diff --git a/chatchat-server/poetry.toml b/chatchat-server/poetry.toml index 42159b24..3b673ef3 100644 --- a/chatchat-server/poetry.toml +++ b/chatchat-server/poetry.toml @@ -3,3 +3,7 @@ in-project = true [installer] modern-installation = false + +[plugins] +[plugins.pypi_mirror] +url = "https://pypi.tuna.tsinghua.edu.cn/simple" diff --git a/chatchat-server/pyproject.toml b/chatchat-server/pyproject.toml index 070c50fd..38de8c8c 100644 --- a/chatchat-server/pyproject.toml +++ b/chatchat-server/pyproject.toml @@ -6,7 +6,7 @@ authors = ["chatchat"] readme = "README.md" [tool.poetry.dependencies] -python = ">=3.8,<4.0,!=3.9.7" +python = ">=3.8.1,<4.0,!=3.9.7" chatchat-model-providers = "^0.3.0" langchain = "0.1.5" langchainhub = "0.1.14" @@ -35,9 +35,9 @@ strsimpy = ">=0.2.1" markdownify = ">=0.11.6" tqdm = ">=4.66.1" websockets = ">=12.0" -numpy = ">=1.26.3" +numpy = "1.24.4" pandas = "~2.1.4" -pydantic = "1.10.14" +pydantic = "2.6.4" httpx = {version = ">=0.25.2", extras = ["brotli", "http2", "socks"]} python-multipart = "0.0.9" @@ -194,7 +194,7 @@ omit = [ ] [build-system] -requires = ["poetry-core>=1.0.0"] +requires = ["poetry-core>=1.0.0", "poetry-plugin-pypi-mirror==0.4.2"] build-backend = "poetry.core.masonry.api" [tool.pytest.ini_options] diff --git a/chatchat/configs/__init__.py b/chatchat/chatchat/configs/__init__.py similarity index 100% rename from chatchat/configs/__init__.py rename to chatchat/chatchat/configs/__init__.py diff --git a/chatchat/chatchat/configs/basic_config.py b/chatchat/chatchat/configs/basic_config.py new file mode 100644 index 00000000..61ec7c70 --- /dev/null +++ b/chatchat/chatchat/configs/basic_config.py @@ -0,0 +1,47 @@ +import logging +import os +from pathlib import Path + +import langchain + + +# 是否显示详细日志 +log_verbose = True +langchain.verbose = log_verbose + +# 通常情况下不需要更改以下内容 + +# 用户数据根目录 +DATA_PATH = str(Path(__file__).absolute().parent.parent / "data") +if not os.path.exists(DATA_PATH): + os.mkdir(DATA_PATH) + +# nltk 模型存储路径 +NLTK_DATA_PATH = os.path.join(DATA_PATH, "nltk_data") +import nltk +nltk.data.path = [NLTK_DATA_PATH] + nltk.data.path + +# 日志格式 +LOG_FORMAT = "%(asctime)s - %(filename)s[line:%(lineno)d] - %(levelname)s: %(message)s" +logger = logging.getLogger() +logger.setLevel(logging.INFO) +logging.basicConfig(format=LOG_FORMAT) + + +# 日志存储路径 +LOG_PATH = os.path.join(DATA_PATH, "logs") +if not os.path.exists(LOG_PATH): + os.mkdir(LOG_PATH) + +# 模型生成内容（图片、视频、音频等）保存位置 +MEDIA_PATH = os.path.join(DATA_PATH, "media") +if not os.path.exists(MEDIA_PATH): + os.mkdir(MEDIA_PATH) + os.mkdir(os.path.join(MEDIA_PATH, "image")) + os.mkdir(os.path.join(MEDIA_PATH, "audio")) + os.mkdir(os.path.join(MEDIA_PATH, "video")) + +# 临时文件目录，主要用于文件对话 +BASE_TEMP_DIR = os.path.join(DATA_PATH, "temp") +if not os.path.exists(BASE_TEMP_DIR): + os.mkdir(BASE_TEMP_DIR) diff --git a/chatchat/configs/basic_config.py.example b/chatchat/chatchat/configs/basic_config.py.example similarity index 100% rename from chatchat/configs/basic_config.py.example rename to chatchat/chatchat/configs/basic_config.py.example diff --git a/chatchat/chatchat/configs/kb_config.py b/chatchat/chatchat/configs/kb_config.py new file mode 100644 index 00000000..a330e80f --- /dev/null +++ b/chatchat/chatchat/configs/kb_config.py @@ -0,0 +1,148 @@ +import os + +from configs.basic_config import DATA_PATH + + +# 默认使用的知识库 +DEFAULT_KNOWLEDGE_BASE = "samples" + +# 默认向量库/全文检索引擎类型。可选：faiss, milvus(离线) & zilliz(在线), pgvector,全文检索引擎es +DEFAULT_VS_TYPE = "faiss" + +# 缓存向量库数量（针对FAISS） +CACHED_VS_NUM = 1 + +# 缓存临时向量库数量（针对FAISS），用于文件对话 +CACHED_MEMO_VS_NUM = 10 + +# 知识库中单段文本长度(不适用MarkdownHeaderTextSplitter) +CHUNK_SIZE = 250 + +# 知识库中相邻文本重合长度(不适用MarkdownHeaderTextSplitter) +OVERLAP_SIZE = 50 + +# 知识库匹配向量数量 +VECTOR_SEARCH_TOP_K = 3 + +# 知识库匹配相关度阈值，取值范围在0-1之间，SCORE越小，相关度越高，取到1相当于不筛选，建议设置在0.5左右 +SCORE_THRESHOLD = 1 + +# 默认搜索引擎。可选：bing, duckduckgo, metaphor +DEFAULT_SEARCH_ENGINE = "metaphor" + +# 搜索引擎匹配结题数量 +SEARCH_ENGINE_TOP_K = 3 + + +# 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 = "b31d23d7b96742ab959f4cc07a605f72" + +# metaphor搜索需要KEY +METAPHOR_API_KEY = "f8c9f98f-141a-4a55-9be7-ae675ccacd7a" + +# 心知天气 API KEY，用于天气Agent。申请：https://www.seniverse.com/ +SENIVERSE_API_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 + +# PDF OCR 控制：只对宽高超过页面一定比例（图片宽/页面宽，图片高/页面高）的图片进行 OCR。 +# 这样可以避免 PDF 中一些小图片的干扰，提高非扫描版 PDF 处理速度 +PDF_OCR_THRESHOLD = (0.6, 0.6) + +# 每个知识库的初始化介绍，用于在初始化知识库时显示和Agent调用，没写则没有介绍，不会被Agent调用。 +KB_INFO = { + "samples": "关于本项目issue的解答", +} + + +# 通常情况下不需要更改以下内容 + +# 知识库默认存储路径 +KB_ROOT_PATH = os.path.join(DATA_PATH, "knowledge_base") +if not os.path.exists(KB_ROOT_PATH): + os.mkdir(KB_ROOT_PATH) + +# 数据库默认存储路径。 +# 如果使用sqlite，可以直接修改DB_ROOT_PATH；如果使用其它数据库，请直接修改SQLALCHEMY_DATABASE_URI。 +DB_ROOT_PATH = os.path.join(KB_ROOT_PATH, "info.db") +SQLALCHEMY_DATABASE_URI = f"sqlite:///{DB_ROOT_PATH}" + +# 可选向量库类型及对应配置 +kbs_config = { + "faiss": { + }, + "milvus": { + "host": "127.0.0.1", + "port": "19530", + "user": "", + "password": "", + "secure": False, + }, + "zilliz": { + "host": "in01-a7ce524e41e3935.ali-cn-hangzhou.vectordb.zilliz.com.cn", + "port": "19530", + "user": "", + "password": "", + "secure": True, + }, + "pg": { + "connection_uri": "postgresql://postgres:postgres@127.0.0.1:5432/langchain_chatchat", + }, + + "es": { + "host": "127.0.0.1", + "port": "9200", + "index_name": "test_index", + "user": "", + "password": "" + }, + "milvus_kwargs":{ + "search_params":{"metric_type": "L2"}, #在此处增加search_params + "index_params":{"metric_type": "L2","index_type": "HNSW"} # 在此处增加index_params + }, + "chromadb": {} +} + +# TextSplitter配置项，如果你不明白其中的含义，就不要修改。 +text_splitter_dict = { + "ChineseRecursiveTextSplitter": { + "source": "", ## 选择tiktoken则使用openai的方法 + "tokenizer_name_or_path": "", + }, + "SpacyTextSplitter": { + "source": "", + "tokenizer_name_or_path": "", + }, + "RecursiveCharacterTextSplitter": { + "source": "tiktoken", + "tokenizer_name_or_path": "cl100k_base", + }, + "MarkdownHeaderTextSplitter": { + "headers_to_split_on": + [ + ("#", "head1"), + ("##", "head2"), + ("###", "head3"), + ("####", "head4"), + ] + }, +} + +# TEXT_SPLITTER 名称 +TEXT_SPLITTER_NAME = "ChineseRecursiveTextSplitter" + +# Embedding模型定制词语的词表文件 +EMBEDDING_KEYWORD_FILE = "embedding_keywords.txt" diff --git a/chatchat/configs/kb_config.py.example b/chatchat/chatchat/configs/kb_config.py.example similarity index 100% rename from chatchat/configs/kb_config.py.example rename to chatchat/chatchat/configs/kb_config.py.example diff --git a/chatchat/configs/loom.yaml.example b/chatchat/chatchat/configs/loom.yaml.example similarity index 100% rename from chatchat/configs/loom.yaml.example rename to chatchat/chatchat/configs/loom.yaml.example diff --git a/chatchat/chatchat/configs/model_config.py b/chatchat/chatchat/configs/model_config.py new file mode 100644 index 00000000..b0f3c454 --- /dev/null +++ b/chatchat/chatchat/configs/model_config.py @@ -0,0 +1,170 @@ +import os + + +# 默认选用的 LLM 名称 +DEFAULT_LLM_MODEL = "qwen" + +# 默认选用的 Embedding 名称 +DEFAULT_EMBEDDING_MODEL = "bge" + + +# 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]) +Agent_MODEL = None + +# 历史对话轮数 +HISTORY_LEN = 3 + +# 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 +MAX_TOKENS = None + +# LLM通用对话参数 +TEMPERATURE = 0.7 +# TOP_P = 0.95 # ChatOpenAI暂不支持该参数 + +SUPPORT_AGENT_MODELS = [ + "chatglm3-6b", + "openai-api", + "Qwen-14B-Chat", + "Qwen-7B-Chat", + "qwen", +] + + +LLM_MODEL_CONFIG = { + # 意图识别不需要输出，模型后台知道就行 + "preprocess_model": { + DEFAULT_LLM_MODEL: { + "temperature": 0.05, + "max_tokens": 4096, + "history_len": 100, + "prompt_name": "default", + "callbacks": False + }, + }, + "llm_model": { + DEFAULT_LLM_MODEL: { + "temperature": 0.9, + "max_tokens": 4096, + "history_len": 10, + "prompt_name": "default", + "callbacks": True + }, + }, + "action_model": { + DEFAULT_LLM_MODEL: { + "temperature": 0.01, + "max_tokens": 4096, + "callbacks": True + }, + }, + "postprocess_model": { + DEFAULT_LLM_MODEL: { + "temperature": 0.01, + "max_tokens": 4096, + "prompt_name": "default", + "callbacks": True + } + }, + "image_model": { + "sd-turbo": { + "size": "256*256", + } + }, + "multimodal_model": { + "qwen-vl": {} + }, +} + +# 可以通过 loom/xinference/oneapi/fastchat 启动模型服务，然后将其 URL 和 KEY 配置过来即可。 +# - platform_name 可以任意填写，不要重复即可 +# - platform_type 可选：openai, xinference, oneapi, fastchat。以后可能根据平台类型做一些功能区分 +# - 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。 + +MODEL_PLATFORMS = [ + # { + # "platform_name": "openai-api", + # "platform_type": "openai", + # "api_base_url": "https://api.openai.com/v1", + # "api_key": "sk-yBuaCpqEVUBarBP9700e7224A2D743AeA329334d19C0A336", + # "api_proxy": "https://qujhzynu.cloud.sealos.io/v1", + # "api_concurrencies": 5, + # "llm_models": [ + # "gpt-3.5-turbo", + # ], + # "embed_models": [], + # "image_models": [], + # "multimodal_models": [], + # }, + + { + "platform_name": "xinference", + "platform_type": "xinference", + "api_base_url": "http://127.0.0.1:9997/v1", + "api_key": "EMPTY", + "api_concurrencies": 5, + # 注意：这里填写的是 xinference 部署的模型 UID，而非模型名称 + "llm_models": [ + "qwen", + "glm3", + ], + "embed_models": [ + "bge", + ], + "image_models": [ + "sd-turbo", + ], + "multimodal_models": [ + "qwen-vl", + ], + }, + + { + "platform_name": "oneapi", + "platform_type": "oneapi", + "api_base_url": "http://127.0.0.1:3000/v1", + "api_key": "sk-Mlft68FXoTYqLfQr06F0E2D77e6e4220B6F420999d25383f", + "api_concurrencies": 5, + "llm_models": [ + # 智谱 API + "chatglm_pro", + "chatglm_turbo", + "chatglm_std", + "chatglm_lite", + # 千问 API + "qwen-turbo", + "qwen-plus", + "qwen-max", + "qwen-max-longcontext", + # 千帆 API + "ERNIE-Bot", + "ERNIE-Bot-turbo", + "ERNIE-Bot-4", + # 星火 API + "SparkDesk", + ], + "embed_models": [ + # 千问 API + "text-embedding-v1", + # 千帆 API + "Embedding-V1", + ], + "image_models": [], + "multimodal_models": [], + }, + + # { + # "platform_name": "loom", + # "platform_type": "loom", + # "api_base_url": "http://127.0.0.1:7860/v1", + # "api_key": "88296d2f9bbd9ab222c1086e39f5fbb2.FbC0YSrAMcaEF2gB", + # "api_concurrencies": 5, + # "llm_models": [ + # "chatglm3-6b", + # ], + # "embed_models": [], + # "image_models": [], + # "multimodal_models": [], + # }, +] + +LOOM_CONFIG = os.path.join(os.path.dirname(os.path.abspath(__file__)), "loom.yaml") diff --git a/chatchat/configs/model_config.py.example b/chatchat/chatchat/configs/model_config.py.example similarity index 100% rename from chatchat/configs/model_config.py.example rename to chatchat/chatchat/configs/model_config.py.example diff --git a/chatchat/configs/openai-plugins-list.json b/chatchat/chatchat/configs/openai-plugins-list.json similarity index 100% rename from chatchat/configs/openai-plugins-list.json rename to chatchat/chatchat/configs/openai-plugins-list.json diff --git a/chatchat/chatchat/configs/prompt_config.py b/chatchat/chatchat/configs/prompt_config.py new file mode 100644 index 00000000..58ce1e0c --- /dev/null +++ b/chatchat/chatchat/configs/prompt_config.py @@ -0,0 +1,209 @@ +PROMPT_TEMPLATES = { + "preprocess_model": { + "default": + '你只要回复0 和 1 ，代表不需要使用工具。以下几种问题不需要使用工具:' + '1. 需要联网查询的内容\n' + '2. 需要计算的内容\n' + '3. 需要查询实时性的内容\n' + '如果我的输入满足这几种情况，返回1。其他输入，请你回复0，你只要返回一个数字\n' + '这是我的问题:' + }, + "llm_model": { + "default": + '{{input}}', + "with_history": + 'The following is a friendly conversation between a human and an AI. ' + 'The AI is talkative and provides lots of specific details from its context. ' + 'If the AI does not know the answer to a question, it truthfully says it does not know.\n\n' + 'Current conversation:\n' + '{history}\n' + 'Human: {input}\n' + 'AI:', + }, + "action_model": { + "GPT-4": + 'Answer the following questions as best you can. You have access to the following tools:\n' + 'The way you use the tools is by specifying a json blob.\n' + 'Specifically, this json should have a `action` key (with the name of the tool to use) and a `action_input` key (with the input to the tool going here).\n' + 'The only values that should be in the "action" field are: {tool_names}\n' + 'The $JSON_BLOB should only contain a SINGLE action, do NOT return a list of multiple actions. Here is an example of a valid $JSON_BLOB:\n' + '```\n\n' + '{{{{\n' + ' "action": $TOOL_NAME,\n' + ' "action_input": $INPUT\n' + '}}}}\n' + '```\n\n' + 'ALWAYS use the following format:\n' + 'Question: the input question you must answer\n' + 'Thought: you should always think about what to do\n' + 'Action:\n' + '```\n\n' + '$JSON_BLOB' + '```\n\n' + 'Observation: the result of the action\n' + '... (this Thought/Action/Observation can repeat N times)\n' + 'Thought: I now know the final answer\n' + 'Final Answer: the final answer to the original input question\n' + 'Begin! Reminder to always use the exact characters `Final Answer` when responding.\n' + 'Question:{input}\n' + 'Thought:{agent_scratchpad}\n', + + "ChatGLM3": + 'You can answer using the tools.Respond to the human as helpfully and accurately as possible.\n' + 'You have access to the following tools:\n' + '{tools}\n' + 'Use a json blob to specify a tool by providing an action key (tool name)\n' + 'and an action_input key (tool input).\n' + 'Valid "action" values: "Final Answer" or [{tool_names}]\n' + 'Provide only ONE action per $JSON_BLOB, as shown:\n\n' + '```\n' + '{{{{\n' + ' "action": $TOOL_NAME,\n' + ' "action_input": $INPUT\n' + '}}}}\n' + '```\n\n' + 'Follow this format:\n\n' + 'Question: input question to answer\n' + 'Thought: consider previous and subsequent steps\n' + 'Action:\n' + '```\n' + '$JSON_BLOB\n' + '```\n' + 'Observation: action result\n' + '... (repeat Thought/Action/Observation N times)\n' + 'Thought: I know what to respond\n' + 'Action:\n' + '```\n' + '{{{{\n' + ' "action": "Final Answer",\n' + ' "action_input": "Final response to human"\n' + '}}}}\n' + 'Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary.\n' + 'Respond directly if appropriate. Format is Action:```$JSON_BLOB```then Observation:.\n' + 'Question: {input}\n\n' + '{agent_scratchpad}\n', + "qwen": + 'Answer the following questions as best you can. You have access to the following APIs:\n\n' + '{tools}\n\n' + 'Use the following format:\n\n' + 'Question: the input question you must answer\n' + 'Thought: you should always think about what to do\n' + 'Action: the action to take, should be one of [{tool_names}]\n' + 'Action Input: the input to the action\n' + 'Observation: the result of the action\n' + '... (this Thought/Action/Action Input/Observation can be repeated zero or more times)\n' + 'Thought: I now know the final answer\n' + 'Final Answer: the final answer to the original input question\n\n' + 'Format the Action Input as a JSON object.\n\n' + 'Begin!\n\n' + 'Question: {input}\n\n' + '{agent_scratchpad}\n\n', + "structured-chat-agent": + 'Respond to the human as helpfully and accurately as possible. You have access to the following tools:\n\n' + '{tools}\n\n' + 'Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input).\n\n' + 'Valid "action" values: "Final Answer" or {tool_names}\n\n' + 'Provide only ONE action per $JSON_BLOB, as shown:\n\n' + '```\n{{\n "action": $TOOL_NAME,\n "action_input": $INPUT\n}}\n```\n\n' + 'Follow this format:\n\n' + 'Question: input question to answer\n' + 'Thought: consider previous and subsequent steps\n' + 'Action:\n```\n$JSON_BLOB\n```\n' + 'Observation: action result\n' + '... (repeat Thought/Action/Observation N times)\n' + 'Thought: I know what to respond\n' + 'Action:\n```\n{{\n "action": "Final Answer",\n "action_input": "Final response to human"\n}}\n\n' + 'Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary. Respond directly if appropriate. Format is Action:```$JSON_BLOB```then Observation\n' + '{input}\n\n' + '{agent_scratchpad}\n\n' + # '(reminder to respond in a JSON blob no matter what)' + }, + "postprocess_model": { + "default": "{{input}}", + } +} + +TOOL_CONFIG = { + "search_local_knowledgebase": { + "use": False, + "top_k": 3, + "score_threshold": 1, + "conclude_prompt": { + "with_result": + '<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"，' + '不允许在答案中添加编造成分，答案请使用中文。 \n' + '<已知信息>{{ context }}\n' + '<问题>{{ question }}\n', + "without_result": + '请你根据我的提问回答我的问题:\n' + '{{ question }}\n' + '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', + } + }, + "search_internet": { + "use": False, + "search_engine_name": "bing", + "search_engine_config": + { + "bing": { + "result_len": 3, + "bing_search_url": "https://api.bing.microsoft.com/v7.0/search", + "bing_key": "680a39347d7242c5bd2d7a9576a125b7", + }, + "metaphor": { + "result_len": 3, + "metaphor_api_key": "", + "split_result": False, + "chunk_size": 500, + "chunk_overlap": 0, + }, + "duckduckgo": { + "result_len": 3 + } + }, + "top_k": 10, + "verbose": "Origin", + "conclude_prompt": + "<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”。 " + "\n<已知信息>{{ context }}\n" + "<问题>\n" + "{{ question }}\n" + "\n" + }, + "arxiv": { + "use": False, + }, + "shell": { + "use": False, + }, + "weather_check": { + "use": False, + "api-key": "S8vrB4U_-c5mvAMiK", + }, + "search_youtube": { + "use": False, + }, + "wolfram": { + "use": False, + }, + "calculate": { + "use": False, + }, + "vqa_processor": { + "use": False, + "model_path": "your model path", + "tokenizer_path": "your tokenizer path", + "device": "cuda:1" + }, + "aqa_processor": { + "use": False, + "model_path": "your model path", + "tokenizer_path": "yout tokenizer path", + "device": "cuda:2" + }, + + "text2images": { + "use": False, + }, + +} diff --git a/chatchat/configs/prompt_config.py.example b/chatchat/chatchat/configs/prompt_config.py.example similarity index 100% rename from chatchat/configs/prompt_config.py.example rename to chatchat/chatchat/configs/prompt_config.py.example diff --git a/chatchat/chatchat/configs/server_config.py b/chatchat/chatchat/configs/server_config.py new file mode 100644 index 00000000..40485250 --- /dev/null +++ b/chatchat/chatchat/configs/server_config.py @@ -0,0 +1,25 @@ +import sys + + +# httpx 请求默认超时时间（秒）。如果加载模型或对话较慢，出现超时错误，可以适当加大该值。 +HTTPX_DEFAULT_TIMEOUT = 300.0 + +# API 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 = True + +# 各服务器默认绑定host。如改为"0.0.0.0"需要修改下方所有XX_SERVER的host +DEFAULT_BIND_HOST = "127.0.0.1" if sys.platform != "win32" else "127.0.0.1" + + +# webui.py server +WEBUI_SERVER = { + "host": DEFAULT_BIND_HOST, + "port": 8501, +} + +# api.py server +API_SERVER = { + "host": DEFAULT_BIND_HOST, + "port": 7861, +} diff --git a/chatchat/configs/server_config.py.example b/chatchat/chatchat/configs/server_config.py.example similarity index 100% rename from chatchat/configs/server_config.py.example rename to chatchat/chatchat/configs/server_config.py.example diff --git a/chatchat/copy_config_example.py b/chatchat/chatchat/copy_config_example.py similarity index 100% rename from chatchat/copy_config_example.py rename to chatchat/chatchat/copy_config_example.py diff --git a/chatchat/chatchat/data/knowledge_base/info.db b/chatchat/chatchat/data/knowledge_base/info.db new file mode 100644 index 00000000..95597ecf Binary files /dev/null and b/chatchat/chatchat/data/knowledge_base/info.db differ diff --git a/chatchat/chatchat/data/knowledge_base/samples/content/README.md b/chatchat/chatchat/data/knowledge_base/samples/content/README.md new file mode 100644 index 00000000..67aa6ef8 --- /dev/null +++ b/chatchat/chatchat/data/knowledge_base/samples/content/README.md @@ -0,0 +1,159 @@ +![](img/logo-long-chatchat-trans-v2.png) + + +🌍 [READ THIS IN ENGLISH](README_en.md) + +📃 **LangChain-Chatchat** (原 Langchain-ChatGLM) + +基于 ChatGLM 等大语言模型与 Langchain 等应用框架实现，开源、可离线部署的检索增强生成(RAG)大模型知识库项目。 + +--- + +## 目录 + +* [介绍](README.md#介绍) +* [解决的痛点](README.md#解决的痛点) +* [快速上手](README.md#快速上手) + * [1. 环境配置](README.md#1-环境配置) + * [2. 模型下载](README.md#2-模型下载) + * [3. 初始化知识库和配置文件](README.md#3-初始化知识库和配置文件) + * [4. 一键启动](README.md#4-一键启动) + * [5. 启动界面示例](README.md#5-启动界面示例) +* [联系我们](README.md#联系我们) + + +## 介绍 + +🤖️ 一种利用 [langchain](https://github.com/hwchase17/langchain) 思想实现的基于本地知识库的问答应用，目标期望建立一套对中文场景与开源模型支持友好、可离线运行的知识库问答解决方案。 + +💡 受 [GanymedeNil](https://github.com/GanymedeNil) 的项目 [document.ai](https://github.com/GanymedeNil/document.ai) 和 [AlexZhangji](https://github.com/AlexZhangji) 创建的 [ChatGLM-6B Pull Request](https://github.com/THUDM/ChatGLM-6B/pull/216) 启发，建立了全流程可使用开源模型实现的本地知识库问答应用。本项目的最新版本中通过使用 [FastChat](https://github.com/lm-sys/FastChat) 接入 Vicuna, Alpaca, LLaMA, Koala, RWKV 等模型，依托于 [langchain](https://github.com/langchain-ai/langchain) 框架支持通过基于 [FastAPI](https://github.com/tiangolo/fastapi) 提供的 API 调用服务，或使用基于 [Streamlit](https://github.com/streamlit/streamlit) 的 WebUI 进行操作。 + +✅ 依托于本项目支持的开源 LLM 与 Embedding 模型，本项目可实现全部使用**开源**模型**离线私有部署**。与此同时，本项目也支持 OpenAI GPT API 的调用，并将在后续持续扩充对各类模型及模型 API 的接入。 + +⛓️ 本项目实现原理如下图所示，过程包括加载文件 -> 读取文本 -> 文本分割 -> 文本向量化 -> 问句向量化 -> 在文本向量中匹配出与问句向量最相似的 `top k`个 -> 匹配出的文本作为上下文和问题一起添加到 `prompt`中 -> 提交给 `LLM`生成回答。 + +📺 [原理介绍视频](https://www.bilibili.com/video/BV13M4y1e7cN/?share_source=copy_web&vd_source=e6c5aafe684f30fbe41925d61ca6d514) + +![实现原理图](img/langchain+chatglm.png) + +从文档处理角度来看，实现流程如下： + +![实现原理图2](img/langchain+chatglm2.png) + +🚩 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。 + +🌐 [AutoDL 镜像](https://www.codewithgpu.com/i/chatchat-space/Langchain-Chatchat/Langchain-Chatchat) 中 `v11` 版本所使用代码已更新至本项目 `v0.2.7` 版本。 + +🐳 [Docker 镜像](registry.cn-beijing.aliyuncs.com/chatchat/chatchat:0.2.6) 已经更新到 ```0.2.7``` 版本。 + +🌲 一行命令运行 Docker ： + +```shell +docker run -d --gpus all -p 80:8501 registry.cn-beijing.aliyuncs.com/chatchat/chatchat:0.2.7 +``` + +🧩 本项目有一个非常完整的[Wiki](https://github.com/chatchat-space/Langchain-Chatchat/wiki/) ， README只是一个简单的介绍，__仅仅是入门教程，能够基础运行__。 如果你想要更深入的了解本项目，或者想对本项目做出贡献。请移步 [Wiki](https://github.com/chatchat-space/Langchain-Chatchat/wiki/) 界面 + +## 解决的痛点 + +该项目是一个可以实现 __完全本地化__推理的知识库增强方案, 重点解决数据安全保护，私域化部署的企业痛点。 +本开源方案采用```Apache License```，可以免费商用，无需付费。 + +我们支持市面上主流的本地大预言模型和Embedding模型，支持开源的本地向量数据库。 +支持列表详见[Wiki](https://github.com/chatchat-space/Langchain-Chatchat/wiki/) + + +## 快速上手 + +### 1. 环境配置 + ++ 首先，确保你的机器安装了 Python 3.8 - 3.10 +``` +$ python --version +Python 3.10.12 +``` +接着，创建一个虚拟环境，并在虚拟环境内安装项目的依赖 +```shell + +# 拉取仓库 +$ git clone https://github.com/chatchat-space/Langchain-Chatchat.git + +# 进入目录 +$ cd Langchain-Chatchat + +# 安装全部依赖 +$ pip install -r requirements.txt +$ pip install -r requirements_api.txt +$ pip install -r requirements_webui.txt + +# 默认依赖包括基本运行环境（FAISS向量库）。如果要使用 milvus/pg_vector 等向量库，请将 requirements.txt 中相应依赖取消注释再安装。 +``` +### 2， 模型下载 + +如需在本地或离线环境下运行本项目，需要首先将项目所需的模型下载至本地，通常开源 LLM 与 Embedding 模型可以从 [HuggingFace](https://huggingface.co/models) 下载。 + +以本项目中默认使用的 LLM 模型 [THUDM/ChatGLM2-6B](https://huggingface.co/THUDM/chatglm2-6b) 与 Embedding 模型 [moka-ai/m3e-base](https://huggingface.co/moka-ai/m3e-base) 为例： + +下载模型需要先[安装 Git LFS](https://docs.github.com/zh/repositories/working-with-files/managing-large-files/installing-git-large-file-storage)，然后运行 + +```Shell +$ git lfs install +$ git clone https://huggingface.co/THUDM/chatglm2-6b +$ git clone https://huggingface.co/moka-ai/m3e-base +``` +### 3. 初始化知识库和配置文件 + +按照下列方式初始化自己的知识库和简单的复制配置文件 +```shell +$ python copy_config_example.py +$ python init_database.py --recreate-vs + ``` +### 4. 一键启动 + +按照以下命令启动项目 +```shell +$ python startup.py -a +``` +### 5. 启动界面示例 + +如果正常启动，你将能看到以下界面 + +1. FastAPI Docs 界面 + +![](img/fastapi_docs_026.png) + +2. Web UI 启动界面示例： + +- Web UI 对话界面： + +![img](img/LLM_success.png) + +- Web UI 知识库管理页面： + +![](img/init_knowledge_base.jpg) + + +### 注意 + +以上方式只是为了快速上手，如果需要更多的功能和自定义启动方式 ，请参考[Wiki](https://github.com/chatchat-space/Langchain-Chatchat/wiki/) + + +--- +## 项目里程碑 + + +--- +## 联系我们 +### Telegram +[![Telegram](https://img.shields.io/badge/Telegram-2CA5E0?style=for-the-badge&logo=telegram&logoColor=white "langchain-chatglm")](https://t.me/+RjliQ3jnJ1YyN2E9) + +### 项目交流群 +二维码 + +🎉 Langchain-Chatchat 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。 + +### 公众号 + +二维码 + +🎉 Langchain-Chatchat 项目官方公众号，欢迎扫码关注。 diff --git a/chatchat/data/knowledge_base/samples/content/llm/img/分布式训练技术原理-幕布图片-124076-270516.jpg b/chatchat/chatchat/data/knowledge_base/samples/content/llm/img/分布式训练技术原理-幕布图片-124076-270516.jpg similarity index 100% rename from chatchat/data/knowledge_base/samples/content/llm/img/分布式训练技术原理-幕布图片-124076-270516.jpg rename to 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a/chatchat/data/knowledge_base/samples/content/test_files/langchain.pdf b/chatchat/chatchat/data/knowledge_base/samples/content/test_files/langchain.pdf similarity index 100% rename from chatchat/data/knowledge_base/samples/content/test_files/langchain.pdf rename to chatchat/chatchat/data/knowledge_base/samples/content/test_files/langchain.pdf diff --git a/chatchat/data/knowledge_base/samples/content/test_files/test.txt b/chatchat/chatchat/data/knowledge_base/samples/content/test_files/test.txt similarity index 100% rename from chatchat/data/knowledge_base/samples/content/test_files/test.txt rename to chatchat/chatchat/data/knowledge_base/samples/content/test_files/test.txt diff --git a/chatchat/chatchat/data/knowledge_base/samples/content/webui2.py b/chatchat/chatchat/data/knowledge_base/samples/content/webui2.py new file mode 100644 index 00000000..b2637adc --- /dev/null +++ b/chatchat/chatchat/data/knowledge_base/samples/content/webui2.py @@ -0,0 +1,237 @@ +from nicegui import ui, Client, app, run +from nicegui.events import ValueChangeEventArguments +from configs import (VERSION, LLM_MODELS, TEMPERATURE, HISTORY_LEN, + VECTOR_SEARCH_TOP_K, SEARCH_ENGINE_TOP_K) +from webui_pages.utils import AsyncApiRequest +import asyncio +from typing import Any, List, Dict, Any + + +app.add_static_files("/image", "img") + + +class Session: + def __init__(self) -> None: + user = app.storage.user + for k, v in self._attrs().items(): + user.setdefault(k, v) + + def _attrs(self) -> Dict[str, Any]: + return { + "messages": [], + "query": "", + "thinking": False, + "cur_kb": "", + "cur_temperature": TEMPERATURE, + "chat_list": [], + "cur_chat": "", + } + + @property + def user(self): + return app.storage.user + + def __getattr__(self, attr: str) -> Any: + if attr in self._attrs(): + return self.user[attr] + else: + raise AttributeError(attr) + + def __setattr__(self, attr: str, val: Any) -> None: + if attr in self._attrs(): + self.user[attr] = val + else: + raise AttributeError(attr) + + +def make_header(left_drawer, right_drawer): + with ui.header().classes("bg-black p-2") as header: + with ui.link(): + ui.icon("menu", size="md").on("click", lambda: left_drawer.toggle()) + ui.image("img/logo-long-chatchat-trans-v2.png").props("fit=scale-down").classes("h-8 w-48 float-left") + left_header = ui.row().props('id="left-header"') + ui.element("q-space") + right_header = ui.row().props('id="right-header"') + ui.label(f"(Version: {VERSION})").classes("text-grey text-xs pt-4") + with ui.link(): + ui.icon("menu", size="md").on("click", lambda: right_drawer.toggle()) + return left_header, right_header + + +def make_left_drawer(links: List, current: str): + with ui.left_drawer(bordered=True, elevated=True) as drawer: + return drawer + + +@ui.refreshable +async def output_messages(): + session = Session() + + for msg in session.messages: + is_user = msg["role"] == "user" + if is_user: + name = "User" + avatar = "/image/user_avatar.png" + else: + name = "AI" + avatar = "/image/chatchat_icon_blue_square_v2.png" + ele = ui.chat_message([], sent=False, name=None, avatar=avatar) + with ele.add_slot("default"): + ui.markdown(msg["content"]) + + ui.query("img.q-message-avatar").classes("self-start") + (ui.query("div.q-message-text--received") + .classes("bg-green-100") + .style("border-radius: 5px;")) + # (ui.query("div.q-message-text--received") + # .run_method("remove_classes", ["q-message-text--received"])) + # await ui.run_javascript("window.sc") + + +@ui.page("/", title="Langchain-Chatchat WebUI") +async def index(client: Client): + ui.add_head_html('''''') + + async def send(): + question = query.value.strip() + query.value = "" + + if not question: + return + + if question == "/clear": + session.messages = [] + output_messages.refresh() + return + + session.thinking = True + session.messages.append({"role": "user", "content": question}) + session.messages.append({"role": "assistant", "content": "Thinking..."}) + output_messages.refresh() + await asyncio.sleep(0.1) + + text = "" + async for chunk in api.chat_chat(question, + stream=True, + conversation_id=None, + model=cur_llm_model.value, + temperature=temperature.value): + text += chunk.get("text", "") + tail = " ▌" + if text.count("```") % 2 == 1: + if text[-1] != "`": + tail += "\n```\n" + elif text[-2:] == "``": + tail += "`\n" + elif text[-1:] == "`": + tail += "``\n" + session.messages[-1]["content"] = text + tail + output_messages.refresh() + await asyncio.sleep(0.1) + + session.messages[-1]["content"] = text + output_messages.refresh() + await asyncio.sleep(0.1) + session.thinking = False + + session = Session() + api = AsyncApiRequest() + + left_drawer = make_left_drawer([], "") + + with ui.right_drawer(bordered=True, elevated=True) as right_drawer: + ui.markdown("### 灵感大全") + user_name = ui.input("用户名称", value="用户") + system_message = (ui.input("AI系统消息", + value="你是一个聪明的人工智能助手，可以回答用户提出的问题。") + .props("autogrow")) + chat_image = ui.upload(label="上传图片").classes("w-full mt-5") + chat_file = ui.upload(label="上传文件").classes("w-full mt-5") + + left_header, right_header = make_header(left_drawer, right_drawer) + + with left_header: + chat_session = (ui.radio(["会话1", "会话2"], value="会话1") + .props("inline") + .classes("p-0")) + + with left_drawer: + ui.markdown("### 配置项") + + def on_chat_mode_change(e: ValueChangeEventArguments): + if e.value == "Agent对话": + session.cur_temperature = temperature.value + temperature.set_value(0.01) + else: + temperature.set_value(session.cur_temperature) + + chat_mode = ui.select(["LLM 对话", "知识库问答", "搜索引擎问答", "Agent对话"], + label="对话模式", + value="LLM 对话", + on_change=on_chat_mode_change, + ) + ui.separator() + + with ui.expansion("模型配置", icon="psychology", value=True): + running_models = await api.list_running_models() + config_models = await api.list_config_models() + models = {x: f"{x}(running)" for x in running_models} + for v in config_models.values(): + for m in v: + if m not in running_models: + models.update({m: m}) + cur_llm_model = ui.select(models, label="LLM模型", value=LLM_MODELS[0], with_input=True, clearable=True) + temperature = ui.number("Temperature", value=TEMPERATURE, min=0, max=1, step=0.01) + history_len = ui.number("历史对话轮数", value=HISTORY_LEN, min=0, max=10) + + with (ui.expansion("知识库配置", icon="book", value=True) + .bind_visibility_from(chat_mode, "value", value="知识库问答")): + def on_kb_change(e: ValueChangeEventArguments): + session.cur_kb = e.value + + kb_names = await api.list_knowledge_bases() + kb_name = ui.select(kb_names, + label="知识库", + value=session.cur_kb or kb_names[0], + on_change=on_kb_change, + ) + vector_top_k = ui.number("Top K", value=VECTOR_SEARCH_TOP_K, min=1, max=10) + + with (ui.expansion("搜索引擎配置", icon="travel_explore", value=True) + .bind_visibility_from(chat_mode, "value", value="搜索引擎问答")): + search_engine = ui.select(["Bing", "Duckduckgo"], value="Bing") + search_top_k = ui.number("Top K", value=SEARCH_ENGINE_TOP_K, min=1, max=10) + + await client.connected() + with ui.column(): + await output_messages() + + with ui.row().classes("absolute bottom-2 left-20 right-20"): + # command = ui.select(["/clear", "/upload"]).classes("w-1/4") + query = (ui.input(autocomplete=["/clear", "/upload"], + placeholder="input your question here.") + .classes("flex-grow") + .props('autogrow outlined autofocus counter dense clearable') + .bind_value(session, "query") + .on("keydown.enter.prevent", send) + ) + with query.add_slot("after"): + ui.button(icon="send", on_click=send).classes("self-center").props("small dense p-0 m-0") + # query._props["autofocus"] = True + # query._props["autogrow"] = True + # query._props["placeholder"] = "input your question here." + # query._props[":list"] = '["/clear", "/upload"]' + # query._props["shadow-text"] = ["/clear", "/upload"] + # ui.input(autocomplete=["/clear", "/upload"]) + + + +# TODO: +# 右侧栏上下文：system_message, picture, file, 知识库文档预览 + + +if __name__ in {"__main__", "__mp_main__"}: + ui.run(port=5000, storage_secret="111111", reload=True) diff --git a/chatchat/chatchat/data/knowledge_base/samples/content/wiki/Home.md b/chatchat/chatchat/data/knowledge_base/samples/content/wiki/Home.md new file mode 100644 index 00000000..71a324cb --- /dev/null +++ b/chatchat/chatchat/data/knowledge_base/samples/content/wiki/Home.md @@ -0,0 +1,74 @@ + +![](https://github.com/chatchat-space/Langchain-Chatchat/blob/master/img/logo-long-chatchat-trans-v2.png) + +> 欢迎来到 Langchain‐Chatchat 的 Wiki , 在这里开启 Langchain 与大模型的邂逅! + + +## 项目简介 + +📃 **LangChain-Chatchat** (原 Langchain-ChatGLM): 基于 Langchain 与 ChatGLM 等大语言模型的本地知识库问答应用实现。 + +🤖️ 一种利用 [langchain](https://github.com/hwchase17/langchain) 思想实现的基于本地知识库的问答应用，目标期望建立一套对中文场景与开源模型支持友好、可离线运行的知识库问答解决方案。 + +💡 受 [GanymedeNil](https://github.com/GanymedeNil) 的项目 [document.ai](https://github.com/GanymedeNil/document.ai) 和 [AlexZhangji](https://github.com/AlexZhangji) 创建的 [ChatGLM-6B Pull Request](https://github.com/THUDM/ChatGLM-6B/pull/216) 启发，建立了全流程可使用开源模型实现的本地知识库问答应用。本项目的最新版本中通过使用 [FastChat](https://github.com/lm-sys/FastChat) 接入 Vicuna, Alpaca, LLaMA, Koala, RWKV 等模型，依托于 [langchain](https://github.com/langchain-ai/langchain) 框架支持通过基于 [FastAPI](https://github.com/tiangolo/fastapi) 提供的 API 调用服务，或使用基于 [Streamlit](https://github.com/streamlit/streamlit) 的 WebUI 进行操作。 + +✅ 依托于本项目支持的开源 LLM 与 Embedding 模型，本项目可实现全部使用**开源**模型**离线私有部署**。与此同时，本项目也支持 OpenAI GPT API 的调用，并将在后续持续扩充对各类模型及模型 API 的接入。 + +⛓️ 本项目实现原理如下图所示，过程包括加载文件 -> 读取文本 -> 文本分割 -> 文本向量化 -> 问句向量化 -> 在文本向量中匹配出与问句向量最相似的 `top k`个 -> 匹配出的文本作为上下文和问题一起添加到 `prompt`中 -> 提交给 `LLM`生成回答。 + + +## 算法流程 + +大家可以前往Bilibili平台查看原理介绍视频： + +📺 [原理介绍视频](https://www.bilibili.com/video/BV13M4y1e7cN/?share_source=copy_web&vd_source=e6c5aafe684f30fbe41925d61ca6d514) + +开发组也为大家绘制了一张实现原理图，效果如下： + +![实现原理图](https://github.com/chatchat-space/Langchain-Chatchat/blob/master/img/langchain+chatglm.png) + +从文档处理角度来看，实现流程如下： + +![实现原理图2](https://github.com/chatchat-space/Langchain-Chatchat/blob/master/img/langchain+chatglm2.png) + + +## 技术路线图（截止0.2.10） + +- [X] Langchain 应用 + - [X] 本地数据接入 + - [X] 接入非结构化文档 + - [X] .txt, .rtf, .epub, .srt + - [X] .eml, .msg + - [X] .html, .xml, .toml, .mhtml + - [X] .json, .jsonl + - [X] .md, .rst + - [X] .docx, .doc, .pptx, .ppt, .odt + - [X] .enex + - [X] .pdf + - [X] .jpg, .jpeg, .png, .bmp + - [X] .py, .ipynb + - [X] 结构化数据接入 + - [X] .csv, .tsv + - [X] .xlsx, .xls, .xlsd + - [X] 分词及召回 + - [X] 接入不同类型 TextSplitter + - [X] 优化依据中文标点符号设计的 ChineseTextSplitter + - [X] 搜索引擎接入 + - [X] Bing 搜索 + - [X] DuckDuckGo 搜索 + - [X] Metaphor 搜索 + - [X] Agent 实现 + - [X] 基础React形式的Agent实现，包括调用计算器等 + - [X] Langchain 自带的Agent实现和调用 + - [X] 智能调用不同的数据库和联网知识 +- [X] LLM 模型接入 + - [X] 支持通过调用 [FastChat](https://github.com/lm-sys/fastchat) api 调用 llm + - [X] 支持 ChatGLM API 等 LLM API 的接入 + - [X] 支持 Langchain 框架支持的LLM API 接入 +- [X] Embedding 模型接入 + - [X] 支持调用 HuggingFace 中各开源 Emebdding 模型 + - [X] 支持 OpenAI Embedding API 等 Embedding API 的接入 + - [X] 支持 智谱AI、百度千帆、千问、MiniMax 等在线 Embedding API 的接入 +- [X] 基于 FastAPI 的 API 方式调用 +- [X] Web UI + - [X] 基于 Streamlit 的 Web UI \ No newline at end of file diff --git a/chatchat/chatchat/data/knowledge_base/samples/content/wiki/_Sidebar.md b/chatchat/chatchat/data/knowledge_base/samples/content/wiki/_Sidebar.md new file mode 100644 index 00000000..6d7fb6f6 --- /dev/null +++ b/chatchat/chatchat/data/knowledge_base/samples/content/wiki/_Sidebar.md @@ -0,0 +1,58 @@ +__导航栏，一切从这里出发__ +## [Home](https://github.com/chatchat-space/Langchain-Chatchat/wiki) +## [支持列表](https://github.com/chatchat-space/Langchain-Chatchat/wiki/%E6%94%AF%E6%8C%81%E5%88%97%E8%A1%A8) +* [LLM 模型支持列表](https://github.com/chatchat-space/Langchain-Chatchat/wiki/%E6%94%AF%E6%8C%81%E5%88%97%E8%A1%A8#llm-%E6%A8%A1%E5%9E%8B%E6%94%AF%E6%8C%81%E5%88%97%E8%A1%A8) +* [Embedding 模型支持列表](https://github.com/chatchat-space/Langchain-Chatchat/wiki/%E6%94%AF%E6%8C%81%E5%88%97%E8%A1%A8#embedding-%E6%A8%A1%E5%9E%8B%E6%94%AF%E6%8C%81%E5%88%97%E8%A1%A8) +* [分词器支持列表](https://github.com/chatchat-space/Langchain-Chatchat/wiki/%E6%94%AF%E6%8C%81%E5%88%97%E8%A1%A8#%E5%88%86%E8%AF%8D%E5%99%A8%E6%94%AF%E6%8C%81%E5%88%97%E8%A1%A8) +* [向量数据库支持列表](https://github.com/chatchat-space/Langchain-Chatchat/wiki/%E6%94%AF%E6%8C%81%E5%88%97%E8%A1%A8#%E5%90%91%E9%87%8F%E6%95%B0%E6%8D%AE%E5%BA%93%E6%94%AF%E6%8C%81%E5%88%97%E8%A1%A8) +* [工具支持列表](https://github.com/chatchat-space/Langchain-Chatchat/wiki/%E6%94%AF%E6%8C%81%E5%88%97%E8%A1%A8#%E5%B7%A5%E5%85%B7%E6%94%AF%E6%8C%81%E5%88%97%E8%A1%A8) + +## [开发环境部署](https://github.com/chatchat-space/Langchain-Chatchat/wiki/%E5%BC%80%E5%8F%91%E7%8E%AF%E5%A2%83%E9%83%A8%E7%BD%B2) + +### 前期准备 + * [软件要求](https://github.com/chatchat-space/Langchain-Chatchat/wiki/%E5%BC%80%E5%8F%91%E7%8E%AF%E5%A2%83%E9%83%A8%E7%BD%B2#%E8%BD%AF%E4%BB%B6%E8%A6%81%E6%B1%82) + * [硬件要求](https://github.com/chatchat-space/Langchain-Chatchat/wiki/%E5%BC%80%E5%8F%91%E7%8E%AF%E5%A2%83%E9%83%A8%E7%BD%B2#%E7%A1%AC%E4%BB%B6%E8%A6%81%E6%B1%82) + * [VPN](https://github.com/chatchat-space/Langchain-Chatchat/wiki/%E5%BC%80%E5%8F%91%E7%8E%AF%E5%A2%83%E9%83%A8%E7%BD%B2#vpn) + +### 部署代码 + * [Docker 部署](https://github.com/chatchat-space/Langchain-Chatchat/wiki/%E5%BC%80%E5%8F%91%E7%8E%AF%E5%A2%83%E9%83%A8%E7%BD%B2#docker-%E9%83%A8%E7%BD%B2) + * [最轻模式部署方案](https://github.com/chatchat-space/Langchain-Chatchat/wiki/%E5%BC%80%E5%8F%91%E7%8E%AF%E5%A2%83%E9%83%A8%E7%BD%B2#%E6%9C%80%E8%BD%BB%E6%A8%A1%E5%BC%8F%E6%9C%AC%E5%9C%B0%E9%83%A8%E7%BD%B2%E6%96%B9%E6%A1%88) + * [常规模式本地部署方案](https://github.com/chatchat-space/Langchain-Chatchat/wiki/%E5%BC%80%E5%8F%91%E7%8E%AF%E5%A2%83%E9%83%A8%E7%BD%B2#%E5%B8%B8%E8%A7%84%E6%A8%A1%E5%BC%8F%E6%9C%AC%E5%9C%B0%E9%83%A8%E7%BD%B2%E6%96%B9%E6%A1%88) + + [环境安装](https://github.com/chatchat-space/Langchain-Chatchat/wiki/%E5%BC%80%E5%8F%91%E7%8E%AF%E5%A2%83%E9%83%A8%E7%BD%B2#%E6%9C%AC%E5%9C%B0%E9%83%A8%E7%BD%B2%E7%8E%AF%E5%A2%83%E5%AE%89%E8%A3%85) + + [模型下载](https://github.com/chatchat-space/Langchain-Chatchat/wiki/%E5%BC%80%E5%8F%91%E7%8E%AF%E5%A2%83%E9%83%A8%E7%BD%B2#%E6%A8%A1%E5%9E%8B%E4%B8%8B%E8%BD%BD) + + [初始化知识库](https://github.com/chatchat-space/Langchain-Chatchat/wiki/%E5%BC%80%E5%8F%91%E7%8E%AF%E5%A2%83%E9%83%A8%E7%BD%B2#%E5%88%9D%E5%A7%8B%E5%8C%96%E7%9F%A5%E8%AF%86%E5%BA%93) + + [一键启动](https://github.com/chatchat-space/Langchain-Chatchat/wiki/%E5%BC%80%E5%8F%91%E7%8E%AF%E5%A2%83%E9%83%A8%E7%BD%B2#%E4%B8%80%E9%94%AE%E5%90%AF%E5%8A%A8) + + [多卡加载](https://github.com/chatchat-space/Langchain-Chatchat/wiki/%E5%BC%80%E5%8F%91%E7%8E%AF%E5%A2%83%E9%83%A8%E7%BD%B2#%E5%A4%9A%E5%8D%A1%E5%8A%A0%E8%BD%BD) + +## [参数配置](https://github.com/chatchat-space/Langchain-Chatchat/wiki/%E5%8F%82%E6%95%B0%E9%85%8D%E7%BD%AE) + +* [基础配置项 basic_config.py](https://github.com/chatchat-space/Langchain-Chatchat/wiki/%E5%8F%82%E6%95%B0%E9%85%8D%E7%BD%AE#%E5%9F%BA%E7%A1%80%E9%85%8D%E7%BD%AE%E9%A1%B9-basic_configpy) +* [模型配置项 model_config.py](https://github.com/chatchat-space/Langchain-Chatchat/wiki/%E5%8F%82%E6%95%B0%E9%85%8D%E7%BD%AE#%E6%A8%A1%E5%9E%8B%E9%85%8D%E7%BD%AE%E9%A1%B9-model_configpy) +* [提示词配置项 prompt_config.py](https://github.com/chatchat-space/Langchain-Chatchat/wiki/%E5%8F%82%E6%95%B0%E9%85%8D%E7%BD%AE#%E6%8F%90%E7%A4%BA%E8%AF%8D%E9%85%8D%E7%BD%AE%E9%A1%B9-prompt_configpy) +* [数据库配置 kb_config.py](https://github.com/chatchat-space/Langchain-Chatchat/wiki/%E5%8F%82%E6%95%B0%E9%85%8D%E7%BD%AE#%E6%95%B0%E6%8D%AE%E5%BA%93%E9%85%8D%E7%BD%AE-kb_configpy) +* [服务和端口配置项 server_config.py](https://github.com/chatchat-space/Langchain-Chatchat/wiki/%E5%8F%82%E6%95%B0%E9%85%8D%E7%BD%AE#%E6%9C%8D%E5%8A%A1%E5%92%8C%E7%AB%AF%E5%8F%A3%E9%85%8D%E7%BD%AE%E9%A1%B9-server_configpy) +* [覆盖配置文件 或者配置 startup.py](https://github.com/chatchat-space/Langchain-Chatchat/wiki/%E5%8F%82%E6%95%B0%E9%85%8D%E7%BD%AE#%E8%A6%86%E7%9B%96%E9%85%8D%E7%BD%AE%E6%96%87%E4%BB%B6-%E6%88%96%E8%80%85%E9%85%8D%E7%BD%AE-startuppy) + +## [自定义](https://github.com/chatchat-space/Langchain-Chatchat/wiki/%E8%87%AA%E5%AE%9A%E4%B9%89) + +* [使用自定义的分词器](https://github.com/chatchat-space/Langchain-Chatchat/wiki/%E8%87%AA%E5%AE%9A%E4%B9%89#%E4%BD%BF%E7%94%A8%E8%87%AA%E5%AE%9A%E4%B9%89%E7%9A%84%E5%88%86%E8%AF%8D%E5%99%A8) +* [使用自定义的 Agent 工具](https://github.com/chatchat-space/Langchain-Chatchat/wiki/%E8%87%AA%E5%AE%9A%E4%B9%89#%E4%BD%BF%E7%94%A8%E8%87%AA%E5%AE%9A%E4%B9%89%E7%9A%84-agent-%E5%B7%A5%E5%85%B7) +* [使用自定义的微调模型](https://github.com/chatchat-space/Langchain-Chatchat/wiki/%E8%87%AA%E5%AE%9A%E4%B9%89#%E4%BD%BF%E7%94%A8%E8%87%AA%E5%AE%9A%E4%B9%89%E7%9A%84%E5%BE%AE%E8%B0%83%E6%A8%A1%E5%9E%8B) +* [使用自定义的嵌入模型](https://github.com/chatchat-space/Langchain-Chatchat/wiki/%E8%87%AA%E5%AE%9A%E4%B9%89#%E4%BD%BF%E7%94%A8%E8%87%AA%E5%AE%9A%E4%B9%89%E7%9A%84%E5%B5%8C%E5%85%A5%E6%A8%A1%E5%9E%8B) +* [日志功能](https://github.com/chatchat-space/Langchain-Chatchat/wiki/%E6%9C%80%E4%BD%B3%E5%AE%9E%E8%B7%B5#%E6%97%A5%E5%BF%97%E5%8A%9F%E8%83%BD) + +## [最佳实践](https://github.com/chatchat-space/Langchain-Chatchat/wiki/%E6%9C%80%E4%BD%B3%E5%AE%9E%E8%B7%B5) +* [推荐的模型组合](https://github.com/chatchat-space/Langchain-Chatchat/wiki/%E6%9C%80%E4%BD%B3%E5%AE%9E%E8%B7%B5#%E6%8E%A8%E8%8D%90%E7%9A%84%E6%A8%A1%E5%9E%8B%E7%BB%84%E5%90%88) +* [微调模型加载实操](https://github.com/chatchat-space/Langchain-Chatchat/wiki/%E6%9C%80%E4%BD%B3%E5%AE%9E%E8%B7%B5#%E5%BE%AE%E8%B0%83%E6%A8%A1%E5%9E%8B%E5%8A%A0%E8%BD%BD%E5%AE%9E%E6%93%8D) +* [预处理知识库文件](https://github.com/chatchat-space/Langchain-Chatchat/wiki/%E6%9C%80%E4%BD%B3%E5%AE%9E%E8%B7%B5#%E9%A2%84%E5%A4%84%E7%90%86%E7%9F%A5%E8%AF%86%E5%BA%93%E6%96%87%E4%BB%B6) +* [自定义的关键词调整Embedding模型](https://github.com/chatchat-space/Langchain-Chatchat/wiki/%E6%9C%80%E4%BD%B3%E5%AE%9E%E8%B7%B5#%E8%87%AA%E5%AE%9A%E4%B9%89%E7%9A%84%E5%85%B3%E9%94%AE%E8%AF%8D%E8%B0%83%E6%95%B4embedding%E6%A8%A1%E5%9E%8B) +* [实际使用效果](https://github.com/chatchat-space/Langchain-Chatchat/wiki/%E6%9C%80%E4%BD%B3%E5%AE%9E%E8%B7%B5#%E5%AE%9E%E9%99%85%E4%BD%BF%E7%94%A8%E6%95%88%E6%9E%9C) + +## [做出贡献](https://github.com/chatchat-space/Langchain-Chatchat/wiki/%E5%81%9A%E5%87%BA%E8%B4%A1%E7%8C%AE) + +* [Issue 规范](https://github.com/chatchat-space/Langchain-Chatchat/wiki/%E5%81%9A%E5%87%BA%E8%B4%A1%E7%8C%AE#issue-%E8%A7%84%E8%8C%83) +* [PR 规范](https://github.com/chatchat-space/Langchain-Chatchat/wiki/%E5%81%9A%E5%87%BA%E8%B4%A1%E7%8C%AE#pr-%E8%A7%84%E8%8C%83) + +## [合作伙伴](https://github.com/chatchat-space/Langchain-Chatchat/wiki/%E5%90%88%E4%BD%9C%E4%BC%99%E4%BC%B4) + +## [常见问题](https://github.com/chatchat-space/Langchain-Chatchat/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98) \ No newline at end of file diff --git a/chatchat/chatchat/data/knowledge_base/samples/content/wiki/做出贡献.md b/chatchat/chatchat/data/knowledge_base/samples/content/wiki/做出贡献.md new file mode 100644 index 00000000..50d73b7f --- /dev/null +++ b/chatchat/chatchat/data/knowledge_base/samples/content/wiki/做出贡献.md @@ -0,0 +1,51 @@ +## Issue 规范 +> 什么样的 issue 是不会被回复的 + +1. 在提出issue前，请查看您的提出的问题是否已经在 issue 列表或者 discussion 内出现，提出重复的问题将 **被关闭** 。 +2. 非项目推荐配置的任何关于环境配置问题的 issue 通常将 **不会由官方回复**，请您在微信沟通群内咨询。 +3. 与项目无关的 issue 将 **不会被回复** 。 +4. 超过30天没有更新动态的 issue 将 **被关闭** 。 +5. 语言非中文和英语的 issue 将 **被关闭** 。 +6. 没有尝试过解决方案的 issue 将 **被关闭** 。 +7. 没有提出任何贡献（例如PR，论文）的 feature / enhancement 将会 **被关闭** 。您可以在 discussion 中的 **希望开发的功能** 讨论区中留言，我们开发组会进行回复。 +8. 不按照 Issue 规范提出的 issue 可能将 **被关闭** 。 + +> 如何提 issue + +1. 简要阐述你的问题 +2. 配上报错日志以(运行报错)或者运行不理想的效果图(原本期望和现实的) +3. 配上对应的配置文件以你的环境 +4. 你尝试过的解决方法。（非常重要） +5. 按照模板提出Issue + +## PR 规范 + +> 什么样的 PR 是不会被接受的 +1. 非紧急bug修复的PR并直接提交到```master```的PR。 +2. 仅仅修改```Readme.md```和```配置文件```的。 +3. 跟项目组已经开发的内容冲突的(dev版本)，将可能被拒绝。 + +首先请注意所有的PR需要以dev分支为基准，master分支仅用来发行与紧急bug修复。 + +> 提出新的通用自定义分词器 + +1. 将您的分词器所在的代码文件放在```text_splitter```文件夹下，文件名为您的分词器名字`my_splitter.py`，然后在`__init__.py`中导入您的分词器。 +2. 发起PR，并说明您的分词器面向的场景或者改进之处。我们非常期待您能举例一个具体的应用场景。 + +> 提出新的 Agent 工具 + +1. 将您的Agent工具所在的代码放在 ```server/agent```文件夹下，文件名为您的工具名字`my_tools.py`，然后在`tools.py`中导入您的工具。 +2. 发起PR，说明您的工具面向的场景或改进之处，并说明如何进行测试和调用。我们非常期待您能举例一个具体的应用场景。 + +> 提出新的自定义模型 + +1. 将您的模型贡献到huggingface平台上，并开放给开发人员下载。 +2. 发起PR，说明您的工具面向的场景或改进之处，并说明如何进行测试和调用。我们非常期待您能举例一个具体的应用场景。 +3. 由开发人员测试通过后，将您的模型添加到合作模型名单中。 + + +> 修复 Bug & 增加其他新功能 + +1. 一个 PR 中必须 **只有一个或者一类功能增加，或者修复一个bug** ，多个功能混合的 PR 将 **不会被接受** 。 +2. 说明您增加的功能或者改进之处，并说明如何进行测试和调用。我们非常期待您能举例一个具体的应用场景。 + diff --git a/chatchat/chatchat/data/knowledge_base/samples/content/wiki/参数配置.md b/chatchat/chatchat/data/knowledge_base/samples/content/wiki/参数配置.md new file mode 100644 index 00000000..23408613 --- /dev/null +++ b/chatchat/chatchat/data/knowledge_base/samples/content/wiki/参数配置.md @@ -0,0 +1,156 @@ +在开始参数配置之前，先执行以下脚本 +```shell +python copy_config_example.py +``` +该脚本将会将所有```config```目录下的配置文件样例复制一份到```config```目录下，方便开发者进行配置。 +接着，开发者可以根据自己的需求，对配置文件进行修改。 + +## 基础配置项 basic_config.py +该配置基负责记录日志的格式和储存路径，通常不需要修改。 + +## 模型配置项 model_config.py +本文件包含本地LLM模型、本地Embeddings模型、在线LLM模型API的相关配置。 + +- 本地模型路径配置。建议将所有下载的模型放到一个统一的目录下，然后将`MODEL_ROOT_PATH`指定为该目录，只要模型目录名称符合下列情况之一（以text2vec为例），即可自动识别加载： + - text2vec，即MODEL_PATH中的键 + - GanymedeNil/text2vec-large-chinese，即MODEL_PATH中的值 + - text2vec-large-chinese，即MODEL_PATH中的值的简写形式 + +- 在线模型API配置。在`ONLINE_LLM_MODEL`已经预先写好了所有支持的在线API服务，通常只需要把申请的API_KEY等填入即可。 +有些在线API服务需要安装额外的依赖： + - zhipu-api: zhipuai + - fangzhou-api: volcengine>=1.0.106 + - qianfan-api: qianfan + - qwen-api: dashscope + +- HISTORY_LEN。历史对话轮数通常不建议设置超过10，因为这可能导致以下问题 + 1. 显存占用过高：尤其是部分模型，本身就已经要占用满显存的情况下，保留太多历史，一次传入token太多，可能会爆显存。 + 2. 速度处理很慢：还是因为一次传入了太多token，导致速度很慢。 + +- TEMPERATURE。通常不建议设置过高。 +在Agent对话模式和知识库问答中，我们强烈建议将要其设置成0或者接近于0。 + +- Agent_MODEL = None +我们支持用户使用“模型接力赛”的用法，即： +选择的大模型仅能调用工具，但是在工具中表现较差，则这个工具作为 “模型调用工具” +如果用户设置了Agent_MODEL，则在 Agent 中，使用Agent_MODEL来执行任务，否则，使用LLM_MODEL + + +## 提示词配置项 prompt_config.py + +提示词配置分为三个板块，分别对应三种聊天类型。 +- llm_chat: 基础的对话提示词， 通常来说，直接是用户输入的内容，没有系统提示词。 +- knowledge_base_chat: 与知识库对话的提示词，在模板中，我们为开发者设计了一个系统提示词，开发者可以自行更改。 +- agent_chat: 与Agent对话的提示词，同样，我们为开发者设计了一个系统提示词，开发者可以自行更改。 + +prompt模板使用Jinja2语法，简单点就是用双大括号代替f-string的单大括号 +请注意，本配置文件支持热加载，修改prompt模板后无需重启服务。 + +## 数据库配置 kb_config.py +请确认本地分词器路径是否已经填写，如： + +``` +text_splitter_dict = { + "ChineseRecursiveTextSplitter": { + "source":"huggingface", # 选择tiktoken则使用openai的方法,不填写则默认为字符长度切割方法。 + "tokenizer_name_or_path":"", # 空格不填则默认使用大模型的分词器。 + } +} +``` +设置好的分词器需要再```TEXT_SPLITTER_NAME```中指定并应用。 + +在这里，通常使用```huggingface```的方法，并且，我们推荐使用大模型自带的分词器来完成任务。 + +请注意，使用```gpt2```分词器将要访问huggingface官网下载权重。 + +我们还支持使用```tiktoken``` 和传统的 按照长度分词的方式，开发者可以自行配置。 + +如果希望调用自己的分词器，请参考[最佳实践]部分。 + +```kbs_config```设置了使用的向量数据库，目前可以选择 +- ```faiss```: 使用faiss数据库，需要安装faiss-gpu +- ```milvus```: 使用milvus数据库，需要安装milvus并进行端口配置 +- ```pg```: 使用pg数据库，需要配置connection_uri + +## 服务和端口配置项 server_config.py + +通常，这个页面并不需要进行大量的修改，仅需确保对应的端口打开，并不互相冲突即可。 + +如果你是Linux系统推荐设置 + +``` +DEFAULT_BIND_HOST ="0.0.0.0" +``` +如果使用联网模型，则需要关注联网模型的端口。 + +这些模型必须是在model_config.MODEL_PATH或ONLINE_MODEL中正确配置的。 + +#在启动startup.py时，可用通过`--model-worker --model-name xxxx`指定模型，不指定则为LLM_MODEL + + +## 覆盖配置文件 或者配置 startup.py + +在 ```server_config.py```中有以下配置文件被注释了 + +``` +"gpus": None, # 使用的GPU，以str的格式指定，如"0,1"，如失效请使用CUDA_VISIBLE_DEVICES="0,1"等形式指定 +"num_gpus": 1, # 使用GPU的数量 +"max_gpu_memory":"20GiB", # 每个GPU占用的最大显存 + + 以下为model_worker非常用参数，可根据需要配置 +"load_8bit": False, # 开启8bit量化 +"cpu_offloading": None, +"gptq_ckpt": None, +"gptq_wbits": 16, +"gptq_groupsize": -1, +"gptq_act_order": False, +"awq_ckpt": None, +"awq_wbits": 16, +"awq_groupsize": -1, +"model_names": [LLM_MODEL], +"conv_template": None, +"limit_worker_concurrency": 5, +"stream_interval": 2, +"no_register": False, +"embed_in_truncate": False, + + 以下为vllm_woker配置参数,注意使用vllm必须有gpu，仅在Linux测试通过 + + tokenizer = model_path # 如果tokenizer与model_path不一致在此处添加 + 'tokenizer_mode':'auto', + 'trust_remote_code':True, + 'download_dir':None, + 'load_format':'auto', + 'dtype':'auto', + 'seed':0, + 'worker_use_ray':False, + 'pipeline_parallel_size':1, + 'tensor_parallel_size':1, + 'block_size':16, + 'swap_space':4 , # GiB + 'gpu_memory_utilization':0.90, + 'max_num_batched_tokens':2560, + 'max_num_seqs':256, + 'disable_log_stats':False, + 'conv_template':None, + 'limit_worker_concurrency':5, + 'no_register':False, + 'num_gpus': 1 + 'engine_use_ray': False, + 'disable_log_requests': False +``` + +在这些参数中，如果没有设置，则使用```startup.py```中的默认值，如果设置了，则使用设置的值。 +因此，强烈建议开发不要在```startup.py```中进行配置，而应该在```server_config.py```中进行配置。避免配置文件覆盖。 + +## 选择使用的模型 +在```model_config.py```完成模型配置后，还不能直接使用，需要在该文件下配置本地模型的运行方式或在线模型的API，例如 +``` + "agentlm-7b": { # 使用default中的IP和端口 + "device": "cuda", + }, + "zhipu-api": { # 请为每个要运行的在线API设置不同的端口 + "port": 21001, + }, +``` +本地模型使用default中的IP和端口，在线模型可以自己选择端口 \ No newline at end of file diff --git a/chatchat/chatchat/data/knowledge_base/samples/content/wiki/合作伙伴.md b/chatchat/chatchat/data/knowledge_base/samples/content/wiki/合作伙伴.md new file mode 100644 index 00000000..e84b59c8 --- /dev/null +++ b/chatchat/chatchat/data/knowledge_base/samples/content/wiki/合作伙伴.md @@ -0,0 +1,37 @@ +## 合作伙伴名单 +🎉 Langchain-Chatchat 项目合作伙伴，感谢以下合作伙伴对本项目的支持。 + + + + + + + + + + + + + + + + + + +
