diff --git a/.gitignore b/.gitignore
index 849ddff3..d16f774f 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1 +1,4 @@
 dist/
+**/__pycache__/*
+*.py[cod]
+.mypy_cache/
\ No newline at end of file
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
new file mode 100644
index 00000000..34f891f9
--- /dev/null
+++ b/.pre-commit-config.yaml
@@ -0,0 +1,6 @@
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.5.0  # Use the ref you want to point at
+    hooks:
+    - id: trailing-whitespace
+    # -   id: ...
\ No newline at end of file
diff --git a/README.md b/README.md
index e69de29b..d021e195 100644
--- a/README.md
+++ b/README.md
@@ -0,0 +1,3 @@
+# Neo4j GenAI package for Python
+
+This repository contains the official Neo4j GenAI features for Python.
diff --git a/examples/similarity_search_for_text.py b/examples/similarity_search_for_text.py
new file mode 100644
index 00000000..1b93b225
--- /dev/null
+++ b/examples/similarity_search_for_text.py
@@ -0,0 +1,54 @@
+from typing import List
+from neo4j import GraphDatabase
+from neo4j.exceptions import DatabaseError
+from neo4j_genai.client import GenAIClient
+
+from random import random
+from neo4j_genai.embeddings import Embeddings
+
+URI = "neo4j://localhost:7687"
+AUTH = ("neo4j", "password")
+
+INDEX_NAME = "embedding-name"
+DIMENSION = 1536
+
+# Connect to Neo4j database
+driver = GraphDatabase.driver(URI, auth=AUTH)
+
+
+# Create Embeddings object
+class CustomEmbeddings(Embeddings):
+    def embed_query(self, text: str) -> List[float]:
+        return [random() for _ in range(DIMENSION)]
+
+
+embeddings = CustomEmbeddings()
+
+# Initialize the client
+client = GenAIClient(driver, embeddings)
+
+# Creating the index
+client.create_index(
+    INDEX_NAME,
+    label="Document",
+    property="propertyKey",
+    dimensions=DIMENSION,
+    similarity_fn="euclidean",
+)
+
+# Upsert the query
+vector = [random() for _ in range(DIMENSION)]
+insert_query = (
+    "MERGE (n:Document)"
+    "WITH n "
+    "CALL db.create.setNodeVectorProperty(n, 'propertyKey', $vector)"
+    "RETURN n"
+)
+parameters = {
+    "vector": vector,
+}
+client.database_query(insert_query, params=parameters)
+
+# Perform the similarity search for a text query
+query_text = "hello world"
+print(client.similarity_search(INDEX_NAME, query_text=query_text, top_k=5))
diff --git a/examples/similarity_search_for_vector.py b/examples/similarity_search_for_vector.py
new file mode 100644
index 00000000..157dc29b
--- /dev/null
+++ b/examples/similarity_search_for_vector.py
@@ -0,0 +1,42 @@
+from neo4j import GraphDatabase
+from neo4j_genai.client import GenAIClient
+
+from random import random
+
+URI = "neo4j://localhost:7687"
+AUTH = ("neo4j", "password")
+
+INDEX_NAME = "embedding-name"
+DIMENSION = 1536
+
+# Connect to Neo4j database
+driver = GraphDatabase.driver(URI, auth=AUTH)
+
+# Initialize the client
+client = GenAIClient(driver)
+
+# Creating the index
+client.create_index(
+    INDEX_NAME,
+    label="Document",
+    property="propertyKey",
+    dimensions=DIMENSION,
+    similarity_fn="euclidean",
+)
+
+# Upsert the vector
+vector = [random() for _ in range(DIMENSION)]
+insert_query = (
+    "MERGE (n:Document)"
+    "WITH n "
+    "CALL db.create.setNodeVectorProperty(n, 'propertyKey', $vector)"
+    "RETURN n"
+)
+parameters = {
+    "vector": vector,
+}
+client.database_query(insert_query, params=parameters)
+
+# Perform the similarity search for a vector query
+query_vector = [random() for _ in range(DIMENSION)]
+print(client.similarity_search(INDEX_NAME, query_vector=query_vector, top_k=5))
diff --git a/neo4j_genai/__pycache__/__init__.cpython-311.pyc b/neo4j_genai/__pycache__/__init__.cpython-311.pyc
deleted file mode 100644
index 8c801ec1..00000000
Binary files a/neo4j_genai/__pycache__/__init__.cpython-311.pyc and /dev/null differ
