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You are working inside a LangGraph-based Python project.

Create project planning documentation for a new AI service named "InterviewGraph".

Your task: Generate two markdown documents:

  1. README.md (high-level overview)
  2. docs/PLAN.md (detailed product & technical planning document)

The project name is: InterviewGraph

The core idea: InterviewGraph takes a resume PDF as input and generates structured, difficulty-rated interview questions based on the resume content.

==================================== README.md REQUIREMENTS

README.md must include:

  1. Project Title
  2. Short Description (3~5 lines)
  3. Core Features (bullet points)
  4. Architecture Overview (high-level explanation of LangGraph pipeline)
  5. Example Flow (PDF → Questions)
  6. Tech Stack (Python, LangGraph, FastAPI, LLM provider)
  7. MVP Scope
  8. Future Roadmap (short bullet list)
  9. How to Run (placeholder instructions acceptable)

Tone:

  • Professional
  • Clear
  • Developer-focused
  • No marketing exaggeration

==================================== docs/PLAN.md REQUIREMENTS

PLAN.md must include structured sections:

1. Project Vision

  • Why this project exists
  • Target users

2. User Scenarios

  • Primary scenario: resume upload → question generation
  • Failure scenario: text extraction failure

3. Functional Requirements

Include:

  • PDF input handling
  • Resume section parsing
  • Signal extraction (skills, projects, keywords)
  • Interview question generation (15 questions)
  • Difficulty rating (1~5)
  • Structured JSON output
  • Markdown output

4. Non-Functional Requirements

Include:

  • Privacy considerations (no raw resume logging)
  • LLM output schema validation
  • Error handling & retry
  • Stateless default design

5. LangGraph Architecture Design

List required nodes:

  • extract_text
  • parse_sections
  • extract_signals
  • generate_questions
  • rate_difficulty
  • format_output

Describe:

  • State design (raw_text, sections, signals, questions, markdown, errors)
  • Linear pipeline for MVP
  • Possible conditional branch for error handling

6. Data Model Design

Define structured interview question format:

  • id
  • category (tech | project | system | deep-dive)
  • difficulty (1~5)
  • question
  • expected_points
  • followups

7. MVP Definition of Done

Clearly define what counts as completed MVP.

8. Out of Scope (for MVP)

Explicitly list:

  • OCR
  • Vector DB / RAG
  • Multi-agent system
  • Mock interview answer evaluation

9. Development Phases

Phase 1: Schema & PDF extraction Phase 2: Section parsing & signal extraction Phase 3: Question generation & rating Phase 4: API integration

====================================

Formatting Rules:

  • Use clean markdown formatting
  • Use clear headers
  • No emojis
  • No casual tone
  • No unnecessary verbosity

Do not generate code. Only generate the two markdown documents.

If docs/ directory does not exist, create it logically in output structure.