| Name | neo4j-graphrag JSON |
| Version |
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| Summary | Python package to allow easy integration to Neo4j's GraphRAG features |
| upload_time | 2025-01-15 08:49:12 |
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| author | Neo4j, Inc |
| requires_python | <4.0.0,>=3.9.0 |
| license | Apache License, Version 2.0 |
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# Neo4j GraphRAG Package for Python
The official Neo4j GraphRAG package for Python enables developers to build [graph retrieval augmented generation (GraphRAG)](https://neo4j.com/blog/graphrag-manifesto/) applications using the power of Neo4j and Python.
As a first-party library, it offers a robust, feature-rich, and high-performance solution, with the added assurance of long-term support and maintenance directly from Neo4j.
## 📄 Documentation
Documentation can be found [here](https://neo4j.com/docs/neo4j-graphrag-python/)
### Resources
A series of blog posts demonstrating how to use this package:
- Build a Knowledge Graph and use GenAI to answer questions:
- [GraphRAG Python Package: Accelerating GenAI With Knowledge Graphs](https://neo4j.com/blog/graphrag-python-package/)
- Retrievers: when the Neo4j graph is already populated:
- [Getting Started With the Neo4j GraphRAG Python Package](https://neo4j.com/developer-blog/get-started-graphrag-python-package/)
- [Enriching Vector Search With Graph Traversal Using the GraphRAG Python Package](https://neo4j.com/developer-blog/graph-traversal-graphrag-python-package/)
- [Hybrid Retrieval for GraphRAG Applications Using the GraphRAG Python Package](https://neo4j.com/developer-blog/hybrid-retrieval-graphrag-python-package/)
- [Enhancing Hybrid Retrieval With Graph Traversal Using the GraphRAG Python Package](https://neo4j.com/developer-blog/enhancing-hybrid-retrieval-graphrag-python-package/)
- [Effortless RAG With Text2CypherRetriever](https://medium.com/neo4j/effortless-rag-with-text2cypherretriever-cb1a781ca53c)
A list of Neo4j GenAI-related features can also be found at [Neo4j GenAI Ecosystem](https://neo4j.com/labs/genai-ecosystem/).
## 🐍 Python Version Support
| Version | Supported? |
| ------- | ---------: |
| 3.12 | ✓ |
| 3.11 | ✓ |
| 3.10 | ✓ |
| 3.9 | ✓ |
| 3.8 | ✗ |
## 📦 Installation
To install the latest stable version, run:
```shell
pip install neo4j-graphrag
```
### Optional Dependencies
This package has some optional features that can be enabled using
the extra dependencies described below:
- LLM providers (at least one is required for RAG and KG Builder Pipeline):
- **ollama**: LLMs from Ollama
- **openai**: LLMs from OpenAI (including AzureOpenAI)
- **google**: LLMs from Vertex AI
- **cohere**: LLMs from Cohere
- **anthropic**: LLMs from Anthropic
- **mistralai**: LLMs from MistralAI
- **sentence-transformers** : to use embeddings from the `sentence-transformers` Python package
- Vector database (to use :ref:`External Retrievers`):
- **weaviate**: store vectors in Weaviate
- **pinecone**: store vectors in Pinecone
- **qdrant**: store vectors in Qdrant
- **experimental**: experimental features mainly related to the Knowledge Graph creation pipelines.
- Warning: this dependency group requires `pygraphviz`. See below for installation instructions.
Install package with optional dependencies with (for instance):
```shell
pip install "neo4j-graphrag[openai]"
```
#### pygraphviz
`pygraphviz` is used for visualizing pipelines.
Installation instructions can be found [here](https://pygraphviz.github.io/documentation/stable/install.html).
## 💻 Example Usage
The scripts below demonstrate how to get started with the package and make use of its key features.
To run these examples, ensure that you have a Neo4j instance up and running and update the `NEO4J_URI`, `NEO4J_USERNAME`, and `NEO4J_PASSWORD` variables in each script with the details of your Neo4j instance.
For the examples, make sure to export your OpenAI key as an environment variable named `OPENAI_API_KEY`.
Additional examples are available in the `examples` folder.
### Knowledge Graph Construction
**NOTE: The [APOC core library](https://neo4j.com/labs/apoc/) must be installed in your Neo4j instance in order to use this feature**
This package offers two methods for constructing a knowledge graph.
The `Pipeline` class provides extensive customization options, making it ideal for advanced use cases.
See the `examples/pipeline` folder for examples of how to use this class.
For a more streamlined approach, the `SimpleKGPipeline` class offers a simplified abstraction layer over the `Pipeline`, making it easier to build knowledge graphs.
Both classes support working directly with text and PDFs.
```python
import asyncio
from neo4j import GraphDatabase
from neo4j_graphrag.embeddings import OpenAIEmbeddings
from neo4j_graphrag.experimental.pipeline.kg_builder import SimpleKGPipeline
from neo4j_graphrag.llm.openai_llm import OpenAILLM
NEO4J_URI = "neo4j://localhost:7687"
NEO4J_USERNAME = "neo4j"
NEO4J_PASSWORD = "password"
# Connect to the Neo4j database
driver = GraphDatabase.driver(NEO4J_URI, auth=(NEO4J_USERNAME, NEO4J_PASSWORD))
# List the entities and relations the LLM should look for in the text
entities = ["Person", "House", "Planet"]
relations = ["PARENT_OF", "HEIR_OF", "RULES"]
potential_schema = [
("Person", "PARENT_OF", "Person"),
("Person", "HEIR_OF", "House"),
("House", "RULES", "Planet"),
]
# Create an Embedder object
embedder = OpenAIEmbeddings(model="text-embedding-3-large")
# Instantiate the LLM
llm = OpenAILLM(
model_name="gpt-4o",
model_params={
"max_tokens": 2000,
"response_format": {"type": "json_object"},
"temperature": 0,
},
)
# Instantiate the SimpleKGPipeline
kg_builder = SimpleKGPipeline(
llm=llm,
driver=driver,
embedder=embedder,
entities=entities,
relations=relations,
on_error="IGNORE",
from_pdf=False,
)
# Run the pipeline on a piece of text
text = (
"The son of Duke Leto Atreides and the Lady Jessica, Paul is the heir of House "
"Atreides, an aristocratic family that rules the planet Caladan."
)
asyncio.run(kg_builder.run_async(text=text))
driver.close()
```
Example knowledge graph created using the above script:
### 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 your embeddings have.
```python
from neo4j import GraphDatabase
from neo4j_graphrag.indexes import create_vector_index
NEO4J_URI = "neo4j://localhost:7687"
NEO4J_USERNAME = "neo4j"
NEO4J_PASSWORD = "password"
INDEX_NAME = "vector-index-name"
# Connect to the Neo4j database
driver = GraphDatabase.driver(NEO4J_URI, auth=(NEO4J_USERNAME, NEO4J_PASSWORD))
# Create the index
create_vector_index(
driver,
INDEX_NAME,
label="Chunk",
embedding_property="embedding",
dimensions=3072,
similarity_fn="euclidean",
)
driver.close()
```
### Populating a Vector Index
This example demonstrates one method for upserting data in your Neo4j database.
It's important to note that there are alternative approaches, such as using the [Neo4j Python driver](https://github.com/neo4j/neo4j-python-driver).
Ensure that your vector index is created prior to executing this example.
```python
from neo4j import GraphDatabase
from neo4j_graphrag.embeddings import OpenAIEmbeddings
from neo4j_graphrag.indexes import upsert_vector
NEO4J_URI = "neo4j://localhost:7687"
NEO4J_USERNAME = "neo4j"
NEO4J_PASSWORD = "password"
# Connect to the Neo4j database
driver = GraphDatabase.driver(NEO4J_URI, auth=(NEO4J_USERNAME, NEO4J_PASSWORD))
# Create an Embedder object
embedder = OpenAIEmbeddings(model="text-embedding-3-large")
# Generate an embedding for some text
text = (
"The son of Duke Leto Atreides and the Lady Jessica, Paul is the heir of House "
"Atreides, an aristocratic family that rules the planet Caladan."
)
vector = embedder.embed_query(text)
# Upsert the vector
upsert_vector(
driver,
node_id=0,
embedding_property="embedding",
vector=vector,
)
driver.close()
```
### Performing a Similarity Search
Please note that when querying a Neo4j vector index _approximate_ nearest neighbor search is used, which may not always deliver exact results.
For more information, refer to the Neo4j documentation on [limitations and issues of vector indexes](https://neo4j.com/docs/cypher-manual/current/indexes/semantic-indexes/vector-indexes/#limitations-and-issues).
In the example below, we perform a simple vector search using a retriever that conducts a similarity search over the `vector-index-name` vector index.
This library provides more retrievers beyond just the `VectorRetriever`.
See the `examples` folder for examples of how to use these retrievers.
Before running this example, make sure your vector index has been created and populated.
```python
from neo4j import GraphDatabase
from neo4j_graphrag.embeddings import OpenAIEmbeddings
from neo4j_graphrag.generation import GraphRAG
from neo4j_graphrag.llm import OpenAILLM
from neo4j_graphrag.retrievers import VectorRetriever
NEO4J_URI = "neo4j://localhost:7687"
NEO4J_USERNAME = "neo4j"
NEO4J_PASSWORD = "password"
INDEX_NAME = "vector-index-name"
# Connect to the Neo4j database
driver = GraphDatabase.driver(NEO4J_URI, auth=(NEO4J_USERNAME, NEO4J_PASSWORD))
# Create an Embedder object
embedder = OpenAIEmbeddings(model="text-embedding-3-large")
# Initialize the retriever
retriever = VectorRetriever(driver, INDEX_NAME, embedder)
# Instantiate the LLM
llm = OpenAILLM(model_name="gpt-4o", model_params={"temperature": 0})
# Instantiate the RAG pipeline
rag = GraphRAG(retriever=retriever, llm=llm)
# Query the graph
query_text = "Who is Paul Atreides?"
response = rag.search(query_text=query_text, retriever_config={"top_k": 5})
print(response.answer)
driver.close()
```
## 🤝 Contributing
You must sign the [contributors license agreement](https://neo4j.com/developer/contributing-code/#sign-cla) in order to make contributions to this project.
### Install Dependencies
Our Python dependencies are managed using Poetry.
If Poetry is not yet installed on your system, you can follow the instructions [here](https://python-poetry.org/) to set it up.
To begin development on this project, start by cloning the repository and then install all necessary dependencies, including the development dependencies, with the following command:
```bash
poetry install --with dev
```
### Reporting Issues
If you have a bug to report or feature to request, first
[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).
If a related issue doesn't exist, please raise a new issue using the [issue form](https://github.com/neo4j/neo4j-graphrag-python/issues/new/choose).
If you're a Neo4j Enterprise customer, you can also reach out to [Customer Support](http://support.neo4j.com/).
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](https://community.neo4j.com/)
and/or [Discord](https://discord.gg/neo4j).
### Workflow for Contributions
1. Fork the repository.
2. Install Python and Poetry.
3. Create a working branch from `main` and start with your changes!
### Code Formatting and Linting
Our codebase follows strict formatting and linting standards using [Ruff](https://docs.astral.sh/ruff/) for code quality checks and [Mypy](https://github.com/python/mypy) for type checking.
Before contributing, ensure that all code is properly formatted, free of linting issues, and includes accurate type annotations.
