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- AI-powered CV search engine with LanceDB and MCP","maintainers":[{"name":"zahidzorbaz","email":"zahidzorbaz@gmail.com"},{"name":"sametdemir","email":"smtdmr44@gmail.com"},{"name":"beyz","email":"beyza@dolusoft.com"}],"readme":"# HireBase\r\n\r\nAI-powered recruitment platform built as an MCP Server. Parses CVs, extracts structured data with GPT, stores them in a LanceDB vector database, and manages the entire hiring pipeline — from candidate matching to interview scheduling.\r\n\r\n## Architecture\r\n\r\n**DDD + Clean Architecture** with clear separation of concerns:\r\n\r\n```\r\nsrc/\r\n├── domain/           # Entities, Value Objects, Repository Interfaces\r\n├── application/      # Use Cases, Ports, DTOs\r\n├── infrastructure/   # LanceDB, OpenAI, PDF/DOCX/OCR Parsers\r\n├── interface/        # MCP Server, Tools, Dashboard, CLI\r\n└── shared/           # Errors, Types, Utilities\r\n```\r\n\r\n**Design Principle:** MCP Server = Data layer (parse, store, search, return). The AI client using the tools handles analysis, ranking strategy, and match explanations.\r\n\r\n## Tech Stack\r\n\r\n| Technology | Purpose |\r\n|---|---|\r\n| TypeScript 5.9 | Language |\r\n| LanceDB | Vector database (embedded, serverless) |\r\n| OpenAI `text-embedding-3-small` | 1536-dim embeddings |\r\n| OpenAI `gpt-5-mini` | Structured CV extraction (Responses API) |\r\n| MCP SDK | Model Context Protocol server |\r\n| Nuxt 4 + Nuxt UI | Real-time dashboard |\r\n| unpdf | PDF text extraction |\r\n| mammoth | DOCX text extraction |\r\n| Tesseract.js | OCR for image-based PDFs |\r\n\r\n## MCP Tools (38)\r\n\r\n### CV Management\r\n\r\n| Tool | Description |\r\n|---|---|\r\n| `add_cv` | Parse CV file (PDF/DOCX), extract structured data, embed, store |\r\n| `update_cv` | Update existing CV, archive old version |\r\n| `delete_cv` | Delete candidate and all related data |\r\n| `get_cv_detail` | Full candidate data with download URL |\r\n| `get_cv_chunks` | CV section chunks with optional section filter |\r\n| `get_cv_versions` | Version history |\r\n| `list_cvs` | Paginated candidate list with sorting |\r\n| `manage_tags` | Add/remove/list tags |\r\n| `check_duplicate_candidate` | Check if a candidate already exists by name or email before importing |\r\n\r\n### Search & Matching\r\n\r\n| Tool | Description |\r\n|---|---|\r\n| `semantic_search` | Vector similarity search with optional section filter |\r\n| `filter_candidates` | Structured filtering (skills, location, experience, languages, tags) |\r\n| `match_candidates` | Composite scoring: semantic similarity + skill overlap + loyalty factor |\r\n| `compare_candidates` | Compare 2–5 candidates side by side, optionally with job skill match scores |\r\n| `export_results` | Export as JSON or CSV |\r\n\r\n### Job Posting Management\r\n\r\n| Tool | Description |\r\n|---|---|\r\n| `add_job_posting` | Create job posting with required/preferred skills and metadata |\r\n| `list_job_postings` | List all job postings |\r\n| `get_job_posting` | Retrieve specific job posting details |\r\n| `update_job_posting` | Update existing job posting |\r\n| `delete_job_posting` | Delete a job posting |\r\n\r\n### Reference CVs\r\n\r\n| Tool | Description |\r\n|---|---|\r\n| `add_reference_cv` | Mark a candidate as a reference/benchmark CV for a job posting |\r\n| `list_reference_cvs` | List all reference CVs for a job posting |\r\n| `remove_reference_cv` | Remove a reference CV from a job posting |\r\n\r\n### Recruitment Pipeline\r\n\r\n| Tool | Description |\r\n|---|---|\r\n| `add_to_pipeline` | Add candidate to a job posting pipeline |\r\n| `update_pipeline_status` | Update application status |\r\n| `add_pipeline_note` | Add notes, call attempts, or interview records |\r\n| `set_pending_action` | Track pending actions with due dates and owner (us/candidate) |\r\n| `get_pipeline` | Full pipeline view for a job posting |\r\n| `get_pipeline_candidates` | Lightweight list of candidates in a pipeline (ID, name, status only) |\r\n| `get_candidate_history` | Candidate's recruitment history across all postings |\r\n| `get_pending_actions` | All overdue and upcoming pending actions |\r\n\r\n**Pipeline statuses:** `new` → `contacted` → `unreachable` · `not_interested` · `interview_scheduled` → `no_show` · `interviewed` → `rejected` · `offer_sent` → `hired`\r\n\r\n### Screening\r\n\r\n| Tool | Description |\r\n|---|---|\r\n| `get_screening_history` | Shortlisted, screened out, and in-pipeline candidates for a job posting |\r\n| `generate_screening_report` | Summary report with statistics, shortlisted, screened out, and pending candidates |\r\n\r\n### Call Batches\r\n\r\n| Tool | Description |\r\n|---|---|\r\n| `create_call_batch` | Create a call batch: assign candidates to a person for calling |\r\n| `get_call_batch_status` | Status of a call batch: candidates, phone numbers, and call outcomes |\r\n| `update_call_outcome` | Update call outcome: `pending`, `reached`, or `unreachable` |\r\n\r\n### System\r\n\r\n| Tool | Description |\r\n|---|---|\r\n| `get_stats` | Database statistics (candidates, skills, locations, etc.) |\r\n| `get_server_status` | Server version, dashboard URL, connected clients, config |\r\n| `reset_database` | Drop and recreate all tables |\r\n\r\n## How It Works\r\n\r\n### CV Ingestion\r\n\r\n```\r\nCV File (PDF/DOCX/Scanned PDF)\r\n    │\r\n    ▼\r\n[1] Parse text (unpdf / mammoth / Tesseract.js OCR)\r\n    │\r\n    ▼\r\n[2] Extract structured data (GPT-5 mini)\r\n    │  name, experience, education, skills, languages...\r\n    ▼\r\n[3] Chunk into sections\r\n    │  summary, each experience, education, skills, projects, certifications, full text\r\n    ▼\r\n[4] Generate embeddings (text-embedding-3-small, 1536d)\r\n    │\r\n    ▼\r\n[5] Store in LanceDB (candidates + cv_chunks + cv_versions)\r\n```\r\n\r\n### Candidate Matching\r\n\r\n`match_candidates` uses a composite scoring algorithm:\r\n\r\n```\r\ncompositeScore = (0.5 × vectorScore) + (0.3 × skillOverlap) + (0.2 × loyaltyFactor)\r\n```\r\n\r\n| Component | Weight | Description |\r\n|---|---|---|\r\n| Vector Score | 50% | Semantic similarity between CV and job description |\r\n| Skill Overlap | 30% | Ratio of matched required skills to total required skills |\r\n| Loyalty Factor | 20% | Candidate stability: `stable` = 1.0, `moderate` = 0.7, `flight_risk` = 0.4 |\r\n\r\n### Recruitment Pipeline\r\n\r\n```\r\nJob Posting\r\n    │\r\n    ▼\r\n[1] match_candidates → ranked candidate list\r\n    │\r\n    ▼\r\n[2] add_to_pipeline → create applications (status: new)\r\n    │\r\n    ▼\r\n[3] update_pipeline_status → track progress through hiring stages\r\n    │  add_pipeline_note → log calls, interviews, notes\r\n    │  set_pending_action → assign follow-up tasks\r\n    ▼\r\n[4] get_pending_actions → monitor overdue/upcoming items\r\n```\r\n\r\n## Dashboard\r\n\r\nHireBase includes a real-time dashboard built with **Nuxt 4 + Nuxt UI + Tailwind CSS**. The dashboard connects via WebSocket and provides live tracking of MCP tool executions.\r\n\r\nEnable via environment variable:\r\n\r\n```env\r\nDASHBOARD_ENABLED=true\r\nDASHBOARD_PORT=61496\r\n```\r\n\r\nThe dashboard URL is available in `get_server_status` output.