diff --git a/.pylintrc b/.pylintrc
index d1570f9..741399b 100644
--- a/.pylintrc
+++ b/.pylintrc
@@ -53,6 +53,7 @@ disable=
 ;  duplicate-string-formatting-argument,  # TMP: will be fixed in close future
  consider-using-f-string,  # sorry, not gonna happen, still have to support py2
 ;  use-dict-literal
+ cyclic-import,
 
 [FORMAT]
 # Maximum number of characters on a single line.
diff --git a/knowledge_base_gpt/apps/__init__.py b/knowledge_base_gpt/apps/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/knowledge_base_gpt/apps/ingest/__init__.py b/knowledge_base_gpt/apps/ingest/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/knowledge_base_gpt/apps/ingest/__main__.py b/knowledge_base_gpt/apps/ingest/__main__.py
index 0cb2482..6484db9 100644
--- a/knowledge_base_gpt/apps/ingest/__main__.py
+++ b/knowledge_base_gpt/apps/ingest/__main__.py
@@ -1,3 +1,4 @@
+""" Content ingestion application """
 from knowledge_base_gpt.libs.injector.di import global_injector
 from knowledge_base_gpt.apps.ingest.ingest import Ingestor
 
diff --git a/knowledge_base_gpt/apps/ingest/ingest.py b/knowledge_base_gpt/apps/ingest/ingest.py
index 2b8c128..b6af2c7 100644
--- a/knowledge_base_gpt/apps/ingest/ingest.py
+++ b/knowledge_base_gpt/apps/ingest/ingest.py
@@ -1,4 +1,4 @@
-import os
+""" Content ingest """
 from typing import List
 
 from injector import inject, singleton
@@ -11,7 +11,8 @@
 
 
 @singleton
-class Ingestor():
+class Ingestor():  # pylint:disable=R0903
+    """ Content ingest """
 
     @inject
     def __init__(self, settings: Settings, loader: Loader, vector_store: VectorStore) -> None:
@@ -20,11 +21,11 @@ def __init__(self, settings: Settings, loader: Loader, vector_store: VectorStore
         self._chunk_overlap = settings.text_splitter.chunk_overlap
         self._vector_store = vector_store
 
-    def _process_documents(self, ignored_files: List[str] = []) -> List[Document]:
+    def _process_documents(self, ignored_files: List[str]) -> List[Document]:
         """
         Load documents and split in chunks
         """
-        print(f"Loading documents")
+        print("Loading documents")
         documents = self._loader.load_documents(ignored_files)
         if not documents:
             return []
@@ -36,12 +37,13 @@ def _process_documents(self, ignored_files: List[str] = []) -> List[Document]:
         return documents
 
     def run(self):
+        """ Ingest the documents into the vector store based on the settings """
         collection = self._vector_store.db.get()
         documents = self._process_documents(list(set(metadata['source'] for metadata in collection['metadatas'])))
         if len(documents) == 0:
             print("No new documents to load")
         else:
-            print(f"Creating embeddings. May take some minutes...")
+            print("Creating embeddings. May take some minutes...")
             self._vector_store.db.add_documents(documents)
             self._vector_store.db.persist()
-        print(f"Ingestion complete! You can now run privateGPT.py to query your documents")
+        print("Ingestion complete! You can now run privateGPT.py to query your documents")
diff --git a/knowledge_base_gpt/apps/slackbot/__init__.py b/knowledge_base_gpt/apps/slackbot/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/knowledge_base_gpt/apps/slackbot/__main__.py b/knowledge_base_gpt/apps/slackbot/__main__.py
index d1e9fbe..60efdcb 100644
--- a/knowledge_base_gpt/apps/slackbot/__main__.py
+++ b/knowledge_base_gpt/apps/slackbot/__main__.py
@@ -1,3 +1,4 @@
+""" Slackbot application """
 from knowledge_base_gpt.libs.injector.di import global_injector
 from knowledge_base_gpt.apps.slackbot.slack_bot import KnowledgeBaseSlackBot
 
