Neo4j Vector Index
Neo4j is an open-source graph database with integrated support for vector similarity search
It supports:
- approximate nearest neighbor search
- Euclidean similarity and cosine similarity
- Hybrid search combining vector and keyword searches
This notebook shows how to use the Neo4j vector index (Neo4jVector).
See the installation instruction.
# Pip install necessary package
%pip install --upgrade --quiet neo4j
%pip install --upgrade --quiet langchain-openai langchain-neo4j
%pip install --upgrade --quiet tiktoken
We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.
import getpass
import os
if "OPENAI_API_KEY" not in os.environ:
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
OpenAI API Key: ········
from langchain_community.document_loaders import TextLoader
from langchain_core.documents import Document
from langchain_neo4j import Neo4jVector
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
loader = TextLoader("../../how_to/state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
# Neo4jVector requires the Neo4j database credentials
url = "bolt://localhost:7687"
username = "neo4j"
password = "password"
# You can also use environment variables instead of directly passing named parameters
# os.environ["NEO4J_URI"] = "bolt://localhost:7687"
# os.environ["NEO4J_USERNAME"] = "neo4j"
# os.environ["NEO4J_PASSWORD"] = "pleaseletmein"
Similarity Search with Cosine Distance (Default)
# The Neo4jVector Module will connect to Neo4j and create a vector index if needed.
db = Neo4jVector.from_documents(
docs, OpenAIEmbeddings(), url=url, username=username, password=password
)
query = "What did the president say about Ketanji Brown Jackson"
docs_with_score = db.similarity_search_with_score(query, k=2)
for doc, score in docs_with_score:
print("-" * 80)
print("Score: ", score)
print(doc.page_content)
print("-" * 80)
--------------------------------------------------------------------------------
Score: 0.9076391458511353
Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections.
Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.
One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.
And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
Score: 0.8912242650985718
A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.
And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system.
We can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling.
We’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.
We’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster.
We’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.
--------------------------------------------------------------------------------
Working with vectorstore
Above, we created a vectorstore from scratch. However, often times we want to work with an existing vectorstore. In order to do that, we can initialize it directly.
index_name = "vector" # default index name
store = Neo4jVector.from_existing_index(
OpenAIEmbeddings(),
url=url,
username=username,
password=password,
index_name=index_name,
)
We can also initialize a vectorstore from existing graph using the from_existing_graph method. This method pulls relevant text information from the database, and calculates and stores the text embeddings back to the database.
# First we create sample data in graph
store.query(
"CREATE (p:Person {name: 'Tomaz', location:'Slovenia', hobby:'Bicycle', age: 33})"
)
[]
# Now we initialize from existing graph
existing_graph = Neo4jVector.from_existing_graph(
embedding=OpenAIEmbeddings(),
url=url,
username=username,
password=password,
index_name="person_index",
node_label="Person",
text_node_properties=["name", "location"],
embedding_node_property="embedding",
)
result = existing_graph.similarity_search("Slovenia", k=1)
result[0]
Document(page_content='\nname: Tomaz\nlocation: Slovenia', metadata={'age': 33, 'hobby': 'Bicycle'})
Neo4j also supports relationship vector indexes, where an embedding is stored as a relationship property and indexed. A relationship vector index cannot be populated via LangChain, but you can connect it to existing relationship vector indexes.
# First we create sample data and index in graph
store.query(
"MERGE (p:Person {name: 'Tomaz'}) "
"MERGE (p1:Person {name:'Leann'}) "
"MERGE (p1)-[:FRIEND {text:'example text', embedding:$embedding}]->(p2)",
params={"embedding": OpenAIEmbeddings().embed_query("example text")},
)
# Create a vector index
relationship_index = "relationship_vector"
store.query(
"""
CREATE VECTOR INDEX $relationship_index
IF NOT EXISTS
FOR ()-[r:FRIEND]-() ON (r.embedding)
OPTIONS {indexConfig: {
`vector.dimensions`: 1536,
`vector.similarity_function`: 'cosine'
}}
""",
params={"relationship_index": relationship_index},
)
[]
relationship_vector = Neo4jVector.from_existing_relationship_index(
OpenAIEmbeddings(),
url=url,
username=username,
password=password,
index_name=relationship_index,
text_node_property="text",
)
relationship_vector.similarity_search("Example")
[Document(page_content='example text')]
Metadata filtering
Neo4j vector store also supports metadata filtering by combining parallel runtime and exact nearest neighbor search. Requires Neo4j 5.18 or greater version.
