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Repository: svcvit/Awesome-Dify-Workflow
Branch: main
Commit: 7fdc2e7747d5
Files: 52
Total size: 944.0 KB
Directory structure:
gitextract__t742dxo/
├── DSL/
│ ├── AgentFlow.yml
│ ├── Agent工具调用.yml
│ ├── Artifact.yml
│ ├── Claude3 Code Translation.yml
│ ├── Deep Researcher On Dify .yml
│ ├── Demo-tod_agent.yml
│ ├── Dify 运营一条龙.yml
│ ├── Document_chat_template.yml
│ ├── DuckDuckGo翻译+LLM二次翻译.yml
│ ├── File_read.yml
│ ├── Form表单聊天Demo.yml
│ ├── Jina Reader Jinja.yml
│ ├── LanguageConsistencyChecker.yml
│ ├── MCP-amap.yml
│ ├── MCP.yml
│ ├── Python Coding Prompt.yml
│ ├── SEO Slug Generator.yml
│ ├── Text to Card Iteration.yml
│ ├── chart_demo.yml
│ ├── dify_course_demo.yml
│ ├── jieba.yml
│ ├── json-repair.yml
│ ├── json_translate.yml
│ ├── llm2o1.cn.yml
│ ├── matplotlib.yml
│ ├── runLLMCode.yml
│ ├── simple-kimi.yml
│ ├── translation_workflow.yml
│ ├── 中译英.yml
│ ├── 全书翻译.yml
│ ├── 图文知识库/
│ │ ├── 图文知识库.yml
│ │ └── 知识库内容/
│ │ ├── coffbox开启Gemini和Anthropic代理，chatwise连接方法.md
│ │ └── 我是技术小白，如何用好DIFY.md
│ ├── 完蛋！我被LLM包围了！ .yml
│ ├── 完蛋！我被LLM包围了！（战绩排行版）.yml
│ ├── 宝玉的英译中优化版.yml
│ ├── 小支付-DEMO.yml
│ ├── 思考助手.yml
│ ├── 搜索大师.yml
│ ├── 文章仿写-单图_多图自动搭配.yml
│ ├── 旅行Demo.yml
│ ├── 春联生成器 (“福”到了版本).yml
│ ├── 春联生成器.yml
│ ├── 标题党创作.yml
│ ├── 根据用户的意图进行回复.yml
│ ├── 瞎说新语v2.yml
│ ├── 腾讯云SubtitleInfo.yml
│ └── 记忆测试.yml
├── LICENSE
├── README.md
├── README_EN.md
└── chat_history.md
================================================
FILE CONTENTS
================================================
================================================
FILE: DSL/AgentFlow.yml
================================================
app:
description: ''
icon: 🤖
icon_background: '#FFEAD5'
mode: advanced-chat
name: AgentFlow
use_icon_as_answer_icon: false
dependencies: []
kind: app
version: 0.1.5
workflow:
conversation_variables: []
environment_variables: []
features:
file_upload:
allowed_file_extensions:
- .JPG
- .JPEG
- .PNG
- .GIF
- .WEBP
- .SVG
allowed_file_types:
- image
allowed_file_upload_methods:
- local_file
- remote_url
enabled: false
image:
enabled: false
number_limits: 3
transfer_methods:
- local_file
- remote_url
number_limits: 3
opening_statement: ''
retriever_resource:
enabled: true
sensitive_word_avoidance:
enabled: false
speech_to_text:
enabled: false
suggested_questions: []
suggested_questions_after_answer:
enabled: false
text_to_speech:
enabled: false
language: ''
voice: ''
graph:
edges:
- data:
isInIteration: false
sourceType: start
targetType: agent
id: 1740638793787-source-1740645185279-target
selected: false
source: '1740638793787'
sourceHandle: source
target: '1740645185279'
targetHandle: target
type: custom
zIndex: 0
- data:
isInIteration: false
sourceType: agent
targetType: answer
id: 1740645185279-source-answer-target
selected: false
source: '1740645185279'
sourceHandle: source
target: answer
targetHandle: target
type: custom
zIndex: 0
nodes:
- data:
desc: ''
selected: false
title: 开始
type: start
variables: []
height: 52
id: '1740638793787'
position:
x: 80
y: 282
positionAbsolute:
x: 80
y: 282
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 243
- data:
answer: '{{#1740645185279.text#}}'
desc: ''
selected: false
title: 直接回复
type: answer
variables: []
height: 101
id: answer
position:
x: 707.4166600208597
y: 288.61610048493054
positionAbsolute:
x: 707.4166600208597
y: 288.61610048493054
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 243
- data:
agent_parameters:
model:
type: constant
value:
completion_params: {}
mode: chat
model: gpt-4o-mini
model_type: llm
provider: langgenius/openai/openai
type: model-selector
query:
type: constant
value: '{{#sys.query#}}'
storage_key:
type: constant
value: '{{#sys.conversation_id#}}'
task_schema:
type: constant
value: " {\n \"fields\": [\n {\n \"name\"\
: \"destination\",\n \"question\": \"请问您想去哪里旅行？\",\n \
\ \"required\": true\n },\n {\n \
\ \"name\": \"duration\",\n \"question\": \"您计划旅行多长时间？\"\
,\n \"required\": true\n },\n {\n \
\ \"name\": \"budget\",\n \"question\": \"您的预算大约是多少？\"\
,\n \"required\": true\n }\n ]\n }"
agent_strategy_label: 多轮对话
agent_strategy_name: TOD
agent_strategy_provider_name: afeaad50-3ca8-4d6b-8e95-ca6993bb2951/agent/agent
desc: ''
output_schema: null
plugin_unique_identifier: afeaad50-3ca8-4d6b-8e95-ca6993bb2951/agent:0.0.1@041f354ba09c869b9e16a69d6a5dfea1f46acdb24fedd355e26a93c9bbc3b531
selected: false
title: Agent
type: agent
height: 144
id: '1740645185279'
position:
x: 382.6767799030139
y: 282
positionAbsolute:
x: 382.6767799030139
y: 282
selected: true
sourcePosition: right
targetPosition: left
type: custom
width: 243
viewport:
x: -41.014528411562424
y: 38.07301894574641
zoom: 0.8637955574370334
================================================
FILE: DSL/Agent工具调用.yml
================================================
app:
description: ''
icon: 🤖
icon_background: '#FFEAD5'
mode: advanced-chat
name: Agent工具调用
use_icon_as_answer_icon: false
dependencies: []
kind: app
version: 0.1.5
workflow:
conversation_variables: []
environment_variables: []
features:
file_upload:
allowed_file_extensions:
- .JPG
- .JPEG
- .PNG
- .GIF
- .WEBP
- .SVG
allowed_file_types:
- image
allowed_file_upload_methods:
- local_file
- remote_url
enabled: false
fileUploadConfig:
audio_file_size_limit: 50
batch_count_limit: 5
file_size_limit: 15
image_file_size_limit: 5
video_file_size_limit: 100
workflow_file_upload_limit: 10
image:
enabled: false
number_limits: 3
transfer_methods:
- local_file
- remote_url
number_limits: 3
opening_statement: ''
retriever_resource:
enabled: true
sensitive_word_avoidance:
enabled: false
speech_to_text:
enabled: false
suggested_questions: []
suggested_questions_after_answer:
enabled: false
text_to_speech:
enabled: false
language: ''
voice: ''
graph:
edges:
- data:
isInIteration: false
sourceType: start
targetType: agent
id: 1739781961838-source-1739781971571-target
source: '1739781961838'
sourceHandle: source
target: '1739781971571'
targetHandle: target
type: custom
zIndex: 0
- data:
isInIteration: false
sourceType: agent
targetType: answer
id: 1739781971571-source-answer-target
source: '1739781971571'
sourceHandle: source
target: answer
targetHandle: target
type: custom
zIndex: 0
nodes:
- data:
desc: ''
selected: false
title: 开始
type: start
variables: []
height: 54
id: '1739781961838'
position:
x: 80
y: 282
positionAbsolute:
x: 80
y: 282
sourcePosition: right
targetPosition: left
type: custom
width: 244
- data:
agent_parameters:
instruction:
type: constant
value: 根据用户的需求，调用不同工具，回复用的内容。
model:
type: constant
value:
completion_params: {}
mode: chat
model: gpt-4o-mini
model_type: llm
provider: langgenius/openai/openai
type: model-selector
query:
type: constant
value: '{{#sys.query#}}'
tools:
type: constant
value:
- enabled: true
extra:
description: ''
parameters: {}
provider_name: time
schemas:
- auto_generate: null
default: '%Y-%m-%d %H:%M:%S'
form: form
human_description:
en_US: Time format in strftime standard.
ja_JP: Time format in strftime standard.
pt_BR: Time format in strftime standard.