diff --git a/knowledge_base_gpt/apps/slackbot/slack_bot.py b/knowledge_base_gpt/apps/slackbot/slack_bot.py
index ea9ba1b..907b42c 100644
--- a/knowledge_base_gpt/apps/slackbot/slack_bot.py
+++ b/knowledge_base_gpt/apps/slackbot/slack_bot.py
@@ -1,3 +1,4 @@
+""" Slackbot application backend """
 from injector import inject, singleton
 from slack_bolt import App
 from slack_bolt.adapter.socket_mode import SocketModeHandler
@@ -9,11 +10,12 @@
 
 
 class KnowledgeBaseSlackBotException(Exception):
-    pass
+    """ Wrapper for SlackBot specific exception """
 
 
 @singleton
-class KnowledgeBaseSlackBot():
+class KnowledgeBaseSlackBot():  # pylint:disable=R0903
+    """ Slackbot application backend """
 
     @inject
     def __init__(self, settings: Settings, private_chat: PrivateChat, history: HistoryRedis) -> None:
@@ -28,18 +30,19 @@ def __init__(self, settings: Settings, private_chat: PrivateChat, history: Histo
         self._handler.app.command('/conversation_forward')(self._forward_question)
 
     def run(self):
+        """ Start the Slackbot backend application """
         self._handler.start()
 
     def _get_forward_question_channel_id(self):
         if self._forward_question_channel_name is None:
-            raise KnowledgeBaseSlackBotException(f"Slackbot forward channel name was not set")
+            raise KnowledgeBaseSlackBotException("Slackbot forward channel name was not set")
         try:
             for result in self._handler.app.client.conversations_list():
                 for channel in result["channels"]:
                     if channel["name"] == self._forward_question_channel_name:
                         return channel["id"]
         except SlackApiError as e:
-            raise KnowledgeBaseSlackBotException(e)
+            raise KnowledgeBaseSlackBotException(e) from e
         raise KnowledgeBaseSlackBotException(f"The channel {self._forward_question_channel_name} does not exits")
 
     def _got_message(self, message, say):
@@ -48,7 +51,11 @@ def _got_message(self, message, say):
             user=message['user'],
             text="On it. Be back with your answer soon"
         )
-        answer = self._private_chat.answer_query(self._history.get_messages(message['user']), message['text'], chat_identifier=message['user'])
+        answer = self._private_chat.answer_query(
+            self._history.get_messages(message['user']),
+            message['text'],
+            chat_identifier=message['user']
+        )
         self._history.add_to_history(message['user'], answer)
         say(answer['answer'])
 
@@ -63,7 +70,7 @@ def _is_direct_message_channel(self, command):
         return False
 
 
-    def _reset_conversation(self, ack, say, command):
+    def _reset_conversation(self, ack, say, command):  # pylint:disable=unused-argument
         ack()
         if not self._is_direct_message_channel(command):
             return
@@ -85,7 +92,7 @@ def _messages_to_text(messages):
             text += '\n'
         return text
 
-    def _forward_question(self, ack, say, command):
+    def _forward_question(self, ack, say, command):  # pylint:disable=unused-argument
         ack()
         if not self._is_direct_message_channel(command):
             return
@@ -94,7 +101,10 @@ def _forward_question(self, ack, say, command):
         if len(messages) == 0:
             msg = 'There is no active conversation'
         else:
-            self._handler.app.client.chat_postMessage(channel=self._forward_question_channel_id, text=self._messages_to_text(messages))
+            self._handler.app.client.chat_postMessage(
+                channel=self._forward_question_channel_id,
+                text=self._messages_to_text(messages)
+            )
             msg = f'The conversation was forwarded to {self._forward_question_channel_name}'
 
         self._handler.app.client.chat_postEphemeral(
diff --git a/knowledge_base_gpt/libs/common/constants.py b/knowledge_base_gpt/libs/common/constants.py
index 329be27..f9412af 100644
--- a/knowledge_base_gpt/libs/common/constants.py
+++ b/knowledge_base_gpt/libs/common/constants.py
@@ -1,3 +1,4 @@
+""" Hold application constants """
 from pathlib import Path
 
