Merge branch 'dev' into dev

README.md

@@ -23,13 +23,17 @@
 
 🚩 本项目未涉及微调、训练过程，但可利用微调或训练对本项目效果进行优化。
 
+🐳 Docker镜像：registry.cn-beijing.aliyuncs.com/isafetech/chatmydata:1.0 （感谢 @InkSong🌲 ）
+
+💻 运行方式：docker run -d -p 80:7860 --gpus all registry.cn-beijing.aliyuncs.com/isafetech/chatmydata:1.0 
+
 🌐 [AutoDL 镜像](https://www.codewithgpu.com/i/imClumsyPanda/langchain-ChatGLM/langchain-ChatGLM)
 
 📓 [ModelWhale 在线运行项目](https://www.heywhale.com/mw/project/643977aa446c45f4592a1e59)
 
 ## 变更日志
 
-参见 [变更日志](docs/CHANGELOG.md)。
+参见 [版本更新日志](https://github.com/imClumsyPanda/langchain-ChatGLM/releases)。
 
 ## 硬件需求
 
@@ -60,6 +64,23 @@
 
     本项目中默认选用的 Embedding 模型 [GanymedeNil/text2vec-large-chinese](https://huggingface.co/GanymedeNil/text2vec-large-chinese/tree/main) 约占用显存 3GB，也可修改为在 CPU 中运行。
 
