小学语文老师智能体样例

examples/sample_apps/primary_chinese_teacher_agent/.gitignore

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+docs/source
+
+# From https://raw.githubusercontent.com/github/gitignore/main/Python.gitignore
+
+ Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+.idea/
+.DS_Store
+
+
+*.pyc
+
+custom_key.toml

examples/sample_apps/primary_chinese_teacher_agent/__init__.py

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+# !/usr/bin/env python3
+# -*- coding:utf-8 -*-

examples/sample_apps/primary_chinese_teacher_agent/bootstrap/__init__.py

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+# !/usr/bin/env python3
+# -*- coding:utf-8 -*-
+

examples/sample_apps/primary_chinese_teacher_agent/bootstrap/intelligence/__init__.py

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+# !/usr/bin/env python3
+# -*- coding:utf-8 -*-
+

examples/sample_apps/primary_chinese_teacher_agent/bootstrap/intelligence/mcp_application.py

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+# !/usr/bin/env python3
+# -*- coding:utf-8 -*-
+
+# @Time    : 2024/4/8 20:58
+# @Author  : jerry.zzw
+# @Email   : jerry.zzw@antgroup.com
+# @FileName: mcp_application.py
+from agentuniverse.agent_serve.web.mcp.mcp_server_manager import MCPServerManager
+from agentuniverse.base.agentuniverse import AgentUniverse
+
+
+class ServerApplication:
+    """
+    Server application.
+    """
+
+    @classmethod
+    def start(cls):
+        AgentUniverse().start(core_mode=True)
+        MCPServerManager().start_server()
+
+
+if __name__ == "__main__":
+    ServerApplication.start()
+    

examples/sample_apps/primary_chinese_teacher_agent/bootstrap/intelligence/server_application.py

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+# !/usr/bin/env python3
+# -*- coding:utf-8 -*-
+
+# @Time    : 2024/4/8 20:58
+# @Author  : jerry.zzw
+# @Email   : jerry.zzw@antgroup.com
+# @FileName: server_application.py
+from agentuniverse.agent_serve.web.web_booster import start_web_server
+from agentuniverse.base.agentuniverse import AgentUniverse
+
+
+class ServerApplication:
+    """
+    Server application.
+    """
+
+    @classmethod
+    def start(cls):
+        AgentUniverse().start()
+        start_web_server()
+
+
+if __name__ == "__main__":
+    ServerApplication.start()