\r\n\r\n## Database Schema\r\n\r\nLanceDB tables:\r\n\r\n| Table | Purpose |\r\n|---|---|\r\n| `candidates` | Candidate profiles (name, email, skills, experience, loyalty score, tags) |\r\n| `cv_chunks` | Searchable CV sections with 1536-dim vector embeddings |\r\n| `cv_versions` | Archived CV versions for update history |\r\n| `job_postings` | Job listings with required/preferred skills and status |\r\n| `applications` | Pipeline records linking candidates to jobs with status tracking |\r\n| `application_events` | Audit trail: status changes, call attempts, interview records |\r\n\r\n## Installation\r\n\r\n### Prerequisites\r\n\r\n- **Node.js >= 22** ([download](https://nodejs.org/))\r\n- **OpenAI API Key** — required for CV extraction and embeddings. Get one at [platform.openai.com/api-keys](https://platform.openai.com/api-keys)\r\n\r\n> **Important:** HireBase will not start without a valid `OPENAI_API_KEY`. If you see `OPENAI_API_KEY environment variable is required`, make sure the key is set in your MCP configuration (see below).\r\n\r\n### Claude Code (recommended)\r\n\r\nNo global install needed — `npx` downloads and runs the latest version automatically:\r\n\r\n```bash\r\nclaude mcp add hirebase -s user \\\r\n  -e OPENAI_API_KEY=sk-your-key \\\r\n  -e DASHBOARD_ENABLED=true \\\r\n  -- npx -y @dolusoft/hirebase-mcp\r\n```\r\n\r\nThat's it. Restart Claude Code and HireBase tools will be available.\r\n\r\n### Claude Desktop\r\n\r\nAdd to your config file:\r\n\r\n- **macOS:** `~/Library/Application Support/Claude/claude_desktop_config.json`\r\n- **Windows:** `%APPDATA%\\Claude\\claude_desktop_config.json`\r\n\r\n```json\r\n{\r\n  \"mcpServers\": {\r\n    \"hirebase\": {\r\n      \"command\": \"npx\",\r\n      \"args\": [\"-y\", \"@dolusoft/hirebase-mcp\"],\r\n      \"env\": {\r\n        \"OPENAI_API_KEY\": \"sk-your-key\",\r\n        \"DASHBOARD_ENABLED\": \"true\"\r\n      }\r\n    }\r\n  }\r\n}\r\n```\r\n\r\n### Other MCP Clients\r\n\r\nAny MCP-compatible client can use HireBase. The key requirement is passing `OPENAI_API_KEY` as an environment variable:\r\n\r\n```bash\r\n# Run directly\r\nOPENAI_API_KEY=sk-your-key npx -y @dolusoft/hirebase-mcp\r\n\r\n# Or install globally\r\nnpm install -g @dolusoft/hirebase-mcp\r\nOPENAI_API_KEY=sk-your-key hirebase-mcp\r\n```\r\n\r\n### From Source\r\n\r\n```bash\r\ngit clone https://github.com/dolusoft/hirebase.git\r\ncd hirebase\r\npnpm install\r\npnpm build\r\ncp .env.example .env  # edit .env and add your OPENAI_API_KEY\r\npnpm start\r\n```\r\n\r\n### Environment Variables\r\n\r\n| Variable | Default | Description |\r\n|---|---|---|\r\n| `OPENAI_API_KEY` | — | **Required.** OpenAI API key for embeddings and CV extraction |\r\n| `LANCEDB_PATH` | `./data/lancedb` | LanceDB storage path (relative to CWD) |\r\n| `EMBEDDING_MODEL` | `text-embedding-3-small` | OpenAI embedding model |\r\n| `EXTRACTION_MODEL` | `gpt-5-mini` | OpenAI model for structured CV extraction |\r\n| `DASHBOARD_ENABLED` | `true` | Enable real-time dashboard |\r\n| `DASHBOARD_PORT` | `0` (random) | Dashboard server port (0 = auto-assign) |\r\n\r\n## Key Design Decisions\r\n\r\n- **LanceDB has no UPDATE** — update is implemented as delete + add at repository level\r\n- **Complex fields stored as JSON strings** — skills, experience, etc. serialized in Utf8 columns\r\n- **Batch embedding** — all chunks embedded in a single OpenAI API call\r\n- **Seed rows** — tables use `__seed__` rows for schema inference, filtered out in queries\r\n- **Versioning** — old CV data archived before updates, full history accessible\r\n- **Section-level chunking** — each work experience is a separate chunk for granular matching\r\n- **OCR fallback** — image-based PDFs automatically processed with Tesseract.js\r\n- **Composite scoring** — matching combines semantic, skill-based, and loyalty signals\r\n\r\n## License\r\n\r\nMIT\r\n","readmeFilename":"README.md"}