From 61126b6474db29ac973a4a151288ff8a9679a163 Mon Sep 17 00:00:00 2001 From: Woojin Son Date: Mon, 23 Feb 2026 21:56:27 +0900 Subject: [PATCH 1/2] =?UTF-8?q?=E2=9C=A8feat(resume=5Fingestor):=20add=20l?= =?UTF-8?q?lm=20support?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .env.example | 27 ++- casts/resume_ingestor/modules/models.py | 51 +++- casts/resume_ingestor/modules/nodes.py | 232 ++++++++++++++++++- casts/resume_ingestor/modules/prompts.py | 84 +++++-- casts/resume_ingestor/modules/state.py | 1 + pyproject.toml | 2 + uv.lock | 281 +++++++++++++++++++++++ 7 files changed, 623 insertions(+), 55 deletions(-) diff --git a/.env.example b/.env.example index 9a7e9a0..7e4453a 100644 --- a/.env.example +++ b/.env.example @@ -1,17 +1,16 @@ -# .env.example file -# This file provides examples of environment variables needed for the project. -# For actual use, you will need to copy this file to create an .env file and enter the actual values. +# InterviewGraph environment example +# Copy this file to .env and replace placeholder values. -# Langsmith Project Tracking -# LangSmith is a tool for monitoring and debugging -LANGSMITH_PROJECT=act... # Project name to be used by LangSmith. -LANGSMITH_API_KEY=lsv2... # LangSmith API key (must be replaced with real key) +# Optional observability +LANGSMITH_PROJECT=interviewgraph +LANGSMITH_API_KEY= -# Depending on the configuration you choose, you will need the following environment variables. +# LLM generation runtime config +# Supported providers in this repo: openai, anthropic +INTERVIEWGRAPH_LLM_PROVIDER=openai +INTERVIEWGRAPH_LLM_MODEL=gpt-4o-mini +INTERVIEWGRAPH_LLM_TEMPERATURE=0.2 -## LLM API Keys: -# OpenAI API Key - required to use the GPT model. -# You can get it from the OpenAI website (https://platform.openai.com/). -OPENAI_API_KEY=sk... - -# Others... +# Provider API keys (set the one matching provider) +OPENAI_API_KEY= +ANTHROPIC_API_KEY= diff --git a/casts/resume_ingestor/modules/models.py b/casts/resume_ingestor/modules/models.py index 36bde1c..7f2c4f7 100644 --- a/casts/resume_ingestor/modules/models.py +++ b/casts/resume_ingestor/modules/models.py @@ -10,13 +10,48 @@ - Embedding Models: https://docs.langchain.com/oss/python/integrations/text_embedding """ -# from langchain.chat_models import init_chat_model -# from langchain_openai import ChatOpenAI +from __future__ import annotations +import os +from typing import Any -def get_sample_model(): - # return init_chat_model( - # model="", - # model_provider="openai", - # ) - pass + +def get_generation_model() -> Any | None: + """Returns a configured LangChain chat model, or None if unavailable. + + Configuration (all optional): + - INTERVIEWGRAPH_LLM_PROVIDER (default: openai) + - INTERVIEWGRAPH_LLM_MODEL (default: gpt-4o-mini) + - INTERVIEWGRAPH_LLM_TEMPERATURE (default: 0.2) + """ + + provider = os.getenv("INTERVIEWGRAPH_LLM_PROVIDER", "openai").strip().lower() + model = os.getenv("INTERVIEWGRAPH_LLM_MODEL", "gpt-4o-mini").strip() + temp_raw = os.getenv("INTERVIEWGRAPH_LLM_TEMPERATURE", "0.2").strip() + + if not _has_provider_credentials(provider): + return None + + try: + temperature = float(temp_raw) + except ValueError: + temperature = 0.2 + + try: + from langchain.chat_models import init_chat_model + + return init_chat_model( + model=model, + model_provider=provider, + temperature=temperature, + ) + except Exception: + return None + + +def _has_provider_credentials(provider: str) -> bool: + if provider == "openai": + return bool(os.getenv("OPENAI_API_KEY", "").strip()) + if provider == "anthropic": + return bool(os.getenv("ANTHROPIC_API_KEY", "").strip()) + return False diff --git a/casts/resume_ingestor/modules/nodes.py b/casts/resume_ingestor/modules/nodes.py index f5ee330..04b1b5d 100644 --- a/casts/resume_ingestor/modules/nodes.py +++ b/casts/resume_ingestor/modules/nodes.py @@ -2,11 +2,14 @@ from __future__ import annotations +import json import re from collections import Counter from pathlib import Path from casts.base_node import BaseNode +from casts.resume_ingestor.modules.models import get_generation_model +from casts.resume_ingestor.modules.prompts import build_question_generation_messages def _error_item( @@ -37,7 +40,12 @@ def execute(self, state): return { "raw_text": resume_text.strip(), "sections": {}, - "signals": {"skills": [], "projects": [], "keywords": []}, + "signals": { + "skills": [], + "projects": [], + "keywords": [], + "evidence": [], + }, "questions": [], "markdown": "", "errors": [], @@ -49,7 +57,12 @@ def execute(self, state): return { "raw_text": "", "sections": {}, - "signals": {"skills": [], "projects": [], "keywords": []}, + "signals": { + "skills": [], + "projects": [], + "keywords": [], + "evidence": [], + }, "questions": [], "markdown": "", "errors": [ @@ -68,7 +81,12 @@ def execute(self, state): return { "raw_text": "", "sections": {}, - "signals": {"skills": [], "projects": [], "keywords": []}, + "signals": { + "skills": [], + "projects": [], + "keywords": [], + "evidence": [], + }, "questions": [], "markdown": "", "errors": [ @@ -85,7 +103,12 @@ def execute(self, state): return { "raw_text": "", "sections": {}, - "signals": {"skills": [], "projects": [], "keywords": []}, + "signals": { + "skills": [], + "projects": [], + "keywords": [], + "evidence": [], + }, "questions": [], "markdown": "", "errors": [ @@ -101,7 +124,12 @@ def execute(self, state): return { "raw_text": loaded_text, "sections": {}, - "signals": {"skills": [], "projects": [], "keywords": []}, + "signals": { + "skills": [], + "projects": [], + "keywords": [], + "evidence": [], + }, "questions": [], "markdown": "", "errors": [], @@ -110,7 +138,12 @@ def execute(self, state): return { "raw_text": "", "sections": {}, - "signals": {"skills": [], "projects": [], "keywords": []}, + "signals": { + "skills": [], + "projects": [], + "keywords": [], + "evidence": [], + }, "questions": [], "markdown": "", "errors": [ @@ -228,12 +261,24 @@ class ExtractSignalsNode(BaseNode): def execute(self, state): existing_errors = list(state.get("errors", [])) if existing_errors: - return {"signals": {"skills": [], "projects": [], "keywords": []}} + return { + "signals": { + "skills": [], + "projects": [], + "keywords": [], + "evidence": [], + } + } sections = state.get("sections") if not isinstance(sections, dict) or not sections: return { - "signals": {"skills": [], "projects": [], "keywords": []}, + "signals": { + "skills": [], + "projects": [], + "keywords": [], + "evidence": [], + }, "errors": existing_errors + [ _error_item( @@ -248,12 +293,14 @@ def execute(self, state): skills = self._extract_skills(sections.get("skills", "")) projects = self._extract_projects(sections.get("projects", "")) keywords = self._extract_keywords(sections) + evidence = self._extract_evidence(sections) return { "signals": { "skills": skills, "projects": projects, "keywords": keywords, + "evidence": evidence, } } @@ -283,6 +330,25 @@ def _extract_keywords(self, sections: dict[str, object]) -> list[str]: sorted_tokens = sorted(ranked.items(), key=lambda item: (-item[1], item[0])) return [token for token, _count in sorted_tokens[:12]] + def _extract_evidence(self, sections: dict[str, object]) -> list[str]: + evidence: list[str] = [] + for key in ("experience", "projects", "summary"): + text = sections.get(key) + if not isinstance(text, str) or not text.strip(): + continue + + for line in text.splitlines(): + cleaned = line.strip(" -*\t") + if len(cleaned) < 16: + continue + if re.search( + r"\b(led|built|designed|improved|scaled|reduced|increased|owned|migrated)\b", + cleaned.lower(), + ): + evidence.append(cleaned) + + return self._dedupe(evidence)[:12] + def _dedupe(self, values: list[str]) -> list[str]: seen: set[str] = set() deduped: list[str] = [] @@ -305,8 +371,9 @@ def execute(self, state): if existing_errors: return {"questions": []} + sections = state.get("sections") signals = state.get("signals") - if not isinstance(signals, dict): + if not isinstance(signals, dict) or not isinstance(sections, dict): return { "questions": [], "errors": existing_errors @@ -314,7 +381,7 @@ def execute(self, state): _error_item( node="generate_questions", code="MISSING_SIGNALS", - message="Cannot generate questions without extracted signals.", + message="Cannot generate questions without extracted signals and sections.", retryable=False, ) ], @@ -323,21 +390,158 @@ def execute(self, state): skills = self._as_list(signals.get("skills")) projects = self._as_list(signals.get("projects")) keywords = self._as_list(signals.get("keywords")) + evidence = self._as_list(signals.get("evidence")) - prompts = self._build_prompt_seeds(skills, projects, keywords) + llm_questions = self._generate_questions_with_llm( + raw_text=str(state.get("raw_text", "")), + sections=sections, + signals={ + "skills": skills, + "projects": projects, + "keywords": keywords, + "evidence": evidence, + }, + ) + if llm_questions is not None: + return {"questions": llm_questions} + + prompts = self._build_prompt_seeds(skills, projects, keywords, evidence) questions = [ self._make_question(index=idx + 1, seed=seed) for idx, seed in enumerate(prompts[:15]) ] return {"questions": questions} + def _generate_questions_with_llm( + self, + *, + raw_text: str, + sections: dict[str, object], + signals: dict[str, object], + ) -> list[dict[str, object]] | None: + model = get_generation_model() + if model is None: + return None + + try: + messages = build_question_generation_messages( + raw_text=raw_text, + sections=sections, + signals=signals, + ) + response = model.invoke(messages) + return self._parse_llm_questions(response) + except Exception: + return None + + def _parse_llm_questions(self, response: object) -> list[dict[str, object]] | None: + content = getattr(response, "content", response) + if isinstance(content, list): + content = "\n".join( + str(item.get("text", "")) if isinstance(item, dict) else str(item) + for item in content + ) + if not isinstance(content, str) or not content.strip(): + return None + + payload_text = self._extract_json_object(content) + if payload_text is None: + return None + + try: + payload = json.loads(payload_text) + except json.JSONDecodeError: + return None + + questions = payload.get("questions") if isinstance(payload, dict) else None + if not isinstance(questions, list) or not questions: + return None + + parsed: list[dict[str, object]] = [] + for index, item in enumerate(questions[:15]): + if not isinstance(item, dict): + continue + category = str(item.get("category", "tech")) + if category not in self._CATEGORIES: + category = "tech" + + difficulty = item.get("difficulty", 3) + if not isinstance(difficulty, int) or not 1 <= difficulty <= 5: + difficulty = 3 + + question_text = str(item.get("question", "")).strip() + if not question_text: + continue + + expected_points = item.get("expected_points", []) + if not isinstance(expected_points, list): + expected_points = [] + expected_points = [ + point.strip() + for point in expected_points + if isinstance(point, str) and point.strip() + ][:3] + if not expected_points: + expected_points = [ + "Problem context and constraints", + "Technical choices and trade-offs", + "Measured outcome and lessons learned", + ] + + followups = item.get("followups", []) + if not isinstance(followups, list): + followups = [] + followups = [ + followup.strip() + for followup in followups + if isinstance(followup, str) and followup.strip() + ][:2] + if not followups: + followups = [ + "What constraints most influenced your decision?", + "What would you improve