examples/sample_apps/primary_chinese_teacher_agent/config/config.toml

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+[BASE_INFO]
+# The app name will be applied to all processes including agent service integration.
+appname = 'demo_app'
+
+[CORE_PACKAGE]
+# Perform a full component scan and registration for all the paths under this list.
+default = ['primary_chinese_teacher_agent.intelligence.agentic']
+# Scan and register agent components for all paths under this list, with priority over the default.
+agent = ['primary_chinese_teacher_agent.intelligence.agentic.agent']
+# Scan and register knowledge components for all paths under this list, with priority over the default.
+knowledge = ['primary_chinese_teacher_agent.intelligence.agentic.knowledge']
+# Scan and register llm components for all paths under this list, with priority over the default.
+llm = ['primary_chinese_teacher_agent.intelligence.agentic.llm']
+# Scan and register planner components for all paths under this list, with priority over the default.
+planner = []
+# Scan and register tool components for all paths under this list, with priority over the default.
+tool = ['primary_chinese_teacher_agent.intelligence.agentic.tool']
+# Scan and register memory components for all paths under this list, with priority over the default.
+memory = ['primary_chinese_teacher_agent.intelligence.agentic.memory']
+# Scan and register service components for all paths under this list, with priority over the default.
+service = ['primary_chinese_teacher_agent.intelligence.service.agent_service']
+# Scan and register prompt components for all paths under this list, with priority over the default.
+prompt = ['primary_chinese_teacher_agent.intelligence.agentic.prompt']
+# Scan and register product components for all paths under this list, with priority over the default.
+#product = ['au_sample_standard_app.platform.difizen.product']
+# Scan and register workflow components for all paths under this list, with priority over the default.
+#workflow = ['au_sample_standard_app.platform.difizen.workflow']
+# Scan and register store components for all paths under this list, with priority over the default.
+store = ['primary_chinese_teacher_agent.intelligence.agentic.knowledge.store']
+# Scan and register rag_router components for all paths under this list, with priority over the default.
+rag_router = ['primary_chinese_teacher_agent.intelligence.agentic.knowledge.rag_router']
+# Scan and register doc_processor components for all paths under this list, with priority over the default.
+doc_processor = ['primary_chinese_teacher_agent.intelligence.agentic.knowledge.doc_processor']
+# Scan and register query_paraphraser components for all paths under this list, with priority over the default.
+query_paraphraser = ['primary_chinese_teacher_agent.intelligence.agentic.knowledge.query_paraphraser']
+# Scan and register memory_compressor components for all paths under this list, with priority over the default.
+memory_compressor = ['primary_chinese_teacher_agent.intelligence.agentic.memory.memory_compressor']
+# Scan and register memory_storage components for all paths under this list, with priority over the default.
+memory_storage = ['primary_chinese_teacher_agent.intelligence.agentic.memory.memory_storage']
+
+[SUB_CONFIG_PATH]
+# Log config file path, an absolute path or a relative path based on the dir where the current config file is located.
+log_config_path = './log_config.toml'
+# Custom key file path, use to save your own secret key like open ai or sth else. REMEMBER TO ADD IT TO .gitignore.
+custom_key_path = './custom_key.toml'
+
+[DB]
+# A sqlalchemy db uri used for storing various info, for example, service request, generated during application running.
+# If it's empty, agentUniverse will create a local sqlite db as default choice.
+system_db_uri = ''
+
+[GUNICORN]
+# Use gunicorn as http server when activate is 'true', or only use flask.
+activate = 'false'
+# Gunicorn config file path, an absolute path or a relative path based on the dir where the current config file is located.
+gunicorn_config_path = './gunicorn_config.toml'
+
+[GRPC]
+activate = 'false'
+max_workers = 10
+server_port = 50051
+
+[MONITOR]
+activate = false
+dir = './monitor'

examples/sample_apps/primary_chinese_teacher_agent/config/custom_key.toml.sample

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+# Example file of custom_key.toml. Rename to custom_key.toml while using.
+[KEY_LIST]
+# Perform a full component scan and registration for all the paths under this list.
+example_key = 'AnExampleKey'
+
+# models
+#kimi default name: default_kimi_llm
+#KIMI_API_KEY='sk-xxxxxxxx'
+#
+##Qwen default name: default_qwen_llm
+#DASHSCOPE_API_KEY='sk-xxxxxx'
+#
+##Opean default name: default_openai_llm
+#OPENAI_API_KEY='sk-xxxxxx'
+#
+##DEEPSEEK default name: default_deepseek_llm
+#DEEPSEEK_API_KEY='sk-xxxxxxx'
+#DEEPSEEK_API_BASE='https://api.deepseek.com/v1'
+#
+## WenXin default name: default_wenxin_llm
+#QIANFAN_AK='xxxx'
+#QIANFAN_SK='xxxx'
+#
+##Ollama default name: default_ollama_llm
+#OLLAMA_BASE_URL='xxxxxx'
+#
+##claude default name: default_claude_llm
+#ANTHROPIC_API_KEY='xxxxxx'
+#ANTHROPIC_API_URL='xxxxxx'
+#
+##baichuan default name: default_baichuan_llm
+#BAICHUAN_API_KEY='xxxxxx'
+#
+##ZHIPU default name: default_zhipu_llm
+#ZHIPU_API_KEY='xxxxxx'
+#ZHIPU_API_BASE='https://open.bigmodel.cn/api/paas/v4/'
+
+# search
+#Google search
+#SERPER_API_KEY='xxxxxx'
+#
+##search api
+#SEARCHAPI_API_KEY='xxxxxx'
+#
+##bing search
+#BING_SUBSCRIPTION_KEY='xxxxxx'

examples/sample_apps/primary_chinese_teacher_agent/config/gunicorn_config.toml

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+[GUNICORN_CONFIG]
+bind = '0.0.0.0:8888'
+backlog = 2048
+worker_class = 'gthread'
+threads = 4
+workers = 5
+timeout = 60
+keepalive = 10

