From 98d564b23aa33a59bafa994447765fc67b6cb750 Mon Sep 17 00:00:00 2001 From: karosi12 Date: Wed, 22 Apr 2026 14:18:34 +0100 Subject: [PATCH 1/3] feat: implement langGraph, create tools and score resume against a job url --- backend/app/api/router.py | 2 + backend/app/api/v1/endpoints.py | 161 ++++ backend/app/core/config.py | 5 +- backend/app/mcp/__init__.py | 5 + backend/app/mcp/server.py | 184 ++++ backend/app/services/langgraph/__init__.py | 14 + backend/app/services/langgraph/workflow.py | 355 ++++++++ backend/app/tools/__init__.py | 11 + backend/app/tools/ats_scorer.py | 346 ++++++++ backend/app/tools/job_fetcher.py | 149 ++++ backend/app/tools/models.py | 94 ++ backend/app/tools/resume_parser.py | 246 ++++++ backend/pyproject.toml | 8 + backend/uv.lock | 953 +++++++++++++++++++++ 14 files changed, 2532 insertions(+), 1 deletion(-) create mode 100644 backend/app/api/v1/endpoints.py create mode 100644 backend/app/mcp/__init__.py create mode 100644 backend/app/mcp/server.py create mode 100644 backend/app/services/langgraph/workflow.py create mode 100644 backend/app/tools/__init__.py create mode 100644 backend/app/tools/ats_scorer.py create mode 100644 backend/app/tools/job_fetcher.py create mode 100644 backend/app/tools/models.py create mode 100644 backend/app/tools/resume_parser.py diff --git a/backend/app/api/router.py b/backend/app/api/router.py index 4704f95..eec04ce 100644 --- a/backend/app/api/router.py +++ b/backend/app/api/router.py @@ -1,6 +1,8 @@ from fastapi import APIRouter from app.api.v1 import health +from app.api.v1 import endpoints api_router = APIRouter() api_router.include_router(health.router, prefix="/v1", tags=["health"]) +api_router.include_router(endpoints.router, prefix="/v1", tags=["talentstream"]) diff --git a/backend/app/api/v1/endpoints.py b/backend/app/api/v1/endpoints.py new file mode 100644 index 0000000..acf8438 --- /dev/null +++ b/backend/app/api/v1/endpoints.py @@ -0,0 +1,161 @@ +"""API v1 endpoints for TalentStreamAI.""" + +from fastapi import APIRouter, HTTPException, UploadFile, File +from fastapi.responses import JSONResponse +from pydantic import BaseModel, Field + +from app.services.langgraph import run_talentstream_workflow +from app.tools.job_fetcher import fetch_job_description +from app.tools.resume_parser import parse_resume +from app.tools.ats_scorer import ats_score_resume + +router = APIRouter() + + +class ApplyRequest(BaseModel): + job_url: str = Field(..., description="URL of the job posting") + + +class ApplyResponse(BaseModel): + status: str + job_data: dict | None = None + resume_data: dict | None = None + ats_score: dict | None = None + gap_analysis: dict | None = None + tailored_resume: str | None = None + cover_letter: str | None = None + email_draft: str | None = None + + +class FetchJobResponse(BaseModel): + status: str + job_data: dict + + +class ParseResumeResponse(BaseModel): + status: str + resume_data: dict + + +class ScoreATSRequest(BaseModel): + job_url: str = Field(..., description="URL of the job posting") + + +class ScoreATSResponse(BaseModel): + status: str + ats_score: dict + + +@router.post("/apply", response_model=ApplyResponse) +async def apply_to_job( + job_url: str = File(..., description="URL of the job posting"), + resume: UploadFile = File(..., description="Resume file (PDF or DOCX)"), +) -> ApplyResponse: + """Run complete TalentStreamAI workflow to generate application materials.""" + import base64 + + ext = resume.filename.rsplit(".", 1)[-1].lower() if resume.filename else "" + if not ext or ext not in ["pdf", "docx", "doc"]: + raise HTTPException( + status_code=400, + detail="Unsupported file format. Use PDF or DOCX.", + ) + + file_content = await resume.read() + file_b64 = base64.b64encode(file_content).decode("utf-8") + + try: + result = await run_talentstream_workflow( + job_url=job_url, + resume_file=file_b64, + resume_ext=ext, + ) + except ValueError as e: + raise HTTPException(status_code=500, detail=str(e)) + except Exception as e: + raise HTTPException(status_code=500, detail=f"Workflow failed: {str(e)}") + + if result.get("error"): + raise HTTPException(status_code=500, detail=result["error"]) + + return ApplyResponse( + status="success", + job_data=result.get("job_data"), + resume_data=result.get("resume_data"), + ats_score=result.get("ats_score"), + gap_analysis=result.get("gap_analysis"), + tailored_resume=result.get("tailored_resume"), + cover_letter=result.get("cover_letter"), + email_draft=result.get("email_draft"), + ) + + +@router.post("/fetch-job", response_model=FetchJobResponse) +async def fetch_job(job_url: str = File(...)) -> FetchJobResponse: + """Fetch and parse a job description from URL.""" + try: + result = fetch_job_description.invoke({"url": job_url}) + return FetchJobResponse(status="success", job_data=result) + except Exception as e: + raise HTTPException(status_code=500, detail=f"Failed to fetch job: {str(e)}") + + +@router.post("/parse-resume", response_model=ParseResumeResponse) +async def parse_resume_endpoint( + resume: UploadFile = File(...), +) -> ParseResumeResponse: + """Parse a resume file (PDF or DOCX).""" + import base64 + + ext = resume.filename.rsplit(".", 1)[-1].lower() if resume.filename else "" + if not ext or ext not in ["pdf", "docx", "doc"]: + raise HTTPException( + status_code=400, + detail="Unsupported file format. Use PDF or DOCX.", + ) + + file_content = await resume.read() + file_b64 = base64.b64encode(file_content).decode("utf-8") + + try: + result = parse_resume.invoke( + { + "file_content": file_b64, + "file_extension": ext, + } + ) + return ParseResumeResponse(status="success", resume_data=result) + except Exception as e: + raise HTTPException(status_code=500, detail=f"Failed to parse resume: {str(e)}") + + +@router.post("/score-ats", response_model=ScoreATSResponse) +async def score_ats( + job_url: str = File(...), + resume: UploadFile = File(...), +) -> ScoreATSResponse: + """Score resume against job description for ATS compatibility.""" + import base64 + + ext = resume.filename.rsplit(".", 1)[-1].lower() if resume.filename else "" + + file_content = await resume.read() + file_b64 = base64.b64encode(file_content).decode("utf-8") + + try: + job_data = fetch_job_description.invoke({"url": job_url}) + resume_data = parse_resume.invoke( + { + "file_content": file_b64, + "file_extension": ext, + } + ) + score = ats_score_resume.invoke( + { + "resume_data": resume_data, + "job_data": job_data, + } + ) + return ScoreATSResponse(status="success", ats_score=score) + except Exception as e: + raise HTTPException(status_code=500, detail=f"Failed to score: {str(e)}") diff --git a/backend/app/core/config.py b/backend/app/core/config.py index b00a5dc..471e304 100644 --- a/backend/app/core/config.py +++ b/backend/app/core/config.py @@ -17,10 +17,13 @@ class Settings(BaseSettings): api_port: int = 8000 