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Merge branch 'dev' of https://github.com/geekan/MetaGPT into geekan/dev

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莘权 马 2024-01-02 16:32:40 +08:00
commit 92f3b81bd1
24 changed files with 60 additions and 53 deletions
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3
metagpt/actions/project_management.py
View file

@ -89,9 +89,6 @@ class WriteTasks(Action):
async def _run_new_tasks(self, context, schema=CONFIG.prompt_schema):
node = await PM_NODE.fill(context, self.llm, schema)
# prompt_template, format_example = get_template(templates, format)
# prompt = prompt_template.format(context=context, format_example=format_example)
# rsp = await self._aask_v1(prompt, "task", OUTPUT_MAPPING, format=format)
return node
async def _merge(self, system_design_doc, task_doc, schema=CONFIG.prompt_schema) -> Document:

2
metagpt/provider/base_llm.py
View file

@ -43,7 +43,7 @@ class BaseLLM(ABC):
if system_msgs:
message = self._system_msgs(system_msgs)
else:
message = [self._default_system_msg()]
message = [self._default_system_msg()] if self.use_system_prompt else []
if format_msgs:
message.extend(format_msgs)
message.append(self._user_msg(msg))

3
metagpt/provider/fireworks_api.py
View file

@ -64,8 +64,9 @@ class FireworksCostManager(CostManager):
token_costs = self.model_grade_token_costs(model)
cost = (prompt_tokens * token_costs["prompt"] + completion_tokens * token_costs["completion"]) / 1000000
self.total_cost += cost
max_budget = CONFIG.max_budget if CONFIG.max_budget else CONFIG.cost_manager.max_budget
logger.info(
f"Total running cost: ${self.total_cost:.4f} | Max budget: ${CONFIG.max_budget:.3f} | "
f"Total running cost: ${self.total_cost:.4f} | Max budget: ${max_budget:.3f} | "
f"Current cost: ${cost:.4f}, prompt_tokens: {prompt_tokens}, completion_tokens: {completion_tokens}"
)
CONFIG.total_cost = self.total_cost

4
metagpt/provider/ollama_api.py
View file

@ -30,9 +30,9 @@ class OllamaCostManager(CostManager):
"""
self.total_prompt_tokens += prompt_tokens
self.total_completion_tokens += completion_tokens
max_budget = CONFIG.max_budget if CONFIG.max_budget else CONFIG.cost_manager.max_budget
logger.info(
f"Max budget: ${CONFIG.max_budget:.3f} | "
f"Max budget: ${max_budget:.3f} | "
f"prompt_tokens: {prompt_tokens}, completion_tokens: {completion_tokens}"
)
CONFIG.total_cost = self.total_cost

4
metagpt/provider/open_llm_api.py
View file

@ -26,9 +26,9 @@ class OpenLLMCostManager(CostManager):
"""
self.total_prompt_tokens += prompt_tokens
self.total_completion_tokens += completion_tokens
max_budget = CONFIG.max_budget if CONFIG.max_budget else CONFIG.cost_manager.max_budget
logger.info(
f"Max budget: ${CONFIG.max_budget:.3f} | reference "
f"Max budget: ${max_budget:.3f} | reference "
f"prompt_tokens: {prompt_tokens}, completion_tokens: {completion_tokens}"
)

5
metagpt/provider/zhipuai/zhipu_model_api.py
View file

@ -2,6 +2,8 @@
# -*- coding: utf-8 -*-
# @Desc : zhipu model api to support sync & async for invoke & sse_invoke
import json
import zhipuai
from zhipuai.model_api.api import InvokeType, ModelAPI
from zhipuai.utils.http_client import headers as zhipuai_default_headers
@ -51,7 +53,6 @@ class ZhiPuModelAPI(ModelAPI):
params=kwargs,
request_timeout=zhipuai.api_timeout_seconds,
)
return result
@classmethod
@ -61,6 +62,8 @@ class ZhiPuModelAPI(ModelAPI):
resp = await cls.arequest(
invoke_type=InvokeType.SYNC, stream=False, method="post", headers=headers, kwargs=kwargs
)
resp = resp.decode("utf-8")
resp = json.loads(resp)
return resp
@classmethod

4
metagpt/provider/zhipuai_api.py
View file

@ -38,12 +38,11 @@ class ZhiPuAILLM(BaseLLM):
From now, there is only one model named `chatglm_turbo`
"""
use_system_prompt: bool = False # zhipuai has no system prompt when use api
def __init__(self):
self.__init_zhipuai(CONFIG)
self.llm = ZhiPuModelAPI
self.model = "chatglm_turbo" # so far only one model, just use it
self.use_system_prompt: bool = False # zhipuai has no system prompt when use api
def __init_zhipuai(self, config: CONFIG):
assert config.zhipuai_api_key
@ -101,7 +100,6 @@ class ZhiPuAILLM(BaseLLM):
elif event.event == ZhiPuEvent.ERROR.value or event.event == ZhiPuEvent.INTERRUPTED.value:
content = event.data
logger.error(f"event error: {content}", end="")
collected_content.append([content])
elif event.event == ZhiPuEvent.FINISH.value:
"""
event.meta

11
metagpt/schema.py
View file

@ -55,7 +55,16 @@ from metagpt.utils.serialize import (
class SerializationMixin(BaseModel):
"""SereDeserMixin for subclass' ser&deser"""
"""
PolyMorphic subclasses Serialization / Deserialization Mixin
- First of all, we need to know that pydantic is not designed for polymorphism.
- If Engineer is subclass of Role, it would be serialized as Role. If we want to serialize it as Engineer, we need
to add `class name` to Engineer. So we need Engineer inherit SerializationMixin.
More details:
- https://docs.pydantic.dev/latest/concepts/serialization/
- https://github.com/pydantic/pydantic/discussions/7008 discuss about avoid `__get_pydantic_core_schema__`
"""
__is_polymorphic_base = False
__subclasses_map__ = {}

4
metagpt/strategy/examples/__init__.py
View file

@ -1,4 +0,0 @@
# -*- coding: utf-8 -*-
# @Date : 12/26/2023 3:32 PM
# @Author : stellahong (stellahong@fuzhi.ai)
# @Desc :

73
metagpt/strategy/examples/creative_writing.py
View file

@ -1,73 +0,0 @@
# -*- coding: utf-8 -*-
# @Date : 12/25/2023 1:06 PM
# @Author : stellahong (stellahong@fuzhi.ai)
# @Desc :
import re
from metagpt.strategy.prompt_templates.creative_writing import cot_prompt, vote_prompt
from metagpt.strategy.tot import TreeofThought
from metagpt.strategy.tot_schema import (
BaseEvaluator,
BaseParser,
Strategy,
ThoughtSolverConfig,
)
class TextGenParser(BaseParser):
propose_prompt: str = cot_prompt
value_prompt: str = vote_prompt
def __call__(self, input_text: str) -> str:
return input_text
def propose(self, current_state: str, **kwargs) -> str:
return self.propose_prompt.format(input=current_state, **kwargs)
def value(self, input: str = "", **kwargs) -> str:
# node_result = self(input)
id = kwargs.get("node_id", "0")
return self.value_prompt + f"Choice {id}:\n{input}\n"
class TextGenEvaluator(BaseEvaluator):
value_map = {"impossible": 0.001, "likely": 1, "sure": 20} # TODO: ad hoc
status_map = {val: key for key, val in value_map.items()}
def __call__(self, evaluation: str, **kwargs) -> float:
try:
value = 0
node_id = kwargs.get("node_id", "0")
pattern = r".*best choice is .*(\d+).*"
match = re.match(pattern, evaluation, re.DOTALL)
if match:
vote = int(match.groups()[0])
print(vote)
if vote == int(node_id):
value = 1
except:
value = 0
return value
def status_verify(self, value):
status = False
if value in self.status_map:
status_value = self.status_map[value]
if status_value != "impossible":
status = True
return status
if __name__ == "__main__":
import asyncio
initial_prompt = """It isn't difficult to do a handstand if you just stand on your hands. It caught him off guard that space smelled of seared steak. When she didn’t like a guy who was trying to pick her up, she started using sign language. Each person who knows you has a different perception of who you are."""
parser = TextGenParser()
evaluator = TextGenEvaluator()
config = ThoughtSolverConfig(n_generate_sample=3, parser=parser, evaluator=evaluator)
tot_base = TreeofThought(strategy=Strategy.BFS, config=config)
asyncio.run(tot_base.solve(init_prompt=initial_prompt))

