linter

app.py

@@ -1,50 +1,62 @@
+import asyncio
 import logging
 import os
+
 import yaml
-import asyncio
 from browser_use import Agent
-from langchain_openai import ChatOpenAI, OpenAIEmbeddings
-from langchain_community.document_loaders import DirectoryLoader
-from langchain_text_splitters import RecursiveCharacterTextSplitter
-from langchain_community.vectorstores import FAISS
-from langchain.memory import ConversationBufferMemory
 from dotenv import load_dotenv
+from langchain.memory import ConversationBufferMemory
+from langchain_community.document_loaders import DirectoryLoader
+from langchain_community.vectorstores import FAISS
+from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+from langchain_text_splitters import RecursiveCharacterTextSplitter
 
 load_dotenv()
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logging.basicConfig(
+    level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s'
+)
 
 with open('config.yaml') as f:
     config = yaml.safe_load(f)
 
+
 def load_documents():
     loader = DirectoryLoader(config['documents_dir'], glob="**/*.txt")
     docs = loader.load()
     text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
     return text_splitter.split_documents(docs)
 
+
 def setup_rag():
     docs = load_documents()
     vectorstore = FAISS.from_documents(docs, OpenAIEmbeddings())
     return vectorstore.as_retriever()
 
+
 memory = ConversationBufferMemory()
 
+
 def generate_response(query):
     retriever = setup_rag()
     relevant_docs = retriever.get_relevant_documents(query)
     context = "\n\n".join([doc.page_content for doc in relevant_docs])
-    
+
     memory.save_context({"input": query}, {"output": ""})
     history = memory.load_memory_variables({})
-    
+
-    llm = ChatOpenAI(model=config['deepseek']['model'], api_key=config['deepseek']['api_key'])
+    llm = ChatOpenAI(
-    response = llm.invoke(f"Context: {context}\n\nHistory: {history}\n\nQuestion: {query}")
+        model=config['deepseek']['model'], api_key=config['deepseek']['api_key']
+    )
+    response = llm.invoke(
+        f"Context: {context}\n\nHistory: {history}\n\nQuestion: {query}"
+    )
     return f"{config['bot_disclaimer']}\n\n{response.content}"
 
+
 async def process_messages():
     agent = Agent(
         llm=ChatOpenAI(model="gpt-4o"),
-        task="Log in to LinkedIn, go to messages, check for new messages, and reply based on the provided knowledge base."
+        task="Log in to LinkedIn, go to messages, check for new messages, and reply based on the provided knowledge base.",
     )
     while True:
         try:
@@ -55,7 +67,7 @@ async def process_messages():
             logging.error(f"Error: {str(e)}")
             time.sleep(300)
 
+
 if __name__ == "__main__":
     logging.info("Starting bot...")
     asyncio.run(process_messages())
-