Ordis

Ordis is a local-first tool and library that turns messy, unstructured text into clean, structured data using a schema-driven extraction pipeline powered by LLMs. You give it a schema that describes the fields you expect, point it at some raw text, and choose any OpenAI-compatible model. Ordis builds the prompt, calls the model, validates the output, and returns either a correct structured record or a clear error.

Ordis does for LLM extraction what Prisma does for databases: strict schemas, predictable output and no more glue code.

Status

✅ CLI functional - Core extraction pipeline working with real LLMs. Ready for testing and feedback.

✅ Programmatic API - Can be used as an npm package in Node.js applications.

Features

• Local-first extraction: Supports Ollama, LM Studio, or any OpenAI-compatible endpoint
• Schema-first workflow: Define your data structure upfront
• Deterministic output: Returns validated records or structured failures
• Token budget awareness: Automatic token counting with warnings and limits
• Dual-purpose: Use as a CLI or import as a library
• TypeScript support: Full type definitions included

Example

ordis extract \
  --schema examples/invoice.schema.json \
  --input examples/invoice.txt \
  --base http://localhost:11434/v1 \
  --model llama3.1:8b \
  --debug

Sample schema (invoice.schema.json):

{
  "fields": {
    "invoice_id": { "type": "string" },
    "amount": { "type": "number" },
    "currency": { "type": "string", "enum": ["USD", "SGD", "EUR"] },
    "date": { "type": "string", "format": "date-time", "optional": true }
  }
}

Model Compatibility

Works with any service exposing an OpenAI-compatible API:

• Ollama
• LM Studio
• OpenRouter
• Mistral
• Groq
• OpenAI
• vLLM servers

Installation

From npm (recommended)

Install globally to use the CLI anywhere:

npm install -g @ordis-dev/ordis
ordis --help

Or install locally in your project:

npm install @ordis-dev/ordis

From Source

git clone https://github.com/ordis-dev/ordis
cd ordis
npm install
npm run build
node dist/cli.js --help

Usage

CLI Usage

Extract data from text using a schema:

ordis extract \
  --schema examples/invoice.schema.json \
  --input examples/invoice.txt \
  --base http://localhost:11434/v1 \
  --model llama3.1:8b \
  --debug

With API key (for providers like OpenAI, Deepseek, etc.):

ordis extract \
  --schema examples/invoice.schema.json \
  --input examples/invoice.txt \
  --base https://api.deepseek.com/v1 \
  --model deepseek-chat \
  --api-key your-api-key-here

Programmatic Usage

Use ordis as a library in your Node.js application:

import { extract, loadSchema, LLMClient } from '@ordis-dev/ordis';

// Load schema from file
const schema = await loadSchema('./invoice.schema.json');

// Or create schema from object
import { loadSchemaFromObject } from 'ordis-cli';
const schema = loadSchemaFromObject({
  fields: {
    invoice_id: { type: 'string' },
    amount: { type: 'number' },
    currency: { type: 'string', enum: ['USD', 'EUR', 'SGD'] }
  }
});

// Configure LLM
const llmConfig = {
  baseURL: 'http://localhost:11434/v1',
  model: 'llama3.2:3b'
};

// Extract data
const result = await extract({
  input: 'Invoice #INV-2024-0042 for $1,250.00 USD',
  schema,
  llmConfig
});

if (result.success) {
  console.log(result.data);
  // { invoice_id: 'INV-2024-0042', amount: 1250, currency: 'USD' }
  console.log('Confidence:', result.confidence);
} else {
  console.error('Extraction failed:', result.errors);
}

Using LLM Presets:

import { extract, loadSchema, LLMPresets } from '@ordis-dev/ordis';

const schema = await loadSchema('./schema.json');

// Use preset configurations
const result = await extract({
  input: text,
  schema,
  llmConfig: LLMPresets.ollama('llama3.2:3b')
  // Or: LLMPresets.openai(apiKey, 'gpt-4o-mini')
  // Or: LLMPresets.lmStudio('local-model')
});

What Works

• ✅ Schema loader and validator
• ✅ Prompt builder with confidence scoring
• ✅ Universal LLM client (OpenAI-compatible APIs)
• ✅ Token budget awareness with warnings and errors
• ✅ Structured error system
• ✅ CLI extraction command
• ✅ Programmatic API for library usage
• ✅ Field-level confidence tracking
• ✅ TypeScript type definitions
• ✅ Performance benchmarks

Performance

Pipeline overhead is negligible (~1-2ms). LLM calls dominate execution time (1-10s depending on model). See benchmarks/README.md for detailed metrics.

Run benchmarks:

npm run benchmark

Roadmap

Completed in v0.1.0:

• ✅ Core extraction pipeline with schema validation
• ✅ Token budget awareness and management
• ✅ Confidence scoring for extracted data
• ✅ Programmatic API for library usage
• ✅ CLI tool with debug mode
• ✅ Comprehensive test suite and benchmarks
• ✅ Support for any OpenAI-compatible API

Upcoming:

• [ ] Smart input truncation (#40)
• [ ] Multi-pass extraction for large inputs (#41)
• [ ] Config file support (#16)
• [ ] Output formatting options (#14)
• [ ] Batch extraction (#19)
• [ ] More example schemas (#13)

Contributing

Contributions are welcome!