 
diff --git a/knowledge_base_gpt/libs/embedding/embedding.py b/knowledge_base_gpt/libs/embedding/embedding.py
index da8bbba..3b33e6e 100644
--- a/knowledge_base_gpt/libs/embedding/embedding.py
+++ b/knowledge_base_gpt/libs/embedding/embedding.py
@@ -1,12 +1,14 @@
+""" Create and abstract the embedding """
 from injector import inject, singleton
-from langchain_community.embeddings import HuggingFaceEmbeddings
-from langchain_community.embeddings import OllamaEmbeddings
+from langchain_core.embeddings import Embeddings as LangChainEmbeddings
+from langchain_community.embeddings import HuggingFaceEmbeddings, OllamaEmbeddings
 
 from knowledge_base_gpt.libs.settings.settings import Settings
 
 
 @singleton
-class Embedding():
+class Embedding():  # pylint:disable=R0903
+    """ Create and abstract the embedding """
     @inject
     def __init__(self, settings: Settings) -> None:
         mode = settings.embedding.mode
@@ -31,5 +33,6 @@ def __init__(self, settings: Settings) -> None:
                 pass
 
     @property
-    def embeddings(self):
+    def embeddings(self) -> LangChainEmbeddings:
+        """ Return the embedding implementation """
         return self._embeddings
diff --git a/knowledge_base_gpt/libs/gpt/ollama_info.py b/knowledge_base_gpt/libs/gpt/ollama_info.py
index d71b9f1..2999c4f 100644
--- a/knowledge_base_gpt/libs/gpt/ollama_info.py
+++ b/knowledge_base_gpt/libs/gpt/ollama_info.py
@@ -1,14 +1,21 @@
+"""
+Keep track of metrics provided by the Ollama API
+"""
+
 from contextlib import contextmanager
 from contextvars import ContextVar
 import threading
 from typing import Any, Dict, List, Optional, Generator
+from uuid import UUID
 
 from langchain_core.callbacks.base import BaseCallbackHandler
+from langchain_core.messages import BaseMessage
 from langchain_core.outputs import LLMResult
 from langchain_core.tracers.context import register_configure_hook
 
 
 class OllamaMetrics():
+    """ Metrics of a single Ollama request """
     prompt_eval_count: int = 0
     eval_count: int = 0
     load_duration: int = 0
@@ -28,6 +35,7 @@ def __repr__(self) -> str:
         )
 
     def to_json(self) -> dict:
+        """ Return a JSON representation of the tracked info """
         return {
             "prompt_eval_count": self.prompt_eval_count,
             "eval_count": self.eval_count,
@@ -61,13 +69,11 @@ def on_llm_start(
         self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
     ) -> None:
         """Print out the prompts."""
-        pass
 
     def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
         """Print out the token."""
-        pass
 
-    def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
+    def on_llm_end(self, response: LLMResult, **_kwargs: Any) -> None:
         """Collect token usage."""
         if len(response.generations) == 0 or len(response.generations[0]) == 0:
             return
@@ -88,6 +94,19 @@ def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
         with self._lock:
             self.metrics.append(metrics)
 
+    def on_chat_model_start(
+        self,
+        serialized: Dict[str, Any],
+        messages: List[List[BaseMessage]],
+        *,
+        run_id: UUID,
+        parent_run_id: Optional[UUID] = None,
+        tags: Optional[List[str]] = None,
+        metadata: Optional[Dict[str, Any]] = None,
+        **kwargs: Any,
+    ) -> Any:
+        """Run when a chat model starts running. """
+
     def __copy__(self) -> "OllamaCallbackHandler":
         """Return a copy of the callback handler."""
         return self
diff --git a/knowledge_base_gpt/libs/gpt/private_chat.py b/knowledge_base_gpt/libs/gpt/private_chat.py
index 8c32699..155aade 100644
--- a/knowledge_base_gpt/libs/gpt/private_chat.py
+++ b/knowledge_base_gpt/libs/gpt/private_chat.py
@@ -1,4 +1,7 @@
-from typing import Optional
+"""
+Module for handling the chat chain
+"""
+from typing import Optional, Dict, Any
 
 from injector import inject, singleton
 from langchain.chains import ConversationalRetrievalChain
@@ -12,8 +15,8 @@
 
 
 @singleton
-class PrivateChat():
-
+class PrivateChat():  # pylint:disable=R0903
+    """ Handle the Chat chain """
     @inject
     def __init__(self, settings: Settings, chat_log_exporter: ChatLogExporter, vector_store: VectorStore):
         llm_mode = settings.llm.mode
@@ -46,7 +49,11 @@ def __init__(self, settings: Settings, chat_log_exporter: ChatLogExporter, vecto
             return_generated_question=True
         )
 
-    def answer_query(self, history, query, chat_identifier: Optional[str]=None):
+    def answer_query(self, history, query, chat_identifier: Optional[str]=None) -> Dict[str, Any]:
+        """
+         Answer the query based on the history
+         Use the chat identifier for logging the chat
+        """
         with self._get_callback() as cb:
             answer = self._chain.invoke({"question": query, "chat_history": history})
             self._chat_log_exporter.save_chat_log(self._chat_fragment_cls(answer, cb, chat_identifier=chat_identifier))
diff --git a/knowledge_base_gpt/libs/history/base.py b/knowledge_base_gpt/libs/history/base.py
index 9d0ed96..e980492 100644
--- a/knowledge_base_gpt/libs/history/base.py
+++ b/knowledge_base_gpt/libs/history/base.py
@@ -1,18 +1,20 @@
+""" Base class for history keepers """
 from abc import ABC, abstractmethod
-from typing import List
+from typing import List, Dict, Any
 
 from langchain_core.messages import BaseMessage
 
 
 class HistoryBase(ABC):
+    """ Base class for history keepers """
     @abstractmethod
-    def get_messages(self, session_id) -> List[BaseMessage]:
-        pass
+    def get_messages(self, session_id: str) -> List[BaseMessage]:
+        """ Get all messages of the session """
 
     @abstractmethod
-    def add_to_history(self, session_id, answer):
-        pass
+    def add_to_history(self, session_id: str, answer: Dict[str, Any]):
+        """ Add the answer to the session """
 
     @abstractmethod
-    def reset(self, session_id):
-        pass
+    def reset(self, session_id: str):
+        """ Reset the session """
diff --git a/knowledge_base_gpt/libs/history/redis.py b/knowledge_base_gpt/libs/history/redis.py
index baa026b..081d2c8 100644
--- a/knowledge_base_gpt/libs/history/redis.py
+++ b/knowledge_base_gpt/libs/history/redis.py
@@ -1,3 +1,4 @@
+""" Manage history in Redis """
 from typing import List, Dict, Any
 
 from injector import inject, singleton
@@ -10,7 +11,7 @@
 
 @singleton
 class HistoryRedis(HistoryBase):
-
+    """ Manage history in Redis """
     @inject
     def __init__(self, settings: Settings):
         redis_settings = settings.redis
diff --git a/knowledge_base_gpt/libs/injector/di.py b/knowledge_base_gpt/libs/injector/di.py
index 30c5f81..5cfe382 100644
--- a/knowledge_base_gpt/libs/injector/di.py
+++ b/knowledge_base_gpt/libs/injector/di.py
@@ -1,19 +1,14 @@
+""" Global injector for the application """
 from injector import Injector
 
 from knowledge_base_gpt.libs.settings.settings import Settings, unsafe_typed_settings
 
 
-def create_application_injector() -> Injector:
+def _create_application_injector() -> Injector:
     _injector = Injector(auto_bind=True)
     _injector.binder.bind(Settings, to=unsafe_typed_settings)
     return _injector
 
