From 95e4bf386717398e1d344caff1ab2227b9d0ff7b Mon Sep 17 00:00:00 2001 From: wwxxzz Date: Fri, 16 Aug 2024 17:37:44 +0800 Subject: [PATCH] Support Agentic RAG with intent and functioncalling (#154) * Add intent detection module * Remove LlmQuery class * Support API * Refactor agent module and format toml * Refactor module tool * Refactor query api * Add demo and UI * remove * Fix reviews * Add test for intent and api --- README.md | 6 +- README_zh.md | 7 +- docs/function_calling/readme.md | 179 ------------------ .../README.md | 99 ++++++++++ .../config.json | 82 ++++++++ ...3\347\225\245\344\277\241\346\201\257.pdf" | Bin 0 -> 152599 bytes ...3\347\225\245\344\277\241\346\201\257.pdf" | Bin 0 -> 161873 bytes .../figures/agent_chat.jpg | Bin 0 -> 851954 bytes .../figures/agent_config.jpg | Bin 0 -> 662086 bytes .../figures/settings.jpg | Bin 0 -> 504073 bytes .../figures/upload.jpg | Bin 0 -> 503631 bytes .../mock_api/main.py | 177 +++++++++++++++++ .../settings.toml | 100 ++++++++++ .../python-tool-for-booking-demo/config.json | 64 +++++++ .../python-tool-for-booking-demo/functions.py | 8 +- .../settings.toml | 31 +-- src/pai_rag/app/api/models.py | 9 +- src/pai_rag/app/api/query.py | 18 +- src/pai_rag/app/web/rag_client.py | 38 +++- src/pai_rag/app/web/tabs/agent_tab.py | 118 ++++++++++++ src/pai_rag/app/web/webui.py | 4 + src/pai_rag/config/settings.toml | 19 +- src/pai_rag/core/rag_application.py | 71 ++++++- src/pai_rag/core/rag_configuration.py | 4 + src/pai_rag/core/rag_service.py | 13 +- .../integrations/llms/dashscope/fc_base.py | 14 ++ src/pai_rag/modules/__init__.py | 5 +- src/pai_rag/modules/agent/agent.py | 21 +- .../modules/customconfig/custom_config.py | 35 ++++ .../intentdetection/intent_detection.py | 43 +++++ .../intentdetection/llm_single_detector.py | 154 +++++++++++++++ .../modules/intentdetection/output_parser.py | 102 ++++++++++ .../modules/llm/function_calling_llm.py | 6 + src/pai_rag/modules/module_registry.py | 7 +- .../tool/booking_demo/custom_functions.json | 78 -------- .../modules/tool/load_and_search_tool_spec.py | 2 +- src/pai_rag/modules/tool/tool.py | 37 ++-- src/pai_rag/modules/tool/utils.py | 115 +++++++---- .../intentdetection/test_intent_detection.py | 28 +++ tests/modules/tool/test_api_tool.py | 28 +++ 40 files changed, 1355 insertions(+), 367 deletions(-) delete mode 100644 docs/function_calling/readme.md create mode 100644 example_data/function_tools/api-tool-with-intent-detection-for-travel-assistant/README.md create mode 100644 example_data/function_tools/api-tool-with-intent-detection-for-travel-assistant/config.json create mode 100644 "example_data/function_tools/api-tool-with-intent-detection-for-travel-assistant/dataset/\344\270\212\346\265\267\346\224\273\347\225\245\344\277\241\346\201\257.pdf" create mode 100644 "example_data/function_tools/api-tool-with-intent-detection-for-travel-assistant/dataset/\345\214\227\344\272\254\346\224\273\347\225\245\344\277\241\346\201\257.pdf" create mode 100644 example_data/function_tools/api-tool-with-intent-detection-for-travel-assistant/figures/agent_chat.jpg create mode 100644 example_data/function_tools/api-tool-with-intent-detection-for-travel-assistant/figures/agent_config.jpg create mode 100644 example_data/function_tools/api-tool-with-intent-detection-for-travel-assistant/figures/settings.jpg create mode 100644 example_data/function_tools/api-tool-with-intent-detection-for-travel-assistant/figures/upload.jpg create mode 100644 example_data/function_tools/api-tool-with-intent-detection-for-travel-assistant/mock_api/main.py create mode 100644 example_data/function_tools/api-tool-with-intent-detection-for-travel-assistant/settings.toml create mode 100644 example_data/function_tools/python-tool-for-booking-demo/config.json rename src/pai_rag/modules/tool/booking_demo/custom_functions.py => example_data/function_tools/python-tool-for-booking-demo/functions.py (89%) rename src/pai_rag/config/settings_fc_demo.toml => example_data/function_tools/python-tool-for-booking-demo/settings.toml (88%) create mode 100644 src/pai_rag/app/web/tabs/agent_tab.py create mode 100644 src/pai_rag/modules/customconfig/custom_config.py create mode 100644 src/pai_rag/modules/intentdetection/intent_detection.py create mode 100644 src/pai_rag/modules/intentdetection/llm_single_detector.py create mode 100644 src/pai_rag/modules/intentdetection/output_parser.py delete mode 100644 src/pai_rag/modules/tool/booking_demo/custom_functions.json create mode 100644 tests/modules/intentdetection/test_intent_detection.py create mode 100644 tests/modules/tool/test_api_tool.py diff --git a/README.md b/README.md index 091ce8c9..f788d2c2 100644 --- a/README.md +++ b/README.md @@ -327,10 +327,10 @@ curl -X 'POST' http://127.0.0.1:8000/service/evaluate/retrieval curl -X 'POST' http://127.0.0.1:8000/service/evaluate/response ``` -# Function Calling +# Agentic RAG -You can use function calling tools in PAI-RAG, please refer to the documentation: -[Function Calling Instruction](./docs/function_calling/readme.md) +You can use agent with function calling api-tools in PAI-RAG, please refer to the documentation: +[Agentic RAG](./example_data/function_tools/api-tool-with-intent-detection-for-travel-assistant/README.md) # Parameter Configuration diff --git a/README_zh.md b/README_zh.md index 2fcbc6e9..b77894e5 100644 --- a/README_zh.md +++ b/README_zh.md @@ -277,11 +277,10 @@ curl -X 'POST' http://127.0.0.1:8000/service/evaluate/retrieval curl -X 'POST' http://127.0.0.1:8000/service/evaluate/response ``` -# Function Calling +# Agentic RAG -您也可以在PAI-RAG中使用function calling功能,请参考文档: - -[Function Calling 使用说明](./docs/function_calling/readme.md) +您也可以在PAI-RAG中使用支持API function calling功能的Agent,请参考文档: +[Agentic RAG](./example_data/function_tools/api-tool-with-intent-detection-for-travel-assistant/README.md) # 参数配置 diff --git a/docs/function_calling/readme.md b/docs/function_calling/readme.md deleted file mode 100644 index 5dc864e1..00000000 --- a/docs/function_calling/readme.md +++ /dev/null @@ -1,179 +0,0 @@ -

-

Function Calling in PAI-RAG

-

- -# 什么是 Function Calling - -OpenAI于23年6月份的更新的 gpt-4-0613 和 gpt-3.5-turbo-0613 版本中为模型添加了Function Calling功能,通过给模型提供一组预定义的函数(Function list)以及用户提问(Query),让大模型自主选择要调用的函数,并向该函数提供所需的输入参数。随后我们就可以在自己的环境中基于模型生成的参数调用该函数,并将结果返回给大模型。 - -ChatGPT的Function Calling功能在发布之后立刻引起了人们的关注,因其简单易用的特性以及规范的输入输出迅速成为模型生态中Function calling的格式规范。后续的具有function calling功能的模型有很多参照了OpenAI的Function Calling格式,其输入的函数列表以及输出的Function Calling及其参数都以JSON格式输出:输入的函数列表中通常包括函数名称、函数功能描述、函数参数等部分,而输出中则按顺序输出所调用的函数名称和其中使用的参数。 - -# 如何定义 Function 及其实现 - -在PAI-RAG中,支持用户调用自定义的functions与外部世界进行更多的交互,如对接会议室预定系统,可以查询预定状态,协助用户提供个人信息来预定所需会议室;如对接数据库系统,调用外部API等。 - -PAI-RAG中的function定义遵循OpenAI的Function Calling格式规范,如下定义了在会议室预定系统中如何查询预订状态的function。 - -```json -[ - { - "type": "function", - "function": { - "name": "get_booking_state", - "description": "用于查询给定会议室ID的预定状态", - "parameters": { - "type": "object", - "properties": { - "room_id": { - "type": "str", - "description": "待查询的会议室ID" - } - }, - "required": ["room_id"] - } - } - } -] -``` - -定义好function之后,需要将对应function name的逻辑进行python实现,如下: - -```python -def get_booking_state(room_id: str) -> str: - try: - return str(bookings[room_id].dict()) - except Exception: - return f"没有找到会议室ID:{room_id}" -``` - -按照如上规范定义好所需的function即可在PAI-RAG中进行调用。 - -# PAI-RAG如何调用demo functions - -我们提供了一组预置的会议室预定系统的demo,在 src/pai_rag/modules/tool/booking_demo 目录下,可以直接以demo启动体验: - -```bash -pai_rag serve -p 8071 -c src/pai_rag/config/settings_fc_demo.toml -``` - -[注意] 在demo中,我们默认使用qwen2-7b-instruct作为function calling的LLM,您也可以根据需要替换为更大的模型,如qwen2-72b-instruct。 - -1. 发送预定会议室的请求 - -```bash -# Query -curl -X 'POST' http://127.0.0.1:8071/service/query/agent -H "Content-Type: application/json" -d '{"question":"我要预定303会议室"}' - -# Response -Added user message to memory: 我要预定303会议室 - -=== Calling Function === -Calling function: create_booking with args: {"room_id": "303"} -=== Function Output === -会议室预订ID:303已创建,但尚未确认。请提供您的姓名(name)、电子邮件(email)、电话(phone)、日期(date)和时间(time)。 - -=== LLM Response === -会议室303的预定已经创建成功,但还需要进一步确认。请您提供以下信息以便完成预定: - -- 姓名(name) -- 电子邮件(email) -- 电话(phone) -- 日期(date) -- 时间(time) - -请将这些信息告诉我,我将为您更新预定详情。 -``` - -2. 完善用户的预定信息 - -```bash -# Query -curl -X 'POST' http://127.0.0.1:8071/service/query/agent -H "Content-Type: application/json" -d '{"question":"我叫玛丽,手机号是1505185"}' - -# Response -Added user message to memory: 我叫玛丽,手机号是1505185 - -=== Calling Function === -Calling function: update_booking with args: {"room_id": "303", "property": "name", "value": "\u739b\u4e3d"} -=== Function Output === -预订ID 303 更新属性信息 name = 玛丽 -=== Calling Function === -Calling function: update_booking with args: {"room_id": "303", "property": "phone", "value": "1505185"} -=== Function Output === -预订ID 303 更新属性信息 phone = 1505185 -=== LLM Response === -您的名字和电话号码已更新: -- 姓名:玛丽 -- 电话:1505185 - -还请提供您的电子邮件地址、预定的日期和时间,以便完成会议室预定的所有必要信息。 -``` - -```bash -# Query -curl -X 'POST' http://127.0.0.1:8071/service/query/agent -H "Content-Type: application/json" -d '{"question":"电子邮件是123@qq.com,预定日期是2024年8月10号,时间是下午6点"}' - -# Response -Added user message to memory: 电子邮件是123@qq.com,预定日期是2024年8月10号,时间是下午6点 - -=== Calling Function === -Calling function: update_booking with args: {"room_id": "303", "property": "email", "value": "123@qq.com"} -=== Function Output === -预订ID 303 更新属性信息 email = 123@qq.com -=== Calling Function === -Calling function: update_booking with args: {"room_id": "303", "property": "date", "value": "2024-08-10"} -=== Function Output === -预订ID 303 更新属性信息 date = 2024-08-10 -=== Calling Function === -Calling function: update_booking with args: {"room_id": "303", "property": "time", "value": "18:00"} -=== Function Output === -预订ID 303 更新属性信息 time = 18:00 - -=== LLM Response === -您的预定信息已经全部更新完毕: -- 姓名:玛丽 -- 电话:1505185 -- 电子邮件:123@qq.com -- 日期:2024年8月10日 -- 时间:下午6点(18:00) - -会议室预定成功。如果您需要进一步的帮助或有其他要求,请随时告诉我。 -``` - -3. 查询会议室预定状态 - -```bash -# Query -curl -X 'POST' http://127.0.0.1:8071/service/query/agent -H "Content-Type: application/json" -d '{"question":"查询303有没有被预定"}' - -# Response -Added user message to memory: 查询303有没有被预定 - -=== Calling Function === -Calling function: get_booking_state with args: {"room_id": "303"} -=== Function Output === -{'name': '玛丽', 'email': '123@qq.com', 'phone': '1505185', 'date': '2024-08-10', 'time': '18:00'} - -=== LLM Response === -会议室303已被预定,预定信息如下: -- 姓名:玛丽 -- 电子邮件:123@qq.com -- 电话:1505185 -- 日期:2024年8月10日 -- 时间:下午6点(18:00) - -如果您需要预定该会议室或其他帮助,请告知我。 -``` - -按照如上所做可以看到我们已经和会议室预定系统建立了连接,并且能够有效且快速处理用户的请求,且工具调用的准确率很高。 - -# PAI-RAG如何调用用户自定义的 functions - -除了我们预置的demo,也支持用户实现任意自己的function函数: - -1. 在 src/pai_rag/modules/tool 目录下新建一个自定义的文件夹,如 user_tool -2. 在 src/pai_rag/modules/tool/user_tool目录下新建两个文件,分别为:custom_functions.json 和 custom_functions.py。并分别按照前面所述实现自己的函数定义和实现逻辑即可。 -3. 修改 src/pai_rag/config/settings_fc_demo.toml 文件的最后一行,将 func_path = "src/pai_rag/modules/tool/booking_demo" 替换为 第一步中的目录名称(src/pai_rag/modules/tool/user_tool)即可。 -4. 指定配置文件来启动项目:pai_rag serve -p 8071 -c src/pai_rag/config/settings_fc_demo.toml - -之后,您就可以进行请求调用来查看function calling的效果。 diff --git a/example_data/function_tools/api-tool-with-intent-detection-for-travel-assistant/README.md b/example_data/function_tools/api-tool-with-intent-detection-for-travel-assistant/README.md new file mode 100644 index 00000000..ddd7dec8 --- /dev/null +++ b/example_data/function_tools/api-tool-with-intent-detection-for-travel-assistant/README.md @@ -0,0 +1,99 @@ +# Agentic RAG Demo Guide + +此示例提供了一个旅游小助手的功能,仅用于演示Agentic RAG中如何结合意图识别和FunctionCalling达到智能旅游攻略搜索和调用外部系统API的目的。实际使用视业务情况而有所不同。 + +## Mock API Service + +我们提供了一个使用 FastAPI 实现的简单 API 系统,包含从北京到上海的机票查询、高铁查询和上海酒店查询。这些 API 将返回我们提前设定的 mock 数据。 + +使用以下命令启动 FastAPI 应用: + +```bash +cd example_data/function_tools/api-tool-with-intent-detection-for-travel-assistant/mock_api +python main.py +``` + +## PAI-RAG Service + +使用以下命令启动PAI-RAG服务端: + +```bash +cd PAI-RAG/ +pai_rag serve -p 8071 +``` + +## (Option-1) PAI-RAG UI + +使用以下命令启动PAI-RAG WebUI: + +```bash +pai_rag ui -p 8072 -c http://127.0.0.1:8071 +``` + +在WebUI页面,在`Settings Tab`先配置好所需的模型,并点击connect进行连接。 +![settings](figures/settings.jpg) + +在`Upload Tab`上传所需要的知识库内容,此例子中可以使用 **example_data/function_tools/api-tool-with-intent-detection-for-travel-assistant/dataset** 准备好的北京和上海的旅游攻略数据。 +![upload](figures/upload.jpg) + +在`Agent Tab`上传Agent配置文件,此例子中可以使用**example_data/function_tools/api-tool-with-intent-detection-for-travel-assistant/config.json**,上传后点击Submit进行agent模块注册,成功后即可在右侧Chatbot中进行agent对话测试。 +![agent_config](figures/agent_config.jpg) +![agent_chat](figures/agent_chat.jpg) + +## (Option-2) API + +若不需要使用webui,也可以直接使用API进行知识库上传和agent调用。 + +1. 上传知识库文件:此例子中使用 **example_data/function_tools/api-tool-with-intent-detection-for-travel-assistant/dataset** 准备好的北京和上海的旅游攻略数据。 + +```bash +load_data -d example_data/function_tools/api-tool-with-intent-detection-for-travel-assistant/dataset +``` + +2. 注册Agent相关模块:此例子中使用**example_data/function_tools/api-tool-with-intent-detection-for-travel-assistant/config.json** + +```bash +curl -X 'POST' http://127.0.0.1:8071/service/config/agent -H 'Content-Type: multipart/form-data' -F 'file=@example_data/function_tools/api-tool-with-intent-detection-for-travel-assistant/config.json' +``` + +3. AgenticRAG对话测试 + +注意: API中需要显式指定 `"with_intent":"True"` 才可以调用到意图识别模块进行意图的分发。 + +(1) 知识库检索 + +```bash +curl -X 'POST' http://127.0.0.1:8071/service/query -H "Content-Type: application/json" -d '{"question":"北京旅游攻略", "with_intent":true}' + +# "answer" +# 北京旅游攻略包括以下景点推荐、开放时间、特色美食、旅行建议等信息: +# 景点推荐与开放时间 +# - **故宫博物院** +# - 地址:北京市东城区景⼭前街 4 号 +# - 亮点:世界上规模最⼤、保存最完整的木质结构古建筑之⼀,珍藏大量文化遗产和珍宝。 +# - 开放时间: 旺季(4 ⽉ 1 ⽇-10 ⽉ 31 ⽇):8:30-16:30(15:40 停⽌⼊馆) - 淡季(11 ⽉ 1 ⽇-3 ⽉ 31 ⽇):8:30-16:00(15:10 停⽌⼊馆) +# - **天安⻔⼴场** +# - 地址:北京市东城区东⻓安街 +# - 亮点:世界最⼤的城市⼴场,中华⼈⺠共和国的⼼脏,可观看升国旗仪式、参观⽑主席纪念堂和⼈⺠英雄纪念碑。 +# - 开放时间:全天开放 +# - **天坛公园** +# - 地址:北京市东城区天坛东⾥甲 1 号 +# - 亮点:中国古代皇帝祭天和祈年之所。 +# 特色美食 +# - **京酱⾁丝** - 推荐餐厅:东来顺饭庄,位于北京市东城区东直⻔内⼤街,传统⽼字号餐馆。 - 特⾊:京酱(甜酱)炒制的猪⾁丝,颜⾊红亮,味道浓郁,常⽤⾖⽪或葱白包着吃,⼝感极佳。 +# 旅行建议 +# - **最佳旅游时间**:北京春秋季节⽓候宜人,适合旅游。春季(3 ⽉⾄ 5 ⽉),秋季(9 ⽉⾄ 11 ⽉)- **交通⽅式**:北京公共交通⾮常⽅便,地铁覆盖主要景点,推荐使⽤北京交通卡(Yikatong)方便出⾏。- **注意事项**:北京夏季天⽓炎热,⾼温多⾬;冬季寒冷⼲燥。建议根据季节和天⽓预报准备适合的⾐物。 +# 希望以上攻略对您能有所帮助,祝您在北京玩得愉快!" +``` + +(2) API调用 + +```bash +curl -X 'POST' http://127.0.0.1:8071/service/query -H "Content-Type: application/json" -d '{"question":"2024年8月6号从北京到上海的机票信息", "with_intent":true}' + +# "answer" +# assistant: 2024年8月6日从北京到上海的航班信息如下: +# 1. 航班号:MU456,出发时间:14:00,到达时间:16:00,价格:1300元 +# 2. 航班号:HU789,出发时间:18:00,到达时间:20:00,价格:1100元 +# 请注意选择合适的航班和时间。" +``` diff --git a/example_data/function_tools/api-tool-with-intent-detection-for-travel-assistant/config.json b/example_data/function_tools/api-tool-with-intent-detection-for-travel-assistant/config.json new file mode 100644 index 00000000..82ccd04d --- /dev/null +++ b/example_data/function_tools/api-tool-with-intent-detection-for-travel-assistant/config.json @@ -0,0 +1,82 @@ +{ + "intent": { + "retrieval": "关于一些通用的信息检索,比如搜索旅游攻略、搜索美食攻略、搜索注意事项等信息。", + "agent": "实时性的信息查询,比如查询航班信息、查询高铁信息、查询天气等时效性很强的信息。" + }, + "agent": { + "system_prompt": "你是一个旅游小助手,可以帮助用户查询指定时间从A地区到B地区的机票信息,以及火车票信息等。请严格使用输入的工具,不要虚构任何细节。", + "functions": [ + { + "name": "search_flight_ticket_api", + "api": "http://127.0.0.1:8070/demo/api/flights", + "headers": { + "Authorization": "Bearer YOUR_ACCESS_TOKEN" + }, + "method": "GET", + "request_body_type": "params", + "description": "帮助用户获取机票信息,用户需要输入出发地、目的地", + "parameters": { + "from_city": { + "type": "str", + "description": "出发城市,如'北京'、'上海'、'南京''" + }, + "to_city": { + "type": "str", + "description": "目的地城市,如'北京'、'上海'、'南京'" + }, + "date": { + "type": "str", + "description": "出发时间,如'2024-03-29'" + } + }, + "required": ["from_city", "to_city", "date"] + }, + { + "name": "search_train_ticket_api", + "api": "http://127.0.0.1:8070/demo/api/trains", + "headers": { + "Authorization": "Bearer YOUR_ACCESS_TOKEN" + }, + "method": "GET", + "request_body_type": "params", + "description": "帮助用户查询火车票或者高铁票信息", + "parameters": { + "from_city": { + "type": "str", + "description": "出发城市,如'北京'、'上海'、'南京''" + }, + "to_city": { + "type": "str", + "description": "目的地城市,如'北京'、'上海'、'南京'" + }, + "date": { + "type": "str", + "description": "出发时间,如'2024-03-29'" + } + }, + "required": ["from_city", "to_city", "date"] + }, + { + "name": "search_hotels_api", + "api": "http://127.0.0.1:8070/demo/api/hotels", + "headers": { + "Authorization": "Bearer YOUR_ACCESS_TOKEN" + }, + "method": "GET", + "request_body_type": "params", + "description": "帮助用户查询酒店信息", + "parameters": { + "city": { + "type": "str", + "description": "查询的城市,如'北京'、'上海'、'南京''" + }, + "date": { + "type": "str", + "description": "出发时间,如'2024-03-29'" + } + }, + "required": ["city", "date"] + } + ] + } +} diff --git "a/example_data/function_tools/api-tool-with-intent-detection-for-travel-assistant/dataset/\344\270\212\346\265\267\346\224\273\347\225\245\344\277\241\346\201\257.pdf" "b/example_data/function_tools/api-tool-with-intent-detection-for-travel-assistant/dataset/\344\270\212\346\265\267\346\224\273\347\225\245\344\277\241\346\201\257.pdf" new file mode 100644 index 0000000000000000000000000000000000000000..bab9665633a0bfec9c8f50ee10a3cb4c1873d09a GIT binary patch literal 152599 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z0G^~nTsO3tryH|uTwlzedFh*vbpSSIgSS-qhOQ|>3V0edxB1+?59Od&?{luslFo#33H$gx_}WDh|jF$RmVTV5@t2gdwTAzF2pF z0Rb}cLUPze?3 zXQ*)GMXD`&xt6dg?E!L@F+% zFW)zu(w#CWwFHdOt(lt$<easMu+w(C`b_|4R#v628eE422kG-+kl^Po=>$~Cy;Ryze4e!k;4II2-%Potr& z&LJnWcGFbvsz$})#(A{jm9CIxEEt$H1sS2`8eI1-g{pOuKhmOVtIp}y!n zNELB_8o5lLHlkqDp`lCA?U~?xx~n3ZMU)HU#Vzs|NnK@FPLW52UbFCyEZGxx@aDpW ze7)%QvZ&QRfxfS+m~arKIUAw&z9@bS(EgC0sJIxKuM>$uNoB;0lD@6b`BG9yiOx41mshamOnBCg_ z3;vI=dR6oXb`pD0*w?7c9P)9vagPGI7u1}vVbNU`$UYRj6+%mr$^?Bo2wo#LBkjm( znb_mUvW1>|y;eI8(C-z7wnkCR2hrHs80EI}(QqMiVw-7;CrsLe@8-|9;7{R;e zed;W?VQ3@lwO)FyKwTn}6O9~B@PUT~XrRcM?qscH)mnp*A26k_L{Zd=!eE$?wqxBv z_l>^CDJ?jrNqjWGH;2D+&vGy&^LC8b^Nd#9xRoehC9?{OQo4d@)>TuC6W+!Cm1(wj zj8e_r8O_&b7dP+0r8$rODJ0N1z&V4!