trelasticext 0.2.0

Elasticsearch Extensions for Hebrew and Multi-language Text Processing

TRelasticExt

A Python package for enhanced Elasticsearch (8.x.x) operations with specialized support for Hebrew text processing.

Installation

pip install trelasticext

Features

• Elasticsearch document management (create, update, delete)
• Hebrew text tokenization and analysis
• Text similarity search with customizable parameters
• Bulk operations for efficient data handling
• Support for cross-index and cross-cluster operations

Quick Start

import trelasticext as ee

# Connect to Elasticsearch
es_params = {"ehost": "http://localhost:9200", "index": "my_index"}

# Search for documents
results = ee.get_es_records_by_field("location", "document_location", es_params)

# Tokenize Hebrew text
tokens = ee.ftokens("שלום עולם")

# Build a custom query
query = ee.query_builder(
    text="מילים לחיפוש",
    fields=["content", "title"],
    fuzziness=1,
    query_type="multimatch"
)

Advanced Usage

Document Operations

# Add a document
doc = {"title": "Sample Document", "content": "This is a test"}
ee.post_es_record(doc, es_params)

# Update a document by ID
update_doc = {"doc": {"title": "Updated Title"}}
ee.update_by_id("doc_id", update_doc, es_params["index"], es_params)

# Delete a document
ee.delete_es_record("doc_id", es_params)

Bulk Operations

# Copy data between Elasticsearch clusters
source_params = {"ehost": "http://source-es:9200", "index": "source_index"}
target_params = {"ehost": "http://target-es:9200", "index": "target_index"}

ee.copy_records_between_hosts(source_params, target_params, clear_data=True)

Text Analysis

# Analyze text using Elasticsearch analyzers
analysis = ee.get_es_analyze_text(
    index="my_index",
    analyzer="hebrew",
    text="טקסט לניתוח",
    es_params=es_params
)

# Get tokens with language filtering
hebrew_tokens = ee.ftokens("מילים באנגלית and Hebrew", lang=["HEB"])

API Reference

Main Functions

• get_es_records_by_field(field, value, es_params, les=None, default_result=False, all_records=False)
• get_es_source_by_field(field, value, es_params, les=None)
• post_es_record(doc, es_params, les=None)
• update_by_id(doc_id, doc, index, es_params, les=None)
• delete_es_record(doc_id, es_params, les=None)
• ftokens(text, spliter=None, lang=None)
• query_builder(text, fields=["sentence"], fuzziness=0, ...)

Requirements

• Python 3.6+
• elasticsearch
• pandas
• numpy
• fasttext
• Levenshtein

License

MIT

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.