Skip to main content

Python SDK for Railtown AI Rail Engine - Retrieval

Project description

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

uv pip install rail-engine

Quick Start

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.

License

MIT

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

rail_engine-0.1.7.tar.gz (25.3 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

rail_engine-0.1.7-py3-none-any.whl (17.4 kB view details)

Uploaded Python 3

File details

Details for the file rail_engine-0.1.7.tar.gz.

File metadata

  • Download URL: rail_engine-0.1.7.tar.gz
  • Upload date:
  • Size: 25.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: uv/0.8.22

File hashes

Hashes for rail_engine-0.1.7.tar.gz
Algorithm Hash digest
SHA256 f6bc211e0855049110cbb316d48131e9a771eb51e3bdc663ec3948fa47a0332c
MD5 5822e7ceef7bed3b0718678ab604ad78
BLAKE2b-256 d949ecc1e62f55f3bfd570c220118fbf41cc14301e0bc9ddd1ca99dda3ecb112

See more details on using hashes here.

File details

Details for the file rail_engine-0.1.7-py3-none-any.whl.

File metadata

File hashes

Hashes for rail_engine-0.1.7-py3-none-any.whl
Algorithm Hash digest
SHA256 132fe270f472cadbd28aa6b88cd7f937945adabf7206eb3187406d8ff4c6c71f
MD5 4f3b761359e8ebaf01fa25ab37a73c44
BLAKE2b-256 8f73dc2c4d1a05531e0d7091e5276beccd961f2941eac1749399cf847ac84644

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page