177 lines
6.4 KiB
Python
177 lines
6.4 KiB
Python
import os
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# 可以指定一个绝对路径，统一存放所有的Embedding和LLM模型。
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# 每个模型可以是一个单独的目录，也可以是某个目录下的二级子目录。
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# 如果模型目录名称和 MODEL_PATH 中的 key 或 value 相同，程序会自动检测加载，无需修改 MODEL_PATH 中的路径。
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MODEL_ROOT_PATH = "/home/gc/gangyan/models"
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# 选用的 Embedding 名称
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EMBEDDING_MODEL = "bge-m3-api"
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# 知识库表里或旧环境可能仍为本地键名；迁移内网 embedding API 后统一映射到 ONLINE_LLM_MODEL 的键
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EMBED_MODEL_ALIASES = {
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"bge_m3": "bge-m3-api",
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}
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# LLM 名称别名映射：用于旧前端/历史数据仍请求无权限模型时的兼容与兜底
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# 例如内网网关令牌不允许 Qwen2-72B-Instruct，则统一转到 deepseek-v3
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LLM_MODEL_ALIASES = {
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"Qwen2-72B-Instruct": "deepseek-v3",
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}
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# Embedding 模型运行设备。设为 "auto" 会自动检测(会有警告)，也可手动设定为 "cuda","mps","cpu","xpu" 其中之一。
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EMBEDDING_DEVICE = "cpu"
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# mivlus混合检索条件
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EXPR = ""
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# 选用的reranker模型
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RERANKER_MODEL = "bge-reranker"
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# 是否启用reranker模型
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USE_RERANKER = False
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RERANKER_MAX_LENGTH = 1024
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# 如果需要在 EMBEDDING_MODEL 中增加自定义的关键字时配置
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EMBEDDING_KEYWORD_FILE = "keywords.txt"
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EMBEDDING_MODEL_OUTPUT_PATH = "output"
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#模型数据库的定义
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POLICY_KNOWLEDGE_BASE = "t_policy_total_bge_new_v2"
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# REPORT_KNOWLEDGE_BASE = "t_strategy_report_bge_v2"
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REPORT_KNOWLEDGE_BASE = "gydemo_report_v2"
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JOURNAL_KNOWLEDGE_BASE = "t_journal_article_bge_v1"
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# 润色改写等功能相关模型设置
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STRATEGY_MODEL_DICT = {
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'DEFAULT_EXTRACT_KEYWORDS_MODEL_NAME': "deepseek-v3",
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'DEFAULT_QUERY_REWRITE_MODEL_NAME': "deepseek-v3",
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'DEFAULT_SUMMARY_MODEL_NAME': "deepseek-v3",
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'DEFAULT_RRQJ_MODEL_NAME': "deepseek-v3",
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'CONTINUE_WRITE_MODEL_NAME': "deepseek-v3",
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'REWRITE_MODEL_NAME': "deepseek-v3",
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'EXPAND_WRITE_MODEL_NAME': "deepseek-v3",
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'ABB_REWRITE_MODEL_NAME': "deepseek-v3",
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'EMBELLISH_MODEL_NAME': "deepseek-v3",
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'CHI_TO_ENS_MODEL_NAME': "deepseek-v3",
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'ENS_TO_CHI_MODEL_NAME': "deepseek-v3",
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'FORMAL_STYLE_MODEL_NAME': "deepseek-v3",
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'PARTY_STYLE_MODEL_NAME': "deepseek-v3",
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'COLLOQUIAL_STYLE_MODEL_NAME': "deepseek-v3",
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}
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# 要运行的 LLM 名称，可以包括本地模型和在线模型。列表中本地模型将在启动项目时全部加载。
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# 列表中第一个模型将作为 API 和 WEBUI 的默认模型。
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# 在这里，使用目前主流的两个离线模型，其中，chatglm3-6b 为默认加载模型。
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# 如果你的显存不足，可使用 Qwen-1_8B-Chat, 该模型 FP16 仅需 3.8G显存。
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LLM_MODELS = ["deepseek-v3", "deepseek-r1", "deepseek-chat", "qwen-max", "Qwen2-72B-Instruct"]
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Agent_MODEL = None
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# LLM 模型运行设备。设为"auto"会自动检测(会有警告)，也可手动设定为 "cuda","mps","cpu","xpu" 其中之一。
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LLM_DEVICE = "cuda"
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HISTORY_LEN = 20
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MAX_TOKENS = None
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MAX_CUT_TOKENS = 30 * 1024
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TEMPERATURE = 0.7
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DEEPSEEK_MODELS = ["deepseek-reasoner", "deepseek-chat"]
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CAST_MODELS = ["kexie_0.5b"]
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ONLINE_LLM_MODEL = {
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# 本地部署的大模型 API (10.102.24.75:3000)
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"bge-m3-api": {
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"model_name": "bge-m3",
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"api_base_url": "http://10.102.24.75:3000/v1",
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"api_key": "sk-nDr7vDHOxJQOGFxbmSE6g2wCK0WELtyZst4kD3eo4383P6j5",
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"provider": "OpenAIWorker",
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},
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"bge-reranker": {
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"model_name": "bge-reranker",
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"api_base_url": "http://10.102.24.75:3000/v1",
