# Structured Output with Union Events API

This repository contains a small CLI tool that demonstrates how to parse raw log lines into typed events using **LangChain**'s structured output capabilities and **Pydantic v2**.

## Features

* Two event models:
  * `HttpOkEvent` – successful HTTP request (status 200)
  * `HttpErrorEvent` – error response (4xx/5xx)
* Uses a discriminator field (`kind`) to route the parsed data into the correct model.
* Parses each log line with an LLM via LangChain and returns a validated Pydantic object.
* Prints a simple table of all parsed events.

## Requirements

```text
langchain-core>=1.0
langchain-openai
pydantic>=2
python-dotenv
```

Install them with:

```bash
pip install -r requirements.txt
```

> **Note**: The script uses the `gpt-4o-mini` model by default. Set the environment variable `OPENAI_API_KEY` to your key.

## Usage

### From a file

```bash
python main.py --file logs.txt
```

### From stdin

```bash
echo -e "GET /api/v1/users 200 123ms\nPOST /api/v1/login 404 Not Found" | python main.py
```

The output will look like:

```
kind | path          | status | duration_ms | error_message
-----+---------------+--------+-------------+--------------
ok   | /api/v1/users | 200    | 123         |
error| /api/v1/login | 404    |             | Not Found
```

## How it works

The script builds a LangChain chain that:

1. **Prompts** the LLM to parse a single log line into JSON.
2. Uses `PydanticOutputParser` to validate and convert the JSON into one of the two Pydantic models.
3. Collects all parsed events and prints them in a table.

The union type is handled automatically by LangChain's structured output parser thanks to the discriminator field.

## License
MIT © 2026