cors_origins: str = "http://localhost:3000" deployment_environment: str | None = None + openai_api_key: str | None = None @property def cors_origins_list(self) -> list[str]: - return [origin.strip() for origin in self.cors_origins.split(",") if origin.strip()] + return [ + origin.strip() for origin in self.cors_origins.split(",") if origin.strip() + ] settings = Settings() diff --git a/backend/app/mcp/__init__.py b/backend/app/mcp/__init__.py new file mode 100644 index 0000000..9205e2b --- /dev/null +++ b/backend/app/mcp/__init__.py @@ -0,0 +1,5 @@ +"""MCP server module for TalentStreamAI.""" + +from app.mcp.server import mcp_server + +__all__ = ["mcp_server"] diff --git a/backend/app/mcp/server.py b/backend/app/mcp/server.py new file mode 100644 index 0000000..bfc5b02 --- /dev/null +++ b/backend/app/mcp/server.py @@ -0,0 +1,184 @@ +"""MCP Server for TalentStreamAI using LangGraph.""" + +from typing import Any, Literal +from typing_extensions import TypedDict + +from langgraph.graph import END, StateGraph + +from app.tools.job_fetcher import fetch_job_description +from app.tools.resume_parser import parse_resume +from app.tools.ats_scorer import ats_score_resume + + +class TalentStreamState(TypedDict): + """State for TalentStreamAI workflow.""" + + job_url: str | None + resume_file: str | None + resume_ext: str | None + job_data: dict | None + resume_data: dict | None + ats_score: dict | None + gap_analysis: dict | None + tailored_resume: str | None + cover_letter: str | None + email_draft: str | None + error: str | None + + +class MCPServer: + """MCP Server exposing TalentStreamAI tools via LangGraph.""" + + def __init__(self): + self.graph = self._build_graph() + self.tools = { + "fetch_job_description": self._wrap_tool(fetch_job_description), + "parse_resume": self._wrap_tool(parse_resume), + "ats_score_resume": self._wrap_tool(ats_score_resume), + } + + def _wrap_tool(self, tool): + """Wrap LangChain tool for MCP.""" + tool_schema = { + "name": tool.name, + "description": tool.description, + "input_schema": tool.args_schema, + } + + async def execute(**kwargs): + result = tool.invoke(kwargs) + return result + + return { + "schema": tool_schema, + "execute": execute, + } + + def _build_graph(self) -> StateGraph: + """Build the LangGraph workflow.""" + + def fetch_job(state: TalentStreamState) -> TalentStreamState: + url = state.get("job_url") + if not url: + return {"error": "No job URL provided"} + + try: + result = fetch_job_description.invoke({"url": url}) + return {"job_data": result} + except Exception as e: + return {"error": f"Failed to fetch job: {str(e)}"} + + def parse_resume(state: TalentStreamState) -> TalentStreamState: + file_content = state.get("resume_file") + file_ext = state.get("resume_ext") + + if not file_content or not file_ext: + return {"error": "No resume file provided"} + + try: + result = parse_resume.invoke( + { + "file_content": file_content, + "file_extension": file_ext, + } + ) + return {"resume_data": result} + except Exception as e: + return {"error": f"Failed to parse resume: {str(e)}"} + + def score_resume(state: TalentStreamState) -> TalentStreamState: + job_data = state.get("job_data") + resume_data = state.get("resume_data") + + if not job_data or not resume_data: + return {"error": "Missing job or resume data"} + + try: + result = ats_score_resume.invoke( + { + "resume_data": resume_data, + "job_data": job_data, + } + ) + return {"ats_score": result} + except Exception as e: + return {"error": f"Failed to score resume: {str(e)}"} + + def aggregate_gap_analysis(state: TalentStreamState) -> TalentStreamState: + ats_score = state.get("ats_score", {}) + resume_data = state.get("resume_data", {}) + job_data = state.get("job_data", {}) + + gap_analysis = { + "keyword_gaps": ats_score.get("keyword_gaps", []), + "skill_gaps": ats_score.get("skill_gaps", []), + "experience_gap": ats_score.get("experience", {}), + "education_gap": ats_score.get("education", {}), + "recommendations": ats_score.get("recommendations", []), + } + + return {"gap_analysis": gap_analysis} + + def should_generate_documents( + state: TalentStreamState, + ) -> Literal["generate", "skip"]: + if state.get("error"): + return "skip" + if state.get("gap_analysis"): + return "generate" + return "skip" + + graph = StateGraph(TalentStreamState) + + graph.add_node("fetch_job", fetch_job) + graph.add_node("parse_resume", parse_resume) + graph.add_node("score_resume", score_resume) + graph.add_node("aggregate_gap_analysis", aggregate_gap_analysis) + + graph.set_entry_point("fetch_job") + graph.add_edge("fetch_job", "parse_resume") + graph.add_edge("parse_resume", "score_resume") + graph.add_edge("score_resume", "aggregate_gap_analysis") + graph.add_edge("aggregate_gap_analysis", END) + + return graph.compile() + + async def run_workflow( + self, + job_url: str, + resume_file: str, + resume_ext: str, + ) -> dict[str, Any]: + """Run the complete TalentStreamAI workflow.""" + + initial_state: TalentStreamState = { + "job_url": job_url, + "resume_file": resume_file, + "resume_ext": resume_ext, + "job_data": None, + "resume_data": None, + "ats_score": None, + "gap_analysis": None, + "tailored_resume": None, + "cover_letter": None, + "email_draft": None, + "error": None, + } + + result = await self.graph.ainvoke(initial_state) + return result + + def get_tool_schemas(self) -> list[dict]: + """Get tool schemas for MCP registration.""" + return [tool["schema"] for tool in self.tools.values()] + + async def execute_tool(self, tool_name: str, params: dict) -> Any: + """Execute a specific tool.""" + if tool_name not in self.tools: + raise ValueError(f"Unknown tool: {tool_name}") + + tool = self.tools[tool_name] + return await tool["execute"](**params) + + +mcp_server = MCPServer() diff --git a/backend/app/services/langgraph/__init__.py b/backend/app/services/langgraph/__init__.py index f720d29..e980bd5 100644 --- a/backend/app/services/langgraph/__init__.py +++ b/backend/app/services/langgraph/__init__.py @@ -1 +1,15 @@ """LangGraph workflows and agent graphs will live here.""" + +from app.mcp.server import mcp_server +from app.services.langgraph.workflow import ( + workflow, + run_talentstream_workflow, + build_talentstream_workflow, +) + +__all__ = [ + "mcp_server", + "workflow", + "run_talentstream_workflow", + "build_talentstream_workflow", +] diff --git a/backend/app/services/langgraph/workflow.py b/backend/app/services/langgraph/workflow.py new file mode 100644 index 0000000..96c3138 --- /dev/null +++ b/backend/app/services/langgraph/workflow.py @@ -0,0 +1,355 @@ +"""Complete LangGraph workflow for TalentStreamAI with document generation.""" + +import os +from typing import Any +from typing import Optional + +from langchain_openai import ChatOpenAI +from langchain_core.messages import HumanMessage +from langgraph.graph import END, StateGraph +from pydantic import BaseModel, Field + +from app.tools.ats_scorer import ats_score_resume +from app.tools.job_fetcher import fetch_job_description +from app.tools.resume_parser import parse_resume +from app.core.config import settings + + +class TalentStreamState(BaseModel): + """State for the complete TalentStreamAI workflow.""" + + job_url: Optional[str] = None + resume_file: Optional[str] = None + resume_ext: Optional[str] = None + job_data: Optional[dict] = None + resume_data: Optional[dict] = None + ats_score: Optional[dict] = None + gap_analysis: Optional[dict] = None + tailored_resume: Optional[str] = None + cover_letter: Optional[str] = None + email_draft: Optional[str] = None + error: Optional[str] = None + + +def _get_llm(): + """Get LLM client (uses OpenAI).""" + api_key = settings.openai_api_key or os.environ.get("OPENAI_API_KEY") + if api_key: + return ChatOpenAI(model="gpt-4o", api_key=api_key) + raise ValueError("No LLM API key configured (OPENAI_API_KEY)") + + +def _generate_tailored_resume(state: TalentStreamState) -> TalentStreamState: + """Generate ATS-optimized tailored resume.""" + job_data = state.job_data + resume_data = state.resume_data + gap_analysis = state.gap_analysis + + if not job_data or not resume_data: + return TalentStreamState(error="Missing required data for resume generation") + + prompt = f"""Generate an ATS-optimized tailored resume for this position. + +JOB DETAILS: +Title: {job_data.get("title", "N/A")} +Company: {job_data.get("company", "N/A")} +Requirements: {job_data.get("requirements", "N/A")} +Responsibilities: {job_data.get("responsibilities", "N/A")} + +RESUME DATA: +Contact: {resume_data.get("contact_info", {})} +Summary: {resume_data.get("summary", "N/A")} +Experience: {resume_data.get("experience", [])} +Education: {resume_data.get("education", [])} +Skills: {resume_data.get("skills", [])} + +ATS GAPS TO ADDRESS: +{gap_analysis.get("keyword_gaps", [])} +{gap_analysis.get("skill_gaps", [])} + +Generate a complete tailored resume in markdown format that: +1. Mirrors the job description's vocabulary and key phrases +2. Highlights matching skills and experiences prominently +3. Addresses the identified gaps where possible +4. Uses ATS-friendly formatting (plain text, no tables or graphics) +5. Is maximum 2 pages in length + +Return ONLY the tailored resume content.""" + + try: + llm = _get_llm() + response = llm.invoke([HumanMessage(content=prompt)]) + return TalentStreamState( + tailored_resume=response.content, + job_url=state.job_url, + resume_file=state.resume_file, + resume_ext=state.resume_ext, + job_data=state.job_data, + resume_data=state.resume_data, + ats_score=state.ats_score, + gap_analysis=state.gap_analysis, + ) + except Exception as e: + return TalentStreamState(error=f"Failed to generate resume: {str(e)}") + + +def _generate_cover_letter(state: TalentStreamState) -> TalentStreamState: + """Generate narrative-driven cover letter.""" + job_data = state.job_data + resume_data = state.resume_data + ats_score = state.ats_score + + if not job_data or not resume_data: + return TalentStreamState( + error="Missing required data for cover letter generation" + ) + + contact = resume_data.get("contact_info", {}) + name = contact.get("name", "Candidate") + email = contact.get("email", "") + + prompt = f"""Write a compelling cover letter for this position. + +COMPANY: {job_data.get("company", "the company")} +POSITION: {job_data.get("title", "the position")} +LOCATION: {job_data.get("location", "N/A")} +RESPONSIBILITIES: {job_data.get("responsibilities", "N/A")} +QUALIFICATIONS: {job_data.get("requirements", "N/A")} + +CANDIDATE INFO: +Name: {name} +Email: {email} +Summary: {resume_data.get("summary", "")} +Experience: {resume_data.get("experience", [])} +Education: {resume_data.get("education", [])} +Skills: {resume_data.get("skills", [])} + +ATS SCORE: {ats_score.get("overall_score", 0) if ats_score else "N/A"}% + +Write a professional cover letter that: +1. Opens with a compelling hook referencing the company's mission or recent achievements +2. Connects the candidate's experience directly to the job requirements +3. Uses narrative format (storytelling) rather than bullet lists +4. Addresses gaps in qualifications if any (pivot skills, eagerness to learn) +5. Ends with a clear call to action +6. Is 300-400 words +7. Feels genuine and human-written, NOT AI-generated + +Use proper cover letter format with date, company address block, and signature. +Return ONLY the cover letter content.""" + + try: + llm = _get_llm() + response = llm.invoke([HumanMessage(content=prompt)]) + return TalentStreamState( + cover_letter=response.content, + job_url=state.job_url, + resume_file=state.resume_file, + resume_ext=state.resume_ext, + job_data=state.job_data, + resume_data=state.resume_data, + ats_score=state.ats_score, + gap_analysis=state.gap_analysis, + tailored_resume=state.tailored_resume, + ) + except Exception as e: + return TalentStreamState(error=f"Failed to generate cover letter: {str(e)}") + + +def _generate_email_draft(state: TalentStreamState) -> TalentStreamState: + """Generate ready-to-send Gmail draft.""" + job_data = state.job_data + resume_data = state.resume_data + cover_letter = state.cover_letter + + if not job_data or not resume_data: + return TalentStreamState(error="Missing required data for email generation") + + contact = resume_data.get("contact_info", {}) + name = contact.get("name", "Candidate") + email = contact.get("email", "") + + prompt = f"""Generate a ready-to-send email draft for a job application. + +TARGET COMPANY: {job_data.get("company", "the company")} +TARGET POSITION: {job_data.get("title", "the position")} +JOB URL: {job_data.get("url", "N/A")} + +CANDIDATE INFO: +Name: {name} +Email: {email} + +COVER LETTER SUMMARY (if available): +{cover_letter[:500] if cover_letter else "See attached cover letter."