add configs

configs/model_config.py

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+import torch.cuda
+import torch.backends
+import os
+import logging
+import uuid
+
+LOG_FORMAT = "%(levelname) -5s %(asctime)s" "-1d: %(message)s"
+logger = logging.getLogger()
+logger.setLevel(logging.INFO)
+logging.basicConfig(format=LOG_FORMAT)
+
+# 在以下字典中修改属性值，以指定本地embedding模型存储位置
+# 如将 "text2vec": "GanymedeNil/text2vec-large-chinese" 修改为 "text2vec": "User/Downloads/text2vec-large-chinese"
+# 此处请写绝对路径
+embedding_model_dict = {
+    "ernie-tiny": "nghuyong/ernie-3.0-nano-zh",
+    "ernie-base": "nghuyong/ernie-3.0-base-zh",
+    "text2vec-base": "shibing624/text2vec-base-chinese",
+    "text2vec": "/home/wsy/Langchain-chat/embedding/text2vec-large-chinese",
+    "text2vec-base-multilingual": "shibing624/text2vec-base-multilingual",
+    "text2vec-base-chinese-sentence": "shibing624/text2vec-base-chinese-sentence",
+    "text2vec-base-chinese-paraphrase": "shibing624/text2vec-base-chinese-paraphrase",
+    "m3e-small": "moka-ai/m3e-small",
+    "m3e-base": "moka-ai/m3e-base",
+}
+
+# Embedding model name
+EMBEDDING_MODEL = "text2vec"
+
+# Embedding running device
+EMBEDDING_DEVICE = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
+
+# supported LLM models
+# llm_model_dict 处理了loader的一些预设行为，如加载位置，模型名称，模型处理器实例
+# 在以下字典中修改属性值，以指定本地 LLM 模型存储位置
+# 如将 "chatglm-6b" 的 "local_model_path" 由 None 修改为 "User/Downloads/chatglm-6b"
+# 此处请写绝对路径,且路径中必须包含repo-id的模型名称，因为FastChat是以模型名匹配的
+llm_model_dict = {
+    "chatglm-6b-int4-qe": {
+        "name": "chatglm-6b-int4-qe",
+        "pretrained_model_name": "THUDM/chatglm-6b-int4-qe",
+        "local_model_path": None,
+        "provides": "ChatGLMLLMChain"
+    },
+    "chatglm-6b-int4": {
+        "name": "chatglm-6b-int4",
+        "pretrained_model_name": "THUDM/chatglm-6b-int4",
+        "local_model_path": None,
+        "provides": "ChatGLMLLMChain"
+    },
+    "chatglm-6b-int8": {
+        "name": "chatglm-6b-int8",
+        "pretrained_model_name": "THUDM/chatglm-6b-int8",
+        "local_model_path": None,
+        "provides": "ChatGLMLLMChain"
+    },
+    "chatglm-6b": {
+        "name": "chatglm-6b",
+        "pretrained_model_name": "THUDM/chatglm-6b",
+        "local_model_path": None,
+        "provides": "ChatGLMLLMChain"
+    },
+    # langchain-ChatGLM 用户“帛凡” @BoFan-tunning 基于ChatGLM-6B 训练并提供的权重合并模型和 lora 权重文件 chatglm-fitness-RLHF
+    # 详细信息见 HuggingFace 模型介绍页 https://huggingface.co/fb700/chatglm-fitness-RLHF
+    # 使用该模型或者lora权重文件，对比chatglm-6b、chatglm2-6b、百川7b，甚至其它未经过微调的更高参数的模型，在本项目中，总结能力可获得显著提升。
+    "chatglm-fitness-RLHF": {
+        "name": "chatglm-fitness-RLHF",
+        "pretrained_model_name": "/home/wsy/chatglm-fitness-RLHF",
+        "local_model_path": None,
+        "provides": "ChatGLMLLMChain"
+    },
+    "chatglm2-6b": {
+        "name": "chatglm2-6b",
+        "pretrained_model_name": "/home/xwy/chatglm2-6b",
+        "local_model_path": None,
+        "provides": "ChatGLMLLMChain"
+    },
+    "chatglm2-6b-32k": {
+        "name": "chatglm2-6b-32k",
+        "pretrained_model_name": "THUDM/chatglm2-6b-32k",
+        "local_model_path": None,
+        "provides": "ChatGLMLLMChain"
+    },
+    # 注：chatglm2-cpp已在mac上测试通过，其他系统暂不支持
+    "chatglm2-cpp": {
+        "name": "chatglm2-cpp",