64
metagpt/strategy/examples/game24.py
View file

@ -1,64 +0,0 @@
# -*- coding: utf-8 -*-
# @Date : 12/25/2023 1:36 AM
# @Author : stellahong (stellahong@fuzhi.ai)
# @Desc :
import re
from metagpt.strategy.prompt_templates.game24 import propose_prompt, value_prompt
from metagpt.strategy.tot import TreeofThought
from metagpt.strategy.tot_schema import (
BaseEvaluator,
BaseParser,
Strategy,
ThoughtSolverConfig,
)
class Game24Parser(BaseParser):
propose_prompt: str = propose_prompt
value_prompt: str = value_prompt
def __call__(self, input_text: str) -> str:
last_line = input_text.strip().split("\n")[-1]
return last_line.split("left: ")[-1].split(")")[0]
def propose(self, current_state: str, **kwargs) -> str:
return self.propose_prompt.format(input=current_state, **kwargs)
def value(self, input: str = "", **kwargs) -> str:
node_result = self(input)
return self.value_prompt.format(input=node_result)
class Game24Evaluator(BaseEvaluator):
value_map = {"impossible": 0.001, "likely": 1, "sure": 20} # TODO: ad hoc
status_map = {val: key for key, val in value_map.items()}
def __call__(self, evaluation: str, **kwargs) -> float:
try:
matches = re.findall(r"\b(impossible|sure|likely)\b", evaluation)
value = self.value_map[matches[0]]
except:
value = 0.001
return value
def status_verify(self, value):
status = False
if value in self.status_map:
status_value = self.status_map[value]
if status_value != "impossible":
status = True
return status
if __name__ == "__main__":
import asyncio
initial_prompt = """4 5 6 10"""
parser = Game24Parser()
evaluator = Game24Evaluator()
config = ThoughtSolverConfig(n_generate_sample=5, parser=parser, evaluator=evaluator)
tot = TreeofThought(strategy=Strategy.BFS, config=config)
asyncio.run(tot.solve(init_prompt=initial_prompt))

4
metagpt/strategy/prompt_templates/__init__.py
View file

@ -1,4 +0,0 @@
# -*- coding: utf-8 -*-
# @Date : 12/23/2023 5:21 PM
# @Author : stellahong (stellahong@fuzhi.ai)
# @Desc :

25
metagpt/strategy/prompt_templates/creative_writing.py
View file

@ -1,25 +0,0 @@
standard_prompt = """
Write a coherent passage of 4 short paragraphs. The end sentence of each paragraph must be: {input}
"""
cot_prompt = """
Write a coherent passage of 4 short paragraphs. The end sentence of each paragraph must be: {input}
Make a plan then write. Your output should be of the following format:
Plan:
Your plan here.
Passage:
Your passage here.
"""
vote_prompt = """Given an instruction and several choices, decide which choice is most promising. Analyze each choice in detail, then conclude in the last line "The best choice is {s}", where s the integer id of the choice.
"""
compare_prompt = """Briefly analyze the coherency of the following two passages. Conclude in the last line "The more coherent passage is 1", "The more coherent passage is 2", or "The two passages are similarly coherent".
"""
score_prompt = """Analyze the following passage, then at the last line conclude "Thus the coherency score is {s}", where s is an integer from 1 to 10.
"""

