87 lines
2.5 KiB
Markdown
87 lines
2.5 KiB
Markdown
# Local AI Service
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This service runs a local Hugging Face summarization model and also exposes document text extraction with OCR for supported PDFs and images.
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## Capabilities
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- job/role summarization
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- PDF text extraction
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- OCR fallback for scanned PDFs
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- OCR for image uploads (`png`, `jpg`, `jpeg`, `webp`)
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- DOCX / TXT / MD extraction
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- optional Ollama-backed CV block classification for harder sectioning
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## Install
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Windows:
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```powershell
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python -m venv .venv
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.\.venv\Scripts\Activate.ps1
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pip install -r requirements.txt
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python -m uvicorn app:app --host 127.0.0.1 --port 8001 --workers 1
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```
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Linux / macOS:
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```bash
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python3 -m venv .venv
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source .venv/bin/activate
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pip install -r requirements.txt
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python -m uvicorn app:app --host 127.0.0.1 --port 8001 --workers 1
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```
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If the host is missing `python3-venv` or `pip`, use the bootstrap script instead:
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```bash
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./scripts/bootstrap-and-test.sh bootstrap
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```
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## Docker
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The Dockerfile installs Tesseract OCR so scanned PDFs and supported images can be processed inside the container.
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## Tests
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Run the summarizer unit tests with:
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```bash
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./scripts/bootstrap-and-test.sh test
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```
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The script:
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- creates `.venv` with stdlib `venv` when available
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- falls back to user-space `virtualenv` when host `venv` support is missing
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- installs `requirements-dev.txt`
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- writes pytest cache under `tmp/pytest-cache` to avoid stale root-owned `.pytest_cache` directories
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## API
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- `GET /health` — health check and runtime capabilities, including lazy model state (`model_loaded`, `model_disabled`, `summarize_available`, `model_load_error`) plus Ollama version/model metadata when configured
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- `POST /summarize` — JSON body `{ "text": "...", "max_length": 150, "min_length": 30 }`
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- `POST /extract-text` — multipart file upload, returns extracted text and OCR metadata
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- `POST /cv/classify-block` — JSON body `{ "block": "..." }`, uses Ollama when `OLLAMA_MODEL` is configured
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## Ollama
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Set these before starting the service if you want the hybrid CV classifier enabled:
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```bash
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export OLLAMA_BASE_URL=http://ollama:11434
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export OLLAMA_MODEL=qwen2.5:7b
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```
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Choose the model by setting `OLLAMA_MODEL` and then warming it with the helper script:
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```bash
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OLLAMA_MODEL=qwen2.5:7b ./scripts/start-ollama-cv.sh
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```
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Equivalent manual flow:
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```bash
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docker compose up -d ollama
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docker compose exec ollama ollama pull qwen2.5:7b
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docker compose up -d ai-service
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```
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- Model weights are downloaded on first pull.
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- OCR quality depends on scan quality and language support.
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- Default OCR language is English (`eng`).
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