examples/sample_apps/primary_chinese_teacher_agent/config/log_config.toml

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+[LOG_CONFIG]
+[LOG_CONFIG.BASIC_CONFIG]
+# Loguru log level.
+log_level = "INFO"
+# Output path of the log file. If value is empty, agentuniverse will create a subdir under your workdir to save logs.
+log_path = "./.test_log_dir"
+# Specifies the log rotation policy, controlling when a new log file is created. It can be a time period
+# (e.g., "1 week"), a file size (e.g., "100 MB"), or a function returning True when rotation should occur.
+log_rotation = "100 MB"
+# Specifies the duration to keep old log files. It can be a time span (e.g., "30 days") or a function to filter the
+# files to be retained. Files outside this policy are purged.
+log_retention = "7 days"
+
+[LOG_CONFIG.EXTEND_MODULE]
+# Whether you use Aliyun Simple Log Service (SLS), if the value is "True", you should fill in the ALIYUN_SLS_CONFIG below.
+sls_log = "False"
+
+[LOG_CONFIG.ALIYUN_SLS_CONFIG]
+# Aliyun sls endpoint.
+sls_endpoint = "mock_endpoint"
+# Your sls log project name.
+sls_project = "mock_project"
+# Your sls log store name.
+sls_log_store = "mock_log_store"
+# Aliyun sls access_key_id.
+access_key_id = "mock_key_id"
+# Aliyun sls access_key_secret.
+access_key_secret = "mock_key_secret"
+# Log queue max size, agentuniverse uses a queue to save the logs to be sent, they will be sent periodically.
+sls_log_queue_max_size = 1000
+# Interval of sending logs to aliyun sls.
+sls_log_send_interval = 3.0

examples/sample_apps/primary_chinese_teacher_agent/intelligence/__init__.py

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+# !/usr/bin/env python3
+# -*- coding:utf-8 -*-
+

examples/sample_apps/primary_chinese_teacher_agent/intelligence/agentic/__init__.py

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+# !/usr/bin/env python3
+# -*- coding:utf-8 -*-

examples/sample_apps/primary_chinese_teacher_agent/intelligence/agentic/agent/__init__.py

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+# !/usr/bin/env python3
+# -*- coding:utf-8 -*-
+

examples/sample_apps/primary_chinese_teacher_agent/intelligence/agentic/agent/agent_instance/__init__.py

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+# !/usr/bin/env python3
+# -*- coding:utf-8 -*-
+

examples/sample_apps/primary_chinese_teacher_agent/intelligence/agentic/agent/agent_instance/primary_chinese_teacher_agent.py

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+# !/usr/bin/env python3
+# -*- coding:utf-8 -*-
+
+# @Time    : 2024/12/26 17:10
+# @Author  : wangchongshi
+# @Email   : wangchongshi.wcs@antgroup.com
+# @FileName: primary_chinese_teacher_agent.py
+from langchain_core.output_parsers import StrOutputParser
+
+from agentuniverse.agent.agent import Agent
+from agentuniverse.agent.input_object import InputObject
+from agentuniverse.base.util.prompt_util import process_llm_token
+from agentuniverse.llm.llm import LLM
+from agentuniverse.prompt.prompt import Prompt
+from primary_chinese_teacher_agent.intelligence.utils.constant.prod_description import \
+    PROD_DESCRIPTION_A
+
+
+class PrimaryChineseTeacherAgent(Agent):
+
+    def input_keys(self) -> list[str]:
+        return ['input']
+
+    def output_keys(self) -> list[str]:
+        return ['output']
+
+    def parse_input(self, input_object: InputObject, agent_input: dict) -> dict:
+        agent_input['input'] = input_object.get_data('input')
+        return agent_input
+
+    def parse_result(self, agent_result: dict) -> dict:
+        return {**agent_result, 'output': agent_result['output']}
+
+    def execute(self, input_object: InputObject, agent_input: dict, **kwargs) -> dict:
+        """Execute pet insurance agent instance.
+
+        Args:
+            input_object (InputObject): input parameters passed by the user.
+            agent_input (dict): agent input parsed from `input_object` by the user.
+
+        Returns:
+            dict: agent result.
+        """
+        # 1. get the llm instance.
+        llm: LLM = self.process_llm(**kwargs)
+        # 2. assemble the background.
+        agent_input['background'] = PROD_DESCRIPTION_A
+        # 3. get the agent prompt.
+        prompt: Prompt = self.process_prompt(agent_input, **kwargs)
+        process_llm_token(llm, prompt.as_langchain(), self.agent_model.profile, agent_input)
+        # 4. invoke agent.
+        chain = prompt.as_langchain() | llm.as_langchain_runnable(
+            self.agent_model.llm_params()) | StrOutputParser()
+        res = self.invoke_chain(chain, agent_input, input_object, **kwargs)
+        # 5. return result.
+        return {**agent_input, 'output': res}

examples/sample_apps/primary_chinese_teacher_agent/intelligence/agentic/agent/agent_instance/primary_chinese_teacher_agent.yaml

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+info:
+  name: 'primary_chinese_teacher_agent'
+  description: '小学一年级语文老师智能体'
+profile:
+  introduction: 你是一位有爱心、耐心、懂教育心理学的小学一年级语文老师。
+  target: 你的目标是根据学生或家长提出的问题，提供清晰、亲切、专业的语文学习指导。
+  instruction: |
+    你需要遵守的规则是：
+      1. 必须使用中文回答，语气温和、简洁、适合一年级学生理解。
+      2. 若问题来自家长，应体现专业指导性；若来自学生，应体现引导与鼓励。
+      3. 回答要结构清晰，必要时分点陈述或使用空行提升可读性。
+      4. 答案应贴近小学一年级语文教学内容，包括识字、拼音、朗读、写字、课文理解等。
+      5. 不使用复杂的专业术语或超纲知识。
+      6. 若提问内容超出一年级语文教学范围，可温和说明，并引导至合适的学习阶段或方法。
+      7. 保持鼓励与启发性，适当使用温馨提示或小贴士风格。
+
+    背景信息是：
+    {background}
+
+    开始！
+    需要回答的问题是：{input}
+  llm_model:
+    name: 'deepseek-llm'
+    model_name: 'deepseek-chat'
+    temperature: 0.2
+action:
+  tool:
+metadata:
+  type: 'AGENT'
+  module: 'primary_chinese_teacher_agent.intelligence.agentic.agent.agent_instance.primary_chinese_teacher_agent'
+  class: 'PrimaryChineseTeacherAgent'

examples/sample_apps/primary_chinese_teacher_agent/intelligence/agentic/agent/agent_template/__init__.py

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+# !/usr/bin/env python3
+# -*- coding:utf-8 -*-
+

examples/sample_apps/primary_chinese_teacher_agent/intelligence/agentic/knowledge/__init__.py

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+# !/usr/bin/env python3
+# -*- coding:utf-8 -*-
+

examples/sample_apps/primary_chinese_teacher_agent/intelligence/agentic/knowledge/doc_processor/__init__.py

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+# !/usr/bin/env python3
+# -*- coding:utf-8 -*-
+

examples/sample_apps/primary_chinese_teacher_agent/intelligence/agentic/knowledge/query_paraphraser/__init__.py

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+# !/usr/bin/env python3
+# -*- coding:utf-8 -*-
+

examples/sample_apps/primary_chinese_teacher_agent/intelligence/agentic/knowledge/rag_router/__init__.py

@@ -0,0 +1,3 @@
+# !/usr/bin/env python3
+# -*- coding:utf-8 -*-
+

examples/sample_apps/primary_chinese_teacher_agent/intelligence/agentic/knowledge/store/__init__.py

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+# !/usr/bin/env python3
+# -*- coding:utf-8 -*-
+

examples/sample_apps/primary_chinese_teacher_agent/intelligence/agentic/llm/__init__.py

@@ -0,0 +1,2 @@
+# !/usr/bin/env python3
+# -*- coding:utf-8 -*-

examples/sample_apps/primary_chinese_teacher_agent/intelligence/agentic/llm/deepseek_llm.yaml

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+name: 'deepseek-llm'
+description: 'deepseek-chat'
+model_name: 'deepseek-chat'
+max_tokens: 2000
+api_key: '${DEEPSEEK_API_KEY}'
+metadata:
+  type: 'LLM'
+  module: 'agentuniverse.llm.default.deep_seek_openai_style_llm'
+  class: 'DefaultDeepSeekLLM'

examples/sample_apps/primary_chinese_teacher_agent/intelligence/agentic/llm/langchian_instance/langchain_instance.py

