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+# Neo4j Store
+
+This readme walks you through setting up `Neo4jVectorStore` to store document embeddings and perform similarity searches.
+
+## What is Neo4j?
+
+[Neo4j](https://neo4j.com) is an open source NoSQL graph database.
+It is a fully transactional database (ACID) that stores data structured as graphs consisting of nodes, connected by relationships.
+Inspired by the structure of the real world, it allows for high query performance on complex data, while remaining intuitive and simple for the developer. 
+
+## What is Neo4j Vector Search?
+
+[Neo4j's Vector Search](https://neo4j.com/docs/cypher-manual/current/indexes-for-vector-search/) got introduced in Neo4j 5.11 and was considered GA with the release of version 5.13.
+Embeddings can be stored on _Node_ properties and can be queried with the [`db.index.vector.queryNodes()`](https://neo4j.com/docs/operations-manual/5/reference/procedures/#procedure_db_index_vector_queryNodes) function.
+Those indexes are powered by Lucene using a Hierarchical Navigable Small World Graph (HNSW) to perform a k approximate nearest neighbors (k-ANN) query over the vector fields.
+
+## Prerequisites
+
+1. 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).
+
+2. A running Neo4j (5.13+) instance 
+    1. [Docker](https://hub.docker.com/_/neo4j) image _neo4j:5.13_
+	2. [Neo4j Desktop](https://neo4j.com/download/)
+    3. [Neo4j Aura](https://neo4j.com/cloud/aura-free/)
+	4. [Neo4j Server](https://neo4j.com/deployment-center/) instance
+
+## Configuration
+
+To connect to Neo4j and use the `Neo4jVectorStore`, you need to provide (e.g. via `application.properties`) configurations for your instance.
+
+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'
+```
+
+## Repository
+
+To acquire Spring AI artifacts, declare the Spring Snapshot repository:
+
+```xml
+<repository>
+	<id>spring-snapshots</id>
+	<name>Spring Snapshots</name>
+	<url>https://repo.spring.io/snapshot</url>
+	<releases>
+		<enabled>false</enabled>
+	</releases>
+</repository>
+```
+
+## Dependencies
+
+Add these dependencies to your project:
+
+1. 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>
+```
+  
+2. Neo4j Vector Store
+
+```xml
+<dependency>
+	<groupId>org.springframework.experimental.ai</groupId>
+	<artifactId>spring-ai-neo4j-store</artifactId>
+	<version>0.7.0-SNAPSHOT</version>
+</dependency>
+```
+
+## Sample Code
+
+To configure `Neo4jVectorStore` in your application, you can use the following setup:
+
+Add to `application.properties` (using your Neo4j credentials):
+
+```
+spring.neo4j.uri=neo4j://localhost:7687
+spring.neo4j.authentication.username=neo4j
+spring.neo4j.authentication.password=password
+```
+
+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
+public VectorStore vectorStore(Driver driver, EmbeddingClient embeddingClient) {
+	return new Neo4jVectorStore(driver, embeddingClient,
+			Neo4jVectorStore.Neo4jVectorStoreConfig.defaultConfig());
+}
+```
+
+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!!" as the first result.
+
+## Neo4jVectorStore config
+
+As you have already noticed, the `Neo4jVectorStore` accepts a configuration parameter.
+The default configuration should fit for most of the basic use-cases, but if you want to tweak it a little bit for you needs, you can edit those defaults.
+
+The default params
+* embedding dimension = 1536
+* distance type = cosine 
+* document node label = "Document"
+* node property for embedding = "embedding"
+* database name = "neo4j"
+
+can be configured with
+
+```java
+Neo4jVectorStore.Neo4jVectorStoreConfig.builder()
+		.withDatabaseName("databaseName")
+		.withDistanceType(Neo4jVectorStore.Neo4jDistanceType.COSINE / Neo4jVectorStore.Neo4jDistanceType.EUCLIDEAN)
+		.withLabel("CustomLabel")
+		.withEmbeddingProperty("vectorEmbedding")
+		.withEmbeddingDimension(1024)
+```