import openai #from langchain.embeddings import HuggingFaceEmbeddings from langchain.embeddings import LocalAIEmbeddings import uuid import requests import ast import sys from contextlib import redirect_stdout from loguru import logger from ascii_magic import AsciiArt from duckduckgo_search import DDGS from typing import Dict, List, Optional import subprocess # these three lines swap the stdlib sqlite3 lib with the pysqlite3 package for chroma __import__('pysqlite3') import sys sys.modules['sqlite3'] = sys.modules.pop('pysqlite3') from langchain.vectorstores import Chroma from chromadb.config import Settings import json import os from io import StringIO # Parse arguments such as system prompt and batch mode import argparse parser = argparse.ArgumentParser(description='LocalAGI') # System prompt parser.add_argument('--system-prompt', dest='system_prompt', action='store', help='System prompt to use') # Batch mode parser.add_argument('--prompt', dest='prompt', action='store', default=False, help='Prompt mode') # Interactive mode parser.add_argument('--interactive', dest='interactive', action='store_true', default=False, help='Interactive mode. Can be used with --prompt to start an interactive session') # skip avatar creation parser.add_argument('--skip-avatar', dest='skip_avatar', action='store_true', default=False, help='Skip avatar creation') # Reevaluate parser.add_argument('--re-evaluate', dest='re_evaluate', action='store_true', default=False, help='Reevaluate if another action is needed or we have completed the user request') # Postprocess parser.add_argument('--postprocess', dest='postprocess', action='store_true', default=False, help='Postprocess the reasoning') # Subtask context parser.add_argument('--subtask-context', dest='subtaskContext', action='store_true', default=False, help='Include context in subtasks') # Search results number parser.add_argument('--search-results', dest='search_results', type=int, action='store', default=2, help='Number of search results to return') # Plan message parser.add_argument('--plan-message', dest='plan_message', action='store', help="What message to use during planning", ) DEFAULT_PROMPT="floating hair, portrait, ((loli)), ((one girl)), cute face, hidden hands, asymmetrical bangs, beautiful detailed eyes, eye shadow, hair ornament, ribbons, bowties, buttons, pleated skirt, (((masterpiece))), ((best quality)), colorful|((part of the head)), ((((mutated hands and fingers)))), deformed, blurry, bad anatomy, disfigured, poorly drawn face, mutation, mutated, extra limb, ugly, poorly drawn hands, missing limb, blurry, floating limbs, disconnected limbs, malformed hands, blur, out of focus, long neck, long body, Octane renderer, lowres, bad anatomy, bad hands, text" DEFAULT_API_BASE = os.environ.get("DEFAULT_API_BASE", "http://api:8080") # TTS api base parser.add_argument('--tts-api-base', dest='tts_api_base', action='store', default=DEFAULT_API_BASE, help='TTS api base') # LocalAI api base parser.add_argument('--localai-api-base', dest='localai_api_base', action='store', default=DEFAULT_API_BASE, help='LocalAI api base') # Images api base parser.add_argument('--images-api-base', dest='images_api_base', action='store', default=DEFAULT_API_BASE, help='Images api base') # Embeddings api base parser.add_argument('--embeddings-api-base', dest='embeddings_api_base', action='store', default=DEFAULT_API_BASE, help='Embeddings api base') # Functions model parser.add_argument('--functions-model', dest='functions_model', action='store', default="functions", help='Functions model') # Embeddings model parser.add_argument('--embeddings-model', dest='embeddings_model', action='store', default="all-MiniLM-L6-v2", help='Embeddings model') # LLM model parser.add_argument('--llm-model', dest='llm_model', action='store', default="gpt-4", help='LLM model') # Voice model parser.add_argument('--tts-model', dest='tts_model', action='store', default="en-us-kathleen-low.onnx", help='TTS model') # Stable diffusion model parser.add_argument('--stablediffusion-model', dest='stablediffusion_model', action='store', default="stablediffusion", help='Stable diffusion model') # Stable diffusion prompt parser.add_argument('--stablediffusion-prompt', dest='stablediffusion_prompt', action='store', default=DEFAULT_PROMPT, help='Stable diffusion prompt') # Parse arguments args = parser.parse_args() FUNCTIONS_MODEL = os.environ.get("FUNCTIONS_MODEL", args.functions_model) EMBEDDINGS_MODEL = os.environ.get("EMBEDDINGS_MODEL", args.embeddings_model) LLM_MODEL = os.environ.get("LLM_MODEL", args.llm_model) VOICE_MODEL= os.environ.get("TTS_MODEL",args.tts_model) STABLEDIFFUSION_MODEL = os.environ.get("STABLEDIFFUSION_MODEL",args.stablediffusion_model) STABLEDIFFUSION_PROMPT = os.environ.get("STABLEDIFFUSION_PROMPT", args.stablediffusion_prompt) PERSISTENT_DIR = os.environ.get("PERSISTENT_DIR", "/data") SYSTEM_PROMPT = "" if os.environ.get("SYSTEM_PROMPT") or args.system_prompt: SYSTEM_PROMPT = os.environ.get("SYSTEM_PROMPT", args.system_prompt) LOCALAI_API_BASE = args.localai_api_base TTS_API_BASE = args.tts_api_base IMAGE_API_BASE = args.images_api_base EMBEDDINGS_API_BASE = args.embeddings_api_base ## Constants REPLY_ACTION = "reply" PLAN_ACTION = "plan" embeddings = LocalAIEmbeddings(model=EMBEDDINGS_MODEL,openai_api_base=EMBEDDINGS_API_BASE) chroma_client = Chroma(collection_name="memories", persist_directory="db", embedding_function=embeddings) # Function to create images with LocalAI def display_avatar(input_text=STABLEDIFFUSION_PROMPT, model=STABLEDIFFUSION_MODEL): response = openai.Image.create( prompt=input_text, n=1, size="128x128", api_base=IMAGE_API_BASE+"/v1" ) image_url = response['data'][0]['url'] # convert the image to ascii art my_art = AsciiArt.from_url(image_url) my_art.to_terminal() # Function to create audio with LocalAI def tts(input_text, model=VOICE_MODEL): # strip newlines from text input_text = input_text.replace("\n", ".") # Create a temp file to store the audio output output_file_path = '/tmp/output.wav' # get from OPENAI_API_BASE env var url = TTS_API_BASE + '/tts' headers = {'Content-Type': 'application/json'} data = { "input": input_text, "model": model } response = requests.post(url, headers=headers, data=json.dumps(data)) if response.status_code == 200: with open(output_file_path, 'wb') as f: f.write(response.content) logger.info('Audio file saved successfully:', output_file_path) else: logger.info('Request failed with status code', response.status_code) try: # Use aplay to play the audio os.system('aplay ' + output_file_path) # remove the audio file os.remove(output_file_path) except: logger.info('Unable to play audio') # Function to analyze the user input and pick the next action to do def needs_to_do_action(user_input,agent_actions={}): # Get the descriptions and the actions name (the keys) descriptions=action_description("", agent_actions) messages = [ {"role": "user", "content": f"""Transcript of AI assistant responding to user requests. Replies with the action to perform and the reasoning. {descriptions}"""}, {"role": "user", "content": f"""{user_input} Function call: """ } ] functions = [ { "name": "intent", "description": """Decide to do an action.""", "parameters": { "type": "object", "properties": { "confidence": { "type": "number", "description": "confidence of the action" }, "reasoning": { "type": "string", "description": "reasoning behind the intent" }, # "observation": { # "type": "string", # "description": "reasoning behind the intent" # }, "action": { "type": "string", "enum": list(agent_actions.keys()), "description": "user intent" }, }, "required": ["action"] } }, ] response = openai.ChatCompletion.create( #model="gpt-3.5-turbo", model=FUNCTIONS_MODEL, messages=messages, request_timeout=1200, functions=functions, api_base=LOCALAI_API_BASE+"/v1", stop=None, temperature=0.1, #function_call="auto" function_call={"name": "intent"}, ) response_message = response["choices"][0]["message"] if response_message.get("function_call"): function_name = response.choices[0].message["function_call"].name function_parameters = response.choices[0].message["function_call"].arguments # read the json from the string res = json.loads(function_parameters) logger.info(">>> function name: "+function_name) logger.info(">>> function parameters: "+function_parameters) return res return {"action": REPLY_ACTION} # This is used to collect the descriptions of the agent actions, used to populate the LLM prompt def action_description(action, agent_actions): descriptions="" # generate descriptions of actions that the agent can pick for a in agent_actions: if ( action != "" and action == a ) or (action == ""): descriptions+=agent_actions[a]["description"]+"\n" return descriptions ## This function is called to ask the user if does agree on the action to take and execute def ask_user_confirmation(action_name, action_parameters): logger.info("==> Ask user confirmation") logger.info("==> action_name: {action_name}", action_name=action_name) logger.info("==> action_parameters: {action_parameters}", action_parameters=action_parameters) # Ask via stdin logger.info("==> Do you want to execute the action? (y/n)") user_input = input() if user_input == "y": logger.info("==> Executing action") return True else: logger.info("==> Skipping action") return False ### This function is used to process the functions given a user input. ### It picks a function, executes it and returns the list of messages containing the result. def process_functions(user_input, action="", agent_actions={}): descriptions=action_description(action, agent_actions) messages = [ # {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": f"""Transcript of AI assistant responding to user requests. Replies with the action to perform, including reasoning, and the confidence interval from 0 to 100. {descriptions}"""}, {"role": "user", "content": f"""{user_input} Function call: """ } ] response = function_completion(messages, action=action,agent_actions=agent_actions) response_message = response["choices"][0]["message"] response_result = "" function_result = {} if response_message.get("function_call"): function_name = response.choices[0].message["function_call"].name function_parameters = response.choices[0].message["function_call"].arguments logger.info("==> function parameters: {function_parameters}",function_parameters=function_parameters) function_to_call = agent_actions[function_name]["function"] function_result = function_to_call(function_parameters, agent_actions=agent_actions) logger.info("==> function result: {function_result}", function_result=function_result) messages.append( { "role": "assistant", "content": None, "function_call": {"name": function_name, "arguments": function_parameters,}, } ) messages.append( { "role": "function", "name": function_name, "content": str(function_result) } ) return messages, function_result ### function_completion is used to autocomplete functions given a list of messages def function_completion(messages, action="", agent_actions={}): function_call = "auto" if action != "": function_call={"name": action} logger.info("==> function name: {function_call}", function_call=function_call) # get the functions from the signatures of the agent actions, if exists functions = [] for action in agent_actions: if agent_actions[action].get("signature"): functions.append(agent_actions[action]["signature"]) response = openai.ChatCompletion.create( #model="gpt-3.5-turbo", model=FUNCTIONS_MODEL, messages=messages, functions=functions, request_timeout=1200, stop=None, api_base=LOCALAI_API_BASE+"/v1", temperature=0.1, function_call=function_call ) return response # Rework the content of each message in the history in a way that is understandable by the LLM # TODO: switch to templates (?) def process_history(conversation_history): messages = "" for message in conversation_history: # if there is content append it if message.get("content") and message["role"] == "function": messages+="Function result: \n" + message["content"]+"\n" elif message.get("function_call"): # encode message["function_call" to json and appends it fcall = json.dumps(message["function_call"]) parameters = "calling " + message["function_call"]["name"]+" with arguments:" args=json.loads(message["function_call"]["arguments"]) for arg in args: logger.debug(arg) logger.debug(args) v=args[arg] parameters+=f""" {arg}=\"{v}\"""" messages+= parameters+"\n" elif message.get("content") and message["role"] == "user": messages+=message["content"]+"\n" elif message.get("content") and message["role"] == "assistant": messages+="Assistant message: "+message["content"]+"\n" return messages def converse(responses): response = openai.ChatCompletion.create( model=LLM_MODEL, messages=responses, stop=None, api_base=LOCALAI_API_BASE+"/v1", request_timeout=1200, temperature=0.1, ) responses.append( { "role": "assistant", "content": response.choices[0].message["content"], } ) return responses ### Fine tune a string before feeding into the LLM def analyze(responses, prefix="Analyze the following text highlighting the relevant information and identify a list of actions to take if there are any. If there are errors, suggest solutions to fix them"): string = process_history(responses) messages = [ { "role": "user", "content": f"""{prefix}: ``` {string} ``` """, } ] response = openai.ChatCompletion.create( model=LLM_MODEL, messages=messages, stop=None, api_base=LOCALAI_API_BASE+"/v1", request_timeout=1200, temperature=0.1, ) return response.choices[0].message["content"] def post_process(string): messages = [ { "role": "user", "content": f"""Summarize the following text, keeping the relevant information: ``` {string} ``` """, } ] logger.info("==> Post processing: {string}", string=string) # get the response from the model response = openai.ChatCompletion.create( model=LLM_MODEL, messages=messages, api_base=LOCALAI_API_BASE+"/v1", stop=None, temperature=0.1, request_timeout=1200, ) result = response["choices"][0]["message"]["content"] logger.info("==> Processed: {string}", string=result) return result ### Agent capabilities ### These functions are called by the agent to perform actions ### def save(memory, agent_actions={}): q = json.loads(memory) logger.info(">>> saving to memories: ") logger.info(q["content"]) chroma_client.add_texts([q["content"]],[{"id": str(uuid.uuid4())}]) chroma_client.persist() return f"The object was saved permanently to memory." def search_memory(query, agent_actions={}): q = json.loads(query) docs = chroma_client.similarity_search(q["reasoning"]) text_res="Memories found in the database:\n" for doc in docs: text_res+="- "+doc.page_content+"\n" #if args.postprocess: # return post_process(text_res) #return text_res return post_process(text_res) def generate_plan(user_input, agent_actions={}): res = json.loads(user_input) logger.info("--> Calculating plan: {description}", description=res["description"]) descriptions=action_description("",agent_actions) plan_message = "The assistant replies with a plan to answer the request with a list of subtasks with logical steps. The reasoning includes a self-contained, detailed and descriptive instruction to fullfill the task." if args.plan_message: plan_message = args.plan_message # plan_message = "The assistant replies with a plan of 3 steps to answer the request with a list of subtasks with logical steps. The reasoning includes a self-contained, detailed and descriptive instruction to fullfill the task." messages = [ {"role": "user", "content": f"""Transcript of AI assistant responding to user requests. {descriptions} Request: {plan_message} Thought: {res["description"]} Function call: """ } ] # get list of plannable actions plannable_actions = [] for action in agent_actions: if agent_actions[action]["plannable"]: # append the key of the dict to plannable_actions plannable_actions.append(action) functions = [ { "name": "plan", "description": """Decide to do an action.""", "parameters": { "type": "object", "properties": { "subtasks": { "type": "array", "items": { "type": "object", "properties": { "reasoning": { "type": "string", "description": "subtask list", }, "function": { "type": "string", "enum": plannable_actions, }, }, }, }, }, "required": ["subtasks"] } }, ] response = openai.ChatCompletion.create( #model="gpt-3.5-turbo", model=FUNCTIONS_MODEL, messages=messages, functions=functions, api_base=LOCALAI_API_BASE+"/v1", stop=None, temperature=0.1, #function_call="auto" function_call={"name": "plan"}, ) response_message = response["choices"][0]["message"] if response_message.get("function_call"): function_name = response.choices[0].message["function_call"].name function_parameters = response.choices[0].message["function_call"].arguments # read the json from the string res = json.loads(function_parameters) logger.info("<<< function name: {function_name} >>>> parameters: {parameters}", function_name=function_name,parameters=function_parameters) return res return {"action": REPLY_ACTION} # write file to disk with content def write_file(arg, agent_actions={}): arg = json.loads(arg) filename = arg["filename"] content = arg["content"] # create persistent dir if does not exist if not os.path.exists(PERSISTENT_DIR): os.makedirs(PERSISTENT_DIR) # write the file in the directory specified filename = os.path.join(PERSISTENT_DIR, filename) with open(filename, 'w') as f: f.write(content) return f"File {filename} saved successfully." def ddg(query: str, num_results: int, backend: str = "api") -> List[Dict[str, str]]: """Run query through DuckDuckGo and return metadata. Args: query: The query to search for. num_results: The number of results to return. Returns: A list of dictionaries with the following keys: snippet - The description of the result. title - The title of the result. link - The link to the result. """ with DDGS() as ddgs: results = ddgs.text( query, backend=backend, ) if results is None: return [{"Result": "No good DuckDuckGo Search Result was found"}] def to_metadata(result: Dict) -> Dict[str, str]: if backend == "news": return { "date": result["date"], "title": result["title"], "snippet": result["body"], "source": result["source"], "link": result["url"], } return { "snippet": result["body"], "title": result["title"], "link": result["href"], } formatted_results = [] for i, res in enumerate(results, 1): if res is not None: formatted_results.append(to_metadata(res)) if len(formatted_results) == num_results: break return formatted_results ## Search on duckduckgo def search_duckduckgo(a, agent_actions={}): a = json.loads(a) list=ddg(a["query"], args.search_results) text_res="" for doc in list: text_res+=f"""- {doc["snippet"]}. Source: {doc["title"]} - {doc["link"]}\n""" #if args.postprocess: # return post_process(text_res) return text_res #l = json.dumps(list) #return l ### End Agent capabilities ### ### Main evaluate function ### This function evaluates the user input and the conversation history. ### It returns the conversation history with the latest response from the assistant. def evaluate(user_input, conversation_history = [],re_evaluate=False, agent_actions={},re_evaluation_in_progress=False, postprocess=False, subtaskContext=False, processed_messages=0): messages = [ { "role": "user", "content": user_input, } ] conversation_history.extend(messages) # pulling the old history make the context grow exponentially # and most importantly it repeates the first message with the commands again and again. # it needs a bit of cleanup and process the messages and piggyback more LocalAI functions templates # old_history = process_history(conversation_history) # action_picker_message = "Conversation history:\n"+old_history # action_picker_message += "\n" action_picker_message = "Request: "+user_input #if re_evaluate and not re_evaluation_in_progress: # observation = analyze(conversation_history, prefix=True) # action_picker_message+="\n\Thought: "+observation[-1]["content"] if re_evaluation_in_progress: observation = analyze(conversation_history) action_picker_message="Decide from the output below if we have to do another action:\n" action_picker_message+="```\n"+user_input+"\n```" action_picker_message+="\n\nObservation: "+observation # if there is no action to do, we can just reply to the user with REPLY_ACTION try: action = needs_to_do_action(action_picker_message,agent_actions=agent_actions) except Exception as e: logger.error("==> error: ") logger.error(e) action = {"action": REPLY_ACTION} if action["action"] != REPLY_ACTION: logger.info("==> LocalAGI wants to call '{action}'", action=action["action"]) #logger.info("==> Observation '{reasoning}'", reasoning=action["observation"]) logger.info("==> Reasoning '{reasoning}'", reasoning=action["reasoning"]) reasoning = action["reasoning"] if action["action"] == PLAN_ACTION: logger.info("==> LocalAGI wants to create a plan that involves more actions ") #if postprocess: #reasoning = post_process(reasoning) function_completion_message="" if processed_messages > 0: function_completion_message += process_history(conversation_history)+"\n" function_completion_message += "Request: "+user_input+"\nReasoning: "+reasoning responses, function_results = process_functions(function_completion_message, action=action["action"], agent_actions=agent_actions) # if there are no subtasks, we can just reply, # otherwise we execute the subtasks # First we check if it's an object if isinstance(function_results, dict) and function_results.get("subtasks") and len(function_results["subtasks"]) > 0: # cycle subtasks and execute functions subtask_result="" for subtask in function_results["subtasks"]: #ctr="Context: "+user_input+"\nThought: "+action["reasoning"]+ "\nRequest: "+subtask["reasoning"] #cr="Request: "+user_input+"\n" cr="" if subtask_result != "" and subtaskContext: # Include cumulative results of previous subtasks # TODO: this grows context, maybe we should use a different approach or summarize ##if postprocess: ## cr+= "Subtask results: "+post_process(subtask_result)+"\n" ##else: cr+="\n"+subtask_result+"\n" subtask_reasoning = subtask["reasoning"] cr+="Reasoning: "+action["reasoning"]+ "\n" cr+="\nFunction to call:" +subtask["function"]+"\n" logger.info("==> subtask '{subtask}' ({reasoning})", subtask=subtask["function"], reasoning=subtask_reasoning) if postprocess: cr+= "Assistant: "+post_process(subtask_reasoning) else: cr+= "Assistant: "+subtask_reasoning subtask_response, function_results = process_functions(cr, subtask["function"],agent_actions=agent_actions) subtask_result+=str(function_results)+"\n" # if postprocess: # subtask_result=post_process(subtask_result) responses.append(subtask_response[-1]) if re_evaluate: ## Better output or this infinite loops.. logger.info("-> Re-evaluate if another action is needed") ## ? conversation history should go after the user_input maybe? re_eval = "" # This is probably not needed as already in the history: #re_eval = user_input +"\n" #re_eval += "Conversation history: \n" if postprocess: re_eval+= post_process(process_history(responses[1:])) +"\n" else: re_eval+= process_history(responses[1:]) +"\n" responses = evaluate(re_eval, responses, re_evaluate,agent_actions=agent_actions,re_evaluation_in_progress=True) if re_evaluation_in_progress: conversation_history.extend(responses) return conversation_history # TODO: this needs to be optimized responses = analyze(responses, prefix=f"You are an AI assistant. Return an appropriate answer to the user input '{user_input}' given the context below and summarizing the actions taken\n") # add responses to conversation history by extending the list conversation_history.append( { "role": "assistant", "content": responses, } ) # logger.info the latest response from the conversation history logger.info(conversation_history[-1]["content"]) tts(conversation_history[-1]["content"]) else: logger.info("==> no action needed") if re_evaluation_in_progress: logger.info("==> LocalAGI has completed the user