+ ChatGLM Logo + + ChatGLM: 国内最早的开源中文大模型之一 +
+ ChatGLM Logo + + AutoDL 提供弹性、好用、省钱的云GPU租用服务。缺显卡就上 AutoDL.com +
+ ChatGLM Logo + + 全球云计算领导者 +
+ Zhenge Logo + + 我们相信预测未来的最好方式是自己来创造。我们在这里等你。 +
diff --git a/chatchat/chatchat/data/knowledge_base/samples/content/wiki/常见问题.md b/chatchat/chatchat/data/knowledge_base/samples/content/wiki/常见问题.md new file mode 100644 index 00000000..9203d521 --- /dev/null +++ b/chatchat/chatchat/data/knowledge_base/samples/content/wiki/常见问题.md @@ -0,0 +1,277 @@ +> 以下是一些常见的问题和回答 +#### Q: 我要提出问题，怎么办 + +A: 首先，你要观察一下你的问题是否有没有被解决，建议翻看以往的Issue和Discussion，如果有，先按照他们的方法来做。 +如果没有，按照以下步骤 +1. 这是一个bug还是一个讨论问题，如果是讨论问题，放在disscusion，如果是bug和feature，放在issue。 +2. 如果要提出feature，提交一份对应的PR会让开发者更重视你的问题，否则你的问题很有可能被直接关闭。 + +#### Q: ValueError: Found modules on cpu/disk. Using Exllama backend requires all the modules to be on GPU. You can deactivate exllama backend by setting disable_exllama=True in the quantization config object. + +A: 这是Fschat依赖源码的问题，请查看以下解决方式，通过修改'Fschat'库中的对应内容。 + +https://github.com/lm-sys/FastChat/issues/2459 + +https://stackoverflow.com/questions/76983305/fine-tuning-thebloke-llama-2-13b-chat-gptq-model-with-hugging-face-transformers + +--- + +#### Q: AttributeError: 'ChatGLMTokenizer' object has no attribute 'tokenizer' + +A: 查看以下Issue + +https://github.com/chatchat-space/Langchain-Chatchat/issues/1835 + +--- + +#### Q: 使用Qwen API key 报错 multiple wodgets with the same key＝“ + +A: 确保你的key是`dashscope`平台的key。并保证`dashscope`依赖满足我们的依赖版本。 + +--- + +#### Q：linux下向量化PDF文件时出错：`ImportError: 从文件 *.pdf 加载文档时出错：libGL.so.1: cannot open shared object file: No such file or directory` + +A： 这是系统缺少必要的动态库，可以手动安装：`libgl1-mesa-glx` 和 `libglib2.0-0` + +--- + +#### Q: 各种Int4模型无法载入 +A. 由于各种Int4模型与Fp16模型并不相似，且量化技术可能有所不同，无法载入可能是因为fschat不支持或者缺少对应的依赖，需要查看对应仓库的issue获得更多信息。开发组没有针对Int4模型进行优化。 + +--- + +#### Q1: 本项目支持哪些文件格式？ + +A1: 目前已测试支持 txt、docx、md、pdf、csv、html、json 等格式文件 + +更多文件格式请参考 [langchain 文档](https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/unstructured_file.html)。目前已知文档中若含有特殊字符，可能存在文件无法加载的问题。 + +--- + +#### Q2: 使用过程中 Python 包 `nltk`发生了 `Resource punkt not found.`报错，该如何解决？ + +A2: 方法一：https://github.com/nltk/nltk_data/raw/gh-pages/packages/tokenizers/punkt.zip 中的 `packages/tokenizers` 解压，放到 `nltk_data/tokenizers` 存储路径下。 + +`nltk_data` 存储路径可以通过 `nltk.data.path` 查询。 + +方法二：执行python代码 + +``` +import nltk +nltk.download() +``` + +--- + +#### Q3: 使用过程中 Python 包 `nltk`发生了 `Resource averaged_perceptron_tagger not found.`报错，该如何解决？ + +A3: + +方法一：将 https://github.com/nltk/nltk_data/blob/gh-pages/packages/taggers/averaged_perceptron_tagger.zip 下载，解压放到 `nltk_data/taggers` 存储路径下。 + +`nltk_data` 存储路径可以通过 `nltk.data.path` 查询。 + +方法二：执行python代码 + +``` +import nltk +nltk.download() +``` + +--- + +#### Q4: 本项目可否在 colab 中运行？ + +A4: 可以尝试使用 chatglm-6b-int4 模型在 colab 中运行，需要注意的是，如需在 colab 中运行 Web UI，需将 `webui.py`中 `demo.queue(concurrency_count=3).launch( server_name='0.0.0.0', share=False, inbrowser=False)`中参数 `share`设置为 `True`。 + +--- + +#### Q5: 在 Anaconda 中使用 pip 安装包无效如何解决？ + +A5: 此问题是系统环境问题，详细见 [在Anaconda中使用pip安装包无效问题](在Anaconda中使用pip安装包无效问题.md) + +--- + +#### Q6: 本项目中所需模型如何下载至本地？ + +A6: 本项目中使用的模型均为 `huggingface.com` 中可下载的开源模型，以默认选择的 `chatglm-6b`和 `text2vec-large-chinese`模型为例，下载模型可执行如下代码： + +```shell +# 安装 git lfs +$ git lfs install + +# 下载 LLM 模型 +$ git clone https://huggingface.co/THUDM/chatglm-6b /your_path/chatglm-6b + +# 下载 Embedding 模型 +$ git clone https://huggingface.co/GanymedeNil/text2vec-large-chinese /your_path/text2vec + +# 模型需要更新时，可打开模型所在文件夹后拉取最新模型文件/代码 +$ git pull +``` + +--- + +#### Q7: `huggingface.com`中模型下载速度较慢怎么办？ + +A7: 可使用本项目用到的模型权重文件百度网盘地址： + +- ernie-3.0-base-zh.zip 链接: https://pan.baidu.com/s/1CIvKnD3qzE-orFouA8qvNQ?pwd=4wih +- ernie-3.0-nano-zh.zip 链接: https://pan.baidu.com/s/1Fh8fgzVdavf5P1omAJJ-Zw?pwd=q6s5 +- text2vec-large-chinese.zip 链接: https://pan.baidu.com/s/1sMyPzBIXdEzHygftEoyBuA?pwd=4xs7 +- chatglm-6b-int4-qe.zip 链接: https://pan.baidu.com/s/1DDKMOMHtNZccOOBGWIOYww?pwd=22ji +- chatglm-6b-int4.zip 链接: https://pan.baidu.com/s/1pvZ6pMzovjhkA6uPcRLuJA?pwd=3gjd +- chatglm-6b.zip 链接: https://pan.baidu.com/s/1B-MpsVVs1GHhteVBetaquw?pwd=djay + +--- + +#### Q8: 老版本和新版本无法兼容怎么办？ + +A8: 保存老版本的配置文件，删除老版本代码并下载新版本代码后，根据新版本的配置文件格式进行修改。 + +在 ```0.2.6```后，运行环境和配置文件发生重大变化，建议重新配置环境和配置文件，并重建知识库。 + + +--- + +#### Q9: 显卡内存爆了，提示 "OutOfMemoryError: CUDA out of memory" + +A9: `VECTOR_SEARCH_TOP_K` 和 `HISTORY_LEN` 的值调低，比如 `VECTOR_SEARCH_TOP_K = 3` 和 `LLM_HISTORY_LEN = 2`，这样由 `query` 和 `context` 拼接得到的 `prompt` 会变短，会减少内存的占用。或者使用量化模型减少显存占用。 + +--- + +#### Q10: 执行 `pip install -r requirements.txt` 过程中遇到 python 包，如 langchain 找不到对应版本的问题 + +A10: 更换 pypi 源后重新安装，如阿里源、清华源等，网络条件允许时建议直接使用 pypi.org 源，具体操作命令如下： + +```shell +# 使用 pypi 源 +$ pip install -r requirements.txt -i https://pypi.python.org/simple +``` + +或 + +```shell +# 使用阿里源 +$ pip install -r requirements.txt -i http://mirrors.aliyun.com/pypi/simple/ +``` + +或 + +```shell +# 使用清华源 +$ pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple/ +``` + +--- + +#### Q11: 启动 api.py 时 upload_file 接口抛出 `partially initialized module 'charset_normalizer' has no attribute 'md__mypyc' (most likely due to a circular import)` + +A11: 这是由于 charset_normalizer 模块版本过高导致的，需要降低低 charset_normalizer 的版本,测试在 charset_normalizer==2.1.0 上可用。 + +--- + +#### Q12: 调用api中的 `bing_search_chat` 接口时，报出 `Failed to establish a new connection: [Errno 110] Connection timed out` + +A12: 这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--! + +--- + +#### Q13: 加载 chatglm-6b-int8 或 chatglm-6b-int4 抛出 `RuntimeError: Only Tensors of floating point andcomplex dtype can require gradients` + +A13: 疑为 chatglm 的 quantization 的问题或 torch 版本差异问题，针对已经变为 Parameter 的 torch.zeros 矩阵也执行 Parameter 操作，从而抛出 `RuntimeError: Only Tensors of floating point andcomplex dtype can require gradients`。解决办法是在 chatglm 项目的原始文件中的 quantization.py 文件 374 行改为： + +``` + try: + self.weight =Parameter(self.weight.to(kwargs["device"]), requires_grad=False) + except Exception as e: + pass +``` + + 如果上述方式不起作用，则在.cache/hugggingface/modules/目录下针对chatglm项目的原始文件中的quantization.py文件执行上述操作，若软链接不止一个，按照错误提示选择正确的路径。 + +注：虽然模型可以顺利加载但在cpu上仍存在推理失败的可能：即针对每个问题，模型一直输出gugugugu。 + + 因此，最好不要试图用cpu加载量化模型，原因可能是目前python主流量化包的量化操作是在gpu上执行的,会天然地存在gap。 + +--- + +#### Q14: 修改配置中路径后，加载 text2vec-large-chinese 依然提示 `WARNING: No sentence-transformers model found with name text2vec-large-chinese. Creating a new one with MEAN pooling.` + +A14: 尝试更换 embedding，如 text2vec-base-chinese，请在 [configs/model_config.py](../configs/model_config.py) 文件中，修改 `text2vec-base`参数为本地路径，绝对路径或者相对路径均可 + +--- + +#### Q16: 使用pg向量库建表报错 + +A15: 需要手动安装对应的vector扩展(连接pg执行 CREATE EXTENSION IF NOT EXISTS vector) + +--- + +#### Q16: pymilvus 连接超时 + +A16.pymilvus版本需要匹配和milvus对应否则会超时参考pymilvus==2.1.3 + +--- + +#### Q17: 使用vllm推理加速框架时，已经下载了模型但出现HuggingFace通信问题 + +A17: 参照如下代码修改python环境下/site-packages/vllm/model_executor/weight_utils.py文件的prepare_hf_model_weights函数如下对应代码： + +```python + + if not is_local: + # Use file lock to prevent multiple processes from + # downloading the same model weights at the same time. + model_path_temp = os.path.join( + os.getenv("HOME"), + ".cache/huggingface/hub", + "models--" + model_name_or_path.replace("/", "--"), + "snapshots/", + ) + downloaded = False + if os.path.exists(model_path_temp): + temp_last_dir = os.listdir(model_path_temp)[-1] + model_path_temp = os.path.join(model_path_temp, temp_last_dir) + base_pattern = os.path.join(model_path_temp, "pytorch_model*.bin") + files = glob.glob(base_pattern) + if len(files) > 0: + downloaded = True + + if downloaded: + hf_folder = model_path_temp + else: + with get_lock(model_name_or_path, cache_dir): + hf_folder = snapshot_download(model_name_or_path, + allow_patterns=allow_patterns, + cache_dir=cache_dir, + tqdm_class=Disabledtqdm) + else: + hf_folder = model_name_or_path +``` + +--- + +#### Q18: `/xxx/base_model_worer.py` 报 `assert r.status_code == 200` 错误 + +A：这个错误是本地模型进程注册到 fastchat controller 失败了。一般有两种原因：1、开了系统全局代理，关闭即可。2、DEFAULT_BIND_HOST 设为'0.0.0.0'，改成'127.0.0.1' 或 本机实际 IP 即可。或者更新到最新版本代码也可以解决。 + + +#### Q19: 使用vllm后端加速，无返回且不报错。 + +A: fschat=0.2.33的vllm_worker脚本代码有bug, 如需使用，需源码修改fastchat.server.vllm_worker，将103行中sampling_params = SamplingParams的参数stop=list(stop)修改为stop= [i for i in stop if i!=""] + + +#### Q20: chatglm3-6b对话中出现"<|user|>"标签，且自问自答。 + +A20: chatglm3官方目前已经修复了chatglm3-6b的问题，若使用的模型为chatglm3-6b，仅需更新chatglm3-6b模型代码即可;请前往 Huggingface 下载最新的权重。 +并更新fschat版本到 0.2.34以上。 + +#### Q21: 为什么启动的时候一直出现 +``` +"device not in ['cuda', 'mps', 'cpu','xpu'], device = auto" +``` +的警告 + +A21: 这是因为你没有在对应的启动选项设定设备，请在`model_config.py`中设定 DEVICE，不过，就算不设定，auto也能正常使用 \ No newline at end of file diff --git a/chatchat/chatchat/data/knowledge_base/samples/content/wiki/开发环境部署.md b/chatchat/chatchat/data/knowledge_base/samples/content/wiki/开发环境部署.md new file mode 100644 index 00000000..9e996fdd --- /dev/null +++ b/chatchat/chatchat/data/knowledge_base/samples/content/wiki/开发环境部署.md @@ -0,0 +1,281 @@ +## 软件要求 + +要顺利运行本代码，请按照以下系统要求进行配置 + +**已经测试过的系统** + ++ Linux Ubuntu 22.04.5 kernel version 6.7 + +其他系统可能出现系统兼容性问题。 + +**最低要求** + +该要求仅针对标准模式，轻量模式使用在线模型，不需要安装torch等库，也不需要显卡即可运行。 + ++ Python 版本: >= 3.8(很不稳定), < 3.12 ++ CUDA 版本: >= 12.1 + +**推荐要求** + +开发者在以下环境下进行代码调试，在该环境下能够避免最多环境问题。 + ++ Python 版本 == 3.11.7 ++ CUDA 版本: == 12.1 + +## 硬件要求 + +本框架使用 `fschat`驱动，统一使用 `huggingface`进行推理，其他推理方式(如 `llama-cpp`，`TensorRT加速引擎` 建议通过推理引擎以 API 形式接入我们的框架)。 + +同时, 我们没有对 `Int4` 模型进行适配，不保证`Int4`模型能够正常运行。因此，量化版本暂时需要由开发者自行适配, 我们可能在未来放。 + +如果想要顺利在GPU运行本地模型的 **FP16** 版本，你至少需要以下的硬件配置，来保证在我们框架下能够实现 **稳定连续对话** + ++ ChatGLM3-6B & LLaMA-7B-Chat 等 7B模型 + + 最低显存要求: 14GB + + 推荐显卡: RTX 4080 ++ Qwen-14B-Chat 等 14B模型 + + 最低显存要求: 30GB + + 推荐显卡: V100 ++ Yi-34B-Chat 等 34B模型 + + 最低显存要求: 69GB + + 推荐显卡: A100 ++ Qwen-72B-Chat 等 72B模型 + + 最低显存要求: 145GB + + 推荐显卡：多卡 A100 以上 + +一种简单的估算方式为： +``` +FP16: 显存占用(GB) = 模型量级 x 2 +Int4: 显存占用(GB) = 模型量级 x 0.75 +``` +以上数据仅为估算，实际情况以 **nvidia-smi** 占用为准。 +请注意，如果使用最低配置，仅能保证代码能够运行，但运行速度较慢，体验不佳。 + +同时，Embedding 模型将会占用 1-2G 的显存，历史记录最多会占用 数GB 的显存，因此，需要多冗余一些显存。 + +内存最低要求: 内存要求至少应该比模型运行的显存大。 + +例如，运行ChatGLM3-6B `FP16` 模型，显存占用13G，推荐使用16G以上内存。 + +### 部分测试用机配置参考，在以下机器下开发组成员已经进行原生模拟测试（创建新环境并根据要求下载后运行），确保能流畅运行全部功能的代码框架。 ++ 服务器 +``` +处理器: Intel® Xeon® Platinum 8558P Processor (260M Cache, 2.7 GHz) +内存: 4 TB +显卡组: NVIDIA H800 SXM5 80GB x 8 +硬盘: 6 PB +操作系统: Ubuntu 22.04 LTS,Linux kernel 5.15.0-60-generic +显卡驱动版本: 535.129.03 +Cuda版本: 12.1 +Python版本: 3.11.7 +网络IP地址：美国，洛杉矶 +``` ++ 个人PC +``` +处理器: Intel® Core™ i9 processor 14900K +内存: 256 GB DDR5 +显卡组: NVIDIA RTX4090 X 1 / NVIDIA RTXA6000 X 1 +硬盘: 1 TB +操作系统: Ubuntu 22.04 LTS / Arch Linux, Linux Kernel 6.6.7 +显卡驱动版本: 545.29.06 +Cuda版本: 12.3 Update 1 +Python版本: 3.11.7 +网络IP地址：中国，上海 +``` + +## VPN + +如果您位于中国(含港，澳，台) 需要调用 OpenAI 或者 其他境外模型的 API，需要使用 VPN 工具或访问镜像站。 + +从 Huggingface 下载模型或者从本仓库拉取最新的代码时，需要开发者自行设置代理。本项目不涉及任何代理工具设置和使用，也不解决任何关于代理的问题。 + +## Docker 部署 + +开发组为开发者们提供了一键部署的 docker 镜像文件懒人包。开发者们可以在 AutoDL 平台和 Docker 平台一键部署。 + +🌐 [AutoDL 镜像](https://www.codewithgpu.com/i/chatchat-space/Langchain-Chatchat/Langchain-Chatchat) ，已经更新到`V13`版本,对应`0.2.9` + +🐳 [Docker 镜像](registry.cn-beijing.aliyuncs.com/chatchat/chatchat:0.2.7) + +💻 一行命令运行 Docker 🌲： + +```shell +docker run -d --gpus all -p 80:8501 registry.cn-beijing.aliyuncs.com/chatchat/chatchat:0.2.7 +``` + +- 该版本镜像大小 `43.1GB`，使用 `v0.2.6`，以 `nvidia/cuda:12.1.1-cudnn8-devel-ubuntu22.04` 为基础镜像 +- 该版本为正常版本，非轻量化版本 +- 该版本内置两个 Embedding 模型：`m3e-large`，`text2vec-bge-large-chinese`，默认启用后者，内置 `chatglm2-6b-32k` +- 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 ` 查看日志 +- 如遇到启动过程卡在 `Waiting..` 步骤，建议使用 `docker exec -it bash` 进入 `/logs/` 目录查看对应阶段日志 + +## 常规模式本地部署方案 + +```shell +# 首先，确信你的机器安装了 Python 3.8 - 3.10 版本 +$ python --version +Python 3.8.13 + +# 如果低于这个版本，可使用conda安装环境 +$ conda create -p /your_path/env_name python=3.8 + +# 激活环境 +$ source activate /your_path/env_name + +# 或，conda安装，不指定路径, 注意以下，都将/your_path/env_name替换为env_name +$ conda create -n env_name python=3.8 +$ conda activate env_name # Activate the environment + +# 更新py库 +$ pip3 install --upgrade pip + +# 关闭环境 +$ source deactivate /your_path/env_name + +# 删除环境 +$ conda env remove -p /your_path/env_name +``` +接着，开始安装项目的依赖 + +```shell +# 拉取仓库 +$ git clone --recursive https://github.com/chatchat-space/Langchain-Chatchat.git + +# 进入目录 +$ cd Langchain-Chatchat + +# 安装全部依赖 +$ pip install -r requirements.txt + +# 默认依赖包括基本运行环境（FAISS向量库）。以下是可选依赖： +- 如果要使用 milvus/pg_vector 等向量库，请将 requirements.txt 中相应依赖取消注释再安装。 +- 如果要开启 OCR GPU 加速，请安装 rapidocr_paddle[gpu] +- 如果要使用在线 API 模型，请安装对用的 SDK + +``` + +此外，为方便用户 API 与 webui 分离运行，可单独根据运行需求安装依赖包。 + +- 如果只需运行 API，可执行： + ```shell + $ pip install -r requirements_api.txt + + # 默认依赖包括基本运行环境（FAISS向量库）。如果要使用 milvus/pg_vector 等向量库，请将 requirements.txt 中相应依赖取消注释再安装。 + ``` + +- 如果只需运行 WebUI，可执行： + ```shell + $ pip install -r requirements_webui.txt + ``` + +注：使用 `langchain.document_loaders.UnstructuredFileLoader`进行 `.docx` 等格式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)。 + + +需要注意的是，对于以下依赖，我们建议源码安装依赖或者定期检查是否为最新版本，我们的框架可能会大量使用这些依赖的最新特性。 ++ transformers ++ fastchat ++ fastapi ++ streamlit 以及其组件 ++ langchain 以及其组件 ++ xformers + +## 模型下载 + +如需在本地或离线环境下运行本项目，需要首先将项目所需的模型下载至本地，通常开源 LLM 与 Embedding 模型可以从 [HuggingFace](https://huggingface.co/models) 下载。 + +以本项目中默认使用的 LLM 模型 [THUDM/chatglm2-6b](https://huggingface.co/THUDM/chatglm2-6b) 与 Embedding 模型 [moka-ai/m3e-base](https://huggingface.co/moka-ai/m3e-base) 为例： + +下载模型需要先[安装Git LFS](https://docs.github.com/zh/repositories/working-with-files/managing-large-files/installing-git-large-file-storage)，然后运行 + +```Shell +$ git lfs install +$ git clone https://huggingface.co/THUDM/chatglm2-6b +$ git clone https://huggingface.co/moka-ai/m3e-base +``` + +## 初始化知识库 + +当前项目的知识库信息存储在数据库中，在正式运行项目之前请先初始化数据库（我们强烈建议您在执行操作前备份您的知识文件）。 +- 如果您已经有创建过知识库，可以先执行以下命令创建或更新数据库表： + ```shell + $ python init_database.py --create-tables + ``` + 如果可以正常运行，则无需再重建知识库。 + +- 如果您是第一次运行本项目，知识库尚未建立，或者之前使用的是低于最新master分支版本的框架，或者配置文件中的知识库类型、嵌入模型发生变化，或者之前的向量库没有开启 `normalize_L2`，需要以下命令初始化或重建知识库： + + ```shell + $ python init_database.py --recreate-vs + ``` + +## 一键启动 +启动前，确保已经按照[参数配置](https://github.com/chatchat-space/Langchain-Chatchat/wiki/%E5%8F%82%E6%95%B0%E9%85%8D%E7%BD%AE)正确配置各config模块。 + +一键启动脚本 startup.py， 一键启动所有 Fastchat 服务、API 服务、WebUI 服务，示例代码： + +```shell +$ python startup.py -a +``` + +并可使用 `Ctrl + C` 直接关闭所有运行服务。如果一次结束不了，可以多按几次。 + +可选参数包括 `-a (或--all-webui)`, `--all-api`, `--llm-api`, `-c (或--controller)`, `--openai-api`, +`-m (或--model-worker)`, `--api`, `--webui`，其中： + +- `--all-webui` 为一键启动 WebUI 所有依赖服务； +- `--all-api` 为一键启动 API 所有依赖服务； +- `--llm-api` 为一键启动 Fastchat 所有依赖的 LLM 服务； +- `--openai-api` 为仅启动 FastChat 的 controller 和 openai-api-server 服务； +- 其他为单独服务启动选项。 + +若想指定非默认模型，需要用 `--model-name` 选项，示例： + +```shell +$ python startup.py --all-webui --model-name Qwen-7B-Chat +``` + +更多信息可通过 `python startup.py -h` 查看。 + +## 多卡加载 +项目支持多卡加载，需在 startup.py 中的 create_model_worker_app 函数中，修改如下三个参数: + +```python +gpus=None, +num_gpus= 1, +max_gpu_memory="20GiB" +``` + +其中，`gpus` 控制使用的显卡的ID，例如 "0,1"; + +`num_gpus` 控制使用的卡数; + +`max_gpu_memory` 控制每个卡使用的显存容量。 + +注1：server_config.py的FSCHAT_MODEL_WORKERS字典中也增加了相关配置，如有需要也可通过修改FSCHAT_MODEL_WORKERS字典中对应参数实现多卡加载，且需注意server_config.py的配置会覆盖create_model_worker_app 函数的配置。 + +注2：少数情况下，gpus参数会不生效，此时需要通过设置环境变量CUDA_VISIBLE_DEVICES来指定torch可见的gpu,示例代码： + +```shell +CUDA_VISIBLE_DEVICES=0,1 python startup.py -a +``` + +## 最轻模式本地部署方案 + +该模式的配置方式与常规模式相同，但无需安装 `torch` 等重依赖，通过在线API实现 LLM 和 Ebeddings 相关功能，适合没有显卡的电脑使用。 + +```shell +$ pip install -r requirements_lite.txt +$ python startup.py -a --lite +``` + +该模式支持的在线 Embeddings 包括： +- [智谱AI](http://open.bigmodel.cn) +- [MiniMax](https://api.minimax.chat) +- [百度千帆](https://cloud.baidu.com/product/wenxinworkshop?track=dingbutonglan) +- [阿里云通义千问](https://dashscope.aliyun.com/) + +在 model_config.py 中 将 LLM_MODELS 和 EMBEDDING_MODEL 设置为可用的在线 API 名称即可。 + +注意：在对话过程中并不要求 LLM 模型与 Embeddings 模型一致，你可以在知识库管理页面中使用 zhipu-api 作为嵌入模型，在知识库对话页面使用其它模型。 diff --git a/chatchat/chatchat/data/knowledge_base/samples/content/wiki/支持列表.md b/chatchat/chatchat/data/knowledge_base/samples/content/wiki/支持列表.md new file mode 100644 index 00000000..8937ed79 --- /dev/null +++ b/chatchat/chatchat/data/knowledge_base/samples/content/wiki/支持列表.md @@ -0,0 +1,176 @@ +## LLM 模型支持列表 +> 本地模型 + +本地 LLM 模型接入基于 [FastChat](https://github.com/lm-sys/FastChat) 实现，支持模型如下： + +- [ChatGLM 全系类对话模型](https://huggingface.co/THUDM/) +- [Orion 全系列对话模型](https://huggingface.co/OrionStarAI/)，必须安装flash-attn 才能使用 +- [Qwen 全系列对话模型](https://huggingface.co/Qwen/) +- [internlm 全系列对话模型](https://huggingface.co/internlm) +- [Baichuan 全系列对话模型](https://huggingface.co/baichuan-inc)，必须降级transformer才能使用 +- [llama 全系列对话模型](https://huggingface.co/meta-llama) +- [Vicuna 全系列对话模型](https://huggingface.co/lmsys) +- [mistral 全系列对话模型](https://huggingface.co/mistralai) + +- [vivo-ai/BlueLM-7B-Chat](https://huggingface.co/vivo-ai/BlueLM-7B-Chat) +- [01-ai/Yi-34B-Chat](https://huggingface.co/01-ai/Yi-34B-Chat) +- [BlinkDL/RWKV-4-Raven](https://huggingface.co/BlinkDL/rwkv-4-raven) +- [camel-ai/CAMEL-13B-Combined-Data](https://huggingface.co/camel-ai/CAMEL-13B-Combined-Data) +- [databricks/dolly-v2-12b](https://huggingface.co/databricks/dolly-v2-12b) +- [FreedomIntelligence/phoenix-inst-chat-7b](https://huggingface.co/FreedomIntelligence/phoenix-inst-chat-7b) +- [h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b](https://huggingface.co/h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b) +- [lcw99/polyglot-ko-12.8b-chang-instruct-chat](https://huggingface.co/lcw99/polyglot-ko-12.8b-chang-instruct-chat) +- [lmsys/fastchat-t5-3b-v1.0](https://huggingface.co/lmsys/fastchat-t5) +- [mosaicml/mpt-7b-chat](https://huggingface.co/mosaicml/mpt-7b-chat) +- [Neutralzz/BiLLa-7B-SFT](https://huggingface.co/Neutralzz/BiLLa-7B-SFT) +- [nomic-ai/gpt4all-13b-snoozy](https://huggingface.co/nomic-ai/gpt4all-13b-snoozy) +- [NousResearch/Nous-Hermes-13b](https://huggingface.co/NousResearch/Nous-Hermes-13b) +- [openaccess-ai-collective/manticore-13b-chat-pyg](https://huggingface.co/openaccess-ai-collective/manticore-13b-chat-pyg) +- [OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5](https://huggingface.co/OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5) +- [project-baize/baize-v2-7b](https://huggingface.co/project-baize/baize-v2-7b) +- [Salesforce/codet5p-6b](https://huggingface.co/Salesforce/codet5p-6b) +- [StabilityAI/stablelm-tuned-alpha-7b](https://huggingface.co/stabilityai/stablelm-tuned-alpha-7b) +- [tiiuae/falcon-40b](https://huggingface.co/tiiuae/falcon-40b) +- [timdettmers/guanaco-33b-merged](https://huggingface.co/timdettmers/guanaco-33b-merged) +- [togethercomputer/RedPajama-INCITE-7B-Chat](https://huggingface.co/togethercomputer/RedPajama-INCITE-7B-Chat) +- [WizardLM/WizardLM-13B-V1.0](https://huggingface.co/WizardLM/WizardLM-13B-V1.0) +- [WizardLM/WizardCoder-15B-V1.0](https://huggingface.co/WizardLM/WizardCoder-15B-V1.0) +- [HuggingFaceH4/starchat-beta](https://huggingface.co/HuggingFaceH4/starchat-beta) +- [FlagAlpha/Llama2-Chinese-13b-Chat](https://huggingface.co/FlagAlpha/Llama2-Chinese-13b-Chat) and others +- [BAAI/AquilaChat-7B](https://huggingface.co/BAAI/AquilaChat-7B) +- [all models of OpenOrca](https://huggingface.co/Open-Orca) +- [Spicyboros](https://huggingface.co/jondurbin/spicyboros-7b-2.2?not-for-all-audiences=true) +- [airoboros 2.2](https://huggingface.co/jondurbin/airoboros-l2-13b-2.2) +- [VMware's OpenLLaMa OpenInstruct](https://huggingface.co/VMware/open-llama-7b-open-instruct) +- 任何 [EleutherAI](https://huggingface.co/EleutherAI) 的 pythia 模型，如 [pythia-6.9b](https://huggingface.co/EleutherAI/pythia-6.9b) +- 在以上模型基础上训练的任何 [Peft](https://github.com/huggingface/peft) 适配器。为了激活，模型路径中必须有 `peft` 。注意：如果加载多个peft模型，你可以通过在任何模型工作器中设置环境变量 `PEFT_SHARE_BASE_WEIGHTS=true` 来使它们共享基础模型的权重。 + +以上模型支持列表可能随 [FastChat](https://github.com/lm-sys/FastChat) 更新而持续更新，可参考 [FastChat 已支持模型列表](https://github.com/lm-sys/FastChat/blob/main/docs/model_support.md)。 + +> 联网模型 + +支持的联网模型 +- [智谱AI](http://open.bigmodel.cn) **临时解决方案，不支持流式输出，尽情期待0.3.x** +- [阿里云通义千问](https://dashscope.aliyun.com/) +- [百川](https://www.baichuan-ai.com/) +- [ChatGPT](https://api.openai.com) +- [Gimini](https://makersuite.google.com/app/apikey) +- [Azure OpenAI](https://azure.microsoft.com/en-us/products/ai-services/openai-service) +- [MiniMax](https://api.minimax.chat) +- [讯飞星火](https://xinghuo.xfyun.cn) +- [百度千帆](https://cloud.baidu.com/product/wenxinworkshop?track=dingbutonglan) +- [字节火山方舟](https://www.volcengine.com) + +## Embedding 模型支持列表 + +> 本地模型 + +本项目支持调用 [HuggingFace](https://huggingface.co/models?pipeline_tag=sentence-similarity) 中的 Embedding 模型，已支持的 Embedding 模型如下： + + +MokaAI系列嵌入模型 + +- [moka-ai/m3e-small](https://huggingface.co/moka-ai/m3e-small) +- [moka-ai/m3e-base](https://huggingface.co/moka-ai/m3e-base) +- [moka-ai/m3e-large](https://huggingface.co/moka-ai/m3e-large) + +BAAI系列嵌入模型 +- [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) +- [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) +- [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) +- [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) +- [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) +- [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) +- [BAAI/bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) +- [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) +- [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) + + +text2vec系列嵌入模型 +- [shibing624/text2vec-base-chinese-sentence](https://huggingface.co/shibing624/text2vec-base-chinese-sentence) +- [shibing624/text2vec-base-chinese-paraphrase](https://huggingface.co/shibing624/text2vec-base-chinese-paraphrase) +- [shibing624/text2vec-base-multilingual](https://huggingface.co/shibing624/text2vec-base-multilingual) +- [shibing624/text2vec-base-chinese](https://huggingface.co/shibing624/text2vec-base-chinese) +- [shibing624/text2vec-bge-large-chinese](https://huggingface.co/shibing624/text2vec-bge-large-chinese) +- [GanymedeNil/text2vec-large-chinese](https://huggingface.co/GanymedeNil/text2vec-large-chinese) + +其他模型 +- [sensenova/piccolo-base-zh](https://huggingface.co/sensenova/piccolo-base-zh) +- [sensenova/piccolo-large-zh](https://huggingface.co/sensenova/piccolo-large-zh) +- [nghuyong/ernie-3.0-nano-zh](https://huggingface.co/nghuyong/ernie-3.0-nano-zh) +- [nghuyong/ernie-3.0-base-zh](https://huggingface.co/nghuyong/ernie-3.0-base-zh) + +达摩院系列嵌入模型 +- [damo/nlp_gte_sentence-embedding_chinese-large](https://modelscope.cn/models/damo/nlp_gte_sentence-embedding_chinese-large) + +> 联网模型 + +除本地模型外，本项目也支持直接接入 OpenAI的在线嵌入模型。 +支持的联网模型 +- [OpenAI/text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) +- [智谱AI](http://open.bigmodel.cn) +- [MiniMax](https://api.minimax.chat) +- [百度千帆](https://cloud.baidu.com/product/wenxinworkshop?track=dingbutonglan) +- [阿里云通义千问](https://dashscope.aliyun.com/) + +## 分词器支持列表 + +> Langchain 中的分词器 + +本项目支持调用 [Langchain](https://api.python.langchain.com/en/latest/api_reference.html#module-langchain.text_splitter) 的 Text Splitter 分词器以及基于此改进的自定义分词器，已支持的 Text Splitter 类型如下： +- CharacterTextSplitter +- LatexTextSplitter +- MarkdownHeaderTextSplitter +- MarkdownTextSplitter +- NLTKTextSplitter +- PythonCodeTextSplitter +- RecursiveCharacterTextSplitter +- SentenceTransformersTokenTextSplitter +- SpacyTextSplitter + +> 自定义分词器 + +已经支持的定制分词器如下： + +- [AliTextSplitter](text_splitter/ali_text_splitter.py) +- [ChineseRecursiveTextSplitter](text_splitter/chinese_recursive_text_splitter.py) +- [ChineseTextSplitter](text_splitter/chinese_text_splitter.py) + + +## 向量数据库支持列表 + +> 本地向量数据库 + +目前支持的本地向量数据库列表如下： + +- [FAISS](https://github.com/facebookresearch/faiss) +- [Milvus](https://github.com/milvus-io/milvus) +- [PGVector](https://github.com/pgvector/pgvector) + +> 联网向量数据库 + +- [Zilliz](https://zilliz.com) + +## 工具支持列表 +> Langchain工具 + +- Shell 工具，用于模拟当前的Linux Shell环境 +- Youtube 工具，用于搜索Youtube的相关视频链接 +- Wolfram 工具，用Wolfram来实现数学计算等 + +其他Langchain自带的工具也可以按照上述三个工具的方式来自己实现 + +> 本地工具 + +- 翻译工具，实现对输入的任意语言翻译。 +- 数学工具，使用LLMMathChain 实现数学计算。 +- 高级知识库工具，智能选择调用多个或者单个知识库并查询内容。 +- 进阶知识库工具，智能选择调用一个最相近的知识库并查询内容。 +- 基础知识库工具，选择指定的一个知识库并回答。 + +> 联网工具 + +- 天气工具，使用自定义的LLMWetherChain实现天气查询，调用和风天气API。 +- 搜索工具，使用我们的搜索API来实现搜索并概括内容。 + +- 我们期待开发者共享更多的工具，帮助项目生态完善 diff --git a/chatchat/chatchat/data/knowledge_base/samples/content/wiki/最佳实践.md b/chatchat/chatchat/data/knowledge_base/samples/content/wiki/最佳实践.md new file mode 100644 index 00000000..91e85a03 --- /dev/null +++ b/chatchat/chatchat/data/knowledge_base/samples/content/wiki/最佳实践.md @@ -0,0 +1,889 @@ +## 推荐的模型组合 + ++ 在默认的配置文件中，我们提供了以下模型组合 +``` +LLM: Chatglm2-6b +Embedding Models: m3e-base +TextSplitter: ChineseRecursiveTextSplitter +Kb_dataset: faiss +``` + ++ 我们推荐开发者根据自己的业务需求进行模型微调，如果不需要微调且配置充足，可选择以下性能较好的配置 +``` +model_config.py +LLM: Qwen-14B-Chat 或 Baichuan2-13B-Chat +Embedding Models: piccolo-large-zh 或 bge-large-zh-v1.5 +HISTORY_LEN = 20 +TEMPERATURE = 0.1 +``` +使用该模型将需要更高的硬件要求 +``` +1张 RTX A6000 或者 A40 等 48GB 显存以上的显卡。推荐 1 x A100 以上。 +(使用多张显卡拼接也能运行，但是速度非常慢，2张4090拼接运行大概为一秒一个字的速度) + +64GB 内存用于加载模型而不被Kill + +服务器级的CPU，推荐 Xeon(R) Platinum 8358P 以上 +``` + ++ 如果开发者知识库较大，有大量文档，大文件，我们推荐开发者使用 ```pg``` 向量数据库 ++ 如果开发者的知识库具有一定的关键词特征，例如： + + 问答对文件(以Q + A 为一个组合的json文件) + + Markdown文件 + + 并排的pdf文件 + + 具有多个表格的pdf文件 + + 我们推荐开发者自行开发分词器，以达到更好的效果。 + ++ 如果开发者想使用更全面的 Agent 功能，我们推荐开发者使用以下配置 +``` +LLM: Qwen-14B-Chat, AgentLM-70B 或 GPT-4 +Tools 的工具控制在10个之内 +``` + +## 微调模型加载实操 + +### 非p-tuning类PEFT加载 +本项目基于 FastChat 加载 LLM 服务，故需以 FastChat 加载 PEFT 路径，针对chatglm,falcon，codet5p以外的模型，以及非p-tuning以外的peft方法，需对peft文件进行修改，步骤如下： + +1. 将config.json文件修改为adapter_config.json; +2. 保证文件夹包含pytorch_model.bin文件； +3. 修改文件夹名称，保证文件夹包含'peft'一词； +4. 将peft文件夹移入项目目录下； +5. 确保adapter_config.json文件夹中base_model_name_or_path指向基础模型； +6. 将peft路径添加到model_config.py的llm_dict中，键为模型名，值为peft路径，注意使用相对路径，如"peft"； +7. 开启 `PEFT_SHARE_BASE_WEIGHTS=true`环境变量，再执行python startup.py -a + +针对p-tuning和chatglm模型，需要对fastchat进行较大幅度的修改。 + +### p-tuning加载 + +P-tuning虽然是一种peft方法，但并不能于huggingface的peft python包兼容，而fastchat在多处以字符串匹配的方式进行硬编码加载模型，因此导致fastchat和chatchat不能兼容p-tuning，经langchain-chatchat开发组多次尝试，给出如下指南进行p-tuning加载。 + +#### 1. peft文件夹修改 + +1. 将config.json文件修改为adapter_config.json; +2. 保证文件夹包含pytorch_model.bin文件； +3. 修改文件夹名称，保证文件夹包含'peft'一词； +4. 在adapter_config.json文件中增加如下字段： + + ```json + "base_model_name_or_path": "/root/model/chatglm2-6b/" + "task_type": "CAUSAL_LM", + "peft_type": "PREFIX_TUNING", + "inference_mode": true, + "revision": "main", + "num_virtual_tokens": 16 + ``` + + **其中,"base_model_name_or_path"为基础模型的存在位置**； +5. 将文件夹移入项目文件夹中，如Langchain-Chatchat项目文件夹目录下； + +#### 2. fastchat包代码修改 + +##### 2.1 fastchat.model.model_adapter文件修改 + +1. 将fastchat.model.model_adapter.py文件的load_model函数修改为： + + ```python + def load_model( + model_path: str, + device: str = "cuda", + num_gpus: int = 1, + max_gpu_memory: Optional[str] = None, + dtype: Optional[torch.dtype] = None, + load_8bit: bool = False, + cpu_offloading: bool = False, + gptq_config: Optional[GptqConfig] = None, + awq_config: Optional[AWQConfig] = None, + revision: str = "main", + debug: bool = False, + load_kwargs = {} + ): + """Load a model from Hugging Face.""" + # get model adapter + adapter = get_model_adapter(model_path) + kwargs = load_kwargs + # Handle device mapping + cpu_offloading = raise_warning_for_incompatible_cpu_offloading_configuration( + device, load_8bit, cpu_offloading + ) + if device == "cpu": + kwargs["torch_dtype"]= torch.float32 + if CPU_ISA in ["avx512_bf16", "amx"]: + try: + import intel_extension_for_pytorch as ipex + + kwargs ["torch_dtype"]= torch.bfloat16 + except ImportError: + warnings.warn( + "Intel Extension for PyTorch is not installed, it can be installed to accelerate cpu inference" + ) + elif device == "cuda": + kwargs["torch_dtype"] = torch.float16 + if num_gpus != 1: + kwargs["device_map"] = "auto" + if max_gpu_memory is None: + kwargs[ + "device_map" + ] = "sequential" # This is important for not the same VRAM sizes + available_gpu_memory = get_gpu_memory(num_gpus) + kwargs["max_memory"] = { + i: str(int(available_gpu_memory[i] * 0.85)) + "GiB" + for i in range(num_gpus) + } + else: + kwargs["max_memory"] = {i: max_gpu_memory for i in range(num_gpus)} + elif device == "mps": + kwargs["torch_dtype"] = torch.float16 + # Avoid bugs in mps backend by not using in-place operations. + replace_llama_attn_with_non_inplace_operations() + elif device == "xpu": + kwargs["torch_dtype"] = torch.bfloat16 + # Try to load ipex, while it looks unused, it links into torch for xpu support + try: + import intel_extension_for_pytorch as ipex + except ImportError: + warnings.warn( + "Intel Extension for PyTorch is not installed, but is required for xpu inference." + ) + elif device == "npu": + kwargs["torch_dtype"]= torch.float16 + # Try to load ipex, while it looks unused, it links into torch for xpu support + try: + import torch_npu + except ImportError: + warnings.warn("Ascend Extension for PyTorch is not installed.") + else: + raise ValueError(f"Invalid device: {device}") + + if cpu_offloading: + # raises an error on incompatible platforms + from transformers import BitsAndBytesConfig + + if "max_memory" in kwargs: + kwargs["max_memory"]["cpu"] = ( + str(math.floor(psutil.virtual_memory().available / 2**20)) + "Mib" + ) + kwargs["quantization_config"] = BitsAndBytesConfig( + load_in_8bit_fp32_cpu_offload=cpu_offloading + ) + kwargs["load_in_8bit"] = load_8bit + elif load_8bit: + if num_gpus != 1: + warnings.warn( + "8-bit quantization is not supported for multi-gpu inference." + ) + else: + model, tokenizer = adapter.load_compress_model( + model_path=model_path, + device=device, + torch_dtype=kwargs["torch_dtype"], + revision=revision, + ) + if debug: + print(model) + return model, tokenizer + elif awq_config and awq_config.wbits < 16: + assert ( + awq_config.wbits == 4 + ), "Currently we only support 4-bit inference for AWQ." + model, tokenizer = load_awq_quantized(model_path, awq_config, device) + if num_gpus != 1: + device_map = accelerate.infer_auto_device_map( + model, + max_memory=kwargs["max_memory"], + no_split_module_classes=[ + "OPTDecoderLayer", + "LlamaDecoderLayer", + "BloomBlock", + "MPTBlock", + "DecoderLayer", + ], + ) + model = accelerate.dispatch_model( + model, device_map=device_map, offload_buffers=True + ) + else: + model.to(device) + return model, tokenizer + elif gptq_config and gptq_config.wbits < 16: + model, tokenizer = load_gptq_quantized(model_path, gptq_config) + if num_gpus != 1: + device_map = accelerate.infer_auto_device_map( + model, + max_memory=kwargs["max_memory"], + no_split_module_classes=["LlamaDecoderLayer"], + ) + model = accelerate.dispatch_model( + model, device_map=device_map, offload_buffers=True + ) + else: + model.to(device) + return model, tokenizer + kwargs["revision"] = revision + + if dtype is not None: # Overwrite dtype if it is provided in the arguments. + kwargs["torch_dtype"] = dtype + + # Load model + model, tokenizer = adapter.load_model(model_path, kwargs) + + if ( + device == "cpu" + and kwargs["torch_dtype"] is torch.bfloat16 + and CPU_ISA is not None + ): + model = ipex.optimize(model, dtype=kwargs["torch_dtype"]) + + if (device == "cuda" and num_gpus == 1 and not cpu_offloading) or device in ( + "mps", + "xpu", + "npu", + ): + model.to(device) + + if device == "xpu": + model = torch.xpu.optimize(model, dtype=kwargs["torch_dtype"], inplace=True) + + if debug: + print(model) + + return model, tokenizer + ``` +2. 