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* publish 0.2.10 (#2797) 新功能： - 优化 PDF 文件的 OCR，过滤无意义的小图片 by @liunux4odoo #2525 - 支持 Gemini 在线模型 by @yhfgyyf #2630 - 支持 GLM4 在线模型 by @zRzRzRzRzRzRzR - elasticsearch更新https连接 by @xldistance #2390 - 增强对PPT、DOC知识库文件的OCR识别 by @596192804 #2013 - 更新 Agent 对话功能 by @zRzRzRzRzRzRzR - 每次创建对象时从连接池获取连接，避免每次执行方法时都新建连接 by @Lijia0 #2480 - 实现 ChatOpenAI 判断token有没有超过模型的context上下文长度 by @glide-the - 更新运行数据库报错和项目里程碑 by @zRzRzRzRzRzRzR #2659 - 更新配置文件/文档/依赖 by @imClumsyPanda @zRzRzRzRzRzRzR - 添加日文版 readme by @eltociear #2787 修复： - langchain 更新后，PGVector 向量库连接错误 by @HALIndex #2591 - Minimax's model worker 错误 by @xyhshen - ES库无法向量检索.添加mappings创建向量索引 by MSZheng20 #2688 * Update README.md * Add files via upload * Update README.md * 修复PDF旋转的BUG * Support Chroma * perf delete unused import * 忽略测试代码 * 更新文件 * API前端丢失问题解决 * 更新了chromadb的打印的符号 * autodl代号错误 * Update README.md * Update README.md * Update README.md * 修复milvus相关bug * 支持星火3.5模型 * 修复es 知识库查询bug (#2848) * 修复es 知识库查询bug (#2848) * 更新zhipuai请求方式 * 增加对 .htm 扩展名的显式支持 * 更新readme * Docker镜像制作与K8S YAML部署操作说明 (#2892) * Dev (#2280) * 修复Azure 不设置Max token的bug * 重写agent 1. 修改Agent实现方式，支持多参数，仅剩 ChatGLM3-6b和 OpenAI GPT4 支持，剩余模型将在暂时缺席Agent功能 2. 删除agent_chat 集成到llm_chat中 3. 重写大部分工具，适应新Agent * 更新架构 * 删除web_chat,自动融合 * 移除所有聊天，都变成Agent控制 * 更新配置文件 * 更新配置模板和提示词 * 更改参数选择bug * 修复模型选择的bug * 更新一些内容 * 更新多模态 语音 视觉的内容 1. 更新本地模型语音 视觉多模态功能并设置了对应工具 * 支持多模态Grounding 1. 美化了chat的代码 2. 支持视觉工具输出Grounding任务 3. 完善工具调用的流程 * 支持XPU，修改了glm3部分agent * 添加 qwen agent * 对其ChatGLM3-6B与Qwen-14B * fix callback handler * 更新Agent工具返回 * fix: LLMChain no output when no tools selected * 跟新了langchain 0.1.x需要的依赖和修改的代码 * 更新chatGLM3 langchain0.1.x Agent写法 * 按照 langchain 0.1 重写 qwen agent * 修复 callback 无效的问题 * 添加文生图工具 * webui 支持文生图 * 集成openai plugins插件 * 删除fastchat的配置 * 增加openai插件 * 集成openai plugins插件 * 更新模型执行列表和今晚修改的内容 * 集成openai_plugins/imitater插件 * 集成openai_plugins/imitater插件 * 集成openai_plugins/imitater插件 * 减少错误的显示 * 标准配置 * vllm参数配置 * 增加智谱插件 * 删除本地fschat配置 * 删除本地fschat配置,pydantic升级到2 * 删除本地fschat workers * openai-plugins-list.json * 升级agent,pydantic升级到2 * fix model_config是系统关键词问题 * embeddings模块集成openai plugins插件，使用统一api调用 * loom模型服务update_store更新逻辑 * 集成LOOM在线embedding业务 * 本地知识库搜索字段修改 * 知识库在线api接入点配置在线api接入点配置更新逻辑 * Update model_config.py.example * 修改模型配置方式，所有模型以 openai 兼容框架的形式接入，chatchat 自身不再加载模型。 改变 Embeddings 模型改为使用框架 API，不再手动加载，删除自定义 Embeddings Keyword 代码 修改依赖文件，移除 torch transformers 等重依赖 暂时移出对 loom 的集成 后续： 1、优化目录结构 2、检查合并中有无被覆盖的 0.2.10 内容 * move document_loaders & text_splitter under server * make torch & transformers optional import pydantic Model & Field from langchain.pydantic_v1 instead of pydantic.v1 * - pydantic 限定为 v1，并统一项目中所有 pydantic 导入路径，为以后升级 v2 做准备 - 重构 api.py： - 按模块划分为不同的 router - 添加 openai 兼容的转发接口，项目默认使用该接口以实现模型负载均衡 - 添加 /tools 接口，可以获取/调用编写的 agent tools - 移除所有 EmbeddingFuncAdapter，统一改用 get_Embeddings - 待办： - /chat/chat 接口改为 openai 兼容 - 添加 /chat/kb_chat 接口，openai 兼容 - 改变 ntlk/knowledge_base/logs 等数据目录位置 * 移除 llama-index 依赖；修复 /v1/models 错误 * 原因：windows下启动失败提示补充python-multipart包 (#3184) 改动：requirements添加python-multipart==0.0.9 版本：0.0.9 Requires: Python >=3.8 Co-authored-by: XuCai <liangxc@akulaku.com> * 添加 xinference 本地模型和自定义模型配置 UI: streamlit run model_loaders/xinference_manager.py * update xinference manager ui * fix merge conflict * model_config 中补充 oneapi 默认在线模型；/v1/models 接口支持 oneapi 平台，统一返回模型列表 * 重写 calculate 工具 * 调整根目录结构，kb/logs/media/nltk_data 移动到专用数据目录（可配置，默认 data）。注意知识库文件要做相应移动 * update kb_config.py.example * 优化 ES 知识库 - 开发者 - get_OpenAIClient 的 local_wrap 默认值改为 False，避免 API 服务未启动导致其它功能受阻（如Embeddings） - 修改 ES 知识库服务： - 检索策略改为 ApproxRetrievalStrategy - 设置 timeout 为 60， 避免文档过多导致 ConnecitonTimeout Error - 修改 LocalAIEmbeddings，使用多线程进行 embed_texts，效果不明显，瓶颈可能主要在提供 Embedding 的服务器上 * 修复glm3 agent被注释的agent会话文本结构解析代码 看起来输出的文本占位符如下，目前解析代码是有问题的 Thought <|assistant|> Action\r ```python tool_call(action_input) ```<|observation|> * make qwen agent work with langchain>=0.1 (#3228) * make xinference model manager support xinference 0.9.x * 使用多进程提高导入知识库的速度 (#3276) * xinference的代码 先传 我后面来改 * Delete server/xinference directory * Create khazic * diiii diii * Revert "xinference的代码" * fix markdown header split (#1825) (#3324) * dify model_providers configuration This module provides the interface for invoking and authenticating various models, and offers Dify a unified information and credentials form rule for model providers. * fix merge conflict: langchain Embeddings not imported in server.utils * 添加 react 编写的新版 WEBUI (#3417) * feat:提交前端代码 * feat:提交logo样式切换 * feat:替换avatar、部分位置icon、chatchat相关说明、git链接、Wiki链接、关于、设置、反馈与建议等功能，关闭lobehub自检更新功能 * fix:移除多余代码 --------- Co-authored-by: liunux4odoo <41217877+liunux4odoo@users.noreply.github.com> * model_providers bootstrap * model_providers bootstrap * update to pydantic v2 (#3486) * 使用poetry管理项目 * 使用poetry管理项目 * dev分支解决pydantic版本冲突问题，增加ollama配置，支持ollama会话和向量接口 (#3508) * dev分支解决pydantic版本冲突问题，增加ollama配置，支持ollama会话和向量接口 1、因dev版本的pydantic升级到了v2版本，由于在class History(BaseModel)中使用了from server.pydantic_v1，而fastapi的引用已变为pydantic的v2版本，所以fastapi用v2版本去校验用v1版本定义的对象，当会话历史histtory不为空的时候，会报错：TypeError: BaseModel.validate() takes 2 positional arguments but 3 were given。经测试，解方法为在class History(BaseModel)中也使用v2版本即可; 2、配置文件参照其它平台配置，增加了ollama平台相关配置，会话模型用户可根据实际情况自行添加，向量模型目前支持nomic-embed-text（必须升级ollama到0.1.29以上）。 