Add Java vector database examples redis-developer#9

vector-database/java/LICENSE

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+MIT License
+
+Copyright (c) 2023 Michael Yuan
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

vector-database/java/README.md

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+# Redis VSS OpenAI Examples in Java
+
+## Contents
+1.  [Summary](#summary)
+2.  [Features](#features)
+3.  [Prerequisites](#prerequisites)
+4.  [Installation](#installation)
+5.  [Usage](#usage)
+6.  [Execution](#execution)
+
+## Summary <a name="summary"></a>
+This provides a series of Java code examples of how to use Redis VSS with vector embeddings generated with OpenAI.
+
+## Features <a name="features"></a>
+- Java source code for implementing Redis VSS on JSON documents using an ecommerce dataset of products and obtaining vector embeddings from OpenAI.
+- Java source code for implementing Redis VSS on Redis Hashes using a dataset with wikipedia articles with OpenAI vector embeddings.
+- Docker compose file to start up a Redis Stack instance.
+
+## Prerequisites <a name="prerequisites"></a>
+- Docker
+- Java JDK
+- [OpenAI key](https://platform.openai.com)
+
+## Installation <a name="installation"></a>
+1.  Clone this repo.
+2.  CD to the java directory
+3.  Export an environment variable with your OpenAI API key: ```export OPENAI_API_KEY="<YOUR_KEY>"```
+4.  Start up Redis Stack:  docker compose up -d
+5.  Using a JDK or Java IDE build the maven project and run the examples
+
+## Execution <a name="execution"></a>
+### Redis Client Connection 
+ ```java
+client = new JedisPooled(prop.getProperty("redis.host"),
+                    Integer.parseInt(prop.getProperty("redis.port")));
+ ```
+ ### OpenAI Client Connection
+ ```java
+service = new OpenAiService(token);
+ ```
+ ### Flat Index Build
+ ```java
+// Define index schema
+Schema schema = new Schema().addNumericField("id")
+        .addTextField("title", 3.0).as("title")
+        .addTextField("url", 1.0).as("url")
+        .addTextField("text", 2.0).as("text")
+        .addVectorField("title_vector", Schema.VectorField.VectorAlgo.FLAT, attr).as("title_vector")
+        .addVectorField("content_vector", Schema.VectorField.VectorAlgo.FLAT, attr).as("content_vector");
+IndexDefinition rule = new IndexDefinition(IndexDefinition.Type.HASH)
+        .setPrefixes(new String[] { "wiki:" });
+client.ftCreate(INDEX_NAME, IndexOptions.defaultOptions().setDefinition(rule), schema);
+ ```
+ ### HNSW Index Build
+ ```java
+// Define index schema
+Schema schema = new Schema().addNumericField("id")
+        .addTextField("title", 3.0).as("title")
+        .addTextField("url", 1.0).as("url")
+        .addTextField("text", 2.0).as("text")
+        .addVectorField("title_vector", Schema.VectorField.VectorAlgo.HNSW, attr).as("title_vector")
+        .addVectorField("content_vector", Schema.VectorField.VectorAlgo.HNSW, attr).as("content_vector");
+IndexDefinition rule = new IndexDefinition(IndexDefinition.Type.HASH)
+        .setPrefixes(new String[] { "wiki:" });
+client.ftCreate(INDEX_NAME_HNSW, IndexOptions.defaultOptions().setDefinition(rule), schema);
+```
+### Redis Hash Data Load
+```java
+Map<byte[], byte[]> map = new HashMap<>();
+map.put("id".getBytes(), record[0].getBytes());
+map.put("url".getBytes(), record[1].getBytes());
+map.put("title".getBytes(), record[2].getBytes());
+map.put("text".getBytes(), record[3].getBytes());
+map.put("title_vector".getBytes(), doubleToByte(title_vector));
+map.put("content_vector".getBytes(), doubleToByte(content_vector));
+map.put("vector_id".getBytes(), record[6].getBytes());
+client.hset(key.getBytes(), map);
+```