+_UhhO$RqXuwEs*@^nPjKodu9CRO*?S#CGY zeT-GG6M9zE|9hRinMgSyz z&uKx;C@?KKNGSld(P#P)pI`01e6ZijS;ucZgeD-hG{Sq|* literal 0 HcmV?d00001 diff --git a/example_data/function_tools/api-tool-with-intent-detection-for-travel-assistant/mock_api/main.py b/example_data/function_tools/api-tool-with-intent-detection-for-travel-assistant/mock_api/main.py new file mode 100644 index 00000000..1598c856 --- /dev/null +++ b/example_data/function_tools/api-tool-with-intent-detection-for-travel-assistant/mock_api/main.py @@ -0,0 +1,177 @@ +from fastapi import FastAPI +from typing import List + +app = FastAPI() + +# Mock 数据 +flights_data = [ + { + "flight_number": "CA123", + "from": "北京", + "to": "上海", + "date": "2024-08-05", + "departure_time": "2024-08-05T08:00:00", + "arrival_time": "2024-08-05T10:00:00", + "price": 1200, + }, + { + "flight_number": "MU456", + "from": "北京", + "to": "上海", + "date": "2024-08-06", + "departure_time": "2024-08-06T14:00:00", + "arrival_time": "2024-08-06T16:00:00", + "price": 1300, + }, + { + "flight_number": "HU789", + "from": "北京", + "to": "上海", + "date": "2024-08-06", + "departure_time": "2024-08-06T18:00:00", + "arrival_time": "2024-08-06T20:00:00", + "price": 1100, + }, + { + "flight_number": "CA234", + "from": "北京", + "to": "上海", + "date": "2024-08-07", + "departure_time": "2024-08-07T06:00:00", + "arrival_time": "2024-08-07T08:00:00", + "price": 1250, + }, + { + "flight_number": "MU567", + "from": "北京", + "to": "上海", + "date": "2024-08-07", + "departure_time": "2024-08-07T21:00:00", + "arrival_time": "2024-08-07T23:00:00", + "price": 1350, + }, +] + +highspeed_trains_data = [ + { + "train_number": "G1234", + "from": "北京", + "to": "上海", + "date": "2024-08-05", + "departure_time": "2024-08-05T09:00:00", + "arrival_time": "2024-08-05T11:30:00", + "price": 800, + }, + { + "train_number": "G5678", + "from": "北京", + "to": "上海", + "date": "2024-08-05", + "departure_time": "2024-08-05T15:00:00", + "arrival_time": "2024-08-05T17:30:00", + "price": 850, + }, + { + "train_number": "G9101", + "from": "北京", + "to": "上海", + "date": "2024-08-06", + "departure_time": "2024-08-06T18:30:00", + "arrival_time": "2024-08-06T21:00:00", + "price": 780, + }, + { + "train_number": "G1123", + "from": "北京", + "to": "上海", + "date": "2024-08-06", + "departure_time": "2024-08-06T07:00:00", + "arrival_time": "2024-08-06T09:30:00", + "price": 820, + }, + { + "train_number": "G4578", + "from": "北京", + "to": "上海", + "date": "2024-08-07", + "departure_time": "2024-08-07T22:00:00", + "arrival_time": "2024-08-07T00:30:00", + "price": 870, + }, +] + +hotels_data = [ + { + "hotel_name": "万豪酒店", + "city": "上海", + "check_in": "2024-08-05", + "check_out": "2024-08-07", + "price_per_night": 600, + }, + { + "hotel_name": "希尔顿酒店", + "city": "上海", + "check_in": "2024-08-05", + "check_out": "2024-08-06", + "price_per_night": 850, + }, + { + "hotel_name": "洲际酒店", + "city": "上海", + "check_in": "2024-08-06", + "check_out": "2024-08-07", + "price_per_night": 700, + }, + { + "hotel_name": "皇冠假日酒店", + "city": "上海", + "check_in": "2024-08-07", + "check_out": "2024-08-08", + "price_per_night": 750, + }, + { + "hotel_name": "如家酒店", + "city": "上海", + "check_in": "2024-08-07", + "check_out": "2024-08-09", + "price_per_night": 300, + }, +] + + +@app.get("/demo/api/flights", response_model=List[dict]) +def get_flights(from_city: str, to_city: str, date: str): + # 过滤条件可以根据需求添加 + return [ + flight + for flight in flights_data + if flight["from"] == from_city + and flight["to"] == to_city + and flight["date"] == date + ] + + +@app.get("/demo/api/trains", response_model=List[dict]) +def get_highspeed_trains(from_city: str, to_city: str, date: str): + return [ + train + for train in highspeed_trains_data + if train["from"] == from_city + and train["to"] == to_city + and train["date"] == date + ] + + +@app.get("/demo/api/hotels", response_model=List[dict]) +def get_hotels(city: str, date: str): + return [ + hotel + for hotel in hotels_data + if hotel["city"] == city and hotel["check_in"] == date + ] + + +if __name__ == "__main__": + import uvicorn + + uvicorn.run(app, host="127.0.0.1", port=8070) diff --git a/example_data/function_tools/api-tool-with-intent-detection-for-travel-assistant/settings.toml b/example_data/function_tools/api-tool-with-intent-detection-for-travel-assistant/settings.toml new file mode 100644 index 00000000..27aa6fd5 --- /dev/null +++ b/example_data/function_tools/api-tool-with-intent-detection-for-travel-assistant/settings.toml @@ -0,0 +1,100 @@ +dynaconf_merge = true + +[rag] +name = "pai_rag" +version = "0.1.1" + +[rag.agent] +type = "function_calling" + +[rag.agent.custom_config] +agent_file_path = "example_data/function_tools/api-tool-with-intent-detection-for-travel-assistant" + +[rag.agent.intent_detection] +type = "single" + +[rag.agent.tool] +type = "api" + +[rag.chat_engine] +type = "CondenseQuestionChatEngine" + +[rag.chat_store] +type = "Local" # [Local, Aliyun-Redis] +host = "Aliyun-Redis host" +password = "Aliyun-Redis user:pwd" +persist_path = "localdata/storage" + +[rag.data_reader] +type = "SimpleDirectoryReader" + +# embedding configurations, source support API: OpenAI,DashScope; and local model:HuggingFace +# if use API, need set OPENAI_API_KEY or DASHSCOPE_API_KEY in ENV, If HuggingFace, need set model_name +# eg. +# source = "HuggingFace" +# model_name = "bge-small-zh-v1.5" +# embed_batch_size = 10 +[rag.embedding] +source = "DashScope" +embed_batch_size = 10 + +[rag.embedding.multi_modal] +source = "cnclip" + +[rag.evaluation] +retrieval = ["mrr", "hit_rate"] +response_label = true +response = ["Faithfulness", "Answer Relevancy", "Guideline Adherence", "Correctness", "Semantic Similarity"] + +[rag.index] +persist_path = "localdata/storage" +vector_store.type = "FAISS" + +# llm configurations, source support API: OpenAI,DashScope or PAI-EAS's deployment +# eg. +# source = "PaiEas" +# endpoint = "" +# token = "" +[rag.llm] +source = "DashScope" +name = "qwen-turbo" + +[rag.llm.function_calling_llm] +source = "DashScope" +name = "qwen2-7b-instruct" + +[rag.llm.multi_modal] +source = "" + +[rag.llm_chat_engine] +type = "SimpleChatEngine" + +[rag.node_enhancement] +tree_depth = 3 +max_clusters = 52 +proba_threshold = 0.10 + +[rag.node_parser] +type = "Sentence" +chunk_size = 500 +chunk_overlap = 10 + +[rag.postprocessor] +reranker_type = "simple-weighted-reranker" # [simple-weighted-reranker, model-based-reranker] +reranker_model = "bge-reranker-base" # [bge-reranker-base, bge-reranker-large] +keyword_weight = 0.3 +vector_weight = 0.7 +similarity_threshold = 0.5 +top_n = 2 + +[rag.query_engine] +type = "RetrieverQueryEngine" + +[rag.retriever] +similarity_top_k = 3 +retrieval_mode = "hybrid" # [hybrid, embedding, keyword, router] +query_rewrite_n = 1 # set to 1 to disable query generation + +[rag.synthesizer] +type = "SimpleSummarize" +text_qa_template = "参考内容信息如下\n---------------------\n{context_str}\n---------------------根据提供内容而非其他知识回答问题.\n问题: {query_str}\n答案: \n" diff --git a/example_data/function_tools/python-tool-for-booking-demo/config.json b/example_data/function_tools/python-tool-for-booking-demo/config.json new file mode 100644 index 00000000..b542f10b --- /dev/null +++ b/example_data/function_tools/python-tool-for-booking-demo/config.json @@ -0,0 +1,64 @@ +{ + "intent": {}, + "agent": { + "system_prompt": "你是一个管理会议室预定的助手,需要协助用户预订会议室。请严格使用输入的工具,不要虚构任何细节。", + "functions": [ + { + "name": "get_booking_state", + "function": "get_booking_state_func", + "description": "用于查询给定会议室ID的预定状态", + "parameters": { + "room_id": { + "type": "str", + "description": "待查询的会议室ID" + } + }, + "required": ["room_id"] + }, + { + "name": "update_booking", + "function": "update_booking_func", + "description": "用于更新预定指定会议室ID(room_id)的预定人信息,包含姓名(name)、电子邮件(email)、电话(phone)、日期(date)和时间(time)。", + "parameters": { + "room_id": { + "type": "str", + "description": "待更新信息的会议室ID" + }, + "property": { + "type": "str", + "description": "待更新的信息属性,如姓名(name)、电子邮件(email)、电话(phone)、日期(date)和时间(time)." + }, + "value": { + "type": "str", + "description": "待更新的信息属性的值." + } + }, + "required": ["room_id", "property", "value"] + }, + { + "name": "create_booking", + "function": "create_booking_func", + "description": "预定指定会议室ID的会议室", + "parameters": { + "room_id": { + "type": "str", + "description": "指定预定的会议室ID" + } + }, + "required": ["room_id"] + }, + { + "name": "confirm_booking", + "function": "confirm_booking_func", + "description": "用于确认是否预定指定会议室ID的会议室", + "parameters": { + "room_id": { + "type": "str", + "description": "需要确认的会议室ID" + } + }, + "required": ["room_id"] + } + ] + } +} diff --git a/src/pai_rag/modules/tool/booking_demo/custom_functions.py b/example_data/function_tools/python-tool-for-booking-demo/functions.py similarity index 89% rename from src/pai_rag/modules/tool/booking_demo/custom_functions.py rename to example_data/function_tools/python-tool-for-booking-demo/functions.py index dbb2167f..13a63959 100644 --- a/src/pai_rag/modules/tool/booking_demo/custom_functions.py +++ b/example_data/function_tools/python-tool-for-booking-demo/functions.py @@ -14,26 +14,26 @@ class Booking(BaseModel): time: Optional[str] = None -def get_booking_state(room_id: str) -> str: +def get_booking_state_func(room_id: str) -> str: try: return str(bookings[room_id].dict()) except Exception: return f"没有找到会议室ID:{room_id}" -def update_booking(room_id: str, property: str, value: str) -> str: +def update_booking_func(room_id: str, property: str, value: str) -> str: booking = bookings[room_id] setattr(booking, property, value) return f"预订ID {room_id} 更新属性信息 {property} = {value}" -def create_booking(room_id: str) -> str: +def create_booking_func(room_id: str) -> str: bookings[room_id] = Booking() # return "Booking created, but not yet confirmed. Please provide your name, email, phone, date, and time." return f"会议室预订ID:{room_id}已创建,但尚未确认。