Refactors, make repl also return output
This commit is contained in:
mudler
252
main.py
252
main.py
@@ -18,6 +18,7 @@ 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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from io import StringIO
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# Parse arguments such as system prompt and batch mode
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import argparse
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@@ -275,97 +276,9 @@ def process_history(conversation_history):
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messages+="Assistant message: "+message["content"]+"\n"
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return messages
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### Main evaluate function
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### This function evaluates in a continuous loop the user input and the conversation history.
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### It returns the conversation history with the latest response from the assistant.
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def evaluate(user_input, conversation_history = [],re_evaluate=False, agent_actions={},re_evaluation_in_progress=False, postprocess=False, subtaskContext=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("==> μAGI 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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if postprocess:
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action["reasoning"] = post_process(action["reasoning"])
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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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#cr=""
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if subtask_result != "" and subtaskContext:
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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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if postprocess:
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cr+= "Subtask results: "+post_process(subtask_result)+"\n"
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else:
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cr+="Subtask results: "+subtask_result+"\n"
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if postprocess:
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cr+= "Request: "+post_process(subtask["reasoning"])
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else:
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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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if postprocess:
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subtask_result=post_process(subtask_result)
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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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## ? conversation history should go after the user_input maybe?
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re_eval = user_input +"\n"
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re_eval += "Conversation history: \n"
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if postprocess:
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re_eval+= post_process(process_history(responses[1:])) +"\n"
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else:
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re_eval+= process_history(responses[1:]) +"\n"
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responses = evaluate(re_eval, 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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def clarify(responses, prefix=True):
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# TODO: this needs to be optimized
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if prefix:
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responses.append(
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{
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"role": "system",
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@@ -386,34 +299,7 @@ def evaluate(user_input, conversation_history = [],re_evaluate=False, agent_acti
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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("==> μAGI has completed the user request")
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logger.info("==> μAGI will reply to the user")
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return conversation_history
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# TODO: this needs to be optimized
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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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return responses
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### Fine tune a string before feeding into the LLM
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def post_process(string):
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@@ -464,7 +350,18 @@ def search(query, agent_actions={}):
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def python_repl(args, agent_actions={}):
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args = json.loads(args)
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try:
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return eval(args["code"])
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# evaluate the code
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# redirect standard output and standard error to a string
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sys.stdout = sys.stderr = mystdout = StringIO()
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# eval the code
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r=eval(args["code"])
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# restore stdout and stderr
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sys.stdout = sys.__stdout__
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sys.stderr = sys.__stderr__
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# get the output
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output = mystdout.getvalue()
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# return the output
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return { "result": r, "output": output}
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except Exception as e:
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return str(e)
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@@ -610,6 +507,123 @@ def search_duckduckgo(args, agent_actions={}):
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### End Agent capabilities
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###
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### Main evaluate function
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### This function evaluates the user input and the conversation history.
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### It returns the conversation history with the latest response from the assistant.
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def evaluate(user_input, conversation_history = [],re_evaluate=False, agent_actions={},re_evaluation_in_progress=False, postprocess=False, subtaskContext=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("==> μAGI 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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if postprocess:
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action["reasoning"] = post_process(action["reasoning"])
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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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#cr=""
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if subtask_result != "" and subtaskContext:
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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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if postprocess:
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cr+= "Subtask results: "+post_process(subtask_result)+"\n"
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else:
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cr+="Subtask results: "+subtask_result+"\n"
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if postprocess:
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cr+= "Request: "+post_process(subtask["reasoning"])
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else:
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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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if postprocess:
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subtask_result=post_process(subtask_result)
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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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## ? conversation history should go after the user_input maybe?
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re_eval = user_input +"\n"
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re_eval += "Conversation history: \n"
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if postprocess:
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re_eval+= post_process(process_history(responses[1:])) +"\n"
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else:
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re_eval+= process_history(responses[1:]) +"\n"
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responses = evaluate(re_eval, 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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# TODO: this needs to be optimized
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responses = clarify(responses, prefix=True)
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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("==> μAGI has completed the user request")
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logger.info("==> μAGI will reply to the user")
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return conversation_history
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# TODO: this needs to be optimized
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# get the response from the model
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response = clarify(conversation_history, prefix=False)
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# add the response to the conversation history by extending the list
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conversation_history.append(response)
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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 action definitions
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agent_actions = {
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