From e6db0e91b9ee00f704df5a06136df9a9e4c13d3c Mon Sep 17 00:00:00 2001
From: Will Tai <wtaisen@gmail.com>
Date: Mon, 8 Apr 2024 17:22:24 +0100
Subject: [PATCH] Added content to README

---
 README.md                                | 66 ++++++++++++++++++++++++
 examples/openai_search.py                |  4 +-
 examples/similarity_search_for_text.py   |  3 +-
 examples/similarity_search_for_vector.py |  4 +-
 4 files changed, 73 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index d021e195b..7909f49ef 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,69 @@
 # Neo4j GenAI package for Python
 
 This repository contains the official Neo4j GenAI features for Python.
+
+## Installation
+
+This package requires Python (>=3.8.1).
+
+To install the latest stable version, use:
+
+```shell
+pip install neo4j-genai
+```
+
+## Example
+After setting up a Neo4j database instance:
+```python
+from neo4j import GraphDatabase
+from neo4j_genai import VectorRetriever
+
+from random import random
+
+from neo4j_genai.indexes import create_vector_index
+
+URI = "neo4j://localhost:7687"
+AUTH = ("neo4j", "password")
+
+INDEX_NAME = "embedding-name"
+DIMENSION = 1536
+
+# Connect to Neo4j database
+driver = GraphDatabase.driver(URI, auth=AUTH)
+
+# Creating the index
+create_vector_index(
+    driver,
+    INDEX_NAME,
+    label="Document",
+    property="propertyKey",
+    dimensions=DIMENSION,
+    similarity_fn="euclidean",
+)
+
+# Initialize the retriever
+retriever = VectorRetriever(driver, INDEX_NAME)
+
+# Upsert the vector
+vector = [random() for _ in range(DIMENSION)]
+insert_query = (
+    "MERGE (n:Document {id: $id})"
+    "WITH n "
+    "CALL db.create.setNodeVectorProperty(n, 'propertyKey', $vector)"
+    "RETURN n"
+)
+parameters = {
+    "id": 0,
+    "vector": vector,
+}
+driver.execute_query(insert_query, parameters)
+
+# Perform the similarity search for a vector query
+query_vector = [random() for _ in range(DIMENSION)]
+print(retriever.search(query_vector=query_vector, top_k=5))
+
+```
+
+## Further 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/)
diff --git a/examples/openai_search.py b/examples/openai_search.py
index f42f76962..87864bdd9 100644
--- a/examples/openai_search.py
+++ b/examples/openai_search.py
@@ -34,13 +34,15 @@
 
 # Upsert the query
 vector = [random() for _ in range(DIMENSION)]
+
 insert_query = (
-    "MERGE (n:Document)"
+    "MERGE (n:Document {id: $id})"
     "WITH n "
     "CALL db.create.setNodeVectorProperty(n, 'propertyKey', $vector)"
     "RETURN n"
 )
 parameters = {
+    "id": 0,
     "vector": vector,
 }
 driver.execute_query(insert_query, parameters)
diff --git a/examples/similarity_search_for_text.py b/examples/similarity_search_for_text.py
index 3104bbc6e..ad28ad0e8 100644
--- a/examples/similarity_search_for_text.py
+++ b/examples/similarity_search_for_text.py
@@ -40,12 +40,13 @@ def embed_query(self, text: str) -> List[float]:
 # Upsert the query
 vector = [random() for _ in range(DIMENSION)]
 insert_query = (
-    "MERGE (n:Document)"
+    "MERGE (n:Document {id: $id})"
     "WITH n "
     "CALL db.create.setNodeVectorProperty(n, 'propertyKey', $vector)"
     "RETURN n"
 )
 parameters = {
+    "id": 0,
     "vector": vector,
 }
 driver.execute_query(insert_query, parameters)
diff --git a/examples/similarity_search_for_vector.py b/examples/similarity_search_for_vector.py
index 310456760..4fc4c7ac8 100644
--- a/examples/similarity_search_for_vector.py
+++ b/examples/similarity_search_for_vector.py
@@ -30,15 +30,15 @@
 # Upsert the vector
 vector = [random() for _ in range(DIMENSION)]
 insert_query = (
-    "MERGE (n:Document)"
+    "MERGE (n:Document {id: $id})"
     "WITH n "
     "CALL db.create.setNodeVectorProperty(n, 'propertyKey', $vector)"
     "RETURN n"
 )
 parameters = {
+    "id": 0,
     "vector": vector,
 }
-
 driver.execute_query(insert_query, parameters)
 
 # Perform the similarity search for a vector query