# Show HN: ParseHawk – 100% Local Document AI with API, CLI, and Web UI > Source: > Published: 2026-06-25 10:57:50+00:00 **Local-first document AI. Run 100% locally by default, with API, CLI, and Web UI.** [Quickstart](#quickstart) · [First extraction](#first-extraction) · [API, CLI, and Web UI](#api-cli-and-web-ui) · [Requirements](#requirements) · [Development](#development) ParseHawk turns PDFs, scans, images, text files, and Markdown into structured JSON without sending sensitive documents to a third-party AI API. It is built for developers and teams working with private data: invoices, receipts, contracts, internal documents, customer files, medical or financial records, and other unstructured inputs that should stay under your control. The default setup runs fully locally. ParseHawk uses vLLM on Linux NVIDIA machines and vLLM Metal on macOS Apple Silicon, so you can run practical document extraction on a server or even on your MacBook. You can drive the same workflow from the browser, from `curl` , or from the `parsehawk` CLI. - Extract structured JSON from unstructured PDFs, scans, images, text, and Markdown - Define your own schemas for the data you want back - Run zero-shot extraction with only instructions and a schema - Add few-shot examples when a document type needs more guidance - Improve extraction quality without training a model - Improve extractors over time with better instructions, schemas, and examples - Get validated JSON output using JSON Schema Draft 2020-12 - Keep files, jobs, extractors, and results local by default - Use the Web UI for humans and the REST API or CLI for scripts, services, and agents - Control both the local stack and the extraction API from one `parsehawk` CLI - Run on Linux with vLLM or on macOS Apple Silicon with vLLM Metal ParseHawk runs on macOS Apple Silicon and Linux x86_64 with an NVIDIA GPU. Windows is not supported yet. ## macOS Apple Silicon details Required: `uv` - Docker Desktop - Xcode Command Line Tools - Apple Silicon Mac with enough unified memory for NuExtract3-W4A16 Verified: - MacBook Pro M3 Pro with 18 GB unified memory - MacBook Pro M3 Pro with 36 GB unified memory Recommended: - 16 GB unified memory minimum for the default local workflow - 32 GB or more for larger context lengths ## Linux NVIDIA details Required: `uv` - Docker Engine - Docker Compose - NVIDIA driver - NVIDIA Container Toolkit - NVIDIA GPU with enough VRAM for NuExtract3-W4A16 Verified: - NVIDIA L4 with 24 GB VRAM Recommended: - 16 GB VRAM minimum for the default local workflow - 24 GB VRAM or more for larger context lengths Run ParseHawk from a Git checkout with [ uv](https://docs.astral.sh/uv/getting-started/installation/) and install the CLI as an editable local tool: ``` git clone https://github.com/parsehawk/parsehawk.git cd parsehawk uv tool install --editable . parsehawk start ``` Then open: - Web UI: [http://127.0.0.1:5173](http://127.0.0.1:5173) - API docs: [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs) - OpenAPI JSON: [http://127.0.0.1:8000/openapi.json](http://127.0.0.1:8000/openapi.json) Stop ParseHawk: ``` parsehawk stop ``` Check your local setup: ``` parsehawk doctor ``` The easiest first run is image-to-JSON extraction with the bundled receipt image and the seeded prebuilt `Receipt` extractor. - Start ParseHawk with `parsehawk start` . - Open [http://127.0.0.1:5173](http://127.0.0.1:5173). - Upload .`tests/fixtures/receipt/receipt.jpg` - Select the prebuilt `Receipt` extractor. - Select the uploaded file and click **Run extraction**. - Inspect the extracted fields and JSON result. Expected fields include: ``` { "merchant_name": "PARSEHAWK COFFEE", "receipt_id": "R-1001", "date": "2026-06-21", "total": 11.22, "currency": "EUR" } parsehawk files upload tests/fixtures/receipt/receipt.jpg parsehawk extractors list parsehawk extract \ tests/fixtures/receipt/receipt.jpg \ --extractor extractor_... \ --wait ``` Use the `Receipt` extractor ID from `extractors list` . ``` API=http://127.0.0.1:8000 EXTRACTOR_ID=$( curl -s "$API/v1/extractors" | jq -r '.