Improve pgvector docs

vector-stores/spring-ai-pgvector-store/README.md

@@ -1,9 +1,114 @@
-
 # PGvector VectorStore
 
-Pgvector is an open-source extension for PostgreSQL that enables storing and searching over machine learning-generated embeddings. It provides different capabilities that let users identify both exact and approximate nearest neighbors. It is designed to work seamlessly with other PostgreSQL features, including indexing and querying.
+This readme will walk you through setting up the PGvector VectorStore to store document embeddings and perform similarity searches.
 
-## Start Postgres+PGVecgor DB:
+## What is PGvector?
+
+[PGvector](https://github.com/pgvector/pgvector) is an open-source extension for PostgreSQL that enables storing and searching over machine learning-generated embeddings. It provides different capabilities that let users identify both exact and approximate nearest neighbors. It is designed to work seamlessly with other PostgreSQL features, including indexing and querying.
+
+## Prerequisites
+
+1. Access to PostgresSQL instance with required database credentials.
+For test purposes you can deploy a local, Docker PostgresSQL/PGvector instance. See the [Run Postgres & PGVector DB locally](#appendix_a) appendix.
+2. OpenAI Account: Create an account at [OpenAI Signup](https://platform.openai.com/signup) and generate the token at [API Keys](https://platform.openai.com/account/api-keys)
+
+## Configuration
+
+To set up PgVectorStore, you need to provide (via application.yaml) configurations to your PostgresSQL database.
+
+Additionally, you'll need to provide your OpenAI API Key. Set it as an environment variable like so:
+
+```bash
+export SPRING_AI_OPENAI_API_KEY='Your_OpenAI_API_Key'
+```
+
+## Dependencies
+
+Add these dependencies to your project:
+
+1. PostgresSQL connection and JdbcTemplate auto-configuration.
+
+	```xml
+	<dependency>
+		<groupId>org.springframework.boot</groupId>
+		<artifactId>spring-boot-starter-jdbc</artifactId>
+	</dependency>
+
+	<dependency>
+		<groupId>org.postgresql</groupId>
+		<artifactId>postgresql</artifactId>
+		<scope>runtime</scope>
+	</dependency>
+	```
+
+2. OpenAI: Required for calculating embeddings.
+
+	```xml
+	<dependency>
+		<groupId>org.springframework.experimental.ai</groupId>
+		<artifactId>spring-ai-openai-spring-boot-starter</artifactId>
+		<version>0.7.0-SNAPSHOT</version>
+	</dependency>
+	```
+
+3. PGvector
+
+	```xml
+	<dependency>
+		<groupId>org.springframework.experimental.ai</groupId>
+		<artifactId>spring-ai-pgvector-store</artifactId>
+		<version>0.7.0-SNAPSHOT</version>
+	</dependency>
+	```
+
+## Sample Code
+
+To configure PgVectorStore in your application, you can use the following setup:
+
+Add to `application.yml` (using your DB credentials):
+
+```yml
+spring:
+	datasource:
+		url: jdbc:postgresql://localhost:5432/vector_store
+		username: postgres
+		password: postgres
+```
+
+Integrate with OpenAI's embeddings by adding the Spring Boot OpenAI starter to your project.
+This provides you with an implementation of the Embeddings client:
+
+```java
+@Bean
+public VectorStore vectorStore(JdbcTemplate jdbcTemplate, EmbeddingClient embeddingClient) {
+	return new PgVectorStore(jdbcTemplate, embeddingClient);
+}
+```
+
+In your main code, create some documents
+
+```java
+List<Document> documents = List.of(
+	new Document("Spring AI rocks!! Spring AI rocks!! Spring AI rocks!! Spring AI rocks!! Spring AI rocks!!", Map.of("meta1", "meta1")),
+	new Document("The World is Big and Salvation Lurks Around the Corner"),
+	new Document("You walk forward facing the past and you turn back toward the future.", Map.of("meta2", "meta2")));
+```
+
+Add the documents to your vector store:
+
+```java
+vectorStore.add(List.of(document));
+```
+
+And finally, retrieve documents similar to a query:
+
+```java
+List<Document> results = vectorStore.similaritySearch("Spring", 5);
+```
+
+If all goes well, you should retrieve the document containing the text "Spring AI rocks!!".
+
+## <a name="appendix_a" /> Appendix A: Run Postgres & PGVector DB locally
 
 ```
 docker run -it --rm --name postgres -p 5432:5432 -e POSTGRES_USER=postgres -e POSTGRES_PASSWORD=postgres ankane/pgvector
@@ -13,4 +118,4 @@ You can connect to this server like this:
 
 ```
 psql -U postgres -h localhost -p 5432
-```
+```