import openai #from langchain.embeddings import HuggingFaceEmbeddings from langchain.embeddings import LocalAIEmbeddings import uuid import requests import sys from loguru import logger from ascii_magic import AsciiArt from duckduckgo_search import DDGS from typing import Dict, List, Optional logger.add(sys.stderr, format="{time} {level} {message}", filter="miniAGI", level="INFO") # 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 FUNCTIONS_MODEL = os.environ.get("FUNCTIONS_MODEL", "functions") LLM_MODEL = os.environ.get("LLM_MODEL", "gpt-4") VOICE_MODEL= os.environ.get("TTS_MODEL","en-us-kathleen-low.onnx") DEFAULT_SD_MODEL = os.environ.get("DEFAULT_SD_MODEL", "stablediffusion") DEFAULT_SD_PROMPT = os.environ.get("DEFAULT_SD_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") REPLY_ACTION = "reply" #embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2") embeddings = LocalAIEmbeddings(model="all-MiniLM-L6-v2") chroma_client = Chroma(collection_name="memories", persist_directory="db", embedding_function=embeddings) # Function to create images with OpenAI def display_avatar(input_text=DEFAULT_SD_PROMPT, model=DEFAULT_SD_MODEL): response = openai.Image.create( prompt=input_text, n=1, size="128x128", api_base=os.environ.get("OPENAI_API_BASE", "http://api:8080")+"/v1" ) image_url = response['data'][0]['url'] # convert the image to ascii art my_art = AsciiArt.from_url(image_url) my_art.to_terminal() 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 = os.environ.get("OPENAI_API_BASE", "http://api:8080") + '/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) print('Audio file saved successfully:', output_file_path) else: print('Request failed with status code', response.status_code) # Use aplay to play the audio os.system('aplay ' + output_file_path) # remove the audio file os.remove(output_file_path) def needs_to_do_action(user_input,agent_actions={}): # Get the descriptions and the actions name (the keys) descriptions="" for action in agent_actions: descriptions+=agent_actions[action]["description"]+"\n" messages = [ {"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} Request: {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" }, "action": { "type": "string", "enum": list(agent_actions.keys()), "description": "user intent" }, "reasoning": { "type": "string", "description": "reasoning behind the intent" }, }, "required": ["action"] } }, ] response = openai.ChatCompletion.create( #model="gpt-3.5-turbo", model=FUNCTIONS_MODEL, messages=messages, functions=functions, max_tokens=200, stop=None, temperature=0.5, #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) print(">>> function name: "+function_name) print(">>> function parameters: "+function_parameters) return res return {"action": REPLY_ACTION} def process_functions(user_input, action="", agent_actions={}): descriptions="" for a in agent_actions: descriptions+=agent_actions[a]["description"]+"\n" messages = [ # {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": f"""Transcript of AI assistant responding to user requests. {descriptions} Request: {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 print("==> function name: ") print(function_name) print("==> function parameters: ") print(function_parameters) function_to_call = agent_actions[function_name]["function"] function_result = function_to_call(function_parameters, agent_actions=agent_actions) print("==> function result: ") print(function_result) messages = [ # {"role": "system", "content": "You are a helpful assistant."}, { "role": "user", "content": user_input, } ] messages.append( { "role": "assistant", "content": None, "function_call": {"name": function_name, "arguments": function_parameters,}, } ) messages.append( { "role": "function", "name": function_name, "content": f'{{"result": "{str(function_result)}"}}' } ) return messages, function_result def function_completion(messages, action="", agent_actions={}): function_call = "auto" if action != "": function_call={"name": action} print("==> function_call: ") print(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, max_tokens=200, stop=None, temperature=0.1, function_call=function_call ) return response # Gets the content of each message in the history def process_history(conversation_history): messages = "" for message in conversation_history: # if there is content append it if message.get("content"): messages+=message["content"]+"\n" if message.get("function_call"): # encode message["function_call" to json and appends it fcall = json.dumps(message["function_call"]) messages+=fcall+"\n" return messages def evaluate(user_input, conversation_history = [],re_evaluate=False, agent_actions={}): try: action = needs_to_do_action(user_input,agent_actions=agent_actions) except Exception as e: print("==> error: ") print(e) action = {"action": REPLY_ACTION} if action["action"] != REPLY_ACTION: print("==> miniAGI wants to do an action: ") print(action["action"]) print("==> Reasoning: ") print(action["reasoning"]) if action["action"] == "generate_plan": print("==> It's a plan <==: ") responses, function_results = process_functions(user_input+"\nReasoning: "+action["reasoning"], 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 for subtask in function_results["subtasks"]: print("==> subtask: ") print(subtask) subtask_response, function_results = process_functions(subtask["reasoning"], subtask["function"],agent_actions=agent_actions) responses.extend(subtask_response) if re_evaluate: all = process_history(responses) print("==> all: ") print(all) ## Better output or this infinite loops.. print("-> Re-evaluate if another action is needed") responses = evaluate(user_input+process_history(responses), responses, re_evaluate,agent_actions=agent_actions) response = openai.ChatCompletion.create( model=LLM_MODEL, messages=responses, max_tokens=200, stop=None, temperature=0.5, ) responses.append( { "role": "assistant", "content": response.choices[0].message["content"], } ) # add responses to conversation history by extending the list conversation_history.extend(responses) # print the latest response from the conversation history print(conversation_history[-1]["content"]) tts(conversation_history[-1]["content"]) else: print("==> no action needed") # construct the message and add it to the conversation history message = {"role": "user", "content": user_input} conversation_history.append(message) #conversation_history.append({ "role": "assistant", "content": "No action needed from my side."}) # get the response from the model response = openai.ChatCompletion.create( model=LLM_MODEL, messages=conversation_history, max_tokens=200, stop=None, temperature=0.5, ) # add the response to the conversation history by extending the list conversation_history.append({ "role": "assistant", "content": response.choices[0].message["content"]}) # print the latest response from the conversation history print(conversation_history[-1]["content"]) tts(conversation_history[-1]["content"]) return conversation_history ### Agent capabilities def save(memory, agent_actions={}): print(">>> saving to memories: ") print(memory) chroma_client.add_texts([memory],[{"id": str(uuid.uuid4())}]) chroma_client.persist() return f"The object was saved permanently to memory." def search(query, agent_actions={}): res = chroma_client.similarity_search(query) print(">>> query: ") print(query) print(">>> retrieved memories: ") print(res) return res def calculate_plan(user_input, agent_actions={}): res = json.loads(user_input) logger.info("--> Calculating plan: {description}", description=res["description"]) messages = [ {"role": "user", "content": f"""Transcript of AI assistant responding to user requests. Replies with a plan to achieve the user's goal with a list of subtasks with logical steps. Request: {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, max_tokens=200, stop=None, temperature=0.5, #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"] 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(args, agent_actions={}): args = json.loads(args) list=ddg(args["query"], 5) l = json.dumps(list) return l ### End Agent capabilities 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": 'For writing a file to disk with content, the assistant replies with the action "write_file" and the filename and content to save.', "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": 'For saving a memory, the assistant replies with the action "save_memory" and the string to save.', "signature": { "name": "save_memory", "description": """Save or store informations into memory.""", "parameters": { "type": "object", "properties": { "thought": { "type": "string", "description": "information to save" }, }, "required": ["thought"] } }, }, "search_memory": { "function": search, "plannable": True, "description": 'For searching a memory, the assistant replies with the action "search_memory" and the query to search to find information stored previously.', "signature": { "name": "search_memory", "description": """Search in memory""", "parameters": { "type": "object", "properties": { "query": { "type": "string", "description": "The query to be used to search informations" }, "reasoning": { "type": "string", "description": "reasoning behind the intent" }, }, "required": ["query"] } }, }, "generate_plan": { "function": calculate_plan, "plannable": False, "description": 'For generating a plan for complex tasks, the assistant replies with the action "generate_plan" and a detailed list of all the subtasks needed to execute the user goal using the available actions.', "signature": { "name": "generate_plan", "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 = [] display_avatar() # TODO: process functions also considering the conversation history? conversation history + input while True: user_input = input("> ") conversation_history=evaluate(user_input, conversation_history, re_evaluate=False, agent_actions=agent_actions)