semvec/pypi-packages huggingface dataset:
rail-engine-ingest 0.1.6
Python SDK for Railtown AI Rail Engine - Ingestion
License: MIT
Requires Python: >=3.10
Keywords: railtown, rail-engine, ingestion, sdk

# Rail Engine Ingestion SDK

Python SDK for ingesting data into Railtown AI Rail Engine - handles embeddings, storage, indexing, and webhook publishing.

## Overview

The `rail-engine-ingest` package provides a Pythonic interface for publishing data to Rail Engine. It supports async/await patterns, Pydantic model validation, and familiar configuration patterns.

## Installation

```bash
uv pip install rail-engine-ingest
```

## Quick Start

```python

# Copy your ENGINE_TOKEN from https://cndr.railtown.ai Project Settings

import asyncio
from railtown.engine.ingest import RailengineIngest
import base64
import json

engine_token = os.getenv("ENGINE_TOKEN")

async def main():
    # Initialize client
    async with RailengineIngest(engine_token=engine_token) as client:
        # Ingest data
        data = {
            "name": "Chicken Shawarma",
            "meal": "lunch",
            "calories": 95
        }
        response = await client.upsert(data)
        print(f"Status: {response.status_code}")

asyncio.run(main())
```

## Configuration

### Environment Variable

- `ENGINE_TOKEN` - Base64-encoded JSON string containing ingestion credentials

### Constructor Parameters

- `engine_token` (optional) - ENGINE_TOKEN string (if not provided, reads from env)
- `model` (optional) - Pydantic model type for validating ingested data

## Features

- **Single `upsert()` method** - Unified interface for ingesting data
- **Multiple data formats** - Accepts Pydantic models, dictionaries, or JSON strings
- **Model validation** - Optional Pydantic model validation
- **Direct JSON payload** - Sends data directly as JSON to the ingestion endpoint
- **Async/await support** - Built for modern async Python applications

## Error Handling

The SDK provides custom exception classes:

- `RailtownError` - Base exception
- `RailtownBadRequestError` - 400 Bad Request
- `RailtownUnauthorizedError` - 401 Unauthorized
- `RailtownNotFoundError` - 404 Not Found
- `RailtownConflictError` - 409 Conflict
- `RailtownServerError` - 5xx Server errors

Exceptions are raised on errors.

## Requirements

- uv
- Python 3.10+
- httpx >= 0.24.0
- pydantic >= 1.10.0

## Related Package

For retrieving and searching data from Rail Engine, see [`rail-engine-ingest`](https://pypi.org/project/rail-engine-ingest/).

## License

MIT
rail-engine 0.1.6
Python SDK for Railtown AI Rail Engine - Retrieval
License: MIT
Requires Python: >=3.10
Keywords: railtown, rail-engine, retrieval, sdk

# Rail Engine Retrieval SDK

Python SDK for retrieving and searching data from Railtown AI Rail Engine - handles embeddings, storage documents, and indexed content.

## Overview

The `rail-engine` package provides a Pythonic interface for retrieving and searching data from Rail Engine. It supports async/await patterns, client-side filtering, automatic pagination, and Pydantic model deserialization.

## Installation

```bash
uv pip install rail-engine
```

## Quick Start

```python
import asyncio
from railtown.engine import Railengine

async def main():
    # Initialize client (reads from ENGINE_PAT and ENGINE_ID env vars)
    async with Railengine() as client:
        # Search vector store
        results = client.search_vector_store(
            query="apple"
        )
        async for item in results:
            print(item)

asyncio.run(main())
```

## Configuration

### Environment Variables

- `ENGINE_PAT` (required) - Personal Access Token
- `ENGINE_ID` (required) - Engine ID (can also be passed to constructor)
- `RAILTOWN_API_URL` (optional) - Base API URL (defaults to `https://cndr.railtown.ai/api`)

### Constructor Parameters

- `pat` (optional) - PAT token (if not provided, reads from `ENGINE_PAT` env)
- `engine_id` (optional) - Engine ID (if not provided, reads from `ENGINE_ID` env, required if not in env)
- `api_url` (optional) - Base API URL (if not provided, reads from `RAILTOWN_API_URL` env or defaults to production)
- `model` (optional) - Pydantic model type for deserializing retrieved data

## Features

- **Multiple retrieval methods**:
  - `search_vector_store()` - Semantic search in vector stores
  - `get_storage_document_by_event_id()` - Get document by EventId
  - `get_storage_document_by_customer_key()` - Get documents by customer key
  - `query_storage_by_jsonpath()` - Query using JSONPath
  - `list_storage_documents()` - List all documents with pagination
  - `search_index()` - Full-text search using Azure Search
- **Client-side filtering** - Filter results using `filter_fn` parameter
- **Automatic pagination** - Handles pagination automatically
- **Model deserialization** - Optional Pydantic model support with per-call override
- **Graceful error handling** - Returns None or empty iterables on errors
- **Async/await support** - Built for modern async Python applications

## Error Handling

The SDK provides custom exception classes:

- `RailtownError` - Base exception
- `RailtownBadRequestError` - 400 Bad Request
- `RailtownUnauthorizedError` - 401 Unauthorized
- `RailtownNotFoundError` - 404 Not Found
- `RailtownConflictError` - 409 Conflict
- `RailtownServerError` - 5xx Server errors

Returns `None` or empty iterables on errors (graceful degradation).

## Requirements

- uv
- Python 3.10+
- httpx >= 0.24.0
- pydantic >= 1.10.0

## Related Package

For ingesting data into Rail Engine, see [`rail-engine`](https://pypi.org/project/rail-engine/).

## License

MIT