diff --git a/.gitignore b/.gitignore
index 7fcd246..78f9161 100644
--- a/.gitignore
+++ b/.gitignore
@@ -181,3 +181,9 @@ cython_debug/
 
 # VSCode configuration
 .vscode/
+
+/.quarto/
+**/*.quarto_ipynb
+_site/
+docs/
+
diff --git a/.pre-commit-config-NO-DOCKER.yaml b/.pre-commit-config-NO-DOCKER.yaml
index 08a134b..626b8e7 100644
--- a/.pre-commit-config-NO-DOCKER.yaml
+++ b/.pre-commit-config-NO-DOCKER.yaml
@@ -39,11 +39,6 @@ repos:
       # Run the formatter.
       - id: ruff-format
 
-  - repo: https://github.com/fpgmaas/deptry.git
-    rev: 0.24.0
-    hooks:
-      - id: deptry
-
   - repo: local
     hooks:
       - id: forbid-new-init
@@ -56,3 +51,9 @@ repos:
         name: scan code with bandit
         entry: make check-python-security
         language: system
+
+      - id: deptry-uv
+        name: deptry (uv)
+        language: system
+        pass_filenames: false # deptry expects a project path, not filenames
+        entry: uv run deptry --per-rule-ignores "DEP003=plum,DEP004=quartodoc|numpydoc" .
diff --git a/README.md b/README.md
index d31cbcc..d174b9b 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,7 @@
 ![ONS Logo](./ONS_Logo_Digital_Colour_Landscape_English_RGB.svg)
 
+### A tool produced by the UK Office for National Statistics - Central Data Science & AI group
+
 # ClassifAI
 
 ClassifAI is a Python package that simplifies semantic search and Retrieval Augmented Generation (RAG) pipelines for classification tasks in the production of official statistics. It is designed to help data professionals build applications and pipelines to label new text samples to official statistical classifications, by leveraging (augmented) semantic search over a knowledgebase of previously coded examples.
@@ -69,17 +71,24 @@ The comparison on other aspects, such as per-request speed or hardware requireme
 
 ## Installation
 
-Install the package directly from GitHub in your Python environment
+You can install the package directly from GitHub in your Python environment, using your preferred package manager.
+By default, only the minimum dependencies of the base versions will be installed; you must specify 
+`classifai[all]` to install all sets of optional dependencies, or `classifai[huggingface, ...]` to install one or more specific sets of optional dependencies.
+The current sets of optional dependencies are `[all, huggingface, ollama, gcp]`.
 
+##### Pip
 ```bash
-pip install "https://github.com/datasciencecampus/classifai/releases/download/v0.2.1/classifai-0.2.1-py3-none-any.whl"
-pip install "classifai[huggingface]"
+pip install "classifai[<dependency list(s)>] @ https://github.com/datasciencecampus/classifai/releases/download/v<version e.g. 0.2.1>/classifai-<version e.g. 0.2.1>-py3-none-any.whl"
 ```
 
-or if you are using Astral UV
-
+##### Astral UV
+one-off add to environment:
+```bash
+uv pip install "classifai[<dependency list(s)>] @ https://github.com/datasciencecampus/classifai/releases/download/v<version e.g. 0.2.1>/classifai-<version e.g. 0.2.1>-py3-none-any.whl"
+```
+persist as an environment requirement:
 ```bash
-uv pip install "https://github.com/datasciencecampus/classifai/releases/download/v0.2.1/classifai-0.2.1-py3-none-any.whl[huggingface]"
+uv add "classifai[<dependency list(s)>] @ https://github.com/datasciencecampus/classifai/releases/download/v<version e.g. 0.2.1>/classifai-<version e.g. 0.2.1>-py3-none-any.whl"
 ```
 
