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feat(redis): Advanced pre-filter for document metadata
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‎examples/src/langchain-classic/indexes/vector_stores/redis/redis_advanced_filtering.ts‎

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/**
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* Advanced Redis Vector Store Filtering Examples
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*
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* This example demonstrates all the advanced filtering capabilities of RedisVectorStore:
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* - Tag filters for categorical data
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* - Numeric filters for ranges and comparisons
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* - Text filters for full-text search
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* - Geo filters for location-based queries
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* - Timestamp filters for date/time queries
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* - Complex combinations with AND/OR logic
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* - Custom filters with raw RediSearch query syntax
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*
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* Note: Timestamps are stored as numeric fields (Unix epoch timestamps).
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* Date objects are automatically converted during serialization and returned
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* as numbers during deserialization.
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*/
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import { createClient } from "redis";
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import { OpenAIEmbeddings } from "@langchain/openai";
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import {
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RedisVectorStore,
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Tag,
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Num,
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Text,
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Geo,
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Timestamp,
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Custom,
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} from "@langchain/redis";
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import { Document } from "@langchain/core/documents";
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// Connect to Redis
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const client = createClient({
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url: process.env.REDIS_URL ?? "redis://localhost:6379",
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});
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await client.connect();
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// Sample documents with rich metadata
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const docs = [
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new Document({
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metadata: {
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category: "electronics",
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price: 299.99,
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title: "Wireless Bluetooth Headphones",
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location: [-122.4194, 37.7749], // San Francisco
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created_at: new Date("2023-01-15"),
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brand: "TechCorp",
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rating: 4.5,
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},
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pageContent:
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"High-quality wireless Bluetooth headphones with noise cancellation",
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}),
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new Document({
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metadata: {
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category: "books",
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price: 24.99,
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title: "JavaScript Programming Guide",
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location: [-74.006, 40.7128], // New York
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created_at: new Date("2023-03-20"),
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author: "John Smith",
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pages: 450,
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},
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pageContent:
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"Comprehensive guide to modern JavaScript programming techniques",
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}),
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new Document({
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metadata: {
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category: "electronics",
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price: 899.99,
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title: "4K Smart TV",
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location: [-118.2437, 34.0522], // Los Angeles
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created_at: new Date("2023-02-10"),
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brand: "ViewTech",
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screen_size: 55,
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},
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pageContent:
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"Ultra HD 4K Smart TV with streaming capabilities and voice control",
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}),
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new Document({
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metadata: {
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category: "books",
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price: 19.99,
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title: "Machine Learning Basics",
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location: [-122.4194, 37.7749], // San Francisco
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created_at: new Date("2023-04-05"),
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author: "Jane Doe",
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pages: 320,
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},
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pageContent:
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"Introduction to machine learning concepts and practical applications",
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}),
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];
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// Create vector store with metadata schema for proper indexing
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const vectorStore = await RedisVectorStore.fromDocuments(
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docs,
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new OpenAIEmbeddings(),
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{
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redisClient: client,
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indexName: "advanced_products",
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customSchema: [
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{ name: "category", type: "tag" },
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{ name: "price", type: "numeric", options: { sortable: true } },
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{ name: "title", type: "text", options: { weight: 2.0 } },
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{ name: "location", type: "geo" },
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// Timestamps are stored as numeric fields (Unix epoch timestamps)
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{ name: "created_at", type: "numeric", options: { sortable: true } },
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{ name: "brand", type: "tag" },
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{ name: "author", type: "tag" },
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{ name: "rating", type: "numeric" },
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{ name: "pages", type: "numeric" },
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{ name: "screen_size", type: "numeric" },
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],
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}
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);
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console.log("=== Advanced Redis Vector Store Filtering Examples ===\n");
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// Example 1: Simple tag filtering
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console.log("1. Simple tag filtering - Electronics only:");
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const electronicsFilter = Tag("category").eq("electronics");
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const electronicsResults = await vectorStore.similaritySearch(
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"high quality device",
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5,
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electronicsFilter
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);
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console.log(`Found ${electronicsResults.length} electronics items`);
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electronicsResults.forEach((doc) =>
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console.log(`- ${doc.metadata.title} ($${doc.metadata.price})`)
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);
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console.log();
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// Example 2: Numeric range filtering
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console.log("2. Numeric range filtering - Products between $20-$500:");
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const priceFilter = Num("price").between(20, 500);
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const priceResults = await vectorStore.similaritySearch(
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"quality product",
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5,
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priceFilter
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);
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console.log(`Found ${priceResults.length} products in price range`);
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priceResults.forEach((doc) =>
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console.log(`- ${doc.metadata.title} ($${doc.metadata.price})`)
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);
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console.log();
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// Example 3: Text search filtering
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console.log("3. Text search filtering - Titles containing 'programming':");
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const textFilter = Text("title").wildcard("*programming*");
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const textResults = await vectorStore.similaritySearch(