+### Redis JSON Data Load
+```java
+Product product = productList.get(i);
+product.addVector(embeddings.get(i).getEmbedding().stream().mapToDouble(Double::doubleValue)
+    .toArray());
+client.jsonSet("product:" + product.id, gson.toJson(product));
+```
+### VSS query: 'modern art in Europe' in 'title_vector'
+```text
+1. Museum of Modern Art (Score: 0.8751771)
+2. Western Europe (Score: 0.8674411)
+3. Renaissance art (Score: 0.86415625)
+4. Pop art (Score: 0.8603469)
+5. Northern Europe (Score: 0.85465807)
+6. Hellenistic art (Score: 0.8527923)
+7. Modernist literature (Score: 0.84703135)
+8. Art film (Score: 0.84327316)
+9. Central Europe (Score: 0.84258366)
+10. European (Score: 0.84141064)
+```
+### VSS query: 'Famous battles in Scottish history' in 'content_vector'
+```text
+1. Battle of Bannockburn (Score: 0.86933625)
+2. Wars of Scottish Independence (Score: 0.8614707)
+3. 1651 (Score: 0.85258836)
+4. First War of Scottish Independence (Score: 0.84962213)
+5. Robert I of Scotland (Score: 0.84621406)
+6. 841 (Score: 0.84399074)
+7. 1716 (Score: 0.84390485)
+8. 1314 (Score: 0.83721495)
+9. 1263 (Score: 0.8364166)
+10. William Wallace (Score: 0.83534056)
+```
+### VSS query: 'man blue jeans' in 'productVector'
+```text
+1. John Players Men Blue Jeans (Score: 0.79446274)
+2. Lee Men Tino Blue Jeans (Score: 0.7797863)
+3. Lee Men Blue Chicago Fit Jeans (Score: 0.77107173)
+4. Lee Men Blue Chicago Fit Jeans (Score: 0.7710503)
+5. Peter England Men Party Blue Jeans (Score: 0.7699358)
+6. Locomotive Men Washed Blue Jeans (Score: 0.74747217)
+7. Locomotive Men Washed Blue Jeans (Score: 0.74747217)
+8. French Connection Men Blue Jeans (Score: 0.7463001)
+9. Palm Tree Kids Boy Washed Blue Jeans (Score: 0.7440362)
+10. Lee Men Elvira Rinse Blue Chicago Fit Jeans (Score: 0.7366651)
+```
+### VSS query: 'man blue jeans' in 'productVector' with hybrid filters: @productDisplayName:"slim fit"
+```text
+1. Lee Rinse Navy Blue Slim Fit Jeans (Score: 0.71524847)
+2. Basics Men Blue Slim Fit Checked Shirt (Score: 0.71524143)
+3. Basics Men Blue Slim Fit Checked Shirt (Score: 0.71524143)
+4. Tokyo Talkies Women Navy Slim Fit Jeans (Score: 0.6794758)
+5. Basics Men Navy Slim Fit Checked Shirt (Score: 0.6708832)
+6. Basics Men Red Slim Fit Checked Shirt (Score: 0.6275975)
+7. Basics Men White Slim Fit Striped Shirt (Score: 0.622632)
+8. ADIDAS Men's Slim Fit White T-shirt (Score: 0.5857945)
+```
+### VSS query: 'man blue jeans' in 'productVector' with hybrid filters: (@year:[2011 2012] @season:{Summer})
+```text
+1. John Players Men Blue Jeans (Score: 0.79437006)
+2. Peter England Men Party Blue Jeans (Score: 0.7699023)
+3. French Connection Men Blue Jeans (Score: 0.746287)
+4. Denizen Women Blue Jeans (Score: 0.7350321)
+5. Do U Speak Green Men Blue Shorts (Score: 0.7293731)
+6. John Players Men Check Blue Shirt (Score: 0.7255274)
+7. Jealous 21 Women Washed Blue Jeans (Score: 0.7209517)
+8. Jealous 21 Women Washed Blue Jeans (Score: 0.7209517)
+9. Lee Men Solid Blue Shirts (Score: 0.7166283)
+10. Gini and Jony Boys Check Blue Shirt (Score: 0.7127073)
+```

vector-database/java/docker-compose.yml

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+version: '3.7'
+services:
+
+  vector-db:
+    image: redis/redis-stack:latest
+    ports:
+      - 6379:6379
+      - 8001:8001
+    environment:
+      - REDISEARCH_ARGS=CONCURRENT_WRITE_MODE
+    volumes:
+      - vector-db:/var/lib/redis
+      - ./redis.conf:/usr/local/etc/redis/redis.conf
+    healthcheck:
+      test: ["CMD", "redis-cli", "-h", "localhost", "-p", "6379", "ping"]
+      interval: 2s
+      timeout: 1m30s
+      retries: 5
+      start_period: 5s
+
+volumes:
+  vector-db: