mirror of
https://github.com/RYDE-WORK/Langchain-Chatchat.git
synced 2026-01-27 09:13:25 +08:00
449 lines
19 KiB
Python
449 lines
19 KiB
Python
import os
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import logging
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import sys
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from pathlib import Path
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from typing import Any, Optional, List, Dict
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from dataclasses import dataclass
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sys.path.append(str(Path(__file__).parent))
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import _core_config as core_config
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logger = logging.getLogger()
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class ConfigModel(core_config.Config):
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DEFAULT_LLM_MODEL: Optional[str] = None
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"""默认选用的 LLM 名称"""
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DEFAULT_EMBEDDING_MODEL: Optional[str] = None
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"""默认选用的 Embedding 名称"""
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Agent_MODEL: Optional[str] = None
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"""AgentLM模型的名称 (可以不指定，指定之后就锁定进入Agent之后的Chain的模型，不指定就是LLM_MODELS[0])"""
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HISTORY_LEN: Optional[int] = None
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"""历史对话轮数"""
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MAX_TOKENS: Optional[int] = None
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"""大模型最长支持的长度，如果不填写，则使用模型默认的最大长度，如果填写，则为用户设定的最大长度"""
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TEMPERATURE: Optional[float] = None
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"""LLM通用对话参数"""
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SUPPORT_AGENT_MODELS: Optional[List[str]] = None
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"""支持的Agent模型"""
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LLM_MODEL_CONFIG: Optional[Dict[str, Dict[str, Any]]] = None
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"""LLM模型配置，包括了不同模态初始化参数"""
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MODEL_PLATFORMS: Optional[List[Dict[str, Any]]] = None
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"""模型平台配置"""
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MODEL_PROVIDERS_CFG_PATH_CONFIG: Optional[str] = None
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"""模型平台配置文件路径"""
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MODEL_PROVIDERS_CFG_HOST: Optional[str] = None
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"""模型平台配置服务host"""
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MODEL_PROVIDERS_CFG_PORT: Optional[int] = None
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"""模型平台配置服务port"""
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TOOL_CONFIG: Optional[Dict[str, Any]] = None
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"""工具配置项"""
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@classmethod
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def class_name(cls) -> str:
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return cls.__name__
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def __str__(self):
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return self.to_json()
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@dataclass
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class ConfigModelFactory(core_config.ConfigFactory[ConfigModel]):
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"""ConfigModel工厂类"""
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def __init__(self):
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# 默认选用的 LLM 名称
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self.DEFAULT_LLM_MODEL = "chatglm3-6b"
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# 默认选用的 Embedding 名称
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self.DEFAULT_EMBEDDING_MODEL = "bge-large-zh-v1.5"
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# AgentLM模型的名称 (可以不指定，指定之后就锁定进入Agent之后的Chain的模型，不指定就是LLM_MODELS[0])
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self.Agent_MODEL = None
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# 历史对话轮数
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self.HISTORY_LEN = 3
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# 大模型最长支持的长度，如果不填写，则使用模型默认的最大长度，如果填写，则为用户设定的最大长度
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self.MAX_TOKENS = None
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# LLM通用对话参数
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self.TEMPERATURE = 0.7
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# TOP_P = 0.95 # ChatOpenAI暂不支持该参数
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self.SUPPORT_AGENT_MODELS = [
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"chatglm3-6b",
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"openai-api",
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"Qwen-14B-Chat",
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"Qwen-7B-Chat",
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"qwen-turbo",
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]
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self.MODEL_PROVIDERS_CFG_PATH_CONFIG = os.path.join(os.path.dirname(os.path.abspath(__file__)),
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"model_providers.yaml")
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self.MODEL_PROVIDERS_CFG_HOST = "127.0.0.1"
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self.MODEL_PROVIDERS_CFG_PORT = 20000
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# 可以通过 model_providers 提供转换不同平台的接口为openai endpoint的能力，启动后下面变量会自动增加相应的平台
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# ### 如果您已经有了一个openai endpoint的能力的地址，可以在这里直接配置
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# - platform_name 可以任意填写，不要重复即可
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# - platform_type 以后可能根据平台类型做一些功能区分,与platform_name一致即可
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# - 将框架部署的模型填写到对应列表即可。不同框架可以加载同名模型，项目会自动做负载均衡。
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# 创建一个全局的共享字典
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self.MODEL_PLATFORMS = [
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{
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"platform_name": "oneapi",
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"platform_type": "oneapi",
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"api_base_url": "http://127.0.0.1:3000/v1",
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"api_key": "sk-",
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"api_concurrencies": 5,
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"llm_models": [
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# 智谱 API
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"chatglm_pro",
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"chatglm_turbo",
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"chatglm_std",
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"chatglm_lite",
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# 千问 API
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"qwen-turbo",
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"qwen-plus",
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"qwen-max",
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"qwen-max-longcontext",
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# 千帆 API
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"ERNIE-Bot",