+## Docker 整合包
+🐳 Docker镜像地址：`registry.cn-beijing.aliyuncs.com/isafetech/chatmydata:1.0 `🌲
+
+💻 一行命令运行：
+```shell
+docker run -d -p 80:7860 --gpus all registry.cn-beijing.aliyuncs.com/isafetech/chatmydata:1.0
+```
+
+- 该版本镜像大小`25.2G`，使用[v0.1.16](https://github.com/imClumsyPanda/langchain-ChatGLM/releases/tag/v0.1.16)，以`nvidia/cuda:12.1.1-cudnn8-runtime-ubuntu22.04`为基础镜像
+- 该版本内置两个`embedding`模型：`m3e-base`，`text2vec-large-chinese`，内置`fastchat+chatglm-6b`
+- 该版本目标为方便一键部署使用，请确保您已经在Linux发行版上安装了NVIDIA驱动程序
+- 请注意，您不需要在主机系统上安装CUDA工具包，但需要安装`NVIDIA Driver`以及`NVIDIA Container Toolkit`，请参考[安装指南](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)
+- 首次拉取和启动均需要一定时间，首次启动时请参照下图使用`docker logs -f <container id>`查看日志
+- 如遇到启动过程卡在`Waiting..`步骤，建议使用`docker exec -it <container id> bash`进入`/logs/`目录查看对应阶段日志
+![](img/docker_logs.png)
+
+
 ## Docker 部署
 为了能让容器使用主机GPU资源，需要在主机上安装 [NVIDIA Container Toolkit](https://github.com/NVIDIA/nvidia-container-toolkit)。具体安装步骤如下：
 ```shell
@@ -198,6 +219,7 @@ Web UI 可以实现如下功能：
   - [ ] 知识图谱/图数据库接入
   - [ ] Agent 实现
 - [x] 增加更多 LLM 模型支持
+  - [x] [THUDM/chatglm2-6b](https://huggingface.co/THUDM/chatglm2-6b)
   - [x] [THUDM/chatglm-6b](https://huggingface.co/THUDM/chatglm-6b)
   - [x] [THUDM/chatglm-6b-int8](https://huggingface.co/THUDM/chatglm-6b-int8)
   - [x] [THUDM/chatglm-6b-int4](https://huggingface.co/THUDM/chatglm-6b-int4)
@@ -221,7 +243,7 @@ Web UI 可以实现如下功能：
     - [x] 选择知识库开始问答
     - [x] 上传文件/文件夹至知识库
     - [x] 知识库测试
-    - [ ] 删除知识库中文件
+    - [x] 删除知识库中文件
   - [x] 支持搜索引擎问答
 - [ ] 增加 API 支持
   - [x] 利用 fastapi 实现 API 部署方式
@@ -229,7 +251,7 @@ Web UI 可以实现如下功能：
 - [x] VUE 前端
 
 ## 项目交流群
-<img src="img/qr_code_32.jpg" alt="二维码" width="300" height="300" />
+<img src="img/qr_code_42.jpg" alt="二维码" width="300" height="300" />
 
 
 🎉 langchain-ChatGLM 项目微信交流群，如果你也对本项目感兴趣，欢迎加入群聊参与讨论交流。

chains/local_doc_qa.py

@@ -17,6 +17,7 @@ import models.shared as shared
 from agent import bing_search
 from langchain.docstore.document import Document
 from functools import lru_cache
+from textsplitter.zh_title_enhance import zh_title_enhance
 
 
 # patch HuggingFaceEmbeddings to make it hashable
@@ -56,7 +57,7 @@ def tree(filepath, ignore_dir_names=None, ignore_file_names=None):
     return ret_list, [os.path.basename(p) for p in ret_list]
 
 
-def load_file(filepath, sentence_size=SENTENCE_SIZE):
+def load_file(filepath, sentence_size=SENTENCE_SIZE, using_zh_title_enhance=ZH_TITLE_ENHANCE):
     if filepath.lower().endswith(".md"):
         loader = UnstructuredFileLoader(filepath, mode="elements")
         docs = loader.load()
@@ -79,6 +80,8 @@ def load_file(filepath, sentence_size=SENTENCE_SIZE):
         loader = UnstructuredFileLoader(filepath, mode="elements")
         textsplitter = ChineseTextSplitter(pdf=False, sentence_size=sentence_size)
         docs = loader.load_and_split(text_splitter=textsplitter)
+    if using_zh_title_enhance:
+        docs = zh_title_enhance(docs)
     write_check_file(filepath, docs)
     return docs
 

cli.py

@@ -42,7 +42,9 @@ def start():
 @start.command(name="api", context_settings=dict(help_option_names=['-h', '--help']))
 @click.option('-i', '--ip', default='0.0.0.0', show_default=True, type=str, help='api_server listen address.')
 @click.option('-p', '--port', default=7861, show_default=True, type=int, help='api_server listen port.')
-def start_api(ip, port):
+@click.option('-k', '--ssl_keyfile', type=int, help='enable api https/wss service, specify the ssl keyfile path.')
+@click.option('-c', '--ssl_certfile', type=int, help='enable api https/wss service, specify the ssl certificate file path.')
+def start_api(ip, port, **kwargs):
     # 调用api_start之前需要先loadCheckPoint,并传入加载检查点的参数，
     # 理论上可以用click包进行包装，但过于繁琐，改动较大，
     # 此处仍用parser包，并以models.loader.args.DEFAULT_ARGS的参数为默认参数
@@ -51,7 +53,7 @@ def start_api(ip, port):
     from models.loader import LoaderCheckPoint
     from models.loader.args import DEFAULT_ARGS
     shared.loaderCheckPoint = LoaderCheckPoint(DEFAULT_ARGS)
-    api_start(host=ip, port=port)
+    api_start(host=ip, port=port, **kwargs)
 
 #     # 通过cli.py调用cli_demo时需要在cli.py里初始化模型，否则会报错：
     # langchain-ChatGLM: error: unrecognized arguments: start cli

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
@@ -58,12 +57,29 @@ llm_model_dict = {
         "local_model_path": None,
         "provides": "ChatGLM"
     },
-
+    "chatglm2-6b": {
+        "name": "chatglm2-6b",
+        "pretrained_model_name": "THUDM/chatglm2-6b",
+        "local_model_path": None,
+        "provides": "ChatGLM"
+    },
+    "chatglm2-6b-int4": {
+        "name": "chatglm2-6b-int4",
+        "pretrained_model_name": "THUDM/chatglm2-6b-int4",
+        "local_model_path": None,
+        "provides": "ChatGLM"
+    },
+    "chatglm2-6b-int8": {
+        "name": "chatglm2-6b-int8",
+        "pretrained_model_name": "THUDM/chatglm2-6b-int8",
+        "local_model_path": None,
+        "provides": "ChatGLM"
+    },
     "chatyuan": {
         "name": "chatyuan",
         "pretrained_model_name": "ClueAI/ChatYuan-large-v2",
         "local_model_path": None,
-        "provides": None
+        "provides": "MOSSLLM"
     },
     "moss": {
         "name": "moss",
@@ -77,6 +93,46 @@ llm_model_dict = {
         "local_model_path": None,
         "provides": "LLamaLLM"
     },
+    # 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": "MOSSLLM"
+
+    },
+    # 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": "MOSSLLM"
+
+    },
+    "baichuan-7b": {
+        "name": "baichuan-7b",
+        "pretrained_model_name": "baichuan-inc/baichuan-7B",
+        "local_model_path": None,
+        "provides": "MOSSLLM"
+    },
+    # 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": "LLamaLLM"
+    },
 
     # 通过 fastchat 调用的模型请参考如下格式
     "fastchat-chatglm-6b": {
@@ -84,6 +140,14 @@ llm_model_dict = {
         "pretrained_model_name": "chatglm-6b",
         "local_model_path": None,
         "provides": "FastChatOpenAILLM",  # 使用fastchat api时，需保证"provides"为"FastChatOpenAILLM"
+        "api_base_url": "http://localhost:8000/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": "FastChatOpenAILLM",  # 使用fastchat api时，需保证"provides"为"FastChatOpenAILLM"
         "api_base_url": "http://localhost:8000/v1"  # "name"修改为fastchat服务中的"api_base_url"
     },
 
@@ -93,8 +157,18 @@ llm_model_dict = {
         "pretrained_model_name": "vicuna-13b-hf",
         "local_model_path": None,
         "provides": "FastChatOpenAILLM",  # 使用fastchat api时，需保证"provides"为"FastChatOpenAILLM"
-        "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"
     },
+    "openai-chatgpt-3.5": {
+        "name": "gpt-3.5-turbo",
+        "pretrained_model_name": "gpt-3.5-turbo",
+        "provides": "FastChatOpenAILLM",
+        "local_model_path": None,
+        "api_base_url": "https://api.openapi.com/v1",
+        "api_key": ""
+    },
+
 }
 
 # LLM 名称
@@ -128,7 +202,7 @@ 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
@@ -174,3 +248,8 @@ BING_SEARCH_URL = "https://api.bing.microsoft.com/v7.0/search"
 # 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 = False

docs/INSTALL.md

@@ -44,4 +44,12 @@ $ pip install -r requirements.txt
 $ python loader/image_loader.py
 
