llm kira
0.7.4
تم حذف مكتبة LLM-Kira بسبب تعارض مع آلية تسمية المكون الإضافي الأساسي. لم يعد بإمكانك تنفيذ pip install llm-kira على جهاز جديد
بدلاً من ذلك ، إذا كنت في حاجة إليها ، يمكنك استنساخ المشروع للاستخدام
git clone https://github.com/LlmKira/llm-kira/tree/0.7.4
pip install poetry
cd llm-kira
poetry install
تمت إزالة مكتبة LLM-Kira بسبب تعارض مع آلية تسمية المكون الإضافي. لم يعد بإمكانك تنفيذ pip install llm-kira على جهاز جديد
بدلاً من ذلك ، إذا كنت بحاجة ، يمكنك استنساخ المشروع والاستخدام
git clone https://github.com/LlmKira/llm-kira/tree/0.7.4
pip install poetry
cd llm-kira
poetry install
طراز متعدد اللغات خفيفة الوزن غير متزامن إطار روبوت الدردشة.
pip install -U llm-kira
init
import llm_kira
llm_kira . setting . redisSetting = llm_kira . setting . RedisConfig ( host = "localhost" ,
port = 6379 ,
db = 0 ,
password = None )
llm_kira . setting . dbFile = "client_memory.db"
llm_kira . setting . proxyUrl = None # "127.0.0.1"
# Plugin
llm_kira . setting . webServerUrlFilter = False
llm_kira . setting . webServerStopSentence = [ "广告" , "营销号" ] # 有默认值 دلالة المزيد من الأمثلة على الاستخدام في test/test.py
خذ openai كمثال
import asyncio
import random
import llm_kira
from llm_kira . creator import Optimizer
from llm_kira . types import PromptItem , Interaction
from llm_kira . llms import OpenAiParam
from typing import List
openaiApiKey = [ "key1" , "key2" ]
openaiApiKey : List [ str ]
receiver = llm_kira . client
conversation = receiver . Conversation (
start_name = "Human:" ,
restart_name = "AI:" ,
conversation_id = 10093 , # random.randint(1, 10000000),
)
llm = llm_kira . client . llms . OpenAi (
profile = conversation ,
api_key = openaiApiKey ,
token_limit = 3700 ,
auto_penalty = False ,
call_func = None ,
)
mem = receiver . MemoryManager ( profile = conversation )
chat_client = receiver . ChatBot ( profile = conversation ,
llm_model = llm
)
async def chat ():
promptManager = llm_kira . creator . PromptEngine (
reverse_prompt_buffer = False , # 设定是首条还是末尾的 prompt 当 input
profile = conversation ,
connect_words = " n " ,
memory_manger = mem ,
llm_model = llm ,
description = "这是一段对话" , # 推荐在这里进行强注入
reference_ratio = 0.5 ,
forget_words = [ "忘掉对话" ],
optimizer = Optimizer . SinglePoint ,
)
# 第三人称
promptManager . insert_prompt ( prompt = PromptItem ( start = "Neko" , text = "喵喵喵" ))
# 直接添加
promptManager . insert_interaction ( Interaction ( single = True , ask = PromptItem ( start = "Neko" , text = "MewMewMewMew" )))
# 添加交互
promptManager . insert_interaction ( Interaction ( single = False ,
ask = PromptItem ( start = "Neko" , text = "MewMewMewMew" ),
reply = PromptItem ( start = "Neko" , text = "MewMewMewMew" ))
)
# 添加新内容
promptManager . insert_prompt ( prompt = PromptItem ( start = conversation . start_name , text = input ( "TestPrompt:" )))
response = await chat_client . predict (
prompt = promptManager ,
llm_param = OpenAiParam ( model_name = "text-davinci-003" , temperature = 0.8 , presence_penalty = 0.1 , n = 1 , best_of = 1 ),
predict_tokens = 1000 ,
)
print ( f"id { response . conversation_id } " )
print ( f"ask { response . ask } " )
print ( f"reply { response . reply } " )
print ( f"usage: { response . llm . usage } " )
print ( f"usage: { response . llm . raw } " )
print ( f"--- { response . llm . time } ---" )
promptManager . clean ( clean_prompt = True , clean_knowledge = False , clean_memory = False )
promptManager . insert_prompt ( prompt = PromptItem ( start = conversation . start_name , text = '今天天气怎么样' ))
response = await chat_client . predict ( llm_param = OpenAiParam ( model_name = "text-davinci-003" ),
prompt = promptManager ,
predict_tokens = 500 ,
# parse_reply=None
)
_info = "parse_reply 函数回调会处理 llm 的回复字段，比如 list 等，传入list，传出 str 的回复。必须是 str。"
_info2 = "The parse_reply function callback handles the reply fields of llm, such as list, etc. Pass in list and pass out str for the reply."
print ( f"id { response . conversation_id } " )
print ( f"ask { response . ask } " )
print ( f"reply { response . reply } " )
print ( f"usage: { response . llm . usage } " )
print ( f"usage: { response . llm . raw } " )
print ( f"--- { response . llm . time } ---" )
asyncio . run ( chat ()) import llm_kira
from llm_kira . creator . think import ThinkEngine , Hook
conversation = llm_kira . client . Conversation (
start_name = "Human:" ,
restart_name = "AI:" ,
conversation_id = 10093 , # random.randint(1, 10000000),
)
_think = ThinkEngine ( profile = conversation )
_think . register_hook ( Hook ( name = "happy" , trigger = "happy" , value = 2 , last = 60 , time = int ( time . time ()))) # 60s
# Hook
_think . hook ( "happy" )
print ( _think . hook_pool )
print ( _think . build_status ( rank = 5 ))
# rank=sum(value,value,value) ├── client
│ ├── agent.py // 基本类
│ ├── anchor.py // 代理端
│ ├── enhance.py // 外部接口方法
│ ├── __init__.py
│ ├── llms // 大语言模型类
│ ├── module // 注入模组
│ ├── Optimizer.py // 优化器
│ ├── test //测试
│ ├── text_analysis_tools
│ ├── types.py // 类型
│ └── vocab.json
├── creator // 提示构建引擎
│ ├── engine.py
│ ├── __init__.py
├── error.py // 通用错误类型
├── __init__.py //主入口
├── radio // 外部通信类型，知识池
│ ├── anchor.py
│ ├── crawer.py
│ ├── decomposer.py
│ └── setting.py
├── requirements.txt
├── tool // LLM 工具类型
│ ├── __init__.py
│ ├── openai
└── utils // 工具类型/语言探测
├── chat.py
├── data.py
├── fatlangdetect
├── langdetect
├── network.py
└── setting.py