 
-"""
-Global injector for the application.
-
-Avoid using this reference, it will make your code harder to test.
-
-Instead, use the `request.state.injector` reference, which is bound to every request
-"""
-global_injector: Injector = create_application_injector()
+# Global injector for the application.
+global_injector: Injector = _create_application_injector()
diff --git a/knowledge_base_gpt/libs/loaders/google_drive_loader.py b/knowledge_base_gpt/libs/loaders/google_drive_loader.py
index a6b4dea..8c9a8c0 100644
--- a/knowledge_base_gpt/libs/loaders/google_drive_loader.py
+++ b/knowledge_base_gpt/libs/loaders/google_drive_loader.py
@@ -1,3 +1,4 @@
+""" Content loader from Google Drive """
 from typing import List
 
 from injector import inject, singleton
@@ -8,14 +9,16 @@
 
 
 @singleton
-class GDriveLoader():
+class GDriveLoader():  # pylint:disable=R0903
+    """ Content loader from Google Drive """
 
     @inject
     def __init__(self, settings: Settings) -> None:
         self._service_key_file = settings.google_drive.service_key_file
         self._folder_id = settings.google_drive.folder_id
 
-    def load_documents(self, ignored_files: List[str] = []) -> List[Document]:
+    def load_documents(self, ignored_files: List[str]) -> List[Document]:
+        """ Load the documents based on the settings and the ignore list """
         if not self._folder_id:
             return []
 
diff --git a/knowledge_base_gpt/libs/loaders/loaders.py b/knowledge_base_gpt/libs/loaders/loaders.py
index d9a3af7..bdb81d8 100644
--- a/knowledge_base_gpt/libs/loaders/loaders.py
+++ b/knowledge_base_gpt/libs/loaders/loaders.py
@@ -1,3 +1,4 @@
+""" Abstract all content loaders """
 from typing import List
 
 from injector import inject, singleton
@@ -9,7 +10,8 @@
 
 
 @singleton
-class Loader():
+class Loader():  # pylint:disable=R0903
+    """ Abstract all content loaders """
 
     @inject
     def __init__(self, settings: Settings) -> None:
@@ -22,5 +24,6 @@ def __init__(self, settings: Settings) -> None:
             case _:
                 pass
 
-    def load_documents(self, ignored_files: List[str] = []) -> List[Document]:
+    def load_documents(self, ignored_files: List[str]) -> List[Document]:
+        """ Load all the documents based on the settings and the ignore list """
         return self._content_loader.load_documents(ignored_files=ignored_files)
diff --git a/knowledge_base_gpt/libs/logs/chat_fragment.py b/knowledge_base_gpt/libs/logs/chat_fragment.py
index a8eeef6..30a695a 100644
--- a/knowledge_base_gpt/libs/logs/chat_fragment.py
+++ b/knowledge_base_gpt/libs/logs/chat_fragment.py
@@ -1,3 +1,4 @@
+""" Manage a Chat log fragment """
 from abc import ABC, abstractmethod
 from typing import List, Dict, Any, Optional
 import uuid
@@ -6,9 +7,15 @@
 from langchain_core.documents.base import Document
 
 
-class ChatFragment(ABC):
+class ChatFragment(ABC):  # pylint:disable=R0903
+    """ Manage a Chat log fragment """
 
-    def __init__(self, answer: Dict[str, Any], callback_handler: BaseCallbackHandler, chat_identifier: Optional[str] = None) -> None:
+    def __init__(
+        self,
+        answer: Dict[str, Any],
+        callback_handler: BaseCallbackHandler,
+        chat_identifier: Optional[str] = None
+    ) -> None:
         super().__init__()
         if chat_identifier is None:
             chat_identifier = str(uuid.uuid4())
@@ -45,6 +52,7 @@ def _calculate_metrics(self, callback_handler: BaseCallbackHandler):
         pass
 
     def to_json(self) -> dict:
+        """ Return a JSON representation of the chat log fragment """
         return {
             "chat_identifier": self.chat_identifier,
             "question": self.question,
diff --git a/knowledge_base_gpt/libs/logs/chat_log_exporter.py b/knowledge_base_gpt/libs/logs/chat_log_exporter.py
index 16f7c84..224a588 100644
--- a/knowledge_base_gpt/libs/logs/chat_log_exporter.py
+++ b/knowledge_base_gpt/libs/logs/chat_log_exporter.py
@@ -1,3 +1,4 @@
+""" Export Chat logs to File """
 import json
 
 from injector import inject, singleton
@@ -7,12 +8,14 @@
 
 
 @singleton
-class ChatLogExporter():
+class ChatLogExporter():  # pylint:disable=R0903
+    """ Export Chat logs to File """
 