 ```
+
 注：使用 `langchain.document_loaders.UnstructuredFileLoader` 进行非结构化文件接入时，可能需要依据文档进行其他依赖包的安装，请参考 [langchain 文档](https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/unstructured_file.html)。
+
+## llama-cpp模型调用的说明
+
+1. 首先从huggingface hub中下载对应的模型，如https://huggingface.co/vicuna/ggml-vicuna-13b-1.1/的[ggml-vic13b-q5_1.bin](https://huggingface.co/vicuna/ggml-vicuna-13b-1.1/blob/main/ggml-vic13b-q5_1.bin)，建议使用huggingface_hub库的snapshot_download下载。
+2. 将下载的模型重命名。通过huggingface_hub下载的模型会被重命名为随机序列，因此需要重命名为原始文件名，如[ggml-vic13b-q5_1.bin](https://huggingface.co/vicuna/ggml-vicuna-13b-1.1/blob/main/ggml-vic13b-q5_1.bin)。
+3. 基于下载模型的ggml的加载时间，推测对应的llama-cpp版本，下载对应的llama-cpp-python库的wheel文件，实测[ggml-vic13b-q5_1.bin](https://huggingface.co/vicuna/ggml-vicuna-13b-1.1/blob/main/ggml-vic13b-q5_1.bin)与llama-cpp-python库兼容,然后手动安装wheel文件。
+4. 将下载的模型信息写入configs/model_config.py文件里 `llm_model_dict`中，注意保证参数的兼容性，一些参数组合可能会报错.

docs/启动API服务.md

+# 启动API服务
+# 启动API服务
+
+## 通过py文件启动
+可以通过直接执行`api.py`文件启动API服务，默认以ip:0.0.0.0和port:7861启动http和ws服务。
+```shell
+python api.py
+```
+同时，启动时支持StartOption所列的模型加载参数，同时还支持IP和端口设置。
+```shell
+python api.py --model-name chatglm-6b-int8 --port 7862 
+```
+
+## 通过cli.bat/cli.sh启动
+也可以通过命令行控制文件继续启动。
+```shell
+cli.sh api --help
+```
+其他可设置参数和上述py文件启动方式相同。
+
+
+# 以https、wss启动API服务
+## 本地创建ssl相关证书文件
+如果没有正式签发的CA证书，可以[安装mkcert](https://github.com/FiloSottile/mkcert#installation)工具， 然后用如下指令生成本地CA证书：
+```shell
+mkcert -install
+mkcert api.example.com 47.123.123.123 localhost 127.0.0.1 ::1
+```
+默认回车保存在当前目录下，会有以生成指令第一个域名命名为前缀命名的两个pem文件。
+
+附带两个文件参数启动即可。
+````shell
+python api --port 7862 --ssl_keyfile api.example.com+4-key.pem --ssl_certfile api.example.com+4.pem
+
+./cli.sh api --port 7862 --ssl_keyfile api.example.com+4-key.pem --ssl_certfile api.example.com+4.pem
+````
+
+此外可以通过前置Nginx转发实现类似效果，可另行查阅相关资料。
\ No newline at end of file

loader/image_loader.py

@@ -5,9 +5,6 @@ from langchain.document_loaders.unstructured import UnstructuredFileLoader
 from paddleocr import PaddleOCR
 import os
 import nltk
-from configs.model_config import NLTK_DATA_PATH
-
-nltk.data.path = [NLTK_DATA_PATH] + nltk.data.path
 
 class UnstructuredPaddleImageLoader(UnstructuredFileLoader):
     """Loader that uses unstructured to load image files, such as PNGs and JPGs."""
@@ -35,6 +32,10 @@ class UnstructuredPaddleImageLoader(UnstructuredFileLoader):
 if __name__ == "__main__":
     import sys
     sys.path.append(os.path.dirname(os.path.dirname(__file__)))
+    
+    from configs.model_config import NLTK_DATA_PATH
+    nltk.data.path = [NLTK_DATA_PATH] + nltk.data.path
+    
     filepath = os.path.join(os.path.dirname(os.path.dirname(__file__)), "knowledge_base", "samples", "content", "test.jpg")
     loader = UnstructuredPaddleImageLoader(filepath, mode="elements")
     docs = loader.load()

models/chatglm_llm.py

@@ -65,6 +65,7 @@ class ChatGLM(BaseAnswer, LLM, ABC):
                 answer_result.history = history
                 answer_result.llm_output = {"answer": stream_resp}
                 yield answer_result
+            self.checkPoint.clear_torch_cache()
         else:
             response, _ = self.checkPoint.model.chat(
                 self.checkPoint.tokenizer,

models/fastchat_openai_llm.py

@@ -23,6 +23,7 @@ def _build_message_template() -> Dict[str, str]:
 
 
 class FastChatOpenAILLM(RemoteRpcModel, LLM, ABC):
+
     api_base_url: str = "http://localhost:8000/v1"
     model_name: str = "chatglm-6b"
     max_token: int = 10000
@@ -31,8 +32,14 @@ class FastChatOpenAILLM(RemoteRpcModel, LLM, ABC):
     checkPoint: LoaderCheckPoint = None
     history = []
     history_len: int = 10
-
-    def __init__(self, checkPoint: LoaderCheckPoint = None):
+    api_key: str = ""
+
+    def __init__(self, 
+                 checkPoint: LoaderCheckPoint = None,
+                #  api_base_url:str="http://localhost:8000/v1",
+                #  model_name:str="chatglm-6b",
+                #  api_key:str=""
+                 ):
         super().__init__()
         self.checkPoint = checkPoint
 