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"learning guide",
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5,
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textFilter
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);
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console.log(`Found ${textResults.length} programming-related items`);
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textResults.forEach((doc) =>
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console.log(`- ${doc.metadata.title} by ${doc.metadata.author || "N/A"}`)
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);
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console.log();
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// Example 4: Geographic filtering
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console.log("4. Geographic filtering - Items within 50km of San Francisco:");
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const geoFilter = Geo("location").within(-122.4194, 37.7749, 50, "km");
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const geoResults = await vectorStore.similaritySearch(
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"local products",
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5,
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geoFilter
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);
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console.log(`Found ${geoResults.length} items near San Francisco`);
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geoResults.forEach((doc) =>
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console.log(`- ${doc.metadata.title} (${doc.metadata.location})`)
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);
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console.log();
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// Example 5: Timestamp filtering
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console.log("5. Timestamp filtering - Items created after March 1, 2023:");
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const timestampFilter = Timestamp("created_at").gt(new Date("2023-03-01"));
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const timestampResults = await vectorStore.similaritySearch(
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"recent items",
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5,
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timestampFilter
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);
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console.log(`Found ${timestampResults.length} recent items`);
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timestampResults.forEach((doc) => {
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// created_at is stored as a Unix epoch timestamp (number)
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// Convert it back to a Date for display
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const createdDate = new Date((doc.metadata.created_at as number) * 1000);
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console.log(`- ${doc.metadata.title} (${createdDate.toDateString()})`);
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});
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console.log();
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// Example 6: Complex combined filtering
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console.log("6. Complex filtering - Electronics under $400 in California:");
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const complexFilter = Tag("category")
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.eq("electronics")
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.and(Num("price").lt(400))
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.and(Geo("location").within(-119.4179, 36.7783, 500, "km")); // California center
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const complexResults = await vectorStore.similaritySearch(
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"affordable electronics",
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5,
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complexFilter
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);
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console.log(
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`Found ${complexResults.length} affordable electronics in California`
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);
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complexResults.forEach((doc) =>
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console.log(
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`- ${doc.metadata.title} ($${doc.metadata.price}) at ${doc.metadata.location}`
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)
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);
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console.log();
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// Example 7: OR filtering
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console.log("7. OR filtering - Books OR items under $30:");
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const orFilter = Tag("category").eq("books").or(Num("price").lt(30));
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const orResults = await vectorStore.similaritySearch(
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"affordable items",
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5,
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orFilter
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);
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console.log(`Found ${orResults.length} books or cheap items`);
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orResults.forEach((doc) =>
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console.log(
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`- ${doc.metadata.title} (${doc.metadata.category}, $${doc.metadata.price})`
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)
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);
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console.log();
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// Example 8: Multiple tag values
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console.log("8. Multiple tag values - TechCorp OR ViewTech brands:");
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const multiTagFilter = Tag("brand").eq(["TechCorp", "ViewTech"]);
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const multiTagResults = await vectorStore.similaritySearch(
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"branded products",
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5,
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multiTagFilter
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);
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console.log(`Found ${multiTagResults.length} items from specified brands`);
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multiTagResults.forEach((doc) =>
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console.log(`- ${doc.metadata.title} by ${doc.metadata.brand}`)
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);
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console.log();
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// Example 9: Negation filtering
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console.log("9. Negation filtering - NOT electronics:");
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const negationFilter = Tag("category").ne("electronics");
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const negationResults = await vectorStore.similaritySearch(
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"non-electronic items",
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5,
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negationFilter
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);
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console.log(`Found ${negationResults.length} non-electronic items`);
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negationResults.forEach((doc) =>
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console.log(`- ${doc.metadata.title} (${doc.metadata.category})`)
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);
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console.log();
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// Example 10: Custom filter with raw RediSearch syntax
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console.log("10. Custom filter - Raw RediSearch query syntax:");
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const customFilter = Custom("(@category:{electronics} @price:[0 400])");
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const customResults = await vectorStore.similaritySearch(
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"affordable tech",
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5,
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customFilter
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);
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console.log(`Found ${customResults.length} affordable electronics`);
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customResults.forEach((doc) =>
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console.log(`- ${doc.metadata.title} ($${doc.metadata.price})`)
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);
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console.log();
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// Cleanup
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await vectorStore.delete({ deleteAll: true });
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await client.disconnect();
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console.log("\n=== Advanced filtering examples completed! ===");

‎libs/providers/langchain-google-genai/package.json‎

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},
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"dependencies": {
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"@google/generative-ai": "^0.24.0",
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"uuid": "^11.1.0"
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"uuid": "^11.1.0",
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"uuid": "^10.0.0"
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},
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"peerDependencies": {
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"@langchain/core": "^1.0.0-alpha.6"

‎libs/providers/langchain-redis/package.json‎

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"format:check": "prettier --config .prettierrc --check \"src\""
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},
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"dependencies": {
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"uuid": "^10.0.0",
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"redis": "^4.6.13"
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},
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"peerDependencies": {
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"eslint": "^9.34.0",
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"prettier": "^2.8.3",
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"typescript": "~5.8.3",
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"uuid": "^10.0.0",
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"vitest": "^3.2.4"
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},
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"publishConfig": {

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