bharatml-commons 1.0.1

Common utilities and protobuf definitions for BharatML Stack

bharatml_commons

Common utilities and protobuf definitions for BharatML Stack.

Overview

bharatml_commons provides shared utilities, protobuf definitions, and base classes used across all BharatML Stack Python SDKs. This package serves as the foundation for other BharatML Stack components.

Features

• Protobuf Schemas: Shared persist.proto and retrieve.proto definitions
• HTTP Clients: REST API client utilities with authentication
• Feature Metadata Client: Complete REST API client for metadata operations
• Data Processing: Column and feature processing utilities
• Base SDK Classes: Consistent API design across all clients
• Code Generation: Scripts for protobuf Python file generation

Installation

pip install bharatml_commons

Quick Start

Feature Metadata Client

from bharatml_commons import FeatureMetadataClient

# Initialize client
client = FeatureMetadataClient(
    metadata_url="https://api.example.com",
    job_id="my-job",
    job_token="my-token"
)

# Get feature metadata
metadata = client.get_feature_metadata(["user_features"])
features = client.get_features({"user_id": "123"}, ["user_features"])

HTTP Client Utilities

from bharatml_commons import BharatMLHTTPClient

# Low-level HTTP client
client = BharatMLHTTPClient()
response = client.get("https://api.example.com/health")

Column and Feature Utilities

from bharatml_commons import clean_column_name, get_fgs_to_feature_mappings

# Clean column names
clean_name = clean_column_name("feature@name#1")
# Output: "feature_name_1"

# Process feature mappings
mappings = get_fgs_to_feature_mappings(feature_groups_data)

Protobuf Definitions

from bharatml_commons.proto.persist.persist_pb2 import Query, Data
from bharatml_commons.proto.retrieve.retrieve_pb2 import RetrieveQuery

# Use protobuf messages
query = Query(entity_label="user", keys_schema=["user_id"])

API Reference

FeatureMetadataClient

The main client for interacting with feature metadata APIs.

class FeatureMetadataClient:
    def __init__(self, metadata_url: str, job_id: str, job_token: str)
    def get_feature_metadata(self, feature_groups: List[str]) -> Dict
    def get_features(self, entity_keys: Dict, feature_groups: List[str]) -> Dict
    def health_check() -> Dict

Utility Functions

• clean_column_name(name: str) -> str: Clean and normalize column names
• generate_renamed_column(old_name: str, new_name: str) -> str: Generate renamed columns
• get_fgs_to_feature_mappings(data: Dict) -> Dict: Process feature group mappings
• extract_entity_info(metadata: Dict) -> Tuple: Extract entity information

HTTP Client

class BharatMLHTTPClient:
    def get(self, url: str, headers: Dict = None) -> Dict
    def post(self, url: str, data: Dict, headers: Dict = None) -> Dict
    def put(self, url: str, data: Dict, headers: Dict = None) -> Dict
    def delete(self, url: str, headers: Dict = None) -> Dict

Development

Setting up Development Environment

git clone https://github.com/Meesho/BharatMLStack.git
cd BharatMLStack/py-sdk/bharatml_commons
pip install -e .

Running Tests

pytest tests/ -v

Generating Protobuf Files

python -m bharatml_commons.proto.generate_proto

Related Packages

This package is part of the BharatML Stack ecosystem:

• spark_feature_push_client: Spark-based data pipeline client
• grpc_feature_client: High-performance gRPC client for real-time operations

Contributing

We welcome contributions from the community! Please see our Contributing Guide for details on how to get started.

Community & Support

• 💬 Discord: Join our community chat
• 🐛 Issues: Report bugs and request features on GitHub Issues
• 📧 Email: Contact us at [e-mail]

License

BharatMLStack is open-source software licensed under the BharatMLStack Business Source License 1.1.

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