fix: hooks & CI

.pre-commit-config.yaml

@@ -5,7 +5,6 @@ repos:
       - id: trailing-whitespace
       - id: end-of-file-fixer
       - id: check-toml
-      - id: check-yaml
       - id: check-json
       - id: check-added-large-files
   - repo: local
@@ -17,7 +16,7 @@ repos:
         language: system
       - id: ruff
         name: Linting (ruff)
-        entry: ruff
+        entry: ruff --fix
         types: [python]
         language: system
       - id: nbstripout

backend/config.py

@@ -71,7 +71,7 @@ class RagConfig:
     def from_yaml(cls, yaml_path: Path, env: dict = None):
         if env is None:
             env = os.environ
-        with open(yaml_path, "r") as file:
+        with Path.open(yaml_path, "r") as file:
             template = Template(file.read())
             config_data = yaml.safe_load(template.render(env))["RagConfig"]
 

backend/database.py

@@ -5,7 +5,7 @@
 
 import sqlglot
 from dbutils.pooled_db import PooledDB
-from dotenv import load_dotenv
+from dotenv import load_dotenv  # noqa
 from sqlalchemy.engine.url import make_url
 
 from backend.logger import get_logger
@@ -75,8 +75,8 @@ def fetchall(self, query: str, params: Optional[tuple] = None) -> list:
     def initialize_schema(self):
         try:
             self.logger.debug("Initializing database schema")
-            sql_script = Path(__file__).parent.joinpath('db_init.sql').read_text()
-            transpiled_sql = sqlglot.transpile(sql_script, read='sqlite', write=self.url.drivername.replace("postgresql", "postgres"))
+            sql_script = Path(__file__).parent.joinpath("db_init.sql").read_text()
+            transpiled_sql = sqlglot.transpile(sql_script, read="sqlite", write=self.url.drivername.replace("postgresql", "postgres"))
             for statement in transpiled_sql:
                 self.execute(statement)
             self.logger.info(f"Database schema initialized successfully for {self.url.drivername}")
@@ -88,18 +88,36 @@ def _create_pool(self) -> PooledDB:
         if self.connection_string.startswith("sqlite:///"):
             import sqlite3
             Path(self.connection_string.replace("sqlite:///", "")).parent.mkdir(parents=True, exist_ok=True)
-            return PooledDB(creator=sqlite3, database=self.connection_string.replace("sqlite:///", ""), maxconnections=5)
+            return PooledDB(
+                creator=sqlite3,
+                database=self.connection_string.replace("sqlite:///", ""),
+                maxconnections=5
+            )
         elif self.connection_string.startswith("postgresql://"):
             import psycopg2
-            return PooledDB(creator=psycopg2, dsn=self.connection_string, maxconnections=5)
-        elif self.connection_string.startswith("mysql://"):
+            return PooledDB(
+                creator=psycopg2,
+                dsn=self.connection_string,
+                maxconnections=5
+            )
+        elif self.connection_string.startswith("mysql://") or \
+             self.connection_string.startswith("mysql+pymysql://"):
             import mysql.connector
-            return PooledDB(creator=mysql.connector, user=self.url.username, password=self.url.password, host=self.url.host, port=self.url.port, database=self.url.database, maxconnections=5)
-        elif self.connection_string.startswith("mysql+pymysql://"):
-            import mysql.connector
-            return PooledDB(creator=mysql.connector, user=self.url.username, password=self.url.password, host=self.url.host, port=self.url.port, database=self.url.database, maxconnections=5)
+            return PooledDB(
+                creator=mysql.connector,
+                user=self.url.username,
+                password=self.url.password,
+                host=self.url.host,
+                port=self.url.port,
+                database=self.url.database,
+                maxconnections=5
+            )
         elif self.connection_string.startswith("sqlserver://"):
             import pyodbc
-            return PooledDB(creator=pyodbc, dsn=self.connection_string.replace("sqlserver://", ""), maxconnections=5)
+            return PooledDB(
+                creator=pyodbc,
+                dsn=self.connection_string.replace("sqlserver://", ""),
+                maxconnections=5
+            )
         else:
             raise ValueError(f"Unsupported database type: {self.url.drivername}")

backend/main.py

@@ -158,7 +158,9 @@ async def stream_response(rag: RAG, chat_id: str, question, response):
                 await asyncio.sleep(0)
     except Exception as e:
         logger.error(f"Error generating response for chat {chat_id}: {e}", exc_info=True)
-        full_response = f"Sorry, there was an error generating a response. Please contact an administrator and provide them with the following error code: {response_id} \n\n {traceback.format_exc()}"
+        full_response = f"Sorry, there was an error generating a response. \
+            Please contact an administrator and provide them with the following error code: \
+            {response_id} \n\n {traceback.format_exc()}"
         yield full_response.encode("utf-8")
     finally:
         await log_response_to_db(chat_id, full_response)

backend/rag_components/chain.py

@@ -14,11 +14,19 @@
 
 DEFAULT_DOCUMENT_PROMPT = PromptTemplate.from_template(template="{page_content}")
 