@@ -60,7 +67,7 @@ class FastChatOpenAILLM(RemoteRpcModel, LLM, ABC):
         return self.api_base_url
 
     def set_api_key(self, api_key: str):
-        pass
+        self.api_key = api_key
 
     def set_api_base_url(self, api_base_url: str):
         self.api_base_url = api_base_url
@@ -73,7 +80,8 @@ class FastChatOpenAILLM(RemoteRpcModel, LLM, ABC):
         try:
             import openai
             # Not support yet
-            openai.api_key = "EMPTY"
+            # openai.api_key = "EMPTY"
+            openai.key = self.api_key
             openai.api_base = self.api_base_url
         except ImportError:
             raise ValueError(
@@ -116,7 +124,8 @@ class FastChatOpenAILLM(RemoteRpcModel, LLM, ABC):
         try:
             import openai
             # Not support yet
-            openai.api_key = "EMPTY"
+            # openai.api_key = "EMPTY"
+            openai.api_key = self.api_key
             openai.api_base = self.api_base_url
         except ImportError:
             raise ValueError(

models/llama_llm.py

@@ -6,14 +6,17 @@ import torch
 import transformers
 from transformers.generation.logits_process import LogitsProcessor
 from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList
-from typing import Optional, List, Dict, Any
+from typing import Optional, List, Dict, Any,Union
 from models.loader import LoaderCheckPoint
 from models.base import (BaseAnswer,
                          AnswerResult)
 
 
 class InvalidScoreLogitsProcessor(LogitsProcessor):
-    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
+    def __call__(self, input_ids: Union[torch.LongTensor,list], scores: Union[torch.FloatTensor,list]) -> torch.FloatTensor:
+        # llama-cpp模型返回的是list,为兼容性考虑，需要判断input_ids和scores的类型，将list转换为torch.Tensor
+        input_ids = torch.tensor(input_ids) if isinstance(input_ids,list) else input_ids
+        scores = torch.tensor(scores) if isinstance(scores,list) else scores
         if torch.isnan(scores).any() or torch.isinf(scores).any():
             scores.zero_()
             scores[..., 5] = 5e4
@@ -163,7 +166,20 @@ class LLamaLLM(BaseAnswer, LLM, ABC):
             self.stopping_criteria = transformers.StoppingCriteriaList()
         # 观测输出
         gen_kwargs.update({'stopping_criteria': self.stopping_criteria})
-
+        # llama-cpp模型的参数与transformers的参数字段有较大差异，直接调用会返回不支持的字段错误
+        # 因此需要先判断模型是否是llama-cpp模型，然后取gen_kwargs与模型generate方法字段的交集
+        # 仅将交集字段传给模型以保证兼容性
+        # todo llama-cpp模型在本框架下兼容性较差，后续可以考虑重写一个llama_cpp_llm.py模块
+        if "llama_cpp" in self.checkPoint.model.__str__():
+            import inspect
+
+            common_kwargs_keys = set(inspect.getfullargspec(self.checkPoint.model.generate).args)&set(gen_kwargs.keys())
+            common_kwargs = {key:gen_kwargs[key] for key in common_kwargs_keys}
+            #? llama-cpp模型的generate方法似乎只接受.cpu类型的输入，响应很慢，慢到哭泣
+            #?为什么会不支持GPU呢，不应该啊？
+            output_ids = torch.tensor([list(self.checkPoint.model.generate(input_id_i.cpu(),**common_kwargs)) for input_id_i in input_ids])
+
+        else:
             output_ids = self.checkPoint.model.generate(**gen_kwargs)
         new_tokens = len(output_ids[0]) - len(input_ids[0])
         reply = self.decode(output_ids[0][-new_tokens:])

models/loader/loader.py

@@ -67,9 +67,11 @@ class LoaderCheckPoint:
         self.load_in_8bit = params.get('load_in_8bit', False)
         self.bf16 = params.get('bf16', False)
 
+
     def _load_model_config(self, model_name):
 
         if self.model_path:
+            self.model_path = re.sub("\s","",self.model_path)
             checkpoint = Path(f'{self.model_path}')
         else:
             if not self.no_remote_model:
@@ -78,10 +80,12 @@ class LoaderCheckPoint:
                 raise ValueError(
                     "本地模型local_model_path未配置路径"
                 )
-
+        try:
             model_config = AutoConfig.from_pretrained(checkpoint, trust_remote_code=True)
-
             return model_config
+        except Exception as e:
+            print(e)
+            return checkpoint
 
     def _load_model(self, model_name):
         """
@@ -93,6 +97,7 @@ class LoaderCheckPoint:
         t0 = time.time()
 
         if self.model_path:
+            self.model_path = re.sub("\s","",self.model_path)
             checkpoint = Path(f'{self.model_path}')
         else:
             if not self.no_remote_model:
@@ -103,7 +108,7 @@ class LoaderCheckPoint:
                 )
 