3、因ollama官方只在会话部分对openai api做了兼容，向量api暂未适配，好在langchain官方库支持OllamaEmbeddings，因而在get_Embeddings方法中添加了相关支持代码。 * 修复 pydantic 升级到 v2 后 DocumentWithVsID 和 /v1/embeddings 兼容性问题 --------- Co-authored-by: srszzw <srszzw@163.com> Co-authored-by: liunux4odoo <liunux@qq.com> * 对python的要求降级到py38 * fix bugs; make poetry using tsinghua mirror of pypi * update gitignore; remove unignored files * update wiki sub module * 20240326 * 20240326 * qqqq * 删除历史文件 * 移动项目模块 * update .gitignore; fix model version error in api_schemas * 封装ModelManager * - 重写 tool 部分： (#3553) - 简化 tool 的定义方式 - 所有 tool 和 tool_config 支持热加载 - 修复：json_schema_extra warning * 使用yaml加载用户配置适配器 * 格式化代码 * 格式化 * 优化工具定义；添加 openai 兼容的统一 chat 接口 (#3570) - 修复： - Qwen Agent 的 OutputParser 不再抛出异常，遇到非 COT 文本直接返回 - CallbackHandler 正确处理工具调用信息 - 重写 tool 定义方式： - 添加 regist_tool 简化 tool 定义: - 可以指定一个用户友好的名称 - 自动将函数的 __doc__ 作为 tool.description - 支持用 Field 定义参数，不再需要额外定义 ModelSchema - 添加 BaseToolOutput 封装 tool 返回结果，以便同时获取原始值、给LLM的字符串值 - 支持工具热加载（有待测试） - 增加 openai 兼容的统一 chat 接口，通过 tools/tool_choice/extra_body 不同参数组合支持： - Agent 对话 - 指定工具调用（如知识库RAG） - LLM 对话 - 根据后端功能更新 webui * 修复：search_local_knowledge_base 工具返回值错误；/tools 路由错误；webui 中“正在思考”一直显示 (#3571) * 添加 openai 兼容的 files 接口 (#3573) * 使用BootstrapWebBuilder适配RESTFulOpenAIBootstrapBaseWeb加载 * 格式化和代码检查说明 * 模型列表适配 * make format * chat_completions接口报文适配 * make format * xinference 插件示例 * 一些默认参数 * exec path fix * 解决ollama部署的qwen,执行agent,返回的json格式不正确问题。 * provider_configuration.py 查询所有的平台信息，包含计费策略和配置schema_validators(参数必填信息校验规则) /workspaces/current/model-providers 查询平台模型分类的详细默认信息，包含了模型类型，模型参数，模型状态 workspaces/current/models/model-types/{model_type} * 开发手册 * 兼容model_providers，集成webui及API中平台配置的初始化 (#3625) * provider_configuration init of MODEL_PLATFORMS * 开发手册 * 兼容model_providers，集成webui及API中平台配置的初始化 * Dev model providers (#3628) * gemini 初始化参数问题 * gemini 同步工具调用 * embedding convert endpoint * 修复 --api -w命令 * /v1/models 接口返回值由 List[Model] 改为 {'data': List[Model]}，兼容最新版 xinference * 3.8兼容 (#3769) * 增加使用说明 * 3.8兼容性配置 * fix * formater * 不同平台兼容测试用例 * embedding兼容 * 增加日志信息 * pip源仓库设置，一些版本问题，启动说明 配置说明 (#3854) * 仓库设置，一些版本问题 * pip源仓库设置，一些版本问题，启动说明 * 配置说明 * 泛型标记错误 (#3855) * 仓库设置，一些版本问题 * pip源仓库设置，一些版本问题，启动说明 * 配置说明 * 发布的依赖信息 * 泛型标记错误 * 泛型标记错误 * CICD github action build publish pypi、Release Tag (#3886) * 测试用例 * CICD 流程 * CICD 流程 * CICD 流程 * 一些agent数据处理的问题，model_runtime模块的说明文档 (#3943) * 一些agent数据出来的问题 * Changes: - Translated and updated the Model Runtime documentation to reflect the latest changes and features. - Clarified the decoupling benefits of the Model Runtime module from the Chatchat service. - Removed outdated information