This repository contains the official Neo4j GenAI features for Python.
The purpose of this package is to provide a first party package to developers, where Neo4j can guarantee long term commitment and maintenance as well as being fast to ship new features and high performing patterns and methods.
Docs are coming soon!
This package requires Python (>=3.8.1).
To install the latest stable version, use:
pip install neo4j-genaiWhile the library has more retrievers than shown here, the following examples should be able to get you started.
Assumption: Neo4j running with populated vector index in place.
from neo4j import GraphDatabase
from neo4j_genai import VectorRetriever
from langchain_openai import OpenAIEmbeddings
URI = "neo4j://localhost:7687"
AUTH = ("neo4j", "password")
INDEX_NAME = "embedding-name"
# Connect to Neo4j database
driver = GraphDatabase.driver(URI, auth=AUTH)
# Create Embedder object
embedder = OpenAIEmbeddings(model="text-embedding-3-large")
# Initialize the retriever
retriever = VectorRetriever(driver, INDEX_NAME, embedder)
# Run the similarity search
query_text = "How do I do similarity search in Neo4j?"
response = retriever.search(query_text=query_text, top_k=5)When creating a vector index, make sure you match the number of dimensions in the index with the number of dimensions the embeddings have.
Assumption: Neo4j running
from neo4j import GraphDatabase
from neo4j_genai.indexes import create_vector_index
URI = "neo4j://localhost:7687"
AUTH = ("neo4j", "password")
INDEX_NAME = "chunk-index"
# Connect to Neo4j database
driver = GraphDatabase.driver(URI, auth=AUTH)
# Creating the index
create_vector_index(
driver,
INDEX_NAME,
label="Document",
property="textProperty",
dimensions=1536,
similarity_fn="euclidean",
)This library does not write to the database, that is up to you. See below for how to write using Cypher via the Neo4j driver.
Assumption: Neo4j running with a defined vector index
from neo4j import GraphDatabase
from random import random
URI = "neo4j://localhost:7687"
AUTH = ("neo4j", "password")
# Connect to Neo4j database
driver = GraphDatabase.driver(URI, auth=AUTH)
# Upsert the vector
vector = [random() for _ in range(DIMENSION)]
insert_query = (
"MERGE (n:Document {id: $id})"
"WITH n "
"CALL db.create.setNodeVectorProperty(n, 'textProperty', $vector)"
"RETURN n"
)
parameters = {
"id": 0,
"vector": vector,
}
driver.execute_query(insert_query, parameters)poetry installIf you have a bug to report or feature to request, first search to see if an issue already exists. If a related issue doesn't exist, please raise a new issue using the relevant issue form.
If you're a Neo4j Enterprise customer, you can also reach out to Customer Support.
If you don't have a bug to report or feature request, but you need a hand with the library; community support is available via Neo4j Online Community and/or Discord.
- Fork the repository.
- Install Python and Poetry. For more information, see the development guide.
- Create a working branch from
mainand start with your changes!
When you're finished with your changes, create a pull request, also known as a PR.
- Ensure that you have signed the CLA.
- Ensure that the base of your PR is set to
main. - Don't forget to link your PR to an issue if you are solving one.
- Enable the checkbox to allow maintainer edits so that maintainers can make any necessary tweaks and update your branch for merge.
- Reviewers may ask for changes to be made before a PR can be merged, either using suggested changes or normal pull request comments. You can apply suggested changes directly through the UI, and any other changes can be made in your fork and committed to the PR branch.
- As you update your PR and apply changes, mark each conversation as resolved.
Open a new virtual environment and then run the tests.
poetry shell
pytest tests/unit