improve the implementation of naive RAG

.gitignore

@@ -24,7 +24,8 @@ log.jsonl
 
 ai_agent/debug.json
 ai_agent/local_prompts/*
-*/debug.json
+**/debug.json
+**/debug.log*
 debug.json
 ai_agent/log.jsonl
 qwen_agent.egg-info/*

docs/agent.md

@@ -8,7 +8,7 @@ The Agent receives a list of messages as input and produces a generator that yie
 
 Different Agent classes have various workflows. In the [agents](../qwen_agent/agents) directory, we provide several different fundamental Agent subclasses.
 For instance, the [ArticleAgent](../qwen_agent/agents/article_agent.py) returns a message that includes an article;
-the [DocQAAgent](../qwen_agent/agents/docqa_agent.py) returns a message that contains the results of a document Q&A Results.
+the [BasicDocQA](../qwen_agent/agents/doc_qa/basic_doc_qa.py) returns a message that contains the results of a document Q&A Results.
 
 These types of Agents have relatively fixed response patterns and are suited for fairly specific use cases.
 

docs/agent_cn.md

@@ -8,7 +8,7 @@ Agent接收一个消息列表输入，并返回一个消息列表的生成器，
 
 不同Agent类具有不同的工作流程，我们在[agents](../qwen_agent/agents)目录提供了多个不同的基础的Agent子类，
 例如[ArticleAgent](../qwen_agent/agents/article_agent.py)接收消息后，返回消息包含一篇文章；
-[DocQAAgent](../qwen_agent/agents/docqa_agent.py)返回消息包含文档问答的结果。
+[BasicDocQA](../qwen_agent/agents/doc_qa/basic_doc_qa.py)返回消息包含文档问答的结果。
 可以看出，这类Agent回复模式相对固定，使用场景也比较固定。
 
 ### 1.1. Assistant 类

examples/assistant_add_custom_tool.py

@@ -19,26 +19,34 @@ class MyImageGen(BaseTool):
         'name': 'prompt',
         'type': 'string',
         'description': 'Detailed description of the desired image content, in English',
-        'required': True
+        'required': True,
     }]
 
     def call(self, params: str, **kwargs) -> str:
         prompt = json5.loads(params)['prompt']
         prompt = urllib.parse.quote(prompt)
-        return json.dumps({'image_url': f'https://image.pollinations.ai/prompt/{prompt}'}, ensure_ascii=False)
+        return json.dumps(
+            {'image_url': f'https://image.pollinations.ai/prompt/{prompt}'},
+            ensure_ascii=False,
+        )
 
 
 def init_agent_service():
     llm_cfg = {'model': 'qwen-max'}
-    system = ('According to the user\'s request, you first draw a picture and then automatically '
+    system = ("According to the user's request, you first draw a picture and then automatically "
               'run code to download the picture and select an image operation from the given document '
               'to process the image')
 
-    tools = ['my_image_gen', 'code_interpreter']  # code_interpreter is a built-in tool in Qwen-Agent
-    bot = Assistant(llm=llm_cfg,
-                    system_message=system,
-                    function_list=tools,
-                    files=[os.path.join(ROOT_RESOURCE, 'doc.pdf')])
+    tools = [
+        'my_image_gen',
+        'code_interpreter',
+    ]  # code_interpreter is a built-in tool in Qwen-Agent
+    bot = Assistant(
+        llm=llm_cfg,
+        system_message=system,
+        function_list=tools,
+        files=[os.path.join(ROOT_RESOURCE, 'doc.pdf')],
+    )
 
     return bot
 

examples/assistant_growing_girl.py

@@ -10,10 +10,12 @@
 def init_agent_service():
     llm_cfg = {'model': 'qwen-max'}
     tools = ['image_gen']
-    bot = Assistant(llm=llm_cfg,
-                    function_list=tools,
-                    system_message='你扮演一个漫画家，根据我给你的女孩的不同阶段，使用工具画出每个阶段女孩的的图片，'
-                    '并串成一个故事讲述出来。要求图片背景丰富')
+    bot = Assistant(
+        llm=llm_cfg,
+        function_list=tools,
+        system_message='你扮演一个漫画家，根据我给你的女孩的不同阶段，使用工具画出每个阶段女孩的的图片，'
+        '并串成一个故事讲述出来。要求图片背景丰富',
+    )
     return bot
 
 
@@ -42,7 +44,10 @@ def app():
         messages.extend(response)
 
 
-def test(query='请用image_gen开始创作！', file: Optional[str] = os.path.join(ROOT_RESOURCE, 'growing_girl.pdf')):
+def test(
+        query='请用image_gen开始创作！',
+        file: Optional[str] = os.path.join(ROOT_RESOURCE, 'growing_girl.pdf'),
+):
     # Define the agent
     bot = init_agent_service()
 

examples/assistant_weather_bot.py

@@ -13,7 +13,11 @@ def init_agent_service():
               '你需要查询相应地区的天气，然后调用给你的画图工具绘制一张城市的图，并从给定的诗词文档中选一首相关的诗词来描述天气，不要说文档以外的诗词。')
 
     tools = ['image_gen', 'amap_weather']
-    bot = Assistant(llm=llm_cfg, system_message=system, function_list=tools)
+    bot = Assistant(
+        llm=llm_cfg,
+        system_message=system,
+        function_list=tools,
+    )
 
