本文展示一个dify+searxng的组合工作流，制作一个搜索大师的工作流，这个搜索大师能干什么呢？主要是：
1、将输入的问题拆分成多个相识的问题。 2、将拆分的问题挨个使用searxng进行多样化搜索 2、把搜索结果进行聚合 3、在搜索结果的基础上让ai帮我们总结，提炼核心。
工作流类型：chatflow
工作流dsl：
app:
description: ''
icon: 🌐
icon_background: '#E4FBCC'
mode: advanced-chat
name: 搜索大师
use_icon_as_answer_icon: false
dependencies:
- current_identifier: null
type: marketplace
value:
marketplace_plugin_unique_identifier: langgenius/searxng:0.0.2@f7b89266ad7194a5574223198ade08a8f3841857d907ebdc4813fa4c2fba9381
- current_identifier: null
type: marketplace
value:
marketplace_plugin_unique_identifier: langgenius/siliconflow:0.0.8@217f973bd7ced1b099c2f0c669f1356bdf4cc38b8372fd58d7874f9940b95de3
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: 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: 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
id: 1719593592357-source-1719759357501-target
selected: false
source: '1719593592357'
sourceHandle: source
target: '1719759357501'
targetHandle: target
type: custom
zIndex: 0
- data:
isInIteration: false
sourceType: llm
targetType: code
id: 1719759357501-source-1719838886987-target
selected: false
source: '1719759357501'
sourceHandle: source
target: '1719838886987'
targetHandle: target
type: custom
zIndex: 0
- data:
isInIteration: false
sourceType: code
targetType: iteration
id: 1719838886987-source-1719840515773-target
selected: false
source: '1719838886987'
sourceHandle: source
target: '1719840515773'
targetHandle: target
type: custom
zIndex: 0
- data:
isInIteration: false
sourceType: iteration
targetType: template-transform
id: 1719878483248-source-1719840940932-target
selected: false
source: '1719878483248'
sourceHandle: source
target: '1719840940932'
targetHandle: target
type: custom
zIndex: 0
- data:
isInIteration: false
sourceType: template-transform
targetType: llm
id: 1719840940932-source-1719932888279-target
selected: false
source: '1719840940932'
sourceHandle: source
target: '1719932888279'
targetHandle: target
type: custom
zIndex: 0
- data:
isInIteration: false
sourceType: iteration
targetType: code
id: 1719840515773-source-1719933634159-target
selected: false
source: '1719840515773'
sourceHandle: source
target: '1719933634159'
targetHandle: target
type: custom
zIndex: 0
- data:
isInIteration: false
sourceType: code
targetType: iteration
id: 1719933634159-source-1719878483248-target
selected: false
source: '1719933634159'
sourceHandle: source
target: '1719878483248'
targetHandle: target
type: custom
zIndex: 0
- data:
isInIteration: false
sourceType: llm
targetType: answer
id: 1719932888279-source-answer-target
selected: false
source: '1719932888279'
sourceHandle: source
target: answer
targetHandle: target
type: custom
zIndex: 0
- data:
isInIteration: true
iteration_id: '1719840515773'
sourceType: iteration-start
targetType: tool
id: 1719840515773start0-source-1720362263119-target
selected: false
source: 1719840515773start0
sourceHandle: source
target: '1720362263119'
targetHandle: target
type: custom
zIndex: 1002
- data:
isInIteration: true
iteration_id: '1719878483248'
sourceType: http-request
targetType: llm
id: 1735721206584-source-1735721255006-target
source: '1735721206584'
sourceHandle: source
target: '1735721255006'
targetHandle: target
type: custom
zIndex: 1002
- data:
isInIteration: true
iteration_id: '1719878483248'
sourceType: iteration-start
targetType: http-request
id: 1719878483248start1-source-1735721206584-target
source: 1719878483248start1
sourceHandle: source
target: '1735721206584'
targetHandle: target
type: custom
zIndex: 1002
nodes:
- data:
desc: ''
selected: false
title: Start
type: start
variables: []
height: 54
id: '1719593592357'
position:
x: 30
y: 388.5
positionAbsolute:
x: 30
y: 388.5
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 244
- data:
answer: '{{#1719932888279.text#}}'
desc: ''
selected: false
title: Answer
type: answer
variables: []
height: 105
id: answer
position:
x: 3619
y: 388.5
positionAbsolute:
x: 3619
y: 388.5
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 244
- data:
context:
enabled: true
variable_selector:
- sys
- query
desc: ''
model:
completion_params: {}
mode: chat
name: Qwen/Qwen2.5-7B-Instruct
provider: langgenius/siliconflow/siliconflow
prompt_template:
- id: 455a5f0d-bbba-479a-b3c2-1eefa938894a
role: system
text: "You are a helpful assistant that helps the user to ask related questions,\
\ based on question and the related contexts. \n\nPlease make sure that\
\ specifics, like events, names, locations, are included in follow up\
\ questions so they can be asked standalone. For example, if the original\
\ question asks about \"the Manhattan project\", in the follow up question,\
\ do not just say \"the project\", but use the full name \"the Manhattan\
\ project\". Your related questions must be in the same language as the\
\ original question."