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"ERNIE-Bot-turbo",
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"ERNIE-Bot-4",
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# 星火 API
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"SparkDesk",
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],
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"embed_models": [
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# 千问 API
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"text-embedding-v1",
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# 千帆 API
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"Embedding-V1",
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],
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"image_models": [],
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"reranking_models": [],
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"speech2text_models": [],
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"tts_models": [],
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},
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]
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# 工具配置项
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self.TOOL_CONFIG = {
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"search_local_knowledgebase": {
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"use": False,
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"top_k": 3,
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"score_threshold": 1.0,
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"conclude_prompt": {
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"with_result":
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'<指令>根据已知信息，简洁和专业的来回答问题。如果无法从中得到答案，请说 "根据已知信息无法回答该问题"，'
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'不允许在答案中添加编造成分，答案请使用中文。 </指令>\n'
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'<已知信息>{{ context }}</已知信息>\n'
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'<问题>{{ question }}</问题>\n',
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"without_result":
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'请你根据我的提问回答我的问题:\n'
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'{{ question }}\n'
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'请注意，你必须在回答结束后强调，你的回答是根据你的经验回答而不是参考资料回答的。\n',
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}
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},
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"search_internet": {
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"use": False,
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"search_engine_name": "bing",
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"search_engine_config":
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{
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"bing": {
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"result_len": 3,
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"bing_search_url": "https://api.bing.microsoft.com/v7.0/search",
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"bing_key": "",
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},
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"metaphor": {
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"result_len": 3,
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"metaphor_api_key": "",
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"split_result": False,
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"chunk_size": 500,
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"chunk_overlap": 0,
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},
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"duckduckgo": {
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"result_len": 3
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}
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},
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"top_k": 10,
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"verbose": "Origin",
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"conclude_prompt":
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"<指令>这是搜索到的互联网信息，请你根据这些信息进行提取并有调理，简洁的回答问题。如果无法从中得到答案，请说 “无法搜索到能回答问题的内容”。 "
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"</指令>\n<已知信息>{{ context }}</已知信息>\n"
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"<问题>\n"
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"{{ question }}\n"
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"</问题>\n"
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},
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"arxiv": {
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"use": False,
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},
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"shell": {
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"use": False,
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},
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"weather_check": {
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"use": False,
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"api_key": "S8vrB4U_-c5mvAMiK",
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},
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"search_youtube": {
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"use": False,
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},
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"wolfram": {
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"use": False,
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"appid": "",
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},
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"calculate": {
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"use": False,
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},
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"vqa_processor": {
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"use": False,
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"model_path": "your model path",
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"tokenizer_path": "your tokenizer path",
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"device": "cuda:1"
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},
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"aqa_processor": {
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"use": False,
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"model_path": "your model path",
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"tokenizer_path": "yout tokenizer path",
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"device": "cuda:2"
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},
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"text2images": {
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"use": False,
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},
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# text2sql使用建议
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# 1、因大模型生成的sql可能与预期有偏差，请务必在测试环境中进行充分测试、评估；
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# 2、生产环境中，对于查询操作，由于不确定查询效率，推荐数据库采用主从数据库架构，让text2sql连接从数据库，防止可能的慢查询影响主业务；
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# 3、对于写操作应保持谨慎，如不需要写操作，设置read_only为True,最好再从数据库层面收回数据库用户的写权限，防止用户通过自然语言对数据库进行修改操作；
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# 4、text2sql与大模型在意图理解、sql转换等方面的能力有关，可切换不同大模型进行测试；
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# 5、数据库表名、字段名应与其实际作用保持一致、容易理解，且应对数据库表名、字段进行详细的备注说明，帮助大模型更好理解数据库结构；
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# 6、若现有数据库表名难于让大模型理解，可配置下面table_comments字段，补充说明某些表的作用。