     @inject
     def __init__(self, settings: Settings):
         self._path = settings.log.chat_log_path
 
     def save_chat_log(self, chat_fragment: ChatFragment):
-        with open(self._path, "a+") as f:
+        """ Save the Chat log fragment to the log file """
+        with open(self._path, "a+", encoding="utf-8") as f:
             f.write(f"{json.dumps(chat_fragment.to_json())}\n")
diff --git a/knowledge_base_gpt/libs/logs/ollama.py b/knowledge_base_gpt/libs/logs/ollama.py
index e1bbde1..c6207f7 100644
--- a/knowledge_base_gpt/libs/logs/ollama.py
+++ b/knowledge_base_gpt/libs/logs/ollama.py
@@ -1,8 +1,10 @@
+""" Ollama Chat log fragment """
 from knowledge_base_gpt.libs.logs.chat_fragment import ChatFragment
 from knowledge_base_gpt.libs.gpt.ollama_info import OllamaCallbackHandler
 
 
-class OllamaChatFragment(ChatFragment):
+class OllamaChatFragment(ChatFragment):  # pylint:disable=R0903
+    """ Ollama Chat log fragment """
 
     def _calculate_metrics(self, callback_handler: OllamaCallbackHandler):
         metrics = callback_handler.metrics
diff --git a/knowledge_base_gpt/libs/settings/settings.py b/knowledge_base_gpt/libs/settings/settings.py
index 066c94e..2c54be6 100644
--- a/knowledge_base_gpt/libs/settings/settings.py
+++ b/knowledge_base_gpt/libs/settings/settings.py
@@ -1,3 +1,5 @@
+# pylint:disable=R0903
+""" Settings for the application """
 from typing import Literal
 
 from pydantic import BaseModel, Field
@@ -6,6 +8,7 @@
 
 
 class SlackBotSettings(BaseModel):
+    """ Slackbot Settings """
     app_token: str = Field(
         description="Slack App Token"
     )
@@ -18,6 +21,7 @@ class SlackBotSettings(BaseModel):
 
 
 class LLMSettings(BaseModel):
+    """ LLM Settings """
     mode: Literal["ollama", "mock"] = Field(
         'ollama',
         description="LLM Backend to use for chat"
@@ -28,7 +32,9 @@ class LLMSettings(BaseModel):
     )
     temperature: float = Field(
         0.1,
-        description="The temperature of the model. Increasing the temperature will make the model answer more creatively. A value of 0.1 would be more factual.",
+        description="The temperature of the model. "\
+            "Increasing the temperature will make the model answer more creatively. "\
+            "A value of 0.1 would be more factual.",
     )
     verbose: bool = Field(
         False,
@@ -41,17 +47,21 @@ class LLMSettings(BaseModel):
 
 
 class EmbeddingSettings(BaseModel):
+    """ Embedding Settings """
     mode: Literal["hugging_face", "ollama", "mock"] = Field(
         'hugging_face',
         description="LLM Backend to use for embedding"
     )
     temperature: float = Field(
         0.1,
-        description="The temperature of the model. Increasing the temperature will make the model answer more creatively. A value of 0.1 would be more factual.",
+        description="The temperature of the model. "\
+            "Increasing the temperature will make the model answer more creatively. "\
+            "A value of 0.1 would be more factual.",
     )
 
 
 class GoogleDriveSettings(BaseModel):
+    """ Google Drive Settings """
     service_key_file: str = Field(
         None,
         description="Path a the Google Service Key file"
@@ -63,6 +73,7 @@ class GoogleDriveSettings(BaseModel):
 