} + +Generate a concise, professional email that: +1. Has a compelling subject line (includes position title and candidate's unique angle) +2. Opens with personalized hook (why THIS company?) +3. Summarizes key qualifications in 2-3 sentences +4. References attached resume and cover letter +5. Is under 150 words (recruiter scanning email in 6 seconds) +6. Has professional sign-off + +Return in this format: +Subject: [subject line] +To: [recipient email - use placeholder if unknown] +Body: [email content] +Signature: [candidate name and contact info]""" + + try: + llm = _get_llm() + response = llm.invoke([HumanMessage(content=prompt)]) + return TalentStreamState( + email_draft=response.content, + job_url=state.job_url, + resume_file=state.resume_file, + resume_ext=state.resume_ext, + job_data=state.job_data, + resume_data=state.resume_data, + ats_score=state.ats_score, + gap_analysis=state.gap_analysis, + tailored_resume=state.tailored_resume, + cover_letter=state.cover_letter, + ) + except Exception as e: + return TalentStreamState(error=f"Failed to generate email: {str(e)}") + + +def build_talentstream_workflow() -> StateGraph: + """Build the complete TalentStreamAI LangGraph workflow.""" + + graph = StateGraph(TalentStreamState) + + graph.add_node("fetch_job", _fetch_job_node) + graph.add_node("parse_resume", _parse_resume_node) + graph.add_node("score_ats", _score_ats_node) + graph.add_node("analyze_gaps", _analyze_gaps_node) + graph.add_node("generate_resume", _generate_tailored_resume) + graph.add_node("generate_cover_letter", _generate_cover_letter) + graph.add_node("generate_email", _generate_email_draft) + + graph.set_entry_point("fetch_job") + graph.add_edge("fetch_job", "parse_resume") + graph.add_edge("parse_resume", "score_ats") + graph.add_edge("score_ats", "analyze_gaps") + graph.add_edge("analyze_gaps", "generate_resume") + graph.add_edge("generate_resume", "generate_cover_letter") + graph.add_edge("generate_cover_letter", "generate_email") + graph.add_edge("generate_email", END) + + return graph.compile() + + +def _fetch_job_node(state: TalentStreamState) -> TalentStreamState: + url = state.job_url + if not url: + return TalentStreamState(error="No job URL provided") + + try: + result = fetch_job_description.invoke({"url": url}) + return TalentStreamState( + job_data=result, + job_url=state.job_url, + resume_file=state.resume_file, + resume_ext=state.resume_ext, + ) + except Exception as e: + return TalentStreamState(error=f"Failed to fetch job: {str(e)}") + + +def _parse_resume_node(state: TalentStreamState) -> TalentStreamState: + file_content = state.resume_file + file_ext = state.resume_ext + + if not file_content or not file_ext: + return TalentStreamState(error="No resume file provided") + + try: + result = parse_resume.invoke( + { + "file_content": file_content, + "file_extension": file_ext, + } + ) + return TalentStreamState( + resume_data=result, + job_url=state.job_url, + resume_file=state.resume_file, + resume_ext=state.resume_ext, + job_data=state.job_data, + ) + except Exception as e: + return TalentStreamState(error=f"Failed to parse resume: {str(e)}") + + +def _score_ats_node(state: TalentStreamState) -> TalentStreamState: + job_data = state.job_data + resume_data = state.resume_data + + if not job_data or not resume_data: + return TalentStreamState(error="Missing job or resume data") + + try: + result = ats_score_resume.invoke( + { + "resume_data": resume_data, + "job_data": job_data, + } + ) + return TalentStreamState( + ats_score=result, + job_url=state.job_url, + resume_file=state.resume_file, + resume_ext=state.resume_ext, + job_data=state.job_data, + resume_data=state.resume_data, + ) + except Exception as e: + return TalentStreamState(error=f"Failed to score ATS: {str(e)}") + + +def _analyze_gaps_node(state: TalentStreamState) -> TalentStreamState: + ats_score = state.ats_score or {} + resume_data = state.resume_data or {} + + gap_analysis = { + "keyword_gaps": ats_score.get("keyword_gaps", []), + "skill_gaps": ats_score.get("skill_gaps", []), + "experience_gap": ats_score.get("experience", {}), + "education_gap": ats_score.get("education", {}), + "recommendations": ats_score.get("recommendations", []), + "resume_skills": resume_data.get("skills", []), + "resume_experience": resume_data.get("experience", []), + } + + return TalentStreamState( + gap_analysis=gap_analysis, + job_url=state.job_url, + resume_file=state.resume_file, + resume_ext=state.resume_ext, + job_data=state.job_data, + resume_data=state.resume_data, + ats_score=state.ats_score, + ) + + +async def run_talentstream_workflow( + job_url: str, + resume_file: str, + resume_ext: str, +) -> dict[str, Any]: + """Run the complete TalentStreamAI workflow.""" + + graph = build_talentstream_workflow() + + initial_state = TalentStreamState( + job_url=job_url, + resume_file=resume_file, + resume_ext=resume_ext, + ) + + result = await graph.ainvoke(initial_state) + return dict(result) + + +workflow = build_talentstream_workflow() diff --git a/backend/app/tools/__init__.py b/backend/app/tools/__init__.py new file mode 100644 index 0000000..5154a60 --- /dev/null +++ b/backend/app/tools/__init__.py @@ -0,0 +1,11 @@ +"""Tools for TalentStreamAI.""" + +from app.tools.job_fetcher import fetch_job_description +from app.tools.resume_parser import parse_resume +from app.tools.ats_scorer import ats_score_resume + +__all__ = [ + "fetch_job_description", + "parse_resume", + "ats_score_resume", +] diff --git a/backend/app/tools/ats_scorer.py b/backend/app/tools/ats_scorer.py new file mode 100644 index 0000000..35eff84 --- /dev/null +++ b/backend/app/tools/ats_scorer.py @@ -0,0 +1,346 @@ +"""Tool for ATS resume scoring against job descriptions.""" + +import re +from typing import Any + +from langchain_core.tools import tool +from pydantic import BaseModel, Field + +from app.tools.models import ( + ExperienceScore, + EducationScore, + ATSScore, +) + + +class ATSInput(BaseModel): + resume_data: dict = Field(..., description="Parsed resume data") + job_data: dict = Field(..., description="Job description data") + + +@tool(args_schema=ATSInput) +def ats_score_resume(resume_data: dict, job_data: dict) -> dict: + """Score a resume against a job description for ATS compatibility. + + Analyzes keyword matches, skills alignment, experience requirements, + and provides an overall ATS compatibility score. + + Args: + resume_data: Parsed resume data from parse_resume tool. + job_data: Job description data from fetch_job_description tool. + + Returns: + A dictionary containing the ATS score and detailed analysis. + """ + if not resume_data or not job_data: + return {"error": "Missing resume_data or job_data"} + + resume_text = resume_data.get("raw_text", "") or "" + job_text = job_data.get("description", "") or "" + job_requirements = job_data.get("requirements", "") or "" + + if not resume_text or not job_text: + return {"error": "Empty resume or job data"} + + keywords = _extract_keywords(job_text.lower()) + resume_keywords = _extract_keywords(resume_text.lower()) + + keyword_matches = [kw for kw in keywords if kw in resume_text.lower()] + keyword_gaps = [kw for kw in keywords if kw not in resume_text.lower()] + + resume_skills = [s.lower() for s in (resume_data.get("skills") or []) if s] + required_skills = _extract_skills(job_requirements.lower()) + + skill_matches = [ + s for s in required_skills if any(s.lower() in rs for rs in resume_skills) + ] + skill_gaps = [ + s for s in required_skills if not any(s.lower() in rs for rs in resume_skills) + ] + + experience_match = _analyze_experience( + resume_data.get("experience") or [], job_requirements.lower() + ) + + education_match = _analyze_education( + resume_data.get("education") or [], job_text.lower() + ) + + keyword_score = ( + min(len(keyword_matches) / len(keywords) * 40, 40) if keywords else 0 + ) + skill_score = ( + min(len(skill_matches) / len(required_skills) * 30, 30) + if required_skills + else 0 + ) + experience_score = min(experience_match["score"] * 20 / 100, 20) + education_score = min(education_match["score"] * 10 / 100, 10) + + overall_score = min( + keyword_score + skill_score + experience_score + education_score, 100 + ) + + return ATSScore( + overall_score=round(overall_score, 1), + keyword_score=round(keyword_score, 1), + keyword_matches=keyword_matches[:15], + keyword_gaps=keyword_gaps[:15], + skill_score=round(skill_score, 1), + skill_matches=skill_matches[:10], + skill_gaps=skill_gaps[:10], + experience=ExperienceScore(**experience_match), + education=EducationScore(**education_match), + recommendations=_generate_recommendations( + keyword_gaps, skill_gaps, experience_match, overall_score + ), + ).model_dump() + + +def _extract_keywords(text: str) -> list[str]: + common_words = { + "the", + "and", + "a", + "an", + "in", + "to", + "of", + "for", + "is", + "on", + "at", + "by", + "with", + "as", + "be", + "or", + "are", + "was", + "will", + "from", + "that", + "this", + "we", + "you", + "your", + "they", + "their", + "our", + "us", + "can", + "have", + "has", + "had", + "do", + "does", + "did", + "but", + "not", + "all", + "any", + "one", + "when", + "what", + "who", + "how", + "why", + "where", + "which", + "also", + "more", + "most", + "some", + "other", + "into", + "out", + "over", + "under", + "only", + "own", + "same", + "so", + "than", + "too", + "very", + "just", + "now", + "here", + "there", + "then", + "if", + "about", + "would", + "could", + "should", + "may", + "might", + "must", + "need", + "position", + "job", + "work", + "role", + "team", + "company", + "year", + "years", + "experience", + "required", + "preferred", + "qualifications", + } + + words = re.findall(r"[a-zA-Z][a-zA-Z0-9+-]{3,}", text) + + keywords = [ + w + for w in words + if w.lower() not in common_words and len(w) > 2 and not w.isdigit() + ] + + keyword_freq = {} + for kw in keywords: + keyword_freq[kw.lower()] = keyword_freq.get(kw.lower(), 0) + 1 + + sorted_keywords = sorted(keyword_freq.items(), key=lambda x: x[1], reverse=True) + + return [kw[0] for kw in sorted_keywords[:50]] + + +def _extract_skills(text: str) -> list[str]: + skill_patterns = [ + r"\b(Python|Java|JavaScript|TypeScript|C\+\+|Go|Rust|Ruby|SQL|NoSQL)\b", + r"\b(React|Angular|Vue|Node\.js|Express|Django|Flask|FastAPI)\b", + r"\b(AWS|Azure|GCP|Docker|Kubernetes|Jenkins|CI/CD|Git)\b", + r"\b(ML|Machine Learning|AI|Deep Learning|NLP|Data Science)\b", + r"\b(Agile|Scrum|Kanban|TDD|BDD|JIRA)\b", + r"\b(REST API|GraphQL|gRPC|Microservices)\b", + r"\b(PostgreSQL|MySQL|MongoDB|Redis|Elasticsearch)\b", + r"\b(Leadership|Management|Communication|Teamwork)\b", + ] + + skills = [] + for pattern in skill_patterns: + matches = re.findall(pattern, text, re.IGNORECASE) + skills.extend(matches) + + return list(set(skills)) + + +def _analyze_experience(resume_experience: list[dict], job_requirements: str) -> dict: + if not resume_experience: + return { + "score": 0, + "level": "not_found", + "details": "No experience found in resume", + "years": 0, + "required": 0, + } + + years_match = re.search( + r"(\d+)\+?\s*(?:years?|yrs?)\s*(?:of)?\s*experience", job_requirements + ) + required_years = int(years_match.group(1)) if years_match else 0 + + total_years = 0 + for exp in resume_experience: + exp_text = str(exp).lower() + year_match = re.search(r"(\d+)\+?\s*(?:years?|yrs?)", exp_text) + if year_match: + total_years += int(year_match.group(1)) + else: + total_years += 1 + + score = ( + 1.0 if total_years >= required_years else total_years / max(required_years, 1) + ) + score = min(score, 1.0) + + level = "senior" if total_years >= 5 else "mid" if total_years >= 2 else "junior" + + return { + "score": round(score * 100, 1), + "level": level, + "years": total_years, + "required": required_years, + } + + +def _analyze_education(resume_education: list[dict], job_text: str) -> dict: + education_keywords = { + "phd": 4, + "doctoral": 4, + "masters": 3, + "master's": 3, + "ms": 3, + "m.s.": 3, + "bachelor": 2, + "bachelor's": 2, + "bs": 2, + "b.s.": 2, + "associate": 1, + "high school": 0, + } + + required_education = 0 + for level, points in education_keywords.items(): + if level in job_text: + required_education = max(required_education, points) + + resume_education_text = " ".join([str(edu).lower() for edu in resume_education]) + + resume_level = 0 + for level, points in education_keywords.items(): + if level in resume_education_text: + resume_level = max(resume_level, points) + + score = ( + 1.0 + if resume_level >= required_education + else resume_level / max(required_education, 1) + ) + score = min(score, 1.0) + + return { + "score": round(score * 100, 1), + "required_level": required_education, + "resume_level": resume_level, + } + + +def _generate_recommendations( + keyword_gaps: list[str], + skill_gaps: list[str], + experience_match: dict, + overall_score: float, +) -> list[str]: + recommendations = [] + + if overall_score < 70: + recommendations.append( + f"Overall ATS score is {overall_score:.0f}%. Target 70%+ for best results." + ) + + if keyword_gaps: + top_gaps = keyword_gaps[:5] + recommendations.append( + f"Add these keywords to your resume: {', '.join(top_gaps)}" + ) + + if skill_gaps: + recommendations.append( + f"Highlight these missing skills: {', '.join(skill_gaps[:3])}" + ) + + if experience_match.get("required", 0) > 0: + if experience_match.get("years", 0) < experience_match.get("required", 0): + recommendations.append( + f"Job requires {experience_match.get('required')} years of experience. " + f"You have {experience_match.get('years')} years." + ) + + if not recommendations: + recommendations.append("Resume is well-optimized for this position!") + + return recommendations diff --git a/backend/app/tools/job_fetcher.py b/backend/app/tools/job_fetcher.py new file mode 100644 index 0000000..ca288dd --- /dev/null +++ b/backend/app/tools/job_fetcher.py @@ -0,0 +1,149 @@ +"""Tool for fetching and parsing job descriptions from URLs.""" + +import httpx +from bs4 import BeautifulSoup +from langchain_core.tools import tool + +from app.tools.models import JobFetcherInput, JobData + + +@tool(args_schema=JobFetcherInput) +def fetch_job_description(url: str) -> dict: + """Fetch and parse a job description from a URL. + + Extracts the job title, company, location, description, requirements, + and preferred qualifications from the job posting page. + + Args: + url: The URL of the job posting. + + Returns: + A dictionary containing job details matching JobData model. + """ + headers = { + "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36" + } + + with httpx.Client(timeout=30.0) as client: + response = client.get(url, headers=headers) + response.raise_for_status() + + soup = BeautifulSoup(response.text, "html.parser") + + text_content = soup.get_text(separator="\n", strip=True) + lines = [line.strip() for line in text_content.split("\n") if line.strip()] + + description = "\n".join(lines) + + title = _extract_title(soup, lines) or "" + company = _extract_company(soup, url) or "" + location = _extract_location(soup, lines) or "" + + requirements = ( + _extract_section(lines, ["requirements", "required", "qualifications"]) or "" + ) + responsibilities = ( + _extract_section(lines, ["responsibilities", "duties", "what you'll do"]) or "" + ) + benefits = _extract_section(lines, ["benefits", "perks", "what we offer"]) or "" + + return JobData( + url=url, + title=title, + company=company, + location=location, + description=description, + requirements=requirements, + responsibilities=responsibilities, + benefits=benefits, + ).model_dump() + + +def _extract_title(soup: BeautifulSoup, lines: list[str]) -> str | None: + for tag in soup.find_all(["h1", "h2", "h3"]): + text = tag.get_text(strip=True) + if text and len(text) < 200: + return text + + for line in lines[:10]: + if any( + keyword in line.lower() for keyword in ["hiring", "job", "position", "role"] + ): + return line + + return None + + +def _extract_company(soup: BeautifulSoup, url: str) -> str | None: + company_keywords = ["company", "employer", "organization"] + + for tag in soup.find_all( + ["span", "div", "p"], + class_=lambda x: x and any(k in x.lower() for k in company_keywords), + ): + text = tag.get_text(strip=True) + if text and len(text) < 100: + return text + + domain = url.split("/")[2] if "/" in url else "" + if "linkedin" in domain: + return "LinkedIn" + elif "indeed" in domain: + return "Indeed" + elif "glassdoor" in domain: + return "Glassdoor" + + return None + + +def _extract_location(soup: BeautifulSoup, lines: list[str]) -> str | None: + location_keywords = ["location", "location:", "address", "remote"] + + for tag in soup.find_all( + ["span", "div", "p"], + class_=lambda x: x and any(k in x.lower() for k in ["location", "address"]), + ): + text = tag.get_text(strip=True) + if text and len(text) < 100: + return text + + for line in lines[:20]: + lower = line.lower() + if any(kw in lower for kw in location_keywords): + return line.split(":", 1)[-1].strip() + + return None + + +def _extract_section(lines: list[str], keywords: list[str]) -> str | None: + start_idx = None + + for i, line in enumerate(lines): + lower = line.lower() + if any(kw in lower for kw in keywords): + start_idx = i + break + + if start_idx is None: + return None + + section_lines = [] + for line in lines[start_idx + 1 : start_idx + 50]: + if line and len(line) > 2: + if any( + heading in line.lower() + for heading in [ + "responsibilities", + "requirements", + "qualifications", + "benefits", + "about", + "who", + ] + ): + break + section_lines.append(line) + + if section_lines: + return "\n".join(section_lines) + return None diff --git a/backend/app/tools/models.py b/backend/app/tools/models.py new file mode 100644 index 0000000..ce7c166 --- /dev/null +++ b/backend/app/tools/models.py @@ -0,0 +1,94 @@ +"""Pydantic models for tool inputs and outputs.""" + +from typing import Optional + +from pydantic import BaseModel, Field + + +class JobFetcherInput(BaseModel): + url: str = Field(..., description="URL of the job posting") + + +class JobData(BaseModel): + url: str + title: str = "" + company: str = "" + location: str = "" + description: str = "" + requirements: str = "" + responsibilities: str = "" + benefits: str = "" + + +class ResumeParserInput(BaseModel): + file_content: str = Field(..., description="Base64 encoded file content") + file_extension: str = Field(..., description="File extension (.pdf or .docx)") + + +class ContactInfo(BaseModel): + email: Optional[str] = None + phone: Optional[str] = None + linkedin: Optional[str] = None + name: Optional[str] = None + + +class ExperienceEntry(BaseModel): + title: Optional[str] = None + company: Optional[str] = None + duration: Optional[str] = None + + +class EducationEntry(BaseModel): + school: Optional[str] = None + degree: Optional[str] = None + field_of_study: Optional[str] = None + + +class ResumeData(BaseModel): + contact_info: ContactInfo = Field(default_factory=ContactInfo) + summary: Optional[str] = None + experience: list[ExperienceEntry] = Field(default_factory=list) + education: list[EducationEntry] = Field(default_factory=list) + skills: list[str] = Field(default_factory=list) + raw_text: Optional[str] = None + + +class ATSInput(BaseModel): + resume_data: ResumeData + job_data: JobData + + +class ExperienceScore(BaseModel): + score: float + level: str + years: int + required: int + + +class EducationScore(BaseModel): + score: float + required_level: int + resume_level: int + + +class ATSScore(BaseModel): + overall_score: float = Field(..., ge=0, le=100) + keyword_score: float + keyword_matches: list[str] = Field(default_factory=list) + keyword_gaps: list[str] = Field(default_factory=list) + skill_score: float + skill_matches: list[str] = Field(default_factory=list) + skill_gaps: list[str] = Field(default_factory=list) + experience: ExperienceScore + education: EducationScore + recommendations: list[str] = Field(default_factory=list) + + +class GapAnalysis(BaseModel): + keyword_gaps: list[str] = Field(default_factory=list) + skill_gaps: list[str] = Field(default_factory=list) + experience_gap: dict = Field(default_factory=dict) + education_gap: dict = Field(default_factory=dict) + recommendations: list[str] = Field(default_factory=list) + resume_skills: list[str] = Field(default_factory=list) + resume_experience: list[dict] = Field(default_factory=list) diff --git a/backend/app/tools/resume_parser.py b/backend/app/tools/resume_parser.py new file mode 100644 index 0000000..64e25c5 --- /dev/null +++ b/backend/app/tools/resume_parser.py @@ -0,0 +1,246 @@ +"""Tool for parsing resumes from PDF and DOCX files.""" + +import base64 +import io +import re +from pathlib import Path +from typing import Optional + +from docx import Document as DocxDocument +from langchain_core.tools import tool +from pypdf import PdfReader +from pydantic import BaseModel + +from app.tools.models import ( + ContactInfo, + EducationEntry, + ExperienceEntry, + ResumeData, +) + + +class ResumeParserInput(BaseModel): + file_content: str + file_extension: str + + +@tool(args_schema=ResumeParserInput) +def parse_resume(file_content: str, file_extension: str) -> dict: + """Parse a resume from PDF or DOCX file content. + + Extracts contact info, summary, experience, education, and skills. + + Args: + file_content: Base64 encoded file content. + file_extension: The file extension (.pdf or .docx). + + Returns: + A dictionary containing parsed resume data matching ResumeData model. + """ + try: + file_bytes = base64.b64decode(file_content) + except Exception as e: + return {"error": f"Failed to decode file: {str(e)}"} + + ext = file_extension.lower().strip().lstrip(".") + + if ext == "pdf": + return _parse_pdf(file_bytes) + elif ext in ["docx", "doc"]: + return _parse_docx(file_bytes) + else: + return {"error": f"Unsupported file format: {file_extension}"} + + +def _parse_pdf(file_bytes: bytes) -> dict: + try: + reader = PdfReader(io.BytesIO(file_bytes)) + text = "\n".join([page.extract_text() or "" for page in reader.pages]) + except Exception as e: + return {"error": f"Failed to parse PDF: {str(e)}"} + + return _extract_sections(text) + + +def _parse_docx(file_bytes: bytes) -> dict: + try: + doc = DocxDocument(io.BytesIO(file_bytes)) + text = "\n".join([para.text for para in doc.paragraphs]) + except Exception as e: + return {"error": f"Failed to parse DOCX: {str(e)}"} + + return _extract_sections(text) + + +def _extract_sections(text: str) -> dict: + lines = [line.strip() for line in text.split("\n") if line.strip()] + + contact_info = _extract_contact_info(lines) + summary = _extract_summary(text) + experience = _extract_experience(lines) + education = _extract_education(lines) + skills = _extract_skills(lines) + + return ResumeData( + contact_info=contact_info, + summary=summary, + experience=experience, + education=education, + skills=skills, + raw_text=text, + ).model_dump() + + +def _extract_contact_info(lines: list[str]) -> ContactInfo: + contact = {} + + for line in lines[:15]: + line = line.strip() + + if "@" in line and "." in line: + contact["email"] = line + elif any(x in line for x in ["linkedin.com/in/", "github.com/", "portfolio"]): + contact["linkedin"] = line + + phone_match = re.search( + r"(\+?1?[-.\s]?\(?\d{3}\)?[-.\s]?\d{3}[-.\s]?\d{4})", line + ) + if phone_match: + contact["phone"] = phone_match.group(1) + + return ContactInfo(**contact) + + +def _extract_summary(text: str) -> str | None: + lines = [line.strip() for line in text.split("\n")] + + summary_keywords = ["summary", "profile", "objective", "about"] + + for i, line in enumerate(lines): + lower = line.lower().strip() + + if any(kw in lower for kw in summary_keywords): + summary_lines = [] + for next_line in lines[i + 1 : i + 6]: + if not next_line or len(next_line) < 3: + break + if any( + kw in next_line.lower() + for kw in [ + "experience", + "education", + "skills", + "work", + "employment", + ] + ): + break + summary_lines.append(next_line) + + if summary_lines: + return " ".join(summary_lines) + + return None + + +def _extract_experience(lines: list[str]) -> list[ExperienceEntry]: + experience = [] + + exp_keywords = ["experience", "employment", "work history", "professional"] + + for i, line in enumerate(lines): + lower = line.lower().strip() + + if any(kw in lower for kw in exp_keywords) and len(line) < 50: + exp_section_lines = lines[i + 1 : i + 40] + + current_entry = {} + for j, exp_line in enumerate(exp_section_lines): + if not exp_line or len(exp_line) < 3: + if current_entry: + experience.append(ExperienceEntry(**current_entry)) + current_entry = {} + continue + + if any( + kw in exp_line.lower() + for kw in ["experience", "employment", "education", "skills"] + ): + if current_entry: + experience.append(ExperienceEntry(**current_entry)) + break + + colon_idx = exp_line.find(":") + if colon_idx > 0: + key = exp_line[:colon_idx].strip() + value = exp_line[colon_idx + 1 :].strip() + current_entry[key.lower()] = value + elif not current_entry and exp_line and len(exp_line) > 10: + current_entry["title"] = exp_line + + return experience + + +def _extract_education(lines: list[str]) -> list[EducationEntry]: + education = [] + + edu_keywords = ["education", "academic", "degree", "university", "college"] + + for i, line in enumerate(lines): + lower = line.lower().strip() + + if any(kw in lower for kw in edu_keywords) and len(line) < 50: + edu_section_lines = lines[i + 1 : i + 25] + + current_entry = {} + for edu_line in edu_section_lines: + if not edu_line or len(edu_line) < 3: + if current_entry: + education.append(EducationEntry(**current_entry)) + current_entry = {} + continue + + if any( + kw in edu_line.lower() for kw in ["experience", "skills", "work"] + ): + if current_entry: + education.append(EducationEntry(**current_entry)) + break + + colon_idx = edu_line.find(":") + if colon_idx > 0: + key = edu_line[:colon_idx].strip() + value = edu_line[colon_idx + 1 :].strip() + current_entry[key.lower()] = value + elif not current_entry and edu_line and len(edu_line) > 5: + current_entry["school"] = edu_line + + return education + + +def _extract_skills(lines: list[str]) -> list[str]: + skills = [] + + skills_keywords = ["skills", "technical", "competencies", "tools"] + + for i, line in enumerate(lines): + lower = line.lower().strip() + + if any(kw in lower for kw in skills_keywords) and len(line) < 30: + skills_section = lines[i + 1 : i + 15] + + all_skills_text = " ".join(skills_section) + + skill_patterns = [ + r"([A-Z][a-zA-Z]*(?:\s+[A-Z][a-zA-Z]*)*)", + r"([A-Z]{2,})", + ] + + for pattern in skill_patterns: + matches = re.findall(pattern, all_skills_text) + skills.extend([s for s in matches if len(s) > 1 and len(s) < 30]) + + if skills: + break + + return list(set(skills))[:20] diff --git a/backend/pyproject.toml b/backend/pyproject.toml index b9698f4..35d451f 100644 --- a/backend/pyproject.toml +++ b/backend/pyproject.toml @@ -7,6 +7,14 @@ dependencies = [ "fastapi==0.115.12", "uvicorn[standard]==0.34.0", "pydantic-settings==2.8.1", + "langgraph>=0.2.0", + "langchain-core>=0.3.0", + "langchain-openai>=0.3.0", + "beautifulsoup4==4.13.0", + "python-docx==1.1.2", + "pypdf==5.1.0", + "httpx==0.28.1", + "python-multipart==0.0.20", ] [tool.uv] diff --git a/backend/uv.lock b/backend/uv.lock index d0d3bb2..b93359c 100644 --- a/backend/uv.lock +++ b/backend/uv.lock @@ -24,6 +24,101 @@ 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changed, 49 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 043f754..b2643ba 100644 --- a/README.md +++ b/README.md @@ -5,11 +5,56 @@ Squad Five capstone for the Andela AI Engineering Bootcamp. The goal is straight Product direction (for context while you build): - Ingest a resume plus a job posting URL. -- Diff the candidate’s story against the role (ATS-oriented gap analysis). -- Generate refreshed resume copy, a cover letter with narrative structure, and a Gmail-ready draft. + - Diff the candidate's story against the role (ATS-oriented gap analysis). + - Generate refreshed resume copy, a cover letter with narrative structure, and a Gmail-ready draft. -Implementation details for LangGraph, Gmail, and persistence are intentionally left open so feature teams can own them. +## Implementation Status +The backend is now fully implemented with a LangGraph-based workflow for generating job application