 ## Example: Indexing and searching a knowledgebase
diff --git a/_quarto.yml b/_quarto.yml
new file mode 100644
index 0000000..c5fa5ce
--- /dev/null
+++ b/_quarto.yml
@@ -0,0 +1,136 @@
+execute: 
+  freeze: auto
+project:
+  type: website
+  render: 
+    - index.qmd
+    - docs/**.qmd
+    - classifai/**.py
+    - DEMO/**.ipynb
+    - DEMO/**.py
+    - DEMO/**.qmd
+    - README.md
+    - DEMO/README.md
+    - classifai/vectorisers/__init__.py
+    - classifai/indexers/__init__.py
+    - classifai/servers/__init__.py
+
+
+website:
+  title: "ClassifAI"
+  page-navigation: true
+  navbar:
+    background: light
+    search: true
+    left:
+      - file: docs/index.qmd
+        text: "Documentation"
+    right:
+      - icon: github
+        href: https://github.com/datasciencecampus/classifai
+
+  sidebar:
+    - id: index
+    - title: "Documentation"
+    - style: "docked"
+      collapse-level: 1
+      contents:
+        - section: "Overview"
+          contents:
+            - docs/index.qmd
+        - section: "Vectorisers"
+          contents:
+            - section: "Vectorisers Overview"
+              contents:
+              - docs/vectorisers.qmd
+              - docs/vectorisers.base.VectoriserBase.qmd
+              - docs/vectorisers.base.VectoriserBase.transform.qmd
+            - section: "Specific Vectorisers"
+              contents:
+              - docs/vectorisers.huggingface.HuggingFaceVectoriser.qmd
+              - docs/vectorisers.ollama.OllamaVectoriser.qmd
+              - docs/vectorisers.gcp.GcpVectoriser.qmd
+        - section: "Indexers"
+          contents:
+            - docs/indexers.qmd
+            - docs/indexers.VectorStore.qmd
+            - docs/indexers.VectorStore.embed.qmd
+            - docs/indexers.VectorStore.search.qmd
+            - docs/indexers.VectorStore.reverse_search.qmd
+            - docs/indexers.VectorStore.from_filespace.qmd
+        - section: "Servers"
+          contents:
+            - docs/servers.qmd
+            - docs/servers.get_router.qmd
+            - docs/servers.get_server.qmd
+            - docs/servers.run_server.qmd
+            - docs/servers.make_endpoints.qmd
+        - section: "DEMO"
+          contents:
+            - file: DEMO/README.md
+            - file: DEMO/general_workflow_demo.ipynb
+            - file: DEMO/custom_vectoriser.ipynb
+            - file: DEMO/custom_preprocessing_and_postprocessing_hooks.ipynb
+
+interlinks:
+ sources:
+   python:
+     url: https://docs.python.org/3/
+
+format:
+  html:
+    theme: cosmo
+    css: styles.css
+    toc: true
+    grid:
+      sidebar-width: 400px
+      body-width: 900px
+      margin-width: 200px
+      gutter-width: 1.0rem
+
+quartodoc:
+  style: pkgdown
+  dir: docs
+  renderer:
+    style: _renderer.py
+    show_signature_annotations: false
+  # renderer:
+  #   style: markdown
+  package: classifai
+  parser: google
+  sections:
+    - title: Vectorisers
+      desc: "Utilities to project text into numerical representation in a semantic vector space"
+      contents:
+        - vectorisers
+        - vectorisers.base
+        - vectorisers.base.VectoriserBase
+        - vectorisers.base.VectoriserBase.transform
+        - vectorisers.huggingface.HuggingFaceVectoriser
+        - vectorisers.ollama.OllamaVectoriser
+        - vectorisers.gcp.GcpVectoriser
+    - title: Indexers
+      desc: "Creation of Vector Stores for efficient similarity search and retrieval"
+      contents:
+        - indexers
+        - indexers.main
+        - indexers.VectorStore
+        - indexers.dataclasses.VectorStoreSearchInput
+        - indexers.dataclasses.VectorStoreSearchOutput
+        - indexers.dataclasses.VectorStoreReverseSearchInput
+        - indexers.dataclasses.VectorStoreReverseSearchOutput
+        - indexers.dataclasses.VectorStoreEmbedInput
+        - indexers.dataclasses.VectorStoreEmbedOutput
+        - indexers.VectorStore.embed
+        - indexers.VectorStore.search
+        - indexers.VectorStore.reverse_search
+        - indexers.VectorStore.from_filespace
+    - title: Servers
+      desc: "Expose ClassifAI functionality via Fast-API endpoints"
+      contents:
+        - servers
+        - servers.main
+        - servers.get_router
+        - servers.get_server
+        - servers.run_server
+        - servers.make_endpoints
\ No newline at end of file
diff --git a/_renderer.py b/_renderer.py
new file mode 100644
index 0000000..f5c4bfa
--- /dev/null
+++ b/_renderer.py
@@ -0,0 +1,63 @@
+from __future__ import annotations
+
+from numpydoc.docscrape import NumpyDocString
+from plum import dispatch
+from quartodoc import MdRenderer
+from quartodoc import ast as qast
+
+
+class Renderer(MdRenderer):
+    style = "siuba"
+
+    @dispatch
+    def render(self, el):
+        """General render method.
+        Note: overloading of `render` enabled via plum.dispatch to allow different
+        rendering behaviour for some elements.
+        """
+        prev_obj = getattr(self, "crnt_obj", None)
+        self.crnt_obj = el
+        res = super().render(el)
+        self.crnt_obj = prev_obj
+
+        return res
+
+    @dispatch
+    def render(self, el: qast.DocstringSectionSeeAlso):  # noqa: F811
+        """Numpy Docstring style render method.
+        Note: overloading of `render` enabled via plum.dispatch to allow different
+        rendering behaviour for some elements.
+        """
+        lines = el.value.split("\n")
+
+        # each entry in result has form: ([('func1', '<directive>), ...], <description>)
+        parsed = NumpyDocString("")._parse_see_also(lines)
+
+        result = []
+        for funcs, description in parsed:
+            links = [f"[{name}](`{self._name_to_target(name)}`)" for name, role in funcs]
+
+            str_links = ", ".join(links)
+
+            if description:
+                str_description = "<br>".join(description)
+                result.append(f"{str_links}: {str_description}")
+            else:
+                result.append(str_links)
+
+        return "*\n".join(result)
+
+    def _name_to_target(self, name: str):
+        """Helper method to convert a function/class name to a full target path,
+        used for Numpy Docstring style render method.
+        """
+        crnt_path = getattr(self.crnt_obj, "path", None)
+        parent = crnt_path.rsplit(".", 1)[0] + "."
+        pkg = "classifai."
+
+        if crnt_path and not (name.startswith(pkg) or name.startswith(parent)):
+            return f"{parent}{name}"
+        elif not name.startswith(pkg):
+            return f"{pkg}{name}"
+
+        return name
diff --git a/index.qmd b/index.qmd
new file mode 100644
index 0000000..e54d850
--- /dev/null
+++ b/index.qmd
@@ -0,0 +1,3 @@
+# Package Overview {.unnumbered}
+
+{{< include README.md >}}
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
index 90ba5d6..9d82f51 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -127,6 +127,9 @@ dev = [
     "mkdocstrings-python >= 1.16.7",
     "mkdocs-material >= 9.6.9",
     "deptry>=0.24.0",
+    "quarto>=0.1.0",
+    "quartodoc>=0.11.1",
+    "numpydoc>=1.10.0",
     {include-group = "test"},
 ]
 test = [
diff --git a/src/classifai/_optional.py b/src/classifai/_optional.py
index 7b4e574..d9efa9e 100644
--- a/src/classifai/_optional.py
+++ b/src/classifai/_optional.py
@@ -23,7 +23,7 @@ def check_deps(reqs: list[str], extra: str | None = None) -> None:
 