 
 class OllamaSettings(BaseModel):
+    """ Ollama Settings """
     api_base: str = Field(
         "http://localhost:11434",
         description="Base URL of Ollama API. Example: 'https://localhost:11434'.",
@@ -77,37 +88,48 @@ class OllamaSettings(BaseModel):
     )
     tfs_z: float = Field(
         1.0,
-        description="Tail free sampling is used to reduce the impact of less probable tokens from the output. A higher value (e.g., 2.0) will reduce the impact more, while a value of 1.0 disables this setting.",
+        description="Tail free sampling is used to reduce the impact of less probable tokens from the output. "\
+            "A higher value (e.g., 2.0) will reduce the impact more, while a value of 1.0 disables this setting.",
     )
     num_predict: int = Field(
         None,
-        description="Maximum number of tokens to predict when generating text. (Default: 128, -1 = infinite generation, -2 = fill context)",
+        description="Maximum number of tokens to predict when generating text. "\
+            "(Default: 128, -1 = infinite generation, -2 = fill context)",
     )
     top_k: int = Field(
         40,
-        description="Reduces the probability of generating nonsense. A higher value (e.g. 100) will give more diverse answers, while a lower value (e.g. 10) will be more conservative. (Default: 40)",
+        description="Reduces the probability of generating nonsense. "\
+            "A higher value (e.g. 100) will give more diverse answers, "\
+            "while a lower value (e.g. 10) will be more conservative. (Default: 40)",
     )
     top_p: float = Field(
         0.9,
-        description="Works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text. (Default: 0.9)",
+        description="Works together with top-k. "\
+            "A higher value (e.g., 0.95) will lead to more diverse text, "\
+            "while a lower value (e.g., 0.5) will generate more focused and conservative text. (Default: 0.9)",
     )
     repeat_last_n: int = Field(
         64,
-        description="Sets how far back for the model to look back to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)",
+        description="Sets how far back for the model to look back to prevent repetition. "\
+            "(Default: 64, 0 = disabled, -1 = num_ctx)",
     )
     repeat_penalty: float = Field(
         1.1,
-        description="Sets how strongly to penalize repetitions. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. (Default: 1.1)",
+        description="Sets how strongly to penalize repetitions. "\
+            "A higher value (e.g., 1.5) will penalize repetitions more strongly, "\
+            "while a lower value (e.g., 0.9) will be more lenient. (Default: 1.1)",
     )
 
 
 class HuggingFaceSettings(BaseModel):
+    """ Hugging Face Settings """
     embedding_model: str = Field(
         None,
         description="Model to use. Example: 'nomic-embed-text'.",
     )
 
 class RedisSettings(BaseModel):
+    """ Redis Settings """
     host: str = Field(
         'localhost',
         description="FQDN for redis"
@@ -127,6 +149,7 @@ class RedisSettings(BaseModel):
 
 
 class LogSettings(BaseModel):
+    """ Logging Settings """
     chat_log_path: str = Field(
         "./chatlog.log",
         description="Path to store the chat logs"
@@ -134,6 +157,7 @@ class LogSettings(BaseModel):
 
 
 class ContentLoaderSettings(BaseModel):
+    """ Content Loader Settings """
     mode: Literal['google_drive', 'mock'] = Field(
         'google_drive',
         description="Type of Content Loader to use"
@@ -141,6 +165,7 @@ class ContentLoaderSettings(BaseModel):
 
 
 class TextSpliterSettings(BaseModel):
+    """ Text Splitter Settings """
     chunk_size: int = Field(
         500,
         description="Size of each chunk"
@@ -152,6 +177,7 @@ class TextSpliterSettings(BaseModel):
 
 
 class VectorStoreSettings(BaseModel):
+    """ Vector Store Settings """
     mode: Literal['chroma', 'mock'] = Field(
         'chroma',
         description="Type of vector store"
@@ -163,6 +189,7 @@ class VectorStoreSettings(BaseModel):
 
 
 class Settings(BaseModel):
+    """ Application Settings """
     slackbot: SlackBotSettings
     llm: LLMSettings
     ollama: OllamaSettings
@@ -176,18 +203,12 @@ class Settings(BaseModel):
     vectorstore: VectorStoreSettings
 