         self.is_llamacpp = len(list(Path(f'{checkpoint}').glob('ggml*.bin'))) > 0
-        if 'chatglm' in model_name.lower():
+        if 'chatglm' in model_name.lower() or "chatyuan" in model_name.lower():
             LoaderClass = AutoModel
         else:
             LoaderClass = AutoModelForCausalLM
@@ -126,8 +131,14 @@ class LoaderCheckPoint:
                         .half()
                         .cuda()
                     )
+                # 支持自定义cuda设备
+                elif ":" in self.llm_device:
+                    model = LoaderClass.from_pretrained(checkpoint,
+                                                    config=self.model_config,
+                                                    torch_dtype=torch.bfloat16 if self.bf16 else torch.float16,
+                                                    trust_remote_code=True).half().to(self.llm_device)
                 else:
-                    from accelerate import dispatch_model
+                    from accelerate import dispatch_model,infer_auto_device_map
 
                     model = LoaderClass.from_pretrained(checkpoint,
                                                         config=self.model_config,
@@ -151,6 +162,13 @@ class LoaderCheckPoint:
                                                                dtype=torch.float16 if not self.load_in_8bit else torch.int8, 
                                                                max_memory=max_memory,
                                                                no_split_module_classes=model._no_split_modules)
+                            # 对于chaglm和moss意外的模型应使用自动指定，而非调用chatglm的配置方式
+                            # 其他模型定义的层类几乎不可能与chatglm和moss一致，使用chatglm_auto_configure_device_map
+                            # 百分百会报错，使用infer_auto_device_map虽然可能导致负载不均衡，但至少不会报错
+                            # 实测在bloom模型上如此
+#                             self.device_map = infer_auto_device_map(model,
+#                                                                     dtype=torch.int8,
+#                                                                     no_split_module_classes=model._no_split_modules)
 
                     model = dispatch_model(model, device_map=self.device_map)
             else:
@@ -166,7 +184,7 @@ class LoaderCheckPoint:
         elif self.is_llamacpp:
 
             try:
-                from models.extensions.llamacpp_model_alternative import LlamaCppModel
+                from llama_cpp import Llama
 
             except ImportError as exc:
                 raise ValueError(
@@ -177,7 +195,16 @@ class LoaderCheckPoint:
             model_file = list(checkpoint.glob('ggml*.bin'))[0]
             print(f"llama.cpp weights detected: {model_file}\n")
 
-            model, tokenizer = LlamaCppModel.from_pretrained(model_file)
+            model = Llama(model_path=model_file._str)
+
+            # 实测llama-cpp-vicuna13b-q5_1的AutoTokenizer加载tokenizer的速度极慢，应存在优化空间
+            # 但需要对huggingface的AutoTokenizer进行优化
+
+            # tokenizer = model.tokenizer
+            # todo 此处调用AutoTokenizer的tokenizer，但后续可以测试自带tokenizer是不是兼容
+            #* -> 自带的tokenizer不与transoformers的tokenizer兼容,无法使用
+
+            tokenizer = AutoTokenizer.from_pretrained(self.model_name)
             return model, tokenizer
 
         elif self.load_in_8bit:
@@ -267,10 +294,21 @@ class LoaderCheckPoint:
         # 在调用chat或者stream_chat时,input_ids会被放到model.device上
         # 如果transformer.word_embeddings.device和model.device不同,则会导致RuntimeError
         # 因此这里将transformer.word_embeddings,transformer.final_layernorm,lm_head都放到第一张卡上
+        
+        encode = ""
+        if 'chatglm2' in self.model_name:
+            device_map = {
+                f"{layer_prefix}.embedding.word_embeddings": 0,
+                f"{layer_prefix}.rotary_pos_emb": 0,
+                f"{layer_prefix}.output_layer": 0,
+                f"{layer_prefix}.encoder.final_layernorm": 0,
+                f"base_model.model.output_layer": 0 
+            }
+            encode = ".encoder"
+        else:
             device_map = {f'{layer_prefix}.word_embeddings': 0,
                       f'{layer_prefix}.final_layernorm': 0, 'lm_head': 0,
                       f'base_model.model.lm_head': 0, }
-
         used = 2
         gpu_target = 0
         for i in range(num_trans_layers):
@@ -278,7 +316,7 @@ class LoaderCheckPoint:
                 gpu_target += 1
                 used = 0
             assert gpu_target < num_gpus
-            device_map[f'{layer_prefix}.layers.{i}'] = gpu_target
+            device_map[f'{layer_prefix}{encode}.layers.{i}'] = gpu_target
             used += 1
 
         return device_map
@@ -395,7 +433,7 @@ class LoaderCheckPoint:
                     print(
                         "如果您使用的是 macOS 建议将 pytorch 版本升级至 2.0.0 或更高版本，以支持及时清理 torch 产生的内存占用。")
             elif torch.has_cuda:
-                device_id = "0" if torch.cuda.is_available() else None
+                device_id = "0" if torch.cuda.is_available() and (":" not in self.llm_device) else None
                 CUDA_DEVICE = f"{self.llm_device}:{device_id}" if device_id else self.llm_device
                 with torch.cuda.device(CUDA_DEVICE):
                     torch.cuda.empty_cache()
@@ -442,5 +480,6 @@ class LoaderCheckPoint:
                 self.model.transformer.prefix_encoder.float()
             except Exception as e:
                 print("加载PrefixEncoder模型参数失败")
-
+        # llama-cpp模型（至少vicuna-13b）的eval方法就是自身，其没有eval方法
+        if not self.is_llamacpp:
             self.model = self.model.eval()

models/moss_llm.py

@@ -6,7 +6,7 @@ from models.base import (BaseAnswer,
                          AnswerResult)
 
 import torch
-
+# todo 建议重写instruction,在该instruction下，各模型的表现比较差
 META_INSTRUCTION = \
     """You are an AI assistant whose name is MOSS.
     - MOSS is a conversational language model that is developed by Fudan University. It is designed to be helpful, honest, and harmless.
@@ -20,7 +20,7 @@ META_INSTRUCTION = \
     Capabilities and tools that MOSS can possess.
     """
 