zh_Hans: strftime 标准的时间格式。
label:
en_US: Format
ja_JP: Format
pt_BR: Format
zh_Hans: 格式
llm_description: null
max: null
min: null
name: format
options: []
placeholder: null
precision: null
required: false
scope: null
template: null
type: string
- auto_generate: null
default: UTC
form: form
human_description:
en_US: Timezone
ja_JP: Timezone
pt_BR: Timezone
zh_Hans: 时区
label:
en_US: Timezone
ja_JP: Timezone
pt_BR: Timezone
zh_Hans: 时区
llm_description: null
max: null
min: null
name: timezone
options:
- label:
en_US: UTC
ja_JP: UTC
pt_BR: UTC
zh_Hans: UTC
value: UTC
- label:
en_US: America/New_York
ja_JP: America/New_York
pt_BR: America/New_York
zh_Hans: 美洲/纽约
value: America/New_York
- label:
en_US: America/Los_Angeles
ja_JP: America/Los_Angeles
pt_BR: America/Los_Angeles
zh_Hans: 美洲/洛杉矶
value: America/Los_Angeles
- label:
en_US: America/Chicago
ja_JP: America/Chicago
pt_BR: America/Chicago
zh_Hans: 美洲/芝加哥
value: America/Chicago
- label:
en_US: America/Sao_Paulo
ja_JP: America/Sao_Paulo
pt_BR: América/São Paulo
zh_Hans: 美洲/圣保罗
value: America/Sao_Paulo
- label:
en_US: Asia/Shanghai
ja_JP: Asia/Shanghai
pt_BR: Asia/Shanghai
zh_Hans: 亚洲/上海
value: Asia/Shanghai
- label:
en_US: Asia/Ho_Chi_Minh
ja_JP: Asia/Ho_Chi_Minh
pt_BR: Ásia/Ho Chi Minh
zh_Hans: 亚洲/胡志明市
value: Asia/Ho_Chi_Minh
- label:
en_US: Asia/Tokyo
ja_JP: Asia/Tokyo
pt_BR: Asia/Tokyo
zh_Hans: 亚洲/东京
value: Asia/Tokyo
- label:
en_US: Asia/Dubai
ja_JP: Asia/Dubai
pt_BR: Asia/Dubai
zh_Hans: 亚洲/迪拜
value: Asia/Dubai
- label:
en_US: Asia/Kolkata
ja_JP: Asia/Kolkata
pt_BR: Asia/Kolkata
zh_Hans: 亚洲/加尔各答
value: Asia/Kolkata
- label:
en_US: Asia/Seoul
ja_JP: Asia/Seoul
pt_BR: Asia/Seoul
zh_Hans: 亚洲/首尔
value: Asia/Seoul
- label:
en_US: Asia/Singapore
ja_JP: Asia/Singapore
pt_BR: Asia/Singapore
zh_Hans: 亚洲/新加坡
value: Asia/Singapore
- label:
en_US: Europe/London
ja_JP: Europe/London
pt_BR: Europe/London
zh_Hans: 欧洲/伦敦
value: Europe/London
- label:
en_US: Europe/Berlin
ja_JP: Europe/Berlin
pt_BR: Europe/Berlin
zh_Hans: 欧洲/柏林
value: Europe/Berlin
- label:
en_US: Europe/Moscow
ja_JP: Europe/Moscow
pt_BR: Europe/Moscow
zh_Hans: 欧洲/莫斯科
value: Europe/Moscow
- label:
en_US: Australia/Sydney
ja_JP: Australia/Sydney
pt_BR: Australia/Sydney
zh_Hans: 澳大利亚/悉尼
value: Australia/Sydney
- label:
en_US: Pacific/Auckland
ja_JP: Pacific/Auckland
pt_BR: Pacific/Auckland
zh_Hans: 太平洋/奥克兰
value: Pacific/Auckland
- label:
en_US: Africa/Cairo
ja_JP: Africa/Cairo
pt_BR: Africa/Cairo
zh_Hans: 非洲/开罗
value: Africa/Cairo
placeholder: null
precision: null
required: false
scope: null
template: null
type: select
settings:
format:
value: '%Y-%m-%d %H:%M:%S'
timezone:
value: UTC
tool_label: 获取当前时间
tool_name: current_time
type: builtin
- enabled: true
extra:
description: ''
parameters:
query:
auto: 1
value: null
provider_name: langgenius/duckduckgo/duckduckgo
schemas:
- auto_generate: null
default: null
form: llm
human_description:
en_US: The search query.
ja_JP: The search query.
pt_BR: The search query.
zh_Hans: 搜索查询语句。
label:
en_US: Query string
ja_JP: Query string
pt_BR: Query string
zh_Hans: 查询语句
llm_description: Key words for searching
max: null
min: null
name: query
options: []
placeholder: null
precision: null
required: true
scope: null
template: null
type: string
- auto_generate: null
default: 5
form: form
human_description:
en_US: The maximum number of results to return.
ja_JP: The maximum number of results to return.
pt_BR: The maximum number of results to return.
zh_Hans: 最大结果数量
label:
en_US: Max results
ja_JP: Max results
pt_BR: Max results
zh_Hans: 最大结果数量
llm_description: ''
max: null
min: null
name: max_results
options: []
placeholder: null
precision: null
required: true
scope: null
template: null
type: number
- auto_generate: null
default: 0
form: form
human_description:
en_US: Whether to pass the search results to llm for summarization.
ja_JP: Whether to pass the search results to llm for summarization.
pt_BR: Whether to pass the search results to llm for summarization.
zh_Hans: 是否需要将搜索结果传给大模型总结
label:
en_US: Require Summary
ja_JP: Require Summary
pt_BR: Require Summary
zh_Hans: 是否总结
llm_description: ''
max: null
min: null
name: require_summary
options: []
placeholder: null
precision: null
required: false
scope: null
template: null
type: boolean
settings:
max_results:
value: 5
require_summary:
value: 0
tool_label: DuckDuckGo 搜索
tool_name: ddgo_search
type: builtin
- enabled: false
extra:
description: ''
parameters:
city:
auto: 1
value: null
provider_name: langgenius/openweather/openweather
schemas:
- auto_generate: null
default: null
form: llm
human_description:
en_US: Target city for weather forecast query
ja_JP: Target city for weather forecast query
pt_BR: Cidade de destino para consulta de previsão do tempo
zh_Hans: 天气预报查询的目标城市
label:
en_US: city
ja_JP: city
pt_BR: cidade
zh_Hans: 城市
llm_description: If you don't know you can extract the city name from
the question or you can reply：Please tell me your city. You have
to extract the Chinese city name from the question.If the input
region is in Chinese characters for China, it should be replaced
with the corresponding English name, such as '北京' for correct input
is 'Beijing'
max: null
min: null
name: city
options: []
placeholder: null
precision: null
required: true
scope: null
template: null
type: string
- auto_generate: null
default: zh_cn
form: form
human_description:
en_US: language
ja_JP: language
pt_BR: language
zh_Hans: 语言
label:
en_US: language
ja_JP: language
pt_BR: language
zh_Hans: 语言
llm_description: ''
max: null
min: null
name: lang
options:
- label:
en_US: cn
ja_JP: cn
pt_BR: cn
zh_Hans: 中国
value: zh_cn
- label:
en_US: usa
ja_JP: usa
pt_BR: usa
zh_Hans: 美国
value: en_us
placeholder: null
precision: null
required: true
scope: null
template: null
type: select
- auto_generate: null
default: metric
form: form
human_description:
en_US: units for temperature
ja_JP: units for temperature
pt_BR: units for temperature
zh_Hans: 温度单位
label:
en_US: units
ja_JP: units
pt_BR: units
zh_Hans: 单位
llm_description: ''
max: null
min: null
name: units
options:
- label:
en_US: metric
ja_JP: metric
pt_BR: metric
zh_Hans: ℃
value: metric
- label:
en_US: imperial
ja_JP: imperial
pt_BR: imperial
zh_Hans: ℉
value: imperial
placeholder: null
precision: null
required: true
scope: null
template: null
type: select
settings:
lang:
value: zh_cn
units:
value: metric
tool_label: 天气查询
tool_name: weather
type: builtin
agent_strategy_label: FunctionCalling
agent_strategy_name: function_calling
agent_strategy_provider_name: langgenius/agent/agent
desc: ''
output_schema: null
plugin_unique_identifier: langgenius/agent:0.0.4@5eb03c08764cc37249f9ef18b89903a99493f6d02c4d5b8ffb40b9f7ef4e865c
selected: true
title: Agent
type: agent
height: 198
id: '1739781971571'
position:
x: 380
y: 282
positionAbsolute:
x: 380
y: 282
selected: true
sourcePosition: right
targetPosition: left
type: custom
width: 244
- data:
answer: '{{#1739781971571.text#}}'
desc: ''
selected: false
title: 直接回复
type: answer
variables: []
height: 103
id: answer
position:
x: 680
y: 282
positionAbsolute:
x: 680
y: 282
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 244
viewport:
x: 4.833333333333201
y: -42.25
zoom: 0.8333333333333334
================================================
FILE: DSL/Artifact.yml
================================================
app:
description: Artifact
icon: 🤖
icon_background: '#FFEAD5'
mode: advanced-chat
name: Artifact
use_icon_as_answer_icon: false
dependencies:
- current_identifier: null
type: marketplace
value:
marketplace_plugin_unique_identifier: langgenius/volcengine_maas:0.0.7@f8e44422cfa5b9a6ac1f2d3b43ef1069868efdad1e5cec2590de3f53ceac37b0
kind: app
version: 0.2.0
workflow:
conversation_variables: []
environment_variables: []
features:
file_upload:
allowed_file_extensions:
- .JPG
- .JPEG
- .PNG
- .GIF
- .WEBP
- .SVG
allowed_file_types:
- image
allowed_file_upload_methods:
- local_file
- remote_url
enabled: false
fileUploadConfig:
audio_file_size_limit: 50
batch_count_limit: 5
file_size_limit: 15
image_file_size_limit: 10
video_file_size_limit: 100
workflow_file_upload_limit: 10