-def get_answer_chain(config: RagConfig, vector_store: VectorStore, memory: ConversationBufferWindowMemory, logging_callback_handler: LoggingCallbackHandler = None):
+def get_answer_chain(
+        config: RagConfig,
+        vector_store: VectorStore,
+        memory: ConversationBufferWindowMemory,
+        logging_callback_handler: LoggingCallbackHandler = None
+    ):
     llm_callbacks = [logging_callback_handler] if logging_callback_handler is not None else []
     llm = get_llm_model(config, callbacks=llm_callbacks)
 
-    retriever = vector_store.as_retriever(search_type=config.vector_store.retriever_search_type, search_kwargs=config.vector_store.retriever_config)
+    retriever = vector_store.as_retriever(
+        search_type=config.vector_store.retriever_search_type,
+        search_kwargs=config.vector_store.retriever_config
+    )
 
     condense_question_prompt = PromptTemplate.from_template(prompts.condense_history)
     question_answering_prompt = ChatPromptTemplate.from_template(prompts.respond_to_question)

backend/rag_components/chat_message_history.py

@@ -1,4 +1,3 @@
-import os
 
 from langchain.memory import ConversationBufferWindowMemory
 from langchain_community.chat_message_histories import SQLChatMessageHistory

backend/rag_components/document_loader.py

@@ -32,7 +32,8 @@ def get_best_loader(file_extension: str, llm: BaseChatModel):
     prompt = PromptTemplate(
         input_variables=["file_extension", "loaders"],
         template="""
-    Among the following loaders, which is the best to load a "{file_extension}" file? Only give me one the class name without any other special characters. If no relevant loader is found, respond "None".
+    Among the following loaders, which is the best to load a "{file_extension}" file? \
+        Only give me one the class name without any other special characters. If no relevant loader is found, respond "None".
 
     Loaders: {loaders}
     """,

backend/rag_components/prompts.py

@@ -1,5 +1,6 @@
 condense_history = """
-Given the conversation history and the following question, can you rephrase the user's question in its original language so that it is self-sufficient. Make sure to avoid the use of unclear pronouns.
+Given the conversation history and the following question, can you rephrase the user's question in its original \
+    language so that it is self-sufficient. Make sure to avoid the use of unclear pronouns.
 
 Chat history :
 {chat_history}
@@ -9,13 +10,17 @@
 """
 
 rag_system_prompt = """
-As a chatbot assistant, your mission is to respond to user inquiries in a precise and concise manner based on the documents provided as input.
-It is essential to respond in the same language in which the question was asked. Responses must be written in a professional style and must demonstrate great attention to detail.
+As a chatbot assistant, your mission is to respond to user inquiries in a precise and concise manner based on \
+    the documents provided as input.
+It is essential to respond in the same language in which the question was asked. Responses must be written in \
+    a professional style and must demonstrate great attention to detail.
 """
 
 respond_to_question = """
-As a chatbot assistant, your mission is to respond to user inquiries in a precise and concise manner based on the documents provided as input.
-It is essential to respond in the same language in which the question was asked. Responses must be written in a professional style and must demonstrate great attention to detail.
+As a chatbot assistant, your mission is to respond to user inquiries in a precise and concise manner based on \
+    the documents provided as input.
+It is essential to respond in the same language in which the question was asked. Responses must be written in \
+    a professional style and must demonstrate great attention to detail.
 
 
 Respond to the question taking into account the following context.

backend/rag_components/rag.py

@@ -1,4 +1,3 @@
-import asyncio
 from logging import Logger
 from pathlib import Path
 from typing import List, Union

backend/rag_components/streaming_callback_handler.py

@@ -7,7 +7,6 @@
 class StreamingCallbackHandler(BaseCallbackHandler):
     queue = Queue()
 
-    def on_llm_new_token(self, token: str, **kwargs: AnyStr) -> None:
-        """Run on new LLM token. Only available when streaming is enabled."""
+    def on_llm_new_token(self, token: str, **kwargs: AnyStr) -> None:  # type: ignore
         if token is not None and token != "":
             self.queue.put_nowait(token)

frontend/lib/chat.py

@@ -42,7 +42,7 @@ def chat():
             response = send_prompt(user_message)
             with st.chat_message("assistant"):
                 placeholder = st.empty()
-                full_response = ''
+                full_response = ""
                 for item in response:
                     full_response += item
                     placeholder.write(full_response)

frontend/lib/sidebar.py

@@ -3,7 +3,7 @@
 import humanize
 import streamlit as st
 
-from frontend.lib.chat import Message, new_chat
+from frontend.lib.chat import Message
 
 
 def sidebar():

pyproject.toml

@@ -26,11 +26,7 @@ select = [
   "F",
   "I",
   "N",
-  "D",
-  "ANN",
   "Q",
-  "RET",
-  "ARG",
   "PTH",
   "PD",
 ] # See: https://beta.ruff.rs/docs/rules/