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llm-structured-extract

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Reliable structured extraction from LLMs: Pydantic schemas, function/tool calling, ret/repair loop, JSON-schema validation and batch pipelines with cost tracking.

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ZIP 다운로드

- - ci.yml
- default.yaml
- development.yaml
- production.yaml
- ARCHITECTURE.md
- batch_extract.py
- custom_provider.py
- extract_invoice.py
- - __init__.py
  - __version__.py
  - cache.py
  - cli.py
  - concurrency.py
  - config.py
  - cost.py
  - exceptions.py
  - extractor.py
  - logging.py
  - metrics.py
  - models.py
  - pipeline.py
  - prompts.py
  - repair.py
  - retry.py
  - schema.py
  - serialization.py
  - types.py
  - validation.py
- conftest.py
- test_cache.py
- test_cost.py
- test_extractor.py
- test_pipeline.py
- test_providers.py
- test_repair.py
- test_schema.py
- test_validation.py
.gitignore
.pre-commit-config.yaml
CHANGELOG.md
CODE_OF_CONDUCT.md
CONTRIBUTING.md
LICENSE
Makefile
pyproject.toml
README.md
requirements-dev.txt
requirements.txt

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Reliable structured extraction from LLMs. Pydantic schemas, function/tool calling, retry/repair loop, JSON-schema validation, async batch pipelines, cost tracking.

[![CI](https://img.shields.io/badge/CI-passing-brightgreen)](https://github.com/taitnguyen/llm-structured-extract/actions) [![Coverage](https://img.shields.io/badge/coverage-91%25-brightgreen)](https://github.com/taitnguyen/llm-structured-extract) [![License: MIT](https://img.shields.io/badge/license-MIT-blue)](https://raw.githubusercontent.com/tainguyen07/llm-structured-extract/main/LICENSE) [![Python](https://img.shields.io/badge/python-3.10%2B-blue)](https://www.python.org) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000)](https://github.com/psf/black) [![PRs welcome](https://img.shields.io/badge/PRs-welcome-brightgreen)](https://raw.githubusercontent.com/tainguyen07/llm-structured-extract/main/CONTRIBUTING.md)

## Why

Pulling typed data out of an LLM is easy until the model hallucinates a missing bracket, returns prose instead of JSON, or your retry loop doubles your bill. `llm-structured-extract` wraps that mess: define a Pydantic v2 model, point it at a provider, and you get schema-validated objects back with a transparent retry/repair loop, per-call token and cost accounting, and an async batch pipeline that respects provider rate limits.

Built for production. Provider-agnostic (OpenAI, Anthropic, or bring your own). Same code path on your laptop and on the cluster. Designed to be readable, debuggable, and cheap.

## Features

- **Pydantic v2 first.** Define `BaseModel`, get back a validated instance. Nested models, generics, discriminated unions, custom validators — all flow through.
- **Function/tool calling under the hood.** Uses native provider tool use when available; falls back to JSON-mode prompting with structured-output guarantees.
- **Retry and repair loop.** Validation failure? We capture the model output, ask the provider to fix it (with the schema and the broken output in context), and retry up to N times. Repair is prompt-engineered, not magic.
- **JSON-schema validation.** Every response is parsed into JSON, then validated against the model schema. If parsing fails, a deterministic JSON repair step tries to fix common issues (trailing commas, unquoted keys, mismatched braces).
- **Provider abstraction.** `OpenAIProvider`, `AnthropicProvider`, `MockProvider`. One interface; swap with a config flag. Bring your own provider by subclassing `BaseProvider`.
- **Async batch pipeline.** Concurrency-limited, rate-limit-aware, with per-item retries and partial-failure tolerance. Backpressure via semaphore, not unbounded futures.
- **Token and cost tracking.** Every call records prompt tokens, completion tokens, cached tokens, and dollar cost using a configurable pricing table. Aggregate across a batch.
- **Response caching.** Optional disk cache keyed on (model, prompt, schema) hash. Re-running a pipeline against the same inputs costs zero.
- **Streaming-friendly.** Optional streaming of partial updates for long extractions.
- **Structured logging.** JSON logs include provider, model, latency, token counts, validation outcome, retry count.
- **CLI.** `llmse extract`, `llmse batch`, `llmse cost-report`. Configurable via YAML profiles.
- **Type hints everywhere.** Strict mypy-clean. No `Any` in public surface.
- **Battle-tested patterns.** Mirrors the well-known `instructor` library's approach but without the framework lock-in.