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+# !/usr/bin/env python3
+# -*- coding:utf-8 -*-
+
+# @Time    : 2024/12/25 16:39
+# @Author  : wangchongshi
+# @Email   : wangchongshi.wcs@antgroup.com
+# @FileName: langchain_instance.py
+from typing import Optional, List, Any, Iterator, AsyncIterator
+
+from langchain_core.callbacks import CallbackManagerForLLMRun, AsyncCallbackManagerForLLMRun
+
+from agentuniverse.llm.llm import LLM
+
+from langchain_core.language_models import LLM as LangChainLLM
+
+from agentuniverse.llm.llm_output import LLMOutput
+from langchain_core.outputs import GenerationChunk
+
+
+class LangChainInstance(LangChainLLM):
+    llm: LLM = None
+    llm_type: str = "AgentUniverse"
+    streaming: bool = False
+
+    def __init__(self, llm: LLM, llm_type: str, **kwargs):
+        super().__init__(**kwargs)
+        self.llm = llm
+        self.llm_type = llm_type
+
+    def _call(self, prompt: str, stop: Optional[List[str]] = None,
+              run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any) -> str:
+        should_stream = kwargs.pop("streaming", False) if "streaming" in kwargs else self.streaming
+        llm_output = self.llm.call(prompt=prompt, stop=stop, **kwargs)
+        if not should_stream:
+            return llm_output.text
+        return self.parse_stream_result(llm_output, run_manager)
+
+    async def _acall(
+            self,
+            prompt: str,
+            stop: Optional[List[str]] = None,
+            run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+            **kwargs: Any,
+    ) -> str:
+        should_stream = kwargs.pop("streaming", False) if "streaming" in kwargs else self.streaming
+        llm_output = await self.llm.acall(prompt=prompt, stop=stop, **kwargs)
+        if not should_stream:
+            return llm_output.text
+        return await self.aparse_stream_result(llm_output, run_manager)
+
+    def _stream(
+            self,
+            prompt: str,
+            stop: Optional[List[str]] = None,
+            run_manager: Optional[CallbackManagerForLLMRun] = None,
+            **kwargs: Any,
+    ) -> Iterator[GenerationChunk]:
+        kwargs['stream'] = True
+        llm_output = self.llm.call(prompt=prompt, stop=stop, **kwargs)
+        for line in llm_output:
+            yield GenerationChunk(text=line.text, generation_info=line.raw)
+
+    async def _astream(
+            self,
+            prompt: str,
+            stop: Optional[List[str]] = None,
+            run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+            **kwargs: Any,
+    ) -> AsyncIterator[GenerationChunk]:
+        kwargs['stream'] = True
+        llm_output = await self.llm.acall(prompt=prompt, stop=stop, **kwargs)
+        async for line in llm_output:
+            yield GenerationChunk(text=line.text, generation_info=line.raw)
+
+    @staticmethod
+    def parse_stream_result(stream_result: Iterator[LLMOutput],
+                            run_manager: Optional[CallbackManagerForLLMRun] = None) -> str:
+        all_data = ""
+        for line in stream_result:
+            all_data += line.text
+            if run_manager:
+                run_manager.on_llm_new_token(line.text)
+        return all_data
+
+    @staticmethod
+    async def aparse_stream_result(stream_result: AsyncIterator[LLMOutput],
+                                   run_manager: Optional[AsyncCallbackManagerForLLMRun] = None) -> str:
+        all_data = ""
+        async for line in stream_result:
+            all_data += line.text
+            if run_manager:
+                await run_manager.on_llm_new_token(line.text)
+        return all_data
+
+    @property
+    def _llm_type(self) -> str:
+        return self.llm_type
+
+    def get_num_tokens(self, text: str) -> int:
+        return self.llm.get_num_tokens(text)
+
+    def get_token_ids(self, text: str) -> List[int]:
+        return self.llm.get_token_ids(text)

examples/sample_apps/primary_chinese_teacher_agent/intelligence/agentic/llm/maya/__init__.py

@@ -0,0 +1,6 @@
+# !/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# @Time    : 2024/12/10 10:51
+# @Author  : jijiawei
+# @Email   : jijiawei.jjw@antgroup.com
+# @FileName: __init__.py

examples/sample_apps/primary_chinese_teacher_agent/intelligence/agentic/llm/qwen_llm.yaml

@@ -0,0 +1,9 @@
+name: 'qwen_llm'
+description: 'demo qwen llm with spi'
+model_name: 'qwen2-72b-instruct'
+max_tokens: 2500
+api_key: '${DASHSCOPE_API_KEY}'
+metadata:
+  type: 'LLM'
+  module: 'agentuniverse.llm.default.qwen_openai_style_llm'
+  class: 'QWenOpenAIStyleLLM'

examples/sample_apps/primary_chinese_teacher_agent/intelligence/agentic/memory/__init__.py

@@ -0,0 +1,3 @@
+# !/usr/bin/env python3
+# -*- coding:utf-8 -*-
+

examples/sample_apps/primary_chinese_teacher_agent/intelligence/agentic/memory/memory_compressor/__init__.py

@@ -0,0 +1,3 @@
+# !/usr/bin/env python3
+# -*- coding:utf-8 -*-
+

examples/sample_apps/primary_chinese_teacher_agent/intelligence/agentic/memory/memory_storage/__init__.py

@@ -0,0 +1,2 @@
+# !/usr/bin/env python3
+# -*- coding:utf-8 -*-

examples/sample_apps/primary_chinese_teacher_agent/intelligence/agentic/prompt/__init__.py

@@ -0,0 +1,3 @@
+# !/usr/bin/env python3
+# -*- coding:utf-8 -*-
+

examples/sample_apps/primary_chinese_teacher_agent/intelligence/agentic/prompt/primary_chinese_teacher_prompt.yaml

@@ -0,0 +1,18 @@
+introduction: 你是一位温柔耐心的小学一年级语文老师。
+target: 你的目标是根据学生或家长的问题，用简单、生动、易懂的语言讲解小学一年级语文知识，帮助学生理解和学习。
+instruction: |
+  你需要遵守的规则是:
+    1. 必须使用中文回答，语气要温和亲切，符合小学一年级学生的理解能力。
+    2. 回答要简洁清晰，避免使用复杂或抽象的词语。
+    3. 如果讲解知识点，要举出具体例子帮助理解。
+    4. 对学生要多鼓励和表扬，帮助他们树立学习信心。
+    5. 回答可以通过分段或留空行的方式，让内容更容易阅读。
+  
+  背景信息是:
+  {background}
+  
+  开始！
+  需要回答的问题是: {input}
+metadata:
+  type: 'PROMPT'
+  version: 'primary_chinese_teacher.cn'

examples/sample_apps/primary_chinese_teacher_agent/intelligence/agentic/tool/__init__.py

@@ -0,0 +1,3 @@
+# !/usr/bin/env python3
+# -*- coding:utf-8 -*-
+

examples/sample_apps/primary_chinese_teacher_agent/intelligence/agentic/work_pattern/__init__.py

@@ -0,0 +1,3 @@
+# !/usr/bin/env python3
+# -*- coding:utf-8 -*-
+

examples/sample_apps/primary_chinese_teacher_agent/intelligence/service/__init__.py

@@ -0,0 +1,3 @@
+# !/usr/bin/env python3
+# -*- coding:utf-8 -*-
+

examples/sample_apps/primary_chinese_teacher_agent/intelligence/service/agent_service/__init__.py

@@ -0,0 +1,2 @@
+# !/usr/bin/env python3
+# -*- coding:utf-8 -*-

examples/sample_apps/primary_chinese_teacher_agent/intelligence/service/agent_service/primary_chinese_teacher_service.yaml

@@ -0,0 +1,5 @@
+name: 'primary_chinese_teacher_agent_service'
+description: ''
+agent: 'primary_chinese_teacher_agent'
+metadata:
+  type: 'SERVICE'

examples/sample_apps/primary_chinese_teacher_agent/intelligence/service/classic_service/__init__.py

@@ -0,0 +1,2 @@
+# !/usr/bin/env python3
+# -*- coding:utf-8 -*-

examples/sample_apps/primary_chinese_teacher_agent/intelligence/test/__init__.py

@@ -0,0 +1,3 @@
+# !/usr/bin/env python3
+# -*- coding:utf-8 -*-
+

examples/sample_apps/primary_chinese_teacher_agent/intelligence/test/primary_chinese_teacher_agent_test.py