139
metagpt/strategy/prompt_templates/game24.py
View file

@ -1,139 +0,0 @@
# 5-shot
standard_prompt = """Use numbers and basic arithmetic operations (+ - * /) to obtain 24.
Input: 4 4 6 8
Answer: (4 + 8) * (6 - 4) = 24
Input: 2 9 10 12
Answer: 2 * 12 * (10 - 9) = 24
Input: 4 9 10 13
Answer: (13 - 9) * (10 - 4) = 24
Input: 1 4 8 8
Answer: (8 / 4 + 1) * 8 = 24
Input: 5 5 5 9
Answer: 5 + 5 + 5 + 9 = 24
Input: {input}
"""
# 5-shot
cot_prompt = """Use numbers and basic arithmetic operations (+ - * /) to obtain 24. Each step, you are only allowed to choose two of the remaining numbers to obtain a new number.
Input: 4 4 6 8
Steps:
4 + 8 = 12 (left: 4 6 12)
6 - 4 = 2 (left: 2 12)
2 * 12 = 24 (left: 24)
Answer: (6 - 4) * (4 + 8) = 24
Input: 2 9 10 12
Steps:
12 * 2 = 24 (left: 9 10 24)
10 - 9 = 1 (left: 1 24)
24 * 1 = 24 (left: 24)
Answer: (12 * 2) * (10 - 9) = 24
Input: 4 9 10 13
Steps:
13 - 10 = 3 (left: 3 4 9)
9 - 3 = 6 (left: 4 6)
4 * 6 = 24 (left: 24)
Answer: 4 * (9 - (13 - 10)) = 24
Input: 1 4 8 8
Steps:
8 / 4 = 2 (left: 1 2 8)
1 + 2 = 3 (left: 3 8)
3 * 8 = 24 (left: 24)
Answer: (1 + 8 / 4) * 8 = 24
Input: 5 5 5 9
Steps:
5 + 5 = 10 (left: 5 9 10)
10 + 5 = 15 (left: 9 15)
15 + 9 = 24 (left: 24)
Answer: ((5 + 5) + 5) + 9 = 24
Input: {input}
"""
# 1-shot
propose_prompt = """Here is an Example for 1 input and 8 possible thoughts:
Input: 2 8 8 14
Possible next steps:
2 + 8 = 10 (left: 8 10 14)
8 / 2 = 4 (left: 4 8 14)
14 + 2 = 16 (left: 8 8 16)
2 * 8 = 16 (left: 8 14 16)
8 - 2 = 6 (left: 6 8 14)
14 - 8 = 6 (left: 2 6 8)
14 / 2 = 7 (left: 7 8 8)
14 - 2 = 12 (left: 8 8 12)
Here is my task for 1 input and {n_generate_sample} possible thoughts:
Input: {input}
Possible next steps:
"""
value_prompt = """Evaluate if given numbers can reach 24 (sure/likely/impossible)
10 14
10 + 14 = 24
sure
11 12
11 + 12 = 23
12 - 11 = 1
11 * 12 = 132
11 / 12 = 0.91
impossible
4 4 10
4 + 4 + 10 = 8 + 10 = 18
4 * 10 - 4 = 40 - 4 = 36
(10 - 4) * 4 = 6 * 4 = 24
sure
4 9 11
9 + 11 + 4 = 20 + 4 = 24
sure
5 7 8
5 + 7 + 8 = 12 + 8 = 20
(8 - 5) * 7 = 3 * 7 = 21
I cannot obtain 24 now, but numbers are within a reasonable range
likely
5 6 6
5 + 6 + 6 = 17
(6 - 5) * 6 = 1 * 6 = 6
I cannot obtain 24 now, but numbers are within a reasonable range
likely
10 10 11
10 + 10 + 11 = 31
(11 - 10) * 10 = 10
10 10 10 are all too big
impossible
1 3 3
1 * 3 * 3 = 9
(1 + 3) * 3 = 12
1 3 3 are all too small
impossible
{input}
"""
value_last_step_prompt = """Use numbers and basic arithmetic operations (+ - * /) to obtain 24. Given an input and an answer, give a judgement (sure/impossible) if the answer is correct, i.e. it uses each input exactly once and no other numbers, and reach 24.
Input: 4 4 6 8
Answer: (4 + 8) * (6 - 4) = 24
Judge:
sure
Input: 2 9 10 12
Answer: 2 * 12 * (10 - 9) = 24
Judge:
sure
Input: 4 9 10 13
Answer: (13 - 9) * (10 - 4) = 24
Judge:
sure
Input: 4 4 6 8
Answer: (4 + 8) * (6 - 4) + 1 = 25
Judge:
impossible
Input: 2 9 10 12
Answer: 2 * (12 - 10) = 24
Judge:
impossible
Input: 4 9 10 13
Answer: (13 - 4) * (10 - 9) = 24
Judge:
impossible
Input: {input}
Answer: {answer}
Judge:"""

4
metagpt/strategy/tot.py
View file

@ -5,7 +5,7 @@
import asyncio
from typing import Any, List
from pydantic import BaseModel, Field
from pydantic import BaseModel, ConfigDict, Field
from metagpt.llm import LLM
from metagpt.logs import logger
@ -29,6 +29,8 @@ Output a list of jsons following the format:
class ThoughtSolverBase(BaseModel):
model_config = ConfigDict(arbitrary_types_allowed=True)
thought_tree: str = ""
llm: BaseLLM = Field(default_factory=LLM, exclude=True)
config: ThoughtSolverConfig = Field(default_factory=ThoughtSolverConfig)

20
metagpt/utils/get_template.py
View file

@ -1,20 +0,0 @@
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
@Time : 2023/9/19 20:39
@Author : femto Zheng
@File : get_template.py
"""
from metagpt.config import CONFIG
def get_template(templates, schema=CONFIG.prompt_schema):
selected_templates = templates.get(schema)
if selected_templates is None:
raise ValueError(f"Can't find {schema} in passed in templates")
# Extract the selected templates
prompt_template = selected_templates["PROMPT_TEMPLATE"]
format_example = selected_templates["FORMAT_EXAMPLE"]
return prompt_template, format_example