mirror of
https://github.com/FoundationAgents/MetaGPT.git
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feat: merge main
This commit is contained in:
莘权 马
commit
33ca44739d
199 changed files with 7620 additions and 469 deletions
1
.github/ISSUE_TEMPLATE/show_me_the_bug.md
vendored
1
.github/ISSUE_TEMPLATE/show_me_the_bug.md
vendored
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@ -19,6 +19,7 @@
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- LLM type and model name:
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- System version:
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- Python version:
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- MetaGPT version or branch:
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<!-- Dependent packagess：the packages version cause the bug(like `pydantic 1.10.8`), installation method（like `pip install metagpt` or `pip install from source` or `run in docker`） -->
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|
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3
MANIFEST.in
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3
MANIFEST.in
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@ -0,0 +1,3 @@
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recursive-include metagpt/ext/stanford_town/prompts *.txt
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recursive-include metagpt/ext/stanford_town/static_dirs *.csv
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recursive-include metagpt/ext/stanford_town/static_dirs *.json
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93
examples/stanford_town/run_st_game.py
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93
examples/stanford_town/run_st_game.py
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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# @Desc : entry of Stanford Town(ST/st) game
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import asyncio
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from typing import Optional
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import fire
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from metagpt.ext.stanford_town.roles.st_role import STRole
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from metagpt.ext.stanford_town.stanford_town import StanfordTown
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from metagpt.ext.stanford_town.utils.const import STORAGE_PATH
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from metagpt.ext.stanford_town.utils.mg_ga_transform import (
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get_reverie_meta,
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write_curr_sim_code,
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write_curr_step,
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)
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from metagpt.ext.stanford_town.utils.utils import copy_folder
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from metagpt.logs import logger
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async def startup(
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idea: str, fork_sim_code: str, sim_code: str, temp_storage_path: str, investment: float = 30.0, n_round: int = 500
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):
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town = StanfordTown()
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logger.info("StanfordTown init environment")
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# copy `storage/{fork_sim_code}` to `storage/{sim_code}`
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copy_folder(str(STORAGE_PATH.joinpath(fork_sim_code)), str(STORAGE_PATH.joinpath(sim_code)))
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# get role names from `storage/{simulation_name}/reverie/meta.json` and then init roles
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reverie_meta = get_reverie_meta(fork_sim_code)
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roles = []
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sim_path = STORAGE_PATH.joinpath(sim_code)
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sim_path.mkdir(exist_ok=True)
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for idx, role_name in enumerate(reverie_meta["persona_names"]):
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has_inner_voice = True if idx == 0 else False
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role = STRole(
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name=role_name,
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profile=role_name,
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sim_code=sim_code,
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step=reverie_meta.get("step", 0),
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start_time=reverie_meta.get("start_date"),
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curr_time=reverie_meta.get("curr_time"),
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sec_per_step=reverie_meta.get("sec_per_step"),
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has_inner_voice=has_inner_voice,
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)
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roles.append(role)
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# init temp_storage
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write_curr_sim_code({"sim_code": sim_code}, temp_storage_path)
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write_curr_step({"step": reverie_meta.get("step", 0)}, temp_storage_path)
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await town.hire(roles)
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town.invest(investment)
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town.run_project(idea)
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await town.run(n_round)
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def main(
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idea: str,
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fork_sim_code: str,
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sim_code: str,
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temp_storage_path: Optional[str] = None,
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investment: float = 30.0,
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n_round: int = 500,
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):
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"""
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Args:
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idea: idea works as an `inner voice` to the first agent.
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fork_sim_code: old simulation name to start with, choose one inside `generative_agents/environment/frontend_server/storage/`
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sim_code: new simulation name to save simulation result
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temp_storage_path: generative_agents temp_storage path inside `environment/frontend_server` to interact.
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investment: the investment of running agents
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n_round: rounds to run agents
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"""
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asyncio.run(
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startup(
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idea=idea,
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fork_sim_code=fork_sim_code,
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sim_code=sim_code,
|
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temp_storage_path=temp_storage_path,
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investment=investment,
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n_round=n_round,
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)
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)
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if __name__ == "__main__":
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fire.Fire(main)
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4
examples/stanford_town/storage/.gitignore
vendored
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4
examples/stanford_town/storage/.gitignore
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|||
# path to store simulation data
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test_*
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unittest*
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||||
July*
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|
@ -0,0 +1,26 @@
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|||
{
|
||||
"Isabella Rodriguez": {
|
||||
"maze": "the_ville",
|
||||
"x": 72,
|
||||
"y": 14
|
||||
},
|
||||
"Klaus Mueller": {
|
||||
"maze": "the_ville",
|
||||
"x": 126,
|
||||
"y": 46
|
||||
},
|
||||
"Maria Lopez": {
|
||||
"maze": "the_ville",
|
||||
"x": 123,
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"y": 57
|
||||
}
|
||||
}
|
||||
|
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|
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|
||||
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||||
|
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|
|
@ -0,0 +1 @@
|
|||
{}
|
||||
|
|
@ -0,0 +1,2 @@
|
|||
{"kw_strength_event": {},
|
||||
"kw_strength_thought": {}}
|
||||
|
|
@ -0,0 +1 @@
|
|||
{}
|
||||
|
|
@ -0,0 +1,51 @@
|
|||
{
|
||||
"vision_r": 8,
|
||||
"att_bandwidth": 8,
|
||||
"retention": 8,
|
||||
"curr_time": null,
|
||||
"curr_tile": null,
|
||||
"daily_plan_req": "Isabella Rodriguez opens Hobbs Cafe at 8am everyday, and works at the counter until 8pm, at which point she closes the cafe.",
|
||||
"name": "Isabella Rodriguez",
|
||||
"first_name": "Isabella",
|
||||
"last_name": "Rodriguez",
|
||||
"age": 34,
|
||||
"innate": "friendly, outgoing, hospitable",
|
||||
"learned": "Isabella Rodriguez is a cafe owner of Hobbs Cafe who loves to make people feel welcome. She is always looking for ways to make the cafe a place where people can come to relax and enjoy themselves.",
|
||||
"currently": "Isabella Rodriguez is planning on having a Valentine's Day party at Hobbs Cafe with her customers on February 14th, 2023 at 5pm. She is gathering party material, and is telling everyone to join the party at Hobbs Cafe on February 14th, 2023, from 5pm to 7pm.",
|
||||
"lifestyle": "Isabella Rodriguez goes to bed around 11pm, awakes up around 6am.",
|
||||
"living_area": "the Ville:Isabella Rodriguez's apartment:main room",
|
||||
"concept_forget": 100,
|
||||
"daily_reflection_time": 180,
|
||||
"daily_reflection_size": 5,
|
||||
"overlap_reflect_th": 4,
|
||||
"kw_strg_event_reflect_th": 10,
|
||||
"kw_strg_thought_reflect_th": 9,
|
||||
|
||||
"recency_w": 1,
|
||||
"relevance_w": 1,
|
||||
"importance_w": 1,
|
||||
"recency_decay": 0.995,
|
||||
"importance_trigger_max": 150,
|
||||
"importance_trigger_curr": 150,
|
||||
"importance_ele_n": 0,
|
||||
"thought_count": 5,
|
||||
|
||||
"daily_req": [],
|
||||
"f_daily_schedule": [],
|
||||
"f_daily_schedule_hourly_org": [],
|
||||
"act_address": null,
|
||||
"act_start_time": null,
|
||||
"act_duration": null,
|
||||
"act_description": null,
|
||||
"act_pronunciatio": null,
|
||||
"act_event": ["Isabella Rodriguez", null, null],
|
||||
"act_obj_description": null,
|
||||
"act_obj_pronunciatio": null,
|
||||
"act_obj_event": [null, null, null],
|
||||
"chatting_with": null,
|
||||
"chat": null,
|
||||
"chatting_with_buffer": {},
|
||||
"chatting_end_time": null,
|
||||
"act_path_set": false,
|
||||
"planned_path": []
|
||||
}
|
||||
|
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@ -0,0 +1,66 @@
|
|||
{
|
||||
"the Ville": {
|
||||
"Hobbs Cafe": {
|
||||
"cafe": [
|
||||
"refrigerator",
|
||||
"cafe customer seating",
|
||||
"cooking area",
|
||||
"kitchen sink",
|
||||
"behind the cafe counter",
|
||||
"piano"
|
||||
]
|
||||
},
|
||||
"Isabella Rodriguez's apartment": {
|
||||
"main room": [
|
||||
"bed",
|
||||
"desk",
|
||||
"refrigerator",
|
||||
"closet",
|
||||
"shelf"
|
||||
]
|
||||
},
|
||||
"The Rose and Crown Pub": {
|
||||
"pub": [
|
||||
"shelf",
|
||||
"refrigerator",
|
||||
"bar customer seating",
|
||||
"behind the bar counter",
|
||||
"kitchen sink",
|
||||
"cooking area",
|
||||
"microphone"
|
||||
]
|
||||
},
|
||||
"Harvey Oak Supply Store": {
|
||||
"supply store": [
|
||||
"supply store product shelf",
|
||||
"behind the supply store counter",
|
||||
"supply store counter"
|
||||
]
|
||||
},
|
||||
"The Willows Market and Pharmacy": {
|
||||
"store": [
|
||||
"behind the pharmacy counter",
|
||||
"pharmacy store shelf",
|
||||
"pharmacy store counter",
|
||||
"grocery store shelf",
|
||||
"behind the grocery counter",
|
||||
"grocery store counter"
|
||||
]
|
||||
},
|
||||
"Dorm for Oak Hill College": {
|
||||
"garden": [
|
||||
"dorm garden"
|
||||
],
|
||||
"common room": [
|
||||
"common room sofa",
|
||||
"pool table",
|
||||
"common room table"
|
||||
]
|
||||
},
|
||||
"Johnson Park": {
|
||||
"park": [
|
||||
"park garden"
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1 @@
|
|||
{}
|
||||
|
|
@ -0,0 +1,2 @@
|
|||
{"kw_strength_event": {},
|
||||
"kw_strength_thought": {}}
|
||||
|
|
@ -0,0 +1 @@
|
|||
{}
|
||||
|
|
@ -0,0 +1,51 @@
|
|||
{
|
||||
"vision_r": 8,
|
||||
"att_bandwidth": 8,
|
||||
"retention": 8,
|
||||
"curr_time": null,
|
||||
"curr_tile": null,
|
||||
"daily_plan_req": "Klaus Mueller goes to the library at Oak Hill College early in the morning, spends his days writing, and eats at Hobbs Cafe.",
|
||||
"name": "Klaus Mueller",
|
||||
"first_name": "Klaus",
|
||||
"last_name": "Mueller",
|
||||
"age": 20,
|
||||
"innate": "kind, inquisitive, passionate",
|
||||
"learned": "Klaus Mueller is a student at Oak Hill College studying sociology. He is passionate about social justice and loves to explore different perspectives.",
|
||||
"currently": "Klaus Mueller is writing a research paper on the effects of gentrification in low-income communities.",
|
||||
"lifestyle": "Klaus Mueller goes to bed around 11pm, awakes up around 7am, eats dinner around 5pm.",
|
||||
"living_area": "the Ville:Dorm for Oak Hill College:Klaus Mueller's room",
|
||||
"concept_forget": 100,
|
||||
"daily_reflection_time": 180,
|
||||
"daily_reflection_size": 5,
|
||||
"overlap_reflect_th": 4,
|
||||
"kw_strg_event_reflect_th": 10,
|
||||
"kw_strg_thought_reflect_th": 9,
|
||||
|
||||
"recency_w": 1,
|
||||
"relevance_w": 1,
|
||||
"importance_w": 1,
|
||||
"recency_decay": 0.99,
|
||||
"importance_trigger_max": 150,
|
||||
"importance_trigger_curr": 150,
|
||||
"importance_ele_n": 0,
|
||||
"thought_count": 5,
|
||||
|
||||
"daily_req": [],
|
||||
"f_daily_schedule": [],
|
||||
"f_daily_schedule_hourly_org": [],
|
||||
"act_address": null,
|
||||
"act_start_time": null,
|
||||
"act_duration": null,
|
||||
"act_description": null,
|
||||
"act_pronunciatio": null,
|
||||
"act_event": ["Klaus Mueller", null, null],
|
||||
"act_obj_description": null,
|
||||
"act_obj_pronunciatio": null,
|
||||
"act_obj_event": [null, null, null],
|
||||
"chatting_with": null,
|
||||
"chat": null,
|
||||
"chatting_with_buffer": {},
|
||||
"chatting_end_time": null,
|
||||
"act_path_set": false,
|
||||
"planned_path": []
|
||||
}
|
||||
|
|
@ -0,0 +1,86 @@
|
|||
{
|
||||
"the Ville": {
|
||||
"Oak Hill College": {
|
||||
"hallway": [],
|
||||
"library": [
|
||||
"library sofa",
|
||||
"library table",
|
||||
"bookshelf"
|
||||
],
|
||||
"classroom": [
|
||||
"blackboard",
|
||||
"classroom podium",
|
||||
"classroom student seating"
|
||||
]
|
||||
},
|
||||
"Dorm for Oak Hill College": {
|
||||
"garden": [
|
||||
"dorm garden"
|
||||
],
|
||||
"Klaus Mueller's room": [
|
||||
"bed",
|
||||
"game console",
|
||||
"closet",
|
||||
"desk"
|
||||
],
|
||||
"woman's bathroom": [
|
||||
"toilet",
|
||||
"shower",
|
||||
"bathroom sink"
|
||||
],
|
||||
"common room": [
|
||||
"common room sofa",
|
||||
"pool table",
|
||||
"common room table"
|
||||
],
|
||||
"man's bathroom": [
|
||||
"shower",
|
||||
"bathroom sink",
|
||||
"toilet"
|
||||
]
|
||||
},
|
||||
"The Willows Market and Pharmacy": {
|
||||
"store": [
|
||||
"grocery store shelf",
|
||||
"behind the grocery counter",
|
||||
"grocery store counter",
|
||||
"pharmacy store shelf",
|
||||
"pharmacy store counter",
|
||||
"behind the pharmacy counter"
|
||||
]
|
||||
},
|
||||
"Harvey Oak Supply Store": {
|
||||
"supply store": [
|
||||
"supply store product shelf",
|
||||
"behind the supply store counter",
|
||||
"supply store counter"
|
||||
]
|
||||
},
|
||||
"Johnson Park": {
|
||||
"park": [
|
||||
"park garden"
|
||||
]
|
||||
},
|
||||
"The Rose and Crown Pub": {
|
||||
"pub": [
|
||||
"shelf",
|
||||
"refrigerator",
|
||||
"bar customer seating",
|
||||
"behind the bar counter",
|
||||
"kitchen sink",
|
||||
"cooking area",
|
||||
"microphone"
|
||||
]
|
||||
},
|
||||
"Hobbs Cafe": {
|
||||
"cafe": [
|
||||
"refrigerator",
|
||||
"cafe customer seating",
|
||||
"cooking area",
|
||||
"kitchen sink",
|
||||
"behind the cafe counter",
|
||||
"piano"
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1 @@
|
|||
{}
|
||||
|
|
@ -0,0 +1,2 @@
|
|||
{"kw_strength_event": {},
|
||||
"kw_strength_thought": {}}
|
||||
|
|
@ -0,0 +1 @@
|
|||
{}
|
||||
|
|
@ -0,0 +1,51 @@
|
|||
{
|
||||
"vision_r": 8,
|
||||
"att_bandwidth": 8,
|
||||
"retention": 8,
|
||||
"curr_time": null,
|
||||
"curr_tile": null,
|
||||
"daily_plan_req": "Maria Lopez spends at least 3 hours a day Twitch streaming or gaming.",
|
||||
"name": "Maria Lopez",
|
||||
"first_name": "Maria",
|
||||
"last_name": "Lopez",
|
||||
"age": 21,
|
||||
"innate": "energetic, enthusiastic, inquisitive",
|
||||
"learned": "Maria Lopez is a student at Oak Hill College studying physics and a part time Twitch game streamer who loves to connect with people and explore new ideas.",
|
||||
"currently": "Maria Lopez is working on her physics degree and streaming games on Twitch to make some extra money. She visits Hobbs Cafe for studying and eating just about everyday.",
|
||||
"lifestyle": "Maria Lopez goes to bed around 2am, awakes up around 9am, eats dinner around 6pm. She likes to hang out at Hobbs Cafe if it's before 6pm.",
|
||||
"living_area": "the Ville:Dorm for Oak Hill College:Maria Lopez's room",
|
||||
"concept_forget": 100,
|
||||
"daily_reflection_time": 180,
|
||||
"daily_reflection_size": 5,
|
||||
"overlap_reflect_th": 4,
|
||||
"kw_strg_event_reflect_th": 10,
|
||||
"kw_strg_thought_reflect_th": 9,
|
||||
|
||||
"recency_w": 1,
|
||||
"relevance_w": 1,
|
||||
"importance_w": 1,
|
||||
"recency_decay": 0.99,
|
||||
"importance_trigger_max": 150,
|
||||
"importance_trigger_curr": 150,
|
||||
"importance_ele_n": 0,
|
||||
"thought_count": 5,
|
||||
|
||||
"daily_req": [],
|
||||
"f_daily_schedule": [],
|
||||
"f_daily_schedule_hourly_org": [],
|
||||
"act_address": null,
|
||||
"act_start_time": null,
|
||||
"act_duration": null,
|
||||
"act_description": null,
|
||||
"act_pronunciatio": null,
|
||||
"act_event": ["Maria Lopez", null, null],
|
||||
"act_obj_description": null,
|
||||
"act_obj_pronunciatio": null,
|
||||
"act_obj_event": [null, null, null],
|
||||
"chatting_with": null,
|
||||
"chat": null,
|
||||
"chatting_with_buffer": {},
|
||||
"chatting_end_time": null,
|
||||
"act_path_set": false,
|
||||
"planned_path": []
|
||||
}
|
||||
|
|
@ -0,0 +1,87 @@
|
|||
{
|
||||
"the Ville": {
|
||||
"Oak Hill College": {
|
||||
"hallway": [],
|
||||
"library": [
|
||||
"library sofa",
|
||||
"library table",
|
||||
"bookshelf"
|
||||
],
|
||||
"classroom": [
|
||||
"blackboard",
|
||||
"classroom podium",
|
||||
"classroom student seating"
|
||||
]
|
||||
},
|
||||
"Dorm for Oak Hill College": {
|
||||
"garden": [
|
||||
"dorm garden"
|
||||
],
|
||||
"Maria Lopez's room": [
|
||||
"closet",
|
||||
"desk",
|
||||
"bed",
|
||||
"computer",
|
||||
"blackboard"
|
||||
],
|
||||
"woman's bathroom": [
|
||||
"toilet",
|
||||
"shower",
|
||||
"bathroom sink"
|
||||
],
|
||||
"common room": [
|
||||
"common room sofa",
|
||||
"pool table",
|
||||
"common room table"
|
||||
],
|
||||
"man's bathroom": [
|
||||
"shower",
|
||||
"bathroom sink",
|
||||
"toilet"
|
||||
]
|
||||
},
|
||||
"The Willows Market and Pharmacy": {
|
||||
"store": [
|
||||
"grocery store shelf",
|
||||
"behind the grocery counter",
|
||||
"grocery store counter",
|
||||
"pharmacy store shelf",
|
||||
"pharmacy store counter",
|
||||
"behind the pharmacy counter"
|
||||
]
|
||||
},
|
||||
"Harvey Oak Supply Store": {
|
||||
"supply store": [
|
||||
"supply store product shelf",
|
||||
"behind the supply store counter",
|
||||
"supply store counter"
|
||||
]
|
||||
},
|
||||
"Johnson Park": {
|
||||
"park": [
|
||||
"park garden"
|
||||
]
|
||||
},
|
||||
"The Rose and Crown Pub": {
|
||||
"pub": [
|
||||
"shelf",
|
||||
"refrigerator",
|
||||
"bar customer seating",
|
||||
"behind the bar counter",
|
||||
"kitchen sink",
|
||||
"cooking area",
|
||||
"microphone"
|
||||
]
|
||||
},
|
||||
"Hobbs Cafe": {
|
||||
"cafe": [
|
||||
"refrigerator",
|
||||
"cafe customer seating",
|
||||
"cooking area",
|
||||
"kitchen sink",
|
||||
"behind the cafe counter",
|
||||
"piano"
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,13 @@
|
|||
{
|
||||
"fork_sim_code": "base_the_ville_isabella_maria_klaus",
|
||||
"start_date": "February 13, 2023",
|
||||
"curr_time": "February 13, 2023, 00:00:00",
|
||||
"sec_per_step": 10,
|
||||
"maze_name": "the_ville",
|
||||
"persona_names": [
|
||||
"Isabella Rodriguez",
|
||||
"Maria Lopez",
|
||||
"Klaus Mueller"
|
||||
],
|
||||
"step": 0
|
||||
}
|
||||
|
|
@ -21,7 +21,7 @@ ## Usage
|
|||
from metagpt.environment.api.env_api import EnvAPIAbstract
|
||||
|
||||
# get screenshot from ExtEnv
|
||||
screenshot_path: Path = env.observe(
|
||||
screenshot_path: Path = await env.observe(
|
||||
EnvAPIAbstract(
|
||||
api_name="get_screenshot", kwargs={"ss_name": f"{round_count}_before", "local_save_dir": task_dir}
|
||||
)
|
||||
|
|
|
|||
|
|
@ -3,10 +3,10 @@
|
|||
# @Desc :
|
||||
|
||||
from metagpt.environment.base_env import Environment
|
||||
from metagpt.environment.android_env.android_env import AndroidEnv
|
||||
from metagpt.environment.werewolf_env.werewolf_env import WerewolfEnv
|
||||
from metagpt.environment.stanford_town_env.stanford_town_env import StanfordTownEnv
|
||||
from metagpt.environment.software_env.software_env import SoftwareEnv
|
||||
from metagpt.environment.android.android_env import AndroidEnv
|
||||
from metagpt.environment.werewolf.werewolf_env import WerewolfEnv
|
||||
from metagpt.environment.stanford_town.stanford_town_env import StanfordTownEnv
|
||||
from metagpt.environment.software.software_env import SoftwareEnv
|
||||
|
||||
|
||||
__all__ = ["AndroidEnv", "WerewolfEnv", "StanfordTownEnv", "SoftwareEnv", "Environment"]
|
||||
|
|
|
|||
|
|
@ -4,7 +4,7 @@
|
|||
|
||||
from pydantic import Field
|
||||
|
||||
from metagpt.environment.android_env.android_ext_env import AndroidExtEnv
|
||||
from metagpt.environment.android.android_ext_env import AndroidExtEnv
|
||||
from metagpt.environment.base_env import Environment
|
||||
|
||||
|
||||
|
|
@ -8,8 +8,9 @@ from typing import Any, Optional
|
|||
|
||||
from pydantic import Field
|
||||
|
||||
from metagpt.environment.android_env.const import ADB_EXEC_FAIL
|
||||
from metagpt.environment.android.const import ADB_EXEC_FAIL
|
||||
from metagpt.environment.base_env import ExtEnv, mark_as_readable, mark_as_writeable
|
||||
from metagpt.environment.base_env_space import BaseEnvAction, BaseEnvObsParams
|
||||
|
||||
|
||||
class AndroidExtEnv(ExtEnv):
|
||||
|
|
@ -19,6 +20,20 @@ class AndroidExtEnv(ExtEnv):
|
|||
width: int = Field(default=720, description="device screen width")
|
||||
height: int = Field(default=1080, description="device screen height")
|
||||
|
||||
def reset(
|
||||
self,
|
||||
*,
|
||||
seed: Optional[int] = None,
|
||||
options: Optional[dict[str, Any]] = None,
|
||||
) -> tuple[dict[str, Any], dict[str, Any]]:
|
||||
pass
|
||||
|
||||
def observe(self, obs_params: Optional[BaseEnvObsParams] = None) -> Any:
|
||||
pass
|
||||
|
||||
def step(self, action: BaseEnvAction) -> tuple[dict[str, Any], float, bool, bool, dict[str, Any]]:
|
||||
pass
|
||||
|
||||
def __init__(self, **data: Any):
|
||||
super().__init__(**data)
|
||||
if data.get("device_id"):
|
||||
|
|
@ -3,9 +3,12 @@
|
|||
# @Desc : base env of executing environment
|
||||
|
||||
import asyncio
|
||||
from abc import abstractmethod
|
||||
from enum import Enum
|
||||
from typing import TYPE_CHECKING, Any, Dict, Iterable, Optional, Set, Union
|
||||
|
||||
from gymnasium import spaces
|
||||
from gymnasium.core import ActType, ObsType
|
||||
from pydantic import BaseModel, ConfigDict, Field, SerializeAsAny, model_validator
|
||||
|
||||
from metagpt.context import Context
|
||||
|
|
@ -14,6 +17,7 @@ from metagpt.environment.api.env_api import (
|
|||
ReadAPIRegistry,
|
||||
WriteAPIRegistry,
|
||||
)
|
||||
from metagpt.environment.base_env_space import BaseEnvAction, BaseEnvObsParams
|
||||
from metagpt.logs import logger
|
||||
from metagpt.schema import Message
|
||||
from metagpt.utils.common import get_function_schema, is_coroutine_func, is_send_to
|
||||
|
|
@ -49,6 +53,11 @@ def mark_as_writeable(func):
|
|||
class ExtEnv(BaseModel):
|
||||
"""External Env to integrate actual game environment"""
|
||||
|
||||
model_config = ConfigDict(arbitrary_types_allowed=True)
|
||||
|
||||
action_space: spaces.Space[ActType] = Field(default_factory=spaces.Space, exclude=True)
|
||||
observation_space: spaces.Space[ObsType] = Field(default_factory=spaces.Space, exclude=True)
|
||||
|
||||
def _check_api_exist(self, rw_api: Optional[str] = None):
|
||||
if not rw_api:
|
||||
raise ValueError(f"{rw_api} not exists")
|
||||
|
|
@ -61,39 +70,56 @@ class ExtEnv(BaseModel):
|
|||
else:
|
||||
return env_write_api_registry.get_apis()
|
||||
|
||||
async def observe(self, env_action: Union[str, EnvAPIAbstract]):
|
||||
async def read_from_api(self, env_action: Union[str, EnvAPIAbstract]):
|
||||
"""get observation from particular api of ExtEnv"""
|
||||
if isinstance(env_action, str):
|
||||
read_api = env_read_api_registry.get(api_name=env_action)["func"]
|
||||
self._check_api_exist(read_api)
|
||||
if is_coroutine_func(read_api):
|
||||
res = await read_api(self)
|
||||
env_read_api = env_read_api_registry.get(api_name=env_action)["func"]
|
||||
self._check_api_exist(env_read_api)
|
||||
if is_coroutine_func(env_read_api):
|
||||
res = await env_read_api(self)
|
||||
else:
|
||||
res = read_api(self)
|
||||
res = env_read_api(self)
|
||||
elif isinstance(env_action, EnvAPIAbstract):
|
||||
read_api = env_read_api_registry.get(api_name=env_action.api_name)["func"]
|
||||
self._check_api_exist(read_api)
|
||||
if is_coroutine_func(read_api):
|
||||
res = await read_api(self, *env_action.args, **env_action.kwargs)
|
||||
env_read_api = env_read_api_registry.get(api_name=env_action.api_name)["func"]
|
||||
self._check_api_exist(env_read_api)
|
||||
if is_coroutine_func(env_read_api):
|
||||
res = await env_read_api(self, *env_action.args, **env_action.kwargs)
|
||||
else:
|
||||
res = read_api(self, *env_action.args, **env_action.kwargs)
|
||||
res = env_read_api(self, *env_action.args, **env_action.kwargs)
|
||||
return res
|
||||
|
||||
async def step(self, env_action: Union[str, Message, EnvAPIAbstract, list[EnvAPIAbstract]]):
|
||||
async def write_thru_api(self, env_action: Union[str, Message, EnvAPIAbstract, list[EnvAPIAbstract]]):
|
||||
"""execute through particular api of ExtEnv"""
|
||||
res = None
|
||||
if isinstance(env_action, Message):
|
||||
self.publish_message(env_action)
|
||||
elif isinstance(env_action, EnvAPIAbstract):
|
||||
write_api = env_write_api_registry.get(env_action.api_name)["func"]
|
||||
self._check_api_exist(write_api)
|
||||
if is_coroutine_func(write_api):
|
||||
res = await write_api(self, *env_action.args, **env_action.kwargs)
|
||||
env_write_api = env_write_api_registry.get(env_action.api_name)["func"]
|
||||
self._check_api_exist(env_write_api)
|
||||
if is_coroutine_func(env_write_api):
|
||||
res = await env_write_api(self, *env_action.args, **env_action.kwargs)
|
||||
else:
|
||||
res = write_api(self, *env_action.args, **env_action.kwargs)
|
||||
res = env_write_api(self, *env_action.args, **env_action.kwargs)
|
||||
|
||||
return res
|
||||
|
||||
@abstractmethod
|
||||
def reset(
|
||||
self,
|
||||
*,
|
||||
seed: Optional[int] = None,
|
||||
options: Optional[dict[str, Any]] = None,
|
||||
) -> tuple[dict[str, Any], dict[str, Any]]:
|
||||
"""Implement this to get init observation"""
|
||||
|
||||
@abstractmethod
|
||||
def observe(self, obs_params: Optional[BaseEnvObsParams] = None) -> Any:
|
||||
"""Implement this if you want to get partial observation from the env"""
|
||||
|
||||
@abstractmethod
|
||||
def step(self, action: BaseEnvAction) -> tuple[dict[str, Any], float, bool, bool, dict[str, Any]]:
|
||||
"""Implement this to feed a action and then get new observation from the env"""
|
||||
|
||||
|
||||
class Environment(ExtEnv):
|
||||
"""环境，承载一批角色，角色可以向环境发布消息，可以被其他角色观察到
|
||||
|
|
@ -108,6 +134,20 @@ class Environment(ExtEnv):
|
|||
history: str = "" # For debug
|
||||
context: Context = Field(default_factory=Context, exclude=True)
|
||||
|
||||
def reset(
|
||||
self,
|
||||
*,
|
||||
seed: Optional[int] = None,
|
||||
options: Optional[dict[str, Any]] = None,
|
||||
) -> tuple[dict[str, Any], dict[str, Any]]:
|
||||
pass
|
||||
|
||||
def observe(self, obs_params: Optional[BaseEnvObsParams] = None) -> Any:
|
||||
pass
|
||||
|
||||
def step(self, action: BaseEnvAction) -> tuple[dict[str, Any], float, bool, bool, dict[str, Any]]:
|
||||
pass
|
||||
|
||||
@model_validator(mode="after")
|
||||
def init_roles(self):
|
||||
self.add_roles(self.roles.values())
|
||||
|
|
|
|||
33
metagpt/environment/base_env_space.py
Normal file
33
metagpt/environment/base_env_space.py
Normal file
|
|
@ -0,0 +1,33 @@
|
|||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
# @Desc :
|
||||
|
||||
from enum import IntEnum
|
||||
|
||||
from pydantic import BaseModel, ConfigDict, Field
|
||||
|
||||
|
||||
class BaseEnvActionType(IntEnum):
|
||||
# # NONE = 0 # no action to run, just get observation
|
||||
pass
|
||||
|
||||
|
||||
class BaseEnvAction(BaseModel):
|
||||
"""env action type and its related params of action functions/apis"""
|
||||
|
||||
model_config = ConfigDict(arbitrary_types_allowed=True)
|
||||
|
||||
action_type: int = Field(default=0, description="action type")
|
||||
|
||||
|
||||
class BaseEnvObsType(IntEnum):
|
||||
# # NONE = 0 # get whole observation from env
|
||||
pass
|
||||
|
||||
|
||||
class BaseEnvObsParams(BaseModel):
|
||||
"""observation params for different EnvObsType to get its observe result"""
|
||||
|
||||
model_config = ConfigDict(arbitrary_types_allowed=True)
|
||||
|
||||
obs_type: int = Field(default=0, description="observation type")
|
||||
|
|
@ -8,14 +8,14 @@ import re
|
|||
import time
|
||||
from typing import Any, Iterable
|
||||
|
||||
from llama_index.vector_stores.chroma import ChromaVectorStore
|
||||
from pydantic import ConfigDict, Field
|
||||
|
||||
from metagpt.config2 import config as CONFIG
|
||||
from metagpt.environment.base_env import Environment
|
||||
from metagpt.environment.minecraft_env.const import MC_CKPT_DIR
|
||||
from metagpt.environment.minecraft_env.minecraft_ext_env import MinecraftExtEnv
|
||||
from metagpt.environment.minecraft.const import MC_CKPT_DIR
|
||||
from metagpt.environment.minecraft.minecraft_ext_env import MinecraftExtEnv
|
||||
from metagpt.logs import logger
|
||||
from metagpt.rag.vector_stores.chroma import ChromaVectorStore
|
||||
from metagpt.utils.common import load_mc_skills_code, read_json_file, write_json_file
|
||||
|
||||
|
||||
|
|
@ -282,7 +282,7 @@ class MinecraftEnv(Environment, MinecraftExtEnv):
|
|||
position = event["status"]["position"]
|
||||
blocks.append(block)
|
||||
positions.append(position)
|
||||
new_events = self.step(
|
||||
new_events = self._step(
|
||||
f"await givePlacedItemBack(bot, {json.dumps(blocks)}, {json.dumps(positions)})",
|
||||
programs=self.programs,
|
||||
)
|
||||
|
|
@ -323,7 +323,7 @@ class MinecraftEnv(Environment, MinecraftExtEnv):
|
|||
Exception: If there is an issue retrieving events.
|
||||
"""
|
||||
try:
|
||||
self.reset(
|
||||
self._reset(
|
||||
options={
|
||||
"mode": "soft",
|
||||
"wait_ticks": 20,
|
||||
|
|
@ -332,13 +332,13 @@ class MinecraftEnv(Environment, MinecraftExtEnv):
|
|||
# difficulty = "easy" if len(self.completed_tasks) > 15 else "peaceful"
|
||||
difficulty = "peaceful"
|
||||
|
||||
events = self.step("bot.chat(`/time set ${getNextTime()}`);\n" + f"bot.chat('/difficulty {difficulty}');")
|
||||
events = self._step("bot.chat(`/time set ${getNextTime()}`);\n" + f"bot.chat('/difficulty {difficulty}');")
|
||||
self.update_event(events)
|
||||
return events
|
||||
except Exception as e:
|
||||
time.sleep(3) # wait for mineflayer to exit
|
||||
# reset bot status here
|
||||
events = self.reset(
|
||||
events = self._reset(
|
||||
options={
|
||||
"mode": "hard",
|
||||
"wait_ticks": 20,
|
||||
|
|
@ -365,7 +365,7 @@ class MinecraftEnv(Environment, MinecraftExtEnv):
|
|||
Exception: If there is an issue retrieving events.