request") logger.info("==> LocalAGI will reply to the user") return conversation_history # get the response from the model response = converse(conversation_history) # add the response to the conversation history by extending the list conversation_history.append(response) # logger.info the latest response from the conversation history logger.info(conversation_history[-1]["content"]) tts(conversation_history[-1]["content"]) return conversation_history ### Agent action definitions agent_actions = { "search_internet": { "function": search_duckduckgo, "plannable": True, "description": 'For searching the internet with a query, the assistant replies with the action "search_internet" and the query to search.', "signature": { "name": "search_internet", "description": """For searching internet.""", "parameters": { "type": "object", "properties": { "query": { "type": "string", "description": "information to save" }, }, } }, }, "write_file": { "function": write_file, "plannable": True, "description": 'The assistant replies with the action "write_file", the filename and content to save for writing a file to disk permanently. This can be used to store the result of complex actions locally.', "signature": { "name": "write_file", "description": """For saving a file to disk with content.""", "parameters": { "type": "object", "properties": { "filename": { "type": "string", "description": "information to save" }, "content": { "type": "string", "description": "information to save" }, }, } }, }, "save_memory": { "function": save, "plannable": True, "description": 'The assistant replies with the action "save_memory" and the string to remember or store an information that thinks it is relevant permanently.', "signature": { "name": "save_memory", "description": """Save or store informations into memory.""", "parameters": { "type": "object", "properties": { "content": { "type": "string", "description": "information to save" }, }, "required": ["content"] } }, }, "search_memory": { "function": search_memory, "plannable": True, "description": 'The assistant replies with the action "search_memory" for searching between its memories with a query term.', "signature": { "name": "search_memory", "description": """Search in memory""", "parameters": { "type": "object", "properties": { "reasoning": { "type": "string", "description": "reasoning behind the intent" }, }, "required": ["reasoning"] } }, }, PLAN_ACTION: { "function": generate_plan, "plannable": False, "description": 'The assistant for solving complex tasks that involves calling more functions in sequence, replies with the action "'+PLAN_ACTION+'".', "signature": { "name": PLAN_ACTION, "description": """Plan complex tasks.""", "parameters": { "type": "object", "properties": { "description": { "type": "string", "description": "reasoning behind the planning" }, }, "required": ["description"] } }, }, REPLY_ACTION: { "function": None, "plannable": False, "description": 'For replying to the user, the assistant replies with the action "'+REPLY_ACTION+'" and the reply to the user directly when there is nothing to do.', }, } conversation_history = [] # Set a system prompt if SYSTEM_PROMPT is set if SYSTEM_PROMPT != "": conversation_history.append({ "role": "system", "content": SYSTEM_PROMPT }) logger.info("Welcome to LocalAGI") # Skip avatar creation if --skip-avatar is set if not args.skip_avatar: logger.info("Creating avatar, please wait...") display_avatar() if not args.prompt: actions = "" for action in agent_actions: actions+=" '"+action+"'" logger.info("LocalAGI internally can do the following actions:{actions}", actions=actions) if not args.prompt: logger.info(">>> Interactive mode <<<") else: logger.info(">>> Prompt mode <<<") logger.info(args.prompt) processed_messages = 0 # IF in prompt mode just evaluate, otherwise loop if args.prompt: conversation_history=evaluate( args.prompt, conversation_history, re_evaluate=args.re_evaluate, agent_actions=agent_actions, # Enable to lower context usage but increases LLM calls postprocess=args.postprocess, subtaskContext=args.subtaskContext, processed_messages=processed_messages, ) processed_messages+=1 if not args.prompt or args.interactive: # TODO: process functions also considering the conversation history? conversation history + input logger.info(">>> Ready! What can I do for you? ( try with: plan a roadtrip to San Francisco ) <<<") while True: user_input = input(">>> ") # we are going to use the args to change the evaluation behavior conversation_history=evaluate( user_input, conversation_history, re_evaluate=args.re_evaluate, agent_actions=agent_actions, # Enable to lower context usage but increases LLM calls postprocess=args.postprocess, subtaskContext=args.subtaskContext, processed_messages=processed_messages, ) processed_messages+=1