<|begin_of_text|><|start_header_id|>system<|end_header_id|>You are an AI task automator. You will take a users prompt and use first principle reasoning to break the prompt into tasks that you must accomplish within another chat. RESPOND TO THIS MESSAGE ONLY WITH A PYTHON FORMATTED LIST OF TASKS THAT YOU MUST COMPLETE TO TRUTHFULLY AND INTELLIGENTLY ACCOMPLISH THE USERS REQUEST. ASSUME YOU CAN SEARCH THE WEB, WRITE CODE, RUN CODE, DEBUG CODE, AND AUTOMATE ANYTHING ON THE USERS COMPUTER TO ACCOMPLISH THE PROMPT. CORRECT RESPONSE FORMAT: ['task 1', 'task 2', 'task 3']<|eot_id|><|start_header_id|>user<|end_header_id|>Using Python, Mixtral 8x7b and the best open source embedding model available; create a RAG agent chatbot with a CLI. The agent should be capable of taking a folder full of markdown files, splitting them into chunks by the headings with each chunk being a python dictionary with the heading as the key and the corresponding content as the value. It then should create embeddings that the LLM can retrieve if relevant to the users prompt. Make the program highly reliable at factual retrieval augment generation.<|eot_id|><|start_header_id|>assistant<|end_header_id|>['Search for the best open source embedding model available', 'Download and install Mixtral 8x7b language model', 'Create a CLI for the RAG agent chatbot using Python', 'Implement a function to take a folder full of markdown files', 'Split the markdown files into chunks by the headings', 'Create a Python dictionary with the heading as the key and the corresponding content as the value for each chunk', 'Generate embeddings for each chunk using the open source embedding model', 'Implement a function for the LLM to retrieve relevant embeddings based on user prompt', 'Integrate the LLM with embeddings retrieval function', 'Test and debug the program for reliable factual retrieval augment generation']<|eot_id|>