init

.gitignore

@@ -0,0 +1,10 @@
+# Python-generated files
+__pycache__/
+*.py[oc]
+build/
+dist/
+wheels/
+*.egg-info
+
+# Virtual environments
+.venv

.python-version

@@ -0,0 +1 @@
+3.8

README.md

@@ -24,13 +24,13 @@ Automatically respond to LinkedIn messages from recruiters using AI. This bot le
 
 1. **Clone the Repository**:
    ```bash
-   git clone https://github.com/your-username/automatic-linkedin-answer-ai.git
+   git clone https://github.com/iklobato/automatic-linkedin-answer-ai.git
    cd automatic-linkedin-answer-ai
    ```
 
 2. **Install Dependencies**:
    ```bash
-   pip install -r requirements.txt
+   uv install 
    ```
 
 3. **Set Up Environment Variables**:

app.py

@@ -0,0 +1,61 @@
+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
+
+load_dotenv()
+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'])
+    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."
+    )
+    while True:
+        try:
+            result = await agent.run()
+            logging.info(f"Agent result: {result}")
+            time.sleep(60)
+        except Exception as e:
+            logging.error(f"Error: {str(e)}")
+            time.sleep(300)
+
+if __name__ == "__main__":
+    logging.info("Starting bot...")
+    asyncio.run(process_messages())
+

pyproject.toml

@@ -0,0 +1,7 @@
+[project]
+name = "automatic-linkedin-answer-ai"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.8"
+dependencies = []

uv.lock

@@ -0,0 +1,8 @@
+version = 1
+revision = 1
+requires-python = ">=3.8"
+
+[[package]]
+name = "automatic-linkedin-answer-ai"
+version = "0.1.0"
+source = { virtual = "." }