regarding the model configuration storage module. - Detailed the retained functionalities post-removal of the Dify configuration page. - Provided a comprehensive overview of the Model Runtime's three-layered structure. - Included the status of the `fetch-from-remote` feature and its non-implementation in Dify. - Added instructions for custom service provider model capabilities. * - 新功能 (#3944) - streamlit 更新到 1.34，webui 支持 Dialog 操作 - streamlit-chatbox 更新到 1.1.12，更好的多会话支持 - 开发者 - 在 API 中增加项目图片路由(/img/{file_name})，方便前端使用 * 修改包名 * 修改包信息 * ollama配置解析问题 * 用户配置动态加载 (#3951) * version = "0.3.0.20240506" * version = "0.3.0.20240506" * version = "0.3.0.20240506" * version = "0.3.0.20240506" * 启动说明 * 一些bug * 修复了一些配置重载的bug * 配置的加载行为修改 * 配置的加载行为修改 * agent代码优化 * ollama 代码升级，使用openai协议 * 支持deepseek客户端 * contributing (#4043) * 添加了贡献说明 docs/contributing，包含了一些代码仓库说明和开发规范，以及在model_providers下面编写了一些单元测试的示例 * 关于providers的配置说明 * python3.8兼容 * python3.8兼容 * ollama兼容 * ollama兼容 * 一些兼容 pydantic<3,>=1.9.0 的代码， * 一些兼容 pydantic<3,>=1.9.0 model_config 的代码， * make format * test * 更新版本 * get_img_base64 * get_img_base64 * get_img_base64 * get_img_base64 * get_img_base64 * 统一模型类型编码 * 向量处理问题 * 优化目录结构 (#4058) * 优化目录结构 * 修改一些测试问题 --------- Co-authored-by: glide-the <2533736852@qq.com> * repositories * 调整日志 * 调整日志zdf * 增加可选依赖extras * feat:Added some documentation. (#4085) * feat:Added some documentation. * feat:Added some documentation. * feat:Added some documentation. --------- Co-authored-by: yuehuazhang <yuehuazhang@tencent.com> * fix code.md typos * fix chatchat-server/pyproject.toml typos * feat:README (#4118) Co-authored-by: yuehuazhang <yuehuazhang@tencent.com> * 初始化数据库集成model_providers * 关闭守护进程 * 1、修改知识库列表接口，返回全量属性字段，同时修改受影响的相关代码。 (#4119) 2、run_in_process_pool改为run_in_thread_pool，解决兼容性问题。 3、poetry配置文件修复。 * 动态更新Prompt中的知识库描述信息，使大模型更容易判断使用哪个知识库。 (#4121) * 1、修改知识库列表接口，返回全量属性字段，同时修改受影响的相关代码。 2、run_in_process_pool改为run_in_thread_pool，解决兼容性问题。 3、poetry配置文件修复。 * 1、动态更新Prompt中的知识库描述信息，使大模型更容易判断使用哪个知识库。 * fix: 补充 xinference 配置信息 (#4123) * feat:README * feat:补充 xinference 平台 llm 和 embedding 模型配置. --------- Co-authored-by: yuehuazhang <yuehuazhang@tencent.com> * 知识库工具的下拉列表改为动态获取，不必重启服务。 (#4126) * 1、知识库工具的下拉列表改为动态获取，不必重启服务。 * update README and imgs * update README and imgs * update README and imgs * update README and imgs * 修改安装说明描述问题 * make formater * 更新版本"0.3.0.20240606 * Update code.md * 优化知识库相关功能 (#4153) - 新功能 - pypi 包新增 chatchat-kb 命令脚本，对应 init_database.py 功能 - 开发者 - _model_config.py 中默认包含 xinference 配置项 - 所有涉及向量库的操作，前置检查当前 Embed 模型是否可用 - /knowledge_base/create_knowledge_base 接口增加 kb_info 参数 - /knowledge_base/list_files 接口返回所有数据库字段，而非文件名称列表 - 修正 xinference 模型管理脚本 * 消除警告 * 一些依赖问题 * 增加text2sql工具，支持特定表、智能判定表，支持对表名进行额外说明 (#4154) * 1、增加text2sql工具，支持特定表、智能判定表，支持对表名进行额外说明 * 支持SQLAlchemy大部分数据库、新增read-only模式，提高安全性、增加text2sql使用建议 (#4155) * 1、修改text2sql连接配置，支持SQLAlchemy大部分数据库； 2、新增read-only模式，若有数据库写保护需求，会从大模型判断、SQLAlchemy拦截器两个层面进行写拦截，提高安全性; 3、增加text2sql使用建议； * dotenv * dotenv 配置 * 用户工作空间操作 (#4156) 工作空间的配置预设，提供ConfigBasic建造方法产生实例。 