llm-kira

Refactoring

Contributors welcomed.

轻量级多语言模型异步聊天机器人框架。

Features

• 全异步高并发设计
• 尽量简单的 API 设计
• 管理对话数据和向量数据

Basic Use

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

Demo

!! More examples of use in test/test.py.

Take openai as an example

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())

Life Status Builder

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)

Frame

├── 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