     Args:
         reqs (list[str]): A list of package names to check.
-        extra (str, optional): The name of the extra installation group. Defaults to None.
+        extra (str): [optional] The name of the extra installation group. Defaults to None.
 
     Raises:
         OptionalDependencyError: If any of the required packages are not installed.
diff --git a/src/classifai/indexers/__init__.py b/src/classifai/indexers/__init__.py
index 83ad3fa..96cd940 100644
--- a/src/classifai/indexers/__init__.py
+++ b/src/classifai/indexers/__init__.py
@@ -1,4 +1,30 @@
-"""Indexers package."""
+# pylint: disable=C0301
+"""This module provides functionality for creating a vector index from a text file.
+It defines the `VectorStore` class, which is used to model and create vector databases
+from CSV text files using a vectoriser object.
+
+This class interacts with the Vectoriser class from the vectorisers submodule,
+expecting that any vector model used to generate embeddings used in the
+VectorStore objects is an instance of one of these classes, most notably
+that each vectoriser object should have a transform method.
+
+Key Features:
+- Batch processing of input files to handle large datasets.
+- Support for CSV file format (additional formats may be added in future updates).
+- Integration with a custom embedder for generating vector embeddings.
+- Logging for tracking progress and handling errors during processing.
+
+Dependencies:
+- polars: For handling data in tabular format and saving it as a Parquet file.
+- tqdm: For displaying progress bars during batch processing.
+- numpy: for vector cosine similarity calculations
+- A custom file iterator (`iter_csv`) for reading input files in batches.
+
+Usage:
+This module is intended to be used with the Vectoriers mdodule and the
+the servers module from ClassifAI, to created scalable, modular, searchable
+vector databases from your own text data.
+"""
 