将fastchat.model.model_adapter.py的函数修改为： + + ```python + def get_generate_stream_function(model: torch.nn.Module, model_path: str): + """Get the generate_stream function for inference.""" + from fastchat.serve.inference import generate_stream + + model_type = str(type(model)).lower() + + is_chatglm = "chatglm" in model_type + is_falcon = "rwforcausallm" in model_type + is_codet5p = "codet5p" in model_type + is_peft = "peft" in model_type + + if is_chatglm: + return generate_stream_chatglm + elif is_falcon: + return generate_stream_falcon + elif is_codet5p: + return generate_stream_codet5p + elif peft_share_base_weights and is_peft: + # Return a curried stream function that loads the right adapter + # according to the model_name available in this context. This ensures + # the right weights are available. + @torch.inference_mode() + def generate_stream_peft( + model, + tokenizer, + params: Dict, + device: str, + context_len: int, + stream_interval: int = 2, + judge_sent_end: bool = False, + ): + + model.set_adapter(model_path) + if "chatglm" in str(type(model.base_model)).lower(): + model.disable_adapter() + prefix_state_dict = torch.load(os.path.join(model_path, "pytorch_model.bin")) + new_prefix_state_dict = {} + + for k, v in prefix_state_dict.items(): + if k.startswith("transformer.prefix_encoder."): + new_prefix_state_dict[k[len("transformer.prefix_encoder."):]] = v + elif k.startswith("transformer.prompt_encoder."): + new_prefix_state_dict[k[len("transformer.prompt_encoder."):]] = v + model.transformer.prefix_encoder.load_state_dict(new_prefix_state_dict) + for x in generate_stream_chatglm( + model, + tokenizer, + params, + device, + context_len, + stream_interval, + judge_sent_end, + ): + yield x + elif "rwforcausallm" in str(type(model.base_model)).lower(): + + for x in generate_stream_falcon( + model, + tokenizer, + params, + device, + context_len, + stream_interval, + judge_sent_end, + ): + yield x + elif "codet5p" in str(type(model.base_model)).lower(): + + for x in generate_stream_codet5p( + model, + tokenizer, + params, + device, + context_len, + stream_interval, + judge_sent_end, + ): + yield x + else: + + for x in generate_stream( + model, + tokenizer, + params, + device, + context_len, + stream_interval, + judge_sent_end, + ): + yield x + + return generate_stream_peft + else: + return generate_stream + ``` +3. 将fastchat.model.model_adapter.py的PeftModelAdapter类的load_model方法修改为： + + ```python + def load_model(self, model_path: str, from_pretrained_kwargs: dict): + """Loads the base model then the (peft) adapter weights""" + from peft import PeftConfig, PeftModel + + config = PeftConfig.from_pretrained(model_path) + base_model_path = config.base_model_name_or_path + if "peft" in base_model_path: + raise ValueError( + f"PeftModelAdapter cannot load a base model with 'peft' in the name: {config.base_model_name_or_path}" + ) + + # Basic proof of concept for loading peft adapters that share the base + # weights. This is pretty messy because Peft re-writes the underlying + # base model and internally stores a map of adapter layers. + # So, to make this work we: + # 1. Cache the first peft model loaded for a given base models. + # 2. Call `load_model` for any follow on Peft models. + # 3. Make sure we load the adapters by the model_path. Why? This is + # what's accessible during inference time. + # 4. In get_generate_stream_function, make sure we load the right + # adapter before doing inference. This *should* be safe when calls + # are blocked the same semaphore. + if peft_share_base_weights: + if base_model_path in peft_model_cache: + model, tokenizer = peft_model_cache[base_model_path] + # Super important: make sure we use model_path as the + # `adapter_name`. + model.load_adapter(model_path, adapter_name=model_path) + else: + base_adapter = get_model_adapter(base_model_path) + base_model, tokenizer = base_adapter.load_model( + base_model_path, from_pretrained_kwargs + ) + # Super important: make sure we use model_path as the + # `adapter_name`. + from peft import get_peft_model + model = get_peft_model(base_model,config,adapter_name=model_path) + peft_model_cache[base_model_path] = (model, tokenizer) + return model, tokenizer + + # In the normal case, load up the base model weights again. + base_adapter = get_model_adapter(base_model_path) + base_model, tokenizer = base_adapter.load_model( + base_model_path, from_pretrained_kwargs + ) + from peft import get_peft_model + model = get_peft_model(base_model,config,adapter_name=model_path) + return model, tokenizer + + ``` +4. 将fastchat.model.model_adapter.py的ChatglmAdapter类的load_model方法修改为： + + ```python + def load_model(self, model_path: str, from_pretrained_kwargs: dict): + revision = from_pretrained_kwargs.get("revision", "main") + tokenizer = AutoTokenizer.from_pretrained( + model_path, trust_remote_code=True, revision=revision + ) + config = AutoConfig.from_pretrained(model_path, trust_remote_code=True,**from_pretrained_kwargs) + model = AutoModel.from_pretrained( + model_path, trust_remote_code=True, config=config + ) + return model, tokenizer + ``` + +##### 2.2 fastchat.serve.model_worker文件修改 + +1. 将fastchat.serve.model_worker文件的ModelWorker的__init__方法修改如下： + + ```python + class ModelWorker(BaseModelWorker): + def __init__( + self, + controller_addr: str, + worker_addr: str, + worker_id: str, + model_path: str, + model_names: List[str], + limit_worker_concurrency: int, + no_register: bool, + device: str, + num_gpus: int, + max_gpu_memory: str, + dtype: Optional[torch.dtype] = None, + load_8bit: bool = False, + cpu_offloading: bool = False, + gptq_config: Optional[GptqConfig] = None, + awq_config: Optional[AWQConfig] = None, + stream_interval: int = 2, + conv_template: Optional[str] = None, + embed_in_truncate: bool = False, + seed: Optional[int] = None, + load_kwargs = {}, #修改点 + **kwargs, + ): + super().__init__( + controller_addr, + worker_addr, + worker_id, + model_path, + model_names, + limit_worker_concurrency, + conv_template=conv_template, + ) + + logger.info(f"Loading the model {self.model_names} on worker {worker_id} ...") + self.model, self.tokenizer = load_model( + model_path, + device=device, + num_gpus=num_gpus, + max_gpu_memory=max_gpu_memory, + dtype=dtype, + load_8bit=load_8bit, + cpu_offloading=cpu_offloading, + gptq_config=gptq_config, + awq_config=awq_config, + load_kwargs=load_kwargs #修改点 + ) + self.device = device + if self.tokenizer.pad_token == None: + self.tokenizer.pad_token = self.tokenizer.eos_token + self.context_len = get_context_length(self.model.config) + print("**"*100) + self.generate_stream_func = get_generate_stream_function(self.model, model_path) + print(f"self.generate_stream_func{self.generate_stream_func}") + print("*"*100) + self.stream_interval = stream_interval + self.embed_in_truncate = embed_in_truncate + self.seed = seed + + if not no_register: + self.init_heart_beat() + ``` +2. 在fastchat.serve.model_worker文件的create_model_worker增加如下args参数： + + ```python + parser.add_argument("--load_kwargs",type=dict,default={}) + ``` + + 并将如下语句： + +```python + worker = ModelWorker( + args.controller_address, + args.worker_address, + worker_id, + args.model_path, + args.model_names, + args.limit_worker_concurrency, + no_register=args.no_register, + device=args.device, + num_gpus=args.num_gpus, + max_gpu_memory=args.max_gpu_memory, + dtype=str_to_torch_dtype(args.dtype), + load_8bit=args.load_8bit, + cpu_offloading=args.cpu_offloading, + gptq_config=gptq_config, + awq_config=awq_config, + stream_interval=args.stream_interval, + conv_template=args.conv_template, + embed_in_truncate=args.embed_in_truncate, + seed=args.seed, + ) +``` + +修改为： + +```python + worker = ModelWorker( + args.controller_address, + args.worker_address, + worker_id, + args.model_path, + args.model_names, + args.limit_worker_concurrency, + no_register=args.no_register, + device=args.device, + num_gpus=args.num_gpus, + max_gpu_memory=args.max_gpu_memory, + dtype=str_to_torch_dtype(args.dtype), + load_8bit=args.load_8bit, + cpu_offloading=args.cpu_offloading, + gptq_config=gptq_config, + awq_config=awq_config, + stream_interval=args.stream_interval, + conv_template=args.conv_template, + embed_in_truncate=args.embed_in_truncate, + seed=args.seed, + load_kwargs=args.load_kwargs + ) +``` + +至此，我们完成了fastchat加载ptuning的所有修改，在调用fastchat加载p-tuning时，可以通过加入 `PEFT_SHARE_BASE_WEIGHTS=true`，并以字典的形式添加--load_kwargs参数为训练ptuning时的pre_seq_len值即可，例如将2.2.2步骤中的 `parser.add_argument("--load_kwargs",type=dict,default={})`修改为： + +`parser.add_argument("--load_kwargs",type=dict,default={"pre_seq_len":16})` + +#### 3 langchain-chatchat代码修改： + +1. 在configs/serve_config.py中的FSCHAT_MODEL_WORKERS字典中增加如下字段： + + ``` + "load_kwargs": {"pre_seq_len": 16} #值修改为adapter_config.json中的pre_seq_len值 + ``` +2. 将startup.py中的create_model_worker_app修改为： + + ```python + def create_model_worker_app(log_level: str = "INFO", **kwargs) -> FastAPI: + """ + kwargs包含的字段如下： + host: + port: + model_names:[`model_name`] + controller_address: + worker_address: + + + 对于online_api: + online_api:True + worker_class: `provider` + 对于离线模型： + model_path: `model_name_or_path`,huggingface的repo-id或本地路径 + device:`LLM_DEVICE` + """ + import fastchat.constants + fastchat.constants.LOGDIR = LOG_PATH + from fastchat.serve.model_worker import worker_id, logger + import argparse + logger.setLevel(log_level) + + parser = argparse.ArgumentParser() + args = parser.parse_args([]) + + for k, v in kwargs.items(): + setattr(args, k, v) + + # 在线模型API + if worker_class := kwargs.get("worker_class"): + from fastchat.serve.model_worker import app + worker = worker_class(model_names=args.model_names, + controller_addr=args.controller_address, + worker_addr=args.worker_address) + sys.modules["fastchat.serve.model_worker"].worker = worker + # 本地模型 + else: + from configs.model_config import VLLM_MODEL_DICT + if kwargs["model_names"][0] in VLLM_MODEL_DICT and args.infer_turbo == "vllm": + import fastchat.serve.vllm_worker + from fastchat.serve.vllm_worker import VLLMWorker,app + from vllm import AsyncLLMEngine + from vllm.engine.arg_utils import AsyncEngineArgs,EngineArgs + args.tokenizer = args.model_path # 如果tokenizer与model_path不一致在此处添加 + args.tokenizer_mode = 'auto' + args.trust_remote_code= True + args.download_dir= None + args.load_format = 'auto' + args.dtype = 'auto' + args.seed = 0 + args.worker_use_ray = False + args.pipeline_parallel_size = 1 + args.tensor_parallel_size = 1 + args.block_size = 16 + args.swap_space = 4 # GiB + args.gpu_memory_utilization = 0.90 + args.max_num_batched_tokens = 2560 + args.max_num_seqs = 256 + args.disable_log_stats = False + args.conv_template = None + args.limit_worker_concurrency = 5 + args.no_register = False + args.num_gpus = 1 # vllm worker的切分是tensor并行，这里填写显卡的数量 + args.engine_use_ray = False + args.disable_log_requests = False + if args.model_path: + args.model = args.model_path + if args.num_gpus > 1: + args.tensor_parallel_size = args.num_gpus + + for k, v in kwargs.items(): + setattr(args, k, v) + + engine_args = AsyncEngineArgs.from_cli_args(args) + engine = AsyncLLMEngine.from_engine_args(engine_args) + + worker = VLLMWorker( + controller_addr = args.controller_address, + worker_addr = args.worker_address, + worker_id = worker_id, + model_path = args.model_path, + model_names = args.model_names, + limit_worker_concurrency = args.limit_worker_concurrency, + no_register = args.no_register, + llm_engine = engine, + conv_template = args.conv_template, + ) + sys.modules["fastchat.serve.vllm_worker"].engine = engine + sys.modules["fastchat.serve.vllm_worker"].worker = worker + + else: + from fastchat.serve.model_worker import app, GptqConfig, AWQConfig, ModelWorker + args.gpus = "0" # GPU的编号,如果有多个GPU，可以设置为"0,1,2,3" + args.max_gpu_memory = "20GiB" + args.num_gpus = 1 # model worker的切分是model并行，这里填写显卡的数量 + + args.load_8bit = False + args.cpu_offloading = None + args.gptq_ckpt = None + args.gptq_wbits = 16 + args.gptq_groupsize = -1 + args.gptq_act_order = False + args.awq_ckpt = None + args.awq_wbits = 16 + args.awq_groupsize = -1 + args.model_names = [] + args.conv_template = None + args.limit_worker_concurrency = 5 + args.stream_interval = 2 + args.no_register = False + args.embed_in_truncate = False + args.load_kwargs = {"pre_seq_len": 16} # 改************************* + for k, v in kwargs.items(): + setattr(args, k, v) + if args.gpus: + if args.num_gpus is None: + args.num_gpus = len(args.gpus.split(',')) + if len(args.gpus.split(",")) < args.num_gpus: + raise ValueError( + f"Larger --num-gpus ({args.num_gpus}) than --gpus {args.gpus}!" + ) + os.environ["CUDA_VISIBLE_DEVICES"] = args.gpus + gptq_config = GptqConfig( + ckpt=args.gptq_ckpt or args.model_path, + wbits=args.gptq_wbits, + groupsize=args.gptq_groupsize, + act_order=args.gptq_act_order, + ) + awq_config = AWQConfig( + ckpt=args.awq_ckpt or args.model_path, + wbits=args.awq_wbits, + groupsize=args.awq_groupsize, + ) + + worker = ModelWorker( + controller_addr=args.controller_address, + worker_addr=args.worker_address, + worker_id=worker_id, + model_path=args.model_path, + model_names=args.model_names, + limit_worker_concurrency=args.limit_worker_concurrency, + no_register=args.no_register, + device=args.device, + num_gpus=args.num_gpus, + max_gpu_memory=args.max_gpu_memory, + load_8bit=args.load_8bit, + cpu_offloading=args.cpu_offloading, + gptq_config=gptq_config, + awq_config=awq_config, + stream_interval=args.stream_interval, + conv_template=args.conv_template, + embed_in_truncate=args.embed_in_truncate, + load_kwargs=args.load_kwargs #改************************* + ) + sys.modules["fastchat.serve.model_worker"].args = args + sys.modules["fastchat.serve.model_worker"].gptq_config = gptq_config + + sys.modules["fastchat.serve.model_worker"].worker = worker + + MakeFastAPIOffline(app) + app.title = f"FastChat LLM Server ({args.model_names[0]})" + app._worker = worker + return app + ``` + +至此，我们完成了langchain-chatchat加载p-tuning的全部操作，将ptuing的路径添加到model_config的llm_dict，如 +``` +chatglm2-6b: 'p-tuning-peft' +``` + +即可以如下方式加载p-tuning： + +```shell +PEFT_SHARE_BASE_WEIGHTS=true python startup.py -a + +``` + + +## 预处理知识库文件 + +在载入知识库文件的时候，直接上传文档虽然能实现基础的问答，但是，其效果并不能发挥到最佳水平。因此，我们建议开发者对知识库文件做出以下的预处理。 +以下方式的预处理如果执行了，有概率提升模型的召回率。 + +### 1. 使用``` TXT / Markdown ``` 等格式化文件，并按照要点排版 +例如，以下段落应该被处理成如下内容后在嵌入知识库，会有更好的效果。 +``` +原文: PDF类型 +查特查特团队荣获AGI Playground Hackathon黑客松“生产力工具的新想象”赛道季军 +2023年10月16日, Founder Park在近日结束的AGI Playground Hackathon黑客松比赛中，查特查特团队展现出色的实力，荣获了“生产力工具的新想象”赛道季军。本次比赛由Founder Park主办，并由智谱、Dify、Zilliz、声网、AWS云服务等企业协办。 +比赛吸引了120多支参赛团队，最终有36支队伍进入决赛，其中34支队伍成功完成了路演。比赛规定，所有参赛选手必须在短短的48小时内完成一个应用产品开发，同时要求使用智谱大模型及Zilliz向量数据库进行开发。 +查特查特团队的现场参赛人员由两名项目成员组成： +来自A大学的小明负责了Agent旅游助手的开发、场地协调以及团队住宿和行程的安排；在保证团队完赛上做出了主要贡献。作为队长，栋宇坚持自信，创新，沉着的精神，不断提出改进方案并抓紧落实，遇到相关问题积极请教老师，提高了团队开发效率。 +作为核心开发者的B公司小蓝，他则主管Agent智能知识库查询开发、Agent底层框架设计、相关API调整和UI调整。在最后，他代表团队在规定的时间内呈现了产品的特点和优势，并完美的展示了产品demo。为团队最终产品能够得到奖项做出了重要贡献。 +``` +修改后的Markdown文件，具有更高的召回率 +``` +# 查特查特团队荣获AGI Playground Hackathon黑客松“生产力工具的新想象”赛道季军。 + +## 报道简介 +2023年10月16日, Founder Park在近日结束的AGI Playground Hackathon黑客松比赛中，查特查特团队展现出色的实力，荣获了“生产力工具的新想象”赛道季军。本次比赛由Founder Park主办，并由智谱、Dify、Zilliz、声网、AWS云服务等企业协办。 + +## 比赛介绍 + +比赛吸引了120多支参赛团队，最终有36支队伍进入决赛，其中34支队伍成功完成了路演。比赛规定，所有参赛选手必须在短短的48小时内完成一个应用产品开发，同时要求使用智谱大模型及Zilliz向量数据库进行开发。 + +## 获奖队员简介 + ++ 小明，A大学 + + 负责Agent旅游助手的开发、场地协调以及团队住宿和行程的安排 + + 在保证团队完赛上做出了主要贡献。作为队长，栋宇坚持自信，创新，沉着的精神，不断提出改进方案并抓紧落实，遇到相关问题积极请教老师，提高了团队开发效率。 + ++ 小蓝，B公司 + + 主管Agent智能知识库查询开发、Agent底层框架设计、相关API调整和UI调整。 + + 代表团队在规定的时间内呈现了产品的特点和优势，并完美的展示了产品demo。 +``` + +### 2. 减少文件中冲突的内容，分门别类存放数据 + +就像人类寻找相关点一样，如果在多份文件中存在相似的内容，可能会导致模型无法准确的搜索到相关内容。 +因此，需要减少文件中相似的内容，或将其分在不同的知识库中。 +例如，以下两个句子中，如果搜索外籍教师，则具有歧义，非常容易搜索到错误答案。 + +``` +文件一： +在大数据专业中，我们已经拥有超过1/3的外籍博士和教师。 + +文件二： + +本专业具有40%的外籍教师比例， +本专业有博士生10人，研究生12人。 +``` + +### 3. 减少具有歧义的句子 +知识库中应该减少具有歧义的句子和段落，或者汉语的高级用法，例如 +``` +1. 他说他会杀了那个人。 +2. 你说啥子？ +3. 我喜欢你的头发。 +4. 地板真的滑，我差点没摔倒。 +``` +在相似度模型对比的时候，仅仅能搜索句子的表面意思，因此，使用有歧义的句子和段落可能导致搜索错误。 + +### 4. 减少单个文件的大小，减少文件中的特殊符号 ++ 上传知识库的单个文件不建议超过5MB，以免出现向量化中断卡死等情况。同时，上传大文件不要使用faiss数据库。 ++ 减少上传文件中的中文符号，特殊符号，无意义空格等。 + +## 自定义的关键词调整Embedding模型 + +1.首先准备一个关键字的文本文件，每一行是一个关键字。例如： +``` +文件key_words.txt： +iphone13pro +中石油 +``` +2. 配置kb_config.py +``` +EMBEDDING_KEYWORD_FILE = "embedding_keywords.txt" +``` +3. 运行```embeddings/add_embedding_keywords.py``` +``` +输入的文本（这里只是一个没分隔的一串字符）：iphone13pro +生成的token id序列：[101, 21128, 102] +token到token id的映射： +[CLS]->101 +iphone13pro->21128 +[SEP]->102 + +输入的文本：中石油 +生成的token id序列：[101, 21129, 102] +token到token id的映射： +[CLS]->101 +中石油->21129 +[SEP]->102 +``` +这样，你就获得了一个新的带有关键词调整的Embedding模型 +## 实际使用效果 +在这里，我们放置了一些成功调用的效果图，方便开发者进行查看自己是否成功运行了框架。 + +### 检查是否成功上传/管理自己的知识库 + +在WebUI界面上传知识库，则必须保证知识库进行向量化，成功之后，文件会被切分并在向量位置打钩。 +下图展示了成功上传知识库的画面 + +![成功上传知识库](https://github.com/chatchat-space/Langchain-Chatchat/blob/dev/img/init_knowledge_base.jpg) + +请确保所有知识库都已经进行了向量化。 + +### 检查是否成功开启LLM对话 + +若打开webui后，在该模式下能成功跟大模型对话即成功调用。 + +下图为成功调用LLM的效果图: + +![LLM对话](https://github.com/chatchat-space/Langchain-Chatchat/blob/dev/img/LLM_success.png) + +### 检查是否成功调用知识库/搜索 +若成功调用知识库，则你应该能看到，在大模型回答的下方有一个```知识库匹配结果```的展开框，并且内部显示了相关的匹配结果。 +如果没有搜索到相关内容，则会提示```根据已知信息无法回答问题```,并且下拉框中没有任何内容。 + +下图为成功调用知识库效果图： + +![成功调用知识库](https://github.com/chatchat-space/Langchain-Chatchat/blob/dev/img/knowledge_base_success.jpg) + +在这个案例中，第一次用户的提问无法在知识库中寻找到合适的答案，因此，大模型回答了```根据已知信息无法回答问题```。 + +第二次用户的提问能在知识库中寻找到合适的答案，因此，大模型给出了一个正确的回答。 + +__注意__: 知识库的搜索情况取决于嵌入模型的准度，分词器的设置，知识库的排版和大模型的数量，提示词设定等多个因素。因此，需要开发者进行深度的优化和调试。 + +### 检查是否成功调用Agent工具 + +若成功调用Agent工具，则你应该看到大模型完整的思维过程，这会在```思考过程```下拉框中显示出来。如果成功调用Agent工具，则你应该看到Markdown引用效果的工具使用情况。 +在Agent对话模式中，```思考过程```中显示的是大模型的思考过程，而下拉框之前的内容为大模型的```Final Answer```，缺乏中间的运算过程。 + +下图展现了一个成功调用Agent工具的效果图: + +![成功调用单个Agent工具](https://github.com/chatchat-space/Langchain-Chatchat/blob/dev/img/agent_success.png) + +本框架支持模型连续掉用多个Agent工具，下图展示了一个一个提问中大模型连续调用多个Agent工具的效果图: + +![连续调用多个Agent工具](https://github.com/chatchat-space/Langchain-Chatchat/blob/dev/img/agent_continue.png) + +在这个案例中，```3900```是大模型的最终答案，其余都是思考过程。 + + + + + diff --git a/chatchat/chatchat/data/knowledge_base/samples/content/wiki/自定义.md b/chatchat/chatchat/data/knowledge_base/samples/content/wiki/自定义.md new file mode 100644 index 00000000..5e339f46 --- /dev/null +++ b/chatchat/chatchat/data/knowledge_base/samples/content/wiki/自定义.md @@ -0,0 +1,159 @@ +## 使用自定义的分词器 +1. 在```text_splitter```文件夹下新建一个文件，文件名为您的分词器名字，比如`my_splitter.py`，然后在`__init__.py`中导入您的分词器，如下所示： +```python +from .my_splitter import MySplitter +``` + +2. 修改```config/model_config.py```文件，将您的分词器名字添加到```text_splitter_dict```中，如下所示： +```python +MySplitter: { + "source": "huggingface", # 选择tiktoken则使用openai的方法 + "tokenizer_name_or_path": "your tokenizer", #如果选择huggingface则使用huggingface的方法，部分tokenizer需要从Huggingface下载 + } +TEXT_SPLITTER = "MySplitter" +``` + +完成上述步骤后，就能使用自己的分词器了。 + +## 使用自定义的 Agent 工具 + +1. 创建自己的Agent工具 + ++ 开发者在```server/agent```文件中创建一个自己的文件，并将其添加到```tools_select.py```中。这样就完成了Tools的设定。 + ++ 当您创建了一个```custom_agent.py```文件，其中包含一个```work```函数，那么您需要在```tools_select.py```中添加如下代码： +```python +from custom_agent import work +Tool.from_function( + func=work, + name="该函数的名字", + description="" + ) +``` ++ 请注意，如果你确定在某一个工程中不会使用到某个工具，可以将其从Tools中移除，降低模型分类错误导致使用错误工具的风险。 + +2. 修改 ```custom_template.py``` 文件 + +开发者需要根据自己选择的大模型设定适合该模型的Agent Prompt和自自定义返回格式。 +```` +""" +Answer the following questions as best you can. You have access to the following tools: +{tools} +Use the following format: +Question: the input question you must answer +Thought: you should always think about what to do +Action: the action to take, should be one of [{tool_names}] +Action Input: the input to the action +Observation: the result of the action +... (this Thought/Action/Action Input/Observation can be repeated zero or more times) +Thought: I now know the final answer +Final Answer: the final answer to the original input question +Begin! +history: +{history} +Question: {input} +Thought: {agent_scratchpad} +""" +```` +除了使用 `Zero React` 的提示词方案，开发者可以自行对提示词进行修改，或者使用 Langchain 提供的其他的Agent结构。例如，如果您使用的模型为`ChatGLM3-6B`模型，我们提供了一个可以正常运行`ChatGLM3-6B`的Agent提示词，该提示词与 Langchain 的 `struct Agent`相似，其内容如下： +```` + +"ChatGLM3": +""" +You can answer using the tools, or answer directly using your knowledge without using the tools.Respond to the human as helpfully and accurately as possible. +You have access to the following tools: +{tools} +Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input). +Valid "action" values: "Final Answer" or [{tool_names}] +Provide only ONE action per $JSON_BLOB, as shown: + +``` +{{{{ + "action": $TOOL_NAME, + "action_input": $INPUT +}}}} +``` +Follow this format: + +Question: input question to answer +Thought: consider previous and subsequent steps +Action: +``` +$JSON_BLOB +``` +Observation: action result +... (repeat Thought/Action/Observation N times) +Thought: I know what to respond +Action: +``` +{{{{ + "action": "Final Answer", + "action_input": "Final response to human" +}}}} +Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary. Respond directly if appropriate. Format is Action:```$JSON_BLOB```then Observation:. + +history: {history} + +Question: {input} + +Thought: {agent_scratchpad} +""", +```` + +3. 让不支持 Langchain 调用方式的但具备 Agent 能力的模型展现能力 + +以**ChatGLM3-6B**为代表的模型，虽然具有 Function Call 能力，但其对齐格式与 Langchain 提供默认Agent格式并不符合，因此无法使用 Langchain 自身能力实现 Function Call。在我们的框架中，您可以在 ```server/Agent/custom_agent/``` 文件夹中自行复现更多模型的 Agent 能力实现。 + +在完成上述步骤之后，您还需要到```server/chat/agent_chat/```中导入您的模块来实现特殊判定。 + +同时，你应该在调用工具的时候使用自定义的模板，我们以`GLM`系列模型进行演示，如果您在使用`GLM`模型进行工具调用，你应该使用`model_config.py`中的`ChatGLM3`模板。 + +4. 局限性 + +- 由于 React Agent 的脆弱性，temperature 参数的设置对于模型的效果有很大的影响。我们建议开发者在使用自定义 Agent 时，对于不同的模型，将其设置成0.1以下，以达到更好的效果。 +- 目前，官方仅对 **ChatGLM3-6B** 一种模型进行了 非 Langchain 对齐格式下的能力激活，我们欢迎开发者自行探索其他模型，并提交对应的 PR，让框架支持更多的 Agent 模型。 +- 在`0.2.x`版本中，我们没有对`Plan`进行优化，因此，连续调用工具的能力较差，我们会在`0.3.x`中优化这一问题。此外，经过测试，本地模型在工具调用上的表现不如在线模型，我们更推荐使用 `gpt4-1106-Preview` 来完成工具调用的任务。 + +## 使用自定义的微调模型 + +- 本项目基于 FastChat 加载 LLM 服务，故需以 FastChat 加载 PEFT 路径。 +- 开发者需要保证路径名称里必须有 peft 这个词。 +- 配置文件的名字为 ```adapter_config.json``` +- peft 路径下包含.bin 格式的 PEFT 权重， peft路径在startup.py中 ```create_model_worker_app``` 函数的 ```args.model_names``` 中指定 +```python + args.model_names = ["/home/ubuntu/your_peft_folder/peft"] + +``` +- 执行代码之，应该设定环境变量 +``` +PEFT_SHARE_BASE_WEIGHTS=true +``` + +注：如果上述方式启动失败，则需要以标准的 FastChat 服务启动方式分步启动，PEFT加载详细步骤参考以下ISSUE + +[加载lora微调后模型失效](https://github.com/chatchat-space/Langchain-Chatchat/issues/1130#issuecomment-1685291822) + +在```最佳实践```章节中，我们为开发者做了更详细的模型载入文档。 + +__该功能可能还具有一定的Bug，需要开发者仔细适配。__ + + +## 使用自定义的嵌入模型 + +- 使用自定义的嵌入模型，开发者需要将其合并到原始的嵌入模型中，之后仅需将其路径添加到```config/model_config.py```中并选择自己的模型启动即可。 +- 如果想自己在Embedding模型中支持 自定义的关键字，需要在 ```embeddings/embedding_keywords.txt```中设定好自己的关键字 +- 运行 ```embeddings/add_embedding_keywords.py``` +- 将生成的新Embedding模型地址放入```configs/model_config.py```中并选择， +``` +"custom-embedding": "your path", +``` +并设置 +``` +EMBEDDING_MODEL = "custom-embedding" +``` +即可调用加入关键字的embedding模型。 +在```最佳实践```章节中，我们为某几个关键词定制了一个Embed模型。 + +## 日志功能 + 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等应用框架实现，开源、可离线部署的检索增强生成(RAG)大模型知识库项目。 + +--- + +## 目录 + +* [介绍](README.md#介绍) +* [解决的痛点](README.md#解决的痛点) +* [快速上手](README.md#快速上手) + * [1. 环境配置](README.md#1-环境配置) + * [2. 模型下载](README.md#2-模型下载) + * [3. 初始化知识库和配置文件](README.md#3-初始化知识库和配置文件) + * [4. 一键启动](README.md#4-一键启动) + * [5. 启动界面示例](README.md#5-启动界面示例) +* [联系我们](README.md#联系我们) + + +## 介绍 + +🤖️ 一种利用 [langchain](https://github.com/hwchase17/langchain) 思想实现的基于本地知识库的问答应用，目标期望建立一套对中文场景与开源模型支持友好、可离线运行的知识库问答解决方案。 + +💡 受 [GanymedeNil](https://github.com/GanymedeNil) 的项目 [document.ai](https://github.com/GanymedeNil/document.ai) 和 [AlexZhangji](https://github.com/AlexZhangji) 创建的 [ChatGLM-6B Pull Request](https://github.com/THUDM/ChatGLM-6B/pull/216) 启发，建立了全流程可使用开源模型实现的本地知识库问答应用。本项目的最新版本中通过使用 [FastChat](https://github.com/lm-sys/FastChat) 接入 Vicuna, Alpaca, LLaMA, Koala, RWKV 等模型，依托于 [langchain](https://github.com/langchain-ai/langchain) 框架支持通过基于 [FastAPI](https://github.com/tiangolo/fastapi) 提供的 API 调用服务，或使用基于 [Streamlit](https://github.com/streamlit/streamlit) 的 WebUI 进行操作。 + +✅ 依托于本项目支持的开源 LLM 与 Embedding 模型，本项目可实现全部使用**开源**模型**离线私有部署**。与此同时，本项目也支持 OpenAI GPT API 的调用，并将在后续持续扩充对各类模型及模型 API 的接入。 + +⛓️ 本项目实现原理如下图所示，过程包括加载文件 -> 读取文本 -> 文本分割 -> 文本向量化 -> 问句向量化 -> 在文本向量中匹配出与问句向量最相似的 `top k`个 -> 匹配出的文本作为上下文和问题一起添加到 `prompt`中 -> 提交给 `LLM`生成回答。 + +📺 [原理介绍视频](https://www.bilibili.com/video/BV13M4y1e7cN/?share_source=copy_web&vd_source=e6c5aafe684f30fbe41925d61ca6d514) + +![实现原理图](img/langchain+chatglm.png) + +从文档处理角度来看，实现流程如下： + +![实现原理图2](img/langchain+chatglm2.png) + +🚩 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。 + +🌐 [AutoDL 镜像](https://www.codewithgpu.com/i/chatchat-space/Langchain-Chatchat/Langchain-Chatchat) 中 `v11` 版本所使用代码已更新至本项目 `v0.2.7` 版本。 + +🐳 [Docker 镜像](registry.cn-beijing.aliyuncs.com/chatchat/chatchat:0.2.6) 已经更新到 ```0.2.7``` 版本。 + +🌲 一行命令运行 Docker ： + +```shell +docker run -d --gpus all -p 80:8501 registry.cn-beijing.aliyuncs.com/chatchat/chatchat:0.2.7 +``` + +🧩 本项目有一个非常完整的[Wiki](https://github.com/chatchat-space/Langchain-Chatchat/wiki/) ， README只是一个简单的介绍，__仅仅是入门教程，能够基础运行__。 如果你想要更深入的了解本项目，或者想对本项目做出贡献。请移步 [Wiki](https://github.com/chatchat-space/Langchain-Chatchat/wiki/) 界面 + +## 解决的痛点 + +该项目是一个可以实现 __完全本地化__推理的知识库增强方案, 重点解决数据安全保护，私域化部署的企业痛点。 +本开源方案采用```Apache License```，可以免费商用，无需付费。 + +我们支持市面上主流的本地大预言模型和Embedding模型，支持开源的本地向量数据库。 +支持列表详见[Wiki](https://github.com/chatchat-space/Langchain-Chatchat/wiki/) + + +## 快速上手 + +### 1. 环境配置 + ++ 首先，确保你的机器安装了 Python 3.8 - 3.10 +``` +$ python --version +Python 3.10.12 +``` +接着，创建一个虚拟环境，并在虚拟环境内安装项目的依赖 +```shell + +# 拉取仓库 +$ git clone https://github.com/chatchat-space/Langchain-Chatchat.git + +# 进入目录 +$ cd Langchain-Chatchat + +# 安装全部依赖 +$ pip install -r requirements.txt +$ pip install -r requirements_api.txt +$ pip install -r requirements_webui.txt + +# 默认依赖包括基本运行环境（FAISS向量库）。如果要使用 milvus/pg_vector 等向量库，请将 requirements.txt 中相应依赖取消注释再安装。 +``` +### 2， 模型下载 + +如需在本地或离线环境下运行本项目，需要首先将项目所需的模型下载至本地，通常开源 LLM 与 Embedding 模型可以从 [HuggingFace](https://huggingface.co/models) 下载。 + +以本项目中默认使用的 LLM 模型 [THUDM/ChatGLM2-6B](https://huggingface.co/THUDM/chatglm2-6b) 与 Embedding 模型 [moka-ai/m3e-base](https://huggingface.co/moka-ai/m3e-base) 为例： + +下载模型需要先[安装 Git LFS](https://docs.github.com/zh/repositories/working-with-files/managing-large-files/installing-git-large-file-storage)，然后运行 + +```Shell +$ git lfs install +$ git clone https://huggingface.co/THUDM/chatglm2-6b +$ git clone https://huggingface.co/moka-ai/m3e-base +``` +### 3. 初始化知识库和配置文件 + +按照下列方式初始化自己的知识库和简单的复制配置文件 +```shell +$ python copy_config_example.py +$ python init_database.py --recreate-vs + ``` +### 4. 一键启动 + +按照以下命令启动项目 +```shell +$ python startup.py -a +``` +### 5. 启动界面示例 + +如果正常启动，你将能看到以下界面 + +1. FastAPI Docs 界面 + +![](img/fastapi_docs_026.png) + +2. Web UI 启动界面示例： + +- Web UI 对话界面： + +![img](img/LLM_success.png) + +- Web UI 知识库管理页面： + +![](img/init_knowledge_base.jpg) + + +### 注意 + +以上方式只是为了快速上手，如果需要更多的功能和自定义启动方式 ，请参考[Wiki](https://github.com/chatchat-space/Langchain-Chatchat/wiki/) + + +--- +## 项目里程碑 + + +--- +## 联系我们 +### Telegram +[![Telegram](https://img.shields.io/badge/Telegram-2CA5E0?style=for-the-badge&logo=telegram&logoColor=white "langchain-chatglm")](https://t.me/+RjliQ3jnJ1YyN2E9) + +### 项目交流群 +二维码 + +🎉 Langchain-Chatchat 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。 + +### 公众号 + +二维码 + +🎉 Langchain-Chatchat 项目官方公众号，欢迎扫码关注。 diff --git a/chatchat/chatchat/data/knowledge_base/test/vector_store/bge-large-zh-v1.5/index.faiss b/chatchat/chatchat/data/knowledge_base/test/vector_store/bge-large-zh-v1.5/index.faiss new file mode 100644 index 00000000..700502e6 Binary files /dev/null and b/chatchat/chatchat/data/knowledge_base/test/vector_store/bge-large-zh-v1.5/index.faiss differ diff --git 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chatchat/chatchat/data/nltk_data/tokenizers/punkt/swedish.pickle diff --git a/chatchat/data/nltk_data/tokenizers/punkt/turkish.pickle b/chatchat/chatchat/data/nltk_data/tokenizers/punkt/turkish.pickle similarity index 100% rename from chatchat/data/nltk_data/tokenizers/punkt/turkish.pickle rename to chatchat/chatchat/data/nltk_data/tokenizers/punkt/turkish.pickle diff --git a/chatchat/img/LLM_success.png b/chatchat/chatchat/img/LLM_success.png similarity index 100% rename from chatchat/img/LLM_success.png rename to chatchat/chatchat/img/LLM_success.png diff --git a/chatchat/img/agent_continue.png b/chatchat/chatchat/img/agent_continue.png similarity index 100% rename from chatchat/img/agent_continue.png rename to chatchat/chatchat/img/agent_continue.png diff --git a/chatchat/img/agent_success.png b/chatchat/chatchat/img/agent_success.png similarity index 100% rename from chatchat/img/agent_success.png rename to chatchat/chatchat/img/agent_success.png diff --git a/chatchat/img/chatchat-qrcode.jpg b/chatchat/chatchat/img/chatchat-qrcode.jpg similarity index 100% rename from chatchat/img/chatchat-qrcode.jpg rename to chatchat/chatchat/img/chatchat-qrcode.jpg diff --git a/chatchat/img/chatchat_icon_blue_square_v2.png b/chatchat/chatchat/img/chatchat_icon_blue_square_v2.png similarity index 100% rename from chatchat/img/chatchat_icon_blue_square_v2.png rename to chatchat/chatchat/img/chatchat_icon_blue_square_v2.png diff --git a/chatchat/img/docker_logs.png b/chatchat/chatchat/img/docker_logs.png similarity index 100% rename from chatchat/img/docker_logs.png rename to chatchat/chatchat/img/docker_logs.png diff --git a/chatchat/img/fastapi_docs_026.png b/chatchat/chatchat/img/fastapi_docs_026.png similarity index 100% rename from chatchat/img/fastapi_docs_026.png rename to chatchat/chatchat/img/fastapi_docs_026.png diff --git a/chatchat/img/init_knowledge_base.jpg b/chatchat/chatchat/img/init_knowledge_base.jpg similarity index 100% rename from chatchat/img/init_knowledge_base.jpg rename to chatchat/chatchat/img/init_knowledge_base.jpg