mirror of
https://github.com/FoundationAgents/MetaGPT.git
synced 2026-06-05 14:55:18 +02:00
refactor openai api and brain memory
This commit is contained in:
geekan
parent
8351c8ec35
commit
e15de55368
2 changed files with 79 additions and 79 deletions
|
|
@ -10,14 +10,15 @@
|
|||
"""
|
||||
import json
|
||||
import re
|
||||
from typing import Dict, List
|
||||
from typing import Dict, List, Optional
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from metagpt.config import CONFIG
|
||||
from metagpt.const import DEFAULT_LANGUAGE
|
||||
from metagpt.const import DEFAULT_LANGUAGE, DEFAULT_MAX_TOKENS, DEFAULT_TOKEN_SIZE
|
||||
from metagpt.logs import logger
|
||||
from metagpt.provider import MetaGPTAPI
|
||||
from metagpt.provider.base_gpt_api import BaseGPTAPI
|
||||
from metagpt.schema import Message, SimpleMessage
|
||||
from metagpt.utils.redis import Redis
|
||||
|
||||
|
|
@ -30,6 +31,7 @@ class BrainMemory(BaseModel):
|
|||
is_dirty: bool = False
|
||||
last_talk: str = None
|
||||
cacheable: bool = True
|
||||
llm: Optional[BaseGPTAPI] = None
|
||||
|
||||
def add_talk(self, msg: Message):
|
||||
"""
|
||||
|
|
@ -120,6 +122,7 @@ class BrainMemory(BaseModel):
|
|||
if isinstance(llm, MetaGPTAPI):
|
||||
return await self._metagpt_summarize(max_words=max_words)
|
||||
|
||||
self.llm = llm
|
||||
return await self._openai_summarize(llm=llm, max_words=max_words, keep_language=keep_language, limit=limit)
|
||||
|
||||
async def _openai_summarize(self, llm, max_words=200, keep_language: bool = False, limit: int = -1):
|
||||
|
|
@ -131,7 +134,7 @@ class BrainMemory(BaseModel):
|
|||
text_length = len(text)
|
||||
if limit > 0 and text_length < limit:
|
||||
return text
|
||||
summary = await llm.summarize(text=text, max_words=max_words, keep_language=keep_language, limit=limit)
|
||||
summary = await self._summarize(text=text, max_words=max_words, keep_language=keep_language, limit=limit)
|
||||
if summary:
|
||||
await self.set_history_summary(history_summary=summary, redis_key=CONFIG.REDIS_KEY, redis_conf=CONFIG.REDIS)
|
||||
return summary
|
||||
|
|
@ -251,3 +254,74 @@ class BrainMemory(BaseModel):
|
|||
texts.append(t)
|
||||
|
||||
return "\n".join(texts)
|
||||
|
||||
async def _summarize(self, text: str, max_words=200, keep_language: bool = False, limit: int = -1) -> str:
|
||||
max_token_count = DEFAULT_MAX_TOKENS
|
||||
max_count = 100
|
||||
text_length = len(text)
|
||||
if limit > 0 and text_length < limit:
|
||||
return text
|
||||
summary = ""
|
||||
while max_count > 0:
|
||||
if text_length < max_token_count:
|
||||
summary = await self._get_summary(text=text, max_words=max_words, keep_language=keep_language)
|
||||
break
|
||||
|
||||
padding_size = 20 if max_token_count > 20 else 0
|
||||
text_windows = self.split_texts(text, window_size=max_token_count - padding_size)
|
||||
part_max_words = min(int(max_words / len(text_windows)) + 1, 100)
|
||||
summaries = []
|
||||
for ws in text_windows:
|
||||
response = await self._get_summary(text=ws, max_words=part_max_words, keep_language=keep_language)
|
||||
summaries.append(response)
|
||||
if len(summaries) == 1:
|
||||
summary = summaries[0]
|
||||
break
|
||||
|
||||
# Merged and retry
|
||||
text = "\n".join(summaries)
|
||||
text_length = len(text)
|
||||
|
||||
max_count -= 1 # safeguard
|
||||
return summary
|
||||
|
||||
async def _get_summary(self, text: str, max_words=20, keep_language: bool = False):
|
||||
"""Generate text summary"""
|
||||
if len(text) < max_words:
|
||||
return text
|
||||
if keep_language:
|
||||
command = f".Translate the above content into a summary of less than {max_words} words in language of the content strictly."
|
||||
else:
|
||||
command = f"Translate the above content into a summary of less than {max_words} words."
|
||||
msg = text + "\n\n" + command
|
||||
logger.debug(f"summary ask:{msg}")
|
||||
response = await self.llm.aask(msg=msg, system_msgs=[])
|
||||
logger.debug(f"summary rsp: {response}")
|
||||
return response
|
||||
|
||||
@staticmethod
|
||||
def split_texts(text: str, window_size) -> List[str]:
|
||||
"""Splitting long text into sliding windows text"""
|
||||
if window_size <= 0:
|
||||
window_size = DEFAULT_TOKEN_SIZE
|
||||
total_len = len(text)
|
||||
if total_len <= window_size:
|
||||
return [text]
|
||||
|
||||
padding_size = 20 if window_size > 20 else 0
|
||||
windows = []
|
||||
idx = 0
|
||||
data_len = window_size - padding_size
|
||||
while idx < total_len:
|
||||
if window_size + idx > total_len: # 不足一个滑窗
|
||||
windows.append(text[idx:])
|
||||
break
|
||||
# 每个窗口少算padding_size自然就可实现滑窗功能, 比如: [1, 2, 3, 4, 5, 6, 7, ....]
|
||||
# window_size=3, padding_size=1：
|
||||
# [1, 2, 3], [3, 4, 5], [5, 6, 7], ....
|
||||
# idx=2, | idx=5 | idx=8 | ...
|
||||
w = text[idx : idx + window_size]
|
||||
windows.append(w)
|
||||
idx += data_len
|
||||
|
||||
return windows
|
||||
|
|
|
|||
|
|
@ -12,7 +12,7 @@
|
|||
import asyncio
|
||||
import json
|
||||
import time
|
||||
from typing import AsyncIterator, List, Union
|
||||
from typing import AsyncIterator, Union
|
||||
|
||||
import openai
|
||||
from openai import APIConnectionError, AsyncOpenAI, AsyncStream, OpenAI
|
||||
|
|
@ -28,7 +28,6 @@ from tenacity import (
|
|||
)
|
||||
|
||||
from metagpt.config import CONFIG, Config, LLMProviderEnum
|
||||
from metagpt.const import DEFAULT_MAX_TOKENS, DEFAULT_TOKEN_SIZE
|
||||
from metagpt.logs import log_llm_stream, logger
|
||||
from metagpt.provider.base_gpt_api import BaseGPTAPI
|
||||
from metagpt.provider.constant import GENERAL_FUNCTION_SCHEMA, GENERAL_TOOL_CHOICE
|
||||
|
|
@ -190,9 +189,7 @@ class OpenAIGPTAPI(BaseGPTAPI, RateLimiter):
|
|||
return self.get_choice_text(rsp)
|
||||
|
||||
def _func_configs(self, messages: list[dict], timeout=3, **kwargs) -> dict:
|
||||
"""
|
||||
Note: Keep kwargs consistent with the parameters in the https://platform.openai.com/docs/api-reference/chat/create
|
||||
"""
|
||||
"""Note: Keep kwargs consistent with https://platform.openai.com/docs/api-reference/chat/create"""
|
||||
if "tools" not in kwargs:
|
||||
configs = {
|
||||
"tools": [{"type": "function", "function": GENERAL_FUNCTION_SCHEMA}],
|
||||
|
|
@ -353,74 +350,3 @@ class OpenAIGPTAPI(BaseGPTAPI, RateLimiter):
|
|||
if self.async_client:
|
||||
await self.async_client.close()
|
||||
self.async_client = None
|
||||
|
||||
async def summarize(self, text: str, max_words=200, keep_language: bool = False, limit: int = -1) -> str:
|
||||
max_token_count = DEFAULT_MAX_TOKENS
|
||||
max_count = 100
|
||||
text_length = len(text)
|
||||
if limit > 0 and text_length < limit:
|
||||
return text
|
||||
summary = ""
|
||||
while max_count > 0:
|
||||
if text_length < max_token_count:
|
||||
summary = await self._get_summary(text=text, max_words=max_words, keep_language=keep_language)
|
||||
break
|
||||
|
||||
padding_size = 20 if max_token_count > 20 else 0
|
||||
text_windows = self.split_texts(text, window_size=max_token_count - padding_size)
|
||||
part_max_words = min(int(max_words / len(text_windows)) + 1, 100)
|
||||
summaries = []
|
||||
for ws in text_windows:
|
||||
response = await self._get_summary(text=ws, max_words=part_max_words, keep_language=keep_language)
|
||||
summaries.append(response)
|
||||
if len(summaries) == 1:
|
||||
summary = summaries[0]
|
||||
break
|
||||
|
||||
# Merged and retry
|
||||
text = "\n".join(summaries)
|
||||
text_length = len(text)
|
||||
|
||||
max_count -= 1 # safeguard
|
||||
return summary
|
||||
|
||||
async def _get_summary(self, text: str, max_words=20, keep_language: bool = False):
|
||||
"""Generate text summary"""
|
||||
if len(text) < max_words:
|
||||
return text
|
||||
if keep_language:
|
||||
command = f".Translate the above content into a summary of less than {max_words} words in language of the content strictly."
|
||||
else:
|
||||
command = f"Translate the above content into a summary of less than {max_words} words."
|
||||
msg = text + "\n\n" + command
|
||||
logger.debug(f"summary ask:{msg}")
|
||||
response = await self.aask(msg=msg, system_msgs=[])
|
||||
logger.debug(f"summary rsp: {response}")
|
||||
return response
|
||||
|
||||
@staticmethod
|
||||
def split_texts(text: str, window_size) -> List[str]:
|
||||
"""Splitting long text into sliding windows text"""
|
||||
if window_size <= 0:
|
||||
window_size = DEFAULT_TOKEN_SIZE
|
||||
total_len = len(text)
|
||||
if total_len <= window_size:
|
||||
return [text]
|
||||
|
||||
padding_size = 20 if window_size > 20 else 0
|
||||
windows = []
|
||||
idx = 0
|
||||
data_len = window_size - padding_size
|
||||
while idx < total_len:
|
||||
if window_size + idx > total_len: # 不足一个滑窗
|
||||
windows.append(text[idx:])
|
||||
break
|
||||
# 每个窗口少算padding_size自然就可实现滑窗功能, 比如: [1, 2, 3, 4, 5, 6, 7, ....]
|
||||
# window_size=3, padding_size=1：
|
||||
# [1, 2, 3], [3, 4, 5], [5, 6, 7], ....
|
||||
# idx=2, | idx=5 | idx=8 | ...
|
||||
w = text[idx : idx + window_size]
|
||||
windows.append(w)
|
||||
idx += data_len
|
||||
|
||||
return windows
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue