Worked on a research project on distributed consensus in undergrad which is still my favorite area.", + "metadata": {"section": "education"} + } +] diff --git a/examples/voice-agents/candidate-screening/data/candidate_strong_match.json b/examples/voice-agents/candidate-screening/data/candidate_strong_match.json new file mode 100644 index 0000000..5981866 --- /dev/null +++ b/examples/voice-agents/candidate-screening/data/candidate_strong_match.json @@ -0,0 +1,62 @@ +[ + { + "id": "summary", + "text": "Senior backend engineer with seven years of production experience. The last four years have been on payments and ledger systems at two fintech startups. Comfortable owning critical money-movement code end to end, from API design through audit-ready logging.", + "metadata": {"section": "summary"} + }, + { + "id": "current-role", + "text": "Currently a Senior Backend Engineer at Northwind Pay (Series A, ~80 employees, $400M annualized payment volume). Joined as employee number 22 in early 2024. I lead the ledger and reconciliation services and recently shipped a rewrite of the settlement pipeline.", + "metadata": {"section": "experience", "company": "Northwind Pay", "current": true} + }, + { + "id": "settlement-rewrite-project", + "text": "Led the rewrite of Northwind's daily settlement pipeline from a Cron-driven Python script to a Temporal workflow with Postgres-backed idempotency. Reduced settlement-day errors by 90 percent and cut the close-of-books window from six hours to under one hour.", + "metadata": {"section": "project", "skill": "distributed_systems"} + }, + { + "id": "ledger-design", + "text": "Designed and implemented Northwind's double-entry ledger from scratch. Used Postgres with serializable isolation for the entry-write path and an event-sourced read model for reporting. Authored the design document and got sign-off from compliance and the auditor.", + "metadata": {"section": "project", "skill": "payments_domain"} + }, + { + "id": "previous-role-acme", + "text": "Previously a Backend Engineer at Acme Issuing (acquired by Marqeta in 2022). Joined as a mid-level engineer and was promoted to senior in 18 months. Worked on the card authorization path and ISO 8583 message handling.", + "metadata": {"section": "experience", "company": "Acme Issuing"} + }, + { + "id": "iso8583-experience", + "text": "At Acme Issuing I owned the ISO 8583 parser and the authorization decision engine. Wrote and operated the integration with two card networks (Visa and Mastercard) and shipped a fraud-rules engine that reduced false declines by 12 percent in the first quarter.", + "metadata": {"section": "project", "skill": "card_networks"} + }, + { + "id": "skills-python", + "text": "Python: seven years of production experience. Comfortable with asyncio, type hints, modern Python tooling (uv, ruff, pyright), FastAPI, Pydantic, and SQLAlchemy. Have profiled and optimized hot paths in production.", + "metadata": {"section": "skills", "skill": "python"} + }, + { + "id": "skills-postgres", + "text": "Postgres: five years in production, including running it under non-trivial load. Have used logical replication for blue-green migrations, debugged lock contention with pg_stat_activity, and have strong opinions about when to use Postgres versus a queue.", + "metadata": {"section": "skills", "skill": "postgres"} + }, + { + "id": "skills-kafka", + "text": "Kafka: two years of production experience. Owned a Kafka cluster for the event spine at Northwind, wrote consumers in Python, and have debugged consumer lag, partition rebalances, and the joys of exactly-once semantics.", + "metadata": {"section": "skills", "skill": "kafka"} + }, + { + "id": "on-call-experience", + "text": "On-call: have been on a one-week-in-six rotation at both companies. At Northwind I am also the de facto incident commander for payment outages. I have a strong opinion that the goal of on-call is to keep the customer whole, not to be the hero.", + "metadata": {"section": "skills", "skill": "on_call"} + }, + { + "id": "education", + "text": "BS in Computer Science from the University of Waterloo, 2017. Co-op rotations at Stripe and Shopify during undergrad gave early exposure to payments and high-traffic systems.", + "metadata": {"section": "education"} + }, + { + "id": "side-project", + "text": "Side project: I maintain a small open-source library that implements double-entry accounting in pure Python with a Postgres adapter. About 800 stars on GitHub and used by a handful of small fintechs.", + "metadata": {"section": "side_projects"} + } +] diff --git a/examples/voice-agents/candidate-screening/data/job_senior_backend_payments.json b/examples/voice-agents/candidate-screening/data/job_senior_backend_payments.json new file mode 100644 index 0000000..f2790d5 --- /dev/null +++ b/examples/voice-agents/candidate-screening/data/job_senior_backend_payments.json @@ -0,0 +1,87 @@ +[ + { + "id": "company-overview", + "text": "Lattica is a Series B payments infrastructure company. We provide programmable card issuing, ledger, and money-movement APIs to fintechs in the US, Canada, and the EU. We process over four billion dollars in annualized volume across 12 production tenants.", + "metadata": {"section": "company"} + }, + { + "id": "role-summary", + "text": "The Senior Backend Engineer on the Payments team owns critical paths in our money-movement systems: authorization, settlement, ledgering, and dispute handling. You will design and build the services that move customer funds and that our compliance and finance teams audit.", + "metadata": {"section": "summary"} + }, + { + "id": "location-and-remote", + "text": "Location: this role is fully remote within the US, Canada, and the EU. We hold quarterly in-person on-sites for the Payments team, typically in New York or Berlin, and cover travel and lodging for those.", + "metadata": {"section": "location"} + }, + { + "id": "visa-sponsorship", + "text": "Visa sponsorship: Lattica sponsors US H-1B and TN visas for senior roles. Existing OPT and STEM-OPT holders are welcome to apply. We do not sponsor entry-level or mid-level visas at this time.", + "metadata": {"section": "visa"} + }, + { + "id": "must-have-python", + "text": "Required: at least four years of production Python experience. We use Python 3.11+ with type hints throughout. You should be comfortable reading a stack trace, writing async code, and reasoning about performance in a hot path.", + "metadata": {"section": "must_have", "skill": "python"} + }, + { + "id": "must-have-postgres", + "text": "Required: strong working knowledge of Postgres in production. You have written non-trivial SQL, understand transactions and isolation levels, have debugged a slow query with EXPLAIN, and know when to add an index versus when to rewrite the query.", + "metadata": {"section": "must_have", "skill": "postgres"} + }, + { + "id": "must-have-distributed-systems", + "text": "Required: production experience with distributed systems. You have built or operated a service that handles concurrent writes, retries safely, and recovers from partial failures. Familiarity with idempotency, exactly-once semantics, and queue-based architectures.", + "metadata": {"section": "must_have", "skill": "distributed_systems"} + }, + { + "id": "must-have-payments-or-financial", + "text": "Required: hands-on experience in payments, banking, or another regulated financial domain. You understand why double-entry ledgers exist, what a chargeback is, and why eventual consistency is a four-letter word in money movement.", + "metadata": {"section": "must_have", "skill": "payments_domain"} + }, + { + "id": "must-have-on-call", + "text": "Required: willingness to participate in an on-call rotation, roughly one week every six. Pages are rare but can be high-stakes; on-call engineers are expected to triage and either fix or escalate within fifteen minutes.", + "metadata": {"section": "must_have", "skill": "on_call"} + }, + { + "id": "nice-to-have-kafka", + "text": "Nice to have: experience with Kafka, NATS, or a similar log-based messaging system. We use Kafka for the event spine that connects authorization, ledgering, and downstream reporting.", + "metadata": {"section": "nice_to_have", "skill": "kafka"} + }, + { + "id": "nice-to-have-card-networks", + "text": "Nice to have: prior exposure to card-network integrations, ISO 8583 message formats, or processor APIs such as Marqeta, Galileo, or i2c. You will not be writing ISO 8583 from scratch but you will read it.", + "metadata": {"section": "nice_to_have", "skill": "card_networks"} + }, + { + "id": "nice-to-have-rust-or-go", + "text": "Nice to have: production experience in Rust or Go. A small set of latency-sensitive services are written in Rust; we are open to migrations and rewrites where the performance case is clear.", + "metadata": {"section": "nice_to_have", "skill": "rust_or_go"} + }, + { + "id": "tech-stack", + "text": "Tech stack: Python 3.11, FastAPI, Postgres 16, Kafka, Redis, Temporal for orchestration, AWS (EKS, RDS, S3, KMS), Datadog for observability, and Buildkite for CI. Most production services run on Kubernetes with progressive deployment via Argo Rollouts.", + "metadata": {"section": "tech_stack"} + }, + { + "id": "team-structure", + "text": "Team structure: the Payments team has eight engineers, one engineering manager, one product manager, and one designer. We pair on tricky changes, do code review for everything that ships, and run a fortnightly architecture review.", + "metadata": {"section": "team"} + }, + { + "id": "day-to-day", + "text": "Day-to-day: you own services end to end. You will write design documents for non-trivial changes, ship code through code review, instrument and monitor your services, respond to issues found by support and finance, and partner with compliance on audit-ready logging.", + "metadata": {"section": "day_to_day"} + }, + { + "id": "interview-process", + "text": "Interview process: a recruiter screen, a thirty-minute screening with this voice agent, a take-home design exercise reviewed live, two engineering interviews (one systems, one code), and a values conversation. Total elapsed time is