diff --git a/chatchat/img/knowledge_base_success.jpg b/chatchat/chatchat/img/knowledge_base_success.jpg similarity index 100% rename from chatchat/img/knowledge_base_success.jpg rename to chatchat/chatchat/img/knowledge_base_success.jpg diff --git a/chatchat/img/langchain+chatglm.png b/chatchat/chatchat/img/langchain+chatglm.png similarity index 100% rename from chatchat/img/langchain+chatglm.png rename to chatchat/chatchat/img/langchain+chatglm.png diff --git a/chatchat/img/langchain+chatglm2.png b/chatchat/chatchat/img/langchain+chatglm2.png similarity index 100% rename from chatchat/img/langchain+chatglm2.png rename to chatchat/chatchat/img/langchain+chatglm2.png diff --git a/chatchat/img/logo-long-chatchat-trans-v2.png b/chatchat/chatchat/img/logo-long-chatchat-trans-v2.png similarity index 100% rename from chatchat/img/logo-long-chatchat-trans-v2.png rename to chatchat/chatchat/img/logo-long-chatchat-trans-v2.png diff --git a/chatchat/img/official_account_qr.png b/chatchat/chatchat/img/official_account_qr.png similarity index 100% rename from chatchat/img/official_account_qr.png rename to chatchat/chatchat/img/official_account_qr.png diff --git a/chatchat/img/official_wechat_mp_account.png b/chatchat/chatchat/img/official_wechat_mp_account.png similarity index 100% rename from chatchat/img/official_wechat_mp_account.png rename to chatchat/chatchat/img/official_wechat_mp_account.png diff --git a/chatchat/img/partners/autodl.svg b/chatchat/chatchat/img/partners/autodl.svg similarity index 100% rename from chatchat/img/partners/autodl.svg rename to chatchat/chatchat/img/partners/autodl.svg diff --git a/chatchat/img/partners/aws.svg b/chatchat/chatchat/img/partners/aws.svg similarity index 100% rename from chatchat/img/partners/aws.svg rename to chatchat/chatchat/img/partners/aws.svg diff --git a/chatchat/img/partners/chatglm.svg b/chatchat/chatchat/img/partners/chatglm.svg similarity index 100% rename from chatchat/img/partners/chatglm.svg rename to chatchat/chatchat/img/partners/chatglm.svg diff --git a/chatchat/img/partners/zhenfund.svg b/chatchat/chatchat/img/partners/zhenfund.svg similarity index 100% rename from chatchat/img/partners/zhenfund.svg rename to chatchat/chatchat/img/partners/zhenfund.svg diff --git a/chatchat/img/qr_code_90.jpg b/chatchat/chatchat/img/qr_code_90.jpg similarity index 100% rename from chatchat/img/qr_code_90.jpg rename to chatchat/chatchat/img/qr_code_90.jpg diff --git a/chatchat/img/qr_code_90.png b/chatchat/chatchat/img/qr_code_90.png similarity index 100% rename from chatchat/img/qr_code_90.png rename to chatchat/chatchat/img/qr_code_90.png diff --git a/chatchat/img/qr_code_91.jpg b/chatchat/chatchat/img/qr_code_91.jpg similarity index 100% rename from chatchat/img/qr_code_91.jpg rename to chatchat/chatchat/img/qr_code_91.jpg diff --git a/chatchat/img/qr_code_92.jpg b/chatchat/chatchat/img/qr_code_92.jpg similarity index 100% rename from chatchat/img/qr_code_92.jpg rename to chatchat/chatchat/img/qr_code_92.jpg diff --git a/chatchat/img/qr_code_93.jpg b/chatchat/chatchat/img/qr_code_93.jpg similarity index 100% rename from chatchat/img/qr_code_93.jpg rename to chatchat/chatchat/img/qr_code_93.jpg diff --git a/chatchat/img/qr_code_94.jpg b/chatchat/chatchat/img/qr_code_94.jpg similarity index 100% rename from chatchat/img/qr_code_94.jpg rename to chatchat/chatchat/img/qr_code_94.jpg diff --git a/chatchat/img/qr_code_95.jpg b/chatchat/chatchat/img/qr_code_95.jpg similarity index 100% rename from chatchat/img/qr_code_95.jpg rename to chatchat/chatchat/img/qr_code_95.jpg diff --git a/chatchat/img/qr_code_96.jpg b/chatchat/chatchat/img/qr_code_96.jpg similarity index 100% rename from chatchat/img/qr_code_96.jpg rename to chatchat/chatchat/img/qr_code_96.jpg diff --git a/chatchat/img/qrcode_90_2.jpg b/chatchat/chatchat/img/qrcode_90_2.jpg similarity index 100% rename from chatchat/img/qrcode_90_2.jpg rename to chatchat/chatchat/img/qrcode_90_2.jpg diff --git a/chatchat/init_database.py b/chatchat/chatchat/init_database.py similarity index 100% rename from chatchat/init_database.py rename to chatchat/chatchat/init_database.py diff --git a/chatchat/model_loaders/xinference_manager.py b/chatchat/chatchat/model_loaders/xinference_manager.py similarity index 100% rename from chatchat/model_loaders/xinference_manager.py rename to chatchat/chatchat/model_loaders/xinference_manager.py diff --git a/chatchat/startup.py b/chatchat/chatchat/startup.py similarity index 98% rename from chatchat/startup.py rename to chatchat/chatchat/startup.py index eeef005e..839ae8b5 100644 --- a/chatchat/startup.py +++ b/chatchat/chatchat/startup.py @@ -57,13 +57,16 @@ def run_api_server(started_event: mp.Event = None, run_mode: str = None): def run_webui(started_event: mp.Event = None, run_mode: str = None): + import sys from chatchat_server.utils import set_httpx_config + set_httpx_config() host = WEBUI_SERVER["host"] port = WEBUI_SERVER["port"] - cmd = ["streamlit", "run", "webui.py", + st_exe = os.path.join(os.path.dirname(sys.executable),"scripts","streamlit.exe") + cmd = [st_exe, "run", "chatchat/chatchat/webui.py", "--server.address", host, "--server.port", str(port), "--theme.base", "light", diff --git a/chatchat/tests/api/test_kb_api.py b/chatchat/chatchat/tests/api/test_kb_api.py similarity index 100% rename from chatchat/tests/api/test_kb_api.py rename to chatchat/chatchat/tests/api/test_kb_api.py diff --git a/chatchat/tests/api/test_kb_api_request.py b/chatchat/chatchat/tests/api/test_kb_api_request.py similarity index 100% rename from chatchat/tests/api/test_kb_api_request.py rename to chatchat/chatchat/tests/api/test_kb_api_request.py diff --git a/chatchat/tests/api/test_kb_summary_api.py b/chatchat/chatchat/tests/api/test_kb_summary_api.py similarity index 100% rename from chatchat/tests/api/test_kb_summary_api.py rename to chatchat/chatchat/tests/api/test_kb_summary_api.py diff --git a/chatchat/tests/api/test_openai_wrap.py b/chatchat/chatchat/tests/api/test_openai_wrap.py similarity index 100% rename from chatchat/tests/api/test_openai_wrap.py rename to chatchat/chatchat/tests/api/test_openai_wrap.py diff --git a/chatchat/tests/api/test_server_state_api.py b/chatchat/chatchat/tests/api/test_server_state_api.py similarity index 100% rename from chatchat/tests/api/test_server_state_api.py rename to chatchat/chatchat/tests/api/test_server_state_api.py diff --git a/chatchat/tests/api/test_stream_chat_api.py b/chatchat/chatchat/tests/api/test_stream_chat_api.py similarity index 100% rename from chatchat/tests/api/test_stream_chat_api.py rename to chatchat/chatchat/tests/api/test_stream_chat_api.py diff --git a/chatchat/tests/api/test_stream_chat_api_thread.py b/chatchat/chatchat/tests/api/test_stream_chat_api_thread.py similarity index 100% rename from chatchat/tests/api/test_stream_chat_api_thread.py rename to chatchat/chatchat/tests/api/test_stream_chat_api_thread.py diff --git a/chatchat/tests/api/test_tools.py b/chatchat/chatchat/tests/api/test_tools.py similarity index 100% rename from chatchat/tests/api/test_tools.py rename to chatchat/chatchat/tests/api/test_tools.py diff --git a/chatchat/tests/custom_splitter/test_different_splitter.py b/chatchat/chatchat/tests/custom_splitter/test_different_splitter.py similarity index 100% rename from chatchat/tests/custom_splitter/test_different_splitter.py rename to chatchat/chatchat/tests/custom_splitter/test_different_splitter.py diff --git a/chatchat/tests/document_loader/test_imgloader.py b/chatchat/chatchat/tests/document_loader/test_imgloader.py similarity index 100% rename from chatchat/tests/document_loader/test_imgloader.py rename to chatchat/chatchat/tests/document_loader/test_imgloader.py diff --git a/chatchat/tests/document_loader/test_pdfloader.py b/chatchat/chatchat/tests/document_loader/test_pdfloader.py similarity index 100% rename from chatchat/tests/document_loader/test_pdfloader.py rename to chatchat/chatchat/tests/document_loader/test_pdfloader.py diff --git a/chatchat/tests/kb_vector_db/__init__.py b/chatchat/chatchat/tests/kb_vector_db/__init__.py similarity index 100% rename from chatchat/tests/kb_vector_db/__init__.py rename to chatchat/chatchat/tests/kb_vector_db/__init__.py diff --git a/chatchat/tests/kb_vector_db/test_faiss_kb.py b/chatchat/chatchat/tests/kb_vector_db/test_faiss_kb.py similarity index 100% rename from chatchat/tests/kb_vector_db/test_faiss_kb.py rename to chatchat/chatchat/tests/kb_vector_db/test_faiss_kb.py diff --git a/chatchat/tests/kb_vector_db/test_milvus_db.py b/chatchat/chatchat/tests/kb_vector_db/test_milvus_db.py similarity index 100% rename from chatchat/tests/kb_vector_db/test_milvus_db.py rename to chatchat/chatchat/tests/kb_vector_db/test_milvus_db.py diff --git a/chatchat/tests/kb_vector_db/test_pg_db.py b/chatchat/chatchat/tests/kb_vector_db/test_pg_db.py similarity index 100% rename from chatchat/tests/kb_vector_db/test_pg_db.py rename to chatchat/chatchat/tests/kb_vector_db/test_pg_db.py diff --git a/chatchat/tests/samples/ocr_test.docx b/chatchat/chatchat/tests/samples/ocr_test.docx similarity index 100% rename from chatchat/tests/samples/ocr_test.docx rename to chatchat/chatchat/tests/samples/ocr_test.docx diff --git a/chatchat/tests/samples/ocr_test.jpg b/chatchat/chatchat/tests/samples/ocr_test.jpg similarity index 100% rename from chatchat/tests/samples/ocr_test.jpg rename to chatchat/chatchat/tests/samples/ocr_test.jpg diff --git a/chatchat/tests/samples/ocr_test.pdf b/chatchat/chatchat/tests/samples/ocr_test.pdf similarity index 100% rename from chatchat/tests/samples/ocr_test.pdf rename to chatchat/chatchat/tests/samples/ocr_test.pdf diff --git a/chatchat/tests/samples/ocr_test.pptx b/chatchat/chatchat/tests/samples/ocr_test.pptx similarity index 100% rename from chatchat/tests/samples/ocr_test.pptx rename to chatchat/chatchat/tests/samples/ocr_test.pptx diff --git a/chatchat/tests/test_migrate.py b/chatchat/chatchat/tests/test_migrate.py similarity index 100% rename from chatchat/tests/test_migrate.py rename to chatchat/chatchat/tests/test_migrate.py diff --git a/chatchat/tests/test_qwen_agent.py b/chatchat/chatchat/tests/test_qwen_agent.py similarity index 100% rename from chatchat/tests/test_qwen_agent.py rename to chatchat/chatchat/tests/test_qwen_agent.py diff --git a/chatchat/webui.py b/chatchat/chatchat/webui.py similarity index 95% rename from chatchat/webui.py rename to chatchat/chatchat/webui.py index 5520a9cf..396d5d48 100644 --- a/chatchat/webui.py +++ b/chatchat/chatchat/webui.py @@ -24,7 +24,7 @@ if __name__ == "__main__": st.set_page_config( "Langchain-Chatchat WebUI", - os.path.join("img", "chatchat_icon_blue_square_v2.png"), + os.path.join("chatchat", "chatchat", "img", "chatchat_icon_blue_square_v2.png"), initial_sidebar_state="expanded", menu_items={ 'Get Help': 'https://github.com/chatchat-space/Langchain-Chatchat', @@ -75,6 +75,8 @@ if __name__ == "__main__": with st.sidebar: st.image( os.path.join( + "chatchat", + "chatchat", "img", "logo-long-chatchat-trans-v2.png" ), diff --git a/chatchat/data/knowledge_base/samples/content/wiki b/chatchat/data/knowledge_base/samples/content/wiki deleted file mode 160000 index 28f664aa..00000000 --- a/chatchat/data/knowledge_base/samples/content/wiki +++ /dev/null @@ -1 +0,0 @@ -Subproject commit 28f664aa08f8191a70339c9ecbe7a89b35a1032a diff --git a/chatchat/poetry.toml b/chatchat/poetry.toml index 42159b24..3b673ef3 100644 --- a/chatchat/poetry.toml +++ b/chatchat/poetry.toml @@ -3,3 +3,7 @@ in-project = true [installer] modern-installation = false + +[plugins] +[plugins.pypi_mirror] +url = "https://pypi.tuna.tsinghua.edu.cn/simple" diff --git a/chatchat/pyproject.toml b/chatchat/pyproject.toml index f4ff6359..3d996615 100644 --- a/chatchat/pyproject.toml +++ b/chatchat/pyproject.toml @@ -7,7 +7,7 @@ readme = "README.md" [tool.poetry.dependencies] -python = ">=3.8,<4.0,!=3.9.7" +python = ">=3.8.1,<4.0,!=3.9.7" chatchat-model-providers = "^0.3.0" chatchat-server = "^0.3.0" chatchat-webui-pages = "^0.3.0" @@ -170,7 +170,7 @@ omit = [ ] [build-system] -requires = ["poetry-core>=1.0.0"] +requires = ["poetry-core>=1.0.0", "poetry-plugin-pypi-mirror==0.4.2"] build-backend = "poetry.core.masonry.api" [tool.pytest.ini_options] diff --git a/model-providers/poetry.toml b/model-providers/poetry.toml index 42159b24..3b673ef3 100644 --- a/model-providers/poetry.toml +++ b/model-providers/poetry.toml @@ -3,3 +3,7 @@ in-project = true [installer] modern-installation = false + +[plugins] +[plugins.pypi_mirror] +url = "https://pypi.tuna.tsinghua.edu.cn/simple" diff --git a/model-providers/pyproject.toml b/model-providers/pyproject.toml index 2da32369..b53822fc 100644 --- a/model-providers/pyproject.toml +++ b/model-providers/pyproject.toml @@ -6,13 +6,13 @@ authors = ["glide-the <2533736852@qq.com>"] readme = "README.md" [tool.poetry.dependencies] -python = ">=3.8,<4.0,!=3.9.7" +python = ">=3.8.1,<4.0,!=3.9.7" transformers = "4.31.0" fastapi = "^0.109.2" uvicorn = ">=0.27.0.post1" sse-starlette = "^1.8.2" pyyaml = "6.0.1" -pydantic = "1.10.14" +pydantic = "2.6.4" redis = "4.5.4" # modle_runtime openai = "1.13.3" @@ -167,7 +167,7 @@ omit = [ ] [build-system] -requires = ["poetry-core>=1.0.0"] +requires = ["poetry-core>=1.0.0", "poetry-plugin-pypi-mirror==0.4.2"] build-backend = "poetry.core.masonry.api" [tool.pytest.ini_options] diff --git a/poetry.toml b/poetry.toml index 42159b24..3b673ef3 100644 --- a/poetry.toml +++ b/poetry.toml @@ -3,3 +3,7 @@ in-project = true [installer] modern-installation = false + +[plugins] +[plugins.pypi_mirror] +url = "https://pypi.tuna.tsinghua.edu.cn/simple" diff --git a/pyproject.toml b/pyproject.toml index 373a0d4e..e9f0409d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -9,7 +9,7 @@ repository = "https://github.com/chatchat-space/Langchain-Chatchat.git" [tool.poetry.dependencies] -python = ">=3.8,<4.0,!=3.9.7" +python = ">=3.8.1,<4.0,!=3.9.7" [tool.poetry.group.lint.dependencies] diff --git a/webui-pages/chatchat_webui_pages/dialogue/dialogue.py b/webui-pages/chatchat_webui_pages/dialogue/dialogue.py index e3936d59..6f9e286e 100644 --- a/webui-pages/chatchat_webui_pages/dialogue/dialogue.py +++ b/webui-pages/chatchat_webui_pages/dialogue/dialogue.py @@ -19,11 +19,11 @@ from chatchat_server.utils import MsgType, get_config_models import uuid from typing import List, Dict -import streamlit_antd_components as sac - chat_box = ChatBox( assistant_avatar=os.path.join( + "chatchat", + "chatchat", "img", "chatchat_icon_blue_square_v2.png" ) diff --git a/webui-pages/poetry.toml b/webui-pages/poetry.toml index 42159b24..3b673ef3 100644 --- a/webui-pages/poetry.toml +++ b/webui-pages/poetry.toml @@ -3,3 +3,7 @@ in-project = true [installer] modern-installation = false + +[plugins] +[plugins.pypi_mirror] +url = "https://pypi.tuna.tsinghua.edu.cn/simple" diff --git a/webui-pages/pyproject.toml b/webui-pages/pyproject.toml index bd7b6018..d3b1d194 100644 --- a/webui-pages/pyproject.toml +++ b/webui-pages/pyproject.toml @@ -6,7 +6,7 @@ authors = ["chatchat"] readme = "README.md" [tool.poetry.dependencies] -python = ">=3.8,<4.0,!=3.9.7" +python = ">=3.8.1,<4.0,!=3.9.7" chatchat-model-providers = "^0.3.0" chatchat-server = "^0.3.0" streamlit = "1.30.0" @@ -54,7 +54,7 @@ optional = true [tool.poetry.group.dev.dependencies] jupyter = "^1.0.0" setuptools = "^67.6.1" -chatchat-model-providers = { path = "../model_providers", develop = true } +chatchat-model-providers = { path = "../model-providers", develop = true } chatchat-server = { path = "../chatchat-server", develop = true } [tool.poetry.extras] @@ -170,7 +170,7 @@ omit = [ ] [build-system] -requires = ["poetry-core>=1.0.0"] +requires = ["poetry-core>=1.0.0", "poetry-plugin-pypi-mirror==0.4.2"] build-backend = "poetry.core.masonry.api" [tool.pytest.ini_options]