image:
enabled: false
number_limits: 3
transfer_methods:
- local_file
- remote_url
number_limits: 3
opening_statement: ''
retriever_resource:
enabled: true
sensitive_word_avoidance:
enabled: false
speech_to_text:
enabled: false
suggested_questions: []
suggested_questions_after_answer:
enabled: false
text_to_speech:
enabled: false
language: ''
voice: ''
graph:
edges:
- data:
sourceType: start
targetType: llm
id: 1745404962666-llm
source: '1745404962666'
sourceHandle: source
target: llm
targetHandle: target
type: custom
- data:
sourceType: llm
targetType: answer
id: llm-answer
source: llm
sourceHandle: source
target: answer
targetHandle: target
type: custom
nodes:
- data:
desc: ''
selected: false
title: 开始
type: start
variables: []
height: 52
id: '1745404962666'
position:
x: 80
y: 282
positionAbsolute:
x: 80
y: 282
sourcePosition: right
targetPosition: left
type: custom
width: 243
- data:
context:
enabled: false
variable_selector: []
desc: ''
memory:
query_prompt_template: '{{#sys.query#}}'
role_prefix:
assistant: ''
user: ''
window:
enabled: true
size: 10
model:
completion_params: {}
mode: chat
name: deepseek-v3-250324
provider: langgenius/volcengine_maas/volcengine_maas
prompt_template:
- role: system
text: 你是一个小助手
selected: true
title: LLM
type: llm
variables: []
vision:
enabled: false
height: 88
id: llm
position:
x: 380
y: 282
positionAbsolute:
x: 380
y: 282
selected: true
sourcePosition: right
targetPosition: left
type: custom
width: 243
- data:
answer: '{{#llm.text#}}'
desc: ''
selected: false
title: 直接回复
type: answer
variables: []
height: 103
id: answer
position:
x: 680
y: 282
positionAbsolute:
x: 680
y: 282
sourcePosition: right
targetPosition: left
type: custom
width: 243
viewport:
x: 0
y: 0
zoom: 1
================================================
FILE: DSL/Claude3 Code Translation.yml
================================================
app:
description: ''
icon: 🤖
icon_background: '#FFEAD5'
mode: workflow
name: Claude3 Code Translation
use_icon_as_answer_icon: false
kind: app
version: 0.1.2
workflow:
conversation_variables: []
environment_variables: []
features:
file_upload:
allowed_file_extensions:
- .JPG
- .JPEG
- .PNG
- .GIF
- .WEBP
- .SVG
allowed_file_types:
- image
allowed_file_upload_methods:
- local_file
- remote_url
enabled: false
image:
enabled: false
number_limits: 3
transfer_methods:
- local_file
- remote_url
number_limits: 3
opening_statement: ''
retriever_resource:
enabled: false
sensitive_word_avoidance:
enabled: false
speech_to_text:
enabled: false
suggested_questions: []
suggested_questions_after_answer:
enabled: false
text_to_speech:
enabled: false
language: ''
voice: ''
graph:
edges:
- data:
isInIteration: false
sourceType: start
targetType: llm
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targetHandle: target
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source: '1720580108806'
sourceHandle: source
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targetHandle: target
type: custom
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- data:
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selected: false
source: '1720580885073'
sourceHandle: source
target: '1720582990256'
targetHandle: target
type: custom
zIndex: 0
- data:
isInIteration: false
sourceType: llm
targetType: tool
id: 1720506191043-source-1723619551968-target
source: '1720506191043'
sourceHandle: source
target: '1723619551968'
targetHandle: target
type: custom
zIndex: 0
- data:
isInIteration: false
sourceType: tool
targetType: code
id: 1723619551968-source-1720508367130-target
source: '1723619551968'
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target: '1720508367130'
targetHandle: target
type: custom
zIndex: 0
- data:
isInIteration: true
iteration_id: '1720580108806'
sourceType: iteration-start
targetType: llm
id: 1720580108806start0-source-1720580171037-target
source: 1720580108806start0
sourceHandle: source
target: '1720580171037'
targetHandle: target
type: custom
zIndex: 1002
nodes:
- data:
desc: ''
selected: false
title: Start
type: start
variables:
- label: 需要翻译的代码文件内容
max_length: 33024
options: []
required: true
type: paragraph
variable: code_to_translate
- label: 相关依赖库的代码内容
max_length: 33024
options: []
required: false
type: paragraph
variable: related_files_content
- label: 待翻译文件的文件名
max_length: 256
options: []
required: true
type: text-input
variable: code_file_name
- label: 源代码语言(例如Python)
max_length: 48
options:
- python
- java
required: true
type: select
variable: src_lang
- label: 目标编程语言（例如Java）
max_length: 48
options:
- java
- python
required: true
type: select
variable: dest_lang
height: 194
id: '1720505581749'
position:
x: 30
y: 301.5
positionAbsolute:
x: 30
y: 301.5
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 244
- data:
context:
enabled: false
variable_selector: []
desc: ''
model:
completion_params:
stop:
- '```'
temperature: 0.1
mode: chat
name: anthropic.claude-3-5-sonnet-20240620-v1:0
provider: bedrock
prompt_template:
- id: 2e5e4e13-2974-440b-9ee5-c015d9aae4ba
role: system
text: "# Role\n你接下来cosplay一个专业的程序猿，精通python java go 等多种语言。\n\n# Task\n你的任务是帮助我将项目的编程语言从{{#1720505581749.src_lang#}}翻译到{{#1720505581749.dest_lang#}}。但是由于输出长度限制，你需要分两次回复来生成完整的代码。\n\
\n第一次回复的目的是生成代码主体结构，但对于中的类、函数，你只需要给出对应的签名即可，不要给出具体实现。\n注意分析相关代码文件，并据此帮你识别内置依赖库和自定义依赖，在你的回复中，不要遗漏import对应的库。\n\
\n# 相关代码文件\n如下是可能与原始代码有关的文件(可能为空)。\n{{#1720505581749.related_files_content#}}\n\
\n# 输出格式\n\n## 第一次输出\n输出采用YAML格式，其中`type`的取值及定义如下: \n- `import`表示依赖包引入代码\n\
- `class`表示类定义，注意java只允许有一个顶层类(通常是与文件同名的类)是public的，其余顶层类不可以使用public修饰。\n\
- `function`表示函数定义，每个函数都有自己的签名\n- `literal`表示不包含在任何函数或类中的代码行，如果存在，则需要给出完整目标语言的代码。main\
\ 函数中的内容不要放在这里。\n\n### 限制\n- `type`的顺序需要严格按照目标翻译代码的嵌套关系及顺序排列，`type` 不可以为其他值，输出必须要满足YAML格式的有效性。\n\
- 生成的代码必须要符合目标语言{{#1720505581749.dest_lang#}}的语法规则，不要放错函数的位置。如果类中属性需要在此类外使用，则要定义相关的属性访问器。\n\
- `cls_attr` 中的 `need_public_getter` 表示是否需要生成public的getter方法，如果此属性被跨类访问，则必须为true。\n\
### 第一次回复的结构示例\n```yaml\nout_file_name: <outfile.ext>\nthinking: | \n\
\ your thinking if any, better not exceeds 80 words.\n{dest_lang}:\n\
\ - type: import\n codes: |\n import java.util.ArrayList\n\
\ ...\n - type: literal\n codes: |\n int a = 0;\n\
\ ...\n - type: class\n name: Main\n signature: public\
\ static void main(String[] args) # 必须\n attributes: # 必须，除非此类不包含任何属性\n\
\ - type: cls_attr\n signature: private int a;\n \
\ need_public_getter: true | false\n - type: inst_attr\n \
\ signature: String str;\n - type: function\n name:\
\ incr\n signature: static int incr(int a, int b)\n -\
\ type: function\n name: main # this is required for the main\
\ class\n signature: public static void main(String[] args) \n\
\ - type: class\n name: Foo\n signature: public\
\ class Foo\n - ...\n - ...\n```\n\n注意，你不需要给出任何解释，只需要给出代码主体即可。\n\
\n### 第一次回复的示例(python -> java)\n输入：\n- 文件名: dog.py\n```python\nimport\
\ time\n\ncurrent_time = time.time()\n\ndef my_func(a):\n return a\
\ + 1\n\nclass Dog:\n species = \"Canis familiaris\"\n\n def __init__(self,\
\ name, age):\n self.name = name\n self.age = age\n\n \
\ def description(self):\n return self.name + \"is \" + self.age\
\ + \" years old\"\n\n def speak(self, sound):\n return self.name\
\ + \"says \" + sound\n\nmiles = Dog(\"Miles\", 4)\n\nprint(miles.description())\
\ # Output: Miles is 4 years old\n\nprint(\"Miles is a \" + miles.species)\
\ # Output: Miles is a Canis familiaris\n```\n\n输出：\n```yaml\nout_file_name:\
\ Dog.java\nthinking: | \n your thinking ...\njava:\n - type: import\n\
\ codes: |\n import java.time.Instant;\n\n - type: class\n\
\ name: Dog\n signature: public class Dog\n attributes:\n\
\ - type: cls_attr\n signature: static String species\
\ = \"Canis familiaris\";\n need_public_getter: false\n \
\ - type: inst_attr\n signature: String name;\n - type:\
\ inst_attr\n signature: Int age;\n - type: function\n\
\ name: Dog\n signature: Dog(String name, int age)\n\
\ - type: function\n name: description\n signature:\
\ String description()\n - type: function\n name: speak\n\
\ signature: String speak(String sound)\n - type: function\n\
\ name: main\n signature: public static void main(String[]\
\ args)\n```\n\n## 第二次输出\n在第二次输出中，我会让你输出一个或者多个特定的类或者函数的完整实现，直接给出代码即可，无需解释；且第一次生成结果中排在此类/函数之前的代码都已经生成了，你不要重复生成。\n\
你需要等到我的指令才能输出第二次响应。\n\n# 限制\n无论是第几次回复，你都要认真思考，不可以大意或者匆忙给出没有深思熟虑的代码。"
- id: 067b610f-6234-4f0d-bf20-9c7d86e7562d
role: user
text: '# 原始代码
- input_file_name: {{#1720505581749.code_file_name#}}
```{{#1720505581749.src_lang#}}
{{#1720505581749.code_to_translate#}}
```'
- id: e6ece9cd-4b04-42d6-870a-350a9cfcdcf6
role: assistant
text: '```yaml'
selected: false
title: LLM-生成目标代码签名
type: llm
variables: []
vision:
configs:
detail: high
enabled: false
height: 98
id: '1720506191043'
position:
x: 334
y: 301.5
positionAbsolute:
x: 334
y: 301.5
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 244
- data:
code: "import json\n\ndef extract_fn_cls(text: str, dest_lang:str) -> dict:\n\
\ resp = json.loads(text)['json']\n out_file_name = resp['out_file_name']\n\
\ signatures = [\n item['signature'] for item in resp[dest_lang]\
\ if item['type'] in ['function', 'class']\n ]\n return {\n \
\ # \"out_file_name\": out_file_name,\n \"resp\": resp,\n \
\ \"signatures\": signatures,\n }\n\ndef main(text:str, dest_lang:str):\n\
\ return extract_fn_cls(text, dest_lang)"
code_language: python3
desc: ''
outputs:
resp:
children: null
type: object
signatures:
children: null
type: array[string]
selected: false
title: Code - 抽取类/函数签名
type: code
variables:
- value_selector:
- '1723619551968'
- text
variable: text
- value_selector:
- '1720505581749'
- dest_lang
variable: dest_lang
height: 54
id: '1720508367130'
position:
x: 323.4908161111016
y: 530.2430558421229
positionAbsolute:
x: 323.4908161111016
y: 530.2430558421229
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 244
- data:
desc: ''
height: 203
iterator_selector:
- '1720508367130'
- signatures
output_selector:
- '1720580171037'
- text
output_type: array[string]
selected: false
startNodeType: llm
start_node_id: 1720580108806start0
title: Iteration - 生成每个函数/类的代码
type: iteration
width: 377
height: 203
id: '1720580108806'
position:
x: 273.57725260247616
y: 674.0749639522259
positionAbsolute:
x: 273.57725260247616
y: 674.0749639522259
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 377
zIndex: 1
- data:
context:
enabled: false
variable_selector: []
desc: ''
isInIteration: true
isIterationStart: true
iteration_id: '1720580108806'
model:
completion_params:
stop:
- '```'
temperature: 0.7
mode: chat
name: anthropic.claude-3-sonnet-20240229-v1:0
provider: bedrock
prompt_template:
- id: 4741625b-505f-462d-b851-9aef4eab5fcd
role: system
text: "# Role\n你接下来cosplay一个专业的程序猿，精通python java go 等多种语言。\n\n# Task\n你的任务是帮助我将项目的编程语言从{{#1720505581749.src_lang#}}翻译到{{#1720505581749.dest_lang#}}。但是由于输出长度限制，你需要分两次回复来生成完整的代码。\n\
\n第一次回复的目的是生成代码主体结构，但对于中的类、函数，你只需要给出对应的签名即可，不要给出具体实现。\n注意分析相关代码文件，并据此帮你识别内置依赖库和自定义依赖，在你的回复中，不要遗漏import对应的库。\n\
\n# 相关代码文件\n如下是可能与原始代码有关的文件(可能为空)。\n{{#1720505581749.related_files_content#}}\n\
\n# 输出格式\n\n## 第一次输出\n输出采用YAML格式，其中`type`的取值及定义如下: \n- `import`表示依赖包引入代码\n\
- `class`表示类定义，注意java只允许有一个顶层类(通常是与文件同名的类)是public的，其余顶层类不可以使用public修饰。\n\
- `function`表示函数定义，每个函数都有自己的签名\n- `literal`表示不包含在任何函数或类中的代码行，如果存在，则需要给出完整目标语言的代码。main\
\ 函数中的内容不要放在这里。\n\n### 限制\n- `type`的顺序需要严格按照目标翻译代码的嵌套关系及顺序排列，`type` 不可以为其他值，输出必须要满足YAML格式的有效性。\n\
- 生成的代码必须要符合目标语言{{#1720505581749.dest_lang#}}的语法规则，不要放错函数的位置。如果类中属性需要在此类外使用，则要定义相关的属性访问器。\n\
- `cls_attr` 中的 `need_public_getter` 表示是否需要生成public的getter方法，如果此属性被跨类访问，则必须为true。\n\
### 第一次回复的结构示例\n```yaml\nout_file_name: <outfile.ext>\nthinking: | \n\
\ your thinking if any, better not exceeds 80 words.\n{dest_lang}:\n\
\ - type: import\n codes: |\n import java.util.ArrayList\n\
\ ...\n - type: literal\n codes: |\n int a = 0;\n\
\ ...\n - type: class\n name: Main\n signature: public\
\ static void main(String[] args) # 必须\n attributes: # 必须，除非此类不包含任何属性\n\
\ - type: cls_attr\n signature: private int a;\n \
\ need_public_getter: true | false\n - type: inst_attr\n \
\ signature: String str;\n - type: function\n name:\
\ incr\n signature: static int incr(int a, int b)\n -\
\ type: function\n name: main # this is required for the main\
\ class\n signature: public static void main(String[] args) \n\
\ - type: class\n name: Foo\n signature: public\
\ class Foo\n - ...\n - ...\n```\n\n注意，你不需要给出任何解释，只需要给出代码主体即可。\n\
\n### 第一次回复的示例(python -> java)\n输入：\n- 文件名: dog.py\n```python\nimport\
\ time\n\ncurrent_time = time.time()\n\ndef my_func(a):\n return a\
\ + 1\n\nclass Dog:\n species = \"Canis familiaris\"\n\n def __init__(self,\
\ name, age):\n self.name = name\n self.age = age\n\n \
\ def description(self):\n return self.name + \"is \" + self.age\
\ + \" years old\"\n\n def speak(self, sound):\n return self.name\
\ + \"says \" + sound\n\nmiles = Dog(\"Miles\", 4)\n\nprint(miles.description())\
\ # Output: Miles is 4 years old\n\nprint(\"Miles is a \" + miles.species)\
\ # Output: Miles is a Canis familiaris\n```\n\n输出：\n```yaml\nout_file_name:\
\ Dog.java\nthinking: | \n your thinking ...\njava:\n - type: import\n\
\ codes: |\n import java.time.Instant;\n\n - type: class\n\
\ name: Dog\n signature: public class Dog\n attributes:\n\
\ - type: cls_attr\n signature: static String species\
\ = \"Canis familiaris\";\n need_public_getter: false\n \
\ - type: inst_attr\n signature: String name;\n - type:\
\ inst_attr\n signature: Int age;\n - type: function\n\
\ name: Dog\n signature: Dog(String name, int age)\n\
\ - type: function\n name: description\n signature:\
\ String description()\n - type: function\n name: speak\n\
\ signature: String speak(String sound)\n - type: function\n\
\ name: main\n signature: public static void main(String[]\
\ args)\n```\n\n## 第二次输出\n在第二次输出中，我会让你输出一个或者多个特定的类或者函数的完整实现，直接给出代码即可，无需解释；且第一次生成结果中排在此类/函数之前的代码都已经生成了，你不要重复生成。\n\
你需要等到我的指令才能输出第二次响应。\n\n# 限制\n无论是第几次回复，你都要认真思考，不可以大意或者匆忙给出没有深思熟虑的代码。"
- id: d4bc0226-67ce-411e-a44d-d7154ed15ef2
role: user
text: '# 原始代码
- input_file_name: {{#1720505581749.code_file_name#}}
```{{#1720505581749.src_lang#}}
{{#1720505581749.code_to_translate#}}
```'
- id: e2e530d6-35f9-4588-b448-772cae738195
role: assistant
text: '```yaml
{{#1720506191043.text#}}
```'
- id: 7c3b0f56-47cb-4bef-8e8b-abe44638fc83
role: user
text: 只需给出 {{#1720580108806.item#}} 的完整代码，不要重复引入依赖。直接给出代码内容，不要解释。
- id: 59bbf386-14a1-420b-98b4-c04e6bde9a23
role: assistant
text: '```{{#1720505581749.dest_lang#}}'
selected: false
title: LLM - 生成函数/类的代码
type: llm
variables: []
vision:
configs:
detail: high
enabled: true
extent: parent
height: 98
id: '1720580171037'
parentId: '1720580108806'
position:
x: 117
y: 85
positionAbsolute:
x: 390.57725260247616
y: 759.0749639522259
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 244
zIndex: 1001
- data:
code: "\ndef main(resp:dict, sig_impl: list[str], dest_lang:str) -> dict:\n\
\ sig_impl_idx = 0\n for item in resp[dest_lang]:\n _type =\
\ item['type']\n if _type in ['function', 'class']:\n \
\ item['codes'] = sig_impl[sig_impl_idx]\n sig_impl_idx += 1\n\
\n final_code = '\\n'.join([item['codes'] for item in resp[dest_lang]])\n\
\n return {\n \"final_code\": final_code,\n \"resp\": resp,\n\
\ }\n"
code_language: python3
desc: ''
outputs:
final_code:
children: null
type: string
resp:
children: null
type: object
selected: false
title: Code - 合并翻译结果
type: code
variables:
- value_selector:
- '1720580108806'
- output
variable: sig_impl
- value_selector:
- '1720508367130'
- resp
variable: resp
- value_selector:
- '1720505581749'
- dest_lang
variable: dest_lang
height: 54
id: '1720580885073'
position:
x: 721.3762323479173
y: 653.6439149148473
positionAbsolute:
x: 721.3762323479173
y: 653.6439149148473
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 244
- data:
desc: 输出翻译结果
outputs:
- value_selector:
- '1720580885073'
- final_code
variable: final_code
selected: false
title: End
type: end
height: 118
id: '1720582990256'
position:
x: 1032.9006749743028
y: 643.1096952379073
positionAbsolute:
x: 1032.9006749743028
y: 643.1096952379073
selected: true
sourcePosition: right
targetPosition: left
type: custom
width: 244
- data:
author: ybalbert
desc: ''
height: 714
selected: false