[] | select(.name=="Receipt" and .is_prebuilt==true) | .id' ) FILE_ID=$( curl -s -X POST "$API/v1/files" \ -F "upload=@tests/fixtures/receipt/receipt.jpg;type=image/jpeg" | jq -r '.id' ) JOB_ID=$( curl -s -X POST "$API/v1/jobs" \ -H "Content-Type: application/json" \ -d "{\"extractor_id\":\"$EXTRACTOR_ID\",\"file_id\":\"$FILE_ID\"}" | jq -r '.id' ) curl -s "$API/v1/jobs/$JOB_ID" | jq . ``` Jobs are asynchronous. Poll `GET /v1/jobs/{job_id}` until `status` is `completed` or `failed` . ParseHawk exposes one local API. The CLI and Web UI are clients of that API. The CLI has two jobs: it controls the local ParseHawk stack (`start` , `stop` , `status` , `doctor` , `restart` ) and it works with the data plane (`files` , `extractors` , `schemas` , `jobs` , and one-shot `extract` ). Core resources: ``` POST /v1/files GET /v1/files GET /v1/files/{file_id} GET /v1/files/{file_id}/content DELETE /v1/files/{file_id} POST /v1/schemas/validate POST /v1/extractors GET /v1/extractors GET /v1/extractors/{extractor_id} PATCH /v1/extractors/{extractor_id} DELETE /v1/extractors/{extractor_id} POST /v1/jobs GET /v1/jobs GET /v1/jobs/{job_id} DELETE /v1/jobs/{job_id} ``` Jobs return the canonical extracted JSON inline as `job.result.data` once completed. Useful CLI commands: ``` parsehawk files upload document.pdf parsehawk files list parsehawk schemas validate schema.json parsehawk extractors create --name invoice_v1 --schema schema.json --instructions "Extract invoice fields." parsehawk jobs create --extractor extractor_... --file-id file_... parsehawk jobs get job_... parsehawk extract document.pdf --schema schema.json --instructions "Extract invoice fields." --wait ``` Public IDs are TypeID-style strings with resource prefixes such as `file_...` , `extractor_...` , and `job_...` . An extractor combines: - a name - natural-language instructions - JSON Schema Draft 2020-12 - optional few-shot examples - optional thinking mode A minimal extractor schema: ``` { "type": "object", "properties": { "invoice_number": { "type": ["string", "null"], "description": "The invoice number exactly as shown on the document." }, "total_amount": { "type": ["number", "null"], "description": "The final total amount to pay." } }, "required": ["invoice_number", "total_amount"], "additionalProperties": false } ``` Few-shot examples can use inline text or uploaded files: ``` { "name": "invoice_v1", "instructions": "Extract the invoice fields exactly.", "schema": { "type": "object", "properties": { "invoice_number": { "type": ["string", "null"] } }, "required": ["invoice_number"], "additionalProperties": false }, "examples": [ { "input": { "type": "text", "text": "Invoice #A-123" }, "output": { "invoice_number": "A-123" } }, { "input": { "type": "file", "file_id": "file_..." }, "output": { "invoice_number": "B-456" } } ] } ``` ParseHawk validates model output against the schema and stores the canonical result under `job.result.data` . The schema dialect is documented in [ docs/schemas/parsehawk-extraction-schema.schema.json](/parsehawk/parsehawk/blob/main/docs/schemas/parsehawk-extraction-schema.schema.json). It supports JSON Schema plus optional `x-parsehawk.semantic` metadata for NuExtract3-oriented scalar hints.The default model is: ``` numind/NuExtract3-W4A16 ``` ParseHawk talks to the runtime through an OpenAI-compatible API. On macOS, the runtime runs on the host through vLLM Metal because Metal acceleration is not available inside