|
||||
"""
|
||||
try:
|
||||
events = self.step(
|
||||
events = self._step(
|
||||
code=self.code,
|
||||
programs=self.programs,
|
||||
)
|
||||
|
|
@ -374,7 +374,7 @@ class MinecraftEnv(Environment, MinecraftExtEnv):
|
|||
except Exception as e:
|
||||
time.sleep(3) # wait for mineflayer to exit
|
||||
# reset bot status here
|
||||
events = self.reset(
|
||||
events = self._reset(
|
||||
options={
|
||||
"mode": "hard",
|
||||
"wait_ticks": 20,
|
||||
|
|
@ -5,20 +5,21 @@
|
|||
|
||||
import json
|
||||
import time
|
||||
from typing import Optional
|
||||
from typing import Any, Optional
|
||||
|
||||
import requests
|
||||
from pydantic import ConfigDict, Field, model_validator
|
||||
|
||||
from metagpt.environment.base_env import ExtEnv, mark_as_writeable
|
||||
from metagpt.environment.minecraft_env.const import (
|
||||
from metagpt.environment.base_env_space import BaseEnvAction, BaseEnvObsParams
|
||||
from metagpt.environment.minecraft.const import (
|
||||
MC_CKPT_DIR,
|
||||
MC_CORE_INVENTORY_ITEMS,
|
||||
MC_CURRICULUM_OB,
|
||||
MC_DEFAULT_WARMUP,
|
||||
METAGPT_ROOT,
|
||||
)
|
||||
from metagpt.environment.minecraft_env.process_monitor import SubprocessMonitor
|
||||
from metagpt.environment.minecraft.process_monitor import SubprocessMonitor
|
||||
from metagpt.logs import logger
|
||||
|
||||
|
||||
|
|
@ -38,6 +39,20 @@ class MinecraftExtEnv(ExtEnv):
|
|||
server_paused: bool = Field(default=False)
|
||||
warm_up: dict = Field(default=dict())
|
||||
|
||||
def reset(
|
||||
self,
|
||||
*,
|
||||
seed: Optional[int] = None,
|
||||
options: Optional[dict[str, Any]] = None,
|
||||
) -> tuple[dict[str, Any], dict[str, Any]]:
|
||||
pass
|
||||
|
||||
def observe(self, obs_params: Optional[BaseEnvObsParams] = None) -> Any:
|
||||
pass
|
||||
|
||||
def step(self, action: BaseEnvAction) -> tuple[dict[str, Any], float, bool, bool, dict[str, Any]]:
|
||||
pass
|
||||
|
||||
@property
|
||||
def server(self) -> str:
|
||||
return f"{self.server_host}:{self.server_port}"
|
||||
|
|
@ -48,7 +63,7 @@ class MinecraftExtEnv(ExtEnv):
|
|||
self.mineflayer = SubprocessMonitor(
|
||||
commands=[
|
||||
"node",
|
||||
METAGPT_ROOT.joinpath("metagpt", "environment", "minecraft_env", "mineflayer", "index.js"),
|
||||
METAGPT_ROOT.joinpath("metagpt", "environment", "minecraft", "mineflayer", "index.js"),
|
||||
str(self.server_port),
|
||||
],
|
||||
name="mineflayer",
|
||||
|
|
@ -115,7 +130,7 @@ class MinecraftExtEnv(ExtEnv):
|
|||
return res.json()
|
||||
|
||||
@mark_as_writeable
|
||||
def reset(self, *, seed=None, options=None) -> dict:
|
||||
def _reset(self, *, seed=None, options=None) -> dict:
|
||||
if options is None:
|
||||
options = {}
|
||||
if options.get("inventory", {}) and options.get("mode", "hard") != "hard":
|
||||
|
|
@ -145,7 +160,7 @@ class MinecraftExtEnv(ExtEnv):
|
|||
return json.loads(returned_data)
|
||||
|
||||
@mark_as_writeable
|
||||
def step(self, code: str, programs: str = "") -> dict:
|
||||
def _step(self, code: str, programs: str = "") -> dict:
|
||||
if not self.has_reset:
|
||||
raise RuntimeError("Environment has not been reset yet")
|
||||
self.check_process()
|
||||
105
metagpt/environment/stanford_town/env_space.py
Normal file
105
metagpt/environment/stanford_town/env_space.py
Normal file
|
|
@ -0,0 +1,105 @@
|
|||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
# @Desc :
|
||||
|
||||
from typing import Any, Optional, Union
|
||||
|
||||
import numpy as np
|
||||
import numpy.typing as npt
|
||||
from gymnasium import spaces
|
||||
from pydantic import ConfigDict, Field, field_validator
|
||||
|
||||
from metagpt.environment.base_env_space import (
|
||||
BaseEnvAction,
|
||||
BaseEnvActionType,
|
||||
BaseEnvObsParams,
|
||||
BaseEnvObsType,
|
||||
)
|
||||
|
||||
|
||||
class EnvActionType(BaseEnvActionType):
|
||||
NONE = 0 # no action to run, just get observation
|
||||
|
||||
ADD_TILE_EVENT = 1 # Add an event triple to a tile
|
||||
RM_TILE_EVENT = 2 # Remove an event triple from a tile
|
||||
TURN_TILE_EVENT_IDLE = 3 # Turn an event triple from a tile into idle
|
||||
RM_TITLE_SUB_EVENT = 4 # Remove an event triple that has the input subject from a tile
|
||||
|
||||
|
||||
class EnvAction(BaseEnvAction):
|
||||
"""env action type and its related params of action functions/apis"""
|
||||
|
||||
model_config = ConfigDict(arbitrary_types_allowed=True)
|
||||
|
||||
action_type: int = Field(default=EnvActionType.NONE, description="action type")
|
||||
coord: npt.NDArray[np.int64] = Field(
|
||||
default_factory=lambda: np.zeros(2, dtype=np.int64), description="tile coordinate"
|
||||
)
|
||||
subject: str = Field(default="", description="subject name of first element in event")
|
||||
event: tuple[str, Optional[str], Optional[str], Optional[str]] = Field(
|
||||
default=["", None, None, None], description="tile event"
|
||||
)
|
||||
|
||||
@field_validator("coord", mode="before")
|
||||
@classmethod
|
||||
def check_coord(cls, coord) -> npt.NDArray[np.int64]:
|
||||
if not isinstance(coord, np.ndarray):
|
||||
return np.array(coord)
|
||||
|
||||
|
||||
class EnvObsType(BaseEnvObsType):
|
||||
"""get part observation with specific params"""
|
||||
|
||||
NONE = 0 # get whole observation from env
|
||||
|
||||
GET_TITLE = 1 # get the tile detail dictionary with given tile coord
|
||||
TILE_PATH = 2 # get the tile address with given tile coord
|
||||
TILE_NBR = 3 # get the neighbors of given tile coord and its vision radius
|
||||
|
||||
|
||||
class EnvObsParams(BaseEnvObsParams):
|
||||
"""observation params for different EnvObsType"""
|
||||
|
||||
model_config = ConfigDict(arbitrary_types_allowed=True)
|
||||
|
||||
obs_type: int = Field(default=EnvObsType.NONE, description="observation type")
|
||||
coord: npt.NDArray[np.int64] = Field(
|
||||
default_factory=lambda: np.zeros(2, dtype=np.int64), description="tile coordinate"
|
||||
)
|
||||
level: str = Field(default="", description="different level of title")
|
||||
vision_radius: int = Field(default=0, description="the vision radius of current tile")
|
||||
|
||||
@field_validator("coord", mode="before")
|
||||
@classmethod
|
||||
def check_coord(cls, coord) -> npt.NDArray[np.int64]:
|
||||
if not isinstance(coord, np.ndarray):
|
||||
return np.array(coord)
|
||||
|
||||
|
||||
EnvObsValType = Union[list[list[str]], dict[str, set[tuple[int, int]]], list[list[dict[str, Any]]]]
|
||||
|
||||
|
||||
def get_observation_space() -> spaces.Dict:
|
||||
# it's a
|
||||
space = spaces.Dict(
|
||||
{"collision_maze": spaces.Discrete(2), "tiles": spaces.Discrete(2), "address_tiles": spaces.Discrete(2)}
|
||||
)
|
||||
|
||||
return space
|
||||
|
||||
|
||||
def get_action_space(maze_shape: tuple[int, int]) -> spaces.Dict:
|
||||
"""The fields defined by the space correspond to the input parameters of the action except `action_type`"""
|
||||
space = spaces.Dict(
|
||||
{
|
||||
"action_type": spaces.Discrete(len(EnvActionType)),
|
||||
"coord": spaces.Box(
|
||||
np.array([0, 0], dtype=np.int64), np.array([maze_shape[0], maze_shape[1]], dtype=np.int64)
|
||||
), # coord of the tile
|
||||
"subject": spaces.Text(256), # the first element of an tile event
|
||||
"event": spaces.Tuple(
|
||||
(spaces.Text(256), spaces.Text(256), spaces.Text(256), spaces.Text(256))
|
||||
), # event is a tuple of four str
|
||||
}
|
||||
)
|
||||
return space
|
||||
10
metagpt/environment/stanford_town/stanford_town_env.py
Normal file
10
metagpt/environment/stanford_town/stanford_town_env.py
Normal file
|
|
@ -0,0 +1,10 @@
|
|||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
# @Desc : MG StanfordTown Env
|
||||
|
||||
from metagpt.environment.base_env import Environment
|
||||
from metagpt.environment.stanford_town.stanford_town_ext_env import StanfordTownExtEnv
|
||||
|
||||
|
||||
class StanfordTownEnv(StanfordTownExtEnv, Environment):
|
||||
pass
|
||||
|
|
@ -5,11 +5,20 @@
|
|||
|
||||
import math
|
||||
from pathlib import Path
|
||||
from typing import Optional, Tuple
|
||||
from typing import Any, Optional
|
||||
|
||||
from pydantic import ConfigDict, Field, model_validator
|
||||
|
||||
from metagpt.environment.base_env import ExtEnv, mark_as_readable, mark_as_writeable
|
||||
from metagpt.environment.stanford_town.env_space import (
|
||||
EnvAction,
|
||||
EnvActionType,
|
||||
EnvObsParams,
|
||||
EnvObsType,
|
||||
EnvObsValType,
|
||||
get_action_space,
|
||||
get_observation_space,
|
||||
)
|
||||
from metagpt.utils.common import read_csv_to_list, read_json_file
|
||||
|
||||
|
||||
|
|
@ -197,15 +206,82 @@ class StanfordTownExtEnv(ExtEnv):
|
|||
else:
|
||||
address_tiles[add] = set([(j, i)])
|
||||
values["address_tiles"] = address_tiles
|
||||
|
||||
values["action_space"] = get_action_space((maze_width, maze_height))
|
||||
values["observation_space"] = get_observation_space()
|
||||
return values
|
||||
|
||||
def reset(
|
||||
self,
|
||||
*,
|
||||
seed: Optional[int] = None,
|
||||
options: Optional[dict[str, Any]] = None,
|
||||
) -> tuple[dict[str, EnvObsValType], dict[str, Any]]:
|
||||
"""reset env and get the init observation
|
||||
Return results corresponding to `observation, info`
|
||||
"""
|
||||
super().reset(seed=seed, options=options)
|
||||
|
||||
obs = self._get_obs()
|
||||
|
||||
return obs, {}
|
||||
|
||||
def _get_obs(self) -> dict[str, EnvObsValType]:
|
||||
"""Get observation"""
|
||||
return {
|
||||
"collision_maze": self.get_collision_maze(),
|
||||
"tiles": self.tiles,
|
||||
"address_tiles": self.get_address_tiles(),
|
||||
}
|
||||
|
||||
def observe(self, obs_params: Optional[EnvObsParams] = None) -> Any:
|
||||
"""Get partial or full observation from the env"""
|
||||
obs_type = obs_params.obs_type if obs_params else EnvObsType.NONE
|
||||
if obs_type == EnvObsType.NONE:
|
||||
obs = self._get_obs()
|
||||
elif obs_type == EnvObsType.GET_TITLE:
|
||||
obs = self.access_tile(tile=obs_params.coord)
|
||||
elif obs_type == EnvObsType.TILE_PATH:
|
||||
obs = self.get_tile_path(tile=obs_params.coord, level=obs_params.level)
|
||||
elif obs_type == EnvObsType.TILE_NBR:
|
||||
obs = self.get_nearby_tiles(tile=obs_params.coord, vision_r=obs_params.vision_radius)
|
||||
return obs
|
||||
|
||||
def step(self, action: EnvAction) -> tuple[dict[str, EnvObsValType], float, bool, bool, dict[str, Any]]:
|
||||
"""Execute action and then return observation
|
||||
Return results corresponding to `observation, reward, terminated, truncated, info`
|
||||
"""
|
||||
terminated = False
|
||||
try:
|
||||
self._execute_env_action(action)
|
||||
except Exception:
|
||||
terminated = True
|
||||
|
||||
obs = self._get_obs()
|
||||
|
||||
ret = (obs, 1.0, terminated, False, {})
|
||||
return ret
|
||||
|
||||
def _execute_env_action(self, action: EnvAction):
|
||||
action_type = action.action_type
|
||||
if action_type == EnvActionType.NONE:
|
||||
pass
|
||||
elif action_type == EnvActionType.ADD_TILE_EVENT:
|
||||
self.add_event_from_tile(curr_event=action.event, tile=action.coord)
|
||||
elif action_type == EnvActionType.RM_TILE_EVENT:
|
||||
self.remove_event_from_tile(curr_event=action.event, tile=action.coord)
|
||||
elif action_type == EnvActionType.TURN_TILE_EVENT_IDLE:
|
||||
self.turn_event_from_tile_idle(curr_event=action.event, tile=action.coord)
|
||||
elif action_type == EnvActionType.RM_TITLE_SUB_EVENT:
|
||||
self.remove_subject_events_from_tile(subject=action.subject, tile=action.coord)
|
||||
|
||||
def turn_coordinate_to_tile(self, px_coordinate: tuple[int, int]) -> tuple[int, int]:
|
||||
"""
|
||||
Turns a pixel coordinate to a tile coordinate.
|
||||
"""
|
||||
x = math.ceil(px_coordinate[0] / self.sq_tile_size)
|
||||
y = math.ceil(px_coordinate[1] / self.sq_tile_size)
|
||||
return (x, y)
|
||||
return x, y
|
||||
|
||||
@mark_as_readable
|
||||
def get_collision_maze(self) -> list:
|
||||
|
|
@ -316,10 +392,6 @@ class StanfordTownExtEnv(ExtEnv):
|
|||
nearby_tiles += [(i, j)]
|
||||
return nearby_tiles
|
||||
|
||||
@mark_as_writeable
|
||||
def add_tiles_event(self, pt_y: int, pt_x: int, event: Tuple[str, str, str, str]):
|
||||
self.tiles[pt_y][pt_x]["events"].add(event)
|
||||
|
||||
@mark_as_writeable
|
||||
def add_event_from_tile(self, curr_event: tuple[str], tile: tuple[int, int]) -> None:
|
||||
"""
|
||||
|
|
@ -1,12 +0,0 @@
|
|||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
# @Desc : MG StanfordTown Env
|
||||
|
||||
from metagpt.environment.base_env import Environment
|
||||
from metagpt.environment.stanford_town_env.stanford_town_ext_env import (
|
||||
StanfordTownExtEnv,
|
||||
)
|
||||
|
||||
|
||||
class StanfordTownEnv(Environment, StanfordTownExtEnv):
|
||||
pass
|
||||
3
metagpt/environment/werewolf/__init__.py
Normal file
3
metagpt/environment/werewolf/__init__.py
Normal file
|
|
@ -0,0 +1,3 @@
|
|||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
# @Desc :
|
||||
|
|
@ -5,7 +5,7 @@
|
|||
from pydantic import Field
|
||||
|
||||
from metagpt.environment.base_env import Environment
|
||||
from metagpt.environment.werewolf_env.werewolf_ext_env import WerewolfExtEnv
|
||||
from metagpt.environment.werewolf.werewolf_ext_env import WerewolfExtEnv
|
||||
from metagpt.logs import logger
|
||||
from metagpt.schema import Message
|
||||
|
||||
|
|
@ -5,11 +5,12 @@
|
|||
import random
|
||||
from collections import Counter
|
||||
from enum import Enum
|
||||
from typing import Callable, Optional
|
||||
from typing import Any, Callable, Optional
|
||||
|
||||
from pydantic import ConfigDict, Field
|
||||
|
||||
from metagpt.environment.base_env import ExtEnv, mark_as_readable, mark_as_writeable
|
||||
from metagpt.environment.base_env_space import BaseEnvAction, BaseEnvObsParams
|
||||
from metagpt.logs import logger
|
||||
|
||||
|
||||
|
|
@ -128,6 +129,20 @@ class WerewolfExtEnv(ExtEnv):
|
|||
player_poisoned: Optional[str] = Field(default=None)
|
||||
player_current_dead: list[str] = Field(default=[])
|
||||
|
||||
def reset(
|
||||
self,
|
||||
*,
|
||||
seed: Optional[int] = None,
|
||||
options: Optional[dict[str, Any]] = None,
|
||||
) -> tuple[dict[str, Any], dict[str, Any]]:
|
||||
pass
|
||||
|
||||
def observe(self, obs_params: Optional[BaseEnvObsParams] = None) -> Any:
|
||||
pass
|
||||
|
||||
def step(self, action: BaseEnvAction) -> tuple[dict[str, Any], float, bool, bool, dict[str, Any]]:
|
||||
pass
|
||||
|
||||
@property
|
||||
def living_players(self) -> list[str]:
|
||||
player_names = []
|
||||
3
metagpt/ext/__init__.py
Normal file
3
metagpt/ext/__init__.py
Normal file
|
|
@ -0,0 +1,3 @@
|
|||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
# @Desc :
|
||||
36
metagpt/ext/stanford_town/README.md
Normal file
36
metagpt/ext/stanford_town/README.md
Normal file
|
|
@ -0,0 +1,36 @@
|
|||
## Stanford Town Game
|
||||
|
||||
### Pre-Description
|
||||
In order to facilitate GA( [generative_agents](https://github.com/joonspk-research/generative_agents) )'s frontend docking data (to avoid changing its code), you can set the value `temp_storage_path` to `temp_storage` of `generative_agents` when start `run_st_game.py`. like
|
||||
|
||||
`python3 run_st_game.py --temp_storage_path path/to/ga/temp_storage xxx`
|
||||
|
||||
Or change the path under `const.py` like beflow
|
||||
|
||||
```
|
||||
STORAGE_PATH = EXAMPLE_PATH.joinpath("storage")
|
||||
TEMP_STORAGE_PATH = EXAMPLE_PATH.joinpath("temp_storage")
|
||||
# updated
|
||||
STORAGE_PATH = Path("{path/to/ga/storage}")
|
||||
TEMP_STORAGE_PATH = Path("{path/to/ga/temp_storage}")
|
||||
```
|
||||
|
||||
This can be used to achieve docking of simulation data without changing the GA code. Otherwise, the GA code must be modified to adapt to the MG output path.
|
||||
|
||||
If you don't want to start from 0, copy other simulation directories under `generative_agents/environment/frontend_server/storage/` to `examples/stanford_town/storage`, and select a directory named `fork_sim_code`.
|
||||
|
||||
### Backend service startup
|
||||
The execution entry is `python3 run_st_game.py "Host a open lunch party at 13:00 pm" "base_the_ville_isabella_maria_klaus" "test_sim" 10`
|
||||
or
|
||||
`python3 run_st_game.py "Host a open lunch party at 13:00 pm" "base_the_ville_isabella_maria_klaus" "test_sim" 10 --temp_storage_path path/to/ga/temp_storage`
|
||||
|
||||
`idea` is the user's voice to the first Agent, and it is disseminated through this voice to see whether the final multi-agents achieve the goal of hosting or participating in the event.
|
||||
|
||||
### Frontend service startup
|
||||
Enter project folder `generative_agents`
|
||||
|
||||
Enter `environment/frontend_server` and use `python3 manage.py runserver` to start the front-end service.
|
||||
Visit `http://localhost:8000/simulator_home` to enter the current simulation interface.
|
||||
|
||||
## Appreciation
|
||||
The reproduction work has referred the `https://github.com/joonspk-research/generative_agents`, let's make a general statement here.
|
||||
35
metagpt/ext/stanford_town/README_CN.md
Normal file
35
metagpt/ext/stanford_town/README_CN.md
Normal file
|
|
@ -0,0 +1,35 @@
|
|||
## Stanford Town Game
|
||||
|
||||
### 前置
|
||||
为了方便GA（ [generative_agents](https://github.com/joonspk-research/generative_agents) ）的前端对接数据（避免改动它那块的代码），可在启动`run_st_game.py`加上`temp_storage_path`指向`generative_agents`对应的`temp_storage`路径。比如
|
||||
|
||||
`python3 run_st_game.py --temp_storage_path path/to/ga/temp_storage xxx`
|
||||
|
||||
或将`const.py`下的
|
||||
|
||||
```
|
||||
STORAGE_PATH = EXAMPLE_PATH.joinpath("storage")
|
||||
TEMP_STORAGE_PATH = EXAMPLE_PATH.joinpath("temp_storage")
|
||||
# 更新为
|
||||
STORAGE_PATH = Path("{path/to/ga/storage}")
|
||||
TEMP_STORAGE_PATH = Path("{path/to/ga/temp_storage}")
|
||||
```
|
||||
这样可用实现不改变GA代码情况下，实现仿真数据的对接。不然得修改GA的代码来适配MG的输出路径。
|
||||
|
||||
如果你不想从0开始启动，拷贝`generative_agents/environment/frontend_server/storage/`下的其他仿真目录到`examples/stanford_town/storage`，并选择一个目录名作为`fork_sim_code`。
|
||||
|
||||
### 后端服务启动
|
||||
执行入口为：`python3 run_st_game.py "Host a open lunch party at 13:00 pm" "base_the_ville_isabella_maria_klaus" "test_sim" 10`
|
||||
或者
|
||||
`python3 run_st_game.py "Host a open lunch party at 13:00 pm" "base_the_ville_isabella_maria_klaus" "test_sim" 10 --temp_storage_path path/to/ga/temp_storage`
|
||||
|
||||
`idea`为用户给第一个Agent的用户心声，并通过这个心声进行传播，看最后多智能体是否达到举办、参加活动的目标。
|
||||
|
||||
### 前端服务启动
|
||||
进入`generative_agents`项目目录
|
||||
|
||||
进入`environment/frontend_server`，使用`python3 manage.py runserver`启动前端服务。
|
||||
访问`http://localhost:8000/simulator_home` 进入当前的仿真界面。
|
||||
|
||||
## Appreciation
|
||||
The reproduction work has referred the `https://github.com/joonspk-research/generative_agents`, let's make a general statement here.
|
||||
3
metagpt/ext/stanford_town/__init__.py
Normal file
3
metagpt/ext/stanford_town/__init__.py
Normal file
|
|
@ -0,0 +1,3 @@
|
|||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
# @Desc : stanford town implement
|
||||
3
metagpt/ext/stanford_town/actions/__init__.py
Normal file
3
metagpt/ext/stanford_town/actions/__init__.py
Normal file
|
|
@ -0,0 +1,3 @@
|
|||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
# @Desc :
|
||||
39
metagpt/ext/stanford_town/actions/agent_chat_sum_rel.py
Normal file
39
metagpt/ext/stanford_town/actions/agent_chat_sum_rel.py
Normal file
|
|
@ -0,0 +1,39 @@
|
|||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
# @Desc : summarize relationship in a agent chat
|
||||
|
||||
from metagpt.ext.stanford_town.actions.st_action import STAction
|
||||
from metagpt.logs import logger
|
||||
|
||||
|
||||
class AgentChatSumRel(STAction):
|
||||
name: str = "AgentChatSumRel"
|
||||
|
||||
def _func_validate(self, llm_resp: str, prompt: str) -> bool:
|
||||
resp = False
|
||||
try:
|
||||
_ = llm_resp.split('"')[0].strip()
|
||||
resp = True
|
||||
except Exception:
|
||||
pass
|
||||
return resp
|
||||
|
||||
def _func_cleanup(self, llm_resp: str, prompt: str) -> str:
|
||||
return llm_resp.split('"')[0].strip()
|
||||
|
||||
def _func_fail_default_resp(self) -> str:
|
||||
pass
|
||||
|
||||
async def run(self, init_role: "STRole", target_role: "STRole", statements: str) -> str:
|
||||
def create_prompt_input(init_role: "STRole", target_role: "STRole", statements: str) -> str:
|
||||
prompt_input = [statements, init_role.name, target_role.name]
|
||||
return prompt_input
|
||||
|
||||
prompt_input = create_prompt_input(init_role, target_role, statements)
|
||||
prompt = self.generate_prompt_with_tmpl_filename(prompt_input, "summarize_chat_relationship_v2.txt")
|
||||
|
||||
example_output = "Jane Doe is working on a project"
|
||||
special_instruction = "The output should be a string that responds to the question."