-
+# todo 在MOSSLLM类下，各模型的响应速度很慢，后续要检查一下原因
 class MOSSLLM(BaseAnswer, LLM, ABC):
     max_token: int = 2048
     temperature: float = 0.7
@@ -42,10 +42,11 @@ class MOSSLLM(BaseAnswer, LLM, ABC):
         return self.checkPoint
 
     @property
-    def set_history_len(self) -> int:
+    def _history_len(self) -> int:
+
         return self.history_len
 
-    def _set_history_len(self, history_len: int) -> None:
+    def set_history_len(self, history_len: int) -> None:
         self.history_len = history_len
 
     def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
@@ -59,11 +60,13 @@ class MOSSLLM(BaseAnswer, LLM, ABC):
             prompt_w_history = str(history)
             prompt_w_history += '<|Human|>: ' + prompt + '<eoh>'
         else:
-            prompt_w_history = META_INSTRUCTION
+            prompt_w_history = META_INSTRUCTION.replace("MOSS", self.checkPoint.model_name.split("/")[-1])
             prompt_w_history += '<|Human|>: ' + prompt + '<eoh>'
 
         inputs = self.checkPoint.tokenizer(prompt_w_history, return_tensors="pt")
         with torch.no_grad():
+            # max_length似乎可以设的小一些，而repetion_penalty应大一些，否则chatyuan,bloom等模型为满足max会重复输出
+            # 
             outputs = self.checkPoint.model.generate(
                 inputs.input_ids.cuda(),
                 attention_mask=inputs.attention_mask.cuda(),

models/shared.py

@@ -44,4 +44,5 @@ def loaderLLM(llm_model: str = None, no_remote_model: bool = False, use_ptuning_
     if 'FastChatOpenAILLM' in llm_model_info["provides"]:
         modelInsLLM.set_api_base_url(llm_model_info['api_base_url'])
         modelInsLLM.call_model_name(llm_model_info['name'])
+        modelInsLLM.set_api_key(llm_model_info['api_key'])
     return modelInsLLM

requirements.txt

@@ -23,9 +23,13 @@ openai
 #accelerate~=0.18.0
 #peft~=0.3.0
 #bitsandbytes; platform_system != "Windows"
-#llama-cpp-python==0.1.34; platform_system != "Windows"
-#https://github.com/abetlen/llama-cpp-python/releases/download/v0.1.34/llama_cpp_python-0.1.34-cp310-cp310-win_amd64.whl; platform_system == "Windows"
 
+# 要调用llama-cpp模型，如vicuma-13b量化模型需要安装llama-cpp-python库
+# but!!! 实测pip install 不好使，需要手动从ttps://github.com/abetlen/llama-cpp-python/releases/下载
+# 而且注意不同时期的ggml格式并不！兼！容!!!因此需要安装的llama-cpp-python版本也不一致，需要手动测试才能确定
+# 实测ggml-vicuna-13b-1.1在llama-cpp-python 0.1.63上可正常兼容
+# 不过！！！本项目模型加载的方式控制的比较严格，与llama-cpp-python的兼容性较差，很多参数设定不能使用，
+# 建议如非必要还是不要使用llama-cpp
 torch~=2.0.0
 pydantic~=1.10.7
 starlette~=0.26.1
@@ -33,5 +37,4 @@ numpy~=1.23.5
 tqdm~=4.65.0
 requests~=2.28.2
 tenacity~=8.2.2
-# 默认下载的charset_normalizer模块版本过高会抛出，`artially initialized module 'charset_normalizer' has no attribute 'md__mypyc' (most likely due to a circular import)`
 charset_normalizer==2.1.0
\ No newline at end of file

test/textsplitter/test_zh_title_enhance.py

+from configs.model_config import *
+from langchain.embeddings.huggingface import HuggingFaceEmbeddings
+import nltk
+from vectorstores import MyFAISS
+from chains.local_doc_qa import load_file
+
+
+nltk.data.path = [NLTK_DATA_PATH] + nltk.data.path
+
+if __name__ == "__main__":
+    filepath = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(__file__))),
+                            "knowledge_base", "samples", "content", "test.txt")
+    embeddings = HuggingFaceEmbeddings(model_name=embedding_model_dict[EMBEDDING_MODEL],
+                                       model_kwargs={'device': EMBEDDING_DEVICE})
+
+    docs = load_file(filepath, using_zh_title_enhance=True)
+    vector_store = MyFAISS.from_documents(docs, embeddings)
+    query = "指令提示技术有什么示例"
+    search_result = vector_store.similarity_search(query)
+    print(search_result)
+    pass

textsplitter/__init__.py

 from .chinese_text_splitter import ChineseTextSplitter
 from .ali_text_splitter import AliTextSplitter
+from .zh_title_enhance import zh_title_enhance
\ No newline at end of file