a normal Linux container. On Linux, the runtime runs as part of Docker Compose. Current defaults: | Setting | Default | |---|---| | vLLM package | `vllm==0.23.0` | | Linux runtime image | `vllm/vllm-openai:v0.23.0` | | Model | `numind/NuExtract3-W4A16` | | GPU memory utilization | `0.5` | | Max model length | `8192` by default, `32768` on larger Apple Silicon Macs | | PDF render DPI | `170` | | PDF max pages | `25` | Common overrides: ``` PARSEHAWK_VLLM_MAX_MODEL_LEN=16384 parsehawk start PARSEHAWK_VLLM_GPU_MEMORY_UTILIZATION=0.6 parsehawk start PARSEHAWK_VLLM_MODEL=numind/NuExtract3-W4A16 parsehawk start PARSEHAWK_VLLM_IMAGE=vllm/vllm-openai:v0.23.0 parsehawk start ``` ParseHawk uses Pydantic settings. Common environment variables: | Environment variable | Default | Description | |---|---|---| `PARSEHAWK_DATA_DIR` | `data` | Local storage directory for SQLite, uploaded files, logs, and local state. | `PARSEHAWK_DATABASE_PATH` | `data/parsehawk.db` | SQLite database path. | `PARSEHAWK_LOG_LEVEL` | `INFO` | Log level for API, worker, runtime, and Web UI logs. | `PARSEHAWK_LOG_MODEL_IO` | `false` | When `true` and `PARSEHAWK_LOG_LEVEL=DEBUG` , log model-runtime request and response JSON from the API/worker process. Image data URLs are redacted. | `PARSEHAWK_INFERENCE_ENGINE` | `none` | API/worker inference engine. `parsehawk start` sets this to `vllm` when a runtime is configured. | `PARSEHAWK_VLLM_BASE_URL` | `http://127.0.0.1:8080/v1` | OpenAI-compatible model runtime URL. | `PARSEHAWK_VLLM_MODEL` | `numind/NuExtract3-W4A16` | Model name sent to the runtime. | `PARSEHAWK_VLLM_MAX_MODEL_LEN` | platform-specific | vLLM context length. Overrides the automatic local default. | `PARSEHAWK_VLLM_MAX_NUM_SEQS` | `128` | Linux vLLM maximum concurrent decode sequences. | `PARSEHAWK_VLLM_GPU_MEMORY_UTILIZATION` | `0.5` | vLLM memory reservation fraction. | `PARSEHAWK_VLLM_IMAGE` | `vllm/vllm-openai:v0.23.0` | Linux Docker runtime image. | `PARSEHAWK_VLLM_CACHE_HOME` | `~/.cache/vllm` | Linux host cache for vLLM compile artifacts. | `PARSEHAWK_PDF_MAX_PAGES` | `25` | Maximum PDF pages rendered for one extraction. | `PARSEHAWK_PDF_RENDER_DPI` | `170` | PDF page image render DPI. | `PARSEHAWK_TELEMETRY_DISABLED` | `false` | When truthy, disables anonymous usage analytics. | CLI config: ``` parsehawk config list parsehawk config set log.level DEBUG parsehawk restart ``` ParseHawk collects **anonymous usage analytics**. Two events are sent to [PostHog](https://posthog.com): `install` — once per install, the first time you start ParseHawk.`run_started` — each time a user starts an extraction run. Each event carries only coarse, non-identifying data: - a random per-install id stored in `data/telemetry-id` - the input type ( `file` or`text` , on runs) - the ParseHawk version and your operating system name - an approximate location (country/region) ParseHawk never sends file contents, file names, extractor instructions, schemas, or extracted data, and it never creates a personal profile from the per-install id. The first time you run `parsehawk start` or `parsehawk dev` , you will see a notice describing this. To opt out, set either of these before starting ParseHawk: ``` export PARSEHAWK_TELEMETRY_DISABLED=1 export DO_NOT_TRACK=1 ``` When ParseHawk runs in Docker, these variables are passed through to the API and worker containers automatically. Maintainers can tag internal usage instead of dropping it: ``` export PARSEHAWK_TELEMETRY_INTERNAL=1 ``` By default ParseHawk stores local state under `data/` : ``` data/ parsehawk.db files/ logs/ parsehawk-state.json telemetry-id ``` Stop ParseHawk before deleting `data/` : ``` parsehawk stop rm -rf data parsehawk start ``` If `data/` is deleted while ParseHawk is still running, old processes can keep serving from already-open SQLite handles. `parsehawk start` refuses to start when target ports are already occupied without a live state file. In that case, stop the process using the port and start again. Development requires: `git` `just` `uv` `pnpm` Useful commands: ``` just setup # install dependencies and pre-commit hooks just start # product-like Docker mode just dev # local-source development mode just web-dev # Web UI dev server only just smoke # local smoke flow just test # Python tests just e2e # local end-to-end API tests (needs the model runtime up) just format # format Python just lint # Ruff linting just typecheck # ty type checking just web-typecheck # TypeScript checks just web-test # Web UI tests just web-build # production Web UI build just check # all standard checks just hooks-run # run pre-commit on all files ``` Pre-commit hooks are not installed automatically by Git. Run this once per clone: ``` just setup ``` The hooks run Ruff, ty, Python tests, Web UI typecheck, and Web UI tests. CI should still run the same checks; hooks are just the fast local feedback loop. Development mode: ``` parsehawk dev ``` Product-like local mode: ``` parsehawk start ``` Start with the built-in health checks: Check status: ``` parsehawk status ``` Read logs: ``` ls data/logs tail -f data/logs/api.log tail -f data/logs/worker.log tail -f data/logs/runtime.log ``` Restart: ``` parsehawk restart ``` If Docker or the runtime gets into a strange state, stop ParseHawk before removing local data: ``` parsehawk stop rm -rf data parsehawk start ``` If the Model Runtime is slow to become ready, give it a few minutes on first startup while vLLM loads model weights, profiles memory, and warms kernels. To start only the API and Web UI without local inference: ``` parsehawk start --runtime none ``` ParseHawk stands on excellent open-source projects, including: [FastAPI](https://github.com/fastapi/fastapi)for the API framework and OpenAPI docs[vLLM](https://github.com/vllm-project/vllm)and[vLLM Metal](https://github.com/vllm-project/vllm-metal)for local model serving[NuExtract3](https://huggingface.co/numind/NuExtract3-W4A16)for the default extraction model[Pydantic](https://github.com/pydantic/pydantic),[Ruff](https://github.com/astral-sh/ruff), and[uv](https://github.com/astral-sh/uv)for the Python toolchain[React](https://github.com/facebook/react),[Vite](https://github.com/vitejs/vite), and[Tailwind CSS](https://github.com/tailwindlabs/tailwindcss)for the Web UI Near-term focus: - make the macOS and Linux runtime paths boringly reliable - publish an installable CLI package - improve the Web UI schema builder - add stronger end-to-end runtime smoke tests - document deployment options for VPS and container platforms Later: - Python SDK - migrations and PostgreSQL support - batch extraction - review/correction workflows - eval tooling - bring-your-own OpenAI-compatible runtime ParseHawk is developed by Totoy GmbH in Vienna, Austria. If you are interested in an enterprise deployment, private-cloud setup, or managed infrastructure for sensitive document workflows, contact [support@totoy.ai](mailto:support@totoy.ai). ParseHawk follows SemVer. Until `v1.0.0` , ParseHawk is in developer preview. Breaking changes may happen in any minor release, for example from `v0.1.0` to `v0.2.0` . Patch releases, such as `v0.1.1` , are intended to be backward-compatible bug fixes for that minor line. We will move to `v1.0.0` once the core CLI commands, the REST API, and the config file format are stable enough for users to rely on. ParseHawk is open source under the Apache-2.0 license. See [ LICENSE](/parsehawk/parsehawk/blob/main/LICENSE). Third-party dependencies retain their own licenses.