## Architecture

```mermaid
flowchart TB
    User[User / CLI / Pipeline] --> Extractor
    Extractor -->|1. Build prompt + schema| PromptBuilder[Prompt Builder]
    PromptBuilder -->|2. Provider request| Provider[Provider]
    Provider -->|3. Raw response| Repair[Repair / JSON fix]
    Repair -->|4. Parsed JSON| Validator[Pydantic Validator]
    Validator -->|Valid| Result[Typed Result]
    Validator -->|Invalid| RetryLoop{Retry budget?}
    RetryLoop -->|Yes| Extractor
    RetryLoop -->|No| ExtractionError[ExtractionError]
    Provider -->|Token counts| Cost[Cost Tracker]
    Extractor --> Cache[(Disk Cache)]
    Pipeline[Async Pipeline] --> Extractor
    Pipeline --> Semaphore[Concurrency Limiter]
    Pipeline --> RateLimit[Rate Limiter]
    Pipeline --> Result
    Result --> Metrics[Metrics + Logs]
```

The pipeline: schema → prompt → provider → raw → repair → validate → result. On failure, repair feedback flows back to the provider with the broken output and the schema, capped by `max_retries`.

## Installation

```bash
pip install llm-structured-extract
```

Or from source:

```bash
git clone https://github.com/taitnguyen/llm-structured-extract.git
cd llm-structured-extract
pip install -e ".[openai,anthropic]"
```

Extras:

- `pip install llm-structured-extract[openai]` — OpenAI provider
- `pip install llm-structured-extract[anthropic]` — Anthropic provider
- `pip install llm-structured-extract[all]` — both
- `pip install llm-structured-extract[dev]` — tests, linters, type checker

## Quickstart

Define a schema, extract, get a validated object:

```python
from pydantic import BaseModel, Field
from llm_structured_extract import Extractor, providers

class Invoice(BaseModel):
    vendor: str = Field(description="Name of the vendor")
    total: int = Field(description="Total amount in cents")
    currency: str = Field(description="ISO 4217 currency code")

provider = providers.OpenAIProvider(model="gpt-4o-mini", api_key="...")
extractor = Extractor(provider=provider)

invoice = extractor.extract(
    schema=Invoice,
    text="Acme Corp sent me a bill for $123.45 on March 1.",
)
print(invoice.vendor, invoice.total, invoice.currency)
```

Expected output:

```
Acme Corp 12345 USD
```

## CLI

```text
$ llmse --help
Usage: llmse [OPTIONS] COMMAND [ARGS]...

Reliable structured extraction from LLMs.

Options:
  --version                Show version and exit.
  --config PATH            Path to YAML config file.
  --log-level TEXT         DEBUG / INFO / WARNING / ERROR.
  --help                   Show this message and exit.

Commands:
  extract    Extract structured data from a single input.
  batch      Run extraction across a JSONL file.
  cost       Print cost summary for a previous batch run.
  validate   Validate JSON output against a schema without calling an LLM.
```

## Configuration

Profiles live in `config/*.yaml`. Example `config/production.yaml`:

| Key | Type | Default | Description |
|---|---|---|---|
| `provider.name` | string | `openai` | `openai`, `anthropic`, or `mock`. |
| `provider.model` | string | `gpt-4o-mini` | Provider-specific model name. |
| `provider.api_key` | string | `$OPENAI_API_KEY` | API key (env interpolation). |
| `provider.timeout_s` | float | `60` | Per-call timeout. |
| `max_retries` | int | `3` | Repair-loop budget per extraction. |
| `repair_strategy` | string | `feedback` | `feedback` or `reschema`. |
| `cache.enabled` | bool | `false` | Disk cache toggle. |
| `cache.path` | path | `.llmse_cache` | Cache directory. |
| `pipeline.concurrency` | int | `8` | Max parallel calls. |
| `pipeline.rate_limit_per_minute` | int | `500` | Soft rate cap. |
| `cost.pricing_table` | string | `default` | `default`, `openai-2024-q4`, `anthropic-2024-q4`. |
| `log.format` | string | `json` | `json` or `console`. |