if implementing this again?", + ] + + parsed.append( + { + "id": f"q{index + 1:02d}", + "category": category, + "difficulty": difficulty, + "question": question_text, + "expected_points": expected_points, + "followups": followups, + } + ) + + if not parsed: + return None + return parsed + + def _extract_json_object(self, text: str) -> str | None: + stripped = text.strip() + if stripped.startswith("```"): + stripped = re.sub( + r"^```(?:json)?", "", stripped, flags=re.IGNORECASE + ).strip() + stripped = re.sub(r"```$", "", stripped).strip() + + start = stripped.find("{") + end = stripped.rfind("}") + if start == -1 or end == -1 or end <= start: + return None + return stripped[start : end + 1] + def _as_list(self, value: object) -> list[str]: if not isinstance(value, list): return [] return [item for item in value if isinstance(item, str) and item.strip()] def _build_prompt_seeds( - self, skills: list[str], projects: list[str], keywords: list[str] + self, + skills: list[str], + projects: list[str], + keywords: list[str], + evidence: list[str], ) -> list[tuple[str, str]]: seeds: list[tuple[str, str]] = [] @@ -352,6 +556,10 @@ def _build_prompt_seeds( for keyword in keywords: seeds.append(("deep-dive", f"{keyword}")) + for item in evidence: + seeds.append(("project", f"{item}")) + seeds.append(("deep-dive", f"{item}")) + if not seeds: seeds = [ ("tech", "core backend skills"), diff --git a/casts/resume_ingestor/modules/prompts.py b/casts/resume_ingestor/modules/prompts.py index 3fe33c8..0ae1b28 100644 --- a/casts/resume_ingestor/modules/prompts.py +++ b/casts/resume_ingestor/modules/prompts.py @@ -11,24 +11,66 @@ - Claude prompt engineering: https://docs.claude.com/en/docs/build-with-claude/prompt-engineering """ -# from langchain.messages import SystemMessage, HumanMessage, AIMessage - - -def get_sample_dictionary_format_prompt(): - # system_content = "You are a proact operator expert" - # user_content = "Do Proactive" - # ai_content = "I will." - # return [ - # {"role": "system", "content": system_content}, - # {"role": "user", "content": user_content}, - # {"role": "ai", "content": ai_content}, - # ] - pass - - -def get_sample_messages_prompt(): - # system = "You are a proact operator expert" - # human = "Do Proactive" - # ai = "I will." - # return [SystemMessage(system), HumanMessage(human), AIMessage(ai)] - pass +from __future__ import annotations + +import json + + +def build_question_generation_messages( + *, + raw_text: str, + sections: dict[str, object], + signals: dict[str, object], +) -> list[dict[str, str]]: + """Builds a schema-constrained prompt for interview question generation.""" + + system_content = ( + "You generate resume-grounded interview questions. " + "Return only valid JSON. Do not include markdown fences or extra text." + ) + + section_payload = { + key: value + for key, value in sections.items() + if isinstance(value, str) and value.strip() + } + + signal_payload = { + "skills": signals.get("skills", []), + "projects": signals.get("projects", []), + "keywords": signals.get("keywords", []), + "evidence": signals.get("evidence", []), + } + + schema_hint = { + "questions": [ + { + "id": "q01", + "category": "tech", + "difficulty": 3, + "question": "string", + "expected_points": ["string"], + "followups": ["string"], + } + ] + } + + user_content = ( + "Generate exactly 15 interview questions.\n" + "Rules:\n" + "1) Categories must be one of: tech, project, system, deep-dive.\n" + "2) Difficulty must be integer 1-5.\n" + "3) Each question must be anchored to resume evidence (skills/projects/experience).\n" + "4) Avoid generic wording and duplicates.\n" + "5) Include 3 expected_points and 2 followups per item.\n" + "6) Output strict JSON only with top-level key 'questions'.