|
||||
output = await self._run_gpt35(prompt, example_output, special_instruction)
|
||||
logger.info(f"Role: {init_role.name} Action: {self.cls_name} output: {output}")
|
||||
return output
|
||||
97
metagpt/ext/stanford_town/actions/decide_to_talk.py
Normal file
97
metagpt/ext/stanford_town/actions/decide_to_talk.py
Normal file
|
|
@ -0,0 +1,97 @@
|
|||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
# @Desc : device to talk to another role, return yes or no
|
||||
|
||||
from metagpt.ext.stanford_town.actions.st_action import STAction
|
||||
from metagpt.logs import logger
|
||||
|
||||
|
||||
class DecideToTalk(STAction):
|
||||
name: str = "DecideToTalk"
|
||||
|
||||
def _func_validate(self, llm_resp: str, prompt: str) -> bool:
|
||||
resp = False
|
||||
try:
|
||||
if llm_resp.split("Answer in yes or no:")[-1].strip().lower() in ["yes", "no"]:
|
||||
resp = True
|
||||
except ValueError:
|
||||
pass
|
||||
return resp
|
||||
|
||||
def _func_cleanup(self, llm_resp: str, prompt: str) -> str:
|
||||
return llm_resp.split("Answer in yes or no:")[-1].strip().lower()
|
||||
|
||||
def _func_fail_default_resp(self) -> str:
|
||||
return "yes"
|
||||
|
||||
async def run(self, init_role: "STRole", target_role: "STRole", retrieved: dict, *args, **kwargs) -> bool:
|
||||
"""Run action"""
|
||||
|
||||
def create_prompt_input(init_role: "STRole", target_role: "STRole", retrieved: dict) -> str:
|
||||
scratch = init_role.rc.scratch
|
||||
target_scratch = target_role.rc.scratch
|
||||
last_chat = init_role.rc.memory.get_last_chat(target_role.name)
|
||||
last_chatted_time = ""
|
||||
last_chat_about = ""
|
||||
if last_chat:
|
||||
last_chatted_time = last_chat.created.strftime("%B %d, %Y, %H:%M:%S")
|
||||
last_chat_about = last_chat.description
|
||||
|
||||
context = ""
|
||||
for c_node in retrieved["events"]:
|
||||
curr_desc = c_node.description.split(" ")
|
||||
curr_desc[2:3] = ["was"]
|
||||
curr_desc = " ".join(curr_desc)
|
||||
context += f"{curr_desc}. "
|
||||
context += "\n"
|
||||
for c_node in retrieved["thoughts"]:
|
||||
context += f"{c_node.description}. "
|
||||
|
||||
curr_time = scratch.curr_time.strftime("%B %d, %Y, %H:%M:%S %p")
|
||||
init_act_desc = scratch.act_description
|
||||
if "(" in init_act_desc:
|
||||
init_act_desc = init_act_desc.split("(")[-1][:-1]
|
||||
|
||||
if len(scratch.planned_path) == 0 and "waiting" not in init_act_desc:
|
||||
init_p_desc = f"{init_role.name} is already {init_act_desc}"
|
||||
elif "waiting" in init_act_desc:
|
||||
init_p_desc = f"{init_role.name} is {init_act_desc}"
|
||||
else:
|
||||
init_p_desc = f"{init_role.name} is on the way to {init_act_desc}"
|
||||
|
||||
target_act_desc = scratch.act_description
|
||||
if "(" in target_act_desc:
|
||||
target_act_desc = target_act_desc.split("(")[-1][:-1]
|
||||
|
||||
if len(target_scratch.planned_path) == 0 and "waiting" not in init_act_desc:
|
||||
target_p_desc = f"{target_role.name} is already {target_act_desc}"
|
||||
elif "waiting" in init_act_desc:
|
||||
target_p_desc = f"{init_role.name} is {init_act_desc}"
|
||||
else:
|
||||
target_p_desc = f"{target_role.name} is on the way to {target_act_desc}"
|
||||
|
||||
prompt_input = []
|
||||
prompt_input += [context]
|
||||
|
||||
prompt_input += [curr_time]
|
||||
|
||||
prompt_input += [init_role.name]
|
||||
prompt_input += [target_role.name]
|
||||
prompt_input += [last_chatted_time]
|
||||
prompt_input += [last_chat_about]
|
||||
|
||||
prompt_input += [init_p_desc]
|
||||
prompt_input += [target_p_desc]
|
||||
prompt_input += [init_role.name]
|
||||
prompt_input += [target_role.name]
|
||||
return prompt_input
|
||||
|
||||
prompt_input = create_prompt_input(init_role, target_role, retrieved)
|
||||
prompt = self.generate_prompt_with_tmpl_filename(
|
||||
prompt_input=prompt_input, tmpl_filename="decide_to_talk_v2.txt"
|
||||
)
|
||||
self.fail_default_resp = self._func_fail_default_resp()
|
||||
output = await self._run_gpt35_max_tokens(prompt, max_tokens=20) # yes or no
|
||||
result = True if output == "yes" else False
|
||||
logger.info(f"Role: {init_role.name} Action: {self.cls_name} output: {result}")
|
||||
return result
|
||||
20
metagpt/ext/stanford_town/actions/dummy_action.py
Normal file
20
metagpt/ext/stanford_town/actions/dummy_action.py
Normal file
|
|
@ -0,0 +1,20 @@
|
|||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
# @Desc : dummy action to make every STRole can deal DummyMessage which is caused by DummyAction
|
||||
|
||||
from metagpt.actions import Action
|
||||
from metagpt.schema import Message
|
||||
|
||||
|
||||
class DummyAction(Action):
|
||||
async def run(self, *args, **kwargs):
|
||||
raise NotImplementedError
|
||||
|
||||
|
||||
class DummyMessage(Message):
|
||||
"""
|
||||
dummy message to pass to role and make them to have a execution every round
|
||||
"""
|
||||
|
||||
content: str = "dummy"
|
||||
cause_by: str = "DummyAction"
|
||||
401
metagpt/ext/stanford_town/actions/gen_action_details.py
Normal file
401
metagpt/ext/stanford_town/actions/gen_action_details.py
Normal file
|
|
@ -0,0 +1,401 @@
|
|||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
# @Desc : gen_action_details
|
||||
|
||||
import random
|
||||
|
||||
from metagpt.environment.stanford_town.env_space import EnvObsParams, EnvObsType
|
||||
from metagpt.ext.stanford_town.actions.st_action import STAction
|
||||
from metagpt.logs import logger
|
||||
|
||||
|
||||
class GenActionSector(STAction):
|
||||
name: str = "GenActionSector"
|
||||
|
||||
def _func_cleanup(self, llm_resp: str, prompt: str):
|
||||
cleaned_response = llm_resp.split("}")[0]
|
||||
return cleaned_response
|
||||
|
||||
def _func_validate(self, llm_resp: str, prompt: str):
|
||||
if len(llm_resp.strip()) < 1:
|
||||
return False
|
||||
if "}" not in llm_resp:
|
||||
return False
|
||||
if "," in llm_resp:
|
||||
return False
|
||||
return True
|
||||
|
||||
def _func_fail_default_resp(self):
|
||||
fs = "kitchen"
|
||||
return fs
|
||||
|
||||
async def run(self, role: "STRole", access_tile: dict[str, str], act_desp: str):
|
||||
def create_prompt_input(role, access_tile: dict[str, str], act_desp):
|
||||
act_world = f"{access_tile['world']}"
|
||||
|
||||
prompt_input = []
|
||||
|
||||
prompt_input += [role.scratch.get_str_name()]
|
||||
prompt_input += [role.scratch.living_area.split(":")[1]]
|
||||
x = f"{act_world}:{role.scratch.living_area.split(':')[1]}"
|
||||
prompt_input += [role.s_mem.get_str_accessible_sector_arenas(x)]
|
||||
|
||||
prompt_input += [role.scratch.get_str_name()]
|
||||
prompt_input += [f"{access_tile['sector']}"]
|
||||
x = f"{act_world}:{access_tile['sector']}"
|
||||
prompt_input += [role.s_mem.get_str_accessible_sector_arenas(x)]
|
||||
|
||||
if role.scratch.get_str_daily_plan_req() != "":
|
||||
prompt_input += [f"\n{role.scratch.get_str_daily_plan_req()}"]
|
||||
else:
|
||||
prompt_input += [""]
|
||||
|
||||
# MAR 11 TEMP
|
||||
prompt_input = []
|
||||
act_world = access_tile["world"]
|
||||
accessible_sector_str = role.s_mem.get_str_accessible_sectors(act_world)
|
||||
curr = accessible_sector_str.split(", ")
|
||||
fin_accessible_sectors = []
|
||||
for i in curr:
|
||||
if "'s house" in i:
|
||||
if role.scratch.last_name in i:
|
||||
fin_accessible_sectors += [i]
|
||||
else:
|
||||
fin_accessible_sectors += [i]
|
||||
accessible_sector_str = ", ".join(fin_accessible_sectors)
|
||||
# END MAR 11 TEMP
|
||||
|
||||
prompt_input += [accessible_sector_str]
|
||||
|
||||
act_desp_1 = act_desp
|
||||
act_desp_2 = act_desp
|
||||
if "(" in act_desp:
|
||||
act_desp_1 = act_desp.split("(")[0].strip()
|
||||
act_desp_2 = act_desp.split("(")[-1][:-1]
|
||||
prompt_input += [role.scratch.get_str_name()]
|
||||
prompt_input += [act_desp_1]
|
||||
|
||||
prompt_input += [act_desp_2]
|
||||
prompt_input += [role.scratch.get_str_name()]
|
||||
return prompt_input
|
||||
|
||||
prompt_template = "action_location_sector_v1.txt"
|
||||
prompt_input = create_prompt_input(role, access_tile, act_desp)
|
||||
prompt = self.generate_prompt_with_tmpl_filename(prompt_input, prompt_template)
|
||||
|
||||
self.fail_default_resp = self._func_fail_default_resp()
|
||||
output = await self._run_gpt35_max_tokens(prompt, max_tokens=15)
|
||||
y = f"{access_tile['world']}"
|
||||
x = [i.strip() for i in role.s_mem.get_str_accessible_sectors(y).split(",")]
|
||||
if output not in x:
|
||||
# output = random.choice(x)
|
||||
output = role.scratch.living_area.split(":")[1]
|
||||
logger.info(f"Role: {role.name} Action: {self.cls_name} output: {output}")
|
||||
return output
|
||||
|
||||
|
||||
class GenActionArena(STAction):
|
||||
name: str = "GenActionArena"
|
||||
|
||||
def _func_cleanup(self, llm_resp: str, prompt: str):
|
||||
cleaned_response = llm_resp.split("}")[0]
|
||||
return cleaned_response
|
||||
|
||||
def _func_validate(self, llm_resp: str, prompt: str):
|
||||
if len(llm_resp.strip()) < 1:
|
||||
return False
|
||||
if "}" not in llm_resp:
|
||||
return False
|
||||
if "," in llm_resp:
|
||||
return False
|
||||
return True
|
||||
|
||||
def _func_fail_default_resp(self):
|
||||
fs = "kitchen"
|
||||
return fs
|
||||
|
||||
async def run(self, role: "STRole", act_desp: str, act_world: str, act_sector: str):
|
||||
def create_prompt_input(role, act_desp, act_world, act_sector):
|
||||
prompt_input = []
|
||||
prompt_input += [role.scratch.get_str_name()]
|
||||
x = f"{act_world}:{act_sector}"
|
||||
prompt_input += [act_sector]
|
||||
|
||||
# MAR 11 TEMP
|
||||
accessible_arena_str = role.s_mem.get_str_accessible_sector_arenas(x)
|
||||
curr = accessible_arena_str.split(", ")
|
||||
fin_accessible_arenas = []
|
||||
for i in curr:
|
||||
if "'s room" in i:
|
||||
if role.scratch.last_name in i:
|
||||
fin_accessible_arenas += [i]
|
||||
else:
|
||||
fin_accessible_arenas += [i]
|
||||
accessible_arena_str = ", ".join(fin_accessible_arenas)
|
||||
# END MAR 11 TEMP
|
||||
prompt_input += [accessible_arena_str]
|
||||
act_desp_1 = act_desp
|
||||
act_desp_2 = act_desp
|
||||
if "(" in act_desp:
|
||||
act_desp_1 = act_desp.split("(")[0].strip()
|
||||
act_desp_2 = act_desp.split("(")[-1][:-1]
|
||||
prompt_input += [role.scratch.get_str_name()]
|
||||
prompt_input += [act_desp_1]
|
||||
|
||||
prompt_input += [act_desp_2]
|
||||
prompt_input += [role.scratch.get_str_name()]
|
||||
|
||||
prompt_input += [act_sector]
|
||||
prompt_input += [accessible_arena_str]
|
||||
return prompt_input
|
||||
|
||||
prompt_template = "action_location_object_vMar11.txt"
|
||||
prompt_input = create_prompt_input(role, act_desp, act_world, act_sector)
|
||||
prompt = self.generate_prompt_with_tmpl_filename(prompt_input, prompt_template)
|
||||
self.fail_default_resp = self._func_fail_default_resp()
|
||||
output = await self._run_gpt35_max_tokens(prompt, max_tokens=15)
|
||||
logger.info(f"Role: {role.name} Action: {self.cls_name} output: {output}")
|
||||
return output
|
||||
|
||||
|
||||
class GenActionObject(STAction):
|
||||
name: str = "GenActionObject"
|
||||
|
||||
def _func_validate(self, llm_resp: str, prompt: str):
|
||||
if len(llm_resp.strip()) < 1:
|
||||
return False
|
||||
return True
|
||||
|
||||
def _func_cleanup(self, llm_resp: str, prompt: str):
|
||||
cleaned_response = llm_resp.strip()
|
||||
return cleaned_response
|
||||
|
||||
def _func_fail_default_resp(self):
|
||||
fs = "bed"
|
||||
return fs
|
||||
|
||||
async def run(self, role: "STRole", act_desp: str, temp_address: str):
|
||||
def create_prompt_input(role, act_desp, temp_address):
|
||||
prompt_input = []
|
||||
if "(" in act_desp:
|
||||
act_desp = act_desp.split("(")[-1][:-1]
|
||||
|
||||
prompt_input += [act_desp]
|
||||
prompt_input += [role.s_mem.get_str_accessible_arena_game_objects(temp_address)]
|
||||
return prompt_input
|
||||
|
||||
prompt_template = "action_object_v2.txt"
|
||||
prompt_input = create_prompt_input(role, act_desp, temp_address)
|
||||
prompt = self.generate_prompt_with_tmpl_filename(prompt_input, prompt_template)
|
||||
self.fail_default_resp = self._func_fail_default_resp()
|
||||
output = await self._run_gpt35_max_tokens(prompt, max_tokens=15)
|
||||
x = [i.strip() for i in role.s_mem.get_str_accessible_arena_game_objects(temp_address).split(",")]
|
||||
if output not in x:
|
||||
output = random.choice(x)
|
||||
logger.info(f"Role: {role.name} Action: {self.cls_name} output: {output}")
|
||||
return output
|
||||
|
||||
|
||||
class GenPronunciatio(STAction):
|
||||
name: str = "GenPronunciatio"
|
||||
|
||||
def _func_cleanup(self, llm_resp: str, prompt: str):
|
||||
cr = llm_resp.strip()
|
||||
if len(cr) > 3:
|
||||
cr = cr[:3]
|
||||
return cr
|
||||
|
||||
def _func_validate(self, llm_resp: str, prompt: str):
|
||||
try:
|
||||
self._func_cleanup(llm_resp, prompt="")
|
||||
if len(llm_resp) == 0:
|
||||
return False
|
||||
except Exception:
|
||||
return False
|
||||
return True
|
||||
|
||||
def _func_fail_default_resp(self):
|
||||
fs = "😋"
|
||||
return fs
|
||||
|
||||
async def run(self, role: "STRole", act_desp: str):
|
||||
def create_prompt_input(act_desp):
|
||||
if "(" in act_desp:
|
||||
act_desp = act_desp.split("(")[-1].split(")")[0]
|
||||
prompt_input = [act_desp]
|
||||
return prompt_input
|
||||
|
||||
prompt_template = "generate_pronunciatio_v1.txt"
|
||||
prompt_input = create_prompt_input(act_desp)
|
||||
prompt = self.generate_prompt_with_tmpl_filename(prompt_input, prompt_template)
|
||||
example_output = "🛁🧖♀️"
|
||||
special_instruction = "The value for the output must ONLY contain the emojis."
|
||||
self.fail_default_resp = self._func_fail_default_resp()
|
||||
output = await self._run_gpt35(prompt, example_output, special_instruction)
|
||||
logger.info(f"Role: {role.name} Action: {self.cls_name} output: {output}")
|
||||
return output
|
||||
|
||||
|
||||
class GenEventTriple(STAction):
|
||||
name: str = "GenEventTriple"
|
||||
|
||||
def _func_cleanup(self, llm_resp: str, prompt: str):
|
||||
cr = llm_resp.strip()
|
||||
cr = [i.strip() for i in cr.split(")")[0].split(",")]
|
||||
return cr
|
||||
|
||||
def _func_validate(self, llm_resp: str, prompt: str):
|
||||
try:
|
||||
llm_resp = self._func_cleanup(llm_resp, prompt="")
|
||||
if len(llm_resp) != 2:
|
||||
return False
|
||||
except Exception:
|
||||
return False
|
||||
return True
|
||||
|
||||
def _func_fail_default_resp(self, role):
|
||||
fs = (role.name, "is", "idle")
|
||||
return fs
|
||||
|
||||
async def run(self, role: "STRole", act_desp: str):
|
||||
def create_prompt_input(role, act_desp):
|
||||
if "(" in act_desp:
|
||||
act_desp = act_desp.split("(")[-1].split(")")[0]
|
||||
prompt_input = [role.name, act_desp, role.name]
|
||||
return prompt_input
|
||||
|
||||
prompt_template = "generate_event_triple_v1.txt"
|
||||
prompt_input = create_prompt_input(role, act_desp)
|
||||
prompt = self.generate_prompt_with_tmpl_filename(prompt_input, prompt_template)
|
||||
self.fail_default_resp = self._func_fail_default_resp(role)
|
||||
output = await self._run_gpt35_max_tokens(prompt, max_tokens=30)
|
||||
output = (role.name, output[0], output[1])
|
||||
logger.info(f"Role: {role.name} Action: {self.cls_name} output: {output}")
|
||||
return output
|
||||
|
||||
|
||||
class GenActObjDescription(STAction):
|
||||
name: str = "GenActObjDescription"
|
||||
|
||||
def _func_cleanup(self, llm_resp: str, prompt: str):
|
||||
cr = llm_resp.strip()
|
||||
if cr[-1] == ".":
|
||||
cr = cr[:-1]
|
||||
return cr
|
||||
|
||||
def _func_validate(self, llm_resp: str, prompt: str):
|
||||
try:
|
||||
llm_resp = self._func_cleanup(llm_resp, prompt="")
|
||||
except Exception:
|
||||
return False
|
||||
return True
|
||||
|
||||
def _func_fail_default_resp(self, act_game_object):
|
||||
fs = f"{act_game_object} is idle"
|
||||
return fs
|
||||
|
||||
async def run(self, role: "STRole", act_game_object: str, act_desp: str):
|
||||
def create_prompt_input(act_game_object, act_desp, role):
|
||||
prompt_input = [act_game_object, role.name, act_desp, act_game_object, act_game_object]
|
||||
return prompt_input
|
||||
|
||||
prompt_template = "generate_obj_event_v1.txt"
|
||||
prompt_input = create_prompt_input(act_game_object, act_desp, role)
|
||||
prompt = self.generate_prompt_with_tmpl_filename(prompt_input, prompt_template)
|
||||
example_output = "being fixed"
|
||||
special_instruction = "The output should ONLY contain the phrase that should go in <fill in>."