materials. + +### API Endpoints + +| Endpoint | Method | Description | +| --- | --- | --- | +| `/api/v1/apply` | POST | Run the complete TalentStreamAI workflow | +| `/api/v1/fetch-job` | POST | Fetch and parse a job description from URL | +| `/api/v1/parse-resume` | POST | Parse a resume file (PDF or DOCX) | +| `/api/v1/score-ats` | POST | Score resume against job description for ATS compatibility | + +#### Apply Endpoint (Main) + +The main `/api/v1/apply` endpoint accepts: +- `job_url` (str): URL of the job posting +- `resume` (UploadFile): Resume file (PDF or DOCX) + +Returns job data, resume data, ATS score, gap analysis, tailored resume, cover letter, and email draft. + +### LangGraph Workflow + +The workflow consists of these nodes executed in sequence: + +1. **fetch_job**: Fetches job description from URL using web scraping +2. **parse_resume**: Parses PDF/DOCX resume into structured data +3. **score_ats**: Scores resume against job requirements +4. **analyze_gaps**: Identifies keyword and skill gaps +5. **generate_resume**: Generates ATS-optimized tailored resume (LLM) +6. **generate_cover_letter**: Generates narrative cover letter (LLM) +7. **generate_email**: Generates Gmail-ready email draft (LLM) + +### Tools + +Located in `backend/app/tools/`: +- **job_fetcher.py**: Fetches and parses job descriptions from URLs +- **resume_parser.py**: Parses PDF and DOCX resumes +- **ats_scorer.py**: Scores resumes against job requirements for ATS compatibility +- **models.py**: Pydantic models for all tool inputs and outputs + +### Environment Variables + +Required in `.env`: +| Key | Description | +| --- | --- | +| `OPENAI_API_KEY` | OpenAI API key for LLM calls (GPT-4o) | ## Prerequisites @@ -56,6 +101,7 @@ Both FastAPI (`pydantic-settings`) and Next.js (via `dotenv-cli` in `frontend/pa | `CORS_ORIGINS` | API | Comma-separated browser origins allowed to call the API. | | `NEXT_PUBLIC_API_URL` | UI (build + browser) | Public API base URL the browser calls. Leave empty for production builds that should call same-origin `/api/*` through CloudFront. | | `DEPLOYMENT_ENVIRONMENT` | API (`/api/v1/health` metadata) | Optional label such as `local`, `dev`, `staging`, or `prod`. | +| `OPENAI_API_KEY` | API | OpenAI API key for LLM calls (GPT-4o). Required for `/api/v1/apply` endpoint. | ## Run the full stack in Docker From a5214575f397f8762151649f6dbd013edb14e5b5 Mon Sep 17 00:00:00 2001 From: karosi12 Date: Thu, 23 Apr 2026 17:27:03 +0100 Subject: [PATCH 3/3] hot-fix: resolve none value and pass default value --- backend/app/api/v1/endpoints.py | 35 +++++++++++----------- backend/app/services/langgraph/workflow.py | 8 +++-- 2 files changed, 24 insertions(+), 19 deletions(-) diff --git a/backend/app/api/v1/endpoints.py b/backend/app/api/v1/endpoints.py index acf8438..be42812 100644 --- a/backend/app/api/v1/endpoints.py +++ b/backend/app/api/v1/endpoints.py @@ -1,6 +1,6 @@ """API v1 endpoints for TalentStreamAI.""" -from fastapi import APIRouter, HTTPException, UploadFile, File +from fastapi import APIRouter, HTTPException, UploadFile, File, Form from fastapi.responses import JSONResponse from pydantic import BaseModel, Field @@ -12,6 +12,17 @@ router = APIRouter() +def validate_resume_file(resume: UploadFile) -> str: + """Validate resume file format and return the extension.""" + ext = resume.filename.rsplit(".", 1)[-1].lower() if resume.filename else "" + if not ext or ext not in ["pdf", "docx", "doc"]: + raise HTTPException( + status_code=400, + detail="Unsupported file format. Use PDF or DOCX.", + ) + return ext + + class ApplyRequest(BaseModel): job_url: str = Field(..., description="URL of the job posting") @@ -48,18 +59,13 @@ class ScoreATSResponse(BaseModel): @router.post("/apply", response_model=ApplyResponse) async def apply_to_job( - job_url: str = File(..., description="URL of the job posting"), + job_url: str = Form(..., description="URL of the job posting"), resume: UploadFile = File(..., description="Resume file (PDF or DOCX)"), ) -> ApplyResponse: """Run complete TalentStreamAI workflow to generate application materials.""" import base64 - ext = resume.filename.rsplit(".", 1)[-1].lower() if resume.filename else "" - if not ext or ext not in ["pdf", "docx", "doc"]: - raise HTTPException( - status_code=400, - detail="Unsupported file format. Use PDF or DOCX.", - ) + ext = validate_resume_file(resume) file_content = await resume.read() file_b64 = base64.b64encode(file_content).decode("utf-8") @@ -91,7 +97,7 @@ async def apply_to_job( @router.post("/fetch-job", response_model=FetchJobResponse) -async def fetch_job(job_url: str = File(...)) -> FetchJobResponse: +async def fetch_job(job_url: str = Form(...)) -> FetchJobResponse: """Fetch and parse a job description from URL.""" try: result = fetch_job_description.invoke({"url": job_url}) @@ -107,12 +113,7 @@ async def parse_resume_endpoint( """Parse a resume file (PDF or DOCX).""" import base64 - ext = resume.filename.rsplit(".", 1)[-1].lower() if resume.filename else "" - if not ext or ext not in ["pdf", "docx", "doc"]: - raise HTTPException( - status_code=400, - detail="Unsupported file format. Use PDF or DOCX.", - ) + ext = validate_resume_file(resume) file_content = await resume.read() file_b64 = base64.b64encode(file_content).decode("utf-8") @@ -131,13 +132,13 @@ async def parse_resume_endpoint( @router.post("/score-ats", response_model=ScoreATSResponse) async def score_ats( - job_url: str = File(...), + job_url: str = Form(...), resume: UploadFile = File(...), ) -> ScoreATSResponse: """Score resume against job description for ATS compatibility.""" import base64 - ext = resume.filename.rsplit(".", 1)[-1].lower() if resume.filename else "" + ext = validate_resume_file(resume) file_content = await resume.read() file_b64 = base64.b64encode(file_content).decode("utf-8") diff --git a/backend/app/services/langgraph/workflow.py b/backend/app/services/langgraph/workflow.py index 96c3138..55862a7 100644 --- a/backend/app/services/langgraph/workflow.py +++ b/backend/app/services/langgraph/workflow.py @@ -48,6 +48,10 @@ def _generate_tailored_resume(state: TalentStreamState) -> TalentStreamState: if not job_data or not resume_data: return TalentStreamState(error="Missing required data for resume generation") + gaps = gap_analysis or {} + keyword_gaps = gaps.get("keyword_gaps", []) + skill_gaps = gaps.get("skill_gaps", []) + prompt = f"""Generate an ATS-optimized tailored resume for this position. JOB DETAILS: @@ -64,8 +68,8 @@ def _generate_tailored_resume(state: TalentStreamState) -> TalentStreamState: Skills: {resume_data.get("skills", [])} ATS GAPS TO ADDRESS: -{gap_analysis.get("keyword_gaps", [])} -{gap_analysis.get("skill_gaps", [])} +{keyword_gaps} +{skill_gaps} Generate a complete tailored resume in markdown format that: 1. Mirrors the job description's vocabulary and key phrases