     return bot
 

examples/group_chat_chess.py

@@ -9,26 +9,30 @@
 CFGS = {
     'background':
         f'一个五子棋群组，棋盘为5*5，黑棋玩家和白棋玩家交替下棋，每次玩家下棋后，棋盘进行更新并展示。{NPC_NAME}下白棋，{USER_NAME}下黑棋。',
-    'agents': [{
-        'name':
-            '棋盘',
-        'description':
-            '负责更新棋盘',
-        'instructions':
-            '你扮演一个五子棋棋盘，你可以根据原始棋盘和玩家下棋的位置坐标，把新的棋盘用矩阵展示出来。棋盘中用0代表无棋子、用1表示黑棋、用-1表示白棋。用坐标<i,j>表示位置，i代表行，j代表列，棋盘左上角位置为<0,0>。',
-        'selected_tools': ['code_interpreter']
-    }, {
-        'name':
-            NPC_NAME,
-        'description':
-            '白棋玩家',
-        'instructions':
-            '你扮演一个玩五子棋的高手，你下白棋。棋盘中用0代表无棋子、用1黑棋、用-1白棋。用坐标<i,j>表示位置，i代表行，j代表列，棋盘左上角位置为<0,0>，请决定你要下在哪里，你可以随意下到一个位置，不要说你是AI助手不会下！返回格式为坐标：\n<i,j>\n除了这个坐标，不要返回其他任何内容',
-    }, {
-        'name': USER_NAME,
-        'description': '黑棋玩家',
-        'is_human': True
-    }]
+    'agents': [
+        {
+            'name':
+                '棋盘',
+            'description':
+                '负责更新棋盘',
+            'instructions':
+                '你扮演一个五子棋棋盘，你可以根据原始棋盘和玩家下棋的位置坐标，把新的棋盘用矩阵展示出来。棋盘中用0代表无棋子、用1表示黑棋、用-1表示白棋。用坐标<i,j>表示位置，i代表行，j代表列，棋盘左上角位置为<0,0>。',
+            'selected_tools': ['code_interpreter'],
+        },
+        {
+            'name':
+                NPC_NAME,
+            'description':
+                '白棋玩家',
+            'instructions':
+                '你扮演一个玩五子棋的高手，你下白棋。棋盘中用0代表无棋子、用1黑棋、用-1白棋。用坐标<i,j>表示位置，i代表行，j代表列，棋盘左上角位置为<0,0>，请决定你要下在哪里，你可以随意下到一个位置，不要说你是AI助手不会下！返回格式为坐标：\n<i,j>\n除了这个坐标，不要返回其他任何内容',
+        },
+        {
+            'name': USER_NAME,
+            'description': '黑棋玩家',
+            'is_human': True
+        },
+    ],
 }
 
 

examples/multi_agent_router.py

@@ -1,4 +1,5 @@
 """A multi-agent cooperation example implemented by router and assistant"""
+
 import os
 from typing import Optional
 
@@ -14,23 +15,21 @@ def init_agent_service():
     tools = ['image_gen', 'code_interpreter']
 
     # Define a vl agent
-    bot_vl = Assistant(llm=llm_cfg_vl)
+    bot_vl = Assistant(llm=llm_cfg_vl, name='多模态助手', description='可以理解图像内容。')
 
     # Define a tool agent
-    bot_tool = ReActChat(llm=llm_cfg, function_list=tools)
+    bot_tool = ReActChat(
+        llm=llm_cfg,
+        name='工具助手',
+        description='可以使用画图工具和运行代码来解决问题',
+        function_list=tools,
+    )
 
     # Define a router (simultaneously serving as a text agent)
-    bot = Router(llm=llm_cfg,
-                 agents={
-                     'vl': {
-                         'obj': bot_vl,
-                         'desc': '多模态助手，可以理解图像内容。'
-                     },
-                     'tool': {
-                         'obj': bot_tool,
-                         'desc': '工具助手，可以使用画图工具和运行代码来解决问题'
-                     }
-                 })
+    bot = Router(
+        llm=llm_cfg,
+        agents=[bot_vl, bot_tool],
+    )
     return bot
 
 
@@ -65,10 +64,10 @@ def app():
 
 
 def test(
-    query: str = 'hello',
-    image:  # noqa
-    str = 'https://img01.sc115.com/uploads/sc/jpgs/1505/apic11540_sc115.com.jpg',  # noqa
-    file: Optional[str] = os.path.join(ROOT_RESOURCE, 'poem.pdf')):  # noqa
+        query: str = 'hello',
+        image: str = 'https://img01.sc115.com/uploads/sc/jpgs/1505/apic11540_sc115.com.jpg',
+        file: Optional[str] = os.path.join(ROOT_RESOURCE, 'poem.pdf'),
+):
     # Define the agent
     bot = init_agent_service()
 

examples/react_data_analysis.py

@@ -46,10 +46,8 @@ def app():
         messages.extend(response)
 
 
-def test(
-    query:  # noqa
-    str = 'pd.head the file first and then help me draw a line chart to show the changes in stock prices',
-    file: Optional[str] = os.path.join(ROOT_RESOURCE, 'stock_prices.csv')):  # noqa
+def test(query: str = 'pd.head the file first and then help me draw a line chart to show the changes in stock prices',
+         file: Optional[str] = os.path.join(ROOT_RESOURCE, 'stock_prices.csv')):
     # Define the agent
     bot = init_agent_service()
 

examples/visual_storytelling.py

@@ -34,8 +34,8 @@ def _run(self,
              **kwargs) -> Iterator[List[Message]]:
         """Define the workflow"""
 
-        assert (isinstance(messages[-1]['content'], list) and
-                any([item.image for item in messages[-1]['content']])), 'This agent requires input of images'
+        assert isinstance(messages[-1]['content'], list)
+        assert any([item.image for item in messages[-1]['content']]), 'This agent requires input of images'
 
         # Image understanding
         new_messages = copy.deepcopy(messages)

qwen_agent/__init__.py

@@ -1,4 +1,4 @@
-__version__ = '0.0.2'
+__version__ = '0.0.3'
 from .agent import Agent
 
 __all__ = ['Agent']