Spaces:
Sleeping
Sleeping
| title: AI Recruitment Agent | |
| emoji: ⚡ | |
| colorFrom: indigo | |
| colorTo: green | |
| sdk: gradio | |
| sdk_version: "4.44.0" | |
| app_file: app.py | |
| pinned: false | |
| # ⚡ AI Recruitment Agent | |
| A production-grade hybrid candidate matching pipeline using **Groq LLM**, **Pinecone vector DB**, and a **Gradio** UI. | |
| ## Architecture | |
| ``` | |
| CSV Input → Stage 1: Normalize (Groq) | |
| → Stage 2: Embed + Match (Pinecone + SentenceTransformers) → Top 20 | |
| → Stage 3: Deterministic Rerank (Groq) → Top 10 | |
| → Stage 4: LLM Deep Review (Groq) → Top 5 | |
| → Stage 5: Final Synthesis (Groq) → Shortlist | |
| ``` | |
| ## Setup (Local) | |
| ### 1. Install dependencies | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| ### 2. Configure environment | |
| ```bash | |
| cp .env.example .env | |
| # Edit .env and fill in your API keys | |
| ``` | |
| ### 3. Create Pinecone index | |
| In your Pinecone console: | |
| - Create an index named `recruitment-index` (or whatever you set in `PINECONE_INDEX`) | |
| - Dimension: **384** for `all-MiniLM-L6-v2`, **1024** for `BAAI/bge-m3` | |
| - Metric: **cosine** | |
| ### 4. Run | |
| ```bash | |
| python app.py | |
| ``` | |
| Open http://localhost:7860 | |
| ## Setup (Hugging Face Spaces) | |
| Do **not** commit a `.env` file. Instead, go to your Space → **Settings → Repository Secrets** and add: | |
| | Secret | Example value | | |
| |--------|--------------| | |
| | `GROQ_API_KEYS` | `gsk_xxx,gsk_yyy` | | |
| | `GROQ_MODEL` | `llama3-70b-8192` | | |
| | `PINECONE_API_KEY` | `pcsk_xxx` | | |
| | `PINECONE_INDEX` | `recruitment-index` | | |
| | `EMBEDDING_MODEL` | `all-MiniLM-L6-v2` | | |
| | `STAGE2_TOP_K` | `20` | | |
| ## CSV Format | |
| | Column | Variants accepted | | |
| |--------|----------| | |
| | `name` | `full_name`, `candidate_name` | | |
| | `email` | `email_address` | | |
| | `skills` | `parsed_skills`, `technical_skills` | | |
| | `experience` | `parsed_work_experience`, `years_of_experience` | | |
| | `education` | `parsed_metadata_education` | | |
| | `resume_text` | `parsed_summary`, `summary` | | |
| ## Pipeline Stages | |
| | Stage | Method | Input | Output | | |
| |-------|--------|-------|--------| | |
| | 1. Normalize | Groq LLM | All candidates | Structured features | | |
| | 2. Embed & Match | Pinecone + SentenceTransformers | All candidates | Top 20 by similarity | | |
| | 3. Rerank | Groq LLM (deterministic scoring) | Top 20 | Top 10 with scores | | |
| | 4. Deep Review | Groq LLM | Top 5 | Verdicts + signals | | |
| | 5. Final Synthesis | Groq LLM | Top 5 reviews | Final ranked shortlist | | |