textsplitter/zh_title_enhance.py

+from langchain.docstore.document import Document
+import re
+
+
+def under_non_alpha_ratio(text: str, threshold: float = 0.5):
+    """Checks if the proportion of non-alpha characters in the text snippet exceeds a given
+    threshold. This helps prevent text like "-----------BREAK---------" from being tagged
+    as a title or narrative text. The ratio does not count spaces.
+
+    Parameters
+    ----------
+    text
+        The input string to test
+    threshold
+        If the proportion of non-alpha characters exceeds this threshold, the function
+        returns False
+    """
+    if len(text) == 0:
+        return False
+
+    alpha_count = len([char for char in text if char.strip() and char.isalpha()])
+    total_count = len([char for char in text if char.strip()])
+    try:
+        ratio = alpha_count / total_count
+        return ratio < threshold
+    except:
+        return False
+
+
+def is_possible_title(
+        text: str,
+        title_max_word_length: int = 20,
+        non_alpha_threshold: float = 0.5,
+) -> bool:
+    """Checks to see if the text passes all of the checks for a valid title.
+
+    Parameters
+    ----------
+    text
+        The input text to check
+    title_max_word_length
+        The maximum number of words a title can contain
+    non_alpha_threshold
+        The minimum number of alpha characters the text needs to be considered a title
+    """
+
+    # 文本长度为0的话，肯定不是title
+    if len(text) == 0:
+        print("Not a title. Text is empty.")
+        return False
+
+    # 文本中有标点符号，就不是title
+    ENDS_IN_PUNCT_PATTERN = r"[^\w\s]\Z"
+    ENDS_IN_PUNCT_RE = re.compile(ENDS_IN_PUNCT_PATTERN)
+    if ENDS_IN_PUNCT_RE.search(text) is not None:
+        return False
+
+    # 文本长度不能超过设定值，默认20
+    # NOTE(robinson) - splitting on spaces here instead of word tokenizing because it
+    # is less expensive and actual tokenization doesn't add much value for the length check
+    if len(text) > title_max_word_length:
+        return False
+
+    # 文本中数字的占比不能太高，否则不是title
+    if under_non_alpha_ratio(text, threshold=non_alpha_threshold):
+        return False
+
+    # NOTE(robinson) - Prevent flagging salutations like "To My Dearest Friends," as titles
+    if text.endswith((",", ".", "，", "。")):
+        return False
+
+    if text.isnumeric():
+        print(f"Not a title. Text is all numeric:\n\n{text}")  # type: ignore
+        return False
+
+    # 开头的字符内应该有数字，默认5个字符内
+    if len(text) < 5:
+        text_5 = text
+    else:
+        text_5 = text[:5]
+    alpha_in_text_5 = sum(list(map(lambda x: x.isnumeric(), list(text_5))))
+    if not alpha_in_text_5:
+        return False
+
+    return True
+
+
+def zh_title_enhance(docs: Document) -> Document:
+    title = None
+    if len(docs) > 0:
+        for doc in docs:
+            if is_possible_title(doc.page_content):
+                doc.metadata['category'] = 'cn_Title'
+                title = doc.page_content
+            elif title:
+                doc.page_content = f"下文与({title})有关。{doc.page_content}"
+        return docs
+    else:
+        print("文件不存在")

vectorstores/MyFAISS.py

@@ -6,6 +6,8 @@ from langchain.docstore.base import Docstore
 from langchain.docstore.document import Document
 import numpy as np
 import copy
+import os
+from configs.model_config import *
 
 
 class MyFAISS(FAISS, VectorStore):
@@ -22,6 +24,9 @@ class MyFAISS(FAISS, VectorStore):
                          docstore=docstore,
                          index_to_docstore_id=index_to_docstore_id,
                          normalize_L2=normalize_L2)
+        self.score_threshold=VECTOR_SEARCH_SCORE_THRESHOLD
+        self.chunk_size = CHUNK_SIZE
+        self.chunk_conent = False
 
     def seperate_list(self, ls: List[int]) -> List[List[int]]:
         # TODO: 增加是否属于同一文档的判断
@@ -52,7 +57,11 @@ class MyFAISS(FAISS, VectorStore):
             if i == -1 or 0 < self.score_threshold < scores[0][j]:
                 # This happens when not enough docs are returned.
                 continue
+            if i in self.index_to_docstore_id:
                 _id = self.index_to_docstore_id[i]
+            # 执行接下来的操作
+            else:
+                continue
             doc = self.docstore.search(_id)
             if (not self.chunk_conent) or ("context_expand" in doc.metadata and not doc.metadata["context_expand"]):
                 # 匹配出的文本如果不需要扩展上下文则执行如下代码
@@ -113,8 +122,10 @@ class MyFAISS(FAISS, VectorStore):
         try:
             if isinstance(source, str):
                 ids = [k for k, v in self.docstore._dict.items() if v.metadata["source"] == source]
+                vs_path = os.path.join(os.path.split(os.path.split(source)[0])[0], "vector_store")
             else:
                 ids = [k for k, v in self.docstore._dict.items() if v.metadata["source"] in source]
+                vs_path = os.path.join(os.path.split(os.path.split(source[0])[0])[0], "vector_store")
             if len(ids) == 0:
                 return f"docs delete fail"
             else:
@@ -122,6 +133,9 @@ class MyFAISS(FAISS, VectorStore):
                     index = list(self.index_to_docstore_id.keys())[list(self.index_to_docstore_id.values()).index(id)]
                     self.index_to_docstore_id.pop(index)
                     self.docstore._dict.pop(id)
+                # TODO: 从 self.index 中删除对应id
+                # self.index.reset()
+                self.save_local(vs_path)
                 return f"docs delete success"
         except Exception as e:
             print(e)

