644 lines
24 KiB
Python
644 lines
24 KiB
Python
import openai
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#from langchain.embeddings import HuggingFaceEmbeddings
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from langchain.embeddings import LocalAIEmbeddings
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import uuid
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import requests
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import sys
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from loguru import logger
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from ascii_magic import AsciiArt
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from duckduckgo_search import DDGS
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from typing import Dict, List, Optional
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# these three lines swap the stdlib sqlite3 lib with the pysqlite3 package for chroma
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__import__('pysqlite3')
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import sys
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sys.modules['sqlite3'] = sys.modules.pop('pysqlite3')
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from langchain.vectorstores import Chroma
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from chromadb.config import Settings
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import json
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import os
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# Parse arguments such as system prompt and batch mode
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import argparse
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parser = argparse.ArgumentParser(description='microAGI')
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parser.add_argument('--system-prompt', dest='system_prompt', action='store',
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help='System prompt to use')
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parser.add_argument('--batch-mode', dest='batch_mode', action='store_true', default=False,
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help='Batch mode')
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# skip avatar creation
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parser.add_argument('--skip-avatar', dest='skip_avatar', action='store_true', default=False,
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help='Skip avatar creation')
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args = parser.parse_args()
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FUNCTIONS_MODEL = os.environ.get("FUNCTIONS_MODEL", "functions")
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LLM_MODEL = os.environ.get("LLM_MODEL", "gpt-4")
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VOICE_MODEL= os.environ.get("TTS_MODEL","en-us-kathleen-low.onnx")
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DEFAULT_SD_MODEL = os.environ.get("DEFAULT_SD_MODEL", "stablediffusion")
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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")
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PERSISTENT_DIR = os.environ.get("PERSISTENT_DIR", "/data")
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REPLY_ACTION = "reply"
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PLAN_ACTION = "plan"
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#embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
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embeddings = LocalAIEmbeddings(model="all-MiniLM-L6-v2")
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chroma_client = Chroma(collection_name="memories", persist_directory="db", embedding_function=embeddings)
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# Function to create images with OpenAI
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def display_avatar(input_text=DEFAULT_SD_PROMPT, model=DEFAULT_SD_MODEL):
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response = openai.Image.create(
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prompt=input_text,
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n=1,
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size="128x128",
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api_base=os.environ.get("OPENAI_API_BASE", "http://api:8080")+"/v1"
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)
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image_url = response['data'][0]['url']
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# convert the image to ascii art
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my_art = AsciiArt.from_url(image_url)
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my_art.to_terminal()
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def tts(input_text, model=VOICE_MODEL):
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# strip newlines from text
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input_text = input_text.replace("\n", ".")
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# Create a temp file to store the audio output
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output_file_path = '/tmp/output.wav'
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# get from OPENAI_API_BASE env var
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url = os.environ.get("OPENAI_API_BASE", "http://api:8080") + '/tts'
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headers = {'Content-Type': 'application/json'}
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data = {
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"input": input_text,
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"model": model
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}
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response = requests.post(url, headers=headers, data=json.dumps(data))
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if response.status_code == 200:
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with open(output_file_path, 'wb') as f:
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f.write(response.content)
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logger.info('Audio file saved successfully:', output_file_path)
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else:
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logger.info('Request failed with status code', response.status_code)
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# Use aplay to play the audio
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os.system('aplay ' + output_file_path)
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# remove the audio file
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os.remove(output_file_path)
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def needs_to_do_action(user_input,agent_actions={}):
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# Get the descriptions and the actions name (the keys)
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descriptions=""
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for action in agent_actions:
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descriptions+=agent_actions[action]["description"]+"\n"
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messages = [
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{"role": "user",
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"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.
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{descriptions}"""},
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{"role": "user",
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"content": f"""{user_input}
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Function call: """
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}
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]
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functions = [
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{
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"name": "intent",
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"description": """Decide to do an action.""",
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"parameters": {
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"type": "object",
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"properties": {
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"confidence": {
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"type": "number",
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"description": "confidence of the action"
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},
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"action": {
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"type": "string",
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"enum": list(agent_actions.keys()),
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"description": "user intent"
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},
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"reasoning": {
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"type": "string",
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"description": "reasoning behind the intent"
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},
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},
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"required": ["action"]
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}
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},
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]
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response = openai.ChatCompletion.create(
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#model="gpt-3.5-turbo",
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model=FUNCTIONS_MODEL,
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messages=messages,
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request_timeout=1200,
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functions=functions,
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stop=None,
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temperature=0.1,
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#function_call="auto"
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function_call={"name": "intent"},
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)
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response_message = response["choices"][0]["message"]
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if response_message.get("function_call"):
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function_name = response.choices[0].message["function_call"].name
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function_parameters = response.choices[0].message["function_call"].arguments
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# read the json from the string
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res = json.loads(function_parameters)
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logger.info(">>> function name: "+function_name)
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logger.info(">>> function parameters: "+function_parameters)
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return res
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return {"action": REPLY_ACTION}
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def process_functions(user_input, action="", agent_actions={}):
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descriptions=""
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for a in agent_actions:
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descriptions+=agent_actions[a]["description"]+"\n"
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messages = [
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# {"role": "system", "content": "You are a helpful assistant."},
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{"role": "user",
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"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.
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{descriptions}"""},
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{"role": "user",
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"content": f"""{user_input}
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Function call: """
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}
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]
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response = function_completion(messages, action=action,agent_actions=agent_actions)
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response_message = response["choices"][0]["message"]
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response_result = ""
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function_result = {}
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if response_message.get("function_call"):
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function_name = response.choices[0].message["function_call"].name
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function_parameters = response.choices[0].message["function_call"].arguments
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logger.info("==> function name: ")
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logger.info(function_name)
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logger.info("==> function parameters: ")
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logger.info(function_parameters)
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function_to_call = agent_actions[function_name]["function"]
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function_result = function_to_call(function_parameters, agent_actions=agent_actions)
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logger.info("==> function result: ")
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logger.info(function_result)
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messages.append(
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{
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"role": "assistant",
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"content": None,
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"function_call": {"name": function_name, "arguments": function_parameters,},
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}
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)
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messages.append(
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{
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"role": "function",
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"name": function_name,
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"content": str(function_result)
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}
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)
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return messages, function_result
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def function_completion(messages, action="", agent_actions={}):
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function_call = "auto"
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if action != "":
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function_call={"name": action}
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logger.info("==> function_call: ")
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logger.info(function_call)
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# get the functions from the signatures of the agent actions, if exists
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functions = []
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for action in agent_actions:
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if agent_actions[action].get("signature"):
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functions.append(agent_actions[action]["signature"])
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response = openai.ChatCompletion.create(
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#model="gpt-3.5-turbo",
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model=FUNCTIONS_MODEL,
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messages=messages,
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functions=functions,
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request_timeout=1200,
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stop=None,
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temperature=0.1,
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function_call=function_call
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)
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return response
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# Gets the content of each message in the history
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def process_history(conversation_history):
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messages = ""
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for message in conversation_history:
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# if there is content append it
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if message.get("content") and message["role"] == "function":
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messages+="Function result: " + message["content"]+"\n"
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elif message.get("function_call"):
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# encode message["function_call" to json and appends it
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fcall = json.dumps(message["function_call"])
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messages+="Assistant calls function: " +fcall+"\n"
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elif message.get("content") and message["role"] == "user":
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messages+="User message: "+message["content"]+"\n"
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elif message.get("content") and message["role"] == "assistant":
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messages+="Assistant message: "+message["content"]+"\n"
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return messages
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def evaluate(user_input, conversation_history = [],re_evaluate=False, agent_actions={},re_evaluation_in_progress=False):
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messages = [
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{
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"role": "user",
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"content": user_input,
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}
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]
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conversation_history.extend(messages)
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# pulling the old history make the context grow exponentially
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# and most importantly it repeates the first message with the commands again and again.
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# it needs a bit of cleanup and process the messages and piggyback more LocalAI functions templates
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# old_history = process_history(conversation_history)
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# action_picker_message = "Conversation history:\n"+old_history
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# action_picker_message += "\n"
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action_picker_message = "Request: "+user_input
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if re_evaluation_in_progress:
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action_picker_message+="\nRe-evaluation if another action is needed or we have completed the user request."
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action_picker_message+="\nReasoning: If no action is needed, I will use "+REPLY_ACTION+" to reply to the user."