showAuthor: true
text: '{"root":{"children":[{"children":[{"detail":0,"format":1,"mode":"normal","style":"","text":"示例参数：需要翻译的代码文件内容","type":"text","version":1}],"direction":"ltr","format":"","indent":0,"type":"paragraph","version":1,"textFormat":1},{"children":[],"direction":null,"format":"","indent":0,"type":"paragraph","version":1,"textFormat":0},{"children":[{"detail":0,"format":0,"mode":"normal","style":"","text":"import
requests","type":"text","version":1}],"direction":"ltr","format":"","indent":0,"type":"paragraph","version":1,"textFormat":0},{"children":[{"detail":0,"format":0,"mode":"normal","style":"","text":"from
utils import Food","type":"text","version":1}],"direction":"ltr","format":"","indent":0,"type":"paragraph","version":1,"textFormat":0},{"children":[],"direction":"ltr","format":"","indent":0,"type":"paragraph","version":1,"textFormat":0},{"children":[{"detail":0,"format":0,"mode":"normal","style":"","text":"def
send_request():","type":"text","version":1}],"direction":"ltr","format":"","indent":0,"type":"paragraph","version":1,"textFormat":0},{"children":[{"detail":0,"format":0,"mode":"normal","style":"","text":" response
= requests.get(\"https://www.example.com\")","type":"text","version":1}],"direction":"ltr","format":"","indent":0,"type":"paragraph","version":1,"textFormat":0},{"children":[{"detail":0,"format":0,"mode":"normal","style":"","text":" print(response.status_code)","type":"text","version":1}],"direction":"ltr","format":"","indent":0,"type":"paragraph","version":1,"textFormat":0},{"children":[],"direction":"ltr","format":"","indent":0,"type":"paragraph","version":1,"textFormat":0},{"children":[{"detail":0,"format":0,"mode":"normal","style":"","text":"def
foo():","type":"text","version":1}],"direction":"ltr","format":"","indent":0,"type":"paragraph","version":1,"textFormat":0},{"children":[{"detail":0,"format":0,"mode":"normal","style":"","text":" print(\"This
is foo\")","type":"text","version":1}],"direction":"ltr","format":"","indent":0,"type":"paragraph","version":1,"textFormat":0},{"children":[{"detail":0,"format":0,"mode":"normal","style":"","text":" x
= 42","type":"text","version":1}],"direction":"ltr","format":"","indent":0,"type":"paragraph","version":1,"textFormat":0},{"children":[{"detail":0,"format":0,"mode":"normal","style":"","text":" return
x","type":"text","version":1}],"direction":"ltr","format":"","indent":0,"type":"paragraph","version":1,"textFormat":0},{"children":[],"direction":"ltr","format":"","indent":0,"type":"paragraph","version":1,"textFormat":0},{"children":[{"detail":0,"format":0,"mode":"normal","style":"","text":"class
Bar:","type":"text","version":1}],"direction":"ltr","format":"","indent":0,"type":"paragraph","version":1,"textFormat":0},{"children":[{"detail":0,"format":0,"mode":"normal","style":"","text":" def
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source: 1739191680739start
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type: custom
zIndex: 1002
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source: llm
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source: '1739193987241'
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source: '1739189441547'
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id: 1739112773493-source-17391916638460-target
selected: false
source: '1739112773493'
sourceHandle: source
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targetHandle: target
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selected: false
source: '17391916807390'
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target: '17391916827400'
targetHandle: target
type: custom
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targetType: llm
id: 17391136486630-source-17391136521390-target
selected: false
source: '17391136486630'
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target: '17391136521390'
targetHandle: target
type: custom
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source: '1739113756598'
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targetType: answer
id: 1739292584847-true-1739091083891-target
selected: false
source: '1739292584847'
sourceHandle: 'true'
target: '1739091083891'
targetHandle: target
type: custom
zIndex: 0
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sourceType: iteration
targetType: llm
id: 17391916827400-source-1739192479032-target
selected: false
source: '17391916827400'
sourceHandle: source
target: '1739192479032'
targetHandle: target
type: custom
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sourceType: llm
targetType: llm
id: 1739192479032-source-llm-target
selected: false
source: '1739192479032'
sourceHandle: source
target: llm
targetHandle: target
type: custom
zIndex: 0
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sourceType: assigner
targetType: if-else
id: 1739090270335-source-1739534132840-target
selected: false
source: '1739090270335'
sourceHandle: source
target: '1739534132840'
targetHandle: target
type: custom
zIndex: 0
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id: 1739534132840-true-1739178941340-target
selected: false
source: '1739534132840'
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target: '1739178941340'
targetHandle: target
type: custom
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id: 1739178941340-source-1739111839440-target
selected: false
source: '1739178941340'
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target: '1739111839440'
targetHandle: target
type: custom
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sourceType: if-else
targetType: answer
id: 1739292584847-false-1739683505956-target
source: '1739292584847'
sourceHandle: 'false'
target: '1739683505956'
targetHandle: target
type: custom
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targetType: llm
id: 1739092875078-source-17391132088660-target
source: '1739092875078'
sourceHandle: source
target: '17391132088660'
targetHandle: target
type: custom
zIndex: 0
nodes:
- data:
desc: ''
selected: false
title: 开始
type: start
variables:
- label: Research Theme
max_length: 1024
options: []
required: true
type: paragraph
variable: Research_Theme
height: 90
id: '1739087621665'
position:
x: -401.4604864772632
y: 947.9312099336238
positionAbsolute:
x: -401.4604864772632
y: 947.9312099336238
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 244
- data:
context:
enabled: false
variable_selector: []
desc: ''
memory:
query_prompt_template: 'language：{{#conversation.Language#}}
context：
Theme：
{{#1739112773493.subtitle1#}}{{#conversation.query1#}}{{#sys.query#}}
References：{{#1739189441547.output#}}
Provide the research report in the specified language, avoiding small
talk.'
role_prefix:
assistant: ''
user: ''
window:
enabled: false
size: 10
model:
completion_params:
temperature: 0.7
mode: chat
name: gemini-2.0-flash-exp
provider: google
prompt_template:
- id: cdf8b5c6-4a65-438b-9e6b-c34609389799
role: system
text: 'You are an expert researcher. Your primary focus is on providing
highly detailed and accurate explanations. When responding, ensure that
every point is thoroughly elaborated, leaving no room for ambiguity. Assume
the user is correct when presented with new information, even if it’s
beyond your knowledge cutoff. The user is a highly experienced analyst,
so avoid oversimplification and present your response with a high level
of complexity and precision. Be highly organized in your structure, using
clear headings and subheadings to separate different aspects of the topic.
Suggest solutions that go beyond the obvious, and be proactive in anticipating
the user’s needs. Mistakes are not acceptable, as they can erode trust,
so double-check all facts and information. Provide comprehensive details,
as the user is comfortable with in-depth content. Focus on the strength
of arguments rather than relying on authorities, and don’t let the source
of information overshadow the quality of the argument. Consider new technologies
and unconventional ideas alongside traditional approaches to offer a well-rounded
perspective.