views/Dockerfile

@@ -15,7 +15,7 @@ COPY . /app
 
 RUN pnpm run build
 
-
+FROM frontend AS final
 
 COPY --from=frontend /app/dist /app/public
 

views/src/api/chat.ts

@@ -16,6 +16,24 @@ export const chatfile = (params: any) => {
   })
 }
 
+export const getKbsList = () => {
+  return api({
+    url: '/local_doc_qa/list_knowledge_base',
+    method: 'get',
+
+  })
+}
+
+export const deleteKb = (knowledge_base_id: any) => {
+  return api({
+    url: '/local_doc_qa/delete_knowledge_base',
+    method: 'delete',
+    params: {
+      knowledge_base_id,
+    },
+  })
+}
+
 export const getfilelist = (knowledge_base_id: any) => {
   return api({
     url: '/local_doc_qa/list_files',
@@ -35,8 +53,8 @@ export const bing_search = (params: any) => {
 export const deletefile = (params: any) => {
   return api({
     url: '/local_doc_qa/delete_file',
-    method: 'post',
-    data: JSON.stringify(params),
+    method: 'delete',
+    params,
   })
 }
 export const web_url = () => {
@@ -45,3 +63,18 @@ export const web_url = () => {
 export const setapi = () => {
   return window.baseApi
 }
+export const getkblist = (knowledge_base_id: any) => {
+  return api({
+    url: '/local_doc_qa/list_knowledge_base',
+    method: 'get',
+    params: {},
+
+  })
+}
+export const deletekb = (params: any) => {
+  return api({
+    url: '/local_doc_qa/delete_knowledge_base',
+    method: 'post',
+    data: JSON.stringify(params),
+  })
+}

views/src/views/chat/index.vue

@@ -555,7 +555,7 @@ const options = computed(() => {
 
   return common
 })
-function handleSelect(key: 'copyText' | 'delete' | 'toggleRenderType') {
+function handleSelect(key: string) {
   if (key == '清除会话') {
     handleClear()
   }
@@ -658,7 +658,6 @@ function searchfun() {
           <NDropdown
             v-if="isMobile"
             :trigger="isMobile ? 'click' : 'hover'"
-            :placement="!inversion ? 'right' : 'left'"
             :options="options"
             @select="handleSelect"
           >

views/src/views/chat/layout/sider/knowledge-base/index.vue

@@ -3,15 +3,16 @@ import { NButton, NForm, NFormItem, NInput, NPopconfirm } from 'naive-ui'
 import { onMounted, ref } from 'vue'
 import filelist from './filelist.vue'
 import { SvgIcon } from '@/components/common'
-import { deletefile, getfilelist } from '@/api/chat'
+import { deleteKb, getKbsList } from '@/api/chat'
 import { idStore } from '@/store/modules/knowledgebaseid/id'
+
 const items = ref<any>([])
 const choice = ref('')
 const store = idStore()
 
 onMounted(async () => {
   choice.value = store.knowledgeid
-  const res = await getfilelist({})
+  const res = await getKbsList()
   res.data.data.forEach((item: any) => {
     items.value.push({
       value: item,
@@ -52,8 +53,8 @@ const handleClick = () => {
   }
 }
 async function handleDelete(item: any) {
-  await deletefile(item.value)
-  const res = await getfilelist({})
+  await deleteKb(item.value)
+  const res = await getKbsList()
   items.value = []
   res.data.data.forEach((item: any) => {
     items.value.push({

webui.py

@@ -218,7 +218,12 @@ def change_chunk_conent(mode, label_conent, history):
 
 
 def add_vs_name(vs_name, chatbot):
-    if vs_name in get_vs_list():
+    if vs_name is None or vs_name.strip() == "" :
+        vs_status = "知识库名称不能为空，请重新填写知识库名称"
+        chatbot = chatbot + [[None, vs_status]]
+        return gr.update(visible=True), gr.update(visible=True), gr.update(visible=True), gr.update(
+            visible=False), chatbot, gr.update(visible=False)
+    elif vs_name in get_vs_list():
         vs_status = "与已有知识库名称冲突，请重新选择其他名称后提交"
         chatbot = chatbot + [[None, vs_status]]
         return gr.update(visible=True), gr.update(visible=True), gr.update(visible=True), gr.update(

webui_st.py

@@ -143,7 +143,7 @@ def init_model(llm_model: str = 'chat-glm-6b', embedding_model: str = 'text2vec'
 #     return history + [[None, model_status]]
 
 
-def get_vector_store(vs_id, files, sentence_size, history, one_conent, one_content_segmentation):
+def get_vector_store(local_doc_qa, vs_id, files, sentence_size, history, one_conent, one_content_segmentation):
     vs_path = os.path.join(KB_ROOT_PATH, vs_id, "vector_store")
     filelist = []
     if not os.path.exists(os.path.join(KB_ROOT_PATH, vs_id, "content")):
@@ -455,6 +455,8 @@ with st.sidebar:
             cols = st.columns([12, 10])
             kb_name = cols[0].text_input(
                 '新知识库名称', placeholder='新知识库名称', label_visibility='collapsed')
+            if 'kb_name' not in st.session_state:
+                st.session_state.kb_name = kb_name
             cols[1].button('新建知识库', on_click=on_new_kb)
             vs_path = st.selectbox(
                 '选择知识库', vs_list, on_change=on_vs_change, key='vs_path')