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.. neo4j-graphrag-python documentation master file, created by
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sphinx-quickstart on Tue Apr 9 16:36:43 2024.
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You can adapt this file completely to your liking, but it should at least
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contain the root `toctree` directive.
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GraphRAG for Python
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===================
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This package contains the official Neo4j GraphRAG features for Python.
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The purpose of this package is to provide a first party package to developers,
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where Neo4j can guarantee long term commitment and maintenance as well as being
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fast to ship new features and high performing patterns and methods.
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⚠️ This package is a renamed continuation of `neo4j-genai`.
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The package `neo4j-genai` is deprecated and will no longer be maintained.
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We encourage all users to migrate to this new package to continue receiving updates and support.
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Neo4j versions supported:
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* Neo4j >=5.18.1
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* Neo4j Aura >=5.18.0
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* Neo4j 2026.01+ (enables SEARCH clause with in-index filtering)
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Python versions supported:
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* Python 3.14
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* Python 3.13
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* Python 3.12
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* Python 3.11
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* Python 3.10
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******
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Topics
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******
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+ :ref:`user-guide-rag`
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+ :ref:`user-guide-kg-builder`
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+ :ref:`user-guide-pipeline`
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+ :ref:`api-documentation`
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+ :ref:`types-documentation`
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.. toctree::
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:maxdepth: 3
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:caption: Contents:
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:hidden:
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Introduction <self>
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user_guide_rag.rst
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user_guide_kg_builder.rst
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user_guide_pipeline.rst
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api.rst
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types.rst
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Usage
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=====
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************
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Installation
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************
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This package requires Python (>=3.10).
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To install the latest stable version, use:
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.. code:: bash
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pip install neo4j-graphrag
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.. note::
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It is always recommended to install python packages for user space in a virtual environment.
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*********************
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Optional Dependencies
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*********************
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Extra dependencies can be installed with:
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.. code:: bash
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pip install "neo4j-graphrag[openai]"
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List of extra dependencies:
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- LLM providers (at least one is required for RAG and KG Builder Pipeline):
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- **ollama**: LLMs from Ollama
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- **openai**: LLMs from OpenAI (including AzureOpenAI)
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- **google**: LLMs from Vertex AI
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- **cohere**: LLMs from Cohere
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- **anthropic**: LLMs from Anthropic
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- **mistralai**: LLMs from MistralAI
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- **sentence-transformers** : to use embeddings from the `sentence-transformers` Python package
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- Vector database (to use :ref:`External Retrievers`):
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- **weaviate**: store vectors in Weaviate
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- **pinecone**: store vectors in Pinecone
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- **qdrant**: store vectors in Qdrant
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- **experimental**: experimental features mainly from the Knowledge Graph creation pipelines.
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- **nlp**: installs spaCy for nlp pipelines, used by `SpaCySemanticMatchResolver` component from the Knowledge Graph creation pipelines.
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- **fuzzy-matching**: installs **rapidfuzz** to fuzzy matching using string similarity, used by `FuzzyMatchResolver` component from the Knowledge Graph creation pipelines.
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.. note::
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The **`nlp`** extra (spaCy) is currently **not supported on Python 3.14** due to an upstream spaCy import-time issue (see `spaCy #13895 <https://github.com/explosion/spaCy/issues/13895>`_). Use Python **3.13 or earlier** for spaCy-based features until that is resolved upstream.
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********
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Examples
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********
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~~~~~~~~~~~~~~~~~~~~~~~
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Creating a vector index
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~~~~~~~~~~~~~~~~~~~~~~~
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When creating a vector index, make sure you match the number of dimensions in the index with the number of dimensions the embeddings have.
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See :ref:`the API documentation<create-vector-index>` for more details.
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.. code:: python
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from neo4j import GraphDatabase
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from neo4j_graphrag.indexes import create_vector_index
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URI = "neo4j://localhost:7687"
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AUTH = ("neo4j", "password")
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INDEX_NAME = "vector-index-name"
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# Connect to Neo4j database
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driver = GraphDatabase.driver(URI, auth=AUTH)
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# Creating the index
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create_vector_index(
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driver,
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INDEX_NAME,
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label="Document",
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embedding_property="vectorProperty",
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dimensions=1536,
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similarity_fn="euclidean",
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)
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.. note::
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Assumed Neo4j is running
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On Neo4j 2026.01+, you can also specify ``filterable_properties`` to enable in-index
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filtering with the ``SEARCH`` clause. See :ref:`filterable-index-creation` for details.
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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Populating the Neo4j Vector Index
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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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 <https://github.com/neo4j/neo4j-python-driver>`_.
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.. code:: python
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from neo4j import GraphDatabase
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from neo4j_graphrag.indexes import upsert_vectors
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from neo4j_graphrag.types import EntityType
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URI = "neo4j://localhost:7687"
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AUTH = ("neo4j", "password")
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# Connect to Neo4j database
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driver = GraphDatabase.driver(URI, auth=AUTH)
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# Upsert the vector
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vector = ...
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upsert_vectors(
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driver,
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ids=["1234"],
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embedding_property="vectorProperty",
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embeddings=[vector],
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entity_type=EntityType.NODE,
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)
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.. note::
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Assumed Neo4j is running with a defined vector index
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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Performing a similarity search
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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While the library has more retrievers than shown here, the following examples should be able to get you started.
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.. code:: python
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from neo4j import GraphDatabase
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from neo4j_graphrag.embeddings.openai import OpenAIEmbeddings
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from neo4j_graphrag.retrievers import VectorRetriever
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URI = "neo4j://localhost:7687"
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AUTH = ("neo4j", "password")
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INDEX_NAME = "vector-index-name"
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# Connect to Neo4j database
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driver = GraphDatabase.driver(URI, auth=AUTH)
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# Create Embedder object
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# Note: An OPENAI_API_KEY environment variable is required here
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embedder = OpenAIEmbeddings(model="text-embedding-3-large")
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# Initialize the retriever
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retriever = VectorRetriever(driver, INDEX_NAME, embedder)
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# Run the similarity search
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query_text = "How do I do similarity search in Neo4j?"
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response = retriever.search(query_text=query_text, top_k=5)
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.. note::
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Assumed Neo4j is running with populated vector index in place.
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***********
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Limitations
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***********
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The query over the vector index is an *approximate* nearest neighbor search and may not give exact results. `See this reference for more details <https://neo4j.com/docs/cypher-manual/current/indexes/semantic-indexes/vector-indexes/#limitations-and-issues>`_.
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Development
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===========
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********************
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Install dependencies
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********************
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.. code:: bash
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uv sync --all-extras
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***************
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Getting started
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***************
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~~~~~~
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Issues
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~~~~~~
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If you have a bug to report or feature to request, first
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`search to see if an issue already exists <https://docs.github.com/en/github/searching-for-information-on-github/searching-on-github/searching-issues-and-pull-requests#search-by-the-title-body-or-comments>`_.
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If a related issue doesn't exist, please raise a new issue using the relevant
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`issue form <https://github.com/neo4j/neo4j-graphrag-python/issues/new/choose>`_.
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If you're a Neo4j Enterprise customer, you can also reach out to `Customer Support <http://support.neo4j.com/>`_.
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If you don't have a bug to report or feature request, but you need a hand with
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the library; community support is available via `Neo4j Online Community <https://community.neo4j.com/>`_
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and/or `Discord <https://discord.gg/neo4j>`_.
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~~~~~~~~~~~~
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Make changes
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~~~~~~~~~~~~
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1. Fork the repository.
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2. Install Python and uv.
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3. Create a working branch from `main` and start with your changes!
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~~~~~~~~~~~~
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Pull request
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~~~~~~~~~~~~
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When you're finished with your changes, create a pull request, also known as a PR.
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- Ensure that you have `signed the CLA <https://neo4j.com/developer/contributing-code/#sign-cla>`_.
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- Ensure that the base of your PR is set to `main`.
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- Don't forget to `link your PR to an issue <https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue>`_
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if you are solving one.
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- Enable the checkbox to `allow maintainer edits <https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/allowing-changes-to-a-pull-request-branch-created-from-a-fork>`_
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so that maintainers can make any necessary tweaks and update your branch for merge.
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- Reviewers may ask for changes to be made before a PR can be merged, either using
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`suggested changes <https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/reviewing-changes-in-pull-requests/incorporating-feedback-in-your-pull-request>`_
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or normal pull request comments. You can apply suggested changes directly through
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the UI, and any other changes can be made in your fork and committed to the PR branch.
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- As you update your PR and apply changes, mark each conversation as `resolved <https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests/commenting-on-a-pull-request#resolving-conversations>`_.
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*********
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Run tests
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*********
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Run the tests using uv.
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.. code:: bash
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uv run pytest
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~~~~~~~~~~
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Unit tests
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~~~~~~~~~~
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This should run out of the box once the dependencies are installed.
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.. code:: bash
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uv run pytest tests/unit
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~~~~~~~~~
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E2E tests
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~~~~~~~~~
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To run e2e tests you'd need to have some services running locally:
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- neo4j
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- weaviate
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- weaviate-text2vec-transformers
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The easiest way to get it up and running is via Docker compose:
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.. code:: bash
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docker compose -f tests/e2e/docker-compose.yml up
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.. note::
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If you suspect something in the databases are cached, run `docker compose -f tests/e2e/docker-compose.yml down` to remove them completely
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Once the services are running, execute the following command to run the e2e tests.
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.. code:: bash
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uv run pytest tests/e2e
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*******************
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Further information
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*******************
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- `The official Neo4j Python driver <https://github.com/neo4j/neo4j-python-driver>`_
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- `Neo4j GenAI integrations <https://neo4j.com/docs/cypher-manual/current/genai-integrations/>`_
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