diff --git a/neo4j_genai/__pycache__/tools.cpython-311.pyc b/neo4j_genai/__pycache__/tools.cpython-311.pyc
deleted file mode 100644
index 719223d0..00000000
Binary files a/neo4j_genai/__pycache__/tools.cpython-311.pyc and /dev/null differ
diff --git a/neo4j_genai/tests/test.py b/neo4j_genai/tests/test.py
deleted file mode 100644
index 16226231..00000000
--- a/neo4j_genai/tests/test.py
+++ /dev/null
@@ -1,35 +0,0 @@
-from neo4j_genai import Client
-from neo4j_genai import GenAI
-from neo4j_genai import GenAIClient
-from neo4j_genai import VectorClient
-
-from neo4j import GraphDatabase, Driver
-from typing import Optional
-from langchain_core.embeddings import Embeddings
-
-from pydantic_v1 import BaseModel
-
-URI = "neo4j://localhost:7687"
-AUTH = ("neo4j", "password")
-
-driver = GraphDatabase.driver(URI, auth=AUTH)
-
-class GenAIClient:
-    def __init__(self, driver: Driver, embeddings: Optional[]) -> None:
-        pass
-client = GenAIClient(driver, embeddings=)
-
-client.create_vector_index()
-client.drop_vector_index()
-
-"""**Embeddings** interface."""
-from abc import ABC, abstractmethod
-from typing import List
-
-
-class Embeddings(ABC):
-    """Interface for embedding models."""
-    
-    @abstractmethod
-    def embed_query(self, text: str) -> List[float]:
-        """Embed query text."""
diff --git a/neo4j_genai/tests/test2.py b/neo4j_genai/tests/test2.py
deleted file mode 100644
index c7103834..00000000
--- a/neo4j_genai/tests/test2.py
+++ /dev/null
@@ -1,68 +0,0 @@
-from neo4j_genai import GenAIClient
-from neo4j import GraphDatabase
-
-URI = "neo4j://localhost:7687"
-AUTH = ("neo4j", "password")
-
-driver = GraphDatabase.driver(URI, auth=AUTH)
-
-client = GenAIClient(driver)
-
-
-client.create_vector_index("indexMovies", "Movie", "embedding", dimensions=666, similarity_function="cosine")
-
-node = driver.execute_query("MATCH (m:Movie {movieId: row.movieId}) RETURN m")
-
-######### EXPLICIT ENCODING ####################
-try:
-    from langchain_community.embeddings import OllamaEmbeddings
-    embeddings = OllamaEmbeddings()
-
-    embedded_vectors = embeddings.embed_query("This is the query")
-except ImportError:
-    embedded_vectors = requests.post("ollama.com/api/embeddings", query="This is the query")
-
-######### EXPLICIT ENDODING ####################
-
-
-client.setNodeVectorProperty(node, "embedding", embedded_vectors)
-
-client.similarity_search("indexMovies", vectors=embedded_vectors)
-
-#############################
-
-from langchain_community.embeddings import OllamaEmbeddings
-from neo4j_genai import GenAIClient
-from neo4j import GraphDatabase
-
-URI = "neo4j://localhost:7687"
-AUTH = ("neo4j", "password")
-
-driver = GraphDatabase.driver(URI, auth=AUTH)
-
-embedding_model = "ollama7b"
-ollama7b_embedding_size = 666
-embeddings = OllamaEmbeddings(embedding_model)
-client = GenAIClient(driver, embeddings=embeddings)
-
-
-
-client.create_vector_index("indexMovies", "Movie", "embedding", dimensions=ollama7b_embedding_size, similarity_function="cosine")
-
-
-node = driver.execute_query("MATCH (m:Movie {movieId: row.movieId}) RETURN m")
-
-
-
-# client.setNodeVectorProperty(node, "embedding", embedded_vectors)
-client.similarity_search("indexMovies", text="Landing on the moon")
-##
-
-# def similarity_search(self, index_name, query_text):
-#     embedded_vectors = self.embedddings.embed_query(query_text)
-
-#     # similary()
-
-
-# FUTURE
-# client.generateEmbeddingsAndSetVectorProperty(node, text_property="plot", vector_property="embedding")
\ No newline at end of file
diff --git a/neo4j_genai/tools.py b/neo4j_genai/tools.py
deleted file mode 100644
index 44d864dd..00000000
--- a/neo4j_genai/tools.py
+++ /dev/null
@@ -1,2 +0,0 @@
-def ping():
-    print("pong")
\ No newline at end of file