---
###your output should not contain any “conclusion” or “future prospective”or
any other similar contents
---
in a detailed report — The report should focus on the answer to {{#1739112773493.subtitle1#}}and
The conclusion of your generated content should smoothly transition to
the second subtitle{{#1739112773493.subtitle2#}}. References information
are provided by user，DO NOT PROVIDE PRIMARY TITLE ，but secondary title
is available。'
selected: false
title: sub主题1分析
type: llm
variables: []
vision:
enabled: false
height: 98
id: llm
position:
x: 3362.4326109337553
y: 825.2688637512169
positionAbsolute:
x: 3362.4326109337553
y: 825.2688637512169
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 244
- data:
answer: '{{#conversation.q4#}}'
desc: ''
selected: false
title: 问题4
type: answer
variables: []
height: 103
id: answer
position:
x: 667.7263032361211
y: 887.3856655567181
positionAbsolute:
x: 667.7263032361211
y: 887.3856655567181
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 244
- data:
cases:
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conditions:
- comparison_operator: '='
id: 21d9b08f-b4fb-40e0-80a1-323cc5fcfcf7
value: '0'
varType: number
variable_selector:
- sys
- dialogue_count
id: 'true'
logical_operator: and
- case_id: 91eeee38-2f3d-41fe-aada-fbb552f38d09
conditions:
- comparison_operator: '='
id: 42d627dd-f6ad-4028-b17e-b5922c8a8555
value: '1'
varType: number
variable_selector:
- sys
- dialogue_count
id: 91eeee38-2f3d-41fe-aada-fbb552f38d09
logical_operator: and
- case_id: ec873050-26b5-4519-ad2d-318d9c8507b4
conditions:
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id: 6e5199ca-f644-44e4-876c-6581f14ec964
value: '2'
varType: number
variable_selector:
- sys
- dialogue_count
id: ec873050-26b5-4519-ad2d-318d9c8507b4
logical_operator: and
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conditions:
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id: 93beb1fd-6a9d-4d8c-a308-4d149f2a5813
value: '3'
varType: number
variable_selector:
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id: 252919df-4a7b-45e9-b845-bcbc14277530
logical_operator: and
- case_id: fdc117e5-9c2d-4b3d-9fac-c1f030b8435d
conditions:
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id: 600fc8f9-83ec-4656-b256-65bbc11bb814
value: '4'
varType: number
variable_selector:
- sys
- dialogue_count
id: fdc117e5-9c2d-4b3d-9fac-c1f030b8435d
logical_operator: or
desc: ''
selected: false
title: 条件分支
type: if-else
height: 318
id: '1739087643444'
position:
x: 65.46491577191637
y: 887.3856655567181
positionAbsolute:
x: 65.46491577191637
y: 887.3856655567181
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 244
- data:
answer: '{{#conversation.q2#}}'
desc: ''
selected: false
title: 问题2
type: answer
variables: []
height: 103
id: '1739087688715'
position:
x: 667.7263032361211
y: 599.4593566990166
positionAbsolute:
x: 667.7263032361211
y: 599.4593566990166
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 244
- data:
context:
enabled: true
variable_selector:
- '1739087621665'
- Research_Theme
desc: ''
model:
completion_params:
temperature: 0.7
mode: chat
name: gemini-2.0-flash-exp
provider: google
prompt_template:
- id: 4f9462cb-d339-4235-acce-3ea44e317d39
role: system
text: 'Given the following query from the user, ask some question about
topics may cause theme mislead。 follow up questions to clarify the research
direction. Return 4 independent questions, but feel free to return less
if the original query is clear: <query>{{#1739087621665.Research_Theme#}}</query>，ONLY
OUTPUT THE QUESTIONS ，no need for any other content，questions shall be
output in {{#conversation.Language#}}'
- role: user
text: '{{#1739087621665.Research_Theme#}}'
selected: false
title: 问题分解
type: llm
variables: []
vision:
enabled: false
height: 98
id: '1739088190247'
position:
x: 947.9076845481827
y: 455.7348090507137
positionAbsolute:
x: 947.9076845481827
y: 455.7348090507137
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 244
- data:
desc: ''
items:
- input_type: variable
operation: over-write
value:
- sys
- query
variable_selector:
- conversation
- Language
write_mode: over-write
selected: false
title: 变量赋值
type: assigner
version: '2'
height: 88
id: '1739088656029'
position:
x: 670.205223622014
y: 455.7348090507137
positionAbsolute:
x: 670.205223622014
y: 455.7348090507137
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 244
- data:
desc: ''
instruction: Extract the return value in the output value as four independent
parameters.Avoids redundancy by tracking previously written content
model:
completion_params:
temperature: 0.7
mode: chat
name: gemini-2.0-flash-exp
provider: google
parameters:
- description: First question provided by previous LLM
name: Question1
required: false
type: string
- description: Second question provided by previous LLM
name: Question2
required: false
type: string
- description: Third question provided by previous LLM
name: Question3
required: false
type: string
- description: Fourth question provided by previous LLM
name: Question4
required: false
type: string
- description: 分组输出所有问题内容
name: Questions
required: false
type: array[string]
query:
- '1739088190247'
- text
reasoning_mode: prompt
selected: false
title: 问题参数提取
type: parameter-extractor
variables: []
vision:
enabled: false
height: 98
id: '1739089738469'
position:
x: 1278.205223622014
y: 455.7348090507137
positionAbsolute:
x: 1278.205223622014
y: 455.7348090507137
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 244
- data:
answer: '为了更好的回答这个问题，我希望先了解一些内容：
{{#1739089738469.Question1#}}'
desc: ''
selected: false
title: 第一次回复&问题1
type: answer
variables: []
height: 135
id: '1739089894656'
position:
x: 1592.4387458428032
y: 301.8019869286369
positionAbsolute:
x: 1592.4387458428032
y: 301.8019869286369
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 244
- data:
desc: ''
items:
- input_type: variable
operation: over-write
value:
- '17391132088660'
- text
variable_selector:
- conversation
- query4
write_mode: over-write
selected: false
title: '上下文变量赋值 '
type: assigner
version: '2'
height: 88
id: '1739090270335'
position:
x: 943.1876788138126
y: 1706.9053326666053
positionAbsolute:
x: 943.1876788138126
y: 1706.9053326666053
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 244
- data:
desc: ''
items:
- input_type: variable
operation: over-write
value:
- '1739089738469'
- Question2
variable_selector:
- conversation
- q2
write_mode: over-write
- input_type: variable
operation: over-write
value:
- '1739089738469'
- Question3
variable_selector:
- conversation
- q3
write_mode: over-write
- input_type: variable
operation: over-write
value:
- '1739089738469'
- Question4
variable_selector:
- conversation
- q4
write_mode: over-write
- input_type: variable
operation: over-write
value:
- '1739089738469'
- Question1
variable_selector:
- conversation
- q1
write_mode: over-write
selected: false
title: 问题变量赋值
type: assigner
version: '2'
height: 172
id: '1739090682795'
position:
x: 1592.4387458428032
y: 568.2569055309016
positionAbsolute:
x: 1592.4387458428032
y: 568.2569055309016
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 244
- data:
answer: '{{#conversation.q3#}}'
desc: ''
selected: false
title: 问题3
type: answer
variables: []
height: 103
id: '1739090734936'
position:
x: 667.7263032361211
y: 736.135874004081
positionAbsolute:
x: 667.7263032361211
y: 736.135874004081
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 244
- data:
answer: '|
报告已生成：
---
{{#1739113756598.output#}}'
desc: ''
selected: false
title: 最终回复
type: answer
variables: []
height: 119
id: '1739091083891'
position:
x: 4305.939971577466
y: 1059.7064421013074
positionAbsolute:
x: 4305.939971577466
y: 1059.7064421013074
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 244
- data:
desc: ''
items:
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operation: over-write
value:
- '1739113013426'
- text
variable_selector:
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- query2
write_mode: over-write
selected: false
title: '上下文变量赋值 '
type: assigner
version: '2'
height: 88
id: '1739091496446'
position:
x: 943.1876788138126
y: 1443.9714391735783
positionAbsolute:
x: 943.1876788138126
y: 1443.9714391735783
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 244
- data:
desc: ''
items:
- input_type: variable
operation: over-write
value:
- '17391132061910'
- text
variable_selector:
- conversation
- query1
write_mode: over-write
selected: false
title: 上下文变量赋值
type: assigner
version: '2'
height: 88
id: '1739092110217'
position:
x: 943.1876788138126
y: 1294.6031523212616
positionAbsolute:
x: 943.1876788138126
y: 1294.6031523212616
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 244
- data:
desc: ''
items:
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operation: over-write
value:
- '17391132110940'
- text
variable_selector:
- conversation
- query3
write_mode: over-write
selected: false
title: 上下文变量赋值
type: assigner
version: '2'
height: 88
id: '1739092875078'
position:
x: 943.1876788138126
y: 1579.4318741274828
positionAbsolute:
x: 943.1876788138126
y: 1579.4318741274828
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 244
- data:
answer: '正在分析研究主题...
'
desc: ''
selected: false
title: 输出：正在生成最终回答
type: answer
variables: []
height: 100
id: '1739111295209'
position:
x: 667.7263032361211
y: 1028.978486275042
positionAbsolute:
x: 667.7263032361211
y: 1028.978486275042
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 244
- data:
context:
enabled: false
variable_selector: []
desc: ''
model:
completion_params:
temperature: 0.7
mode: chat
name: gemini-2.0-flash-exp
provider: google
prompt_template:
- id: 90d59c0f-2542-42ed-a853-5a2ad135af3b
role: system
text: 'according to the original theme{{#1739087621665.Research_Theme#}}
，and tuned theme{{#conversation.research_theme#}}Generate 3 to 5 sub-titles.