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try:
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action = needs_to_do_action(action_picker_message,agent_actions=agent_actions)
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except Exception as e:
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logger.error("==> error: ")
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logger.error(e)
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action = {"action": REPLY_ACTION}
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if action["action"] != REPLY_ACTION:
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logger.info("==> microAGI wants to call '{action}'", action=action["action"])
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logger.info("==> Reasoning '{reasoning}'", reasoning=action["reasoning"])
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if action["action"] == PLAN_ACTION:
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logger.info("==> It's a plan <==: ")
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#function_completion_message = "Conversation history:\n"+old_history+"\n"+
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function_completion_message = "Request: "+user_input+"\nReasoning: "+action["reasoning"]
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responses, function_results = process_functions(function_completion_message, action=action["action"], agent_actions=agent_actions)
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# if there are no subtasks, we can just reply,
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# otherwise we execute the subtasks
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# First we check if it's an object
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if isinstance(function_results, dict) and function_results.get("subtasks") and len(function_results["subtasks"]) > 0:
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# cycle subtasks and execute functions
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subtask_result=""
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for subtask in function_results["subtasks"]:
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logger.info("==> subtask: ")
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logger.info(subtask)
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#ctr="Context: "+user_input+"\nThought: "+action["reasoning"]+ "\nRequest: "+subtask["reasoning"]
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cr="Context: "+user_input+"\n"
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if subtask_result != "":
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# Include cumulative results of previous subtasks
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# TODO: this grows context, maybe we should use a different approach or summarize
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cr+="Subtask results: "+subtask_result+"\n"
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cr+="Request: "+subtask["reasoning"]
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subtask_response, function_results = process_functions(cr, subtask["function"],agent_actions=agent_actions)
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subtask_result+=process_history(subtask_response)
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responses.extend(subtask_response)
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if re_evaluate:
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## Better output or this infinite loops..
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logger.info("-> Re-evaluate if another action is needed")
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responses = evaluate(user_input+"\n Conversation history: \n"+process_history(responses[1:]), responses, re_evaluate,agent_actions=agent_actions,re_evaluation_in_progress=True)
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if re_evaluation_in_progress:
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conversation_history.extend(responses)
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return conversation_history
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responses.append(
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{
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"role": "system",
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"content": "Return an appropriate answer to the user given the context above."
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}
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)
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response = openai.ChatCompletion.create(
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model=LLM_MODEL,
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messages=responses,
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stop=None,
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request_timeout=1200,
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temperature=0.1,
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)
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responses.append(
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{
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"role": "assistant",
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"content": response.choices[0].message["content"],
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}
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)
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# add responses to conversation history by extending the list
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conversation_history.extend(responses)
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# logger.info the latest response from the conversation history
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logger.info(conversation_history[-1]["content"])
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tts(conversation_history[-1]["content"])
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else:
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logger.info("==> no action needed")
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if re_evaluation_in_progress:
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logger.info("==> microAGI has completed the user request")
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logger.info("==> microAGI will reply to the user")
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return conversation_history
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# get the response from the model
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response = openai.ChatCompletion.create(
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model=LLM_MODEL,
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messages=conversation_history,
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stop=None,
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temperature=0.1,
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request_timeout=1200,
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)
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# add the response to the conversation history by extending the list
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conversation_history.append({ "role": "assistant", "content": response.choices[0].message["content"]})
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# logger.info the latest response from the conversation history
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logger.info(conversation_history[-1]["content"])
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tts(conversation_history[-1]["content"])
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return conversation_history
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### Agent capabilities
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def save(memory, agent_actions={}):
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q = json.loads(memory)
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logger.info(">>> saving to memories: ")
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logger.info(q["thought"])
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chroma_client.add_texts([q["thought"]],[{"id": str(uuid.uuid4())}])
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chroma_client.persist()
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return f"The object was saved permanently to memory."
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def search(query, agent_actions={}):
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q = json.loads(query)
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docs = chroma_client.similarity_search(q["reasoning"])
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text_res="Memories found in the database:\n"
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for doc in docs:
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text_res+="- "+doc.page_content+"\n"
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return text_res
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def calculate_plan(user_input, agent_actions={}):
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res = json.loads(user_input)
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logger.info("--> Calculating plan: {description}", description=res["description"])
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messages = [
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{"role": "user",
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"content": f"""Transcript of AI assistant responding to user requests.
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Replies with a plan to achieve the user's goal with a list of subtasks with logical steps. The reasoning includes a self-contained, detailed instruction to fullfill the task.
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Request: {res["description"]}
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Function call: """
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}
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]
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# get list of plannable actions
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plannable_actions = []
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for action in agent_actions:
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if agent_actions[action]["plannable"]:
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# append the key of the dict to plannable_actions
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plannable_actions.append(action)
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functions = [
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{
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"name": "plan",
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"description": """Decide to do an action.""",
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"parameters": {
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"type": "object",
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"properties": {
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"subtasks": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"reasoning": {
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"type": "string",
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"description": "subtask list",
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},
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"function": {
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"type": "string",
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"enum": plannable_actions,
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},
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},
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},
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},
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},
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"required": ["subtasks"]
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}
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},
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]
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response = openai.ChatCompletion.create(
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#model="gpt-3.5-turbo",
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model=FUNCTIONS_MODEL,
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messages=messages,
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functions=functions,
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stop=None,
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temperature=0.1,
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#function_call="auto"
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function_call={"name": "plan"},
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)
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response_message = response["choices"][0]["message"]
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if response_message.get("function_call"):
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function_name = response.choices[0].message["function_call"].name
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function_parameters = response.choices[0].message["function_call"].arguments
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# read the json from the string
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res = json.loads(function_parameters)
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logger.info("<<< function name: {function_name} >>>> parameters: {parameters}", function_name=function_name,parameters=function_parameters)
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return res
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return {"action": REPLY_ACTION}
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# write file to disk with content
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def write_file(arg, agent_actions={}):
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arg = json.loads(arg)
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filename = arg["filename"]
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content = arg["content"]
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# create persistent dir if does not exist
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if not os.path.exists(PERSISTENT_DIR):
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os.makedirs(PERSISTENT_DIR)
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# write the file in the directory specified
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filename = os.path.join(PERSISTENT_DIR, filename)
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with open(filename, 'w') as f:
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f.write(content)
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return f"File {filename} saved successfully."
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def ddg(query: str, num_results: int, backend: str = "api") -> List[Dict[str, str]]:
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"""Run query through DuckDuckGo and return metadata.
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Args:
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query: The query to search for.
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num_results: The number of results to return.
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Returns:
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A list of dictionaries with the following keys:
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snippet - The description of the result.
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title - The title of the result.
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link - The link to the result.
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"""
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with DDGS() as ddgs:
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results = ddgs.text(
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query,
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backend=backend,
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)
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if results is None:
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return [{"Result": "No good DuckDuckGo Search Result was found"}]
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def to_metadata(result: Dict) -> Dict[str, str]:
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if backend == "news":
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return {
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"date": result["date"],
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"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": '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"
|
|
},
|
|
},
|
|
}
|
|
},
|
|
},
|
|
"remember": {
|
|
"function": save,
|
|
"plannable": True,
|
|
"description": 'The assistant replies with the action "remember" and the string to save in order to remember something or save an information that thinks it is relevant permanently.',
|
|
"signature": {
|
|
"name": "remember",
|
|
"description": """Save or store informations into memory.""",
|
|
"parameters": {
|
|
"type": "object",
|
|
"properties": {
|
|
"thought": {
|
|
"type": "string",
|
|
"description": "information to save"
|
|
},
|
|
},
|
|
"required": ["thought"]
|
|
}
|
|
},
|
|
},
|
|
"recall": {
|
|
"function": search,
|
|
"plannable": True,
|
|
"description": 'The assistant replies with the action "recall" for searching between its memories with a query term.',
|
|
"signature": {
|
|
"name": "recall",
|
|
"description": """Search in memory""",
|
|
"parameters": {
|
|
"type": "object",
|
|
"properties": {
|
|
"reasoning": {
|
|
"type": "string",
|
|
"description": "reasoning behind the intent"
|
|
},
|
|
},
|
|
"required": ["reasoning"]
|
|
}
|
|
},
|
|
},
|
|
PLAN_ACTION: {
|
|
"function": calculate_plan,
|
|
"plannable": False,
|
|
"description": 'The assistant for solving complex tasks that involves more than one action or planning actions in sequence, replies with the action "'+PLAN_ACTION+'" and a detailed list of all the subtasks.',
|
|
"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 os.environ.get("SYSTEM_PROMPT"):
|
|
conversation_history.append({
|
|
"role": "system",
|
|
"content": os.environ.get("SYSTEM_PROMPT")
|
|
})
|
|
|
|
logger.info("Welcome to microAGI")
|
|
logger.info("Creating avatar, please wait...")
|
|
|
|
display_avatar()
|
|
|
|
logger.info("Welcome to microAGI")
|
|
logger.info("microAGI has the following actions available at its disposal:")
|
|
for action in agent_actions:
|
|
logger.info("{action} - {description}", action=action, description=agent_actions[action]["description"])
|
|
|
|
# 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=True, agent_actions=agent_actions) |