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index 90a60f18..5a61a6ea 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,13 +1,235 @@
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+]
+
+[package.dependencies]
+typing-extensions = ">=4.6.0,<4.7.0 || >4.7.0"
+
+[[package]]
+name = "pylint"
+version = "3.1.0"
+description = "python code static checker"
+optional = false
+python-versions = ">=3.8.0"
+files = [
+    {file = "pylint-3.1.0-py3-none-any.whl", hash = "sha256:507a5b60953874766d8a366e8e8c7af63e058b26345cfcb5f91f89d987fd6b74"},
+    {file = "pylint-3.1.0.tar.gz", hash = "sha256:6a69beb4a6f63debebaab0a3477ecd0f559aa726af4954fc948c51f7a2549e23"},
+]
+
+[package.dependencies]
+astroid = ">=3.1.0,<=3.2.0-dev0"
+colorama = {version = ">=0.4.5", markers = "sys_platform == \"win32\""}
+dill = [
+    {version = ">=0.2", markers = "python_version < \"3.11\""},
+    {version = ">=0.3.7", markers = "python_version >= \"3.12\""},
+    {version = ">=0.3.6", markers = "python_version >= \"3.11\" and python_version < \"3.12\""},
+]
+isort = ">=4.2.5,<5.13.0 || >5.13.0,<6"
+mccabe = ">=0.6,<0.8"
+platformdirs = ">=2.2.0"
+tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
+tomlkit = ">=0.10.1"
+typing-extensions = {version = ">=3.10.0", markers = "python_version < \"3.10\""}
+
+[package.extras]
+spelling = ["pyenchant (>=3.2,<4.0)"]
+testutils = ["gitpython (>3)"]
+
+[[package]]
+name = "pytest"
+version = "8.0.2"
+description = "pytest: simple powerful testing with Python"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "pytest-8.0.2-py3-none-any.whl", hash = "sha256:edfaaef32ce5172d5466b5127b42e0d6d35ebbe4453f0e3505d96afd93f6b096"},
+    {file = "pytest-8.0.2.tar.gz", hash = "sha256:d4051d623a2e0b7e51960ba963193b09ce6daeb9759a451844a21e4ddedfc1bd"},
+]
+
+[package.dependencies]
+colorama = {version = "*", markers = "sys_platform == \"win32\""}
+exceptiongroup = {version = ">=1.0.0rc8", markers = "python_version < \"3.11\""}
+iniconfig = "*"
+packaging = "*"
+pluggy = ">=1.3.0,<2.0"
+tomli = {version = ">=1.0.0", markers = "python_version < \"3.11\""}
+
+[package.extras]
+testing = ["argcomplete", "attrs (>=19.2.0)", "hypothesis (>=3.56)", "mock", "nose", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
+
+[[package]]
+name = "pytest-mock"
+version = "3.12.0"
+description = "Thin-wrapper around the mock package for easier use with pytest"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "pytest-mock-3.12.0.tar.gz", hash = "sha256:31a40f038c22cad32287bb43932054451ff5583ff094bca6f675df2f8bc1a6e9"},
+    {file = "pytest_mock-3.12.0-py3-none-any.whl", hash = "sha256:0972719a7263072da3a21c7f4773069bcc7486027d7e8e1f81d98a47e701bc4f"},
+]
+
+[package.dependencies]
+pytest = ">=5.0"
+
+[package.extras]
+dev = ["pre-commit", "pytest-asyncio", "tox"]
+
 [[package]]
 name = "pytz"
 version = "2024.1"
@@ -29,7 +482,71 @@ files = [
     {file = "pytz-2024.1.tar.gz", hash = "sha256:2a29735ea9c18baf14b448846bde5a48030ed267578472d8955cd0e7443a9812"},
 ]
 
+[[package]]
+name = "tomli"
+version = "2.0.1"
+description = "A lil' TOML parser"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "tomli-2.0.1-py3-none-any.whl", hash = "sha256:939de3e7a6161af0c887ef91b7d41a53e7c5a1ca976325f429cb46ea9bc30ecc"},
+    {file = "tomli-2.0.1.tar.gz", hash = "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"},
+]
+
+[[package]]
+name = "tomlkit"
+version = "0.12.4"
+description = "Style preserving TOML library"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "tomlkit-0.12.4-py3-none-any.whl", hash = "sha256:5cd82d48a3dd89dee1f9d64420aa20ae65cfbd00668d6f094d7578a78efbb77b"},
+    {file = "tomlkit-0.12.4.tar.gz", hash = "sha256:7ca1cfc12232806517a8515047ba66a19369e71edf2439d0f5824f91032b6cc3"},
+]
+
+[[package]]
+name = "types-requests"
+version = "2.31.0.20240218"