<instructions>
Please generate 4 subheadings for the main title following these steps:
Carefully read the provided main title and related content
Analyze the core theme and key information points of the main title
Ensure the generated subheadings maintain consistency and relevance with
the main title
Each subheading should:
Be concise and appropriate in length
Highlight a unique angle or key point
Capture readers'' interest
Match the overall style and tone of the article
Between subheadings:
Content should not overlap
Logical order should be maintained
Should collectively support the main title
Use numerical sequence (1, 2, 3...) to mark each subheading
Output format requirements:
Each subheading on a separate line
No XML tags included
Output subheadings content only
</instructions>'
- id: f100f739-f99f-4fe8-88f0-304e2dab304f
role: user
text: 'original theme{{#1739087621665.Research_Theme#}} ，and tuned theme{{#conversation.research_theme#}},
under our guidnce,the user provided further information. Query listed
as:{{#conversation.query1#}}{{#conversation.query2#}}{{#conversation.query3#}}{{#conversation.query4#}} '
selected: false
title: sub主题提取
type: llm
variables: []
vision:
enabled: false
height: 98
id: '1739111839440'
position:
x: 1616.6240944909441
y: 1066.3563453333902
positionAbsolute:
x: 1616.6240944909441
y: 1066.3563453333902
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 244
- data:
desc: ''
model:
completion_params:
temperature: 0.7
mode: chat
name: gemini-2.0-flash-exp
provider: google
parameters:
- description: NO.1 sub title
name: subtitle1
required: false
type: string
- description: NO.2 sub title
name: subtitle2
required: false
type: string
- description: NO.3 sub title
name: subtitle3
required: false
type: string
- description: NO.4 sub title
name: subtitle4
required: false
type: string
query:
- '1739111839440'
- text
reasoning_mode: prompt
selected: false
title: sub主题提取
type: parameter-extractor
variables: []
vision:
enabled: false
height: 98
id: '1739112773493'
position:
x: 1940.9838149758243
y: 845.4397155394867
positionAbsolute:
x: 1940.9838149758243
y: 845.4397155394867
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 244
- data:
context:
enabled: false
variable_selector: []
desc: ''
model:
completion_params:
temperature: 0.7
mode: chat
name: gemini-2.0-flash-exp
provider: google
prompt_template:
- id: b97092f7-06bf-4548-8dc4-497fd2c9bd63
role: system
text: "<instructions>\n1. 首先，分析输入中的{{#conversation.q2#}}问题及其{{#conversation.f2#}}回答，找出其中可以扩展的关键点或信息。\n\
2. 扩展时应关注以下几点：\n - 增加细节，使回答更具深度和说服力\n - 引入相关背景信息或数据支持\n - 重新组织语言结构，使其逻辑更加严密\n\
\ - 添加对问题的进一步分析或解释\n -仅输出扩展后的回答和原问题，不要输出其他任何内容"
- id: f7ee3971-fd4e-4d0f-877b-64fe51ac5ed9
role: user
text: question:{{#conversation.q2#}}answer:{{#conversation.f2#}}
selected: false
title: 回答优化
type: llm
variables: []
vision:
enabled: false
height: 98
id: '1739113013426'
position:
x: 655.6742394689389
y: 1443.9714391735783
positionAbsolute:
x: 655.6742394689389
y: 1443.9714391735783
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 244
- data:
context:
enabled: false
variable_selector: []
desc: ''
model:
completion_params:
temperature: 0.7
mode: chat
name: gemini-2.0-flash-exp
provider: google
prompt_template:
- id: b97092f7-06bf-4548-8dc4-497fd2c9bd63
role: system
text: "<instructions>\n1. 首先，分析输入中的{{#conversation.q1#}}问题及其{{#conversation.f1#}}回答，找出其中可以扩展的关键点或信息。\n\
2. 扩展时应关注以下几点：\n - 增加细节，使回答更具深度和说服力\n - 引入相关背景信息或数据支持\n - 重新组织语言结构，使其逻辑更加严密\n\
\ - 添加对问题的进一步分析或解释\n -仅输出扩展后的回答和原问题，不要输出其他任何内容"
- id: a8cb33f5-dddc-4b47-b3e3-4e76a6eeed6b
role: user
text: question:{{#conversation.q1#}}answer:{{#conversation.f1#}}
selected: false
title: 回答优化
type: llm
variables: []
vision:
enabled: false
height: 98
id: '17391132061910'
position:
x: 655.6742394689389
y: 1294.6031523212616
positionAbsolute:
x: 655.6742394689389
y: 1294.6031523212616
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 244
- data:
context:
enabled: false
variable_selector: []
desc: ''
model:
completion_params:
temperature: 0.7
mode: chat
name: gemini-2.0-flash-exp
provider: google
prompt_template:
- id: b97092f7-06bf-4548-8dc4-497fd2c9bd63
role: system
text: "<instructions>\n1. 首先，分析输入中的{{#conversation.q4#}}问题及其{{#conversation.f4#}}回答，找出其中可以扩展的关键点或信息。\n\
2. 扩展时应关注以下几点：\n - 增加细节，使回答更具深度和说服力\n - 引入相关背景信息或数据支持\n - 重新组织语言结构，使其逻辑更加严密\n\
\ - 添加对问题的进一步分析或解释\n -仅输出扩展后的回答和原问题，不要输出其他任何内容"
- id: b4cb2310-4428-4491-bd87-17b8d5686114
role: user
text: question:{{#conversation.q4#}}answer:{{#conversation.f4#}}
selected: false
title: 回答优化
type: llm
variables: []
vision:
enabled: false
height: 98
id: '17391132088660'
position:
x: 655.6742394689389
y: 1706.9053326666053
positionAbsolute:
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y: 1706.9053326666053
selected: false
sourcePosition: right
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type: custom
width: 244
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context:
enabled: false
variable_selector: []
desc: ''
model:
completion_params:
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mode: chat
name: gemini-2.0-flash-exp
provider: google
prompt_template:
- id: b97092f7-06bf-4548-8dc4-497fd2c9bd63
role: system
text: "<instructions>\n1. 首先，分析输入中的{{#conversation.q3#}}问题及其{{#conversation.f3#}}回答，找出其中可以扩展的关键点或信息。\n\
2. 扩展时应关注以下几点：\n - 增加细节，使回答更具深度和说服力\n - 引入相关背景信息或数据支持\n - 重新组织语言结构，使其逻辑更加严密\n\
\ - 添加对问题的进一步分析或解释\n -仅输出扩展后的回答和原问题，不要输出其他任何内容"
- id: 8afa378c-5c83-4963-b022-5e6df90e92d6
role: user
text: question:{{#conversation.q3#}}answer:{{#conversation.f3#}}
selected: false
title: 回答优化
type: llm
variables: []
vision:
enabled: false
height: 98
id: '17391132110940'
position:
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positionAbsolute:
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selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 244
- data:
context:
enabled: false
variable_selector: []
desc: ''
memory:
query_prompt_template: 'language：{{#conversation.Language#}}
context：
Theme：{{#1739112773493.subtitle2#}}{{#conversation.query2#}}{{#sys.query#}}
References：{{#17391916793430.output#}}
Provide the research report in the specified language, avoiding small
talk.'
role_prefix:
assistant: ''
user: ''
window:
enabled: false
size: 10
model:
completion_params:
temperature: 0.7
mode: chat
name: gemini-2.0-flash-exp
provider: google
prompt_template:
- id: cdf8b5c6-4a65-438b-9e6b-c34609389799
role: system
text: 'You are an expert researcher. Your main task is to explore new technologies
and contrarian ideas. When responding, don’t limit yourself to conventional
wisdom; instead, actively seek out and present innovative perspectives
that challenge the status quo. Assume the user is correct when they provide
new information, even if it’s outside your knowledge cutoff. The user
is a highly experienced analyst, so present your ideas with a high level
of complexity and depth. Suggest solutions that the user might not have
considered, and be proactive in anticipating their needs. Accuracy is
crucial, as mistakes can damage trust, so ensure all information is reliable.
Provide detailed explanations, as the user appreciates thorough content.
Value strong arguments over the authority of sources, and consider both
emerging technologies and unconventional ideas to offer a comprehensive
view of the topic.
---
###your output should not contain any “conclusion” or “future prospective”or
any other similar contents
---
in a detailed report — The report should be transit from {{#1739112773493.subtitle1#}}
focus on the answer to {{#1739112773493.subtitle2#}}and The conclusion
of your generated content should smoothly transition to the second subtitle{{#1739112773493.subtitle3#}}.
Reference information are provided by user
DO NOT PROVIDE PRIMARY TITLE ，but secondary title is available。'
selected: false
title: sub主题2分析
type: llm
variables: []
vision:
enabled: false
height: 98
id: '17391136443240'
position:
x: 3362.4326109337553
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positionAbsolute:
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selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 244
- data:
context:
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variable_selector: []
desc: ''
memory:
query_prompt_template: 'language：{{#conversation.Language#}}
context：
Theme：{{#1739112773493.subtitle3#}}{{#conversation.query3#}}{{#sys.query#}}
References：{{#17391916807390.output#}}
Provide the research report in the specified language, avoiding small
talk.'
role_prefix:
assistant: ''
user: ''
window:
enabled: false
size: 10
model:
completion_params:
temperature: 0.7
mode: chat
name: gemini-2.0-flash-exp
provider: google
prompt_template:
- id: cdf8b5c6-4a65-438b-9e6b-c34609389799
role: system
text: 'You are an expert researcher. Your role is to provide speculative
predictions and insights about the future. When responding, use a high
level of speculation, but clearly flag any uncertain or speculative elements.