typically two to three weeks.", + "metadata": {"section": "process"} + }, + { + "id": "compensation-and-equity", + "text": "Compensation: base salary range is $185,000 to $235,000 USD depending on location and level, plus a meaningful equity grant vesting over four years. Lattica provides full health, dental, vision, and a $2,000 annual learning budget.", + "metadata": {"section": "compensation"} + } +] diff --git a/examples/voice-agents/candidate-screening/evals/test_scorecards.py b/examples/voice-agents/candidate-screening/evals/test_scorecards.py new file mode 100644 index 0000000..5f00bd4 --- /dev/null +++ b/examples/voice-agents/candidate-screening/evals/test_scorecards.py @@ -0,0 +1,116 @@ +"""Unit tests for the scorecard logic. + +These tests don't need an LLM or a live Moss index - they verify the +deterministic parts of the agent: rubric capture, recommendation rules, +and scorecard JSON shape. Treat this as the "we don't ship vibes" +floor; expand with LLM-driven scenario tests separately. + +Run from the example folder: + + uv run pytest evals/ +""" + +from __future__ import annotations + +import sys +from pathlib import Path + +import pytest + +# Make the agent module importable without installing the package. +sys.path.insert(0, str(Path(__file__).resolve().parent.parent)) + +from agent import ( # noqa: E402 + RubricEntry, + ScreeningSessionData, + _build_scorecard, + _recommendation_from_rubric, +) + + +@pytest.fixture +def session() -> ScreeningSessionData: + return ScreeningSessionData( + candidate_id="c-test", + role_id="senior-backend-payments", + consent_to_record=True, + ) + + +def _entry(score: int, skill: str = "python", evidence: str = "ok") -> RubricEntry: + return RubricEntry(score=score, skill=skill, evidence=evidence) + + +# -- recommendation rule ----------------------------------------------------- + + +def test_advance_when_all_scores_strong(): + rubric = {f"skill_{i}": _entry(4 if i % 2 else 5) for i in range(5)} + assert _recommendation_from_rubric(rubric) == "advance_to_technical" + + +def test_borderline_when_one_low_score(): + rubric = { + "python": _entry(4), + "postgres": _entry(4), + "payments_domain": _entry(2), + "distributed": _entry(3), + } + assert _recommendation_from_rubric(rubric) == "borderline_review" + + +def test_do_not_advance_when_multiple_lows(): + rubric = { + "python": _entry(2), + "postgres": _entry(2), + "payments_domain": _entry(1), + } + assert _recommendation_from_rubric(rubric) == "do_not_advance" + + +def test_no_signal_when_empty(): + assert _recommendation_from_rubric({}) == "no_signal" + + +# -- scorecard shape --------------------------------------------------------- + + +def test_scorecard_has_required_top_level_keys(session): + session.rubric["python"] = _entry(4, "python", "Used asyncio in production for 3 years") + card = _build_scorecard(session) + for key in ( + "candidate_id", + "role_id", + "duration_sec", + "rubric", + "candidate_questions", + "notes", + "recommendation", + "schema_version", + ): + assert key in card, f"scorecard missing key: {key}" + + +def test_scorecard_rubric_carries_evidence(session): + session.rubric["postgres"] = _entry( + 5, "postgres", "Walked through serializable isolation tradeoffs unprompted" + ) + card = _build_scorecard(session) + assert card["rubric"]["postgres"]["score"] == 5 + assert "serializable" in card["rubric"]["postgres"]["evidence"] + + +def test_scorecard_recommendation_matches_rubric(session): + session.rubric = { + "python": _entry(5), + "postgres": _entry(4), + "payments_domain": _entry(5), + "distributed": _entry(4), + } + card = _build_scorecard(session) + assert card["recommendation"] == "advance_to_technical" + + +def test_scorecard_duration_is_non_negative(session): + card = _build_scorecard(session) + assert card["duration_sec"] >= 0 diff --git a/examples/voice-agents/candidate-screening/pyproject.toml 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This is the closest I have come to working on payments, although Stripe abstracts most of the hard parts.", - "metadata": {"section": "project", "skill": "payments_domain"} - }, - { - "id": "skills-python", - "text": "Python: three years of production experience. Have worked with Django, FastAPI, and Celery. Comfortable with the language but I am still learning the deeper performance and async corners.", - "metadata": {"section": "skills", "skill": "python"} - }, - { - "id": "skills-postgres", - "text": "Postgres: three years of experience as a consumer of the database. Have written reasonably complex queries and added indexes when ORM-generated queries were slow. Have not run Postgres in production myself.", - "metadata": {"section": "skills", "skill": "postgres"} - }, - { - "id": "no-distributed-systems-depth", - "text": "Distributed systems: limited direct experience. Have used Redis as a queue and Celery for background jobs but have not designed a system that needs strict idempotency or exactly-once semantics. Eager to learn.", - "metadata": {"section": "gaps", "skill": "distributed_systems"} - }, - { - "id": "no-kafka", - "text": "Kafka: no production experience. I have read the documentation and built a toy producer-consumer in a weekend project but have not run Kafka under real load.", - "metadata": {"section": "gaps", "skill": "kafka"} - }, - { - "id": "first-job", - "text": "First job out of college was as a Junior Engineer at Pageturn, a small ed-tech company. Worked there for six months on the front-end before moving to backend at Bookstack.", - "metadata": {"section": "experience", "company": "Pageturn"} - }, - { - "id": "on-call-experience", - "text": "On-call: Bookstack does not have a formal on-call rotation. The senior engineers handle pages. I have helped triage a few incidents during business hours but have never been the primary responder.", - "metadata": {"section": "skills", "skill": "on_call"} - }, - { - "id": "education", - "text": "BS in Mathematics from a state university, 2022. Took a few computer science classes but most of my software skills are self-taught and on-the-job.", - "metadata": {"section": "education"} - }, - { - "id": "motivation", - "text": "Motivation for applying: I want to work on something with high stakes where correctness matters more than speed of feature output. Payments feels like the right environment to grow into a real senior engineer.", - "metadata": {"section": "motivation"} + { + "id": "summary", + "text": "Mid-level backend engineer with three years of professional experience. Comfortable with Python and Postgres for typical web-app workloads. Applying to senior roles because I am ambitious and ready to grow, even if the role is a stretch.", + "metadata": { + "section": "summary" } + }, + { + "id": "current-role", + "text": "Currently a Software Engineer at Bookstack (Series A, ~40 employees, B2B SaaS). I have been here for two and a half years and was recently promoted from junior to mid-level. I work on the API and integrations team.", + "metadata": { + "section": "experience", + "company": "Bookstack", + "current": "true" + } + }, + { + "id": "stripe-integration-project", + "text": "Shipped the Stripe billing integration at Bookstack. Wrote the webhook handlers and the subscription state machine on top of Stripe's APIs. This is the closest I have come to working on payments, although Stripe abstracts most of the hard parts.", + "metadata": { + "section": "project", + "skill": "payments_domain" + } + }, + { + "id": "skills-python", + "text": "Python: three years of production experience. Have worked with Django, FastAPI, and Celery. Comfortable with the language but I am still learning the deeper performance and async corners.", + "metadata": { + "section": "skills", + "skill": "python" + } + }, + { + "id": "skills-postgres", + "text": "Postgres: three years of experience as a consumer of the database. Have written reasonably complex queries and added indexes when ORM-generated queries were slow. Have not run Postgres in production myself.", + "metadata": { + "section": "skills", + "skill": "postgres" + } + }, + { + "id": "no-distributed-systems-depth", + "text": "Distributed systems: limited direct experience. Have used Redis as a queue and Celery for background jobs but have not designed a system that needs strict idempotency or exactly-once semantics. Eager to learn.", + "metadata": { + "section": "gaps", + "skill": "distributed_systems" + } + }, + { + "id": "no-kafka", + "text": "Kafka: no production experience. I have read the documentation and built a toy producer-consumer in a weekend project but have not run Kafka under real load.", + "metadata": { + "section": "gaps", + "skill": "kafka" + } + }, + { + "id": "first-job", + "text": "First job out of college was as a Junior Engineer at Pageturn, a small ed-tech company. Worked there for six months on the front-end before moving to backend at Bookstack.", + "metadata": { + "section": "experience", + "company": "Pageturn" + } + }, + { + "id": "on-call-experience", + "text": "On-call: Bookstack does not have a formal on-call rotation. The senior engineers handle pages. I have helped triage a few incidents during business hours but have never been the primary responder.", + "metadata": { + "section": "skills", + "skill": "on_call" + } + }, + { + "id": "education", + "text": "BS in Mathematics from a state university, 2022. Took a few computer science classes but most of my software skills are self-taught and on-the-job.", + "metadata": { + "section": "education" + } + }, + { + "id": "motivation", + "text": "Motivation for applying: I want to work on something with high stakes where correctness matters more than speed of feature output. Payments feels like the right environment to grow into a real senior engineer.", + "metadata": { + "section": "motivation" + } + } ] diff --git a/examples/voice-agents/candidate-screening/data/candidate_partial_match.json b/examples/voice-agents/candidate-screening/data/candidate_partial_match.json index 31f203d..39b0c3f 100644 --- a/examples/voice-agents/candidate-screening/data/candidate_partial_match.json +++ b/examples/voice-agents/candidate-screening/data/candidate_partial_match.json @@ -1,62 +1,97 @@ [ - { - "id": "summary", - "text": "Senior infrastructure engineer with eight years of experience building distributed systems at scale. 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I designed the partitioning scheme and wrote the consumers in Python and Go.", - "metadata": {"section": "project", "skill": "kafka"} - }, - { - "id": "postgres-migration-project", - "text": "Led a multi-quarter Postgres migration that moved Topograph's metadata store from a self-managed Postgres on EC2 to RDS Aurora with logical replication for cutover. Zero downtime, zero data loss, and trimmed monthly database spend by 35 percent.", - "metadata": {"section": "project", "skill": "postgres"} - }, - { - "id": "previous-role-cloudbase", - "text": "Previously a Software Engineer at Cloudbase (an internal-tools company). Worked on the deployment platform team for three years. Wrote a lot of Python and got my first real exposure to running services in production.", - "metadata": {"section": "experience", "company": "Cloudbase"} - }, - { - "id": "skills-python", - "text": "Python: eight years of production experience. Heavy use of asyncio for I/O-bound services, FastAPI for internal APIs, and a fair amount of typed Python with mypy. Comfortable with modern tooling.", - "metadata": {"section": "skills", "skill": "python"} - }, - { - "id": "skills-postgres", - "text": "Postgres: six years. Comfortable with the operational side, including replication, point-in-time recovery, and tuning for read-heavy workloads. Less comfortable with the transactional-correctness corners that I imagine matter for a ledger.", - "metadata": {"section": "skills", "skill": "postgres"} - }, - { - "id": "skills-kafka", - "text": "Kafka: four years of production experience as both a consumer-author and an operator. Have written consumers in Python, Go, and a small amount of Rust. Have debugged consumer lag, rebalances, and ZooKeeper to KRaft migrations.", - "metadata": {"section": "skills", "skill": "kafka"} - }, - { - "id": "skills-go", - "text": "Go: two years of production experience. Wrote a number of small services and a CLI tool. Comfortable enough to be productive but I would not call myself an expert.", - "metadata": {"section": "skills", "skill": "go"} - }, - { - "id": "no-payments-experience", - "text": "Have not worked in payments, banking, or any other regulated financial domain. Familiar with concepts like idempotency and double-entry accounting from reading and side reading, but I have not shipped production payments code.", - "metadata": {"section": "gaps", "skill": "payments_domain"} - }, - { - "id": "on-call-experience", - "text": "On-call: have been on rotation at both companies. Topograph is one week every five. I do not love it but I do not dread it either; I treat it as part of the job.", - "metadata": {"section": "skills", "skill": "on_call"} - }, - { - "id": "education", - "text": "BS in Computer Engineering from the University of Texas at Austin, 2016. Worked on a research project on distributed consensus in undergrad which is still my favorite area.", - "metadata": {"section": "education"} + { + "id": "summary", + "text": "Senior infrastructure engineer with eight years of experience building distributed systems at scale. Strong systems and Python background but no direct payments or financial-domain work. Looking to move from infrastructure into product engineering with real customer-facing impact.", + "metadata": { + "section": "summary" } + }, + { + "id": "current-role", + "text": "Currently a Senior Engineer at Topograph (Series C, ~300 employees, geospatial analytics). On the platform team. I own the data pipeline that ingests and processes around two terabytes of geospatial data per day for analytics customers.", + "metadata": { + "section": "experience", + "company": "Topograph", + "current": "true" + } + }, + { + "id": "kafka-pipeline-project", + "text": "Built and operated Topograph's Kafka-based ingestion pipeline. The pipeline processes roughly 40,000 events per second at peak with end-to-end latency under two seconds. I designed the partitioning scheme and wrote the consumers in Python and Go.", + "metadata": { + "section": "project", + "skill": "kafka" + } + }, + { + "id": "postgres-migration-project", + "text": "Led a multi-quarter Postgres migration that moved Topograph's metadata store from a self-managed Postgres on EC2 to RDS Aurora with logical replication for cutover. Zero downtime, zero data loss, and trimmed monthly database spend by 35 percent.", + "metadata": { + "section": "project", + "skill": "postgres" + } + }, + { + "id": "previous-role-cloudbase", + "text": "Previously a Software Engineer at Cloudbase (an internal-tools company). Worked on the deployment platform team for three years. Wrote a lot of Python and got my first real exposure to running services in production.", + "metadata": { + "section": "experience", + "company": "Cloudbase" + } + }, + { + "id": "skills-python", + "text": "Python: eight years of production experience. Heavy use of asyncio for