+        "pretrained_model_name": "cylee0909/chatglm2cpp",
+        "local_model_path": None,
+        "provides": "ChatGLMCppLLMChain"
+    },
+    "chatglm2-6b-int4": {
+        "name": "chatglm2-6b-int4",
+        "pretrained_model_name": "THUDM/chatglm2-6b-int4",
+        "local_model_path": None,
+        "provides": "ChatGLMLLMChain"
+    },
+    "chatglm2-6b-int8": {
+        "name": "chatglm2-6b-int8",
+        "pretrained_model_name": "THUDM/chatglm2-6b-int8",
+        "local_model_path": None,
+        "provides": "ChatGLMLLMChain"
+    },
+    "chatyuan": {
+        "name": "chatyuan",
+        "pretrained_model_name": "ClueAI/ChatYuan-large-v2",
+        "local_model_path": None,
+        "provides": "MOSSLLMChain"
+    },
+    "moss": {
+        "name": "moss",
+        "pretrained_model_name": "fnlp/moss-moon-003-sft",
+        "local_model_path": None,
+        "provides": "MOSSLLMChain"
+    },
+    "moss-int4": {
+        "name": "moss",
+        "pretrained_model_name": "fnlp/moss-moon-003-sft-int4",
+        "local_model_path": None,
+        "provides": "MOSSLLM"
+    },
+    "vicuna-13b-hf": {
+        "name": "vicuna-13b-hf",
+        "pretrained_model_name": "vicuna-13b-hf",
+        "local_model_path": None,
+        "provides": "LLamaLLMChain"
+    },
+    "vicuna-7b-hf": {
+        "name": "vicuna-13b-hf",
+        "pretrained_model_name": "vicuna-13b-hf",
+        "local_model_path": None,
+        "provides": "LLamaLLMChain"
+    },
+    # 直接调用返回requests.exceptions.ConnectionError错误，需要通过huggingface_hub包里的snapshot_download函数
+    # 下载模型，如果snapshot_download还是返回网络错误，多试几次，一般是可以的，
+    # 如果仍然不行，则应该是网络加了防火墙(在服务器上这种情况比较常见)，基本只能从别的设备上下载，
+    # 然后转移到目标设备了.
+    "bloomz-7b1": {
+        "name": "bloomz-7b1",
+        "pretrained_model_name": "bigscience/bloomz-7b1",
+        "local_model_path": None,
+        "provides": "MOSSLLMChain"
+
+    },
+    # 实测加载bigscience/bloom-3b需要170秒左右，暂不清楚为什么这么慢
+    # 应与它要加载专有token有关
+    "bloom-3b": {
+        "name": "bloom-3b",
+        "pretrained_model_name": "bigscience/bloom-3b",
+        "local_model_path": None,
+        "provides": "MOSSLLMChain"
+
+    },
+    "baichuan-7b": {
+        "name": "baichuan-7b",
+        "pretrained_model_name": "/home/wsy/baichuan7b-chat",
+        "local_model_path": None,
+        "provides": "MOSSLLMChain"
+    },
+    "Baichuan-13b-Chat": {
+        "name": "Baichuan-13b-Chat",
+        "pretrained_model_name": "baichuan-inc/Baichuan-13b-Chat",
+        "local_model_path": None,
+        "provides": "BaichuanLLMChain"
+    },
+    # llama-cpp模型的兼容性问题参考https://github.com/abetlen/llama-cpp-python/issues/204
+    "ggml-vicuna-13b-1.1-q5": {
+        "name": "ggml-vicuna-13b-1.1-q5",
+        "pretrained_model_name": "lmsys/vicuna-13b-delta-v1.1",
+        # 这里需要下载好模型的路径,如果下载模型是默认路径则它会下载到用户工作区的
+        # /.cache/huggingface/hub/models--vicuna--ggml-vicuna-13b-1.1/
+        # 还有就是由于本项目加载模型的方式设置的比较严格，下载完成后仍需手动修改模型的文件名
+        # 将其设置为与Huggface Hub一致的文件名
+        # 此外不同时期的ggml格式并不兼容，因此不同时期的ggml需要安装不同的llama-cpp-python库，且实测pip install 不好使
+        # 需要手动从https://github.com/abetlen/llama-cpp-python/releases/tag/下载对应的wheel安装
+        # 实测v0.1.63与本模型的vicuna/ggml-vicuna-13b-1.1/ggml-vic13b-q5_1.bin可以兼容
+        "local_model_path": f'''{"/".join(os.path.abspath(__file__).split("/")[:3])}/.cache/huggingface/hub/models--vicuna--ggml-vicuna-13b-1.1/blobs/''',