Equality filtering has the following syntax.
existing_graph.similarity_search(
"Slovenia",
filter={"hobby": "Bicycle", "name": "Tomaz"},
)
[Document(page_content='\nname: Tomaz\nlocation: Slovenia', metadata={'age': 33, 'hobby': 'Bicycle'})]
Metadata filtering also support the following operators:
$eq: Equal$ne: Not Equal$lt: Less than$lte: Less than or equal$gt: Greater than$gte: Greater than or equal$in: In a list of values$nin: Not in a list of values$between: Between two values$like: Text contains value$ilike: lowered text contains value
existing_graph.similarity_search(
"Slovenia",
filter={"hobby": {"$eq": "Bicycle"}, "age": {"$gt": 15}},
)
[Document(page_content='\nname: Tomaz\nlocation: Slovenia', metadata={'age': 33, 'hobby': 'Bicycle'})]
You can also use OR operator between filters
existing_graph.similarity_search(
"Slovenia",
filter={"$or": [{"hobby": {"$eq": "Bicycle"}}, {"age": {"$gt": 15}}]},
)
[Document(page_content='\nname: Tomaz\nlocation: Slovenia', metadata={'age': 33, 'hobby': 'Bicycle'})]
Add documents
We can add documents to the existing vectorstore.
store.add_documents([Document(page_content="foo")])
['acbd18db4cc2f85cedef654fccc4a4d8']
docs_with_score = store.similarity_search_with_score("foo")
docs_with_score[0]
(Document(page_content='foo'), 0.9999997615814209)
Customize response with retrieval query
You can also customize responses by using a custom Cypher snippet that can fetch other information from the graph. Under the hood, the final Cypher statement is constructed like so:
read_query = (
"CALL db.index.vector.queryNodes($index, $k, $embedding) "
"YIELD node, score "
) + retrieval_query
The retrieval query must return the following three columns:
text: Union[str, Dict] = Value used to populatepage_contentof a documentscore: Float = Similarity scoremetadata: Dict = Additional metadata of a document
Learn more in this blog post.
retrieval_query = """
RETURN "Name:" + node.name AS text, score, {foo:"bar"} AS metadata
"""
retrieval_example = Neo4jVector.from_existing_index(
OpenAIEmbeddings(),
url=url,
username=username,
password=password,
index_name="person_index",
retrieval_query=retrieval_query,
)
retrieval_example.similarity_search("Foo", k=1)
[Document(page_content='Name:Tomaz', metadata={'foo': 'bar'})]
Here is an example of passing all node properties except for embedding as a dictionary to text column,
retrieval_query = """
RETURN node {.name, .age, .hobby} AS text, score, {foo:"bar"} AS metadata
"""
retrieval_example = Neo4jVector.from_existing_index(
OpenAIEmbeddings(),
url=url,
username=username,
password=password,
index_name="person_index",
retrieval_query=retrieval_query,
)
retrieval_example.similarity_search("Foo", k=1)
[Document(page_content='name: Tomaz\nage: 33\nhobby: Bicycle\n', metadata={'foo': 'bar'})]
You can also pass Cypher parameters to the retrieval query. Parameters can be used for additional filtering, traversals, etc...
retrieval_query = """
RETURN node {.*, embedding:Null, extra: $extra} AS text, score, {foo:"bar"} AS metadata
"""
retrieval_example = Neo4jVector.from_existing_index(
OpenAIEmbeddings(),
url=url,
username=username,
password=password,
index_name="person_index",
retrieval_query=retrieval_query,
)
retrieval_example.similarity_search("Foo", k=1, params={"extra": "ParamInfo"})
[Document(page_content='location: Slovenia\nextra: ParamInfo\nname: Tomaz\nage: 33\nhobby: Bicycle\nembedding: None\n', metadata={'foo': 'bar'})]
Hybrid search (vector + keyword)
Neo4j integrates both vector and keyword indexes, which allows you to use a hybrid search approach
# The Neo4jVector Module will connect to Neo4j and create a vector and keyword indices if needed.
hybrid_db = Neo4jVector.from_documents(
docs,
OpenAIEmbeddings(),
url=url,
username=username,
password=password,
search_type="hybrid",
)
To load the hybrid search from existing indexes, you have to provide both the vector and keyword indices
index_name = "vector" # default index name
keyword_index_name = "keyword" # default keyword index name
store = Neo4jVector.from_existing_index(
OpenAIEmbeddings(),
url=url,
username=username,
password=password,
index_name=index_name,
keyword_index_name=keyword_index_name,
search_type="hybrid",
)
Retriever options
This section shows how to use Neo4jVector as a retriever.
retriever = store.as_retriever()
retriever.invoke(query)[0]
Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../how_to/state_of_the_union.txt'})
Question Answering with Sources
This section goes over how to do question-answering with sources over an Index. It does this by using the RetrievalQAWithSourcesChain, which does the lookup of the documents from an Index.
from langchain.chains import RetrievalQAWithSourcesChain
from langchain_openai import ChatOpenAI
API Reference:RetrievalQAWithSourcesChain | ChatOpenAI
chain = RetrievalQAWithSourcesChain.from_chain_type(
ChatOpenAI(temperature=0), chain_type="stuff", retriever=retriever
)
chain.invoke(
{"question": "What did the president say about Justice Breyer"},
return_only_outputs=True,
)
{'answer': 'The president honored Justice Stephen Breyer for his service to the country and mentioned his retirement from the United States Supreme Court.\n',
'sources': '../../how_to/state_of_the_union.txt'}
Related
- Vector store conceptual guide
- Vector store how-to guides