I/O-bound services, FastAPI for internal APIs, and a fair amount of typed Python with mypy. Comfortable with modern tooling.", + "metadata": { + "section": "skills", + "skill": "python" + } + }, + { + "id": "skills-postgres", + "text": "Postgres: six years. Comfortable with the operational side, including replication, point-in-time recovery, and tuning for read-heavy workloads. Less comfortable with the transactional-correctness corners that I imagine matter for a ledger.", + "metadata": { + "section": "skills", + "skill": "postgres" + } + }, + { + "id": "skills-kafka", + "text": "Kafka: four years of production experience as both a consumer-author and an operator. Have written consumers in Python, Go, and a small amount of Rust. Have debugged consumer lag, rebalances, and ZooKeeper to KRaft migrations.", + "metadata": { + "section": "skills", + "skill": "kafka" + } + }, + { + "id": "skills-go", + "text": "Go: two years of production experience. Wrote a number of small services and a CLI tool. Comfortable enough to be productive but I would not call myself an expert.", + "metadata": { + "section": "skills", + "skill": "go" + } + }, + { + "id": "no-payments-experience", + "text": "Have not worked in payments, banking, or any other regulated financial domain. Familiar with concepts like idempotency and double-entry accounting from reading and side reading, but I have not shipped production payments code.", + "metadata": { + "section": "gaps", + "skill": "payments_domain" + } + }, + { + "id": "on-call-experience", + "text": "On-call: have been on rotation at both companies. Topograph is one week every five. I do not love it but I do not dread it either; I treat it as part of the job.", + "metadata": { + "section": "skills", + "skill": "on_call" + } + }, + { + "id": "education", + "text": "BS in Computer Engineering from the University of Texas at Austin, 2016. Worked on a research project on distributed consensus in undergrad which is still my favorite area.", + "metadata": { + "section": "education" + } + } ] diff --git a/examples/voice-agents/candidate-screening/data/candidate_strong_match.json b/examples/voice-agents/candidate-screening/data/candidate_strong_match.json index 5981866..586796f 100644 --- a/examples/voice-agents/candidate-screening/data/candidate_strong_match.json +++ b/examples/voice-agents/candidate-screening/data/candidate_strong_match.json @@ -1,62 +1,96 @@ [ - { - "id": "summary", - "text": "Senior backend engineer with seven years of production experience. The last four years have been on payments and ledger systems at two fintech startups. Comfortable owning critical money-movement code end to end, from API design through audit-ready logging.", - "metadata": {"section": "summary"} - }, - { - "id": "current-role", - "text": "Currently a Senior Backend Engineer at Northwind Pay (Series A, ~80 employees, $400M annualized payment volume). Joined as employee number 22 in early 2024. I lead the ledger and reconciliation services and recently shipped a rewrite of the settlement pipeline.", - "metadata": {"section": "experience", "company": "Northwind Pay", "current": true} - }, - { - "id": "settlement-rewrite-project", - "text": "Led the rewrite of Northwind's daily settlement pipeline from a Cron-driven Python script to a Temporal workflow with Postgres-backed idempotency. Reduced settlement-day errors by 90 percent and cut the close-of-books window from six hours to under one hour.", - "metadata": {"section": "project", "skill": "distributed_systems"} - }, - { - "id": "ledger-design", - "text": "Designed and implemented Northwind's double-entry ledger from scratch. Used Postgres with serializable isolation for the entry-write path and an event-sourced read model for reporting. Authored the design document and got sign-off from compliance and the auditor.", - "metadata": {"section": "project", "skill": "payments_domain"} - }, - { - "id": "previous-role-acme", - "text": "Previously a Backend Engineer at Acme Issuing (acquired by Marqeta in 2022). Joined as a mid-level engineer and was promoted to senior in 18 months. Worked on the card authorization path and ISO 8583 message handling.", - "metadata": {"section": "experience", "company": "Acme Issuing"} - }, - { - "id": "iso8583-experience", - "text": "At Acme Issuing I owned the ISO 8583 parser and the authorization decision engine. Wrote and operated the integration with two card networks (Visa and Mastercard) and shipped a fraud-rules engine that reduced false declines by 12 percent in the first quarter.", - "metadata": {"section": "project", "skill": "card_networks"} - }, - { - "id": "skills-python", - "text": "Python: seven years of production experience. Comfortable with asyncio, type hints, modern Python tooling (uv, ruff, pyright), FastAPI, Pydantic, and SQLAlchemy. Have profiled and optimized hot paths in production.", - "metadata": {"section": "skills", "skill": "python"} - }, - { - "id": "skills-postgres", - "text": "Postgres: five years in production, including running it under non-trivial load. Have used logical replication for blue-green migrations, debugged lock contention with pg_stat_activity, and have strong opinions about when to use Postgres versus a queue.", - "metadata": {"section": "skills", "skill": "postgres"} - }, - { - "id": "skills-kafka", - "text": "Kafka: two years of production experience. Owned a Kafka cluster for the event spine at Northwind, wrote consumers in Python, and have debugged consumer lag, partition rebalances, and the joys of exactly-once semantics.", - "metadata": {"section": "skills", "skill": "kafka"} - }, - { - "id": "on-call-experience", - "text": "On-call: have been on a one-week-in-six rotation at both companies. At Northwind I am also the de facto incident commander for payment outages. I have a strong opinion that the goal of on-call is to keep the customer whole, not to be the hero.", - "metadata": {"section": "skills", "skill": "on_call"} - }, - { - "id": "education", - "text": "BS in Computer Science from the University of Waterloo, 2017. Co-op rotations at Stripe and Shopify during undergrad gave early exposure to payments and high-traffic systems.", - "metadata": {"section": "education"} - }, - { - "id": "side-project", - "text": "Side project: I maintain a small open-source library that implements double-entry accounting in pure Python with a Postgres adapter. About 800 stars on GitHub and used by a handful of small fintechs.", - "metadata": {"section": "side_projects"} + { + "id": "summary", + "text": "Senior backend engineer with seven years of production experience. The last four years have been on payments and ledger systems at two fintech startups. Comfortable owning critical money-movement code end to end, from API design through audit-ready logging.", + "metadata": { + "section": "summary" } + }, + { + "id": "current-role", + "text": "Currently a Senior Backend Engineer at Northwind Pay (Series A, ~80 employees, $400M annualized payment volume). Joined as employee number 22 in early 2024. I lead the ledger and reconciliation services and recently shipped a rewrite of the settlement pipeline.", + "metadata": { + "section": "experience", + "company": "Northwind Pay", + "current": "true" + } + }, + { + "id": "settlement-rewrite-project", + "text": "Led the rewrite of Northwind's daily settlement pipeline from a Cron-driven Python script to a Temporal workflow with Postgres-backed idempotency. Reduced settlement-day errors by 90 percent and cut the close-of-books window from six hours to under one hour.", + "metadata": { + "section": "project", + "skill": "distributed_systems" + } + }, + { + "id": "ledger-design", + "text": "Designed and implemented Northwind's double-entry ledger from scratch. Used Postgres with serializable isolation for the entry-write path and an event-sourced read model for reporting. Authored the design document and got sign-off from compliance and the auditor.", + "metadata": { + "section": "project", + "skill": "payments_domain" + } + }, + { + "id": "previous-role-acme", + "text": "Previously a Backend Engineer at Acme Issuing (acquired by Marqeta in 2022). Joined as a mid-level engineer and was promoted to senior in 18 months. Worked on the card authorization path and ISO 8583 message handling.", + "metadata": { + "section": "experience", + "company": "Acme Issuing" + } + }, + { + "id": "iso8583-experience", + "text": "At Acme Issuing I owned the ISO 8583 parser and the authorization decision engine. Wrote and operated the integration with two card networks (Visa and Mastercard) and shipped a fraud-rules engine that reduced false declines by 12 percent in the first quarter.", + "metadata": { + "section": "project", + "skill": "card_networks" + } + }, + { + "id": "skills-python", + "text": "Python: seven years of production experience. Comfortable with asyncio, type hints, modern Python tooling (uv, ruff, pyright), FastAPI, Pydantic, and SQLAlchemy. Have profiled and optimized hot paths in production.", + "metadata": { + "section": "skills", + "skill": "python" + } + }, + { + "id": "skills-postgres", + "text": "Postgres: five years in production, including running it under non-trivial load. Have used logical replication for blue-green migrations, debugged lock contention with pg_stat_activity, and have strong opinions about when to use Postgres versus a queue.", + "metadata": { + "section": "skills", + "skill": "postgres" + } + }, + { + "id": "skills-kafka", + "text": "Kafka: two years of production experience. Owned a Kafka cluster for the event spine at Northwind, wrote consumers in Python, and have debugged consumer lag, partition rebalances, and the joys of exactly-once semantics.", + "metadata": { + "section": "skills", + "skill": "kafka" + } + }, + { + "id": "on-call-experience", + "text": "On-call: have been on a one-week-in-six rotation at both companies. At Northwind I am also the de facto incident commander for payment outages. I have a strong opinion that the goal of on-call is to keep the customer whole, not to be the hero.", + "metadata": { + "section": "skills", + "skill": "on_call" + } + }, + { + "id": "education", + "text": "BS in Computer Science from the University of Waterloo, 2017. Co-op rotations at Stripe and Shopify during undergrad gave early exposure to payments and high-traffic systems.", + "metadata": { + "section": "education" + } + }, + { + "id": "side-project", + "text": "Side project: I maintain a small open-source library that implements double-entry accounting in pure Python with a Postgres adapter. About 800 stars on GitHub and used by a handful of small fintechs.", + "metadata": { + "section": "side_projects" + } + } ] From 4ca20f85a6e093fa14dd09986205aa669f157208 Mon Sep 17 00:00:00 2001 From: yatharth Date: Fri, 15 May 2026 17:59:18 -0700 Subject: [PATCH 3/3] fix: update README and agent logic for role context retrieval; sanitize candidate ID in scorecard filename --- .../candidate-screening/README.md | 8 +++--- .../voice-agents/candidate-screening/agent.py | 26 ++++++++++++++----- .../candidate-screening/create_indexes.py | 5 ++-- 3 files changed, 27 insertions(+), 12 deletions(-) diff --git a/examples/voice-agents/candidate-screening/README.md b/examples/voice-agents/candidate-screening/README.md index 7b589f2..3672360 100644 --- a/examples/voice-agents/candidate-screening/README.md +++ b/examples/voice-agents/candidate-screening/README.md @@ -19,7 +19,7 @@ before the agent can ask a good follow-up. ▼ ▼ ┌──────────────────┐ ┌──────────────────┐ │ Moss: JD index │ │ Moss: resume idx │ - │ (15 docs) │ │ (10-12 docs) │ + │ (17 docs) │ │ (11-12 docs) │ └──────────────────┘ └──────────────────┘ lookup_job_requirement lookup_resume_fact ``` @@ -116,8 +116,10 @@ the candidate notices any pause. ## A working interview, condensed ``` -Agent: Hi, I'm calling about your application for the Senior Backend - Engineer role on the Lattica Payments team. This is a short +Agent: [lookup_job_requirement("role title, company name, team") + -> "Senior Backend Engineer on the Payments team... Lattica..."] + Hi, I'm calling about your application for the Senior Backend + Engineer role on Lattica's Payments team. This is a short voice screening - about twenty-five minutes. The conversation is recorded and reviewed by the hiring team - okay to continue? Candidate: Yes, that works. diff --git a/examples/voice-agents/candidate-screening/agent.py b/examples/voice-agents/candidate-screening/agent.py index 7c1cd40..0c88796 100644 --- a/examples/voice-agents/candidate-screening/agent.py +++ b/examples/voice-agents/candidate-screening/agent.py @@ -204,12 +204,21 @@ def __init__(self, moss_client: MossClient): super().__init__(instructions=SYSTEM_PROMPT) async def on_enter(self) -> None: - await self.session.say( - "Hi, I'm calling about your application for the Senior Backend " - "Engineer role on the Lattica Payments team. This is a short " - "voice screening - about twenty-five minutes. Before we get " - "started, this conversation is recorded and reviewed by the " - "hiring team - is that okay with you to continue?" + role_context = await self._query( + JOB_INDEX, + "role title, company name, team", + source="JD", + ) + await self.session.generate_reply( + instructions=( + "Greet the candidate. Identify the role, company, and team " + "using ONLY the context below - do not invent any of them. " + "Tell them this is a short voice screening, about 25 minutes, " + "and that the conversation is recorded and reviewed by the " + "hiring team. Ask if it's okay to continue. Keep it under " + "three sentences and conversational.\n\n" + f"Role context from the JD index:\n{role_context}" + ), ) # ----- Retrieval tools ------------------------------------------------- @@ -340,8 +349,11 @@ async def submit_scorecard(self, context: RunContext) -> str: if data.consent_to_record is not True: return "Cannot submit a scorecard without recorded consent." + safe_candidate_id = "".join( + ch if ch.isalnum() or ch in {"_", "-"} else "_" for ch in data.candidate_id + ) or "candidate" SCORECARD_DIR.mkdir(parents=True, exist_ok=True) - path = SCORECARD_DIR / f"{data.candidate_id}__{int(data.started_at)}.json" + path = SCORECARD_DIR / f"{safe_candidate_id}__{int(data.started_at)}.json" scorecard = _build_scorecard(data) path.write_text(json.dumps(scorecard, indent=2)) logger.info(f"{GREEN}scorecard written: {path}{RESET}") diff --git a/examples/voice-agents/candidate-screening/create_indexes.py b/examples/voice-agents/candidate-screening/create_indexes.py index 932bf3c..1169ff0 100644 --- a/examples/voice-agents/candidate-screening/create_indexes.py +++ b/examples/voice-agents/candidate-screening/create_indexes.py @@ -6,8 +6,9 @@ Both fixtures live in ``data/`` as flat JSON arrays of ``{id, text, metadata}`` documents - the native Moss shape. Index names are derived from the -fixture filename, with the leading ``job_`` / ``candidate_`` and the -``.json`` suffix stripped, then underscores swapped for dashes. +fixture filename by dropping the ``.json`` suffix and swapping +underscores for dashes - the leading ``job_`` / ``candidate_`` prefix +is kept (e.g. ``candidate_strong_match.json`` -> ``candidate-strong-match``). Run before starting the agent: