# llm-kira
A refactored version of the `openai-kira` specification. Use redis or a file database.
Building ChatBot with LLMs.Using `async` requests.
> Contributors welcomed.
## Features
* safely cut context
* usage
* async request api / curl
* multi-Api Key load
* self-design callback
## Basic Use
`pip install -U llm-kira`
**Init**
```python
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
```python
import asyncio
import random
import llm_kira
from llm_kira.client import Optimizer
from llm_kira.client.types import PromptItem
from llm_kira.client.llms.openai 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,
memory_manger=mem,
optimizer=Optimizer.SinglePoint,
llm_model=llm)
async def chat():
promptManager = receiver.PromptManager(profile=conversation,
connect_words="\n",
template="Templates, custom prefixes"
)
promptManager.insert(item=PromptItem(start=conversation.start_name, text="My id is 1596321"))
response = await chat_client.predict(llm_param=OpenAiParam(model_name="text-davinci-003", n=2, best_of=2),
prompt=promptManager,
predict_tokens=500,
increase="External enhancements, or searched result",
)
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()
promptManager.insert(item=PromptItem(start=conversation.start_name, text="Whats my id？"))
response = await chat_client.predict(llm_param=OpenAiParam(model_name="text-davinci-003"),
prompt=promptManager,
predict_tokens=500,
increase="外部增强:每句话后面都要带 “喵”",
# 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())
```
## Frame
```
├── client
│ ├── agent.py //profile class
│ ├── anchor.py // client etc.
│ ├── enhance.py // web search etc.
│ ├── __init__.py
│ ├── llm.py // llm func.
│ ├── module // plugin for enhance
│ ├── Optimizer.py // memory Optimizer (cutter
│ ├── pot.py // test cache
│ ├── test_module.py // test plugin
│ ├── text_analysis_tools // nlp support
│ ├── types.py // data class
│ └── vocab.json // cache?
├── __init__.py
├── openai // func
│ ├── api // data
│ ├── __init__.py
│ └── resouce // func
├── requirements.txt
└── utils // utils... tools...
├── chat.py
├── data.py
├── fatlangdetect //lang detect
├── langdetect
├── network.py
└── setting.py
```
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"description": "# llm-kira\n\nA refactored version of the `openai-kira` specification. Use redis or a file database.\n\nBuilding ChatBot with LLMs.Using `async` requests.\n\n> Contributors welcomed.\n\n## Features\n\n* safely cut context\n* usage\n* async request api / curl\n* multi-Api Key load\n* self-design callback\n\n## Basic Use\n\n`pip install -U llm-kira`\n\n**Init**\n\n```python\nimport llm_kira\n\nllm_kira.setting.redisSetting = llm_kira.setting.RedisConfig(host=\"localhost\",\n port=6379,\n db=0,\n password=None)\nllm_kira.setting.dbFile = \"client_memory.db\"\nllm_kira.setting.proxyUrl = None # \"127.0.0.1\"\n\n# Plugin\nllm_kira.setting.webServerUrlFilter = False\nllm_kira.setting.webServerStopSentence = [\"\u5e7f\u544a\", \"\u8425\u9500\u53f7\"] # \u6709\u9ed8\u8ba4\u503c\n```\n\n## Demo\n\n**More examples of use in `test/test.py`.**\n\nTake `openai` as an example\n\n```python\nimport asyncio\nimport random\nimport llm_kira\nfrom llm_kira.client import Optimizer\nfrom llm_kira.client.types import PromptItem\nfrom llm_kira.client.llms.openai import OpenAiParam\nfrom typing import List\n\nopenaiApiKey = [\"key1\", \"key2\"]\nopenaiApiKey: List[str]\n\nreceiver = llm_kira.client\nconversation = receiver.Conversation(\n start_name=\"Human:\",\n restart_name=\"AI:\",\n conversation_id=10093, # random.randint(1, 10000000),\n)\n\nllm = llm_kira.client.llms.OpenAi(\n profile=conversation,\n api_key=openaiApiKey,\n token_limit=3700,\n auto_penalty=False,\n call_func=None,\n)\n\nmem = receiver.MemoryManager(profile=conversation)\nchat_client = receiver.ChatBot(profile=conversation,\n memory_manger=mem,\n optimizer=Optimizer.SinglePoint,\n llm_model=llm)\n\n\nasync def chat():\n promptManager = receiver.PromptManager(profile=conversation,\n connect_words=\"\\n\",\n template=\"Templates, custom prefixes\"\n )\n promptManager.insert(item=PromptItem(start=conversation.start_name, text=\"My id is 1596321\"))\n response = await chat_client.predict(llm_param=OpenAiParam(model_name=\"text-davinci-003\", n=2, best_of=2),\n prompt=promptManager,\n predict_tokens=500,\n increase=\"External enhancements, or searched result\",\n )\n print(f\"id {response.conversation_id}\")\n print(f\"ask {response.ask}\")\n print(f\"reply {response.reply}\")\n print(f\"usage:{response.llm.usage}\")\n print(f\"usage:{response.llm.raw}\")\n print(f\"---{response.llm.time}---\")\n\n promptManager.clean()\n promptManager.insert(item=PromptItem(start=conversation.start_name, text=\"Whats my id\uff1f\"))\n response = await chat_client.predict(llm_param=OpenAiParam(model_name=\"text-davinci-003\"),\n prompt=promptManager,\n predict_tokens=500,\n increase=\"\u5916\u90e8\u589e\u5f3a:\u6bcf\u53e5\u8bdd\u540e\u9762\u90fd\u8981\u5e26 \u201c\u55b5\u201d\",\n # parse_reply=None\n )\n _info = \"parse_reply \u51fd\u6570\u56de\u8c03\u4f1a\u5904\u7406 llm \u7684\u56de\u590d\u5b57\u6bb5\uff0c\u6bd4\u5982 list \u7b49\uff0c\u4f20\u5165list\uff0c\u4f20\u51fa str \u7684\u56de\u590d\u3002\u5fc5\u987b\u662f str\u3002\"\n _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.\"\n print(f\"id {response.conversation_id}\")\n print(f\"ask {response.ask}\")\n print(f\"reply {response.reply}\")\n print(f\"usage:{response.llm.usage}\")\n print(f\"usage:{response.llm.raw}\")\n print(f\"---{response.llm.time}---\")\n\n\nasyncio.run(chat())\n```\n\n## Frame\n\n```\n\u251c\u2500\u2500 client\n\u2502 \u251c\u2500\u2500 agent.py //profile class\n\u2502 \u251c\u2500\u2500 anchor.py // client etc.\n\u2502 \u251c\u2500\u2500 enhance.py // web search etc.\n\u2502 \u251c\u2500\u2500 __init__.py\n\u2502 \u251c\u2500\u2500 llm.py // llm func.\n\u2502 \u251c\u2500\u2500 module // plugin for enhance\n\u2502 \u251c\u2500\u2500 Optimizer.py // memory Optimizer (cutter\n\u2502 \u251c\u2500\u2500 pot.py // test cache\n\u2502 \u251c\u2500\u2500 test_module.py // test plugin\n\u2502 \u251c\u2500\u2500 text_analysis_tools // nlp support\n\u2502 \u251c\u2500\u2500 types.py // data class\n\u2502 \u2514\u2500\u2500 vocab.json // cache?\n\u251c\u2500\u2500 __init__.py\n\u251c\u2500\u2500 openai // func\n\u2502 \u251c\u2500\u2500 api // data\n\u2502 \u251c\u2500\u2500 __init__.py\n\u2502 \u2514\u2500\u2500 resouce // func\n\u251c\u2500\u2500 requirements.txt\n\u2514\u2500\u2500 utils // utils... tools...\n \u251c\u2500\u2500 chat.py\n \u251c\u2500\u2500 data.py\n \u251c\u2500\u2500 fatlangdetect //lang detect\n \u251c\u2500\u2500 langdetect\n \u251c\u2500\u2500 network.py\n \u2514\u2500\u2500 setting.py\n\n```\n\n",
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