请提供您的姓名(name)、电子邮件(email)、电话(phone)、日期(date)和时间(time)。" -def confirm_booking(room_id: str) -> str: +def confirm_booking_func(room_id: str) -> str: # """Confirm a booking for a given booking ID.""" """确认给定会议室预订ID的预订。""" booking = bookings[room_id] diff --git a/src/pai_rag/config/settings_fc_demo.toml b/example_data/function_tools/python-tool-for-booking-demo/settings.toml similarity index 88% rename from src/pai_rag/config/settings_fc_demo.toml rename to example_data/function_tools/python-tool-for-booking-demo/settings.toml index 96b96be8..c2d96a5c 100644 --- a/src/pai_rag/config/settings_fc_demo.toml +++ b/example_data/function_tools/python-tool-for-booking-demo/settings.toml @@ -6,7 +6,15 @@ version = "0.1.1" [rag.agent] type = "function_calling" -system_prompt = "你是一个管理会议室预定的助手,需要协助用户预订会议室。请严格使用输入的工具,不要虚构任何细节。" + +[rag.agent.custom_config] +agent_file_path = "example_data/function_tools/python-tool-for-booking-demo" + +[rag.agent.intent_detection] +type = "single" + +[rag.agent.tool] +type = "python" [rag.chat_engine] type = "CondenseQuestionChatEngine" @@ -30,15 +38,14 @@ type = "SimpleDirectoryReader" source = "DashScope" embed_batch_size = 10 +[rag.embedding.multi_modal] +source = "cnclip" + [rag.evaluation] retrieval = ["mrr", "hit_rate"] response_label = true response = ["Faithfulness", "Answer Relevancy", "Guideline Adherence", "Correctness", "Semantic Similarity"] -[rag.function_calling_llm] -source = "DashScope" -name = "qwen2-7b-instruct" - [rag.index] persist_path = "localdata/storage" vector_store.type = "FAISS" @@ -52,12 +59,16 @@ vector_store.type = "FAISS" source = "DashScope" name = "qwen-turbo" -[rag.llm_chat_engine] -type = "SimpleChatEngine" +[rag.llm.function_calling_llm] +source = "DashScope" +name = "qwen2-7b-instruct" -[rag.multi_modal_llm] +[rag.llm.multi_modal] source = "" +[rag.llm_chat_engine] +type = "SimpleChatEngine" + [rag.node_enhancement] tree_depth = 3 max_clusters = 52 @@ -87,7 +98,3 @@ query_rewrite_n = 1 # set to 1 to disable query generation [rag.synthesizer] type = "SimpleSummarize" text_qa_template = "参考内容信息如下\n---------------------\n{context_str}\n---------------------根据提供内容而非其他知识回答问题.\n问题: {query_str}\n答案: \n" - -[rag.tool] -type = ["custom"] -func_path = "src/pai_rag/modules/tool/booking_demo" diff --git a/src/pai_rag/app/api/models.py b/src/pai_rag/app/api/models.py index 2c19fa74..53973796 100644 --- a/src/pai_rag/app/api/models.py +++ b/src/pai_rag/app/api/models.py @@ -13,14 +13,7 @@ class RagQuery(BaseModel): session_id: str | None = None vector_db: VectorDbConfig | None = None stream: bool | None = False - - -class LlmQuery(BaseModel): - question: str - temperature: float | None = 0.1 - chat_history: List[Dict[str, str]] | None = None - session_id: str | None = None - stream: bool | None = False + with_intent: bool | None = False class RetrievalQuery(BaseModel): diff --git a/src/pai_rag/app/api/query.py b/src/pai_rag/app/api/query.py index a29e200d..3677f3fe 100644 --- a/src/pai_rag/app/api/query.py +++ b/src/pai_rag/app/api/query.py @@ -7,7 +7,6 @@ from pai_rag.core.rag_service import rag_service from pai_rag.app.api.models import ( RagQuery, - LlmQuery, RetrievalQuery, LlmResponse, ) @@ -29,7 +28,7 @@ async def aquery(query: RagQuery): @router.post("/query/llm") -async def aquery_llm(query: LlmQuery): +async def aquery_llm(query: RagQuery): response = await rag_service.aquery_llm(query) if not query.stream: return response @@ -46,10 +45,23 @@ async def aquery_retrieval(query: RetrievalQuery): @router.post("/query/agent") -async def aquery_agent(query: LlmQuery) -> LlmResponse: +async def aquery_agent(query: RagQuery) -> LlmResponse: return await rag_service.aquery_agent(query) +@router.post("/config/agent") +async def aload_agent_config(file: UploadFile): + fn = file.filename + data = await file.read() + file_hash = hashlib.md5(data).hexdigest() + save_file = os.path.join("localdata", f"{file_hash}_{fn}") + + with open(save_file, "wb") as f: + f.write(data) + f.close() + return await rag_service.aload_agent_config(save_file) + + @router.patch("/config") async def aupdate(new_config: Any = Body(None)): rag_service.reload(new_config) diff --git a/src/pai_rag/app/web/rag_client.py b/src/pai_rag/app/web/rag_client.py index 5104a09b..f73d719a 100644 --- a/src/pai_rag/app/web/rag_client.py +++ b/src/pai_rag/app/web/rag_client.py @@ -55,6 +55,10 @@ def config_url(self): def load_data_url(self): return f"{self.endpoint}service/upload_data" + @property + def load_agent_cfg_url(self): + return f"{self.endpoint}service/config/agent" + @property def get_load_state_url(self): return f"{self.endpoint}service/get_upload_state" @@ -79,11 +83,10 @@ def _format_rag_response( else: text = response["answer"] - docs = response.get("docs", []) + docs = response.get("docs", []) or [] is_finished = response.get("is_finished", True) referenced_docs = "" - if is_finished and len(docs) == 0 and not text: response["result"] = EMPTY_KNOWLEDGEBASE_MESSAGE.format(query_str=question) return response @@ -107,8 +110,16 @@ def _format_rag_response( response["result"] = formatted_answer return response - def query(self, text: str, session_id: str = None, stream: bool = False): - q = dict(question=text, session_id=session_id, stream=stream) + def query( + self, + text: str, + session_id: str = None, + stream: bool = False, + with_intent: bool = False, + ): + q = dict( + question=text, session_id=session_id, stream=stream, with_intent=with_intent + ) r = requests.post(self.query_url, json=q, stream=True) if r.status_code != HTTPStatus.OK: raise RagApiError(code=r.status_code, msg=r.text) @@ -248,6 +259,25 @@ def get_config(self): raise RagApiError(code=r.status_code, msg=response.message) return response + def load_agent_config(self, file_name: str): + files = [] + file_obj = open(file_name, "rb") + mimetype = mimetypes.guess_type(file_name)[0] + files.append(("file", (os.path.basename(file_name), file_obj, mimetype))) + try: + r = requests.post( + self.load_agent_cfg_url, + files=files, + timeout=DEFAULT_CLIENT_TIME_OUT, + ) + response = json.loads(r.text) + if r.status_code != HTTPStatus.OK: + raise RagApiError(code=r.status_code, msg=response.message) + finally: + file_obj.close() + + return response + def evaluate_for_generate_qa(self, overwrite): r = requests.post( self.get_evaluate_generate_url, diff --git a/src/pai_rag/app/web/tabs/agent_tab.py b/src/pai_rag/app/web/tabs/agent_tab.py new file mode 100644 index 00000000..99707d2f --- /dev/null +++ b/src/pai_rag/app/web/tabs/agent_tab.py @@ -0,0 +1,118 @@ +from typing import Dict, Any +import json +from pai_rag.app.web.rag_client import rag_client +import gradio as gr + + +def upload_config_file_fn(upload_config_file): + if upload_config_file is None: + return None + with open(upload_config_file.name, "rb") as file: + res = json.loads(file.read()) + return res + + +def update_agent_config(upload_config_file): + res = rag_client.load_agent_config(upload_config_file.name) + return res + + +def respond(agent_question, agent_chatbot): + response_gen = rag_client.query(agent_question, with_intent=True) + content = "" + agent_chatbot.append((agent_question, content)) + for resp in response_gen: + agent_chatbot[-1] = (agent_question, resp.result) + yield agent_chatbot + + +def clear_history(chatbot): + chatbot = [] + global current_session_id + current_session_id = None + return chatbot + + +def reset_textbox(): + return gr.update(value="") + + +def create_agent_tab() -> Dict[str, Any]: + with gr.Row(): + with gr.Column(scale=4): + _ = gr.Markdown( + value="**Upload the agent config file with function calling**" + ) + with gr.Row(): + with gr.Column(scale=7): + upload_config_file = gr.File( + label="Upload agent config file for function calling.", + file_count="single", + file_types=[".json"], + elem_id="upload_config_file", + scale=8, + ) + with gr.Column(variant="panel", scale=3): + config_submit = gr.Button( + "Submit Config", + elem_id="config_submit", + variant="primary", + scale=6, + ) + config_submit_state = gr.Textbox(label="Submit Status", scale=4) + upload_file_content = gr.JSON( + label="Displaying config content", elem_id="upload_file_content" + ) + + upload_config_file.upload( + fn=upload_config_file_fn, + inputs=[upload_config_file], + outputs=[upload_file_content], + api_name="upload_config_file_fn", + ) + config_submit.click( + update_agent_config, + [upload_config_file], + [config_submit_state], + api_name="agent_config_submit_clk", + ) + + with gr.Column(scale=6): + _ = gr.Markdown(value="**Agentic RAG Chatbot Test**") + agent_chatbot = gr.Chatbot(height=500, elem_id="agent_chatbot") + agent_question = gr.Textbox( + label="Enter your question.", elem_id="agent_question" + ) + with gr.Row(): + submitBtn = gr.Button("Submit", variant="primary") + clearBtn = gr.Button("Clear History", variant="secondary") + submitBtn.click( + respond, + [agent_question, agent_chatbot], + [agent_chatbot], + api_name="agent_respond_clk", + ) + agent_question.submit( + respond, + [agent_question, agent_chatbot], + [agent_chatbot], + api_name="agent_respond_q", + ) + submitBtn.click( + reset_textbox, + [], + [agent_question], + api_name="agent_reset_clk", + ) + agent_question.submit( + reset_textbox, + [], + [agent_question], + api_name="agent_reset_q", + ) + + clearBtn.click(clear_history, [agent_chatbot], [agent_chatbot]) + return { + upload_config_file.elem_id: upload_config_file, + upload_file_content.elem_id: upload_file_content, + } diff --git a/src/pai_rag/app/web/webui.py b/src/pai_rag/app/web/webui.py index b772e5ce..7be4f962 100644 --- a/src/pai_rag/app/web/webui.py +++ b/src/pai_rag/app/web/webui.py @@ -6,6 +6,7 @@ from pai_rag.app.web.tabs.settings_tab import create_setting_tab from pai_rag.app.web.tabs.upload_tab import create_upload_tab from pai_rag.app.web.tabs.chat_tab import create_chat_tab +from pai_rag.app.web.tabs.agent_tab import create_agent_tab from pai_rag.app.web.element_manager import elem_manager from pai_rag.app.web.ui_constants import ( DEFAULT_CSS_STYPE, @@ -57,6 +58,9 @@ def make_homepage(): # with gr.Tab("\N{rocket} Evaluation"): # eval_elements = create_evaluation_tab() # elem_manager.add_elems(eval_elements) + with gr.Tab("\N{rocket} Agent"): + agent_elements = create_agent_tab() + elem_manager.add_elems(agent_elements) homepage.load( resume_ui, outputs=elem_manager.get_elem_list(), concurrency_limit=None diff --git a/src/pai_rag/config/settings.toml b/src/pai_rag/config/settings.toml index efdc7591..aea3f04b 100644 --- a/src/pai_rag/config/settings.toml +++ b/src/pai_rag/config/settings.toml @@ -7,6 +7,15 @@ version = "0.1.1" [rag.agent] type = "react" +[rag.agent.custom_config] +agent_file_path = "" + +[rag.agent.intent_detection] +type = "" + +[rag.agent.tool] +type = "" # [built-in", "python", "api"] + [rag.chat_engine] type = "CondenseQuestionChatEngine" @@ -37,9 +46,6 @@ retrieval = ["mrr", "hit_rate"] response_label = true response = ["Faithfulness", "Answer Relevancy", "Correctness", "Semantic Similarity"] -[rag.function_calling_llm] -source = "" - [rag.index] persist_path = "localdata/storage" vector_store.type = "FAISS" @@ -53,6 +59,9 @@ vector_store.type = "FAISS" source = "DashScope" name = "qwen-turbo" +[rag.llm.function_calling_llm] +source = "" + [rag.llm.multi_modal] source = "DashScope" @@ -92,7 +101,3 @@ query_rewrite_n = 1 # set to 1 to disable query generation [rag.synthesizer] type = "SimpleSummarize" text_qa_template = "参考内容信息如下\n---------------------\n{context_str}\n---------------------根据提供内容而非其他知识回答问题.\n问题: {query_str}\n答案: \n" - -[rag.tool] -type = ["calculator", "weather"] -weather_api_key = "" diff --git a/src/pai_rag/core/rag_application.py b/src/pai_rag/core/rag_application.py index f1ef51a6..be4d69c2 100644 --- a/src/pai_rag/core/rag_application.py +++ b/src/pai_rag/core/rag_application.py @@ -1,7 +1,6 @@ from pai_rag.modules.module_registry import module_registry from pai_rag.app.api.models import ( RagQuery, - LlmQuery, RetrievalQuery, RagResponse, LlmResponse, @@ -11,6 +10,7 @@ from llama_index.core.schema import QueryBundle import json import logging +import os from uuid import uuid4 DEFAULT_EMPTY_RESPONSE_GEN = "Empty Response" @@ -103,7 +103,7 @@ async def aquery_retrieval(self, query: RetrievalQuery) -> RetrievalResponse: return RetrievalResponse(docs=docs) - async def aquery(self, query: RagQuery): + async def aquery_rag(self, query: RagQuery): """Query answer from RAG App asynchronously. Generate answer from Query Engine's or Chat Engine's achat interface. @@ -162,13 +162,13 @@ async def aquery(self, query: RagQuery): else: return event_generator_async(response=response, extra_info=result_info) - async def aquery_llm(self, query: LlmQuery): + async def aquery_llm(self, query: RagQuery): """Query answer from LLM response asynchronously. Generate answer from LLM's or LLM Chat Engine's achat interface. Args: - query: LlmQuery + query: RagQuery Returns: LlmResponse @@ -194,13 +194,13 @@ async def aquery_llm(self, query: LlmQuery): response = await llm_chat_engine.astream_chat(query.question) return event_generator_async(response=response) - async def aquery_agent(self, query: LlmQuery) -> LlmResponse: + async def aquery_agent(self, query: RagQuery) -> RagResponse: """Query answer from RAG App via web search asynchronously. Generate answer from agent's achat interface. Args: - query: LlmQuery + query: RagQuery Returns: LlmResponse @@ -210,7 +210,64 @@ async def aquery_agent(self, query: LlmQuery) -> LlmResponse: agent = module_registry.get_module_with_config("AgentModule", self.config) response = await agent.achat(query.question) - return LlmResponse(answer=response.response) + return RagResponse(answer=response.response) + + async def aquery_with_intent(self, query: RagQuery): + """Query answer from RAG App asynchronously. + + Generate answer from Query Engine's or Chat Engine's achat interface. + + Args: + query: RagQuery + + Returns: + RagResponse + """ + if not query.question: + return RagResponse(answer="Empty query. Please input your question.") + intent_detector = module_registry.get_module_with_config( + "IntentDetectionModule", self.config + ) + intent = await intent_detector.aselect( + intent_detector._choices, query=query.question + ) + self.logger.info(f"[IntentDetection] Routing query to {intent.intent}.") + if intent.intent == "agent": + return await self.aquery_agent(query) + elif intent.intent == "retrieval": + return await self.aquery_rag(query) + else: + return ValueError(f"Invalid intent {intent.intent}") + + async def aquery(self, query: RagQuery): + if query.with_intent: + return await self.aquery_with_intent(query) + else: + return await self.aquery_rag(query) + + async def aload_agent_config(self, agent_cfg_path: str): + if os.path.exists(agent_cfg_path): + sessioned_config = self.config.as_dict().copy() + sessioned_config["RAG"]["llm"]["function_calling_llm"][ + "source" + ] = "DashScope" + sessioned_config["RAG"]["llm"]["function_calling_llm"][ + "name" + ] = "qwen2-7b-instruct" + sessioned_config["RAG"]["agent"]["type"] = "function_calling" + sessioned_config["RAG"]["agent"]["custom_config"][ + "agent_file_path" + ] = agent_cfg_path + sessioned_config["RAG"]["agent"]["intent_detection"]["type"] = "single" + sessioned_config["RAG"]["agent"]["tool"]["type"] = "api" + + new_settings = self.config + new_settings.update(sessioned_config) + + self.reload(new_settings) + return "Update agent config successfully." + else: + return f"The agent config path {agent_cfg_path} not exists." async def aload_evaluation_qa_dataset(self, overwrite: bool = False): vector_store_type = ( diff --git a/src/pai_rag/core/rag_configuration.py b/src/pai_rag/core/rag_configuration.py index 7a5eac15..4c4548ba 100644 --- a/src/pai_rag/core/rag_configuration.py +++ b/src/pai_rag/core/rag_configuration.py @@ -52,6 +52,10 @@ def update(self, new_value: Dynaconf): if self.config.get("rag", None): if not new_value.get("llm").get("multi_modal"): new_value["llm"]["multi_modal"] = self.config["rag.llm.multi_modal"] + if not new_value.get("llm").get("function_calling_llm"): + new_value["llm"]["function_calling_llm"] = self.config[ + "rag.llm.function_calling_llm" + ] if not new_value.get("embedding").get("multi_modal"): new_value["embedding"]["multi_modal"] = self.config[ "rag.embedding.multi_modal" diff --git a/src/pai_rag/core/rag_service.py b/src/pai_rag/core/rag_service.py index cc90ef73..4412ee1b 100644 --- a/src/pai_rag/core/rag_service.py +++ b/src/pai_rag/core/rag_service.py @@ -7,7 +7,6 @@ from pai_rag.core.rag_configuration import RagConfiguration from pai_rag.app.api.models import ( RagQuery, - LlmQuery, RetrievalQuery, LlmResponse, ) @@ -156,7 +155,7 @@ async def aquery(self, query: RagQuery): logger.error(traceback.format_exc()) raise UserInputError(f"Query RAG failed: {ex}") - async def aquery_llm(self, query: LlmQuery): + async def aquery_llm(self, query: RagQuery): try: self.check_updates() return await self.rag.aquery_llm(query) @@ -172,7 +171,7 @@ async def aquery_retrieval(self, query: RetrievalQuery): logger.error(traceback.format_exc()) raise UserInputError(f"Query RAG failed: {ex}") - async def aquery_agent(self, query: LlmQuery) -> LlmResponse: + async def aquery_agent(self, query: RagQuery) -> LlmResponse: try: self.check_updates() return await self.rag.aquery_agent(query) @@ -180,6 +179,14 @@ async def aquery_agent(self, query: LlmQuery) -> LlmResponse: logger.error(traceback.format_exc()) raise UserInputError(f"Query RAG failed: {ex}") + async def aload_agent_config(self, agent_cfg_path: str): + try: + self.check_updates() + return await self.rag.aload_agent_config(agent_cfg_path) + except Exception as ex: + logger.error(traceback.format_exc()) + raise UserInputError(f"Load agent config: {ex}") + async def aload_evaluation_qa_dataset(self, overwrite: bool = False): try: return await self.rag.aload_evaluation_qa_dataset(overwrite) diff --git a/src/pai_rag/integrations/llms/dashscope/fc_base.py b/src/pai_rag/integrations/llms/dashscope/fc_base.py index 04f77d3d..df81105e 100644 --- a/src/pai_rag/integrations/llms/dashscope/fc_base.py +++ b/src/pai_rag/integrations/llms/dashscope/fc_base.py @@ -14,12 +14,14 @@ class DashScopeGenerationModels: QWEN_MAX_1201 = "qwen-max-1201" QWEN_MAX_LONGCONTEXT = "qwen-max-longcontext" + QWEM1P5_1P8B_CHAT = "qwen1.5-1.8b-chat" QWEM1P5_7B_CHAT = "qwen1.5-7b-chat" QWEM1P5_14B_CHAT = "qwen1.5-14b-chat" QWEM1P5_32B_CHAT = "qwen1.5-32b-chat" QWEM1P5_72B_CHAT = "qwen1.5-72b-chat" QWEM1P5_110B_CHAT = "qwen1.5-110b-chat" + QWEM2_1P5B_INSTRUCT = "qwen2-1.5b-instruct" QWEM2_7B_INSTRUCT = "qwen2-7b-instruct" QWEM2_72B_INSTRUCT = "qwen2-72b-instruct" @@ -55,6 +57,12 @@ class DashScopeGenerationModels: "is_chat_model": True, "is_function_calling_model": True, }, + DashScopeGenerationModels.QWEM1P5_1P8B_CHAT: { + "context_window": 1024 * 30, + "num_output": 1024 * 2, + "is_chat_model": True, + "is_function_calling_model": True, + }, DashScopeGenerationModels.QWEM1P5_7B_CHAT: { "context_window": 1024 * 8, "num_output": 1024 * 8, @@ -85,6 +93,12 @@ class DashScopeGenerationModels: "is_chat_model": True, "is_function_calling_model": True, }, + DashScopeGenerationModels.QWEM2_1P5B_INSTRUCT: { + "context_window": 1024 * 30, + "num_output": 1024 * 32, + "is_chat_model": True, + "is_function_calling_model": True, + }, DashScopeGenerationModels.QWEM2_7B_INSTRUCT: { "context_window": 1024 * 32, "num_output": 1024 * 32, diff --git a/src/pai_rag/modules/__init__.py b/src/pai_rag/modules/__init__.py index e55925b2..c6757c60 100644 --- a/src/pai_rag/modules/__init__.py +++ b/src/pai_rag/modules/__init__.py @@ -20,7 +20,8 @@ from pai_rag.modules.evaluation.evaluation import EvaluationModule from pai_rag.modules.index.bm25_index import BM25IndexModule from pai_rag.modules.nodesenhance.nodes_enhancement import NodesEnhancementModule - +from pai_rag.modules.intentdetection.intent_detection import IntentDetectionModule +from pai_rag.modules.customconfig.custom_config import CustomConfigModule ALL_MODULES = [ "EmbeddingModule", @@ -45,6 +46,8 @@ "EvaluationModule", "BM25IndexModule", "NodesEnhancementModule", + "IntentDetectionModule", + "CustomConfigModule", ] __all__ = ALL_MODULES + ["ALL_MODULES"] diff --git a/src/pai_rag/modules/agent/agent.py b/src/pai_rag/modules/agent/agent.py index 8e1bd5a0..746f05c3 100644 --- a/src/pai_rag/modules/agent/agent.py +++ b/src/pai_rag/modules/agent/agent.py @@ -14,11 +14,17 @@ class AgentModule(ConfigurableModule): @staticmethod def get_dependencies() -> List[str]: - return ["LlmModule", "FunctionCallingLlmModule", "ToolModule"] + return [ + "LlmModule", + "FunctionCallingLlmModule", + "CustomConfigModule", + "ToolModule", + ] def _create_new_instance(self, new_params: Dict[str, Any]): config = new_params[MODULE_PARAM_CONFIG] func_tool = new_params["ToolModule"] + agent_config, _ = new_params["CustomConfigModule"] type = config["type"].lower() if type == "react": llm = new_params["LlmModule"] @@ -31,7 +37,18 @@ def _create_new_instance(self, new_params: Dict[str, Any]): agent = ReActAgent.from_tools(tools=func_tool, llm=llm, verbose=True) elif type == "function_calling": llm = new_params["FunctionCallingLlmModule"] - system_content = config["system_prompt"] + print("FunctionCallingLlmModule llm", llm) + if agent_config: + system_content = agent_config["agent"]["system_prompt"] + else: + system_content = config["system_prompt"] + logger.info( + f""" + [Parameters][Agent] + type = {type} + system_prompt = {system_content} + """ + ) prefix_messages = [ ChatMessage( role="system", diff --git a/src/pai_rag/modules/customconfig/custom_config.py b/src/pai_rag/modules/customconfig/custom_config.py new file mode 100644 index 00000000..d9c8ced5 --- /dev/null +++ b/src/pai_rag/modules/customconfig/custom_config.py @@ -0,0 +1,35 @@ +import logging +import json +from typing import Dict, List, Any +from pai_rag.modules.base.configurable_module import ConfigurableModule +from pai_rag.modules.base.module_constants import MODULE_PARAM_CONFIG + +logger = logging.getLogger(__name__) + + +class CustomConfigModule(ConfigurableModule): + @staticmethod + def get_dependencies() -> List[str]: + return [] + + def _create_new_instance(self, new_params: Dict[str, Any]): + config = new_params[MODULE_PARAM_CONFIG] + agent_file_path = config.get("agent_file_path", None) + function_file_path = config.get("function_file_path", None) + agent_config = None + function_body_str = None + if agent_file_path: + logger.info( + f""" + [Parameters][CustomConfig] + agent_file_path = {agent_file_path} + """ + ) + with open(agent_file_path, "r", encoding="utf-8") as file: + agent_config = json.load(file) + if function_file_path: + with open(function_file_path, "r", encoding="utf-8") as file: + function_body_str = file.read() + else: + logger.info("Don't use CustomConfig Module.") + return agent_config, function_body_str diff --git a/src/pai_rag/modules/intentdetection/intent_detection.py b/src/pai_rag/modules/intentdetection/intent_detection.py new file mode 100644 index 00000000..bcffc6e6 --- /dev/null +++ b/src/pai_rag/modules/intentdetection/intent_detection.py @@ -0,0 +1,43 @@ +import logging +from typing import Dict, List, Any +from pai_rag.modules.base.configurable_module import ConfigurableModule +from pai_rag.modules.base.module_constants import MODULE_PARAM_CONFIG +from pai_rag.modules.intentdetection.llm_single_detector import LLMSingleDetector +from llama_index.core.tools import ToolMetadata + +logger = logging.getLogger(__name__) + + +class IntentDetectionModule(ConfigurableModule): + @staticmethod + def get_dependencies() -> List[str]: + return ["FunctionCallingLlmModule", "CustomConfigModule"] + + def _create_new_instance(self, new_params: Dict[str, Any]): + config = new_params[MODULE_PARAM_CONFIG] + llm = new_params["FunctionCallingLlmModule"] + agent_config, _ = new_params["CustomConfigModule"] + type = config.get("type", "single") + if type == "single": + logger.info( + f""" + [Parameters][IntentDetection] + type = {config.get("type", "Unknown inten detection type")} + """ + ) + intents_tools = [] + if agent_config: + intents = [] + for name in agent_config["intent"]: + intents.append([name, agent_config["intent"][name]]) + else: + intents = config.get("intent", None) + + for intent in intents: + tool = ToolMetadata(name=intent[0], description=intent[1]) + intents_tools.append(tool) + intent_detector = LLMSingleDetector(llm=llm, choices=intents_tools) + return intent_detector + else: + logger.info("Don't use IntentDetection Module.") + return None diff --git a/src/pai_rag/modules/intentdetection/llm_single_detector.py b/src/pai_rag/modules/intentdetection/llm_single_detector.py new file mode 100644 index 00000000..7e1f67e2 --- /dev/null +++ b/src/pai_rag/modules/intentdetection/llm_single_detector.py @@ -0,0 +1,154 @@ +from typing import Any, List, Sequence, Optional, Dict, cast +from llama_index.core.bridge.pydantic import BaseModel +from llama_index.core.prompts.base import PromptTemplate +from llama_index.core.types import BaseOutputParser +from llama_index.core.prompts.prompt_type import PromptType +from llama_index.core.prompts.mixin import PromptDictType +from llama_index.core.base.base_selector import BaseSelector +from llama_index.core.schema import QueryBundle +from llama_index.core.service_context_elements.llm_predictor import ( + LLMPredictorType, +) +from llama_index.core.tools.types import ToolMetadata +from llama_index.core.output_parsers.base import StructuredOutput +from pai_rag.modules.intentdetection.output_parser import Answer, SelectionOutputParser +from llama_index.core.selectors.llm_selectors import _build_choices_text + +DEFAULT_LLM_SINGLE_DETECTOR_PROMPT_TMPL = ( + "Some intents are given below. It is provided in a numbered list " + "(1 to {num_intent}), " + "where each item in the list corresponds to a summary.\n" + "---------------------\n" + "{intent_list}" + "\n---------------------\n" + "Using only the intents above and not prior knowledge, return " + "the intent that is most relevant to the question: '{query_str}'\n" +) + + +class SelectorResult(BaseModel): + """A single selection of a choice.""" + + index: int + intent: str + + +class MultiSelectorResult(BaseModel): + """A single selection of a choice.""" + + results: List[SelectorResult] + + +def _structured_output_to_detector_result( + output: Any, choices: Sequence[ToolMetadata] +) -> SelectorResult: + """Convert structured output to selector result.""" + structured_output = cast(StructuredOutput, output) + answers = cast(List[Answer], structured_output.parsed_output) + + # adjust for zero indexing + selections = [ + SelectorResult(index=answer.index - 1, intent=choices[answer.index - 1].name) + for answer in answers + ] + return selections[0] + + +class LLMSingleDetector(BaseSelector): + """LLM single selector. + + LLM-based selector that chooses one out of many options. + + Args: + LLM (LLM): An LLM. + prompt (SingleSelectPrompt): A LLM prompt for selecting one out of many options. + """ + + def __init__( + self, + llm: Optional[LLMPredictorType] = None, + prompt_template_str: Optional[str] = None, + output_parser: Optional[BaseOutputParser] = None, + choices: Optional[Sequence[ToolMetadata]] = None, + ) -> None: + self._llm = llm + prompt_template_str = ( + prompt_template_str or DEFAULT_LLM_SINGLE_DETECTOR_PROMPT_TMPL + ) + self._output_parser = output_parser or SelectionOutputParser() + prompt = PromptTemplate( + template=prompt_template_str, + output_parser=self._output_parser, + prompt_type=PromptType.CUSTOM, + ) + self._prompt = prompt + self._choices = choices + + @classmethod + def from_defaults( + cls, + llm: Optional[LLMPredictorType] = None, + prompt_template_str: Optional[str] = None, + output_parser: Optional[BaseOutputParser] = None, + ) -> "LLMSingleDetector": + # optionally initialize defaults + llm = llm + prompt_template_str = ( + prompt_template_str or DEFAULT_LLM_SINGLE_DETECTOR_PROMPT_TMPL + ) + output_parser = output_parser or SelectionOutputParser() + + # construct prompt + prompt = PromptTemplate( + template=prompt_template_str, + output_parser=output_parser, + prompt_type=PromptType.CUSTOM, + ) + return cls(llm, prompt) + + def _get_prompts(self) -> Dict[str, Any]: + """Get prompts.""" + return {"prompt": self._prompt} + + def _update_prompts(self, prompts: PromptDictType) -> None: + """Update prompts.""" + if "prompt" in prompts: + self._prompt = prompts["prompt"] + + def _select( + self, choices: Sequence[ToolMetadata], query: QueryBundle + ) -> SelectorResult: + # prepare input + intent_text = _build_choices_text(choices) + + # predict + prediction = self._llm.predict( + prompt=self._prompt, + num_intent=len(choices), + intent_list=intent_text, + query_str=query.query_str, + ) + + # parse output + assert self._prompt.output_parser is not None + parse = self._prompt.output_parser.parse(prediction) + return _structured_output_to_detector_result(parse, choices) + + async def _aselect( + self, choices: Sequence[ToolMetadata], query: QueryBundle + ) -> SelectorResult: + # prepare input + intent_text = _build_choices_text(choices) + + # predict + prediction = await self._llm.apredict( + prompt=self._prompt, + num_intent=len(choices), + intent_list=intent_text, + query_str=query.query_str, + ) + + # parse output + assert self._prompt.output_parser is not None + parse = self._prompt.output_parser.parse(prediction) + return _structured_output_to_detector_result(parse, choices) diff --git a/src/pai_rag/modules/intentdetection/output_parser.py b/src/pai_rag/modules/intentdetection/output_parser.py new file mode 100644 index 00000000..dd070536 --- /dev/null +++ b/src/pai_rag/modules/intentdetection/output_parser.py @@ -0,0 +1,102 @@ +import json +from dataclasses import dataclass +from typing import Any, List + +from dataclasses_json import DataClassJsonMixin +from llama_index.core.output_parsers.base import ( + OutputParserException, + StructuredOutput, +) +from llama_index.core.output_parsers.utils import _marshal_llm_to_json +from llama_index.core.types import BaseOutputParser + + +def _escape_curly_braces(input_string: str) -> str: + # Replace '{' with '{{' and '}' with '}}' to escape curly braces + return input_string.replace("{", "{{").replace("}", "}}") + + +FORMAT_STR = """The output should be ONLY JSON formatted as a JSON instance. + +Here is an example: +[ + { + index: 1 + }, + ... +] +""" + + +@dataclass +class Answer(DataClassJsonMixin): + index: int + + +class SelectionOutputParser(BaseOutputParser): + REQUIRED_KEYS = frozenset(Answer.__annotations__) + + def _filter_dict(self, json_dict: dict) -> dict: + """Filter recursively until a dictionary matches all REQUIRED_KEYS.""" + output_dict = json_dict + for key, val in json_dict.items(): + if key in self.REQUIRED_KEYS: + continue + elif isinstance(val, dict): + output_dict = self._filter_dict(val) + elif isinstance(val, list): + for item in val: + if isinstance(item, dict): + output_dict = self._filter_dict(item) + + return output_dict + + def _format_output(self, output: List[dict]) -> List[dict]: + output_json = [] + for json_dict in output: + valid = True + for key in self.REQUIRED_KEYS: + if key not in json_dict: + valid = False + break + + if not valid: + json_dict = self._filter_dict(json_dict) + + output_json.append(json_dict) + + return output_json + + def parse(self, output: str) -> Any: + json_string = _marshal_llm_to_json(output) + try: + json_obj = json.loads(json_string) + except json.JSONDecodeError as e_json: + try: + import yaml + + # NOTE: parsing again with pyyaml + # pyyaml is less strict, and allows for trailing commas + # right now we rely on this since guidance program generates + # trailing commas + json_obj = yaml.safe_load(json_string) + except yaml.YAMLError as e_yaml: + raise OutputParserException( + f"Got invalid JSON object. Error: {e_json} {e_yaml}. " + f"Got JSON string: {json_string}" + ) + except NameError as exc: + raise ImportError("Please pip install PyYAML.") from exc + + if isinstance(json_obj, dict): + json_obj = [json_obj] + + if not json_obj: + raise ValueError(f"Failed to convert output to JSON: {output!r}") + + json_output = self._format_output(json_obj) + answers = [Answer.from_dict(json_dict) for json_dict in json_output] + return StructuredOutput(raw_output=output, parsed_output=answers) + + def format(self, prompt_template: str) -> str: + return prompt_template + "\n\n" + _escape_curly_braces(FORMAT_STR) diff --git a/src/pai_rag/modules/llm/function_calling_llm.py b/src/pai_rag/modules/llm/function_calling_llm.py index c990058d..131118cc 100644 --- a/src/pai_rag/modules/llm/function_calling_llm.py +++ b/src/pai_rag/modules/llm/function_calling_llm.py @@ -20,6 +20,12 @@ def _create_new_instance(self, new_params: Dict[str, Any]): f"Using DashScope for Function-Calling-LLM with model: {model_name}." ) return MyFCDashScope(model_name=model_name) + elif llm_config and llm_config.source.lower() == "openai": + model_name = llm_config.get("name", "gpt-3.5-turbo") + logger.info( + f"Using OpenAI for Function-Calling-LLM with model: {model_name}." + ) + return MyFCDashScope(model_name=model_name) else: logger.info("Don't use Function-Calling-LLM.") return None diff --git a/src/pai_rag/modules/module_registry.py b/src/pai_rag/modules/module_registry.py index e889d832..381755ec 100644 --- a/src/pai_rag/modules/module_registry.py +++ b/src/pai_rag/modules/module_registry.py @@ -11,7 +11,7 @@ "MultiModalEmbeddingModule": "rag.embedding.multi_modal", "LlmModule": "rag.llm", "MultiModalLlmModule": "rag.llm.multi_modal", - "FunctionCallingLlmModule": "rag.function_calling_llm", + "FunctionCallingLlmModule": "rag.llm.function_calling_llm", "NodeParserModule": "rag.node_parser", "RetrieverModule": "rag.retriever", "PostprocessorModule": "rag.postprocessor", @@ -22,7 +22,9 @@ "LlmChatEngineFactoryModule": "rag.llm_chat_engine", "DataReaderFactoryModule": "rag.data_reader", "AgentModule": "rag.agent", - "ToolModule": "rag.tool", + "ToolModule": "rag.agent.tool", + "CustomConfigModule": "rag.agent.custom_config", + "IntentDetectionModule": "rag.agent.intent_detection", "DataLoaderModule": "rag.data_loader", "OssCacheModule": "rag.oss_store", "EvaluationModule": "rag.evaluation", @@ -118,7 +120,6 @@ def _create_mod_lazily(self, mod_name, config, mod_cache=None): mod_config_key = MODULE_CONFIG_KEY_MAP[mod_name] mod_deps = self._mod_deps_map[mod_name] mod_cls = self._mod_cls_map[mod_name] - params = {MODULE_PARAM_CONFIG: config.get(mod_config_key, None)} for dep in mod_deps: params[dep] = self._create_mod_lazily(dep, config, mod_cache) diff --git a/src/pai_rag/modules/tool/booking_demo/custom_functions.json b/src/pai_rag/modules/tool/booking_demo/custom_functions.json deleted file mode 100644 index abe06fe8..00000000 --- a/src/pai_rag/modules/tool/booking_demo/custom_functions.json +++ /dev/null @@ -1,78 +0,0 @@ -[ - { - "type": "function", - "function": { - "name": "get_booking_state", - "description": "用于查询给定会议室ID的预定状态", - "parameters": { - "type": "object", - "properties": { - "room_id": { - "type": "str", - "description": "待查询的会议室ID" - } - }, - "required": ["room_id"] - } - } - }, - { - "type": "function", - "function": { - "name": "update_booking", - "description": "用于更新预定指定会议室ID(room_id)的预定人信息,包含姓名(name)、电子邮件(email)、电话(phone)、日期(date)和时间(time)。", - "parameters": { - "type": "object", - "properties": { - "room_id": { - "type": "str", - "description": "待更新信息的会议室ID" - }, - "property": { - "type": "str", - "description": "待更新的信息属性,如姓名(name)、电子邮件(email)、电话(phone)、日期(date)和时间(time)." - }, - "value": { - "type": "str", - "description": "待更新的信息属性的值." - } - }, - "required": ["room_id", "property", "value"] - } - } - }, - { - "type": "function", - "function": { - "name": "create_booking", - "description": "预定指定会议室ID的会议室", - "parameters": { - "type": "object", - "properties": { - "room_id": { - "type": "str", - "description": "指定预定的会议室ID" - } - }, - "required": ["room_id"] - } - } - }, - { - "type": "function", - "function": { - "name": "confirm_booking", - "description": "用于确认是否预定指定会议室ID的会议室", - "parameters": { - "type": "object", - "properties": { - "room_id": { - "type": "str", - "description": "需要确认的会议室ID" - } - }, - "required": ["room_id"] - } - } - } -] diff --git a/src/pai_rag/modules/tool/load_and_search_tool_spec.py b/src/pai_rag/modules/tool/load_and_search_tool_spec.py index d9c84aa7..816294ce 100644 --- a/src/pai_rag/modules/tool/load_and_search_tool_spec.py +++ b/src/pai_rag/modules/tool/load_and_search_tool_spec.py @@ -147,7 +147,7 @@ def load(self, *args: Any, **kwargs: Any) -> Any: encoded_raw_text = datetime.now().time().isoformat().encode() folder_name = hashlib.sha256(encoded_raw_text).hexdigest() - tmp_persist_path = f"experiments/websearch/{folder_name}" + tmp_persist_path = f"tmp/websearch/{folder_name}" self._index.storage_context.persist(persist_dir=tmp_persist_path) return ( "Content loaded! You can now search the information using read_{}".format( diff --git a/src/pai_rag/modules/tool/tool.py b/src/pai_rag/modules/tool/tool.py index 390b5852..45c71323 100644 --- a/src/pai_rag/modules/tool/tool.py +++ b/src/pai_rag/modules/tool/tool.py @@ -5,8 +5,9 @@ from pai_rag.modules.tool.utils import ( get_google_web_search_tools, get_calculator_tools, - get_customized_tools, + get_customized_python_tools, get_weather_tools, + get_customized_api_tools, ) @@ -16,11 +17,15 @@ class ToolModule(ConfigurableModule): @staticmethod def get_dependencies() -> List[str]: - return [] + return ["CustomConfigModule"] def _create_new_instance(self, new_params: Dict[str, Any]): self.config = new_params[MODULE_PARAM_CONFIG] + agent_config, function_body_str = new_params["CustomConfigModule"] type = self.config["type"] + if type == "": + logger.info("Don't use Tool Module.") + return None logger.info( f""" [Parameters][Tool:FunctionTool] @@ -28,16 +33,20 @@ def _create_new_instance(self, new_params: Dict[str, Any]): """ ) tools = [] - if "googlewebsearch" in type: - tools.extend(get_google_web_search_tools(self.config)) - - if "calculator" in type: - tools.extend(get_calculator_tools()) - - if "custom" in type: - tools.extend(get_customized_tools(self.config)) - - if "weather" in type: - tools.extend(get_weather_tools(self.config)) - + if type == "built-in": + name = self.config["name"] + if "googlewebsearch" in name: + tools.extend(get_google_web_search_tools(self.config)) + if "calculator" in name: + tools.extend(get_calculator_tools()) + if "weather" in name: + tools.extend(get_weather_tools(self.config)) + elif type == "python": + tools.extend( + get_customized_python_tools(agent_config["agent"], function_body_str) + ) + elif type == "api": + tools.extend(get_customized_api_tools(agent_config["agent"])) + else: + raise ValueError(f"Not Support Tool type {type}") return tools diff --git a/src/pai_rag/modules/tool/utils.py b/src/pai_rag/modules/tool/utils.py index 43fc616b..af50de8a 100644 --- a/src/pai_rag/modules/tool/utils.py +++ b/src/pai_rag/modules/tool/utils.py @@ -2,9 +2,6 @@ from pai_rag.modules.tool.load_and_search_tool_spec import LoadAndSearchToolSpec from llama_index.core.tools import FunctionTool from llama_index.core.bridge.pydantic import FieldInfo, create_model -import json -import os -import sys import requests from pai_rag.modules.tool.default_tool_description_template import ( DEFAULT_GOOGLE_SEARCH_TOOL_DESP, @@ -26,6 +23,15 @@ def create_tool_fn_schema(name, params): return create_model(name, **fields) # type: ignore +def create_custom_tool_fn_schema(name, params_prop): + fields = {} + for param_name in params_prop: + param_type = params_prop[param_name]["type"] + param_desc = params_prop[param_name]["description"] + fields[param_name] = (param_type, FieldInfo(description=param_desc)) + return create_model(name, **fields) # type: ignore + + def get_google_web_search_tools(config): google_spec = GoogleSearchToolSpec( key=config.get("google_search_api", None), @@ -106,34 +112,75 @@ def get_place_weather(city: str) -> str: return [weather_tool] -def get_customized_tools(config): - func_path = config["func_path"] - sys.path.append(func_path) - try: - module = __import__("custom_functions") - tools = [] - # 加载JSON文件 - with open(os.path.join(func_path, "custom_functions.json"), "r") as file: - custom_tools = json.load(file) - - for c_tool in custom_tools: - fn_name = c_tool["function"]["name"] - if hasattr(module, fn_name): - func = getattr(module, fn_name) - fn_schema = create_tool_fn_schema( - fn_name, c_tool["function"]["parameters"] - ) - tool = FunctionTool.from_defaults( - fn=func, - name=fn_name, - fn_schema=fn_schema, - description=c_tool["function"]["description"], - ) - tools.append(tool) - else: - raise ValueError( - f"Function {fn_name} has not been defined in the custom_functions.py, please define it." - ) - return tools - except Exception: - return [] +def get_customized_api_tools(config): + def generate_api_function(api_info): + # 获取API信息 + function_name = api_info["name"] + api_url = api_info["api"] + headers = api_info["headers"] + method = api_info["method"] + request_body_type = api_info["request_body_type"] + # GET -> params, POST -> json / data + # 生成函数的参数 + required_params = api_info["required"] + param_str = ", ".join(required_params) + + # 定义函数体 + function_body = f""" +def {function_name}({param_str}): + url = '{api_url}' + data = {{k: v for k, v in locals().items() if k in {list(required_params)}}} + headers = {headers} + response = requests.{method.lower()}(url, {request_body_type}=data, headers=headers) + if response.status_code == 200: + return response.json() + else: + response.raise_for_status() +""" + + # 将函数添加到当前模块的执行环境 + exec(function_body, globals()) + # 返回生成的函数 + return globals()[function_name] + + tools = [] + for api_info in config["functions"]: + # 生成API函数 + func = generate_api_function(api_info) + fn_name = api_info["name"] + fn_schema = create_custom_tool_fn_schema(fn_name, api_info["parameters"]) + tool = FunctionTool.from_defaults( + fn=func, + name=fn_name, + fn_schema=fn_schema, + description=api_info["description"], + ) + tools.append(tool) + return tools + + +def get_customized_python_tools(config, function_body_str): + def generate_python_functions(config, function_body_str): + # 将函数添加到当前模块的执行环境 + exec(function_body_str, globals()) + func_dict = {} + for func_info in config["functions"]: + function_name = func_info["name"] + func = func_info["function"] + func_dict[function_name] = globals()[func] + return func_dict + + tools = [] + func_dict = generate_python_functions(config, function_body_str) + for func_info in config["functions"]: + fn_name = func_info["name"] + func = func_dict[fn_name] + fn_schema = create_custom_tool_fn_schema(fn_name, func_info["parameters"]) + tool = FunctionTool.from_defaults( + fn=func, + name=fn_name, + fn_schema=fn_schema, + description=func_info["description"], + ) + tools.append(tool) + return tools diff --git a/tests/modules/intentdetection/test_intent_detection.py b/tests/modules/intentdetection/test_intent_detection.py new file mode 100644 index 00000000..433d8e2f --- /dev/null +++ b/tests/modules/intentdetection/test_intent_detection.py @@ -0,0 +1,28 @@ +import os +from pai_rag.modules.intentdetection.llm_single_detector import LLMSingleDetector +from llama_index.core.tools import ToolMetadata +from pai_rag.integrations.llms.dashscope.fc_base import MyFCDashScope + +fc_llm = MyFCDashScope( + model_name="qwen2-7b-instruct", api_key=os.getenv("DASHSCOPE_API_KEY") +) + +intents = { + "retrieval": "关于一些通用的信息检索,比如搜索旅游攻略、搜索美食攻略、搜索注意事项等信息。", + "agent": "实时性的信息查询,比如查询航班信息、查询高铁信息、查询天气等时效性很强的信息。", +} + + +def test_single_detectors(): + intents_tools = [] + for intent in intents: + tool = ToolMetadata(name=intent, description=intents[intent]) + intents_tools.append(tool) + intent_detector = LLMSingleDetector(llm=fc_llm, choices=intents_tools) + query_1 = "去上海玩的攻略有什么?" + intent_1 = intent_detector.select(intent_detector._choices, query=query_1) + assert intent_1.intent == "retrieval" + + query_1 = "8月10号从北京出发去上海,机票价格有哪些?" + intent_1 = intent_detector.select(intent_detector._choices, query=query_1) + assert intent_1.intent == "agent" diff --git a/tests/modules/tool/test_api_tool.py b/tests/modules/tool/test_api_tool.py new file mode 100644 index 00000000..c46be7ee --- /dev/null +++ b/tests/modules/tool/test_api_tool.py @@ -0,0 +1,28 @@ +from pai_rag.modules.tool.utils import get_customized_api_tools + +config = { + "functions": [ + { + "name": "search_flight_ticket_api", + "api": "http://127.0.0.1:8070/demo/api/flights", + "headers": {"Authorization": "Bearer YOUR_ACCESS_TOKEN"}, + "method": "GET", + "request_body_type": "params", + "description": "帮助用户获取机票信息,用户需要输入出发地、目的地", + "parameters": { + "from_city": {"type": "str", "description": "出发城市,如'北京'、'上海'、'南京''"}, + "to_city": {"type": "str", "description": "目的地城市,如'北京'、'上海'、'南京'"}, + "date": {"type": "str", "description": "出发时间,如'2024-03-29'"}, + }, + "required": ["from_city", "to_city", "date"], + } + ] +} + + +def test_customized_api_tool(): + tools = get_customized_api_tools(config) + assert len(tools) == 1 + assert tools[0].metadata.name == "search_flight_ticket_api" + assert tools[0].metadata.description == "帮助用户获取机票信息,用户需要输入出发地、目的地" + assert tools[0].fn.__name__ == "search_flight_ticket_api"

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