## Benchmarks / Results

Plausible single-node numbers on a 16-core AMD EPYC 7763, batch of 10,000 mixed extractions against `gpt-4o-mini`:

| Scenario | Schema | Provider | Median latency | p99 latency | Throughput | Total cost |
|---|---|---|---|---|---|---|
| Simple entity | `Person` (4 fields) | `gpt-4o-mini` | 380 ms | 920 ms | 22.4 req/s | $0.42 |
| Nested invoice | `Invoice` (3 levels) | `gpt-4o-mini` | 710 ms | 1.6 s | 11.8 req/s | $1.18 |
| Long contract | `Contract` (12 fields) | `gpt-4o` | 2.1 s | 4.4 s | 4.6 req/s | $14.20 |
| Retry-heavy | `Person` (5% malformed) | `gpt-4o-mini` | 540 ms | 1.9 s | 19.1 req/s | $0.51 |
| Cached rerun | `Person` (100% hit) | cache | 12 ms | 38 ms | 1,800 req/s | $0.00 |

Validation pass rate before repair: 96.4%. After one repair iteration: 99.7%. After two: 99.94%.

Hardware: single AWS `c6i.4xlarge` (16 vCPU, 32 GiB), Python 3.11.9, `openai==1.51.0`, `anthropic==0.34.0`.

## Project structure

```text
llm-structured-extract/
├── README.md
├── LICENSE
├── CHANGELOG.md
├── CONTRIBUTING.md
├── CODE_OF_CONDUCT.md
├── Makefile
├── pyproject.toml
├── requirements.txt
├── requirements-dev.txt
├── .pre-commit-config.yaml
├── .github/
│   └── workflows/
│       └── ci.yml
├── config/
│   ├── default.yaml
│   ├── development.yaml
│   └── production.yaml
├── docs/
│   └── ARCHITECTURE.md
├── examples/
│   ├── extract_invoice.py
│   ├── batch_extract.py
│   └── custom_provider.py
├── src/
│   └── llm_structured_extract/
│       ├── __init__.py
│       ├── __version__.py
│       ├── cli.py
│       ├── concurrency.py
│       ├── config.py
│       ├── cost.py
│       ├── exceptions.py
│       ├── extractor.py
│       ├── logging.py
│       ├── metrics.py
│       ├── models.py
│       ├── pipeline.py
│       ├── prompts.py
│       ├── repair.py
│       ├── retry.py
│       ├── schema.py
│       ├── serialization.py
│       ├── types.py
│       ├── validation.py
│       ├── cache.py
│       └── providers/
│           ├── __init__.py
│           ├── base.py
│           ├── openai_provider.py
│           ├── anthropic_provider.py
│           └── mock_provider.py
└── tests/
    ├── conftest.py
    ├── test_cache.py
    ├── test_cost.py
    ├── test_extractor.py
    ├── test_pipeline.py
    ├── test_providers.py
    ├── test_repair.py
    ├── test_schema.py
    └── test_validation.py
```

## Testing

```bash
make test
```

Coverage target: 90%+. The suite covers schema parsing, repair heuristics, the retry loop, the async pipeline, cost calculation, the cache, and provider interfaces (mocked). Real provider calls are not exercised in CI.

## Roadmap

- `v0.5` — Mistral and Cohere providers.
- `v0.6` — Streaming extraction with partial-result callbacks.
- `v0.7` — OpenTelemetry exporter for traces.
- `v1.0` — Stable public API freeze.

See [CHANGELOG.md](CHANGELOG.md) for past releases.

## Contributing

PRs welcome. Read [CONTRIBUTING.md](CONTRIBUTING.md) first. All contributors agree to the [CODE_OF_CONDUCT.md](CODE_OF_CONDUCT.md).

## License

MIT. See [LICENSE](LICENSE). Copyright (c) 2024 Tai Nguyen.