+        "provides": "LLamaLLMChain"
+    },
+
+    # 通过 fastchat 调用的模型请参考如下格式
+    "fastchat-chatglm-6b": {
+        "name": "chatglm-6b",  # "name"修改为fastchat服务中的"model_name"
+        "pretrained_model_name": "chatglm-6b",
+        "local_model_path": None,
+        "provides": "FastChatOpenAILLMChain",  # 使用fastchat api时，需保证"provides"为"FastChatOpenAILLMChain"
+        "api_base_url": "http://localhost:8000/v1",  # "name"修改为fastchat服务中的"api_base_url"
+        "api_key": "EMPTY"
+    },
+        # 通过 fastchat 调用的模型请参考如下格式
+    "fastchat-chatglm-6b-int4": {
+        "name": "chatglm-6b-int4",  # "name"修改为fastchat服务中的"model_name"
+        "pretrained_model_name": "chatglm-6b-int4",
+        "local_model_path": None,
+        "provides": "FastChatOpenAILLMChain",  # 使用fastchat api时，需保证"provides"为"FastChatOpenAILLMChain"
+        "api_base_url": "http://localhost:8001/v1",  # "name"修改为fastchat服务中的"api_base_url"
+        "api_key": "EMPTY"
+    },
+    "fastchat-chatglm2-6b": {
+        "name": "chatglm2-6b",  # "name"修改为fastchat服务中的"model_name"
+        "pretrained_model_name": "chatglm2-6b",
+        "local_model_path": None,
+        "provides": "FastChatOpenAILLMChain",  # 使用fastchat api时，需保证"provides"为"FastChatOpenAILLMChain"
+        "api_base_url": "http://localhost:8000/v1"  # "name"修改为fastchat服务中的"api_base_url"
+    },
+
+    # 通过 fastchat 调用的模型请参考如下格式
+    "fastchat-vicuna-13b-hf": {
+        "name": "vicuna-13b-hf",  # "name"修改为fastchat服务中的"model_name"
+        "pretrained_model_name": "vicuna-13b-hf",
+        "local_model_path": None,
+        "provides": "FastChatOpenAILLMChain",  # 使用fastchat api时，需保证"provides"为"FastChatOpenAILLMChain"
+        "api_base_url": "http://localhost:8000/v1",  # "name"修改为fastchat服务中的"api_base_url"
+        "api_key": "EMPTY"
+    },
+
+      "fastchat-baichuan2-7b-chat": {
+        "name": "Baichuan2-7B-chat",  # "name"修改为fastchat服务中的"model_name"
+        "pretrained_model_name": "Baichuan2-7B-chat",
+        "local_model_path": None,
+        "provides": "FastChatOpenAILLMChain",  # 使用fastchat api时，需保证"provides"为"FastChatOpenAILLMChain"
+        "api_base_url": "http://localhost:8000/v1",  # "name"修改为fastchat服务中的"api_base_url"
+        "api_key": "EMPTY"
+    },
+    # 调用chatgpt时如果报出： urllib3.exceptions.MaxRetryError: HTTPSConnectionPool(host='api.openai.com', port=443):
+    #  Max retries exceeded with url: /v1/chat/completions
+    # 则需要将urllib3版本修改为1.25.11
+    # 如果依然报urllib3.exceptions.MaxRetryError: HTTPSConnectionPool，则将https改为http
+    # 参考https://zhuanlan.zhihu.com/p/350015032
+
+    # 如果报出：raise NewConnectionError(
+    # urllib3.exceptions.NewConnectionError: <urllib3.connection.HTTPSConnection object at 0x000001FE4BDB85E0>:
+    # Failed to establish a new connection: [WinError 10060]
+    # 则是因为内地和香港的IP都被OPENAI封了，需要切换为日本、新加坡等地
+    "openai-chatgpt-3.5": {
+        "name": "gpt-3.5-turbo",
+        "pretrained_model_name": "gpt-3.5-turbo",
+        "provides": "FastChatOpenAILLMChain",
+        "local_model_path": None,
+        "api_base_url": "https://openai.api2d.net/v1",
+        "api_key": "fk216618-f39L8P2msSmhydRuG51oDh1aDG0CklUV"
+    },
+
+}
+
+# LLM 名称
+LLM_MODEL = "openai-chatgpt-3.5"
+# 量化加载8bit 模型
+LOAD_IN_8BIT = False
+# Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU.