 from .dataclasses import (
     VectorStoreEmbedInput,
diff --git a/src/classifai/indexers/dataclasses.py b/src/classifai/indexers/dataclasses.py
index 3152b18..ecfdf7f 100644
--- a/src/classifai/indexers/dataclasses.py
+++ b/src/classifai/indexers/dataclasses.py
@@ -5,7 +5,7 @@
 ##
 # Search Input DataClass
 ##
-searchInputSchema = pa.DataFrameSchema(
+searchInputSchema: pa.DataFrameSchema = pa.DataFrameSchema(
     {
         "id": pa.Column(str),
         "query": pa.Column(str),
@@ -15,6 +15,8 @@
 
 
 class VectorStoreSearchInput(pd.DataFrame):
+    """TODO."""
+
     _schema = searchInputSchema
 
     def __init__(self, data: dict | pd.DataFrame):
@@ -51,7 +53,7 @@ def query(self) -> pd.Series:
 ##
 # Search Output DataClass
 ##
-searchOutputSchema = pa.DataFrameSchema(
+searchOutputSchema: pa.DataFrameSchema = pa.DataFrameSchema(
     {
         "query_id": pa.Column(str),
         "query_text": pa.Column(str),
@@ -66,6 +68,8 @@ def query(self) -> pd.Series:
 
 
 class VectorStoreSearchOutput(pd.DataFrame):
+    """TODO."""
+
     _schema = searchOutputSchema
 
     def __init__(self, data: dict | pd.DataFrame):
@@ -118,7 +122,7 @@ def score(self) -> pd.Series:
 ##
 # Reverse Search Input DataClass
 ##
-reverseSearchInputSchema = pa.DataFrameSchema(
+reverseSearchInputSchema: pa.DataFrameSchema = pa.DataFrameSchema(
     {
         "id": pa.Column(str),
         "doc_id": pa.Column(str),
@@ -128,6 +132,8 @@ def score(self) -> pd.Series:
 
 
 class VectorStoreReverseSearchInput(pd.DataFrame):
+    """TODO."""
+
     _schema = reverseSearchInputSchema
 
     def __init__(self, data: dict | pd.DataFrame):
@@ -164,7 +170,7 @@ def text(self) -> pd.Series:
 ##
 # Reverse Search Output DataClass
 ##
-reverseSearchOutputSchema = pa.DataFrameSchema(
+reverseSearchOutputSchema: pa.DataFrameSchema = pa.DataFrameSchema(
     {
         "id": pa.Column(str),
         "doc_id": pa.Column(str),
@@ -174,6 +180,8 @@ def text(self) -> pd.Series:
 
 
 class VectorStoreReverseSearchOutput(pd.DataFrame):
+    """TODO."""
+
     _schema = reverseSearchOutputSchema
 
     def __init__(self, data: dict | pd.DataFrame):
@@ -214,7 +222,7 @@ def doc_text(self) -> pd.Series:
 ##
 # Embed Input DataClass
 ##
-embedInputSchema = pa.DataFrameSchema(
+embedInputSchema: pa.DataFrameSchema = pa.DataFrameSchema(
     {
         "id": pa.Column(str),
         "text": pa.Column(str),
@@ -224,6 +232,8 @@ def doc_text(self) -> pd.Series:
 
 
 class VectorStoreEmbedInput(pd.DataFrame):
+    """TODO."""
+
     _schema = embedInputSchema
 
     def __init__(self, data: dict | pd.DataFrame):
@@ -260,7 +270,7 @@ def text(self) -> pd.Series:
 ##
 # Embed Ouput DataClass
 ##
-embedOutputSchema = pa.DataFrameSchema(
+embedOutputSchema: pa.DataFrameSchema = pa.DataFrameSchema(
     {
         "id": pa.Column(str),
         "text": pa.Column(str),
@@ -271,6 +281,8 @@ def text(self) -> pd.Series:
 
 
 class VectorStoreEmbedOutput(pd.DataFrame):
+    """TODO."""
+
     _schema = embedOutputSchema
 
     def __init__(self, data: dict | pd.DataFrame):
diff --git a/src/classifai/indexers/main.py b/src/classifai/indexers/main.py
index 1114fc7..fbe827b 100644
--- a/src/classifai/indexers/main.py
+++ b/src/classifai/indexers/main.py
@@ -32,6 +32,7 @@
 import shutil
 import time
 import uuid
+from typing import Optional, Self, Union  # noqa: F401
 
 import numpy as np
 import polars as pl
@@ -71,7 +72,7 @@ class VectorStore:
         data_type (str): the data type of the original file (curently only csv supported)
         vectoriser (object): A Vectoriser object from the corresponding ClassifAI Pacakge module
         batch_size (int): the batch size to pass to the vectoriser when embedding
-        meta_data (dict[str:type]): key-value pairs of metadata to extract from the input file and their correpsonding types
+        meta_data (dict): key-value pairs of metadata to extract from the input file and their correpsonding types
         output_dir (str): the path to the output directory where the VectorStore will be saved
         vectors (np.array): a numpy array of vectors for the vector DB
         vector_shape (int): the dimension of the vectors
@@ -99,11 +100,11 @@ def __init__(  # noqa: C901, PLR0912, PLR0913, PLR0915
             data_type (str): The type of input data (currently supports only "csv").
             vectoriser (object): The vectoriser object used to transform text into
                                 vector embeddings.
-            batch_size (int, optional): The batch size for processing the input file and batching to
+            batch_size (int): [optional] The batch size for processing the input file and batching to
             vectoriser. Defaults to 8.
-            meta_data (dict, optional): key,value pair metadata column names to extract from the input file and their types.
+            meta_data (dict): key,value pair metadata column names to extract from the input file and their types.
                                 Defaults to None.
-            output_dir (str, optional): The directory where the vector store will be saved.
+            output_dir (str): [optional] The directory where the vector store will be saved.
                                 Defaults to None, where input file name will be used.
             overwrite (bool, optional): If True, allows overwriting existing folders with the same name. Defaults to false to prevent accidental overwrites.
             hooks (dict, optional): A dictionary of user-defined hooks for preprocessing and postprocessing. Defaults to None.
@@ -217,7 +218,7 @@ def __init__(  # noqa: C901, PLR0912, PLR0913, PLR0915
                 context={"cause_type": type(e).__name__, "cause_message": str(e)},
             ) from e
 
-    def _save_metadata(self, path):
+    def _save_metadata(self, path: str):
         """Saves metadata about the vector store to a JSON file.
 
         Args:
@@ -709,7 +710,7 @@ def from_filespace(cls, folder_path, vectoriser, hooks: dict | None = None):  #
             hooks (dict, optional): A dictionary of user-defined hooks for preprocessing and postprocessing. Defaults to None.
 
         Returns:
-            VectorStore: An instance of the `VectorStore` class.
+            (VectorStore): An instance of the `VectorStore` class.
 
         Raises:
             DataValidationError: If input arguments are invalid or if there are issues with the metadata or Parquet files.
diff --git a/src/classifai/servers/__init__.py b/src/classifai/servers/__init__.py
index 88b4065..a20d108 100644
--- a/src/classifai/servers/__init__.py
+++ b/src/classifai/servers/__init__.py
@@ -1,5 +1,12 @@
-"""FastAPI server as a package."""
+"""This module provides functionality for creating a start a restAPI service which
+allows a user to call the search methods of different VectorStore objects, from
+an api-endpoint.
 
-from .main import get_router, run_server
+These functions interact with the ClassifAI PackageIndexer modules
+VectorStore objects, such that their embed and search methods are exposed on
+restAPI endpoints, in a FastAPI restAPI service started with these functions.
+"""
 
-__all__ = ["get_router", "run_server"]
+from .main import get_router, get_server, make_endpoints, run_server
+
+__all__ = ["get_router", "get_server", "make_endpoints", "run_server"]
diff --git a/src/classifai/servers/main.py b/src/classifai/servers/main.py
index 054acad..8cdcbd3 100644
--- a/src/classifai/servers/main.py
+++ b/src/classifai/servers/main.py
@@ -110,7 +110,6 @@ def get_server(vector_stores: list[VectorStore], endpoint_names: list[str]) -> F
     Args:
         vector_stores (list[VectorStore]): A list of vector store objects, each responsible for handling embedding and search operations for a specific endpoint.
         endpoint_names (list[str]): A list of endpoint names corresponding to the vector stores.
-        port (int, optional): The port on which the API server will run. Defaults to 8000.
 
     Returns:
         FastAPI: Server with intialized search endpoints
@@ -153,12 +152,12 @@ def make_endpoints(router: APIRouter | FastAPI, vector_stores_dict: dict[str, Ve
     """
     for endpoint_name, vector_store in vector_stores_dict.items():
         logging.info("Registering endpoints for: %s", endpoint_name)
-        create_embedding_endpoint(router, endpoint_name, vector_store)
-        create_search_endpoint(router, endpoint_name, vector_store)
-        create_reverse_search_endpoint(router, endpoint_name, vector_store)
+        _create_embedding_endpoint(router, endpoint_name, vector_store)
+        _create_search_endpoint(router, endpoint_name, vector_store)
+        _create_reverse_search_endpoint(router, endpoint_name, vector_store)
 
 
-def create_embedding_endpoint(router: APIRouter | FastAPI, endpoint_name: str, vector_store: VectorStore):
+def _create_embedding_endpoint(router: APIRouter | FastAPI, endpoint_name: str, vector_store: VectorStore):
     """Create and register an embedding endpoint for a specific vector store.
 
     Args:
@@ -191,7 +190,7 @@ async def embedding_endpoint(data: ClassifaiData) -> EmbeddingsResponseBody:
         return EmbeddingsResponseBody(data=returnable)
 
 
-def create_search_endpoint(router: APIRouter | FastAPI, endpoint_name: str, vector_store: VectorStore):
+def _create_search_endpoint(router: APIRouter | FastAPI, endpoint_name: str, vector_store: VectorStore):
     """Create and register a search endpoint for a specific vector store.
 
     Args:
@@ -230,7 +229,7 @@ async def search_endpoint(
         return formatted_result
 
 
-def create_reverse_search_endpoint(router: APIRouter | FastAPI, endpoint_name: str, vector_store: VectorStore):
+def _create_reverse_search_endpoint(router: APIRouter | FastAPI, endpoint_name: str, vector_store: VectorStore):
     """Create and register a reverse_search endpoint for a specific vector store.
 
     Args:
diff --git a/src/classifai/vectorisers/gcp.py b/src/classifai/vectorisers/gcp.py
index f1fd29e..dcb484c 100644
--- a/src/classifai/vectorisers/gcp.py
+++ b/src/classifai/vectorisers/gcp.py
@@ -90,11 +90,11 @@ def __init__(
                 context={"vectoriser": "gcp", "cause": str(e), "cause_type": type(e).__name__},
             ) from e
 
-    def transform(self, texts):
+    def transform(self, texts: str | list[str]) -> np.ndarray:
         """Transforms input text(s) into embeddings using the GenAI API.
 
         Args:
-            texts (str or list of str): The input text(s) to embed. Can be a single string or a list of strings.
+            texts (str,list[str]): The input text(s) to embed. Can be a single string or a list of strings.
 
         Returns:
             numpy.ndarray: A 2D array of embeddings, where each row corresponds to an input text.
diff --git a/src/classifai/vectorisers/huggingface.py b/src/classifai/vectorisers/huggingface.py
index 20c181a..4fc0482 100644
--- a/src/classifai/vectorisers/huggingface.py
+++ b/src/classifai/vectorisers/huggingface.py
@@ -1,5 +1,7 @@
 """A module that provides a wrapper for Huggingface Transformers models to generate text embeddings."""
 
+from numpy import ndarray as np_ndarray
+
 from classifai._optional import check_deps
 from classifai.exceptions import ConfigurationError, ExternalServiceError, VectorisationError
 
@@ -88,11 +90,11 @@ def __init__(
                 },
             ) from e
 
-    def transform(self, texts):
+    def transform(self, texts: str | list[str]) -> np_ndarray:
         """Transforms input text(s) into embeddings using the Huggingface model.
 
         Args:
-            texts (str or list of str): The input text(s) to embed. Can be a single string or a list of strings.
+            texts (str,list[str]): The input text(s) to embed. Can be a single string or a list of strings.
 
         Returns:
             numpy.ndarray: A 2D array of embeddings, where each row corresponds to an input text.
diff --git a/src/classifai/vectorisers/ollama.py b/src/classifai/vectorisers/ollama.py
index ee24d41..983fff7 100644
--- a/src/classifai/vectorisers/ollama.py
+++ b/src/classifai/vectorisers/ollama.py
@@ -28,11 +28,11 @@ def __init__(self, model_name: str):
 
         self.model_name = model_name
 
-    def transform(self, texts):
+    def transform(self, texts: str | list[str]) -> np.ndarray:
         """Transforms input text(s) into embeddings using the Huggingface model.
 
         Args:
-            texts (str or list of str): The input text(s) to embed. Can be a single string or a list of strings.
+            texts (str ,list [str]): The input text(s) to embed. Can be a single string or a list of strings.
 
         Returns:
             numpy.ndarray: A 2D array of embeddings, where each row corresponds to an input text.
diff --git a/styles.css b/styles.css
new file mode 100644
index 0000000..a03ce13
--- /dev/null
+++ b/styles.css
@@ -0,0 +1,203 @@
+/* css styles */
+@import url('https://rsms.me/inter/inter.css');
+@import url('https://fonts.googleapis.com/css2?family=Roboto:ital,wght@0,100..900;1,100..900&display=swap');
+html { font-family: 'Roboto', 'Helvetica Neue', 'Inter', Helvetica, Arial, sans-serif;}
+@supports (font-variation-settings: normal) {
+  html { font-family: 'Roboto', 'Helvetica Neue', 'Inter', Helvetica, Arial, sans-serif; }
+}
+
+:root {
+  --bs-body-font-family: inter;
+  --bs-body-font-size: 16px;
+}
+
+:root {
+  --site-color-primary:  #F9F4D8;/*#ebfddd;*/
+  --site-color-primary: #EE8320;
+  --site-color-primary: #C29A68;
+}
+
+#quarto-header {
+  border: 1px solid #dee2e6;
+  background-color: #FFFFFF;
+}
+
+#quarto-header nav {
+  max-width: 1300px;
+  margin: auto;
+}
+
+#quarto-header .nav-link {
+  padding-left: 5.4px !important;
+  padding-right: 5.4px !important;
+  margin-left: 30px !important;
+  margin-right: 5.4px !important;
+  font-size: 18px;
+}
+
+#quarto-header .nav-link .bi {
+  font-size: 20px;
+}
+
+.sidebar a.nav-link {
+  font-size: 14.4px;
+  font-weight: 400;
+}
+
+.nav-item .nav-link.active {
+  /*background-color: #EE8320;*/
+  border-bottom: black 2px solid;
+}
+
+.navbar {
+  /*background-color: var(--site-color-primary);*/
+  background-color: #FFFFFF;
+  /*var(--site-color-primary);*/
+  padding-top: 0px !important;
+  padding-bottom: 0px !important;
+  /*border-bottom: black 2px solid;*/
+}
+
+.navbar-title {
+  color: black !important;
+  font-weight: 800;
+  font-size: 32px;
+}
+
+.navbar-nav .nav-link {
+    color: black !important;
+}
+
+.navbar #quarto-search.type-overlay .aa-Autocomplete svg.aa-SubmitIcon {
+  color: black;
+}
+
+div.sidebar-item-container .active {
+  /*color: #0047ab;*/
+  color: #6699ff;
+  color: #5dba17;
+  color: #4D9089 !important;
+  font-weight: 600;
+}
+
+.sidebar nav[role=doc-toc] ul>li>a.active, .sidebar nav[role=doc-toc] ul>li>ul>li>a.active
+{
+  color: black !important;
+  /*color: #4D9089 !important;*/
+  border-left: 2px solid #4D9089 !important;
+}
+
+.sidebar nav[role=doc-toc]>ul a {
+  border-left: 2px solid white;
+}
+
+/*
+div.sidebar-item-container .active {
+  color: black;
+  background-color: var(--site-color-secondary);
+}
+
+.sidebar nav[role=doc-toc] ul>li>a.active {
+    border-left: 1px solid var(--site-color-secondary);
+    color: var(--site-color-secondary) !important;
+}
+*/
+
+.sidebar-item-container .text-start {
+  font-weight: 600;
+  font-size: 14.4px !important;
+}
+
+.sidebar-item-text {
+  /*color: rgba(60, 60, 60, 0.7);*/
+  font-weight: 500;
+  font-size: 14px;
+  line-height: 22px;
+}
+
+.sidebar-item-section {
+  padding-top: 16px;
+}
+
+.sidebar-section {
+  padding-left: 0px !important;
+}
+
+.dataframe {
+  font-size: 14px;
+}
+
+.dataframe, .dataframe table {
+  width: inherit;
+}
+
+/*
+.dataframe tbody tr th:only-of-type {
+    vertical-align: middle;
+}
+
+.dataframe tbody tr th {
+    vertical-align: top;
+}
+
+.dataframe thead th {
+    text-align: right;
+}
+*/
+
+
+
+
+/* INDEX PAGE ------------------------------------------ */
+
+
+
+
+/*
+ * Buttons
+*/
+
+.btn-action {
+  min-width: 165px;
+  border-radius: 30px;
+  border: none;
+}
+
+/* content block */
+
+.content-block {
+  padding-top: 20px;
+  padding-bottom: 10px;
+  margin-left: 30px;
+  margin-right: 30px;
+}
+
+
+@media(min-width: 900px) {
+.content-block {
+  margin-left: 50px;
+  margin-right: 50px;
+}
+}
+
+@media (min-width: 1200px) {
+.content-block {
+  max-width: 1100px;
+  margin-left: auto;
+  margin-right: auto;
+}
+}
+
+.feature-blocks .grid {
+  padding-top: 20px;
+  padding-bottom: 20px;
+}
+
+.hero-github-links {
+  padding-top: 30px;
+}
+
+.hero-title {
+  font-size: 64px;
+  background-color: #FFFFFF77;
+}
diff --git a/uv.lock b/uv.lock
index 644c09b..acf0803 100644
--- a/uv.lock
+++ b/uv.lock
@@ -5,15 +5,27 @@ resolution-markers = [
     "python_full_version >= '3.14' and sys_platform == 'win32'",
     "python_full_version >= '3.14' and sys_platform == 'emscripten'",
     "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
-    "python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform == 'win32'",
+    "python_full_version == '3.13.*' and sys_platform == 'win32'",
+    "python_full_version == '3.12.*' and sys_platform == 'win32'",
     "python_full_version == '3.11.*' and sys_platform == 'win32'",
-    "python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform == 'emscripten'",
+    "python_full_version == '3.13.*' and sys_platform == 'emscripten'",
+    "python_full_version == '3.12.*' and sys_platform == 'emscripten'",
     "python_full_version == '3.11.*' and sys_platform == 'emscripten'",
-    "python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version == '3.13.*' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version == '3.12.*' and sys_platform != 'emscripten' and sys_platform != 'win32'",
     "python_full_version == '3.11.*' and sys_platform != 'emscripten' and sys_platform != 'win32'",
     "python_full_version < '3.11'",
 ]
 
+[[package]]
+name = "alabaster"
+version = "1.0.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/a6/f8/d9c74d0daf3f742840fd818d69cfae176fa332022fd44e3469487d5a9420/alabaster-1.0.0.tar.gz", hash = "sha256:c00dca57bca26fa62a6d7d0a9fcce65f3e026e9bfe33e9c538fd3fbb2144fd9e", size = 24210, upload-time = "2024-07-26T18:15:03.762Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/7e/b3/6b4067be973ae96ba0d615946e314c5ae35f9f993eca561b356540bb0c2b/alabaster-1.0.0-py3-none-any.whl", hash = "sha256:fc6786402dc3fcb2de3cabd5fe455a2db534b371124f1f21de8731783dec828b", size = 13929, upload-time = "2024-07-26T18:15:02.05Z" },
+]
+
 [[package]]
 name = "annotated-doc"
 version = "0.0.4"
@@ -75,11 +87,11 @@ wheels = [
 
 [[package]]
 name = "babel"
-version = "2.17.0"
+version = "2.18.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/7d/6b/d52e42361e1aa00709585ecc30b3f9684b3ab62530771402248b1b1d6240/babel-2.17.0.tar.gz", hash = "sha256:0c54cffb19f690cdcc52a3b50bcbf71e07a808d1c80d549f2459b9d2cf0afb9d", size = 9951852, upload-time = "2025-02-01T15:17:41.026Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/7d/b2/51899539b6ceeeb420d40ed3cd4b7a40519404f9baf3d4ac99dc413a834b/babel-2.18.0.tar.gz", hash = "sha256:b80b99a14bd085fcacfa15c9165f651fbb3406e66cc603abf11c5750937c992d", size = 9959554, upload-time = "2026-02-01T12:30:56.078Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/b7/b8/3fe70c75fe32afc4bb507f75563d39bc5642255d1d94f1f23604725780bf/babel-2.17.0-py3-none-any.whl", hash = "sha256:4d0b53093fdfb4b21c92b5213dba5a1b23885afa8383709427046b21c366e5f2", size = 10182537, upload-time = "2025-02-01T15:17:37.39Z" },
+    { url = "https://files.pythonhosted.org/packages/77/f5/21d2de20e8b8b0408f0681956ca2c69f1320a3848ac50e6e7f39c6159675/babel-2.18.0-py3-none-any.whl", hash = "sha256:e2b422b277c2b9a9630c1d7903c2a00d0830c409c59ac8cae9081c92f1aeba35", size = 10196845, upload-time = "2026-02-01T12:30:53.445Z" },
 ]
 
 [[package]]
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 ]
 
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+wheels = [
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+[[package]]
+name = "black"
+version = "26.1.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "click" },
+    { name = "mypy-extensions" },
+    { name = "packaging" },
+    { name = "pathspec" },
+    { name = "platformdirs" },
+    { name = "pytokens" },
+    { name = "tomli", marker = "python_full_version < '3.11'" },
+    { name = "typing-extensions", marker = "python_full_version < '3.11'" },
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