|
||||
self.fail_default_resp = self._func_fail_default_resp(act_game_object)
|
||||
output = await self._run_gpt35(prompt, example_output, special_instruction)
|
||||
logger.info(f"Role: {role.name} Action: {self.cls_name} output: {output}")
|
||||
return output
|
||||
|
||||
|
||||
class GenObjEventTriple(STAction):
|
||||
name: str = "GenObjEventTriple"
|
||||
|
||||
def _func_cleanup(self, llm_resp: str, prompt: str):
|
||||
cr = llm_resp.strip()
|
||||
cr = [i.strip() for i in cr.split(")")[0].split(",")]
|
||||
return cr
|
||||
|
||||
def _func_validate(self, llm_resp: str, prompt: str):
|
||||
try:
|
||||
llm_resp = self._func_cleanup(llm_resp, prompt="")
|
||||
if len(llm_resp) != 2:
|
||||
return False
|
||||
except Exception:
|
||||
return False
|
||||
return True
|
||||
|
||||
def _func_fail_default_resp(self, act_game_object: str):
|
||||
fs = (act_game_object, "is", "idle")
|
||||
return fs
|
||||
|
||||
async def run(self, role: "STRole", act_game_object, act_obj_desp):
|
||||
def create_prompt_input(act_game_object, act_obj_desp):
|
||||
prompt_input = [act_game_object, act_obj_desp, act_game_object]
|
||||
return prompt_input
|
||||
|
||||
prompt_template = "generate_event_triple_v1.txt"
|
||||
prompt_input = create_prompt_input(act_game_object, act_obj_desp)
|
||||
prompt = self.generate_prompt_with_tmpl_filename(prompt_input, prompt_template)
|
||||
self.fail_default_resp = self._func_fail_default_resp(act_game_object)
|
||||
output = await self._run_gpt35_max_tokens(prompt, max_tokens=30)
|
||||
output = (act_game_object, output[0], output[1])
|
||||
logger.info(f"Role: {role.name} Action: {self.cls_name} output: {output}")
|
||||
return output
|
||||
|
||||
|
||||
class GenActionDetails(STAction):
|
||||
name: str = "GenActionDetails"
|
||||
|
||||
def _func_cleanup(self, llm_resp: str, prompt: str) -> list:
|
||||
pass
|
||||
|
||||
def _func_validate(self, llm_resp: str, prompt: str) -> bool:
|
||||
# TODO -- this sometimes generates error
|
||||
try:
|
||||
self._func_cleanup(llm_resp)
|
||||
except Exception:
|
||||
return False
|
||||
return True
|
||||
|
||||
def _func_fail_default_resp(self):
|
||||
fs = {}
|
||||
return fs
|
||||
|
||||
async def run(self, role: "STRole", act_desp: str, act_dura):
|
||||
access_tile = role.rc.env.observe(
|
||||
obs_params=EnvObsParams(obs_type=EnvObsType.GET_TITLE, coord=role.scratch.curr_tile)
|
||||
)
|
||||
act_world = access_tile["world"]
|
||||
act_sector = await GenActionSector().run(role, access_tile, act_desp)
|
||||
act_arena = await GenActionArena().run(role, act_desp, act_world, act_sector)
|
||||
act_address = f"{act_world}:{act_sector}:{act_arena}"
|
||||
if not role.s_mem.get_str_accessible_arena_game_objects(act_address):
|
||||
act_game_object = "<random>"
|
||||
else:
|
||||
act_game_object = await GenActionObject().run(role, act_desp, act_address)
|
||||
new_address = f"{act_world}:{act_sector}:{act_arena}:{act_game_object}"
|
||||
act_pron = await GenPronunciatio().run(role, act_desp)
|
||||
act_event = await GenEventTriple().run(role, act_desp)
|
||||
# Persona's actions also influence the object states. We set those up here.
|
||||
act_obj_desp = await GenActObjDescription().run(role, act_game_object, act_desp)
|
||||
act_obj_pron = await GenPronunciatio().run(role, act_obj_desp)
|
||||
act_obj_event = await GenObjEventTriple().run(role, act_game_object, act_obj_desp)
|
||||
result_dict = {
|
||||
"action_address": new_address,
|
||||
"action_duration": int(act_dura),
|
||||
"action_description": act_desp,
|
||||
"action_pronunciatio": act_pron,
|
||||
"action_event": act_event,
|
||||
"chatting_with": None,
|
||||
"chat": None,
|
||||
"chatting_with_buffer": None,
|
||||
"chatting_end_time": None,
|
||||
"act_obj_description": act_obj_desp,
|
||||
"act_obj_pronunciatio": act_obj_pron,
|
||||
"act_obj_event": act_obj_event,
|
||||
}
|
||||
logger.info(f"Role: {role.name} Action: {self.cls_name} output: {result_dict}")
|
||||
return result_dict
|
||||
60
metagpt/ext/stanford_town/actions/gen_daily_schedule.py
Normal file
60
metagpt/ext/stanford_town/actions/gen_daily_schedule.py
Normal file
|
|
@ -0,0 +1,60 @@
|
|||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
# @Desc : gen_daily_schedule
|
||||
|
||||
|
||||
from metagpt.ext.stanford_town.actions.st_action import STAction
|
||||
from metagpt.logs import logger
|
||||
|
||||
|
||||
class GenDailySchedule(STAction):
|
||||
name: str = "GenDailySchedule"
|
||||
|
||||
def _func_validate(self, llm_resp: str, prompt: str) -> bool:
|
||||
try:
|
||||
self._func_cleanup(llm_resp, prompt="")
|
||||
except Exception:
|
||||
return False
|
||||
return True
|
||||
|
||||
def _func_cleanup(self, llm_resp: str, prompt: str) -> list:
|
||||
cr = []
|
||||
_cr = llm_resp.split(")")
|
||||
for i in _cr:
|
||||
if i[-1].isdigit():
|
||||
i = i[:-1].strip()
|
||||
if i[-1] == "." or i[-1] == ",":
|
||||
cr += [i[:-1].strip()]
|
||||
return cr
|
||||
|
||||
def _func_fail_default_resp(self) -> int:
|
||||
fs = [
|
||||
"wake up and complete the morning routine at 6:00 am",
|
||||
"eat breakfast at 7:00 am",
|
||||
"read a book from 8:00 am to 12:00 pm",
|
||||
"have lunch at 12:00 pm",
|
||||
"take a nap from 1:00 pm to 4:00 pm",
|
||||
"relax and watch TV from 7:00 pm to 8:00 pm",
|
||||
"go to bed at 11:00 pm",
|
||||
]
|
||||
return fs
|
||||
|
||||
async def run(self, role: "STRole", wake_up_hour: str):
|
||||
def create_prompt_input(role, wake_up_hour):
|
||||
prompt_input = []
|
||||
prompt_input += [role.scratch.get_str_iss()]
|
||||
prompt_input += [role.scratch.get_str_lifestyle()]
|
||||
prompt_input += [role.scratch.get_str_curr_date_str()]
|
||||
prompt_input += [role.scratch.get_str_firstname()]
|
||||
prompt_input += [f"{str(wake_up_hour)}:00 am"]
|
||||
return prompt_input
|
||||
|
||||
wake_up_hour = int(wake_up_hour)
|
||||
prompt_template = "daily_planning_v6.txt"
|
||||
prompt_input = create_prompt_input(role, wake_up_hour)
|
||||
prompt = self.generate_prompt_with_tmpl_filename(prompt_input, prompt_template)
|
||||
self.fail_default_resp = self._func_fail_default_resp()
|
||||
output = await self._run_gpt35_max_tokens(prompt, max_tokens=500)
|
||||
output = [f"wake up and complete the morning routine at {wake_up_hour}:00 am"] + output
|
||||
logger.info(f"Role: {role.name} Action: {self.cls_name} output: {output}")
|
||||
return output
|
||||
181
metagpt/ext/stanford_town/actions/gen_hourly_schedule.py
Normal file
181
metagpt/ext/stanford_town/actions/gen_hourly_schedule.py
Normal file
|
|
@ -0,0 +1,181 @@
|
|||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
# @Desc : gen_hourly_schedule
|
||||
|
||||
import random
|
||||
import string
|
||||
|
||||
from metagpt.logs import logger
|
||||
|
||||
from .st_action import STAction
|
||||
|
||||
|
||||
def get_random_alphanumeric(i=6, j=6):
|
||||
"""
|
||||
Returns a random alpha numeric strength that has the length of somewhere
|
||||
between i and j.
|
||||
|
||||
INPUT:
|
||||
i: min_range for the length
|
||||
j: max_range for the length
|
||||
OUTPUT:
|
||||
an alpha numeric str with the length of somewhere between i and j.
|
||||
"""
|
||||
k = random.randint(i, j)
|
||||
x = "".join(random.choices(string.ascii_letters + string.digits, k=k))
|
||||
return x
|
||||
|
||||
|
||||
class GenHourlySchedule(STAction):
|
||||
name: str = "GenHourlySchedule"
|
||||
|
||||
def _func_validate(self, llm_resp: str, prompt: str) -> bool:
|
||||
try:
|
||||
self._func_cleanup(llm_resp, prompt="")
|
||||
except Exception:
|
||||
return False
|
||||
return True
|
||||
|
||||
def _func_cleanup(self, llm_resp: str, prompt: str) -> list:
|
||||
cr = llm_resp.strip()
|
||||
if cr[-1] == ".":
|
||||
cr = cr[:-1]
|
||||
# to only use the first line of output
|
||||
cr = cr.split("\n")[0]
|
||||
return cr
|
||||
|
||||
def _func_fail_default_resp(self) -> int:
|
||||
fs = "asleep"
|
||||
return fs
|
||||
|
||||
async def _generate_schedule_for_given_hour(
|
||||
self, role: "STRole", curr_hour_str, p_f_ds_hourly_org, hour_str, intermission2=None
|
||||
):
|
||||
def create_prompt_input(persona, curr_hour_str, p_f_ds_hourly_org, hour_str, intermission2=None):
|
||||
schedule_format = ""
|
||||
for i in hour_str:
|
||||
schedule_format += f"[{persona.scratch.get_str_curr_date_str()} -- {i}]"
|
||||
schedule_format += " Activity: [Fill in]\n"
|
||||
schedule_format = schedule_format[:-1]
|
||||
|
||||
intermission_str = "Here the originally intended hourly breakdown of"
|
||||
intermission_str += f" {persona.scratch.get_str_firstname()}'s schedule today: "
|
||||
for count, i in enumerate(persona.scratch.daily_req):
|
||||
intermission_str += f"{str(count + 1)}) {i}, "
|
||||
intermission_str = intermission_str[:-2]
|
||||
|
||||
prior_schedule = ""
|
||||
if p_f_ds_hourly_org:
|
||||
prior_schedule = "\n"
|
||||
for count, i in enumerate(p_f_ds_hourly_org):
|
||||
prior_schedule += f"[(ID:{get_random_alphanumeric()})"
|
||||
prior_schedule += f" {persona.scratch.get_str_curr_date_str()} --"
|
||||
prior_schedule += f" {hour_str[count]}] Activity:"
|
||||
prior_schedule += f" {persona.scratch.get_str_firstname()}"
|
||||
prior_schedule += f" is {i}\n"
|
||||
|
||||
prompt_ending = f"[(ID:{get_random_alphanumeric()})"
|
||||
prompt_ending += f" {persona.scratch.get_str_curr_date_str()}"
|
||||
prompt_ending += f" -- {curr_hour_str}] Activity:"
|
||||
prompt_ending += f" {persona.scratch.get_str_firstname()} is"
|
||||
|
||||
if intermission2:
|
||||
intermission2 = f"\n{intermission2}"
|
||||
|
||||
prompt_input = []
|
||||
prompt_input += [schedule_format]
|
||||
prompt_input += [persona.scratch.get_str_iss()]
|
||||
|
||||
prompt_input += [prior_schedule + "\n"]
|
||||
prompt_input += [intermission_str]
|
||||
if intermission2:
|
||||
prompt_input += [intermission2]
|
||||
else:
|
||||
prompt_input += [""]
|
||||
prompt_input += [prompt_ending]
|
||||
|
||||
return prompt_input
|
||||
|
||||
prompt_template = "generate_hourly_schedule_v2.txt"
|
||||
prompt_input = create_prompt_input(role, curr_hour_str, p_f_ds_hourly_org, hour_str, intermission2)
|
||||
prompt_input_str = "\n".join(prompt_input)
|
||||
prompt = self.generate_prompt_with_tmpl_filename(prompt_input, prompt_template)
|
||||
self.fail_default_resp = self._func_fail_default_resp()
|
||||
output = await self._run_gpt35_max_tokens(prompt, max_tokens=50)
|
||||
logger.info(
|
||||
f"Role: {role.name} _generate_schedule_for_given_hour prompt_input: {prompt_input_str}, "
|
||||
f"output: {output}"
|
||||
)
|
||||
return output
|
||||
|
||||
async def run(self, role: "STRole", wake_up_hour: int):
|
||||
hour_str = [
|
||||
"00:00 AM",
|
||||
"01:00 AM",
|
||||
"02:00 AM",
|
||||
"03:00 AM",
|
||||
"04:00 AM",
|
||||
"05:00 AM",
|
||||
"06:00 AM",
|
||||
"07:00 AM",
|
||||
"08:00 AM",
|
||||
"09:00 AM",
|
||||
"10:00 AM",
|
||||
"11:00 AM",
|
||||
"12:00 PM",
|
||||
"01:00 PM",
|
||||
"02:00 PM",
|
||||
"03:00 PM",
|
||||
"04:00 PM",
|
||||
"05:00 PM",
|
||||
"06:00 PM",
|
||||
"07:00 PM",
|
||||
"08:00 PM",
|
||||
"09:00 PM",
|
||||
"10:00 PM",
|
||||
"11:00 PM",
|
||||
]
|
||||
n_m1_activity = []
|
||||
diversity_repeat_count = 1 # TODO mg 1->3
|
||||
for i in range(diversity_repeat_count):
|
||||
logger.info(f"diversity_repeat_count idx: {i}")
|
||||
n_m1_activity_set = set(n_m1_activity)
|
||||
if len(n_m1_activity_set) < 5:
|
||||
n_m1_activity = []
|
||||
for count, curr_hour_str in enumerate(hour_str):
|
||||
if wake_up_hour > 0:
|
||||
n_m1_activity += ["sleeping"]
|
||||
wake_up_hour -= 1
|
||||
else:
|
||||
logger.info(f"_generate_schedule_for_given_hour idx: {count}, n_m1_activity: {n_m1_activity}")
|
||||
n_m1_activity += [
|
||||
await self._generate_schedule_for_given_hour(role, curr_hour_str, n_m1_activity, hour_str)
|
||||
]
|
||||
|
||||
# Step 1. Compressing the hourly schedule to the following format:
|
||||
# The integer indicates the number of hours. They should add up to 24.
|
||||
# [['sleeping', 6], ['waking up and starting her morning routine', 1],
|
||||
# ['eating breakfast', 1], ['getting ready for the day', 1],
|
||||
# ['working on her painting', 2], ['taking a break', 1],
|
||||
# ['having lunch', 1], ['working on her painting', 3],
|
||||
# ['taking a break', 2], ['working on her painting', 2],
|
||||
# ['relaxing and watching TV', 1], ['going to bed', 1], ['sleeping', 2]]
|
||||
_n_m1_hourly_compressed = []
|
||||
prev = None
|
||||
prev_count = 0
|
||||
for i in n_m1_activity:
|
||||
if i != prev:
|
||||
prev_count = 1
|
||||
_n_m1_hourly_compressed += [[i, prev_count]]
|
||||
prev = i
|
||||
elif _n_m1_hourly_compressed:
|
||||
_n_m1_hourly_compressed[-1][1] += 1
|
||||
|
||||
# Step 2. Expand to min scale (from hour scale)
|
||||
# [['sleeping', 360], ['waking up and starting her morning routine', 60],
|
||||
# ['eating breakfast', 60],..
|
||||
n_m1_hourly_compressed = []
|
||||
for task, duration in _n_m1_hourly_compressed:
|
||||
n_m1_hourly_compressed += [[task, duration * 60]]
|
||||
logger.info(f"Role: {role.name} Action: {self.cls_name} output: {n_m1_hourly_compressed}")
|
||||
return n_m1_hourly_compressed
|
||||
125
metagpt/ext/stanford_town/actions/gen_iter_chat_utt.py
Normal file
125
metagpt/ext/stanford_town/actions/gen_iter_chat_utt.py
Normal file
|
|
@ -0,0 +1,125 @@
|
|||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
# @Desc : generate_iterative_chat_utt
|
||||
|
||||
from metagpt.environment.stanford_town.env_space import EnvObsParams, EnvObsType
|
||||
from metagpt.ext.stanford_town.actions.st_action import STAction
|
||||
from metagpt.ext.stanford_town.utils.utils import extract_first_json_dict
|
||||
from metagpt.logs import logger
|
||||
|
||||
|
||||
class GenIterChatUTT(STAction):
|
||||
name: str = "GenIterChatUTT"
|
||||
|
||||
def _func_validate(self, llm_resp: str, prompt: str) -> bool:
|
||||
resp = False
|
||||
try:
|
||||
_ = extract_first_json_dict(llm_resp)
|
||||
resp = True
|
||||
except Exception:
|
||||
pass
|
||||
return resp
|
||||
|
||||
def _func_cleanup(self, llm_resp: str, prompt: str) -> dict:
|
||||
gpt_response = extract_first_json_dict(llm_resp)
|
||||
|
||||
cleaned_dict = dict()
|
||||
cleaned = []
|
||||
for key, val in gpt_response.items():
|
||||
cleaned += [val]
|
||||
cleaned_dict["utterance"] = cleaned[0]
|
||||
cleaned_dict["end"] = True
|
||||
if "f" in str(cleaned[1]) or "F" in str(cleaned[1]):
|
||||
cleaned_dict["end"] = False
|
||||
|
||||
return cleaned_dict
|
||||
|
||||
def _func_fail_default_resp(self) -> dict:
|
||||
cleaned_dict = dict()
|
||||
cleaned_dict["utterance"] = "..."