+BF16 = False
+# 本地lora存放的位置
+LORA_DIR = "loras/"
+
+# LORA的名称，如有请指定为列表
+
+LORA_NAME = ""
+USE_LORA = True if LORA_NAME else False
+
+# LLM streaming reponse
+STREAMING = True
+
+# 直接定义baichuan的lora完整路径即可
+LORA_MODEL_PATH_BAICHUAN=""
+
+# Use p-tuning-v2 PrefixEncoder
+USE_PTUNING_V2 = False
+PTUNING_DIR='./ptuning-v2'
+# LLM running device
+LLM_DEVICE = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
+
+# 知识库默认存储路径
+KB_ROOT_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "knowledge_base")
+
+# 基于上下文的prompt模版，请务必保留"{question}"和"{context}"
+PROMPT_TEMPLATE = """已知信息：
+{context} 
+
+你将作为一个python助教, 服务于大学一年级的python基础课堂, 请你根据上述已知信息，简洁和专业的来回答学生们的问题。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” 或 “没有提供足够的相关信息”，不允许在答案中添加编造成分，答案请使用中文，如果不清晰的可以使用带注释的代码解释。 问题是：{question}"""
+
+# 缓存知识库数量,如果是ChatGLM2,ChatGLM2-int4,ChatGLM2-int8模型若检索效果不好可以调成’10’
+CACHED_VS_NUM = 2
+
+# 文本分句长度
+SENTENCE_SIZE = 130
+
+# 匹配后单段上下文长度s
+CHUNK_SIZE = 350
+
+# 传入LLM的历史记录长度
+LLM_HISTORY_LEN = 3
+
+# 知识库检索时返回的匹配内容条数
+VECTOR_SEARCH_TOP_K = 2
+
+# 知识检索内容相关度 Score, 数值范围约为0-1100，如果为0，则不生效，建议设置为500左右，经测试设置为小于500时，匹配结果更精准
+VECTOR_SEARCH_SCORE_THRESHOLD = 500
+
+NLTK_DATA_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "nltk_data")
+
+# FLAG_USER_NAME = uuid.uuid4().hex
+FLAG_USER_NAME = "王思源"
+
+logger.info(f"""
+loading model config
+llm device: {LLM_DEVICE}
+embedding device: {EMBEDDING_DEVICE}
+dir: {os.path.dirname(os.path.dirname(__file__))}
+flagging username: {FLAG_USER_NAME}     
+""")
+
+# 是否开启跨域，默认为False，如果需要开启，请设置为True
+# is open cross domain
+OPEN_CROSS_DOMAIN = False
+
+# Bing 搜索必备变量
+# 使用 Bing 搜索需要使用 Bing Subscription Key,需要在azure port中申请试用bing search
+# 具体申请方式请见
+# https://learn.microsoft.com/en-us/bing/search-apis/bing-web-search/create-bing-search-service-resource
+# 使用python创建bing api 搜索实例详见:
+# https://learn.microsoft.com/en-us/bing/search-apis/bing-web-search/quickstarts/rest/python
+BING_SEARCH_URL = "https://api.bing.microsoft.com/v7.0/search"
+# 注意不是bing Webmaster Tools的api key，
+
+# 此外，如果是在服务器上，报Failed to establish a new connection: [Errno 110] Connection timed out
+# 是因为服务器加了防火墙，需要联系管理员加白名单，如果公司的服务器的话，就别想了GG
+BING_SUBSCRIPTION_KEY = ""
+
+# 是否开启中文标题加强，以及标题增强的相关配置
+# 通过增加标题判断，判断哪些文本为标题，并在metadata中进行标记；
+# 然后将文本与往上一级的标题进行拼合，实现文本信息的增强。
+ZH_TITLE_ENHANCE = True