Neo4j GenAI package for Python¶
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.
Neo4j versions supported:
Neo4j >=5.18.1
Neo4j Aura >=5.18.0
Python versions supported:
Python 3.12
Python 3.11
Python 3.10
Python 3.9
Python 3.8
Topics¶
Usage¶
Installation¶
This package requires Python (>=3.8.1).
To install the latest stable version, use:
pip install neo4j-genai
Note
It is always recommended to install python packages for user space in a virtual environment.
Examples¶
Creating a vector index¶
When creating a vector index, make sure you match the number of dimensions in the index with the number of dimensions the embeddings have.
See the API documentation for more details.
from neo4j import GraphDatabase
from neo4j_genai.indexes import create_vector_index
URI = "neo4j://localhost:7687"
AUTH = ("neo4j", "password")
INDEX_NAME = "vector-index-name"
# Connect to Neo4j database
driver = GraphDatabase.driver(URI, auth=AUTH)
# Creating the index
create_vector_index(
driver,
INDEX_NAME,
label="Document",
embedding_property="vectorProperty",
dimensions=1536,
similarity_fn="euclidean",
)
Note
Assumed Neo4j is running
Populating the Neo4j Vector Index¶
Note that the below example is not the only way you can upsert data into your Neo4j database. For example, you could also leverage the Neo4j Python driver.
from neo4j import GraphDatabase
from neo4j_genai.indexes import upsert_vector
URI = "neo4j://localhost:7687"
AUTH = ("neo4j", "password")
# Connect to Neo4j database
driver = GraphDatabase.driver(URI, auth=AUTH)
# Upsert the vector
vector = ...
upsert_vector(
driver,
node_id=1,
embedding_property="vectorProperty",
vector=vector,
)
Note
Assumed Neo4j is running with a defined vector index
Performing a similarity search¶
While the library has more retrievers than shown here, the following examples should be able to get you started.
from neo4j import GraphDatabase
from neo4j_genai.retrievers import VectorRetriever
from langchain_openai import OpenAIEmbeddings
URI = "neo4j://localhost:7687"
AUTH = ("neo4j", "password")
INDEX_NAME = "vector-index-name"
# Connect to Neo4j database
driver = GraphDatabase.driver(URI, auth=AUTH)
# Create Embedder object
# Note: An OPENAI_API_KEY environment variable is required here
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)
Note
Assumed Neo4j is running with populated vector index in place.
Limitations¶
The query over the vector index is an approximate nearest neighbor search and may not give exact results. See this reference for more details.
Development¶
Install dependencies¶
poetry install
Getting started¶
Issues¶
If 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.
Make changes¶
Fork the repository.
Install Python and Poetry.
Create a working branch from main and start with your changes!
Pull request¶
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.
Run tests¶
Open a new virtual environment and then run the tests.
poetry shell
pytest
Unit tests¶
This should run out of the box once the dependencies are installed.
poetry run pytest tests/unit
E2E tests¶
To run e2e tests you’d need to have some services running locally:
neo4j
weaviate
weaviate-text2vec-transformers
The easiest way to get it up and running is via Docker compose:
docker compose -f tests/e2e/docker-compose.yml up
Note
If you suspect something in the databases are cached, run docker compose -f tests/e2e/docker-compose.yml down to remove them completely
Once the services are running, execute the following command to run the e2e tests.
poetry run pytest tests/e2e