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Neo4jRM

Constructor

Initialize an instance of the Neo4jRM class.

Neo4jRM(
    index_name: str,
    text_node_property: str,
    k: int = 5,
    retrieval_query: str = None,
    embedding_provider: str = "openai",
    embedding_model: str = "text-embedding-ada-002",
)

Environment Variables:

You need to define the credentials as environment variables:

  • NEO4J_USERNAME (str): Specifies the username required for authenticating with the Neo4j database. This is a crucial security measure to ensure that only authorized users can access the database.

  • NEO4J_PASSWORD (str): Defines the password associated with the NEO4J_USERNAME for authentication purposes. This password should be kept secure to prevent unauthorized access to the database.

  • NEO4J_URI (str): Indicates the Uniform Resource Identifier (URI) used to connect to the Neo4j database. This URI typically includes the protocol, hostname, and port, providing the necessary information to establish a connection to the database.

  • NEO4J_DATABASE (str, optional): Specifies the name of the database to connect to within the Neo4j instance. If not set, the system defaults to using "neo4j" as the database name. This allows for flexibility in connecting to different databases within a single Neo4j server.

  • OPENAI_API_KEY (str): Specifies the API key required for authenticiating with OpenAI's services.

Parameters:

  • index_name (str): Specifies the name of the vector index to be used within Neo4j for organizing and querying data.
  • text_node_property (str, optional): Defines the specific property of nodes that will be returned.
  • k (int, optional): The number of top results to return from the retrieval operation. It defaults to 5 if not explicitly specified.
  • retrieval_query (str, optional): A custom query string provided for retrieving data. If not provided, a default query tailored to the text_node_property will be used.
  • embedding_provider (str, optional): The name of the service provider for generating embeddings. Defaults to "openai" if not specified.
  • embedding_model (str, optional): The specific embedding model to use from the provider. By default, it uses the "text-embedding-ada-002" model from OpenAI.

Methods

forward(self, query: [str], k: Optional[int] = None) -> dspy.Prediction

Search the neo4j vector index for the top k passages matching the given query or queries, using embeddings generated via the specified embedding_model.

Parameters:

  • query (str_): The query.
  • k (Optional[int], optional): The number of results to retrieve. If not specified, defaults to the value set during initialization.

Returns:

  • dspy.Prediction: Contains the retrieved passages as a list of string with the prediction signature.

ex:

Prediction(
    passages=['Passage 1 Lorem Ipsum awesome', 'Passage 2 Lorem Ipsum Youppidoo', 'Passage 3 Lorem Ipsum Yassssss']
)

Quick Example how to use Neo4j in a local environment.

from dspy.retrieve.neo4j_rm import Neo4jRM
import os

os.environ["NEO4J_URI"] = 'bolt://localhost:7687'
os.environ["NEO4J_USERNAME"] = 'neo4j'
os.environ["NEO4J_PASSWORD"] = 'password'
os.environ["OPENAI_API_KEY"] = 'sk-'

retriever_model = Neo4jRM(
    index_name="vector",
    text_node_property="text"
)

results = retriever_model("Explore the significance of quantum computing", k=3)

for passage in results:
    print("Document:", passage, "\n")