@@ -0,0 +1,19 @@
+# !/usr/bin/env python3
+# -*- coding:utf-8 -*-
+
+from agentuniverse.agent.output_object import OutputObject
+from agentuniverse.base.agentuniverse import AgentUniverse
+from agentuniverse.agent.agent import Agent
+from agentuniverse.agent.agent_manager import AgentManager
+
+AgentUniverse().start(config_path='../../config/config.toml', core_mode=True)
+
+
+def chat(question: str):
+    instance: Agent = AgentManager().get_instance_obj('primary_chinese_teacher_agent')
+    output_object: OutputObject = instance.run(input=question)
+    print(output_object.get_data('output'))
+
+
+if __name__ == '__main__':
+    chat("我是刚刚幼儿园毕业的小朋友，请问一年级上册要预习哪些课文？")

examples/sample_apps/primary_chinese_teacher_agent/intelligence/utils/__init__.py

@@ -0,0 +1,3 @@
+# !/usr/bin/env python3
+# -*- coding:utf-8 -*-
+

examples/sample_apps/primary_chinese_teacher_agent/intelligence/utils/common/__init__.py

@@ -0,0 +1,3 @@
+# !/usr/bin/env python3
+# -*- coding:utf-8 -*-
+

examples/sample_apps/primary_chinese_teacher_agent/intelligence/utils/common/jsonl_file_util.py

@@ -0,0 +1,91 @@
+# !/usr/bin/env python3
+# -*- coding:utf-8 -*-
+
+# @Time    : 2024/7/1 21:09
+# @Author  : wangchongshi
+# @Email   : wangchongshi.wcs@antgroup.com
+# @FileName: jsonl_file_util.py
+import json
+import os
+import sys
+
+from agentuniverse.base.util.logging.logging_util import LOGGER
+
+DATA_DIR = './data/'
+
+sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
+
+
+class JsonFileOps(object):
+    def __init__(self):
+        return
+
+    @classmethod
+    def is_file_exist(cls, file_path):
+        file_name, ext = os.path.splitext(file_path)
+        if ext.lower() != '.jsonl':
+            raise Exception('Unsupported file extension')
+        return os.path.exists(file_path)
+
+
+class JsonFileReader(object):
+    def __init__(self, file_path: str):
+        self.file_handler = None
+        self.file_name = file_path
+        if JsonFileOps.is_file_exist(file_path):
+            self.file_handler = open(file_path, 'r', encoding='utf-8')
+
+    def read_json_obj(self):
+        if not self.file_handler:
+            raise Exception(f"None json file to read: {self.file_name}")
+        json_line = self.file_handler.readline()
+        if json_line:
+            try:
+                json_obj = json.loads(json_line.strip())
+                return json_obj
+            except Exception as e:
+                LOGGER.warn(f"except[read_json_line]>>>{e}:{json_line}")
+                return json.loads('{}')
+        else:
+            return None
+
+    def read_json_obj_list(self):
+        obj_list = []
+        while True:
+            obj = self.read_json_obj()
+            if obj is None:
+                break
+            obj_list.append(obj)
+        return obj_list
+
+
+class JsonFileWriter(object):
+    def __init__(self, output_file_name: str, extension='jsonl', directory=DATA_DIR):
+        self.outfile_path = directory + output_file_name + '.' + extension
+        directory = os.path.dirname(self.outfile_path)
+        if not os.path.exists(directory):
+            os.makedirs(directory)
+        self.outfile_handler = open(self.outfile_path, 'w', encoding='utf-8')
+
+    def write_json_obj(self, json_obj: dict):
+        try:
+            # confirm that it's a json string and then write.
+            json_line = json.dumps(json_obj, ensure_ascii=False)
+            self.outfile_handler.write(json_line.strip() + '\n')
+            self.outfile_handler.flush()
+        except Exception as e:
+            LOGGER.warn(f"except[write_json_obj]>>>{e}:{json_obj}")
+        return
+
+    def write_json_obj_list(self, json_obj_list: list):
+        for i in range(0, len(json_obj_list)):
+            self.write_json_obj(json_obj_list[i])
+        return
+
+    def write_json_query_answer(self, query: str, answer: str):
+        json_obj = {"query": query, "answer": answer}
+        self.write_json_obj(json_obj)
+
+    def write_json_query_answer_list(self, query_answer_list: list):
+        for i in range(0, len(query_answer_list)):
+            self.write_json_query_answer(query_answer_list[i][0], query_answer_list[i][1])

examples/sample_apps/primary_chinese_teacher_agent/intelligence/utils/common/txt_file_util.py