\n\n" + f"Raw resume text:\n{raw_text[:6000]}\n\n" + f"Parsed sections:\n{json.dumps(section_payload, ensure_ascii=True)}\n\n" + f"Extracted signals:\n{json.dumps(signal_payload, ensure_ascii=True)}\n\n" + f"Output JSON shape example:\n{json.dumps(schema_hint, ensure_ascii=True)}" + ) + + return [ + {"role": "system", "content": system_content}, + {"role": "user", "content": user_content}, + ] diff --git a/casts/resume_ingestor/modules/state.py b/casts/resume_ingestor/modules/state.py index 4ca42db..0b89648 100644 --- a/casts/resume_ingestor/modules/state.py +++ b/casts/resume_ingestor/modules/state.py @@ -24,6 +24,7 @@ class ResumeSignals(TypedDict): skills: list[str] projects: list[str] keywords: list[str] + evidence: list[str] class InterviewQuestion(TypedDict): diff --git a/pyproject.toml b/pyproject.toml index e343f22..5c69529 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -6,7 +6,9 @@ readme = "README.md" requires-python = ">=3.11,<3.14" dependencies = [ 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@@ -0,0 +1,17 @@ +Summary +Senior backend engineer with 8 years building distributed API platforms for B2B SaaS. + +Skills +Python, FastAPI, PostgreSQL, Redis, Kafka, AWS, Terraform, Kubernetes + +Experience +- Led migration from monolith to event-driven services and reduced API p95 latency by 42%. +- Built a rate-limiting and abuse detection layer that reduced fraudulent requests by 67%. +- Owned incident response for payment pipeline outages and introduced SLO-based alerting. + +Projects +- Designed an interview automation service that generated role-specific question sets for hiring teams. +- Built an async document ingestion pipeline that processed 1.2M records per day. + +Education +BS in Computer Science diff --git a/scripts/staging_quality_check.py b/scripts/staging_quality_check.py new file mode 100644 index 0000000..3646a70 --- /dev/null +++ b/scripts/staging_quality_check.py @@ -0,0 +1,125 @@ +from __future__ import annotations + +import argparse +import json +import os +import sys +from pathlib import Path + +from casts.resume_ingestor.graph import resume_ingestor_graph +from casts.resume_ingestor.modules.models import get_generation_model + + +def _parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Run staging quality check") + parser.add_argument( + "--resume-file", + default="docs/samples/staging_resume.txt", + help="Path to plaintext resume sample", + ) + parser.add_argument( + "--allow-fallback", + action="store_true", + help="Allow deterministic fallback when LLM is unavailable", + ) + return parser.parse_args() + + +def _provider_name() -> str: + return os.getenv("INTERVIEWGRAPH_LLM_PROVIDER", "openai").strip().lower() + + +def _has_provider_key() -> bool: + provider = _provider_name() + if provider == "openai": + return bool(os.getenv("OPENAI_API_KEY", "").strip()) + if provider == "anthropic": + return bool(os.getenv("ANTHROPIC_API_KEY", "").strip()) + return False + + +def _quality_report(result: dict[str, object]) -> dict[str, object]: + questions = result.get("questions", []) + errors = result.get("errors", []) + + if not isinstance(questions, list): + questions = [] + if not isinstance(errors, list): + errors = [] + + categories = [] + with_evidence_terms = 0 + for item in questions: + if not isinstance(item, dict): + continue + category = item.get("category") + question = str(item.get("question", "")).lower() + if isinstance(category, str): + categories.append(category) + + if any( + term in question + for term in ("project", "production", "trade-off", "scale", "impact") + ): + with_evidence_terms += 1 + + unique_categories = sorted(set(categories)) + return { + "question_count": len(questions), + "unique_categories": unique_categories, + "category_count": len(unique_categories), + "questions_with_context_terms": with_evidence_terms, + "error_count": len(errors), + } + + +def _as_int(value: object, default: int = 0) -> int: + if