该类的实例对象用于存储工作空间的配置信息，如工作空间的路径等 工作空间的配置信息存储在用户的家目录下的.config/chatchat/workspace/workspace_config.json文件中。 注意：不存在则读取默认 提供了操作入口 指令` chatchat-config` 工作空间配置 options: ``` -h, --help show this help message and exit -v {true,false}, --verbose {true,false} 是否开启详细日志 -d DATA, --data DATA 数据存放路径 -f FORMAT, --format FORMAT 日志格式 --clear 清除配置 ``` * 配置路径问题 * fix faiss_cache bug * Feature(File RAG): add file_rag in chatchat-server, add ensemble retriever and vectorstore retriever. * Feature(File RAG): add file_rag in chatchat-server, add ensemble retriever and vectorstore retriever. * fix xinference manager bug * Fix(File RAG): use jieba instead of cutword * Fix(File RAG): update kb_doc_api.py * 工作空间的配置预设，提供ConfigBasic建造 实例。 (#4158) - ConfigWorkSpace接口说明 ```text ConfigWorkSpace是一个配置工作空间的抽象类，提供基础的配置信息存储和读取功能。 提供ConfigFactory建造方法产生实例。 该类的实例对象用于存储工作空间的配置信息，如工作空间的路径等 工作空间的配置信息存储在用户的家目录下的.chatchat/workspace/workspace_config.json文件中。 注意：不存在则读取默认 ``` * 编写配置说明 * 编写配置说明 --------- Co-authored-by: liunux4odoo <41217877+liunux4odoo@users.noreply.github.com> Co-authored-by: glide-the <2533736852@qq.com> Co-authored-by: tonysong <tonysong@digitalgd.com.cn> Co-authored-by: songpb <songpb@gmail.com> Co-authored-by: showmecodett <showmecodett@gmail.com> Co-authored-by: zR <2448370773@qq.com> Co-authored-by: zqt <1178747941@qq.com> Co-authored-by: zqt996 <67185303+zqt996@users.noreply.github.com> Co-authored-by: fengyaojie <fengyaojie@xdf.cn> Co-authored-by: Hans WAN <hanswan@tom.com> Co-authored-by: thinklover <thinklover@gmail.com> Co-authored-by: liunux4odoo <liunux@qq.com> Co-authored-by: xucailiang <74602715+xucailiang@users.noreply.github.com> Co-authored-by: XuCai <liangxc@akulaku.com> Co-authored-by: dignfei <913015993@qq.com> Co-authored-by: Leb <khazzz1c@gmail.com> Co-authored-by: Sumkor <sumkor@foxmail.com> Co-authored-by: panhong <381500590@qq.com> Co-authored-by: srszzw <741992282@qq.com> Co-authored-by: srszzw <srszzw@163.com> Co-authored-by: yuehua-s <41819795+yuehua-s@users.noreply.github.com> Co-authored-by: yuehuazhang <yuehuazhang@tencent.com>
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"/>
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</defs>
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</svg>
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