+description = "Typing stubs for requests"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "types-requests-2.31.0.20240218.tar.gz", hash = "sha256:f1721dba8385958f504a5386240b92de4734e047a08a40751c1654d1ac3349c5"},
+    {file = "types_requests-2.31.0.20240218-py3-none-any.whl", hash = "sha256:a82807ec6ddce8f00fe0e949da6d6bc1fbf1715420218a9640d695f70a9e5a9b"},
+]
+
+[package.dependencies]
+urllib3 = ">=2"
+
+[[package]]
+name = "typing-extensions"
+version = "4.10.0"
+description = "Backported and Experimental Type Hints for Python 3.8+"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "typing_extensions-4.10.0-py3-none-any.whl", hash = "sha256:69b1a937c3a517342112fb4c6df7e72fc39a38e7891a5730ed4985b5214b5475"},
+    {file = "typing_extensions-4.10.0.tar.gz", hash = "sha256:b0abd7c89e8fb96f98db18d86106ff1d90ab692004eb746cf6eda2682f91b3cb"},
+]
+
+[[package]]
+name = "urllib3"
+version = "2.2.1"
+description = "HTTP library with thread-safe connection pooling, file post, and more."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "urllib3-2.2.1-py3-none-any.whl", hash = "sha256:450b20ec296a467077128bff42b73080516e71b56ff59a60a02bef2232c4fa9d"},
+    {file = "urllib3-2.2.1.tar.gz", hash = "sha256:d0570876c61ab9e520d776c38acbbb5b05a776d3f9ff98a5c8fd5162a444cf19"},
+]
+
+[package.extras]
+brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"]
+h2 = ["h2 (>=4,<5)"]
+socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
+zstd = ["zstandard (>=0.18.0)"]
+
 [metadata]
 lock-version = "2.0"
-python-versions = ">=3.7"
-content-hash = "4638595b14c9dedbce42766fd4745866b8011e8cd03967f27d170a0200602437"
+python-versions = "^3.8"
+content-hash = "e9571707d11b67cf7226bd97e391882098e9ca6065c92ce148da425824aa7718"
diff --git a/pyproject.toml b/pyproject.toml
index f4af4c7f..675c1cee 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,18 +1,42 @@
 [tool.poetry]
 name = "neo4j-genai"
 version = "0.1.0"
-description = ""
+description = "Python package to allow easy integration to Neo4j's GenAI features"
 authors = ["Neo4j, Inc <team-gen-ai@neo4j.com>"]
 license = "Apache License, Version 2.0"
 readme = "README.md"
 
+[[tool.poetry.packages]]
+include = "neo4j_genai"
+from = "src"
+
 [tool.poetry.dependencies]
-python = ">=3.7"
+python = "^3.8"
 neo4j = "^5.17.0"
+types-requests = "^2.31.0.20240218"
+pytest = "^8.0.2"
+pytest-mock = "^3.12.0"
+pydantic = "^2.6.3"
 
+[tool.poetry.group.dev.dependencies]
+pylint = "^3.1.0"
+mypy = "^1.8.0"
+black = "^24.2.0"
 
 [build-system]
 requires = ["poetry-core"]
 build-backend = "poetry.core.masonry.api"
 
-exclude = ["**/tests/"]
\ No newline at end of file
+exclude = ["**/tests/"]
+[tool.black]
+line-length = 88
+target-version = ['py38']
+include = '\.pyi?$'
+exclude = '''
+/(
+    .git
+  | .venv
+  | build
+  | dist
+)/
+'''
diff --git a/neo4j_genai/__init__.py b/src/neo4j_genai/__init__.py
similarity index 100%
rename from neo4j_genai/__init__.py
rename to src/neo4j_genai/__init__.py
diff --git a/src/neo4j_genai/client.py b/src/neo4j_genai/client.py
new file mode 100644
index 00000000..eb9714e2
--- /dev/null
+++ b/src/neo4j_genai/client.py
@@ -0,0 +1,181 @@
+from typing import List, Dict, Any, Optional
+from pydantic import ValidationError
+from neo4j import Driver
+from neo4j.exceptions import CypherSyntaxError
+from neo4j_genai.embeddings import Embeddings
+from neo4j_genai.types import CreateIndexModel, SimilaritySearchModel, Neo4jRecord
+
+
+class GenAIClient:
+    """
+    Provides functionality to use Neo4j's GenAI features
+    """
+
+    def __init__(
+        self,
+        driver: Driver,
+        embeddings: Optional[Embeddings] = None,
+    ) -> None:
+        self.driver = driver
+        self._verify_version()
+        self.embeddings = embeddings
+
+    def _verify_version(self) -> None:
+        """
+        Check if the connected Neo4j database version supports vector indexing.
