删除reqirements的中文

configs/model_config.py

@@ -27,7 +27,6 @@ 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
@@ -38,44 +37,113 @@ llm_model_dict = {
         "name": "chatglm-6b-int4-qe",
         "pretrained_model_name": "THUDM/chatglm-6b-int4-qe",
         "local_model_path": None,
-        "provides": "ChatGLM"
+        "provides": "ChatGLMLLMChain"
     },
     "chatglm-6b-int4": {
         "name": "chatglm-6b-int4",
         "pretrained_model_name": "THUDM/chatglm-6b-int4",
         "local_model_path": None,
-        "provides": "ChatGLM"
+        "provides": "ChatGLMLLMChain"
     },
     "chatglm-6b-int8": {
         "name": "chatglm-6b-int8",
         "pretrained_model_name": "THUDM/chatglm-6b-int8",
         "local_model_path": None,
-        "provides": "ChatGLM"
+        "provides": "ChatGLMLLMChain"
     },
     "chatglm-6b": {
         "name": "chatglm-6b",
         "pretrained_model_name": "THUDM/chatglm-6b",
         "local_model_path": None,
-        "provides": "ChatGLM"
+        "provides": "ChatGLMLLMChain"
+    },
+    "chatglm2-6b": {
+        "name": "chatglm2-6b",
+        "pretrained_model_name": "THUDM/chatglm2-6b",
+        "local_model_path": None,
+        "provides": "ChatGLMLLMChain"
+    },
+    "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": 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": "LLamaLLM"
+        "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": "baichuan-inc/baichuan-7B",
+        "local_model_path": None,
+        "provides": "MOSSLLMChain"
+    },
+    # 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
@@ -83,7 +151,24 @@ llm_model_dict = {
         "name": "chatglm-6b",  # "name"修改为fastchat服务中的"model_name"
         "pretrained_model_name": "chatglm-6b",
         "local_model_path": None,
-        "provides": "FastChatOpenAILLM",  # 使用fastchat api时，需保证"provides"为"FastChatOpenAILLM"
+        "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"
     },
 
@@ -92,9 +177,29 @@ llm_model_dict = {
         "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": "FastChatOpenAILLM",  # 使用fastchat api时，需保证"provides"为"FastChatOpenAILLM"
+        "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_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://api.openapi.com/v1",
+        "api_key": ""
+    },
+
 }
 
 # LLM 名称
@@ -116,8 +221,7 @@ STREAMING = True
 
 # Use p-tuning-v2 PrefixEncoder
 USE_PTUNING_V2 = False
-
+PTUNING_DIR='./ptuning-v2'
-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"
 
@@ -130,7 +234,7 @@ PROMPT_TEMPLATE = """已知信息：
 
 根据上述已知信息，简洁和专业的来回答用户的问题。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。 问题是：{question}"""
 
-# 缓存知识库数量
+# 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 = 1
 
 # 文本分句长度
@@ -145,8 +249,8 @@ LLM_HISTORY_LEN = 3
 # 知识库检索时返回的匹配内容条数
 VECTOR_SEARCH_TOP_K = 5
 
-# 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，经测试设置为小于500时，匹配结果更精准
+# 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 = 0
+VECTOR_SEARCH_SCORE_THRESHOLD = 500
 
 NLTK_DATA_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "nltk_data")