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"text2sql": {
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"use": False,
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# SQLAlchemy连接字符串，支持的数据库有：
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# crate、duckdb、googlesql、mssql、mysql、mariadb、oracle、postgresql、sqlite、clickhouse、prestodb
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# 不同的数据库请查询SQLAlchemy，修改sqlalchemy_connect_str，配置对应的数据库连接，如sqlite为sqlite:///数据库文件路径，下面示例为mysql
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# 如提示缺少对应数据库的驱动，请自行通过poetry安装
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"sqlalchemy_connect_str": "mysql+pymysql://用户名:密码@主机地址/数据库名称e",
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# 务必评估是否需要开启read_only,开启后会对sql语句进行检查，请确认text2sql.py中的intercept_sql拦截器是否满足你使用的数据库只读要求
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# 优先推荐从数据库层面对用户权限进行限制
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"read_only": False,
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# 限定返回的行数
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"top_k": 50,
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# 是否返回中间步骤
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"return_intermediate_steps": True,
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# 如果想指定特定表，请填写表名称，如["sys_user","sys_dept"]，不填写走智能判断应该使用哪些表
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"table_names": [],
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# 对表名进行额外说明，辅助大模型更好的判断应该使用哪些表，尤其是SQLDatabaseSequentialChain模式下,是根据表名做的预测，很容易误判。
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"table_comments": {
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# 如果出现大模型选错表的情况，可尝试根据实际情况填写表名和说明
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# "tableA":"这是一个用户表，存储了用户的基本信息",
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# "tanleB":"角色表",
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}
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},
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}
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self._init_llm_work_config()
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def _init_llm_work_config(self):
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"""初始化知识库runtime的一些配置"""
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self.LLM_MODEL_CONFIG = {
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# 意图识别不需要输出，模型后台知道就行
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"preprocess_model": {
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self.DEFAULT_LLM_MODEL: {
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"temperature": 0.05,
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"max_tokens": 4096,
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"history_len": 100,
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"prompt_name": "default",
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"callbacks": False
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},
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},
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"llm_model": {
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self.DEFAULT_LLM_MODEL: {
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"temperature": 0.9,
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"max_tokens": 4096,
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"history_len": 10,
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"prompt_name": "default",
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"callbacks": True
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},
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},
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"action_model": {
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self.DEFAULT_LLM_MODEL: {
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"temperature": 0.01,
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"max_tokens": 4096,
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"prompt_name": "ChatGLM3",
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"callbacks": True
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},
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},
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"postprocess_model": {
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self.DEFAULT_LLM_MODEL: {
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"temperature": 0.01,
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"max_tokens": 4096,
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"prompt_name": "default",
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"callbacks": True
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}
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},
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"image_model": {
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"sd-turbo": {
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"size": "256*256",
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}
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}
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}
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def default_llm_model(self, llm_model: str):
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self.DEFAULT_LLM_MODEL = llm_model
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def default_embedding_model(self, embedding_model: str):
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self.DEFAULT_EMBEDDING_MODEL = embedding_model
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def agent_model(self, agent_model: str):
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self.Agent_MODEL = agent_model
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def history_len(self, history_len: int):
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self.HISTORY_LEN = history_len
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def max_tokens(self, max_tokens: int):
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self.MAX_TOKENS = max_tokens
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def temperature(self, temperature: float):
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self.TEMPERATURE = temperature
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def support_agent_models(self, support_agent_models: List[str]):
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self.SUPPORT_AGENT_MODELS = support_agent_models
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def model_providers_cfg_path_config(self, model_providers_cfg_path_config: str):
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self.MODEL_PROVIDERS_CFG_PATH_CONFIG = model_providers_cfg_path_config
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def model_providers_cfg_host(self, model_providers_cfg_host: str):
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self.MODEL_PROVIDERS_CFG_HOST = model_providers_cfg_host
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def model_providers_cfg_port(self, model_providers_cfg_port: int):
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self.MODEL_PROVIDERS_CFG_PORT = model_providers_cfg_port
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def model_platforms(self, model_platforms: List[Dict[str, Any]]):
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self.MODEL_PLATFORMS = model_platforms
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def tool_config(self, tool_config: Dict[str, Any]):
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self.TOOL_CONFIG = tool_config
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def get_config(self) -> ConfigModel:
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config = ConfigModel()
