neo4j driver adapter

The neo4j driver adapter is a nb adapter for the Neo4J driver, an open source Java driver for connecting to and performing operations on an instance of a Neo4J/Aura database. The driver is hosted on github at https://github.com/neo4j/neo4j-java-driver.

activity parameters

The following parameters must be supplied to the adapter at runtime in order to successfully connect to an instance of the Neo4J/Aura database:

• db_uri - the URI for the Neo4J instance for the driver to connect to.

Op Templates

The Neo4J adapter supports six different op types:

• sync_autocommit
• async_autocommit
• sync_read_transaction
• async_read_transaction
• sync_write_transaction
• async_write_transaction

A good reference for when to use each is located at https://neo4j.com/docs/driver-manual/1.7/sessions-transactions/

For these different op types, users can specify appropriate Cypher queries to run against the database

Examples

All examples provided are in the scope of leveraging Neo4J's vector index capabilities. Although, arbitrary Cypher queries can be run for most involved graph modeling use cases, only a simple vector search functionality has been properly worked through, currently.

ops:
  example_create_vector_index:
    sync_autocommit: |
      CREATE VECTOR INDEX $index_name IF NOT EXISTS FOR (n:TEMPLATE(node_label,Node))
      ON (n.embedding) OPTIONS
      {indexConfig: {`vector.dimensions`: $dimension, `vector.similarity_function`: $similarity_function}}
    query_params:
      index_name: vector_index
      dimension: TEMPLATE(dimension)
      similarity_function: TEMPLATE(similarity_function,cosine)

  example_insert_node:
    async_write_transaction: |
      CREATE (v:TEMPLATE(node_label,Node) {id: $id, embedding: $vector})
    query_params:
      id: '{id}'
      vector: '{train_vector}'

  example_search:
      async_read_transaction: |
        WITH $query_vector AS queryVector
        CALL db.index.vector.queryNodes($index_name, $k, queryVector)
        YIELD node
        RETURN node.id
      query_params:
        query_vector: '{test_vector}'
        index_name: vector_index
        k: TEMPLATE(k,100)