- To install Ruff, follow the instructions [here](https://docs.astral.sh/ruff/installation/).
- To set up Mypy, follow the steps outlined [here](https://mypy.readthedocs.io/en/stable/getting_started.html#installing-and-running-mypy).
Adherence to these standards is required for contributions to be accepted.
#### Using Pre-commit
We recommend setting up [pre-commit](https://pre-commit.com/) to automate code quality checks.
This ensures your changes meet our guidelines before committing.
1. Install pre-commit by following the [installation guide](https://pre-commit.com/#install).
2. Set up the pre-commit hooks by running:
```bash
pre-commit install
```
3. To manually check if a file meets the quality requirements, run:
```bash
pre-commit run --file path/to/file
```
### Pull Requests
When you're finished with your changes, create a pull request (PR) using the following workflow.
- Ensure you have formatted and linted your code.
- Ensure that you have [signed the CLA](https://neo4j.com/developer/contributing-code/#sign-cla).
- Ensure that the base of your PR is set to `main`.
- 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)
if you are solving one.
- Check 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)
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](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/reviewing-changes-in-pull-requests/incorporating-feedback-in-your-pull-request)
or normal pull request comments. You can apply suggested changes directly through
the UI. 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](https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests/commenting-on-a-pull-request#resolving-conversations).
- Update the `CHANGELOG.md` if you have made significant changes to the project, these include:
- Major changes:
- New features
- Bug fixes with high impact
- Breaking changes
- Minor changes:
- Documentation improvements
- Code refactoring without functional impact
- Minor bug fixes
- Keep `CHANGELOG.md` changes brief and focus on the most important changes.
### Updating the `CHANGELOG.md`
1. You can automatically generate a changelog suggestion for your PR by commenting on it [using CodiumAI](https://github.com/CodiumAI-Agent):
```
@CodiumAI-Agent /update_changelog
```
2. Edit the suggestion if necessary and update the appropriate subsection in the `CHANGELOG.md` file under 'Next'.
3. Commit the changes.
## 🧪 Tests
### Unit Tests
Install the project dependencies then run the following command to run the unit tests locally:
```bash
poetry run pytest tests/unit
```
### E2E tests
To execute end-to-end (e2e) tests, you need the following services to be running locally:
- neo4j
- weaviate
- weaviate-text2vec-transformers
The simplest way to set these up is by using Docker Compose:
```bash
docker compose -f tests/e2e/docker-compose.yml up
```
_(tip: If you encounter any caching issues within the databases, you can completely remove them by running `docker compose -f tests/e2e/docker-compose.yml down`)_
Once all the services are running, execute the following command to run the e2e tests:
```bash
poetry run pytest tests/e2e
```
## ℹ️ Additional Information
- [The official Neo4j Python driver](https://github.com/neo4j/neo4j-python-driver)
- [Neo4j GenAI integrations](https://neo4j.com/docs/cypher-manual/current/genai-integrations/)
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"description": "# Neo4j GraphRAG Package for Python\n\nThe official Neo4j GraphRAG package for Python enables developers to build [graph retrieval augmented generation (GraphRAG)](https://neo4j.com/blog/graphrag-manifesto/) applications using the power of Neo4j and Python.\nAs a first-party library, it offers a robust, feature-rich, and high-performance solution, with the added assurance of long-term support and maintenance directly from Neo4j.\n\n## \ud83d\udcc4 Documentation\n\nDocumentation can be found [here](https://neo4j.com/docs/neo4j-graphrag-python/)\n\n### Resources\n\nA series of blog posts demonstrating how to use this package:\n\n- Build a Knowledge Graph and use GenAI to answer questions:\n - [GraphRAG Python Package: Accelerating GenAI With Knowledge Graphs](https://neo4j.com/blog/graphrag-python-package/)\n- Retrievers: when the Neo4j graph is already populated:\n - [Getting Started With the Neo4j GraphRAG Python Package](https://neo4j.com/developer-blog/get-started-graphrag-python-package/)\n - [Enriching Vector Search With Graph Traversal Using the GraphRAG Python Package](https://neo4j.com/developer-blog/graph-traversal-graphrag-python-package/)\n - [Hybrid Retrieval for GraphRAG Applications Using the GraphRAG Python Package](https://neo4j.com/developer-blog/hybrid-retrieval-graphrag-python-package/)\n - [Enhancing Hybrid Retrieval With Graph Traversal Using the GraphRAG Python Package](https://neo4j.com/developer-blog/enhancing-hybrid-retrieval-graphrag-python-package/)\n - [Effortless RAG With Text2CypherRetriever](https://medium.com/neo4j/effortless-rag-with-text2cypherretriever-cb1a781ca53c)\n\nA list of Neo4j GenAI-related features can also be found at [Neo4j GenAI Ecosystem](https://neo4j.com/labs/genai-ecosystem/).\n\n\n## \ud83d\udc0d Python Version Support\n\n| Version | Supported? |\n| ------- | ---------: |\n| 3.12 | ✓ |\n| 3.11 | ✓ |\n| 3.10 | ✓ |\n| 3.9 | ✓ |\n| 3.8 | ✗ |\n\n## \ud83d\udce6 Installation\n\nTo install the latest stable version, run:\n\n```shell\npip install neo4j-graphrag\n```\n\n### Optional Dependencies\n\nThis package has some optional features that can be enabled using\nthe extra dependencies described below:\n\n- LLM providers (at least one is required for RAG and KG Builder Pipeline):\n - **ollama**: LLMs from Ollama\n - **openai**: LLMs from OpenAI (including AzureOpenAI)\n - **google**: LLMs from Vertex AI\n - **cohere**: LLMs from Cohere\n - **anthropic**: LLMs from Anthropic\n - **mistralai**: LLMs from MistralAI\n- **sentence-transformers** : to use embeddings from the `sentence-transformers` Python package\n- Vector database (to use :ref:`External Retrievers`):\n - **weaviate**: store vectors in Weaviate\n - **pinecone**: store vectors in Pinecone\n - **qdrant**: store vectors in Qdrant\n- **experimental**: experimental features mainly related to the Knowledge Graph creation pipelines.\n - Warning: this dependency group requires `pygraphviz`. See below for installation instructions.\n\n\nInstall package with optional dependencies with (for instance):\n\n```shell\npip install \"neo4j-graphrag[openai]\"\n```\n\n#### pygraphviz\n\n`pygraphviz` is used for visualizing pipelines.\nInstallation instructions can be found [here](https://pygraphviz.github.io/documentation/stable/install.html).\n\n## \ud83d\udcbb Example Usage\n\nThe scripts below demonstrate how to get started with the package and make use of its key features.\nTo run these examples, ensure that you have a Neo4j instance up and running and update the `NEO4J_URI`, `NEO4J_USERNAME`, and `NEO4J_PASSWORD` variables in each script with the details of your Neo4j instance.\nFor the examples, make sure to export your OpenAI key as an environment variable named `OPENAI_API_KEY`.\nAdditional examples are available in the `examples` folder.\n\n### Knowledge Graph Construction\n\n**NOTE: The [APOC core library](https://neo4j.com/labs/apoc/) must be installed in your Neo4j instance in order to use this feature**\n\nThis package offers two methods for constructing a knowledge graph.\n\nThe `Pipeline` class provides extensive customization options, making it ideal for advanced use cases.\nSee the `examples/pipeline` folder for examples of how to use this class.\n\nFor a more streamlined approach, the `SimpleKGPipeline` class offers a simplified abstraction layer over the `Pipeline`, making it easier to build knowledge graphs.\nBoth classes support working directly with text and PDFs.\n\n```python\nimport asyncio\n\nfrom neo4j import GraphDatabase\nfrom neo4j_graphrag.embeddings import OpenAIEmbeddings\nfrom neo4j_graphrag.experimental.pipeline.kg_builder import SimpleKGPipeline\nfrom neo4j_graphrag.llm.openai_llm import OpenAILLM\n\nNEO4J_URI = \"neo4j://localhost:7687\"\nNEO4J_USERNAME = \"neo4j\"\nNEO4J_PASSWORD = \"password\"\n\n# Connect to the Neo4j database\ndriver = GraphDatabase.driver(NEO4J_URI, auth=(NEO4J_USERNAME, NEO4J_PASSWORD))\n\n# List the entities and relations the LLM should look for in the text\nentities = [\"Person\", \"House\", \"Planet\"]\nrelations = [\"PARENT_OF\", \"HEIR_OF\", \"RULES\"]\npotential_schema = [\n (\"Person\", \"PARENT_OF\", \"Person\"),\n (\"Person\", \"HEIR_OF\", \"House\"),\n (\"House\", \"RULES\", \"Planet\"),\n]\n\n# Create an Embedder object\nembedder = OpenAIEmbeddings(model=\"text-embedding-3-large\")\n\n# Instantiate the LLM\nllm = OpenAILLM(\n model_name=\"gpt-4o\",\n model_params={\n \"max_tokens\": 2000,\n \"response_format\": {\"type\": \"json_object\"},\n \"temperature\": 0,\n },\n)\n\n# Instantiate the SimpleKGPipeline\nkg_builder = SimpleKGPipeline(\n llm=llm,\n driver=driver,\n embedder=embedder,\n entities=entities,\n relations=relations,\n on_error=\"IGNORE\",\n from_pdf=False,\n)\n\n# Run the pipeline on a piece of text\ntext = (\n \"The son of Duke Leto Atreides and the Lady Jessica, Paul is the heir of House \"\n \"Atreides, an aristocratic family that rules the planet Caladan.\"\n)\nasyncio.run(kg_builder.run_async(text=text))\ndriver.close()\n```\n\nExample knowledge graph created using the above script:\n\n\n\n### Creating a Vector Index\n\nWhen creating a vector index, make sure you match the number of dimensions in the index with the number of dimensions your embeddings have.\n\n```python\nfrom neo4j import GraphDatabase\nfrom neo4j_graphrag.indexes import create_vector_index\n\nNEO4J_URI = \"neo4j://localhost:7687\"\nNEO4J_USERNAME = \"neo4j\"\nNEO4J_PASSWORD = \"password\"\nINDEX_NAME = \"vector-index-name\"\n\n# Connect to the Neo4j database\ndriver = GraphDatabase.driver(NEO4J_URI, auth=(NEO4J_USERNAME, NEO4J_PASSWORD))\n\n# Create the index\ncreate_vector_index(\n driver,\n INDEX_NAME,\n label=\"Chunk\",\n embedding_property=\"embedding\",\n dimensions=3072,\n similarity_fn=\"euclidean\",\n)\ndriver.close()\n```\n\n### Populating a Vector Index\n\nThis example demonstrates one method for upserting data in your Neo4j database.\nIt's important to note that there are alternative approaches, such as using the [Neo4j Python driver](https://github.com/neo4j/neo4j-python-driver).\n\nEnsure that your vector index is created prior to executing this example.\n\n```python\nfrom neo4j import GraphDatabase\nfrom neo4j_graphrag.embeddings import OpenAIEmbeddings\nfrom neo4j_graphrag.indexes import upsert_vector\n\nNEO4J_URI = \"neo4j://localhost:7687\"\nNEO4J_USERNAME = \"neo4j\"\nNEO4J_PASSWORD = \"password\"\n\n# Connect to the Neo4j database\ndriver = GraphDatabase.driver(NEO4J_URI, auth=(NEO4J_USERNAME, NEO4J_PASSWORD))\n\n# Create an Embedder object\nembedder = OpenAIEmbeddings(model=\"text-embedding-3-large\")\n\n# Generate an embedding for some text\ntext = (\n \"The son of Duke Leto Atreides and the Lady Jessica, Paul is the heir of House \"\n \"Atreides, an aristocratic family that rules the planet Caladan.\"\n)\nvector = embedder.embed_query(text)\n\n# Upsert the vector\nupsert_vector(\n driver,\n node_id=0,\n embedding_property=\"embedding\",\n vector=vector,\n)\ndriver.close()\n```\n\n### Performing a Similarity Search\n\nPlease note that when querying a Neo4j vector index _approximate_ nearest neighbor search is used, which may not always deliver exact results.\nFor more information, refer to the Neo4j documentation on [limitations and issues of vector indexes](https://neo4j.com/docs/cypher-manual/current/indexes/semantic-indexes/vector-indexes/#limitations-and-issues).\n\nIn the example below, we perform a simple vector search using a retriever that conducts a similarity search over the `vector-index-name` vector index.\n\nThis library provides more retrievers beyond just the `VectorRetriever`.\nSee the `examples` folder for examples of how to use these retrievers.\n\nBefore running this example, make sure your vector index has been created and populated.\n\n```python\nfrom neo4j import GraphDatabase\nfrom neo4j_graphrag.embeddings import OpenAIEmbeddings\nfrom neo4j_graphrag.generation import GraphRAG\nfrom neo4j_graphrag.llm import OpenAILLM\nfrom neo4j_graphrag.retrievers import VectorRetriever\n\nNEO4J_URI = \"neo4j://localhost:7687\"\nNEO4J_USERNAME = \"neo4j\"\nNEO4J_PASSWORD = \"password\"\nINDEX_NAME = \"vector-index-name\"\n\n# Connect to the Neo4j database\ndriver = GraphDatabase.driver(NEO4J_URI, auth=(NEO4J_USERNAME, NEO4J_PASSWORD))\n\n# Create an Embedder object\nembedder = OpenAIEmbeddings(model=\"text-embedding-3-large\")\n\n# Initialize the retriever\nretriever = VectorRetriever(driver, INDEX_NAME, embedder)\n\n# Instantiate the LLM\nllm = OpenAILLM(model_name=\"gpt-4o\", model_params={\"temperature\": 0})\n\n# Instantiate the RAG pipeline\nrag = GraphRAG(retriever=retriever, llm=llm)\n\n# Query the graph\nquery_text = \"Who is Paul Atreides?\"\nresponse = rag.search(query_text=query_text, retriever_config={\"top_k\": 5})\nprint(response.answer)\ndriver.close()\n```\n\n## \ud83e\udd1d Contributing\n\nYou must sign the [contributors license agreement](https://neo4j.com/developer/contributing-code/#sign-cla) in order to make contributions to this project.\n\n### Install Dependencies\n\nOur Python dependencies are managed using Poetry.\nIf Poetry is not yet installed on your system, you can follow the instructions [here](https://python-poetry.org/) to set it up.\nTo begin development on this project, start by cloning the repository and then install all necessary dependencies, including the development dependencies, with the following command:\n\n```bash\npoetry install --with dev\n```\n\n### Reporting Issues\n\nIf you have a bug to report or feature to request, first\n[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).\nIf a related issue doesn't exist, please raise a new issue using the [issue form](https://github.com/neo4j/neo4j-graphrag-python/issues/new/choose).\n\nIf you're a Neo4j Enterprise customer, you can also reach out to [Customer Support](http://support.neo4j.com/).\n\nIf you don't have a bug to report or feature request, but you need a hand with\nthe library; community support is available via [Neo4j Online Community](https://community.neo4j.com/)\nand/or [Discord](https://discord.gg/neo4j).\n\n### Workflow for Contributions\n\n1. Fork the repository.\n2. Install Python and Poetry.\n3. Create a working branch from `main` and start with your changes!\n\n### Code Formatting and Linting\n\nOur codebase follows strict formatting and linting standards using [Ruff](https://docs.astral.sh/ruff/) for code quality checks and [Mypy](https://github.com/python/mypy) for type checking.