User Guide: RAG

This guide provides a starting point for using the Neo4j GraphRAG package and configuring it according to specific requirements.

Quickstart

To perform a GraphRAG query using the neo4j-graphrag package, a few components are needed:

1. A Neo4j driver: used to query your Neo4j database.

2. A Retriever: the neo4j-graphrag package provides some implementations (see the dedicated section) and lets you write your own if none of the provided implementations matches your needs (see how to write a custom retriever).

3. An LLM: to generate the answer, we need to call an LLM model. The neo4j-graphrag package’s LLM interface is compatible with LangChain. Developers can also write their own interface if needed.

In practice, it’s done with only a few lines of code:

from neo4j import GraphDatabase
from neo4j_graphrag.retrievers import VectorRetriever
from neo4j_graphrag.llm import OpenAILLM
from neo4j_graphrag.generation import GraphRAG
from neo4j_graphrag.embeddings import OpenAIEmbeddings

# 1. Neo4j driver
URI = "neo4j://localhost:7687"
AUTH = ("neo4j", "password")

INDEX_NAME = "index-name"

# Connect to Neo4j database
driver = GraphDatabase.driver(URI, auth=AUTH)

# 2. Retriever
# Create Embedder object, needed to convert the user question (text) to a vector
embedder = OpenAIEmbeddings(model="text-embedding-3-large")

# Initialize the retriever
retriever = VectorRetriever(driver, INDEX_NAME, embedder)

# 3. LLM
# Note: the OPENAI_API_KEY must be in the env vars
llm = OpenAILLM(model_name="gpt-4o", model_params={"temperature": 0})

# Initialize the RAG pipeline
rag = GraphRAG(retriever=retriever, llm=llm)

# Query the graph
query_text = "How do I do similarity search in Neo4j?"
response = rag.search(query_text=query_text, retriever_config={"top_k": 5})
print(response.answer)

Note

In order to run this code, the openai Python package needs to be installed: pip install openai

The following sections provide more details about how to customize this code.

GraphRAG Configuration

Each component can be configured individually: the LLM and the prompt.

Using Another LLM Model

If OpenAI cannot be used directly, there are a few available alternatives:

• Use Azure OpenAI (GPT…).

• Use Google VertexAI (Gemini…).

• Use Anthropic LLM (Claude…).

• Use Mistral LLM

• Use Cohere.

• Use a local Ollama model.

• Implement a custom interface.

• Utilize any LangChain chat model.

All options are illustrated below.

Using Azure Open AI LLM

It is possible to use Azure OpenAI switching to the AzureOpenAILLM class:

from neo4j_graphrag.llm import AzureOpenAILLM
llm = AzureOpenAILLM(
    model_name="gpt-4o",
    azure_endpoint="https://example-endpoint.openai.azure.com/",  # update with your endpoint
    api_version="2024-06-01",  # update appropriate version
    api_key="...",  # api_key is optional and can also be set with OPENAI_API_KEY env var
)
llm.invoke("say something")

Check the OpenAI Python client documentation. to learn more about the configuration.

Note

In order to run this code, the openai Python package needs to be installed: pip install openai

See AzureOpenAILLM.

Using VertexAI LLM

To use VertexAI, instantiate the VertexAILLM class:

from neo4j_graphrag.llm import VertexAILLM
from vertexai.generative_models import GenerationConfig

generation_config = GenerationConfig(temperature=0.0)
llm = VertexAILLM(
    model_name="gemini-1.5-flash-001", generation_config=generation_config
)
llm.invoke("say something")

Note

In order to run this code, the google-cloud-aiplatform Python package needs to be installed: pip install google-cloud-aiplatform

See VertexAILLM.

Using Anthropic LLM

To use Anthropic, instantiate the AnthropicLLM class:

from neo4j_graphrag.llm import AnthropicLLM

llm = AnthropicLLM(
    model_name="claude-3-opus-20240229",
    model_params={"max_tokens": 1000},  # max_tokens must be specified
    api_key=api_key,  # can also set `ANTHROPIC_API_KEY` in env vars
)
llm.invoke("say something")

Note

In order to run this code, the anthropic Python package needs to be installed: pip install anthropic

See AnthropicLLM.

Using MistralAI LLM

To use MistralAI, instantiate the MistralAILLM class:

from neo4j_graphrag.llm import MistralAILLM

llm = MistralAILLM(
    model_name="mistral-small-latest",
    api_key=api_key,  # can also set `MISTRAL_API_KEY` in env vars
)
llm.invoke("say something")

Note

In order to run this code, the mistralai Python package needs to be installed: pip install mistralai

See MistralAILLM.

Using Cohere LLM

To use Cohere, instantiate the CohereLLM class:

from neo4j_graphrag.llm import CohereLLM

llm = CohereLLM(
    model_name="command-r",
    api_key=api_key,  # can also set `CO_API_KEY` in env vars
)
llm.invoke("say something")

Note

In order to run this code, the cohere Python package needs to be installed: pip install cohere

See CohereLLM.

Using a Local Model via Ollama

Assuming Ollama is running on the default address 127.0.0.1:11434, it can be queried using the following:

from neo4j_graphrag.llm import OllamaLLM
llm = OllamaLLM(model_name="orca-mini")
llm.invoke("say something")

Using a Model from LangChain

The LangChain Python package contains implementations for various LLMs and providers. Its interface is compatible with our GraphRAG interface, facilitating integration:

from neo4j_graphrag.generation import GraphRAG
from langchain_community.chat_models import ChatOllama

# retriever = ...

llm = ChatOllama(model="llama3:8b")
rag = GraphRAG(retriever=retriever, llm=llm)
query_text = "How do I do similarity search in Neo4j?"
response = rag.search(query_text=query_text, retriever_config={"top_k": 5})
print(response.answer)

It is however not mandatory to use LangChain.

Using a Custom Model

If the provided implementations do not match their needs, developers can create a custom LLM class by subclassing the LLMInterface. Here’s an example using the Python Ollama client:

import ollama
from neo4j_graphrag.llm import LLMInterface, LLMResponse

class OllamaLLM(LLMInterface):

    def invoke(self, input: str) -> LLMResponse:
        response = ollama.chat(model=self.model_name, messages=[
          {
            'role': 'user',
            'content': input,
          },
        ])
        return LLMResponse(
            content=response["message"]["content"]
        )

    async def ainvoke(self, input: str) -> LLMResponse:
        return self.invoke(input)  # TODO: implement async with ollama.AsyncClient


# retriever = ...

llm = OllamaLLM("llama3:8b")

rag = GraphRAG(retriever=retriever, llm=llm)
query_text = "How do I do similarity search in Neo4j?"
response = rag.search(query_text=query_text, retriever_config={"top_k": 5})
print(response.answer)

See LLMInterface.

Configuring the Prompt

Prompts are managed through PromptTemplate classes. Specifically, the GraphRAG pipeline utilizes a RagTemplate with a default prompt that can be accessed through rag.prompt_template.template. To use a different prompt, subclass the RagTemplate class and pass it to the GraphRAG pipeline object during initialization:

from neo4j_graphrag.generation import RagTemplate, GraphRAG

# retriever = ...
# llm = ...

prompt_template = RagTemplate(
    prompt="Answer the question {question} using context {context} and examples {examples}",
    expected_inputs=["context", "question", "examples"]
)

rag = GraphRAG(retriever=retriever, llm=llm, prompt_template=prompt_template)

# ...

See PromptTemplate.

The final configurable component in the GraphRAG pipeline is the retriever. Below are descriptions of the various options available.

Retriever Configuration

We provide implementations for the following retrievers:

List of retrievers

Retriever	Description
VectorRetriever	Performs a similarity search based on a Neo4j vector index and a query text or vector. Returns the matched node and similarity score.
VectorCypherRetriever	Performs a similarity search based on a Neo4j vector index and a query text or vector. The returned results can be configured through a retrieval query parameter that will be executed after the index search. It can be used to fetch more context around the matched node.
HybridRetriever	Uses both a vector and a full-text index in Neo4j.
HybridCypherRetriever	Same as HybridRetriever with a retrieval query similar to VectorCypherRetriever.
Text2Cypher	Translates the user question into a Cypher query to be run against a Neo4j database (or Knowledge Graph). The results of the query are then passed to the LLM to generate the final answer.
WeaviateNeo4jRetriever	Use this retriever when vectors are saved in a Weaviate vector database
PineconeNeo4jRetriever	Use this retriever when vectors are saved in a Pinecone vector database
QdrantNeo4jRetriever	Use this retriever when vectors are saved in a Qdrant vector database

Retrievers all expose a search method that we will discuss in the next sections.

Vector Retriever

The simplest method to instantiate a vector retriever is:

from neo4j_graphrag.retrievers import VectorRetriever

retriever = VectorRetriever(
    driver,
    index_name=POSTER_INDEX_NAME,
)

The index_name is the name of the Neo4j vector index that will be used for similarity search.

Warning

Vector index use an approximate nearest neighbor algorithm. Refer to the Neo4j Documentation to learn about its limitations.

Search Similar Vector

To identify the top 3 most similar nodes, perform a search by vector:

vector = []  # a list of floats, same size as the vectors in the Neo4j vector index
retriever_result = retriever.search(query_vector=vector, top_k=3)

However, in most cases, a text (from the user) will be provided instead of a vector. In this scenario, an Embedder is required.

Search Similar Text

When searching for a text, specifying how the retriever transforms (embeds) the text into a vector is required. Therefore, the retriever requires knowledge of an embedder:

embedder = OpenAIEmbeddings(model="text-embedding-3-large")

# Initialize the retriever
retriever = VectorRetriever(
    driver,
    index_name=POSTER_INDEX_NAME,
    embedder=embedder,
)

query_text = "How do I do similarity search in Neo4j?"
retriever_result = retriever.search(query_text=query_text, top_k=3)

Embedders

Currently, this package supports the following embedders:

• OpenAIEmbeddings

• SentenceTransformerEmbeddings

• VertexAIEmbeddings

• MistralAIEmbeddings

• CohereEmbeddings

• AzureOpenAIEmbeddings

• OllamaEmbeddings

The OpenAIEmbeddings was illustrated previously. Here is how to use the SentenceTransformerEmbeddings:

from neo4j_graphrag.embeddings import SentenceTransformerEmbeddings

embedder = SentenceTransformerEmbeddings(model="all-MiniLM-L6-v2")  # Note: this is the default model

If another embedder is desired, a custom embedder can be created, using the Embedder interface.

Other Vector Retriever Configuration

Often, not all node properties are pertinent for the RAG context; only a selected few are relevant for inclusion in the LLM prompt context. You can specify which properties to return using the return_properties parameter:

from neo4j_graphrag.retrievers import VectorRetriever

retriever = VectorRetriever(
    driver,
    index_name=POSTER_INDEX_NAME,
    embedder=embedder,
    return_properties=["title"],
)

Pre-Filters

When performing a similarity search, one may have constraints to apply. For instance, filtering out movies released before 2000. This can be achieved using filters.

Note

Filters are implemented for all retrievers except the Hybrid retrievers. The documentation below is not valid for external retrievers, which use their own filter syntax (see Vector Databases).

from neo4j_graphrag.retrievers import VectorRetriever

retriever = VectorRetriever(
    driver,
    index_name=POSTER_INDEX_NAME,
)

filters = {
    "year": {
        "$gte": 2000,
    }
}

query_text = "How do I do similarity search in Neo4j?"
retriever_result = retriever.search(query_text=query_text, filters=filters)

Warning

When using filters, the similarity search bypasses the vector index and instead utilizes an exact match algorithm Ensure that the pre-filtering is stringent enough to prevent query overload.

The currently supported operators are:

• $eq: equal.

• $ne: not equal.

• $lt: less than.

• $lte: less than or equal to.

• $gt: greater than.

• $gte: greater than or equal to.

• $between: between.

• $in: value is in a given list.

• $nin: not in.

• $like: LIKE operator case-sensitive.

• $ilike: LIKE operator case-insensitive.

Here are examples of valid filter syntaxes and their meaning:

Filters syntax

Filter	Meaning
{“year”: 1999}	year = 1999
{“year”: {“$eq”: 1999}}	year = 1999
{“year”: 2000, “title”: “The Matrix”}	year = 1999 AND title = “The Matrix”
{“$and”: [{“year”: 2000}, {“title”: “The Matrix”}]}	year = 1999 AND title = “The Matrix”
{“$or”: [{“title”: “The Matrix Revolution”}, {“title”: “The Matrix”}]}	title = “The Matrix” OR title = “The Matrix Revolution”
{“title”: {“$like”: “The Matrix”}}	title CONTAINS “The Matrix”
{“title”: {“$ilike”: “the matrix”}}	toLower(title) CONTAINS “The Matrix”

See also VectorRetriever.

Vector Cypher Retriever

The VectorCypherRetriever fully leverages Neo4j’s graph capabilities by combining vector-based similarity searches with graph traversal techniques. It processes a query embedding to perform a similarity search against a specified vector index, retrieves relevant node variables, and then executes a Cypher query to traverse the graph based on these nodes. This integration ensures that retrievals are both semantically meaningful and contextually enriched by the underlying graph structure.

Retrieval Query

When crafting the retrieval query, it’s important to note two available variables are in the query scope:

• node: represents the node retrieved from the vector index search.

• score: denotes the similarity score.

For instance, in a movie graph with actors where the vector index pertains to certain movie properties, the retrieval query can be structured as follows:

retrieval_query = """
    MATCH
    (actor:Actor)-[:ACTED_IN]->(node)
    RETURN
    node.title AS movie_title,
    node.plot AS movie_plot,
    collect(actor.name) AS actors;
"""
retriever = VectorCypherRetriever(
    driver,
    index_name=INDEX_NAME,
    retrieval_query=retrieval_query,
)

It is recommended that the retrieval query returns node properties, as opposed to nodes.

Format the Results

Warning

This API is in beta mode and will be subject to change in the future.

The result_formatter function customizes the output of Cypher retrievers for improved prompt engineering and readability. It converts each Neo4j record into a RetrieverResultItem with two fields: content and metadata.

The content field is a formatted string containing the key information intended for the language model, such as movie titles or descriptions. The metadata field holds additional details, useful for debugging or providing extra context, like scores or node properties.

def result_formatter(record: neo4j.Record) -> RetrieverResultItem:
    return RetrieverResultItem(
        content=f"Movie title: {record.get('movieTitle')}, description: {record.get('movieDescription')}, actors: {record.get('actors')}",
        metadata={
            "title": record.get('movieTitle'),
            "score": record.get("score"),
        }
    )

retriever = VectorCypherRetriever(
    driver,
    index_name=INDEX_NAME,
    retrieval_query="MATCH (node)<-[:ACTED_IN]-(p:Person) RETURN node.title as movieTitle, node.plot as movieDescription, collect(p.name) as actors, score",
    result_formatter=result_formatter,
)

Also see VectorCypherRetriever.

Vector Databases

Note

For external retrievers, the filter syntax depends on the provider. Please refer to the documentation of the Python client for each provider for details.

Weaviate Retrievers

Note

In order to import this retriever, the Weaviate Python client must be installed: pip install weaviate-client

from weaviate.connect.helpers import connect_to_local
from neo4j_graphrag.retrievers import WeaviateNeo4jRetriever

client = connect_to_local()
retriever = WeaviateNeo4jRetriever(
    driver=driver,
    client=client,
    embedder=embedder,
    collection="Movies",
    id_property_external="neo4j_id",
    id_property_neo4j="id",
)

Internally, this retriever performs the vector search in Weaviate, finds the corresponding node by matching the Weaviate metadata id_property_external with a Neo4j node.id_property_neo4j, and returns the matched node.

The return_properties and retrieval_query parameters operate similarly to those in other retrievers.

See WeaviateNeo4jRetriever.

Pinecone Retrievers

Note

In order to import this retriever, the Pinecone Python client must be installed: pip install pinecone-client

from pinecone import Pinecone
from neo4j_graphrag.retrievers import PineconeNeo4jRetriever

client = Pinecone()  # ... create your Pinecone client

retriever = PineconeNeo4jRetriever(
    driver=driver,
    client=client,
    index_name="Movies",
    id_property_neo4j="id",
    embedder=embedder,
)

Also see PineconeNeo4jRetriever.

Qdrant Retrievers

Note

In order to import this retriever, the Qdrant Python client must be installed: pip install qdrant-client

from qdrant_client import QdrantClient
from neo4j_graphrag.retrievers import QdrantNeo4jRetriever

client = QdrantClient(...)  # construct the Qdrant client instance

retriever = QdrantNeo4jRetriever(
    driver=driver,
    client=client,
    collection_name="my-collection",
    id_property_external="neo4j_id",    # The payload field that contains identifier to a corresponding Neo4j node id property
    id_property_neo4j="id",
    embedder=embedder,
)

See QdrantNeo4jRetriever.

Other Retrievers

Hybrid Retrievers

In an hybrid retriever, results are searched for in both a vector and a full-text index. For this reason, a full-text index must also exist in the database, and its name must be provided when instantiating the retriever:

from neo4j_graphrag.retrievers import HybridRetriever

INDEX_NAME = "embedding-name"
FULLTEXT_INDEX_NAME = "fulltext-index-name"

retriever = HybridRetriever(
    driver,
    INDEX_NAME,
    FULLTEXT_INDEX_NAME,
    embedder,
)

See HybridRetriever.

Also note that there is an helper function to create a full-text index (see the API documentation).

Hybrid Cypher Retrievers

In an hybrid cypher retriever, results are searched for in both a vector and a full-text index. Once the similar nodes are identified, a retrieval query can traverse the graph and return more context:

from neo4j_graphrag.retrievers import HybridCypherRetriever

INDEX_NAME = "embedding-name"
FULLTEXT_INDEX_NAME = "fulltext-index-name"

retriever = HybridCypherRetriever(
    driver,
    INDEX_NAME,
    FULLTEXT_INDEX_NAME,
    retrieval_query="MATCH (node)-[:AUTHORED_BY]->(author:Author)" "RETURN author.name"
    embedder=embedder,
)

See HybridCypherRetriever.

Text2Cypher Retriever

This retriever first asks an LLM to generate a Cypher query to fetch the exact information required to answer the question from the database. Then this query is executed and the resulting records are added to the context for the LLM to write the answer to the initial user question. The cypher-generation and answer-generation LLMs can be different.

from neo4j import GraphDatabase
from neo4j_graphrag.retrievers import Text2CypherRetriever
from neo4j_graphrag.llm.openai import OpenAILLM

URI = "neo4j://localhost:7687"
AUTH = ("neo4j", "password")

# Connect to Neo4j database
driver = GraphDatabase.driver(URI, auth=AUTH)

# Create LLM object
llm = OpenAILLM(model_name="gpt-3.5-turbo")

# (Optional) Specify your own Neo4j schema
neo4j_schema = """
Node properties:
Person {name: STRING, born: INTEGER}
Movie {tagline: STRING, title: STRING, released: INTEGER}
Relationship properties:
ACTED_IN {roles: LIST}
REVIEWED {summary: STRING, rating: INTEGER}
The relationships:
(:Person)-[:ACTED_IN]->(:Movie)
(:Person)-[:DIRECTED]->(:Movie)
(:Person)-[:PRODUCED]->(:Movie)
(:Person)-[:WROTE]->(:Movie)
(:Person)-[:FOLLOWS]->(:Person)
(:Person)-[:REVIEWED]->(:Movie)
"""

# (Optional) Provide user input/query pairs for the LLM to use as examples
examples = [
    "USER INPUT: 'Which actors starred in the Matrix?' QUERY: MATCH (p:Person)-[:ACTED_IN]->(m:Movie) WHERE m.title = 'The Matrix' RETURN p.name"
]

# Initialize the retriever
retriever = Text2CypherRetriever(
    driver=driver,
    llm=llm,  # type: ignore
    neo4j_schema=neo4j_schema,
    examples=examples,
)

# Generate a Cypher query using the LLM, send it to the Neo4j database, and return the results
query_text = "Which movies did Hugo Weaving star in?"
print(retriever.search(query_text=query_text))

Note

Since we are not performing any similarity search (vector index), the Text2Cypher retriever does not require any embedder.

Warning

The LLM-generated query is not guaranteed to be syntactically correct. In case it can’t be executed, a Text2CypherRetrievalError is raised.

See Text2CypherRetriever.

Custom Retriever

If the application requires very specific retrieval strategy, it is possible to implement a custom retriever using the Retriever interface:

from neo4j_graphrag.retrievers.base import Retriever

class MyCustomRetriever(Retriever):
    def __init__(
        self,
        driver: neo4j.Driver,
        # any other required parameters
    ) -> None:
        super().__init__(driver)

    def get_search_results(
        self,
        query_vector: Optional[list[float]] = None,
        query_text: Optional[str] = None,
        top_k: int = 5,
        filters: Optional[dict[str, Any]] = None,
    ) -> RawSearchResult:
        pass

See RawSearchResult for a description of the returned type.

DB Operations

See Database Interaction.

Create a Vector Index

from neo4j import GraphDatabase
from neo4j_graphrag.indexes import create_vector_index

URI = "neo4j://localhost:7687"
AUTH = ("neo4j", "password")

INDEX_NAME = "chunk-index"
DIMENSION=1536

# 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=DIMENSION,
    similarity_fn="euclidean",
)

Populate a 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)]
upsert_vector(driver, node_id="1234", embedding_property="embedding", vector=vector)

This will update the node with id(node)=1234 to add (or update) a node.embedding property. This property will also be added to the vector index.

Drop a Vector Index

Warning

This operation is irreversible and should be used with caution.

from neo4j import GraphDatabase

URI = "neo4j://localhost:7687"
AUTH = ("neo4j", "password")

# Connect to Neo4j database
driver = GraphDatabase.driver(URI, auth=AUTH)
drop_index_if_exists(driver, INDEX_NAME)