|
||||
cleaned_dict["end"] = False
|
||||
return cleaned_dict
|
||||
|
||||
async def run(
|
||||
self,
|
||||
init_role: "STRole",
|
||||
target_role: "STRole",
|
||||
retrieved: dict,
|
||||
curr_context: str,
|
||||
curr_chat: list[str],
|
||||
*args,
|
||||
**kwargs,
|
||||
) -> dict:
|
||||
def create_prompt_input(
|
||||
access_tile: dict[str, str],
|
||||
init_role: "STRole",
|
||||
target_role: "STRole",
|
||||
retrieved: dict,
|
||||
curr_context: str,
|
||||
curr_chat: list[str],
|
||||
):
|
||||
role = init_role
|
||||
scratch = role.rc.scratch
|
||||
target_scratch = target_role.rc.scratch
|
||||
prev_convo_insert = "\n"
|
||||
if role.rc.memory.chat_list:
|
||||
for i in role.rc.memory.chat_list:
|
||||
if i.object == target_role.name:
|
||||
v1 = int((scratch.curr_time - i.created).total_seconds() / 60)
|
||||
prev_convo_insert += (
|
||||
f"{str(v1)} minutes ago, {scratch.name} and "
|
||||
f"{target_scratch.name} were already {i.description} "
|
||||
f"This context takes place after that conversation."
|
||||
)
|
||||
break
|
||||
if prev_convo_insert == "\n":
|
||||
prev_convo_insert = ""
|
||||
if role.rc.memory.chat_list:
|
||||
if int((scratch.curr_time - role.rc.memory.chat_list[-1].created).total_seconds() / 60) > 480:
|
||||
prev_convo_insert = ""
|
||||
logger.info(f"prev_convo_insert: {prev_convo_insert}")
|
||||
|
||||
curr_sector = f"{access_tile['sector']}"
|
||||
curr_arena = f"{access_tile['arena']}"
|
||||
curr_location = f"{curr_arena} in {curr_sector}"
|
||||
|
||||
retrieved_str = ""
|
||||
for key, vals in retrieved.items():
|
||||
for v in vals:
|
||||
retrieved_str += f"- {v.description}\n"
|
||||
|
||||
convo_str = ""
|
||||
for i in curr_chat:
|
||||
convo_str += ": ".join(i) + "\n"
|
||||
if convo_str == "":
|
||||
convo_str = "[The conversation has not started yet -- start it!]"
|
||||
|
||||
init_iss = f"Here is Here is a brief description of {scratch.name}.\n{scratch.get_str_iss()}"
|
||||
prompt_input = [
|
||||
init_iss,
|
||||
scratch.name,
|
||||
retrieved_str,
|
||||
prev_convo_insert,
|
||||
curr_location,
|
||||
curr_context,
|
||||
scratch.name,
|
||||
target_scratch.name,
|
||||
convo_str,
|
||||
scratch.name,
|
||||
target_scratch.name,
|
||||
scratch.name,
|
||||
scratch.name,
|
||||
scratch.name,
|
||||
]
|
||||
return prompt_input
|
||||
|
||||
access_tile = init_role.rc.env.observe(
|
||||
obs_params=EnvObsParams(obs_type=EnvObsType.GET_TITLE, coord=init_role.scratch.curr_tile)
|
||||
)
|
||||
prompt_input = create_prompt_input(access_tile, init_role, target_role, retrieved, curr_context, curr_chat)
|
||||
prompt = self.generate_prompt_with_tmpl_filename(prompt_input, "iterative_convo_v1.txt")
|
||||
# original using `ChatGPT_safe_generate_response_OLD`
|
||||
self.fail_default_resp = self._func_fail_default_resp()
|
||||
output = await self._run_gpt35_wo_extra_prompt(prompt)
|
||||
logger.info(f"Role: {init_role.name} Action: {self.cls_name} output: {output}")
|
||||
return output
|
||||
35
metagpt/ext/stanford_town/actions/inner_voice_action.py
Normal file
35
metagpt/ext/stanford_town/actions/inner_voice_action.py
Normal file
|
|
@ -0,0 +1,35 @@
|
|||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
# @Desc :
|
||||
|
||||
from metagpt.ext.stanford_town.actions.st_action import STAction
|
||||
from metagpt.logs import logger
|
||||
|
||||
|
||||
class AgentWhisperThoughtAction(STAction):
|
||||
name: str = "AgentWhisperThoughtAction"
|
||||
|
||||
def _func_validate(self, llm_resp: str, prompt: str) -> bool:
|
||||
try:
|
||||
self._func_cleanup(llm_resp, prompt)
|
||||
return True
|
||||
except Exception:
|
||||
return False
|
||||
|
||||
def _func_cleanup(self, llm_resp: str, prompt: str = "") -> list:
|
||||
return llm_resp.split('"')[0].strip()
|
||||
|
||||
def _func_fail_default_resp(self) -> str:
|
||||
pass
|
||||
|
||||
async def run(self, role: "STRole", statements: str, test_input=None, verbose=False) -> str:
|
||||
def create_prompt_input(role: "STRole", statements, test_input=None):
|
||||
prompt_input = [role.scratch.name, statements]
|
||||
return prompt_input
|
||||
|
||||
prompt_input = create_prompt_input(role, statements)
|
||||
prompt = self.generate_prompt_with_tmpl_filename(prompt_input, "whisper_inner_thought_v1.txt")
|
||||
|
||||
output = await self._run_gpt35_max_tokens(prompt, max_tokens=50)
|
||||
logger.info(f"Role: {role.name} Action: {self.cls_name} output: {output}")
|
||||
return output
|
||||
154
metagpt/ext/stanford_town/actions/new_decomp_schedule.py
Normal file
154
metagpt/ext/stanford_town/actions/new_decomp_schedule.py
Normal file
|
|
@ -0,0 +1,154 @@
|
|||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
# @Desc : new_decomp_schedule
|
||||
|
||||
import datetime
|
||||
|
||||
from metagpt.ext.stanford_town.actions.st_action import STAction
|
||||
from metagpt.logs import logger
|
||||
|
||||
|
||||
class NewDecompSchedule(STAction):
|
||||
name: str = "NewDecompSchedule"
|
||||
|
||||
def _func_validate(self, llm_resp: str, prompt: str) -> bool:
|
||||
resp = False
|
||||
try:
|
||||
llm_resp = self._func_cleanup(llm_resp, prompt)
|
||||
dur_sum = 0
|
||||
for act, dur in llm_resp:
|
||||
dur_sum += dur
|
||||
if isinstance(act, str):
|
||||
return False
|
||||
if isinstance(dur, int):
|
||||
return False
|
||||
x = prompt.split("\n")[0].split("originally planned schedule from")[-1].strip()[:-1]
|
||||
x = [datetime.datetime.strptime(i.strip(), "%H:%M %p") for i in x.split(" to ")]
|
||||
delta_min = int((x[1] - x[0]).total_seconds() / 60)
|
||||
|
||||
if int(dur_sum) != int(delta_min):
|
||||
return False
|
||||
except Exception:
|
||||
pass
|
||||
return resp
|
||||
|
||||
def _func_cleanup(self, llm_resp: str, prompt: str) -> list:
|
||||
new_schedule = prompt + " " + llm_resp.strip()
|
||||
new_schedule = new_schedule.split("The revised schedule:")[-1].strip()
|
||||
new_schedule = new_schedule.split("\n")
|
||||
|
||||
ret_temp = []
|
||||
for i in new_schedule:
|
||||
ret_temp += [i.split(" -- ")]
|
||||
|
||||
ret = []
|
||||
for time_str, action in ret_temp:
|
||||
start_time = time_str.split(" ~ ")[0].strip()
|
||||
end_time = time_str.split(" ~ ")[1].strip()
|
||||
delta = datetime.datetime.strptime(end_time, "%H:%M") - datetime.datetime.strptime(start_time, "%H:%M")
|
||||
delta_min = int(delta.total_seconds() / 60)
|
||||
if delta_min < 0:
|
||||
delta_min = 0
|
||||
ret += [[action, delta_min]]
|
||||
|
||||
return ret
|
||||
|
||||
def _func_fail_default_resp(self, main_act_dur: int, truncated_act_dur: int) -> int:
|
||||
dur_sum = 0
|
||||
for act, dur in main_act_dur:
|
||||
dur_sum += dur
|
||||
|
||||
ret = truncated_act_dur[:]
|
||||
ret += main_act_dur[len(ret) - 1 :]
|
||||
|
||||
# If there are access, we need to trim...
|
||||
ret_dur_sum = 0
|
||||
count = 0
|
||||
over = None
|
||||
for act, dur in ret:
|
||||
ret_dur_sum += dur
|
||||
if ret_dur_sum == dur_sum:
|
||||
break
|
||||
if ret_dur_sum > dur_sum:
|
||||
over = ret_dur_sum - dur_sum
|
||||
break
|
||||
count += 1
|
||||
|
||||
if over:
|
||||
ret = ret[: count + 1]
|
||||
ret[-1][1] -= over
|
||||
|
||||
return ret
|
||||
|
||||
async def run(
|
||||
self,
|
||||
role: "STRole",
|
||||
main_act_dur: int,
|
||||
truncated_act_dur: int,
|
||||
start_time_hour: datetime,
|
||||
end_time_hour: datetime,
|
||||
inserted_act: str,
|
||||
inserted_act_dur: int,
|
||||
*args,
|
||||
**kwargs,
|
||||
):
|
||||
def create_prompt_input(
|
||||
role: "STRole",
|
||||
main_act_dur: int,
|
||||
truncated_act_dur: int,
|
||||
start_time_hour: datetime,
|
||||
end_time_hour: datetime,
|
||||
inserted_act: str,
|
||||
inserted_act_dur: int,
|
||||
):
|
||||
persona_name = role.name
|
||||
start_hour_str = start_time_hour.strftime("%H:%M %p")
|
||||
end_hour_str = end_time_hour.strftime("%H:%M %p")
|
||||
|
||||
original_plan = ""
|
||||
for_time = start_time_hour
|
||||
for i in main_act_dur:
|
||||
original_plan += (
|
||||
f'{for_time.strftime("%H:%M")} ~ '
|
||||
f'{(for_time + datetime.timedelta(minutes=int(i[1]))).strftime("%H:%M")} -- ' + i[0]
|
||||
)
|
||||
original_plan += "\n"
|
||||
for_time += datetime.timedelta(minutes=int(i[1]))
|
||||
|
||||
new_plan_init = ""
|
||||
for_time = start_time_hour
|
||||
for count, i in enumerate(truncated_act_dur):
|
||||
new_plan_init += (
|
||||
f'{for_time.strftime("%H:%M")} ~ '
|
||||
f'{(for_time + datetime.timedelta(minutes=int(i[1]))).strftime("%H:%M")} -- ' + i[0]
|
||||
)
|
||||
new_plan_init += "\n"
|
||||
if count < len(truncated_act_dur) - 1:
|
||||
for_time += datetime.timedelta(minutes=int(i[1]))
|
||||
|
||||
new_plan_init += (for_time + datetime.timedelta(minutes=int(i[1]))).strftime("%H:%M") + " ~"
|
||||
|
||||
prompt_input = [
|
||||
persona_name,
|
||||
start_hour_str,
|
||||
end_hour_str,
|
||||
original_plan,
|
||||
persona_name,
|
||||
inserted_act,
|
||||
inserted_act_dur,
|
||||
persona_name,
|
||||
start_hour_str,
|
||||
end_hour_str,
|
||||
end_hour_str,
|
||||
new_plan_init,
|
||||
]
|
||||
return prompt_input
|
||||
|
||||
prompt_input = create_prompt_input(
|
||||
role, main_act_dur, truncated_act_dur, start_time_hour, end_time_hour, inserted_act, inserted_act_dur
|
||||
)
|
||||
prompt = self.generate_prompt_with_tmpl_filename(prompt_input, "new_decomp_schedule_v1.txt")
|
||||
self.fail_default_resp = self._func_fail_default_resp(main_act_dur, truncated_act_dur)
|
||||
output = await self._run_gpt35_max_tokens(prompt, max_tokens=1000)
|
||||
logger.info(f"Role: {role.name} Action: {self.cls_name} output: {output}")
|
||||
return output
|
||||
277
metagpt/ext/stanford_town/actions/run_reflect_action.py
Normal file
277
metagpt/ext/stanford_town/actions/run_reflect_action.py
Normal file
|
|
@ -0,0 +1,277 @@
|
|||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
# @Desc : Integration Reflect Action
|
||||
|
||||
import re
|
||||
|
||||
from metagpt.ext.stanford_town.actions.st_action import STAction
|
||||
from metagpt.logs import logger
|
||||
|
||||
|
||||
# Run GPT Prompt Focal Point method
|
||||
class AgentFocusPt(STAction):
|
||||
name: str = "AgentFocusPt"
|
||||
|
||||
def _func_validate(self, llm_resp: str, prompt: str) -> bool:
|
||||
try:
|
||||
self._func_cleanup(llm_resp, prompt)
|
||||
return True
|
||||
except Exception:
|
||||
return False
|
||||
|
||||
def _func_cleanup(self, llm_resp: str, prompt: str = "") -> str:
|
||||
try:
|
||||
"""
|
||||
Cleanup handling has been completed for run_v2
|
||||
"""
|
||||
return llm_resp
|
||||
except Exception as exp:
|
||||
logger.error(f"{self.cls_name} with error {exp}")
|
||||
|
||||
def _func_fail_default_resp(self) -> str:
|
||||
pass
|
||||
|
||||
async def run(self, role: "STRole", statements: str, n: int, test_input=None) -> str:
|
||||
def create_prompt_input(role: "STRole", statements, n, test_input=None):
|
||||
prompt_input = [statements, str(n)]
|
||||
return prompt_input
|
||||
|
||||
prompt_input = create_prompt_input(role, statements, n)
|
||||
prompt = self.generate_prompt_with_tmpl_filename(prompt_input, "generate_focal_pt_v1.txt")
|
||||
|
||||
example_output = '["What should Jane do for lunch", "Does Jane like strawberry", "Who is Jane"]'
|
||||
special_instruction = "Output must be a list of str."
|
||||
output = await self._run_gpt35(prompt, example_output, special_instruction)
|
||||
logger.info(f"Role: {role.name} Action: {self.cls_name} output: {output}")
|
||||
return output
|
||||
|
||||
|
||||
# Run GPT Prompt Insight and Guidance
|
||||
class AgentInsightAndGuidance(STAction):
|
||||
name: str = "AgentInsightAndGuidance"
|
||||
|
||||
def _func_validate(self, llm_resp: str, prompt: str) -> bool:
|
||||
try:
|
||||
self._func_cleanup(llm_resp, prompt)
|
||||
return True
|
||||
except Exception:
|
||||
return False
|
||||
|
||||
def _func_cleanup(self, llm_resp: str, prompt: str = "") -> dict:
|
||||
try:
|
||||
llm_resp = "1. " + llm_resp.strip()
|
||||
ret = dict()
|
||||
for i in llm_resp.split("\n"):
|
||||
row = " ".join(i.split(". ")[1:])
|
||||
if "(because of " not in row:
|
||||
continue
|
||||
thought = row.split("(because of ")[0].strip()
|
||||
if ")" not in row.split("(because of ")[1]:
|
||||
continue
|
||||
evi_raw = row.split("(because of ")[1].split(")")[0].strip()
|
||||
evi_raw = re.findall(r"\d+", evi_raw)
|
||||
evi_raw = [int(i.strip()) for i in evi_raw]
|
||||
ret[thought] = evi_raw
|
||||
return ret
|
||||
except Exception as exp:
|
||||
logger.error(f"{self.cls_name} with error {exp}")
|
||||
|
||||
def _func_fail_default_resp(self, n: int) -> str:
|
||||
return ["I am hungry"] * n
|
||||
|
||||
async def run(self, role: "STRole", statements: str, n: int, test_input=None) -> dict:
|
||||
def create_prompt_input(role, statements, n, test_input=None):
|
||||
prompt_input = [statements, str(n)]
|
||||
return prompt_input
|
||||
|
||||
prompt_input = create_prompt_input(role, statements, n)
|
||||
prompt = self.generate_prompt_with_tmpl_filename(prompt_input, "insight_and_evidence_v1.txt")
|
||||
|
||||
self.fail_default_resp = self._func_fail_default_resp(n)
|
||||
output = await self._run_gpt35_max_tokens(prompt, max_tokens=150)
|
||||
logger.info(f"Role: {role.name} Action: {self.cls_name} output: {output}")
|
||||
return output
|
||||
|
||||
|
||||
# Run GPT Prompt Event Triple
|
||||
class AgentEventTriple(STAction):
|
||||
name: str = "AgentEventTriple"
|
||||
|
||||
def _func_validate(self, llm_resp: str, prompt: str) -> bool:
|
||||
try:
|
||||
llm_resp = self._func_cleanup(llm_resp, prompt="")
|
||||
if len(llm_resp) != 2:
|
||||
return False
|
||||
except Exception:
|
||||
return False
|
||||
return True
|
||||
|
||||
def _func_cleanup(self, llm_resp: str, prompt: str = "") -> list:
|
||||
try:
|
||||
cr = llm_resp.strip()
|
||||
cr = [i.strip() for i in cr.split(")")[0].split(",")]
|
||||
if len(cr) != 2:
|
||||
return cr[-2:]
|
||||
return cr
|
||||
except Exception as exp:
|
||||
logger.error(f"{self.cls_name} with error {exp}")
|
||||
|
||||
def _func_fail_default_resp(self) -> str:
|
||||
pass
|
||||
|
||||
async def run(self, statements: str, role: "STRole", verbose=False) -> tuple:
|
||||
def create_prompt_input(statements, role):
|
||||
if "(" in statements:
|
||||
statements = statements.split("(")[-1].split(")")[0]
|
||||
prompt_input = [role.scratch.name, statements, role.scratch.name]
|
||||
return prompt_input
|
||||
|
||||
prompt_input = create_prompt_input(statements, role)
|
||||
prompt = self.generate_prompt_with_tmpl_filename(prompt_input, "generate_event_triple_v1.txt")
|
||||
|
||||
output = await self._run_gpt35_max_tokens(prompt, max_tokens=30)
|
||||
output = (role.scratch.name, output[0], output[1])
|
||||
logger.info(f"Role: {role.name} Action: {self.cls_name} output: {output}")
|
||||
return output
|
||||
|
||||
|
||||
# Run GPT Prompt Event Poignancy
|
||||
class AgentEventPoignancy(STAction):
|
||||
name: str = "AgentEventPoignancy"
|
||||
|
||||
def _func_validate(self, llm_resp: str, prompt: str) -> bool:
|
||||
try:
|
||||
self._func_cleanup(llm_resp, prompt)
|
||||
return True
|
||||
except Exception:
|
||||
return False
|
||||
|
||||
def _func_cleanup(self, llm_resp: str, prompt: str = "") -> int:
|
||||
try:
|
||||
llm_resp = int(llm_resp.strip())
|
||||
return llm_resp
|
||||
except Exception as exp:
|
||||
logger.error(f"{self.cls_name} with error {exp}")
|
||||
|
||||
def _func_fail_default_resp(self) -> str:
|
||||
pass
|
||||
|
||||
async def run(self, role: "STRole", statements: str, test_input=None, verbose=False) -> str:
|
||||
def create_prompt_input(role: "STRole", statements: str, test_input=None):
|
||||
prompt_input = [role.scratch.name, role.scratch.get_str_iss(), role.scratch.name, statements]
|
||||
return prompt_input
|
||||
|
||||
prompt_input = create_prompt_input(role, statements)
|
||||
prompt = self.generate_prompt_with_tmpl_filename(prompt_input, "poignancy_event_v1.txt")
|
||||
|
||||
example_output = "5" # ########
|
||||
special_instruction = "The output should ONLY contain ONE integer value on the scale of 1 to 10."
|
||||
output = await self._run_gpt35(prompt, example_output, special_instruction)
|
||||
logger.info(f"Role: {role.name} Action: {self.cls_name} output: {output}")
|
||||
return output
|
||||
|
||||
|
||||
# Run GPT Prompt Chat Poignancy
|
||||
class AgentChatPoignancy(STAction):
|
||||
name: str = "AgentChatPoignancy"
|
||||
|
||||
def _func_validate(self, llm_resp: str, prompt: str) -> bool:
|
||||
try:
|
||||
self._func_cleanup(llm_resp, prompt)
|
||||
return True
|
||||
except Exception:
|
||||
return False
|
||||
|
||||
def _func_cleanup(self, llm_resp: str, prompt: str = "") -> int:
|
||||
try:
|
||||
llm_resp = int(llm_resp.strip())
|
||||
return llm_resp
|
||||
except Exception as exp:
|
||||
logger.error(f"{self.cls_name} with error {exp}")
|
||||
|
||||
def _func_fail_default_resp(self) -> str:
|
||||
pass
|
||||
|
||||
async def run(self, role: "STRole", statements: str, test_input=None, verbose=False) -> str:
|
||||
def create_prompt_input(role: "STRole", statements, test_input=None):
|
||||
prompt_input = [role.scratch.name, role.scratch.get_str_iss(), role.scratch.name, statements]
|
||||
return prompt_input
|
||||
|
||||
prompt_input = create_prompt_input(role, statements)
|
||||
prompt = self.generate_prompt_with_tmpl_filename(prompt_input, "poignancy_chat_v1.txt")
|
||||
|
||||
example_output = "5" # ########
|
||||
special_instruction = "The output should ONLY contain ONE integer value on the scale of 1 to 10."
|
||||
output = await self._run_gpt35(prompt, example_output, special_instruction)
|
||||
logger.info(f"Role: {role.name} Action: {self.cls_name} output: {output}")
|
||||
return output
|
||||
|
||||
|
||||
# Run GPT Prompt Planning Thought on Convo
|
||||
class AgentPlanThoughtOnConvo(STAction):
|
||||
name: str = "AgentPlanThoughtOnConvo"
|
||||
|
||||
def _func_validate(self, llm_resp: str, prompt: str) -> bool:
|
||||
try:
|
||||
self._func_cleanup(llm_resp, prompt)
|
||||
return True
|
||||
except Exception:
|
||||
return False
|
||||
|
||||
def _func_cleanup(self, llm_resp: str, prompt: str = "") -> str:
|
||||
try:
|
||||
return llm_resp.split('"')[0].strip()
|
||||
except Exception as exp:
|
||||
logger.error(f"{self.cls_name} with error {exp}")
|
||||
|
||||
def _func_fail_default_resp(self) -> str:
|
||||
pass
|
||||
|
||||
async def run(self, role: "STRole", statements: str, test_input=None, verbose=False) -> str:
|
||||
def create_prompt_input(role, statements, test_input=None):
|
||||
prompt_input = [statements, role.scratch.name, role.scratch.name, role.scratch.name]
|
||||
return prompt_input
|
||||
|
||||
prompt_input = create_prompt_input(role, statements)
|
||||
prompt = self.generate_prompt_with_tmpl_filename(prompt_input, "planning_thought_on_convo_v1.txt")
|
||||
|
||||
output = await self._run_gpt35_max_tokens(prompt, max_tokens=50)
|
||||
logger.info(f"Role: {role.name} Action: {self.cls_name} output: {output}")
|
||||
return output
|
||||
|
||||
|
||||
# Run GPT Prompt Memory on Convo
|
||||
class AgentMemoryOnConvo(STAction):
|
||||
name: str = "AgentMemoryOnConvo"
|
||||
|
||||
def _func_validate(self, llm_resp: str, prompt: str) -> bool:
|
||||
try:
|
||||
self._func_cleanup(llm_resp, prompt)
|
||||
return True
|
||||
except Exception:
|
||||
return False
|
||||
|
||||
def _func_cleanup(self, llm_resp: str, prompt: str = "") -> str:
|
||||
try:
|
||||
return llm_resp.split('"')[0].strip()
|
||||
except Exception as exp:
|
||||
logger.error(f"{self.cls_name} with error {exp}")
|
||||
|
||||
def _func_fail_default_resp(self) -> str:
|
||||
pass
|
||||
|
||||
async def run(self, role: "STRole", statements: str, test_input=None, verbose=False) -> str:
|
||||
def create_prompt_input(role, statements, test_input=None):
|
||||
prompt_input = [statements, role.scratch.name, role.scratch.name, role.scratch.name]
|
||||
return prompt_input
|
||||
|
||||
prompt_input = create_prompt_input(role, statements)
|
||||
prompt = self.generate_prompt_with_tmpl_filename(prompt_input, "memo_on_convo_v1.txt")
|
||||
example_output = "Jane Doe was interesting to talk to."
|
||||
special_instruction = (
|
||||
"The output should ONLY contain a string that summarizes anything interesting "
|
||||
"that the agent may have noticed"
|
||||
)
|
||||
output = await self._run_gpt35(prompt, example_output, special_instruction)
|
||||
logger.info(f"Role: {role.name} Action: {self.cls_name} output: {output}")
|
||||
return output
|
||||
119
metagpt/ext/stanford_town/actions/st_action.py
Normal file
119
metagpt/ext/stanford_town/actions/st_action.py
Normal file
|
|
@ -0,0 +1,119 @@
|
|||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
# @Desc : StanfordTown Action
|
||||
import json
|
||||
import time
|
||||
from abc import abstractmethod
|
||||
from pathlib import Path
|
||||
from typing import Any, Optional, Union
|
||||
|
||||
from metagpt.actions.action import Action
|
||||
from metagpt.config2 import config
|
||||
from metagpt.ext.stanford_town.utils.const import PROMPTS_DIR
|
||||
from metagpt.logs import logger
|
||||
|
||||
|
||||
class STAction(Action):
|
||||
name: str = "STAction"
|
||||
prompt_dir: Path = PROMPTS_DIR
|
||||
fail_default_resp: Optional[str] = None
|
||||
|
||||
@property
|
||||
def cls_name(self):
|
||||
return self.__class__.__name__
|
||||
|
||||
@abstractmethod
|
||||
def _func_validate(self, llm_resp: str, prompt: str):
|
||||
raise NotImplementedError
|
||||
|
||||
@abstractmethod
|
||||
def _func_cleanup(self, llm_resp: str, prompt: str):
|
||||
raise NotImplementedError
|
||||
|
||||
@abstractmethod
|
||||
def _func_fail_default_resp(self):
|
||||
raise NotImplementedError
|
||||
|
||||
def generate_prompt_with_tmpl_filename(self, prompt_input: Union[str, list], tmpl_filename) -> str:
|
||||
"""
|
||||
same with `generate_prompt`
|
||||
Args:
|
||||
prompt_input: the input we want to feed in (IF THERE ARE MORE THAN ONE INPUT, THIS CAN BE A LIST.)
|
||||
tmpl_filename: prompt template filename
|
||||
Returns:
|
||||
a str prompt that will be sent to LLM server.
|
||||
"""
|
||||
if isinstance(prompt_input, str):
|
||||
prompt_input = [prompt_input]
|
||||
prompt_input = [str(i) for i in prompt_input]
|
||||
|
||||
f = open(str(self.prompt_dir.joinpath(tmpl_filename)), "r")
|
||||
prompt = f.read()
|
||||
f.close()
|
||||
for count, i in enumerate(prompt_input):
|
||||
prompt = prompt.replace(f"!<INPUT {count}>!", i)
|
||||
if "<commentblockmarker>###</commentblockmarker>" in prompt:
|
||||
prompt = prompt.split("<commentblockmarker>###</commentblockmarker>")[1]
|
||||
return prompt.strip()
|
||||
|
||||
async def _aask(self, prompt: str) -> str:
|
||||
return await self.llm.aask(prompt)
|
||||
|
||||
async def _run_gpt35_max_tokens(self, prompt: str, max_tokens: int = 50, retry: int = 3):
|
||||
for idx in range(retry):
|
||||
try:
|
||||
tmp_max_tokens_rsp = getattr(config.llm, "max_token", 1500)
|
||||
setattr(config.llm, "max_token", max_tokens)
|
||||
self.llm.use_system_prompt = False # to make it behave like a non-chat completions
|
||||
|
||||
llm_resp = await self._aask(prompt)
|
||||
|
||||
setattr(config.llm, "max_token", tmp_max_tokens_rsp)
|
||||
logger.info(f"Action: {self.cls_name} llm _run_gpt35_max_tokens raw resp: {llm_resp}")
|
||||
if self._func_validate(llm_resp, prompt):
|
||||
return self._func_cleanup(llm_resp, prompt)
|
||||
except Exception as exp:
|
||||
logger.warning(f"Action: {self.cls_name} _run_gpt35_max_tokens exp: {exp}")
|
||||
time.sleep(5)
|
||||
return self.fail_default_resp
|
||||
|
||||
async def _run_gpt35(
|
||||
self, prompt: str, example_output: str, special_instruction: str, retry: int = 3
|
||||
) -> Union[bool, Any]:
|
||||
"""same with `gpt_structure.ChatGPT_safe_generate_response`"""
|
||||
prompt = '"""\n' + prompt + '\n"""\n'
|
||||
prompt += f"Output the response to the prompt above in json. {special_instruction}\n"
|
||||
prompt += "Example output json:\n"
|
||||
prompt += '{"output": "' + str(example_output) + '"}'
|
||||
|
||||
for idx in range(retry):
|
||||
try:
|
||||
llm_resp = await self._aask(prompt)
|
||||
logger.info(f"Action: {self.cls_name} llm _run_gpt35 raw resp: {llm_resp}")
|
||||
end_idx = llm_resp.strip().rfind("}") + 1
|
||||
llm_resp = llm_resp[:end_idx]
|
||||
llm_resp = json.loads(llm_resp)["output"]
|
||||
|
||||
if self._func_validate(llm_resp, prompt):
|
||||
return self._func_cleanup(llm_resp, prompt)
|
||||
except Exception as exp:
|
||||
logger.warning(f"Action: {self.cls_name} _run_gpt35 exp: {exp}")
|
||||
time.sleep(5) # usually avoid `Rate limit`
|
||||
return False
|
||||
|
||||
async def _run_gpt35_wo_extra_prompt(self, prompt: str, retry: int = 3) -> str:
|
||||
for idx in range(retry):
|
||||
try:
|
||||
llm_resp = await self._aask(prompt)
|
||||
llm_resp = llm_resp.strip()
|
||||
logger.info(f"Action: {self.cls_name} llm _run_gpt35_wo_extra_prompt raw resp: {llm_resp}")
|
||||
if self._func_validate(llm_resp, prompt):
|
||||
return self._func_cleanup(llm_resp, prompt)
|
||||
except Exception as exp:
|
||||
logger.warning(f"Action: {self.cls_name} _run_gpt35_wo_extra_prompt exp: {exp}")
|
||||
time.sleep(5) # usually avoid `Rate limit`
|
||||
return self.fail_default_resp
|
||||
|
||||
async def run(self, *args, **kwargs):
|
||||
"""Run action"""
|
||||
raise NotImplementedError("The run method should be implemented in a subclass.")
|
||||
47
metagpt/ext/stanford_town/actions/summarize_conv.py
Normal file
47
metagpt/ext/stanford_town/actions/summarize_conv.py
Normal file
|
|
@ -0,0 +1,47 @@
|
|||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
# @Desc : summarize the content of agents' conversation
|
||||
|
||||
from metagpt.ext.stanford_town.actions.st_action import STAction
|
||||
from metagpt.logs import logger
|
||||
|
||||
|
||||
class SummarizeConv(STAction):
|
||||
name: str = "SummarizeConv"
|
||||
|
||||
def _func_validate(self, llm_resp: str, prompt: str) -> bool:
|
||||
resp = False
|
||||
try:
|
||||
_ = self._func_cleanup(llm_resp, prompt)
|
||||
resp = True
|
||||
except Exception:
|
||||
pass
|
||||
return resp
|
||||
|
||||
def _func_cleanup(self, llm_resp: str, prompt: str) -> str:
|
||||
ret = "conversing about " + llm_resp.strip()
|
||||
return ret
|
||||
|
||||
def _func_fail_default_resp(self) -> str:
|
||||
return "conversing with a housemate about morning greetings"
|
||||
|
||||
async def run(self, conv: list):
|
||||
def create_prompt_input(conversation: list):
|
||||
convo_str = ""
|
||||
for row in conversation:
|
||||
convo_str += f'{row[0]}: "{row[1]}"\n'
|
||||
prompt_input = [convo_str]
|
||||
return prompt_input
|
||||
|
||||
prompt_input = create_prompt_input(conv)
|
||||
prompt = self.generate_prompt_with_tmpl_filename(prompt_input, "summarize_conversation_v1.txt")
|
||||
|
||||
example_output = "conversing about what to eat for lunch"
|
||||
special_instruction = (
|
||||
"The output must continue the sentence above by filling in the <fill in> tag. "
|
||||
"Don't start with 'this is a conversation about...' Just finish the sentence "
|
||||
"but do not miss any important details (including who are chatting)."
|
||||
)
|
||||
output = await self._run_gpt35(prompt, example_output, special_instruction)
|
||||
logger.info(f"Action: {self.cls_name} output: {output}")
|
||||
return output
|
||||
173
metagpt/ext/stanford_town/actions/task_decomp.py
Normal file
173
metagpt/ext/stanford_town/actions/task_decomp.py
Normal file
|
|
@ -0,0 +1,173 @@
|
|||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
# @Desc : task_decomp
|
||||
|
||||
import datetime
|
||||
|
||||
from metagpt.ext.stanford_town.actions.st_action import STAction
|
||||
from metagpt.logs import logger
|
||||
|
||||
|
||||
class TaskDecomp(STAction):
|
||||
name: str = "TaskDecomp"
|
||||
|
||||
def _func_cleanup(self, llm_resp: str, prompt: str) -> list:
|
||||
# TODO SOMETHING HERE sometimes fails... See screenshot
|
||||
temp = [i.strip() for i in llm_resp.split("\n")]
|
||||
_cr = []
|
||||
cr = []
|
||||
for count, i in enumerate(temp):
|
||||
if count != 0:
|
||||
_cr += [" ".join([j.strip() for j in i.split(" ")][3:])]
|
||||
else:
|
||||
_cr += [i]
|
||||
for count, i in enumerate(_cr):
|
||||
k = [j.strip() for j in i.split("(duration in minutes:")]
|
||||
task = k[0]
|
||||
if task[-1] == ".":
|
||||
task = task[:-1]
|
||||
duration = int(k[1].split(",")[0].strip())
|
||||
cr += [[task, duration]]
|
||||
|
||||
total_expected_min = int(prompt.split("(total duration in minutes")[-1].split("):")[0].strip())
|
||||
|
||||
# TODO -- now, you need to make sure that this is the same as the sum of
|
||||
# the current action sequence.
|
||||
curr_min_slot = [
|
||||
["dummy", -1],
|
||||
] # (task_name, task_index)
|
||||
for count, i in enumerate(cr):
|
||||
i_task = i[0]
|
||||
i_duration = i[1]
|
||||
|
||||
i_duration -= i_duration % 5
|
||||
if i_duration > 0:
|
||||
for j in range(i_duration):
|
||||
curr_min_slot += [(i_task, count)]
|
||||
curr_min_slot = curr_min_slot[1:]
|
||||
|
||||
if len(curr_min_slot) > total_expected_min:
|
||||
last_task = curr_min_slot[60]
|
||||
for i in range(1, 6):
|
||||
curr_min_slot[-1 * i] = last_task
|
||||
elif len(curr_min_slot) < total_expected_min:
|
||||
last_task = curr_min_slot[-1]
|
||||
for i in range(total_expected_min - len(curr_min_slot)):
|
||||
curr_min_slot += [last_task]
|
||||
|
||||
cr_ret = [
|
||||
["dummy", -1],
|
||||
]
|
||||
for task, task_index in curr_min_slot:
|
||||
if task != cr_ret[-1][0]:
|
||||
cr_ret += [[task, 1]]
|
||||
else:
|
||||
cr_ret[-1][1] += 1
|
||||
cr = cr_ret[1:]
|
||||
|
||||
return cr
|
||||
|
||||
def _func_validate(self, llm_resp: str, prompt: str) -> bool:
|
||||
# TODO -- this sometimes generates error
|
||||
try:
|
||||
self._func_cleanup(llm_resp, prompt)
|
||||
except Exception:
|
||||
return False
|
||||
return True
|
||||
|
||||
def _func_fail_default_resp(self) -> int:
|
||||
fs = [["asleep", 0]]
|
||||
return fs
|
||||
|
||||
async def run(self, role: "STRole", task_desc: int, truncated_act_dur: int, *args, **kwargs):
|
||||
def create_prompt_input(role, task, duration):
|
||||
"""
|
||||
Today is Saturday June 25. From 00:00 ~ 06:00am, Maeve is
|
||||
planning on sleeping, 06:00 ~ 07:00am, Maeve is
|
||||
planning on waking up and doing her morning routine,
|
||||
and from 07:00am ~08:00am, Maeve is planning on having breakfast.
|
||||
"""
|
||||
|
||||
curr_f_org_index = role.scratch.get_f_daily_schedule_hourly_org_index()
|
||||
all_indices = []
|
||||
# if curr_f_org_index > 0:
|
||||
# all_indices += [curr_f_org_index-1]
|
||||
all_indices += [curr_f_org_index]
|
||||
if curr_f_org_index + 1 <= len(role.scratch.f_daily_schedule_hourly_org):
|
||||
all_indices += [curr_f_org_index + 1]
|
||||
if curr_f_org_index + 2 <= len(role.scratch.f_daily_schedule_hourly_org):
|
||||
all_indices += [curr_f_org_index + 2]
|
||||
|
||||
curr_time_range = ""
|
||||
|
||||
logger.debug("DEBUG")
|
||||
logger.debug(role.scratch.f_daily_schedule_hourly_org)
|
||||
logger.debug(all_indices)
|
||||
|
||||
summ_str = f'Today is {role.scratch.curr_time.strftime("%B %d, %Y")}. '
|
||||
summ_str += "From "
|
||||
for index in all_indices:
|
||||
logger.debug(f"index {index}")
|
||||
if index < len(role.scratch.f_daily_schedule_hourly_org):
|
||||
start_min = 0
|
||||
for i in range(index):
|
||||
start_min += role.scratch.f_daily_schedule_hourly_org[i][1]
|
||||
end_min = start_min + role.scratch.f_daily_schedule_hourly_org[index][1]
|
||||
start_time = datetime.datetime.strptime("00:00:00", "%H:%M:%S") + datetime.timedelta(
|
||||
minutes=start_min
|
||||
)
|
||||
end_time = datetime.datetime.strptime("00:00:00", "%H:%M:%S") + datetime.timedelta(
|
||||
minutes=end_min
|
||||
)
|
||||
start_time_str = start_time.strftime("%H:%M%p")
|
||||
end_time_str = end_time.strftime("%H:%M%p")
|
||||
summ_str += (
|
||||
f"{start_time_str} ~ {end_time_str}, {role.name} is planning "
|
||||
f"on {role.scratch.f_daily_schedule_hourly_org[index][0]}, "
|
||||
)
|
||||
if curr_f_org_index + 1 == index:
|
||||
curr_time_range = f"{start_time_str} ~ {end_time_str}"
|
||||
summ_str = summ_str[:-2] + "."
|
||||
|
||||
prompt_input = []
|
||||
prompt_input += [role.scratch.get_str_iss()]
|
||||
prompt_input += [summ_str]
|
||||
# prompt_input += [role.scratch.get_str_curr_date_str()]
|
||||
prompt_input += [role.scratch.get_str_firstname()]
|
||||
prompt_input += [role.scratch.get_str_firstname()]
|
||||
prompt_input += [task]
|
||||
prompt_input += [curr_time_range]
|
||||
prompt_input += [duration]
|
||||
prompt_input += [role.scratch.get_str_firstname()]
|
||||
return prompt_input
|
||||
|
||||
prompt_input = create_prompt_input(role, task_desc, truncated_act_dur)
|
||||
prompt = self.generate_prompt_with_tmpl_filename(prompt_input, "task_decomp_v3.txt")
|
||||
self.fail_default_resp = self._func_fail_default_resp()
|
||||
output = await self._run_gpt35_max_tokens(prompt, max_tokens=1000)
|
||||
logger.info(f"Role: {role.name} {self.cls_name} output: {output}")
|
||||
|
||||
fin_output = []
|
||||
time_sum = 0
|
||||
for i_task, i_duration in output:
|
||||
time_sum += i_duration
|
||||
# HM?????????
|
||||
# if time_sum < duration:
|
||||
if time_sum <= truncated_act_dur:
|
||||
fin_output += [[i_task, i_duration]]
|
||||
else:
|
||||
break
|
||||
ftime_sum = 0
|
||||
for fi_task, fi_duration in fin_output:
|
||||
ftime_sum += fi_duration
|
||||
|
||||
fin_output[-1][1] += truncated_act_dur - ftime_sum
|
||||
output = fin_output
|
||||
|
||||
task_decomp = output
|
||||
ret = []
|
||||
for decomp_task, duration in task_decomp:
|
||||
ret += [[f"{task_desc} ({decomp_task})", duration]]
|
||||
output = ret
|
||||
logger.info(f"Role: {role.name} Action: {self.cls_name} output: {output}")
|
||||
return output
|
||||
42
metagpt/ext/stanford_town/actions/wake_up.py
Normal file
42
metagpt/ext/stanford_town/actions/wake_up.py
Normal file
|
|
@ -0,0 +1,42 @@
|
|||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
# @Desc : wake_up
|
||||
|
||||
|
||||
from metagpt.ext.stanford_town.actions.st_action import STAction
|
||||
from metagpt.logs import logger
|
||||
|
||||
|
||||
class WakeUp(STAction):
|
||||
name: str = "WakeUp"
|
||||
|
||||
def _func_validate(self, llm_resp: str, prompt: str = None) -> bool:
|
||||
try:
|
||||
self._func_cleanup(llm_resp, prompt="")
|
||||
except Exception:
|
||||
return False
|
||||
return True
|
||||
|
||||
def _func_cleanup(self, llm_resp: str, prompt: str) -> int:
|
||||
cr = int(llm_resp.strip().lower().split("am")[0])
|
||||
return cr
|
||||
|
||||
def _func_fail_default_resp(self) -> int:
|
||||
fs = 8
|
||||
return fs
|
||||
|
||||
async def run(self, role: "STRole"):
|
||||
def create_prompt_input(role):
|
||||
prompt_input = [
|
||||
role.scratch.get_str_iss(),
|
||||
role.scratch.get_str_lifestyle(),
|
||||
role.scratch.get_str_firstname(),
|
||||
]
|
||||
return prompt_input
|
||||
|
||||
prompt_input = create_prompt_input(role)
|
||||
prompt = self.generate_prompt_with_tmpl_filename(prompt_input, "wake_up_hour_v1.txt")
|
||||
self.fail_default_resp = self._func_fail_default_resp()
|
||||
output = await self._run_gpt35_max_tokens(prompt, max_tokens=5)
|
||||
logger.info(f"Role: {role.name} Action: {self.cls_name} output: {output}")
|
||||
return output
|
||||
0
metagpt/ext/stanford_town/memory/__init__.py
Normal file
0
metagpt/ext/stanford_town/memory/__init__.py
Normal file
378
metagpt/ext/stanford_town/memory/agent_memory.py
Normal file
378
metagpt/ext/stanford_town/memory/agent_memory.py
Normal file
|
|
@ -0,0 +1,378 @@
|
|||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
# @Desc : BasicMemory,AgentMemory实现
|
||||
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
from typing import Optional
|
||||
|
||||
from pydantic import Field, field_serializer, model_validator
|
||||
|
||||
from metagpt.logs import logger
|
||||
from metagpt.memory.memory import Memory
|
||||
from metagpt.schema import Message
|
||||
from metagpt.utils.common import read_json_file, write_json_file
|
||||
|
||||
|
||||
class BasicMemory(Message):
|
||||
"""
|
||||
BasicMemory继承于MG的Message类，其中content属性替代description属性
|
||||
Message类中对于Chat类型支持的非常好，对于Agent个体的Perceive,Reflection,Plan支持的并不多
|
||||
在Type设计上，我们延续GA的三个种类，但是对于Chat种类的对话进行特别设计（具体怎么设计还没想好）
|
||||
"""
|
||||
|
||||
memory_id: Optional[str] = Field(default=None) # 记忆ID
|
||||
memory_count: int = -1 # 第几个记忆，实际数值与Memory相等
|
||||
type_count: int = -1 # 第几种记忆，类型为整数
|
||||
memory_type: Optional[str] = Field(default=None) # 记忆类型，包含 event,thought,chat三种类型
|
||||
depth: int = -1 # 记忆深度，类型为整数
|
||||
created: Optional[datetime] = Field(default=None) # 创建时间
|
||||
expiration: Optional[datetime] = Field(default=None) # 记忆失效时间，默认为空（）
|
||||
last_accessed: Optional[datetime] = Field(default=None) # 上一次调用的时间，初始化时候与self.created一致
|
||||
subject: Optional[str] = Field(default=None) # 主语
|
||||
predicate: Optional[str] = Field(default=None) # 谓语
|
||||
object: Optional[str] = Field(default=None) # 宾语
|
||||
|
||||
description: Optional[str] = Field(default=None)
|
||||
embedding_key: Optional[str] = Field(default=None) # 内容与self.content一致
|
||||
poignancy: int = -1 # importance值
|
||||
keywords: list[str] = Field(default=[]) # keywords
|
||||
filling: list = Field(default=[]) # 装的与之相关联的memory_id的列表
|
||||
|
||||
__hash__ = object.__hash__ # support hash in AgentMemory
|
||||
|
||||
@model_validator(mode="before")
|
||||
@classmethod
|
||||
def check_values(cls, values):
|
||||
if "created" in values:
|
||||
values["last_accessed"] = values["created"]
|
||||
if "content" in values:
|
||||
values["description"] = values["content"]
|
||||
if "filling" in values:
|
||||
values["filling"] = values["filling"] or []
|
||||
return values
|
||||
|
||||
@field_serializer("created", "expiration")
|
||||
def transform_time_field(self, time_field: Optional[datetime]) -> str:
|
||||
if time_field:
|
||||
time_field = time_field.strftime("%Y-%m-%d %H:%M:%S")
|
||||
return time_field
|
||||
|
||||
def summary(self):
|
||||
return self.subject, self.predicate, self.object
|
||||
|
||||
def save_to_dict(self) -> dict:
|
||||
"""
|
||||
将MemoryBasic类转化为字典，用于存储json文件
|
||||
这里需要注意，cause_by跟GA不兼容，所以需要做一个格式转换
|
||||
"""
|
||||
memory_dict = dict()
|
||||
node_id = self.memory_id
|
||||
basic_mem_obj = self.model_dump(
|
||||
include=[
|
||||
"node_count",
|
||||
"type_count",
|
||||
"type",
|
||||
"depth",
|
||||
"created",
|
||||
"expiration",
|
||||
"subject",
|
||||
"predicate",
|
||||
"object",
|
||||
"description",
|
||||
"embedding_key",
|
||||
"poignancy",
|
||||
"keywords",
|
||||
"filling",
|
||||
"cause_by",
|
||||
]
|
||||
)
|
||||
|
||||
memory_dict[node_id] = basic_mem_obj
|
||||
return memory_dict
|
||||
|
||||
|
||||
class AgentMemory(Memory):
|
||||
"""
|
||||
GA中主要存储三种JSON
|
||||
1. embedding.json (Dict embedding_key:embedding)
|
||||
2. Node.json (Dict Node_id:Node)
|
||||
3. kw_strength.json
|
||||
"""
|
||||
|
||||
storage: list[BasicMemory] = [] # 重写Storage，存储BasicMemory所有节点
|
||||
event_list: list[BasicMemory] = [] # 存储event记忆
|
||||
thought_list: list[BasicMemory] = [] # 存储thought记忆
|
||||
chat_list: list[BasicMemory] = [] # chat-related memory
|
||||
|
||||
event_keywords: dict[str, list[BasicMemory]] = dict() # 存储keywords
|
||||
thought_keywords: dict[str, list[BasicMemory]] = dict()
|
||||
chat_keywords: dict[str, list[BasicMemory]] = dict()
|
||||
|
||||
kw_strength_event: dict[str, int] = dict()
|
||||
kw_strength_thought: dict[str, int] = dict()
|
||||
|
||||
memory_saved: Optional[Path] = Field(default=None)
|
||||
embeddings: dict[str, list[float]] = dict()
|
||||
|
||||
def set_mem_path(self, memory_saved: Path):
|
||||
self.memory_saved = memory_saved
|
||||
self.load(memory_saved)
|
||||
|
||||
def save(self, memory_saved: Path):
|
||||
"""
|
||||
将MemoryBasic类存储为Nodes.json形式。复现GA中的Kw Strength.json形式
|
||||
这里添加一个路径即可
|
||||
TODO 这里在存储时候进行倒序存储，之后需要验证（test_memory通过）
|
||||
"""
|
||||
memory_json = dict()
|
||||
for i in range(len(self.storage)):
|
||||
memory_node = self.storage[len(self.storage) - i - 1]
|
||||
memory_node = memory_node.save_to_dict()
|
||||
memory_json.update(memory_node)
|
||||
write_json_file(memory_saved.joinpath("nodes.json"), memory_json)
|
||||
write_json_file(memory_saved.joinpath("embeddings.json"), self.embeddings)
|
||||
|
||||
strength_json = dict()
|
||||
strength_json["kw_strength_event"] = self.kw_strength_event
|
||||
strength_json["kw_strength_thought"] = self.kw_strength_thought
|
||||
write_json_file(memory_saved.joinpath("kw_strength.json"), strength_json)
|
||||
|
||||
def load(self, memory_saved: Path):
|
||||
"""
|
||||
将GA的JSON解析，填充到AgentMemory类之中
|
||||
"""
|
||||
self.embeddings = read_json_file(memory_saved.joinpath("embeddings.json"))
|
||||
memory_load = read_json_file(memory_saved.joinpath("nodes.json"))
|
||||
for count in range(len(memory_load.keys())):
|
||||
node_id = f"node_{str(count + 1)}"
|
||||
node_details = memory_load[node_id]
|
||||
node_type = node_details["type"]
|
||||
created = datetime.strptime(node_details["created"], "%Y-%m-%d %H:%M:%S")
|
||||
expiration = None
|
||||
if node_details["expiration"]:
|
||||
expiration = datetime.strptime(node_details["expiration"], "%Y-%m-%d %H:%M:%S")
|
||||
|
||||
s = node_details["subject"]
|
||||
p = node_details["predicate"]
|
||||
o = node_details["object"]
|
||||
|
||||
description = node_details["description"]
|
||||
embedding_pair = (node_details["embedding_key"], self.embeddings[node_details["embedding_key"]])
|
||||
poignancy = node_details["poignancy"]
|
||||
keywords = set(node_details["keywords"])
|
||||
filling = node_details["filling"]
|
||||
if node_type == "thought":
|
||||
self.add_thought(
|
||||
created, expiration, s, p, o, description, keywords, poignancy, embedding_pair, filling
|
||||
)
|
||||
if node_type == "event":
|
||||
self.add_event(created, expiration, s, p, o, description, keywords, poignancy, embedding_pair, filling)
|
||||
if node_type == "chat":
|
||||
self.add_chat(created, expiration, s, p, o, description, keywords, poignancy, embedding_pair, filling)
|
||||
|
||||
strength_keywords_load = read_json_file(memory_saved.joinpath("kw_strength.json"))
|
||||
if strength_keywords_load["kw_strength_event"]:
|
||||
self.kw_strength_event = strength_keywords_load["kw_strength_event"]
|
||||
if strength_keywords_load["kw_strength_thought"]:
|
||||
self.kw_strength_thought = strength_keywords_load["kw_strength_thought"]
|
||||
|
||||
def add(self, memory_basic: BasicMemory):
|
||||
"""
|
||||
Add a new message to storage, while updating the index
|
||||
重写add方法，修改原有的Message类为BasicMemory类，并添加不同的记忆类型添加方式
|
||||
"""
|
||||
if memory_basic.memory_id in self.storage:
|
||||
return
|
||||
self.storage.append(memory_basic)
|
||||
if memory_basic.memory_type == "chat":
|
||||
self.chat_list[0:0] = [memory_basic]
|
||||
return
|
||||
if memory_basic.memory_type == "thought":
|
||||
self.thought_list[0:0] = [memory_basic]
|
||||
return
|
||||
if memory_basic.memory_type == "event":
|
||||
self.event_list[0:0] = [memory_basic]
|
||||
return
|
||||
|
||||
def add_chat(
|
||||
self, created, expiration, s, p, o, content, keywords, poignancy, embedding_pair, filling, cause_by=""
|
||||
):
|
||||
"""
|
||||
调用add方法，初始化chat，在创建的时候就需要调用embedding函数
|
||||
"""
|
||||
memory_count = len(self.storage) + 1
|
||||
type_count = len(self.thought_list) + 1
|
||||
memory_type = "chat"
|
||||
memory_id = f"node_{str(memory_count)}"
|
||||
depth = 1
|
||||
|
||||
memory_node = BasicMemory(
|
||||
memory_id=memory_id,
|
||||
memory_count=memory_count,
|
||||
type_count=type_count,
|
||||
memory_type=memory_type,
|
||||
depth=depth,
|
||||
created=created,
|
||||
expiration=expiration,
|
||||
subject=s,
|
||||
predicate=p,
|
||||
object=o,
|
||||
description=content,
|
||||
embedding_key=embedding_pair[0],
|
||||
poignancy=poignancy,
|
||||
keywords=keywords,
|
||||
filling=filling,
|
||||
cause_by=cause_by,
|
||||
)
|
||||
|
||||
keywords = [i.lower() for i in keywords]
|
||||
for kw in keywords:
|
||||
if kw in self.chat_keywords:
|
||||
self.chat_keywords[kw][0:0] = [memory_node]
|
||||
else:
|
||||
self.chat_keywords[kw] = [memory_node]
|
||||
|
||||
self.add(memory_node)
|
||||
|
||||
self.embeddings[embedding_pair[0]] = embedding_pair[1]
|
||||
return memory_node
|
||||
|
||||
def add_thought(self, created, expiration, s, p, o, content, keywords, poignancy, embedding_pair, filling):
|
||||
"""
|
||||
调用add方法，初始化thought
|
||||
"""
|
||||
memory_count = len(self.storage) + 1
|
||||
type_count = len(self.thought_list) + 1
|
||||
memory_type = "thought"
|
||||
memory_id = f"node_{str(memory_count)}"
|
||||
depth = 1
|
||||
|
||||
try:
|
||||
if filling:
|
||||
depth_list = [memory_node.depth for memory_node in self.storage if memory_node.memory_id in filling]
|
||||
depth += max(depth_list)
|
||||
except Exception as exp:
|
||||
logger.warning(f"filling init occur {exp}")
|
||||
pass
|
||||
|
||||
memory_node = BasicMemory(
|
||||
memory_id=memory_id,
|
||||
memory_count=memory_count,
|
||||
type_count=type_count,
|
||||
memory_type=memory_type,
|
||||
depth=depth,
|
||||
created=created,
|
||||
expiration=expiration,
|
||||
subject=s,
|
||||
predicate=p,
|
||||
object=o,
|
||||
description=content,
|
||||
embedding_key=embedding_pair[0],
|
||||
poignancy=poignancy,
|
||||
keywords=keywords,
|
||||
filling=filling,
|
||||
)
|
||||
|
||||
keywords = [i.lower() for i in keywords]
|
||||
for kw in keywords:
|
||||
if kw in self.thought_keywords:
|
||||
self.thought_keywords[kw][0:0] = [memory_node]
|
||||
else:
|
||||
self.thought_keywords[kw] = [memory_node]
|
||||
|
||||
self.add(memory_node)
|
||||
|
||||
if f"{p} {o}" != "is idle":
|
||||
for kw in keywords:
|
||||
if kw in self.kw_strength_thought:
|
||||
self.kw_strength_thought[kw] += 1
|
||||
else:
|
||||
self.kw_strength_thought[kw] = 1
|
||||
|
||||
self.embeddings[embedding_pair[0]] = embedding_pair[1]
|
||||
return memory_node
|
||||
|
||||
def add_event(self, created, expiration, s, p, o, content, keywords, poignancy, embedding_pair, filling):
|
||||
"""
|
||||
调用add方法，初始化event
|
||||
"""
|
||||
memory_count = len(self.storage) + 1
|
||||
type_count = len(self.event_list) + 1
|
||||
memory_type = "event"
|
||||
memory_id = f"node_{str(memory_count)}"
|
||||
depth = 0
|
||||
|
||||
if "(" in content:
|
||||
content = " ".join(content.split()[:3]) + " " + content.split("(")[-1][:-1]
|
||||
|
||||
memory_node = BasicMemory(
|
||||
memory_id=memory_id,
|
||||
memory_count=memory_count,
|
||||
type_count=type_count,
|
||||
memory_type=memory_type,
|
||||
depth=depth,
|
||||
created=created,
|
||||
expiration=expiration,
|
||||
subject=s,
|
||||
predicate=p,
|
||||
object=o,
|
||||
description=content,
|
||||
embedding_key=embedding_pair[0],
|
||||
poignancy=poignancy,
|
||||
keywords=keywords,
|
||||
filling=filling,
|
||||
)
|
||||
|
||||
keywords = [i.lower() for i in keywords]
|
||||
for kw in keywords:
|
||||
if kw in self.event_keywords:
|
||||
self.event_keywords[kw][0:0] = [memory_node]
|
||||
else:
|
||||
self.event_keywords[kw] = [memory_node]
|
||||
|
||||
self.add(memory_node)
|
||||
|
||||
if f"{p} {o}" != "is idle":
|
||||
for kw in keywords:
|
||||
if kw in self.kw_strength_event:
|
||||
self.kw_strength_event[kw] += 1
|
||||
else:
|
||||
self.kw_strength_event[kw] = 1
|
||||
|
||||
self.embeddings[embedding_pair[0]] = embedding_pair[1]
|
||||
return memory_node
|
||||
|
||||
def get_summarized_latest_events(self, retention):
|
||||
ret_set = set()
|
||||
for e_node in self.event_list[:retention]:
|
||||
ret_set.add(e_node.summary())
|
||||
return ret_set
|
||||
|
||||
def get_last_chat(self, target_role_name: str):
|
||||
if target_role_name.lower() in self.chat_keywords:
|
||||
return self.chat_keywords[target_role_name.lower()][0]
|
||||
else:
|
||||
return False
|
||||
|
||||
def retrieve_relevant_thoughts(self, s_content: str, p_content: str, o_content: str) -> set:
|
||||
contents = [s_content, p_content, o_content]
|
||||
|
||||
ret = []
|
||||
for i in contents:
|
||||
if i in self.thought_keywords:
|
||||
ret += self.thought_keywords[i.lower()]
|
||||
|
||||
ret = set(ret)
|
||||
return ret
|
||||
|
||||
def retrieve_relevant_events(self, s_content: str, p_content: str, o_content: str) -> set:
|
||||
contents = [s_content, p_content, o_content]
|
||||
|
||||
ret = []
|
||||
for i in contents:
|
||||
if i in self.event_keywords:
|
||||
ret += self.event_keywords[i]
|
||||
|
||||
ret = set(ret)
|
||||
return ret
|
||||
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Add table
Add a link
Reference in a new issue