isinstance(value, int): + return value + if isinstance(value, str) and value.isdigit(): + return int(value) + return default + + +def main() -> int: + args = _parse_args() + resume_path = Path(args.resume_file) + if not resume_path.exists(): + print(f"Resume sample not found: {resume_path}") + return 2 + + model = get_generation_model() + llm_ready = model is not None and _has_provider_key() + if not llm_ready and not args.allow_fallback: + provider = _provider_name() + print( + "LLM is not configured. Set provider/model vars and API key, then rerun. " + f"provider={provider}" + ) + return 2 + + resume_text = resume_path.read_text(encoding="utf-8") + graph = resume_ingestor_graph() + result = graph.invoke({"resume_text": resume_text}) + report = _quality_report(result) + + print( + json.dumps( + {"llm_ready": llm_ready, "report": report}, ensure_ascii=True, indent=2 + ) + ) + + question_count = _as_int(report.get("question_count"), 0) + category_count = _as_int(report.get("category_count"), 0) + + if question_count != 15: + return 1 + + min_categories = 3 if llm_ready else 2 + if category_count < min_categories: + return 1 + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/tests/node_tests/test_node.py b/tests/node_tests/test_node.py index 30cf6ec..3ad27b2 100644 --- a/tests/node_tests/test_node.py +++ b/tests/node_tests/test_node.py @@ -82,7 +82,12 @@ def test_extract_signals_node_returns_error_without_sections() -> None: node = ExtractSignalsNode() result = node({"sections": {}, "errors": []}) - assert result["signals"] == {"skills": [], "projects": [], "keywords": []} + assert result["signals"] == { + "skills": [], + "projects": [], + "keywords": [], + "evidence": [], + } assert len(result["errors"]) == 1 assert result["errors"][0]["code"] == "MISSING_SECTIONS" @@ -91,10 +96,16 @@ def test_generate_questions_node_creates_15_structured_items() -> None: node = GenerateQuestionsNode() result = node( { + "raw_text": "Backend engineer with production API and cloud infra experience", + "sections": { + "summary": "Backend engineer", + "projects": "Built interview graph service", + }, "signals": { "skills": ["Python", "FastAPI", "AWS"], "projects": ["Built interview graph service"], "keywords": ["backend", "api", "scalability"], + "evidence": ["Built interview graph service used by recruiting teams"], }, "errors": [], } @@ -110,6 +121,48 @@ def test_generate_questions_node_creates_15_structured_items() -> None: assert len(first["followups"]) >= 1 +def test_generate_questions_node_uses_llm_when_available(monkeypatch) -> None: + node = GenerateQuestionsNode() + + class FakeResponse: + content = ( + '{"questions":[{"id":"qx","category":"tech","difficulty":4,' + '"question":"How did you scale FastAPI for burst traffic?",' + '"expected_points":["Autoscaling","Bottleneck analysis","SLO impact"],' + '"followups":["What failed first?","How did you validate?", "extra"]}]}' + ) + + class FakeModel: + def invoke(self, _messages): + return FakeResponse() + + monkeypatch.setattr( + "casts.resume_ingestor.modules.nodes.get_generation_model", + lambda: FakeModel(), + ) + + result = node( + { + "raw_text": "Senior backend engineer", + "sections": {"summary": "Senior backend engineer"}, + "signals": { + "skills": ["FastAPI"], + "projects": ["Built interview graph service"], + "keywords": ["scalability"], + "evidence": ["Scaled API for recruiting platform"], + }, + "errors": [], + } + ) + + assert len(result["questions"]) == 1 + assert result["questions"][0]["id"] == "q01" + assert result["questions"][0]["category"] == "tech" + assert result["questions"][0]["difficulty"] == 4 + assert len(result["questions"][0]["expected_points"]) == 3 + assert len(result["questions"][0]["followups"]) == 2 + + def test_rate_difficulty_node_assigns_1_to_5_scale() -> None: node = RateDifficultyNode() generated_questions = [