@@ -0,0 +1,50 @@
+# !/usr/bin/env python3
+# -*- coding:utf-8 -*-
+
+# @Time    : 2024/7/1 16:05
+# @Author  : wangchongshi
+# @Email   : wangchongshi.wcs@antgroup.com
+# @FileName: txt_file_util.py
+import os
+import sys
+
+sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
+
+
+class TxtFileOps(object):
+    def __init__(self):
+        return
+
+    @classmethod
+    def is_file_exist(cls, file_path):
+        file_name, ext = os.path.splitext(file_path)
+        if ext.lower() != '.txt':
+            raise Exception('Unsupported file extension')
+        return os.path.exists(file_path)
+
+
+class TxtFileReader(object):
+
+    def __init__(self, file_path: str):
+        self.file_handler = None
+        self.file_name = file_path
+        if TxtFileOps.is_file_exist(file_path):
+            self.file_handler = open(file_path, 'r', encoding='utf-8')
+
+    def read_txt_obj(self):
+        if not self.file_handler:
+            raise Exception(f"No txt file to read: {self.file_name}")
+        txt_line = self.file_handler.readline()
+        if txt_line:
+            return txt_line.strip()
+        else:
+            return None
+
+    def read_txt_obj_list(self):
+        obj_list = []
+        while True:
+            obj = self.read_txt_obj()
+            if obj is None:
+                break
+            obj_list.append(obj)
+        return obj_list

examples/sample_apps/primary_chinese_teacher_agent/intelligence/utils/constant/__init__.py

@@ -0,0 +1,2 @@
+# !/usr/bin/env python3
+# -*- coding:utf-8 -*-

examples/sample_apps/primary_chinese_teacher_agent/intelligence/utils/constant/prod_description.py

@@ -0,0 +1,48 @@
+# !/usr/bin/env python3
+# -*- coding:utf-8 -*-
+
+
+# mock的特定背景知识，用于回答小学一年级语文教学相关问题。
+PROD_DESCRIPTION_A = """
+小学一年级语文教学背景知识
+1. 教学目标
+课程目标：帮助学生认识常用汉字、培养阅读兴趣、掌握基础拼音、学习正确的书写姿势与笔顺。
+核心素养：语言积累与运用、阅读理解、表达与交流、文化启蒙。
+
+2. 教学内容
+识字与写字：学习常见汉字约350个，掌握基本笔画和偏旁部首。
+拼音学习：掌握23个声母、24个韵母、4个声调，能正确拼读音节。
+阅读与理解：通过课文、童话、儿歌等内容培养阅读兴趣。
+口语表达：鼓励学生大胆说话，进行简单的日常交流与表达。
+语文活动：朗读比赛、讲故事、识字游戏等。
+
+3. 教学方法
+多感官教学：通过听、说、读、写结合的方式增强记忆。
+游戏化学习：通过识字卡片、拼音接龙等方式提高学习兴趣。
+分层辅导：针对不同学习水平的学生给予个性化指导。
+激励评价：采用表扬、贴纸奖励、朗读之星等方式增强积极性。
+
+4. 教学工具与资源
+教材：《语文》（人教版一年级上、下册）
+辅助材料：识字卡、拼音挂图、绘本、动画朗读课件。
+数字资源：小学语文点读应用、在线拼音学习网站。
+
+5. 教师角色与教学理念
+教师角色：语文启蒙引导者，激发学生语言兴趣与文化情感。
+教学理念：以学生为中心，重视兴趣培养与基础打牢。
+课堂氛围：轻松活泼，鼓励表达与思考。
+
+6. 家校配合建议
+家庭阅读：家长每天陪读10分钟，共同朗读课文。
+书写习惯：督促孩子正确握笔、端正坐姿、按笔顺书写。
+语言交流：多与孩子交流日常生活，引导使用完整句子表达。
+
+7. 评价方式
+形成性评价：课堂表现、朗读态度、识字数量、书写整洁度。
+终结性评价：期末阅读理解、听写测试、朗读展示。
+
+8. 教学特色活动
+汉字趣味日：识字游戏、笔画比赛。
+童话故事周：学生分组表演课文内容。
+拼音小达人：拼读竞赛与口语展示。
+"""