llm-manager 0.4.3

Flash-built LLM workflows

llm-manager 大模型应用开发

简介

llm-manager是一个用于快速开发大模型应用的Python包。支持MCP服务集成和工具转换。内置工具：CodeInterpreter。

依赖性

基础依赖（安装llm-manager时将自动安装）：

• portalocker
• cryptography
• pydantic
• requests
• docker
• fastmcp

安装

# 通过 pip 安装：
pip install llm-manager

配置操作

1. 模型配置写入

from llm_manager.config import EnvConfig

# 配置名为 'qwen' 的大模型
EnvConfig.write("qwen", endpoint_url="https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions", api_key={API_KEY})

# 配置名为 'deepseek' 的大模型
EnvConfig.write("deepseek", endpoint_url="https://api.deepseek.com/chat/completions", api_key={API_KEY})

2. 应用配置写入

from llm_manager.config import EnvConfig

# 应用名为 'chat' 的大模型配置
EnvConfig.write("chat", llm="deepseek", model="deepseek-chat", temperature=0.9, top_p=0.95, stream=True)

# 应用名为 'qwen-vl' 的大模型配置
EnvConfig.write("qwen-vl", llm="qwen", model="qwen-vl-max-latest", stream=True)

# 读取全部配置文件信息
all_config = EnvConfig.read()

工具开发

tools.py文件内容

import datetime
from typing import Any

from llm_manager.tools import ToolModel


# 自动生成schema
class GetCurrentDate(ToolModel):
    """获取当前日期"""

    def __call__(self) -> dict[str, Any]:
        now = datetime.datetime.now()
        return {"isoformat": now.isoformat(), "isoweekday": now.isoweekday()}


# 自定义schema
class GetCurrentDate(ToolModel):

    @classmethod
    def to_schema(cls) -> dict[str, Any]:
        return {
            "type": "function",
            "function": {
                "name": "GetCurrentDate",
                "description": "获取当前日期"
            }
        }

    def __call__(self) -> dict[str, Any]:
        now = datetime.datetime.now()
        return {"isoformat": now.isoformat(), "isoweekday": now.isoweekday()}

chat应用开发

chat/model.py文件内容

from llm_manager.model import BaseAgent
from llm_manager.tools import Tools

from tools import GetCurrentDate


class LLMChat(BaseAgent):
    _raw: bool = True  # 保持完整输出数据结构（默认False，只输出模型回复）
    _tools: Tools = Tools(GetCurrentDate)

    content: str

    @property
    def _section(self) -> str:
        return "chat"

chat/prompt.py文件内容

SYSTEM = """You are a helpful assistant."""

USER = """{content}"""

chat应用示例

from chat.model import LLMChat

chat = LLMChat(content="还有几天周末放假？")
for chunk in chat.run():
    print(chunk)
for chunk in chat.run(content="周末旅游地点推荐"):
    print(chunk)

OCR应用开发

ocr/model.py文件内容

from llm_manager.model import BaseAgent


class VLMOCR(BaseAgent):

    @property
    def _section(self) -> str:
        return "qwen-vl"

ocr/prompt.py文件内容

SYSTEM = """You are an AI specialized in recognizing and extracting text from images. Your mission is to analyze the image document and generate the result in QwenVL Document Parser HTML format using specified tags while maintaining user privacy and data integrity."""

USER = """QwenVL HTML"""

OCR应用示例

import base64
import pathlib

from ocr.model import VLMOCR


def encode_image(filepath: str) -> str:
    with open(filepath, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode("utf-8")


vlm_ocr = VLMOCR(_images=[encode_image("document.png")])

for chunk in vlm_ocr.run():
    print(chunk, end="")