\nBefore contributing, ensure that all code is properly formatted, free of linting issues, and includes accurate type annotations.\n\n- To install Ruff, follow the instructions [here](https://docs.astral.sh/ruff/installation/).\n- To set up Mypy, follow the steps outlined [here](https://mypy.readthedocs.io/en/stable/getting_started.html#installing-and-running-mypy).\n\nAdherence to these standards is required for contributions to be accepted.\n\n#### Using Pre-commit\n\nWe recommend setting up [pre-commit](https://pre-commit.com/) to automate code quality checks.\nThis ensures your changes meet our guidelines before committing.\n\n1. Install pre-commit by following the [installation guide](https://pre-commit.com/#install).\n2. Set up the pre-commit hooks by running:\n\n ```bash\n pre-commit install\n ```\n\n3. To manually check if a file meets the quality requirements, run:\n\n ```bash\n pre-commit run --file path/to/file\n ```\n\n### Pull Requests\n\nWhen you're finished with your changes, create a pull request (PR) using the following workflow.\n\n- Ensure you have formatted and linted your code.\n- Ensure that you have [signed the CLA](https://neo4j.com/developer/contributing-code/#sign-cla).\n- Ensure that the base of your PR is set to `main`.\n- 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)\n if you are solving one.\n- Check 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)\n so that maintainers can make any necessary tweaks and update your branch for merge.\n- Reviewers may ask for changes to be made before a PR can be merged, either using\n [suggested changes](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/reviewing-changes-in-pull-requests/incorporating-feedback-in-your-pull-request)\n or normal pull request comments. You can apply suggested changes directly through\n the UI. Any other changes can be made in your fork and committed to the PR branch.\n- 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).\n- Update the `CHANGELOG.md` if you have made significant changes to the project, these include:\n - Major changes:\n - New features\n - Bug fixes with high impact\n - Breaking changes\n - Minor changes:\n - Documentation improvements\n - Code refactoring without functional impact\n - Minor bug fixes\n- Keep `CHANGELOG.md` changes brief and focus on the most important changes.\n\n### Updating the `CHANGELOG.md`\n\n1. You can automatically generate a changelog suggestion for your PR by commenting on it [using CodiumAI](https://github.com/CodiumAI-Agent):\n\n```\n@CodiumAI-Agent /update_changelog\n```\n\n2. Edit the suggestion if necessary and update the appropriate subsection in the `CHANGELOG.md` file under 'Next'.\n3. Commit the changes.\n\n## \ud83e\uddea Tests\n\n### Unit Tests\n\nInstall the project dependencies then run the following command to run the unit tests locally:\n\n```bash\npoetry run pytest tests/unit\n```\n\n### E2E tests\n\nTo execute end-to-end (e2e) tests, you need the following services to be running locally:\n\n- neo4j\n- weaviate\n- weaviate-text2vec-transformers\n\nThe simplest way to set these up is by using Docker Compose:\n\n```bash\ndocker compose -f tests/e2e/docker-compose.yml up\n```\n\n_(tip: If you encounter any caching issues within the databases, you can completely remove them by running `docker compose -f tests/e2e/docker-compose.yml down`)_\n\nOnce all the services are running, execute the following command to run the e2e tests:\n\n```bash\npoetry run pytest tests/e2e\n```\n\n## \u2139\ufe0f Additional Information\n\n- [The official Neo4j Python driver](https://github.com/neo4j/neo4j-python-driver)\n- [Neo4j GenAI integrations](https://neo4j.com/docs/cypher-manual/current/genai-integrations/)\n\n",
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