kiarina-lib-redisearch 1.15.0

RediSearch client library for kiarina namespace

kiarina-lib-redisearch

A comprehensive Python client library for RediSearch with advanced configuration management, schema definition, and both full-text and vector search capabilities.

Features

• Full-Text Search: Advanced text search with stemming, phonetic matching, and fuzzy search
• Vector Search: Similarity search using FLAT and HNSW algorithms with multiple distance metrics
• Schema Management: Type-safe schema definition with automatic migration support
• Configuration Management: Flexible configuration using pydantic-settings-manager
• Sync & Async: Support for both synchronous and asynchronous operations
• Advanced Filtering: Intuitive query builder with type-safe filter expressions
• Index Management: Complete index lifecycle management (create, migrate, reset, drop)
• Type Safety: Full type hints and Pydantic validation throughout

Installation

pip install kiarina-lib-redisearch

Quick Start

Basic Usage (Sync)

import redis
from kiarina.lib.redisearch import create_redisearch_client, settings_manager

# Define your schema (part of your application code)
schema = [
    {"type": "tag", "name": "category"},
    {"type": "text", "name": "title"},
    {"type": "numeric", "name": "price", "sortable": True},
    {"type": "vector", "name": "embedding", "algorithm": "FLAT", "dims": 1536}
]

# Configure settings (infrastructure configuration)
settings_manager.user_config = {
    "default": {
        "key_prefix": "products:",
        "index_name": "products_index"
    }
}

# Create Redis connection (decode_responses=False is required)
redis_client = redis.Redis(host="localhost", port=6379, decode_responses=False)

# Create RediSearch client with schema
client = create_redisearch_client(
    "default",
    field_dicts=schema,
    redis=redis_client,
)

# Create index
client.create_index()

# Add documents
client.set({
    "category": "electronics",
    "title": "Wireless Headphones",
    "price": 99.99,
    "embedding": [0.1, 0.2, 0.3, ...]
}, id="product_1")

# Full-text search
results = client.find(
    filter=[["category", "==", "electronics"]],
    return_fields=["title", "price"]
)

# Vector similarity search
results = client.search(
    vector=[0.1, 0.2, 0.3, ...],
    limit=10
)

Async Usage

import redis.asyncio
from kiarina.lib.redisearch.asyncio import create_redisearch_client

schema = [
    {"type": "text", "name": "title"},
    {"type": "numeric", "name": "price", "sortable": True}
]

async def main():
    redis_client = redis.asyncio.Redis(host="localhost", port=6379, decode_responses=False)
    client = create_redisearch_client(field_dicts=schema, redis=redis_client)
    
    await client.create_index()
    await client.set({"title": "Example", "price": 99.99}, id="doc_1")
    results = await client.find()

Schema Definition

Define your search schema with type-safe field definitions:

Field Types

# Tag field
{"type": "tag", "name": "category", "separator": ",", "sortable": True}

# Text field
{"type": "text", "name": "description", "weight": 2.0, "no_stem": False}

# Numeric field
{"type": "numeric", "name": "price", "sortable": True}

# Vector field (FLAT)
{
    "type": "vector",
    "name": "embedding",
    "algorithm": "FLAT",
    "dims": 1536,
    "datatype": "FLOAT32",
    "distance_metric": "COSINE"
}

# Vector field (HNSW)
{
    "type": "vector",
    "name": "embedding",
    "algorithm": "HNSW",
    "dims": 1536,
    "datatype": "FLOAT32",
    "distance_metric": "COSINE",
    "m": 16,
    "ef_construction": 200
}

Configuration

Environment Variables

export KIARINA_LIB_REDISEARCH_KEY_PREFIX="myapp:"
export KIARINA_LIB_REDISEARCH_INDEX_NAME="main_index"
export KIARINA_LIB_REDISEARCH_PROTECT_INDEX_DELETION="true"

YAML Configuration

# config.yaml
redisearch:
  development:
    key_prefix: "dev:"
    index_name: "dev_index"
    protect_index_deletion: false
  production:
    key_prefix: "prod:"
    index_name: "prod_index"
    protect_index_deletion: true

import yaml
from kiarina.lib.redisearch import settings_manager

with open("config.yaml") as f:
    config = yaml.safe_load(f)
    settings_manager.user_config = config["redisearch"]

settings_manager.active_key = "production"

API Reference

Index Operations

# Check if index exists
exists = client.exists_index()

# Create index
client.create_index()

# Drop index
client.drop_index(delete_documents=True)

# Reset index (drop and recreate)
client.reset_index()

# Migrate index (auto-detect schema changes)
client.migrate_index()

# Get index information
info = client.get_info()

Document Operations

# Set document
client.set({"title": "Example", "price": 99.99}, id="doc_1")

# Get document
doc = client.get("doc_1")

# Delete document
client.delete("doc_1")

# Get Redis key
key = client.get_key("doc_1")  # Returns "prefix:doc_1"

Search Operations

# Count documents
count_result = client.count(filter=[["category", "==", "electronics"]])

# Full-text search
results = client.find(
    filter=[["category", "==", "electronics"], ["price", "<", 500]],
    sort_by="price",
    sort_desc=False,
    offset=0,
    limit=20,
    return_fields=["title", "price"]
)

# Vector similarity search
results = client.search(
    vector=[0.1, 0.2, ...],
    filter=[["category", "==", "electronics"]],
    offset=0,
    limit=10,
    return_fields=["title", "distance"]
)

Advanced Filtering

import kiarina.lib.redisearch_filter as rf

# Using filter API
filter_expr = (
    (rf.Tag("category") == "electronics") &
    (rf.Numeric("price") < 500) &
    (rf.Text("title") % "*wireless*")
)
results = client.find(filter=filter_expr)

# Using condition lists
conditions = [
    ["category", "==", "electronics"],
    ["price", "<", 500],
    ["title", "like", "*wireless*"]
]
results = client.find(filter=conditions)

Filter Operators

Tag filters:

• == or in: Match tags
• != or not in: Exclude tags

Numeric filters:

• ==, !=, >, <, >=, <=: Numeric comparisons

Text filters:

• ==: Exact match
• !=: Not equal
• % or like: Pattern matching (wildcards, fuzzy search)

Schema Migration

# Update schema in code
new_schema = [
    {"type": "tag", "name": "category"},
    {"type": "text", "name": "title"},
    {"type": "numeric", "name": "rating", "sortable": True},  # New field
]

# Create client with new schema
client = create_redisearch_client(field_dicts=new_schema, redis=redis_client)

# Migrate (auto-detects changes and recreates index)
client.migrate_index()

Testing

Prerequisites

• Python 3.12+
• Redis with RediSearch module
• Docker (for running Redis in tests)

Running Tests

# Start Redis with RediSearch
docker compose up -d redis

# Run all tests for this package
mise run package:test kiarina-lib-redisearch

# Run with coverage
mise run package:test kiarina-lib-redisearch --coverage

Dependencies

• redis - Redis client for Python
• numpy - Numerical computing (for vector operations)
• pydantic - Data validation and settings management
• pydantic-settings - Settings management
• pydantic-settings-manager - Advanced settings management

License

This project is licensed under the MIT License - see the LICENSE file for details.

Related Projects

• kiarina-python - The main monorepo containing this package
• RediSearch - The search and query engine this library connects to
• kiarina-lib-redis - Redis client library for basic Redis operations
• pydantic-settings-manager - Configuration management library used by this package