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config.DEFAULT_LLM_MODEL = self.DEFAULT_LLM_MODEL
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config.DEFAULT_EMBEDDING_MODEL = self.DEFAULT_EMBEDDING_MODEL
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config.Agent_MODEL = self.Agent_MODEL
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config.HISTORY_LEN = self.HISTORY_LEN
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config.MAX_TOKENS = self.MAX_TOKENS
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config.TEMPERATURE = self.TEMPERATURE
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config.SUPPORT_AGENT_MODELS = self.SUPPORT_AGENT_MODELS
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config.LLM_MODEL_CONFIG = self.LLM_MODEL_CONFIG
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config.MODEL_PLATFORMS = self.MODEL_PLATFORMS
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config.MODEL_PROVIDERS_CFG_PATH_CONFIG = self.MODEL_PROVIDERS_CFG_PATH_CONFIG
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config.MODEL_PROVIDERS_CFG_HOST = self.MODEL_PROVIDERS_CFG_HOST
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config.MODEL_PROVIDERS_CFG_PORT = self.MODEL_PROVIDERS_CFG_PORT
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config.TOOL_CONFIG = self.TOOL_CONFIG
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return config
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class ConfigModelWorkSpace(core_config.ConfigWorkSpace[ConfigModelFactory, ConfigModel]):
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"""
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工作空间的配置预设, 提供ConfigModel建造方法产生实例。
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"""
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config_factory_cls = ConfigModelFactory
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def __init__(self):
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super().__init__()
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def _build_config_factory(self, config_json: Any) -> ConfigModelFactory:
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_config_factory = self.config_factory_cls()
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if config_json.get("DEFAULT_LLM_MODEL"):
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_config_factory.default_llm_model(config_json.get("DEFAULT_LLM_MODEL"))
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if config_json.get("DEFAULT_EMBEDDING_MODEL"):
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_config_factory.default_embedding_model(config_json.get("DEFAULT_EMBEDDING_MODEL"))
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if config_json.get("Agent_MODEL"):
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_config_factory.agent_model(config_json.get("Agent_MODEL"))
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if config_json.get("HISTORY_LEN"):
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_config_factory.history_len(config_json.get("HISTORY_LEN"))
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if config_json.get("MAX_TOKENS"):
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_config_factory.max_tokens(config_json.get("MAX_TOKENS"))
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if config_json.get("TEMPERATURE"):
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_config_factory.temperature(config_json.get("TEMPERATURE"))
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if config_json.get("SUPPORT_AGENT_MODELS"):
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_config_factory.support_agent_models(config_json.get("SUPPORT_AGENT_MODELS"))
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if config_json.get("MODEL_PROVIDERS_CFG_PATH_CONFIG"):
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_config_factory.model_providers_cfg_path_config(config_json.get("MODEL_PROVIDERS_CFG_PATH_CONFIG"))
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if config_json.get("MODEL_PROVIDERS_CFG_HOST"):
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_config_factory.model_providers_cfg_host(config_json.get("MODEL_PROVIDERS_CFG_HOST"))
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if config_json.get("MODEL_PROVIDERS_CFG_PORT"):
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_config_factory.model_providers_cfg_port(config_json.get("MODEL_PROVIDERS_CFG_PORT"))
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if config_json.get("MODEL_PLATFORMS"):
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_config_factory.model_platforms(config_json.get("MODEL_PLATFORMS"))
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if config_json.get("TOOL_CONFIG"):
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_config_factory.tool_config(config_json.get("TOOL_CONFIG"))
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return _config_factory
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@classmethod
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def get_type(cls) -> str:
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return ConfigModel.class_name()
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def get_config(self) -> ConfigModel:
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return self._config_factory.get_config()
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def set_default_llm_model(self, llm_model: str):
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self._config_factory.default_llm_model(llm_model)
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self.store_config()
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def set_default_embedding_model(self, embedding_model: str):
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self._config_factory.default_embedding_model(embedding_model)
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self.store_config()
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def set_agent_model(self, agent_model: str):
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self._config_factory.agent_model(agent_model)
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self.store_config()
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def set_history_len(self, history_len: int):
|
||
self._config_factory.history_len(history_len)
|
||
self.store_config()
|
||
|
||
def set_max_tokens(self, max_tokens: int):
|
||
self._config_factory.max_tokens(max_tokens)
|
||
self.store_config()
|
||
|
||
def set_temperature(self, temperature: float):
|
||
self._config_factory.temperature(temperature)
|
||
self.store_config()
|
||
|
||
def set_support_agent_models(self, support_agent_models: List[str]):
|
||
self._config_factory.support_agent_models(support_agent_models)
|
||
self.store_config()
|
||
|
||
def set_model_providers_cfg_path_config(self, model_providers_cfg_path_config: str):
|
||
self._config_factory.model_providers_cfg_path_config(model_providers_cfg_path_config)
|
||
self.store_config()
|
||
|
||
def set_model_providers_cfg_host(self, model_providers_cfg_host: str):
|
||
self._config_factory.model_providers_cfg_host(model_providers_cfg_host)
|
||
self.store_config()
|
||
|
||
def set_model_providers_cfg_port(self, model_providers_cfg_port: int):
|
||
self._config_factory.model_providers_cfg_port(model_providers_cfg_port)
|
||
self.store_config()
|
||
|
||
def set_model_platforms(self, model_platforms: List[Dict[str, Any]]):
|
||
self._config_factory.model_platforms(model_platforms=model_platforms)
|
||
self.store_config()
|
||
|
||
def set_tool_config(self, tool_config: Dict[str, Any]):
|
||
self._config_factory.tool_config(tool_config=tool_config)
|
||
self.store_config()
|
||
|
||
|
||
config_model_workspace: ConfigModelWorkSpace = ConfigModelWorkSpace() |