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"api_key": "sk-nDr7vDHOxJQOGFxbmSE6g2wCK0WELtyZst4kD3eo4383P6j5",
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"provider": "OpenAIWorker",
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},
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"deepseek-v3": {
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"model_name": "deepseek-v3",
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"api_base_url": "http://10.102.24.75:3000/v1",
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"api_key": "sk-BlQIGRrotbVDWE5mXCPBFjVWIvJ83hldzz67xInNwzVo7pPb",
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},
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"deepseek-r1": {
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"model_name": "deepseek-r1",
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"api_base_url": "http://10.102.24.75:3000/v1",
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"api_key": "sk-BlQIGRrotbVDWE5mXCPBFjVWIvJ83hldzz67xInNwzVo7pPb",
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},
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"Qwen2-72B-Instruct": {
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"model_name": "Qwen2-72B-Instruct",
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"api_base_url": "http://10.102.24.75:3000/v1",
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"api_key": "sk-BlQIGRrotbVDWE5mXCPBFjVWIvJ83hldzz67xInNwzVo7pPb",
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},
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# 阿里云通义千问
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# 文档参考 https://help.aliyun.com/zh/model-studio/qwen-api-reference/
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"qwen-max":{
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"model_name":"qwen-max",
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"api_base_url":"https://dashscope.aliyuncs.com/compatible-mode/v1",
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"api_key":"sk-672f9d1fc4404674bf1a713dfd130a14",
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},
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# deepseek
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"deepseek-chat":{
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# DeepSeek-V3.2
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"model_name":"deepseek-chat",
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"api_base_url":"https://api.deepseek.com/v1",
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"api_key":"sk-26858b50690a49828766fcfcf3290de9",
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},
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"deepseek-reasoner":{
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# DeepSeek-V3.2
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"model_name":"deepseek-reasoner",
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"api_base_url":"https://api.deepseek.com/v1",
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"api_key":"sk-26858b50690a49828766fcfcf3290de9",
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},
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# 智谱AI API,具体注册及api key获取请前往 http://open.bigmodel.cn
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"zhipu-api": {
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"api_key": "http://open.bigmodel.cn",
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"version": "glm-4",
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"provider": "ChatGLMWorker",
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},
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}
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# 在以下字典中修改属性值，以指定本地embedding模型存储位置。支持3种设置方法：
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# 1、将对应的值修改为模型绝对路径
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# 2、不修改此处的值（以 text2vec 为例）：
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# 2.1 如果{MODEL_ROOT_PATH}下存在如下任一子目录：
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# - text2vec
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# - GanymedeNil/text2vec-large-chinese
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# - text2vec-large-chinese
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# 2.2 如果以上本地路径不存在，则使用huggingface模型
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MODEL_PATH = {
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"embed_model": {
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"bge_m3": "bge-m3",
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},
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"llm_model": {
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"deepseek-v3": "deepseek-v3",
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"deepseek-r1": "deepseek-r1",
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"qwen-max": "qwen-max",
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"deepseek-chat": "deepseek-chat",
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"deepseek-reasoner": "deepseek-reasoner",
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"Qwen2-72B-Instruct": "Qwen2-72B-Instruct"
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},
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"reranker": {
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"bge-reranker": "bge-reranker",
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"bge-reranker-large": "bge-reranker-large",
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}
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}
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# 通常情况下不需要更改以下内容
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# nltk 模型存储路径
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NLTK_DATA_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "nltk_data")
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# 使用VLLM可能导致模型推理能力下降，无法完成Agent任务
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VLLM_MODEL_DICT = {
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"chatglm3-6b": "chatglm3-6b",
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}
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SUPPORT_AGENT_MODEL = [
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"Qwen", # 所有Qwen系列本地模型
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"chatglm3-6b"
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]
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