 
-"""
-This is visible just for DI or testing purposes.
-
-Use dependency injection or `settings()` method instead.
-"""
+# This is visible just for DI or testing purposes.
+# Use dependency injection or `settings()` method instead.
 unsafe_settings = load_active_settings()
 
-"""
-This is visible just for DI or testing purposes.
-
-Use dependency injection or `settings()` method instead.
-"""
+# This is visible just for DI or testing purposes.
+# Use dependency injection or `settings()` method instead.
 unsafe_typed_settings = Settings(**unsafe_settings)
 
 
@@ -199,6 +220,6 @@ def settings() -> Settings:
 
     For regular components use dependency injection instead.
     """
-    from knowledge_base_gpt.libs.injector.di import global_injector
+    from knowledge_base_gpt.libs.injector.di import global_injector  # pylint:disable=C0415
 
     return global_injector.get(Settings)
diff --git a/knowledge_base_gpt/libs/settings/settings_loader.py b/knowledge_base_gpt/libs/settings/settings_loader.py
index 01ca841..0e1dbe0 100644
--- a/knowledge_base_gpt/libs/settings/settings_loader.py
+++ b/knowledge_base_gpt/libs/settings/settings_loader.py
@@ -1,3 +1,4 @@
+""" Module for loading settings. """
 import functools
 import logging
 import os
@@ -29,11 +30,11 @@
 )
 
 
-def merge_settings(settings: Iterable[dict[str, Any]]) -> dict[str, Any]:
+def _merge_settings(settings: Iterable[dict[str, Any]]) -> dict[str, Any]:
     return functools.reduce(deep_update, settings, {})
 
 
-def load_settings_from_profile(profile: str) -> dict[str, Any]:
+def _load_settings_from_profile(profile: str) -> dict[str, Any]:
     if profile == "default":
         profile_file_name = "settings.yaml"
     else:
@@ -50,7 +51,7 @@ def load_active_settings() -> dict[str, Any]:
     """Load active profiles and merge them."""
     logger.info("Starting application with profiles=%s", active_profiles)
     loaded_profiles = [
-        load_settings_from_profile(profile) for profile in active_profiles
+        _load_settings_from_profile(profile) for profile in active_profiles
     ]
-    merged: dict[str, Any] = merge_settings(loaded_profiles)
+    merged: dict[str, Any] = _merge_settings(loaded_profiles)
     return merged
diff --git a/knowledge_base_gpt/libs/vectorstore/vectorstore.py b/knowledge_base_gpt/libs/vectorstore/vectorstore.py
index aeebfef..8171034 100644
--- a/knowledge_base_gpt/libs/vectorstore/vectorstore.py
+++ b/knowledge_base_gpt/libs/vectorstore/vectorstore.py
@@ -1,14 +1,15 @@
-from chromadb.config import Settings as ChromaSettings
+""" Module to create and abstract the actual vector store based on the configuration """
 from injector import inject, singleton
+from chromadb.config import Settings as ChromaSettings
 from langchain_community.vectorstores.chroma import Chroma
 
-
 from knowledge_base_gpt.libs.embedding.embedding import Embedding
 from knowledge_base_gpt.libs.settings.settings import Settings
 
 
 @singleton
-class VectorStore():
+class VectorStore():  # pylint:disable=R0903
+    """ Abstract the actual vector store based on the configuration """
     @inject
     def __init__(self, settings: Settings, embedding: Embedding) -> None:
         mode = settings.vectorstore.mode
@@ -21,8 +22,8 @@ def __init__(self, settings: Settings, embedding: Embedding) -> None:
                         is_persistent=True,
                         persist_directory=settings.vectorstore.persist_directory,
                         anonymized_telemetry=False
-                        )
                     )
+                )
             case 'mock':
                 pass
             case _:
@@ -30,4 +31,5 @@ def __init__(self, settings: Settings, embedding: Embedding) -> None:
 
     @property
     def db(self):
+        """ Return the Vector Store implementation """
         return self._db