Add readme for Neo4jVectorStore.
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Gerrit Meier
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Mark Pollack
Mark Pollack
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vector-stores/spring-ai-neo4j-store/README.md
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vector-stores/spring-ai-neo4j-store/README.md
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# Neo4j Store
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This readme walks you through setting up `Neo4jVectorStore` to store document embeddings and perform similarity searches.
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## What is Neo4j?
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[Neo4j](https://neo4j.com) is an open source NoSQL graph database.
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It is a fully transactional database (ACID) that stores data structured as graphs consisting of nodes, connected by relationships.
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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.
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## What is Neo4j Vector Search?
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[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.
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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.
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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.
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## Prerequisites
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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).
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2. A running Neo4j (5.13+) instance
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1. [Docker](https://hub.docker.com/_/neo4j) image _neo4j:5.13_
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2. [Neo4j Desktop](https://neo4j.com/download/)
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3. [Neo4j Aura](https://neo4j.com/cloud/aura-free/)
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4. [Neo4j Server](https://neo4j.com/deployment-center/) instance
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## Configuration
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To connect to Neo4j and use the `Neo4jVectorStore`, you need to provide (e.g. via `application.properties`) configurations for your instance.
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Additionally, you'll need to provide your OpenAI API Key. Set it as an environment variable like so:
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```bash
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export SPRING_AI_OPENAI_API_KEY='Your_OpenAI_API_Key'
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```
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## Repository
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To acquire Spring AI artifacts, declare the Spring Snapshot repository:
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```xml
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<repository>
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<id>spring-snapshots</id>
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<name>Spring Snapshots</name>
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<url>https://repo.spring.io/snapshot</url>
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<releases>
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<enabled>false</enabled>
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</releases>
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</repository>
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```
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## Dependencies
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Add these dependencies to your project:
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1. OpenAI: Required for calculating embeddings.
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```xml
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<dependency>
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<groupId>org.springframework.experimental.ai</groupId>
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<artifactId>spring-ai-openai-spring-boot-starter</artifactId>
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<version>0.7.0-SNAPSHOT</version>
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</dependency>
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```
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2. Neo4j Vector Store
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```xml
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<dependency>
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<groupId>org.springframework.experimental.ai</groupId>
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<artifactId>spring-ai-neo4j-store</artifactId>
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<version>0.7.0-SNAPSHOT</version>
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</dependency>
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```
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## Sample Code
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To configure `Neo4jVectorStore` in your application, you can use the following setup:
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Add to `application.properties` (using your Neo4j credentials):
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```
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spring.neo4j.uri=neo4j://localhost:7687
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spring.neo4j.authentication.username=neo4j
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spring.neo4j.authentication.password=password
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```
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Integrate with OpenAI's embeddings by adding the Spring Boot OpenAI Starter to your project.
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This provides you with an implementation of the Embeddings client:
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```java
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public VectorStore vectorStore(Driver driver, EmbeddingClient embeddingClient) {
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return new Neo4jVectorStore(driver, embeddingClient,
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Neo4jVectorStore.Neo4jVectorStoreConfig.defaultConfig());
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}
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```
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In your main code, create some documents:
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```java
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List<Document> documents = List.of(
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new Document("Spring AI rocks!! Spring AI rocks!! Spring AI rocks!! Spring AI rocks!! Spring AI rocks!!", Map.of("meta1", "meta1")),
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new Document("The World is Big and Salvation Lurks Around the Corner"),
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new Document("You walk forward facing the past and you turn back toward the future.", Map.of("meta2", "meta2")));
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```
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Add the documents to your vector store:
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```java
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vectorStore.add(List.of(document));
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```
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And finally, retrieve documents similar to a query:
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```java
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List<Document> results = vectorStore.similaritySearch("Spring", 5);
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```
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If all goes well, you should retrieve the document containing the text "Spring AI rocks!!" as the first result.
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## Neo4jVectorStore config
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As you have already noticed, the `Neo4jVectorStore` accepts a configuration parameter.
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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.
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The default params
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* embedding dimension = 1536
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* distance type = cosine
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* document node label = "Document"
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* node property for embedding = "embedding"
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* database name = "neo4j"
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can be configured with
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```java
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Neo4jVectorStore.Neo4jVectorStoreConfig.builder()
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.withDatabaseName("databaseName")
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.withDistanceType(Neo4jVectorStore.Neo4jDistanceType.COSINE / Neo4jVectorStore.Neo4jDistanceType.EUCLIDEAN)
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.withLabel("CustomLabel")
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.withEmbeddingProperty("vectorEmbedding")
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.withEmbeddingDimension(1024)
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```
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