# Python

```bash
pip install sirdas            # requires Node.js 20+; the engine is the same one the CLI and MCP server use
pip install "sirdas[langchain]"   # or [llamaindex]
```

The client runs `sirdas … --json` locally. It finds the executable in this order: `SIRDAS_BIN`, `sirdas` on `PATH`, `npx -y sirdas@<version>`.

## Read anything

```python
import sirdas

doc = sirdas.read("factura.pdf")          # also .docx, .xlsx, .pptx, .html, .eml, .png…
doc["status"]            # "ok" | raises sirdas.PasswordRequired
doc["document_type"]     # "factura"
for f in doc["fields"]:
    print(f["name"], f["value"], f["page"], f["bbox"])   # bbox: [x0, y0, x1, y1] in 0–1
doc["tables"], doc["form"], doc["documents"], doc["chunks"], doc["next_steps"]
```

Options: `password=`, `anonymize=True`, `ocr=True`, `max_tokens=1500`.

## Other calls

| Call | Returns |
|---|---|
| `sirdas.convert(path)` | `{format, markdown, tables, warnings}` |
| `sirdas.split_documents(path, output_dir=None)` | `{segments, files}` |
| `sirdas.forms(path)` | `{fields, checkboxes}` |
| `sirdas.classify(path)` | `{type, confidence, signals, runnerUp}` |
| `sirdas.chunks(path, max_tokens=1500)` | `[{id, pages, section, text, tokens}]` |
| `sirdas.tables(path)` | tables |
| `sirdas.dataset(paths, out_dir, format="chat", split=True)` | written files, incl. `README.md` dataset card |
| `sirdas.protect(path, password, output)` / `sirdas.unlock(path, output, password="")` | output path |

## LangChain and LlamaIndex

```python
from sirdas.integrations import SirdasLoader, SirdasReader

docs = SirdasLoader("contrato.pdf", max_tokens=800).load()       # langchain_core Documents
nodes = SirdasReader().load_data("historia.pdf")                 # llama_index Documents
```

Each chunk keeps `id` (stable content hash — use it as the vector id), `pages`, `section` and `document_type` in its metadata.

## Fine-tuning dataset in one line

```python
sirdas.dataset(["contratos/"], "ds/", format="alpaca")   # train/validation/test.jsonl + README.md
from datasets import load_dataset
load_dataset("json", data_files={"train": "ds/train.jsonl", "test": "ds/test.jsonl"})
```