haystack-ml-stack 0.4.1

Functions related to Haystack ML

Haystack ML Stack

Currently this project contains a FastAPI-based service designed for low-latency scoring of streams data coming from http requests

🚀 Features

• FastAPI Service: Lightweight and fast web service for ML inference.
  • Asynchronous I/O: Utilizes aiobotocore for non-blocking S3 and DynamoDB operations.
  • Model Loading: Downloads and loads the ML model (using cloudpickle) from a configurable S3 path on startup.
  • Feature Caching: Implements a thread-safe Time-To-Live (TTL) / Least-Recently-Used (LRU) cache (cachetools.TLRUCache) for DynamoDB features, reducing latency and database load.
  • DynamoDB Integration: Fetches stream-specific features from DynamoDB to enrich the data before scoring.
  • Health Check: Provides a /health endpoint to monitor service status and model loading.

📦 Installation

This project requires Python 3.11 or later.

1. Install package: The dependencies associated are listed in pyproject.toml.

   pip install haystack-ml-stack
   

⚙️ Configuration

The service is configured using environment variables, managed by pydantic-settings. You can use a .env file for local development.

Variable Name	Alias	Default	Description
S3_MODEL_PATH	S3_MODEL_PATH	None	Required. The s3://bucket/key URL for the cloudpickled ML model file.
FEATURES_TABLE	FEATURES_TABLE	"features"	Name of the DynamoDB table storing stream features.
LOGS_FRACTION	LOGS_FRACTION	0.01	Fraction of requests to log detailed stream data for sampling/debugging (0.0 to 1.0).
CACHE_MAXSIZE	(none)	50000	Maximum size of the in-memory feature cache.

Example env vars

S3_MODEL_PATH="s3://my-ml-models/stream-scorer/latest.pkl"
FEATURES_TABLE="features"
LOGS_FRACTION=0.05

🌐 Endpoints

Method	Path	Description
GET	/	Root endpoint, returns a simple running message.
GET	/health	Checks if the service is running and if the ML model has been loaded.
POST	/score	Main scoring endpoint. Accepts stream data and returns model predictions.

💻 Technical Details

Model Structure

The ML model file downloaded from S3 is expected to be a cloudpickle-serialized Python dictionary with the following structure:

model = {
    "preprocess": <function>,  # Function to transform request data into model input.
    "predict": <function>,     # Function to perform the actual model inference.
    "params": <dict/any>,      # Optional parameters passed to preprocess/predict.
    "stream_features": <list[str]>, # Optional list of feature names to fetch from DynamoDB.
}

Feature Caching (cache.py)

The ThreadSafeTLRUCache ensures that feature lookups and updates are thread-safe. The _ttu (time-to-use) policy allows features to specify their own TTL via a cache_ttl_in_seconds key in the stored value.

DynamoDB Feature Fetching (dynamo.py)

The set_stream_features function handles:

• Checking the in-memory cache for required stream_features.

• Batch-fetching any missing features from DynamoDB.

• Parsing the low-level DynamoDB items into Python types.

• Populating the cache with the fetched data, respecting the feature's TTL.

• Injecting the fetched feature values back into the streams list in the request payload.