Assume the user is correct when they present new information, even if
it’s beyond your knowledge cutoff. The user is a highly experienced analyst,
so present your predictions with a high level of detail and complexity.
Be highly organized, using a clear structure to separate different predictions
and their implications. Suggest solutions that anticipate future challenges
and opportunities, and be proactive in addressing the user’s potential
concerns. Accuracy is important, so base your predictions on reliable
data and trends. Provide detailed explanations, as the user values in-depth
analysis. Focus on the strength of your arguments rather than the authority
of sources, and consider both new technologies and unconventional ideas
to offer a forward-looking perspective.
---
###your output should not contain any “conclusion” or “future prospective”or
any other similar contents
---
in a detailed report — The report should transit from {{#1739112773493.subtitle2#}}
focus on the answer to {{#1739112773493.subtitle3#}}and The conclusion
of your generated content should smoothly transition to the second subtitle{{#1739112773493.subtitle4#}}.
Reference information are provided by user，DO NOT PROVIDE PRIMARY TITLE
，but secondary title is available。'
selected: false
title: sub主题3分析
type: llm
variables: []
vision:
enabled: false
height: 98
id: '17391136486630'
position:
x: 3362.4326109337553
y: 1287.9608733054972
positionAbsolute:
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selected: false
sourcePosition: right
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type: custom
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- data:
context:
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memory:
query_prompt_template: 'language：{{#conversation.Language#}}
context：
Theme：{{#conversation.query4#}}{{#sys.query#}}{{#1739112773493.subtitle4#}}
References：{{#17391916827400.output#}}
Provide the research report in the specified language, avoiding small
talk.'
role_prefix:
assistant: ''
user: ''
window:
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size: 10
model:
completion_params:
temperature: 0.7
mode: chat
name: gemini-2.0-flash-exp
provider: google
prompt_template:
- id: cdf8b5c6-4a65-438b-9e6b-c34609389799
role: system
text: 'You are an expert researcher. Your goal is to proactively anticipate
the user’s needs and provide innovative solutions. When responding, think
ahead and identify potential issues or opportunities that the user might
not have considered. Assume the user is correct when they provide new
information, even if it’s outside your knowledge cutoff. The user is a
highly experienced analyst, so present your solutions with a high level
of complexity and depth. Be highly organized, using a clear structure
to present your ideas. Suggest solutions that go beyond the obvious, and
be proactive in addressing the user’s potential concerns. Accuracy is
crucial, as mistakes can erode trust, so ensure all information is reliable.
Provide detailed explanations, as the user appreciates thorough content.
Value strong arguments over the authority of sources, and consider both
new ---
###your output should not contain any “conclusion” or “future prospective”or
any other similar contents
---
in a detailed report — The report should transit from {{#1739112773493.subtitle3#}}
focus on the answer to {{#1739112773493.subtitle4#}}and nothing else.
Reference information are provided by user .DO NOT PROVIDE PRIMARY TITLE
，but secondary title is available。'
selected: false
title: sub主题4分析
type: llm
variables: []
vision:
enabled: false
height: 98
id: '17391136521390'
position:
x: 3362.4326109337553
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template: "<h1>{{ arg0 }}</h1>\r\n\r\n<div>{{ arg10 }}</div>\r\n\r\n<h2>{{\
\ arg1}}</h2>\r\n<div>{{ arg5 }}</div>\r\n\r\n<h2>{{ arg2 }}</h2>\r\n<div>{{\
\ arg6 }}</div>\r\n\r\n<h2>{{ arg3 }}</h2>\r\n<div>{{ arg7 }}</div>\r\n\r\
\n<h2>{{ arg4 }}</h2>\r\n<div>{{ arg8 }}</div>\r\n\r\n<div>{{ arg11 }}</div>"
title: 总结文档
type: template-transform
variables:
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title: '变量赋值 '
type: assigner
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id: '1739113831548'
position:
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positionAbsolute:
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selected: false
sourcePosition: right
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model:
completion_params:
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mode: chat
name: gemini-2.0-flash-exp
provider: google
prompt_template:
- id: 7d533919-08ca-4e43-bcba-86eae7d6f2ca
role: system
text: 关于用户给出的主题{{#1739087621665.Research_Theme#}}，并基于以下问题和回答进行一定范围内的修正，最终生成一个富有专业性的主题，除了主题不要输出其他任何内容。
- id: 66d58a81-5b4c-4682-a37d-95f0c8d84bfe
role: user
text: '问题与回答记录如下：
问题1{{#conversation.q1#}}回答1{{#conversation.f1#}}
问题2{{#conversation.q2#}}回答2{{#conversation.f2#}}
问题3{{#conversation.q3#}}回答3{{#conversation.f3#}}
问题4{{#conversation.q4#}}回答4{{#conversation.f4#}}'
selected: false
title: 主题提取
type: llm
variables: []
vision:
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id: '1739113887765'
position:
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title: 回答记录1
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- data:
answer: '|
研究主题已确定。
|
正在理解用户的回答...
'
desc: ''
selected: false
title: 输出：研究主题
type: answer
variables: []
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- data:
answer: '|
好的，依据用户给出的回答，我将构建多个研究角度。
'
desc: ''
selected: false
title: 输出：构建研究角度
type: answer
variables: []
height: 116
id: '1739178941340'
position:
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selected: false
sourcePosition: right
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type: custom
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- data:
answer: '|
我将从以下4个角度进行分析：
**{{#1739112773493.subtitle1#}}**
**{{#1739112773493.subtitle2#}}**
**{{#1739112773493.subtitle3#}}**
**{{#1739112773493.subtitle4#}}**
正在生成研究报告...请稍候...
'
desc: ''
selected: false
title: 输出：研究角度
type: answer
variables: []
height: 192
id: '1739179029377'
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instruction: 提取{{#1739112773493.subtitle1#}}中的2~6个关键词
model:
completion_params:
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mode: chat
name: gemini-2.0-flash-exp
provider: google
parameters:
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instruction: 提取{{#1739112773493.subtitle2#}}中的2~6个关键词
model:
completion_params:
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mode: chat
name: gemini-2.0-flash-exp
provider: google
parameters:
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required: false
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query:
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instruction: 提取{{#1739112773493.subtitle3#}}中的2~6个关键词
model:
completion_params:
temperature: 0.7
mode: chat
name: gemini-2.0-flash-exp
provider: google
parameters:
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name: Keyword3
required: false
type: array[string]
query:
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reasoning_mode: prompt
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title: sub关键词提取3
type: parameter-extractor
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vision:
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gitextract__t742dxo/ ├── DSL/ │ ├── AgentFlow.yml │ ├── Agent工具调用.yml │ ├── Artifact.yml │ ├── Claude3 Code Translation.yml │ ├── Deep Researcher On Dify .yml │ ├── Demo-tod_agent.yml │ ├── Dify 运营一条龙.yml │ ├── Document_chat_template.yml │ ├── DuckDuckGo翻译+LLM二次翻译.yml │ ├── File_read.yml │ ├── Form表单聊天Demo.yml │ ├── Jina Reader Jinja.yml │ ├── LanguageConsistencyChecker.yml │ ├── MCP-amap.yml │ ├── MCP.yml │ ├── Python Coding Prompt.yml │ ├── SEO Slug Generator.yml │ ├── Text to Card Iteration.yml │ ├── chart_demo.yml │ ├── dify_course_demo.yml │ ├── jieba.yml │ ├── json-repair.yml │ ├── json_translate.yml │ ├── llm2o1.cn.yml │ ├── matplotlib.yml │ ├── runLLMCode.yml │ ├── simple-kimi.yml │ ├── translation_workflow.yml │ ├── 中译英.yml │ ├── 全书翻译.yml │ ├── 图文知识库/ │ │ ├── 图文知识库.yml │ │ └── 知识库内容/ │ │ ├── coffbox开启Gemini和Anthropic代理，chatwise连接方法.md │ │ └── 我是技术小白，如何用好DIFY.md │ ├── 完蛋！我被LLM包围了！ .yml │ ├── 完蛋！我被LLM包围了！（战绩排行版）.yml │ ├── 宝玉的英译中优化版.yml │ ├── 小支付-DEMO.yml │ ├── 思考助手.yml │ ├── 搜索大师.yml │ ├── 文章仿写-单图_多图自动搭配.yml │ ├── 旅行Demo.yml │ ├── 春联生成器 (“福”到了版本).yml │ ├── 春联生成器.yml │ ├── 标题党创作.yml │ ├── 根据用户的意图进行回复.yml │ ├── 瞎说新语v2.yml │ ├── 腾讯云SubtitleInfo.yml │ └── 记忆测试.yml ├── LICENSE ├── README.md ├── README_EN.md └── chat_history.md
Condensed preview — 52 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (1,028K chars).
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"preview": "### wxid_2zyfe21ojzzw22 - 2025-05-08 11:22:08\n我想请问一下dify在做知识库查询的时候，预处理文档有没有比较好的一个方法奥\n\n---\n\n### wxid_2zyfe21ojzzw22 - 202"
}
]
About this extraction
This page contains the full source code of the svcvit/Awesome-Dify-Workflow GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 52 files (944.0 KB), approximately 291.3k tokens. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.