Open In Colab

Neo4j vector store#

If you’re opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.

%pip install llama-index-vector-stores-neo4jvector
!pip install llama-index
import os
import openai

openai.api_key = os.environ["OPENAI_API_KEY"]

Initiate Neo4j vector wrapper#

from llama_index.vector_stores.neo4jvector import Neo4jVectorStore

username = "neo4j"
password = "pleaseletmein"
url = "bolt://localhost:7687"
embed_dim = 1536

neo4j_vector = Neo4jVectorStore(username, password, url, embed_dim)

Load documents, build the VectorStoreIndex#

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from IPython.display import Markdown, display

Download Data

!mkdir -p 'data/paul_graham/'
!wget '' -O 'data/paul_graham/paul_graham_essay.txt'
--2023-12-14 18:44:00--
Resolving (,,, ...
Connecting to (||:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 75042 (73K) [text/plain]
Saving to: ‘data/paul_graham/paul_graham_essay.txt’

data/paul_graham/pa 100%[===================>]  73,28K  --.-KB/s    in 0,03s   

2023-12-14 18:44:00 (2,16 MB/s) - ‘data/paul_graham/paul_graham_essay.txt’ saved [75042/75042]
# load documents
documents = SimpleDirectoryReader("./data/paul_graham").load_data()
from llama_index.core import StorageContext

storage_context = StorageContext.from_defaults(vector_store=neo4j_vector)
index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context
query_engine = index.as_query_engine()
response = query_engine.query("What happened at interleaf?")

At Interleaf, they added a scripting language inspired by Emacs and made it a dialect of Lisp. They were looking for a Lisp hacker to write things in this scripting language. The author of the text worked at Interleaf and mentioned that their Lisp was the thinnest icing on a giant C cake. The author also mentioned that they didn’t know C and didn’t want to learn it, so they never understood most of the software at Interleaf. Additionally, the author admitted to being a bad employee and spending much of their time working on a separate project called On Lisp.

Load existing vector index#

In order to connect to an existing vector index, you need to define the index_name and text_node_property parameters:

  • index_name: name of the existing vector index (default is vector)

  • text_node_property: name of the property that containt the text value (default is text)

index_name = "existing_index"
text_node_property = "text"
existing_vector = Neo4jVectorStore(

loaded_index = VectorStoreIndex.from_vector_store(existing_vector)

Customizing responses#

You can customize the retrieved information from the knowledge graph using the retrieval_query parameter.

The retrieval query must return the following four columns:

  • text:str - The text of the returned document

  • score:str - similarity score

  • id:str - node id

  • metadata: Dict - dictionary with additional metadata (must contain _node_type and _node_content keys)

retrieval_query = (
    "RETURN 'Interleaf hired Tomaz' AS text, score, AS id, "
    "{author: 'Tomaz', _node_type:node._node_type, _node_content:node._node_content} AS metadata"
neo4j_vector_retrieval = Neo4jVectorStore(
    username, password, url, embed_dim, retrieval_query=retrieval_query
loaded_index = VectorStoreIndex.from_vector_store(
response = loaded_index.query("What happened at interleaf?")

Interleaf hired Tomaz.