+
+        Queries the Neo4j database to retrieve its version and compares it
+        against a target version (5.11.0) that is known to support vector
+        indexing. Raises a ValueError if the connected Neo4j version is
+        not supported.
+        """
+        version = self.database_query("CALL dbms.components()")[0]["versions"][0]
+        if "aura" in version:
+            version_tuple = (*tuple(map(int, version.split("-")[0].split("."))), 0)
+        else:
+            version_tuple = tuple(map(int, version.split(".")))
+
+        target_version = (5, 11, 0)
+
+        if version_tuple < target_version:
+            raise ValueError(
+                "Version index is only supported in Neo4j version 5.11 or greater"
+            )
+
+    def database_query(self, query: str, params: Dict = {}) -> List[Dict[str, Any]]:
+        """
+        This method sends a Cypher query to the connected Neo4j database
+        and returns the results as a list of dictionaries.
+
+        Args:
+            query (str): The Cypher query to execute.
+            params (Dict, optional): Dictionary of query parameters. Defaults to {}.
+
+        Returns:
+            List[Dict[str, Any]]: List of dictionaries containing the query results.
+        """
+        with self.driver.session() as session:
+            try:
+                data = session.run(query, params)
+                return [r.data() for r in data]
+            except CypherSyntaxError as e:
+                raise ValueError(f"Cypher Statement is not valid\n{e}")
+
+    def create_index(
+        self,
+        name: str,
+        label: str,
+        property: str,
+        dimensions: int,
+        similarity_fn: str,
+    ) -> None:
+        """
+        This method constructs a Cypher query and executes it
+        to create a new vector index in Neo4j.
+
+        See Cypher manual on [Create node index](https://neo4j.com/docs/operations-manual/5/reference/procedures/#procedure_db_index_vector_createNodeIndex)
+
+        Args:
+            name (str): The unique name of the index.
+            label (str): The node label to be indexed.
+            property (str): The property key of a node which contains embedding values.
+            dimensions (int): Vector embedding dimension
+            similarity_fn (str): case-insensitive values for the vector similarity function:
+                ``euclidean`` or ``cosine``.
+
+        Raises:
+            ValueError: If validation of the input arguments fail.
+        """
+        index_data = {
+            "name": name,
+            "label": label,
+            "property": property,
+            "dimensions": dimensions,
+            "similarity_fn": similarity_fn,
+        }
+        try:
+            index_data = CreateIndexModel(**index_data)
+        except ValidationError as e:
+            raise ValueError(f"Error for inputs to create_index {str(e)}")
+
+        query = (
+            "CALL db.index.vector.createNodeIndex("
+            "$name,"
+            "$label,"
+            "$property,"
+            "toInteger($dimensions),"
+            "$similarity_fn )"
+        )
+        self.database_query(query, params=index_data.dict())
+
+    def drop_index(self, name: str) -> None:
+        """
+        This method constructs a Cypher query and executes it
+        to drop a vector index in Neo4j.
+        See Cypher manual on [Drop vector indexes](https://neo4j.com/docs/cypher-manual/current/indexes-for-vector-search/#indexes-vector-drop)
+
+        Args:
+            name (str): The name of the index to delete.
+        """
+        query = "DROP INDEX $name"
+        parameters = {
+            "name": name,
+        }
+        self.database_query(query, params=parameters)
+
+    def similarity_search(
+        self,
+        name: str,
+        query_vector: Optional[List[float]] = None,
+        query_text: Optional[str] = None,
+        top_k: int = 5,
+    ) -> List[Neo4jRecord]:
+        """Get the top_k nearest neighbor embeddings for either provided query_vector or query_text.
+        See the following documentation for more details:
+
+        - [Query a vector index](https://neo4j.com/docs/cypher-manual/current/indexes-for-vector-search/#indexes-vector-query)
+        - [db.index.vector.queryNodes()](https://neo4j.com/docs/operations-manual/5/reference/procedures/#procedure_db_index_vector_queryNodes)
+
+        Args:
+            name (str): Refers to the unique name of the vector index to query.
+            query_vector (Optional[List[float]], optional): The vector embeddings to get the closest neighbors of. Defaults to None.
+            query_text (Optional[str], optional): The text to get the closest neighbors of. Defaults to None.
+            top_k (int, optional): The number of neighbors to return. Defaults to 5.
+
+        Raises:
+            ValueError: If validation of the input arguments fail.
+            ValueError: If no embeddings is provided.
+
+        Returns:
+            List[Neo4jRecord]: The `top_k` neighbors found in vector search with their nodes and scores.
+        """
+        try:
+            validated_data = SimilaritySearchModel(
+                index_name=name,
+                top_k=top_k,
+                query_vector=query_vector,
+                query_text=query_text,
+            )
+        except ValidationError as e:
+            error_details = e.errors()
+            raise ValueError(f"Validation failed: {error_details}")
+
+        parameters = validated_data.dict(exclude_none=True)
+
+        if query_text:
+            if not self.embeddings:
+                raise ValueError("Embedding method required for text query.")
+            query_vector = self.embeddings.embed_query(query_text)
+            parameters["query_vector"] = query_vector
+
+        db_query_string = """
+        CALL db.index.vector.queryNodes($index_name, $top_k, $query_vector) 
+        YIELD node, score
+        """
+        records = self.database_query(db_query_string, params=parameters)
+
+        try:
+            return [Neo4jRecord(node=record["node"], score=record["score"]) for record in records]
+        except ValidationError as e:
+            error_details = e.errors()
+            raise ValueError(f"Validation failed while constructing output: {error_details}")
diff --git a/src/neo4j_genai/embeddings.py b/src/neo4j_genai/embeddings.py
new file mode 100644
index 00000000..1fe801ff
--- /dev/null
+++ b/src/neo4j_genai/embeddings.py
@@ -0,0 +1,17 @@
+from abc import ABC, abstractmethod
+from neo4j_genai.types import EmbeddingVector
+
+
+class Embeddings(ABC):
+    """Interface for embedding models."""
+
+    @abstractmethod
+    def embed_query(self, text: str) -> EmbeddingVector:
+        """Embed query text.
+
+        Args:
+            text (str): Text to convert to vector embedding
+
+        Returns:
+            EmbeddingVector: A vector embedding.
+        """
diff --git a/src/neo4j_genai/types.py b/src/neo4j_genai/types.py
new file mode 100644
index 00000000..82da6699
--- /dev/null
+++ b/src/neo4j_genai/types.py
@@ -0,0 +1,38 @@
+from typing import List, Any, Literal, Optional
+from pydantic import BaseModel, PositiveInt, Field, root_validator
+
+
+class Neo4jRecord(BaseModel):
+    node: Any
+    score: float
+
+
+class EmbeddingVector(BaseModel):
+    vector: List[float]
+
+
+class CreateIndexModel(BaseModel):
+    name: str
+    label: str
+    property: str
+    dimensions: int = Field(ge=1, le=2048)
+    similarity_fn: Literal["euclidean", "cosine"]
+
+
+class SimilaritySearchModel(BaseModel):
+    index_name: str
+    top_k: PositiveInt = 5
+    query_vector: Optional[List[float]] = None
+    query_text: Optional[str] = None
+
+    @root_validator(pre=True)
+    def check_query(cls, values):
+        """
+        Validates that one of either query_vector or query_text is provided exclusively.
+        """
+        query_vector, query_text = values.get("query_vector"), values.get("query_text")
+        if not (bool(query_vector) ^ bool(query_text)):
+            raise ValueError(
+                "You must provide exactly one of query_vector or query_text."
+            )
+        return values
diff --git a/tests/__init__.py b/tests/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/tests/test_client.py b/tests/test_client.py
new file mode 100644
index 00000000..e69de29b