- id: 6ba785ec-0bba-4a19-8a6d-63dbeacce7d2
role: user
text: "Here is the question: {{#sys.query#}}\n\nUnderstand the question\
\ first, then identify worthwhile topics that can be follow-ups, provide\
\ 3 questions, each question no longer than 20 words. \nDo NOT repeat\
\ the original question. the output language MUST same as ‘ {{#context#}}\
\ ‘ and do NOT return any translation. "
selected: false
title: 问题分解器
type: llm
variables: []
vision:
enabled: false
height: 96
id: '1719759357501'
position:
x: 334.5929876945846
y: 388.5
positionAbsolute:
x: 334.5929876945846
y: 388.5
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 244
- data:
code: "\ndef main(arg1: str) -> dict:\n questions = arg1.split('\\n')\n\
\ return {\n \"result\": questions,\n }\n"
code_language: python3
desc: ''
outputs:
result:
children: null
type: array[string]
selected: false
title: 问题数组
type: code
variables:
- value_selector:
- '1719759357501'
- text
variable: arg1
height: 54
id: '1719838886987'
position:
x: 638
y: 388.5
positionAbsolute:
x: 638
y: 388.5
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 244
- data:
desc: ''
error_handle_mode: terminated
height: 247
is_parallel: false
iterator_selector:
- '1719838886987'
- result
output_selector:
- '1720362263119'
- json
output_type: array[string]
parallel_nums: 10
selected: false
startNodeType: tool
start_node_id: 1719840515773start0
title: 循环搜索
type: iteration
width: 377
height: 247
id: '1719840515773'
position:
x: 943.6915776724429
y: 388.5
positionAbsolute:
x: 943.6915776724429
y: 388.5
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 377
zIndex: 1
- data:
desc: ''
selected: false
template: '{{ arg1 }}
'
title: 聚合内容
type: template-transform
variables:
- value_selector:
- '1719878483248'
- output
variable: arg1
height: 54
id: '1719840940932'
position:
x: 3011.7993474263017
y: 388.5
positionAbsolute:
x: 3011.7993474263017
y: 388.5
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 244
- data:
desc: ''
error_handle_mode: terminated
height: 377
is_parallel: false
iterator_selector:
- '1719933634159'
- result
output_selector:
- '1735721255006'
- text
output_type: array[string]
parallel_nums: 10
selected: false
startNodeType: http-request
start_node_id: 1719878483248start1
title: 整理收集结果
type: iteration
width: 1286
height: 377
id: '1719878483248'
position:
x: 1577.4861397281613
y: 344.5358915534005
positionAbsolute:
x: 1577.4861397281613
y: 344.5358915534005
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 1286
zIndex: 1
- data:
context:
enabled: false
variable_selector: []
desc: ''
memory:
query_prompt_template: '{{#sys.query#}}{{#1719759357501.text#}}'
role_prefix:
assistant: ''
user: ''
window:
enabled: false
size: 50
model:
completion_params: {}
mode: chat
name: Qwen/Qwen2.5-7B-Instruct
provider: langgenius/siliconflow/siliconflow
prompt_template:
- id: fd1a7c6d-67ae-4124-ad91-65fe8868c4ab
role: system
text: 'You are a large language AI assistant built by winson. Your answer
must be correct, accurate and written by an expert using an unbiased and
professional tone.
'
- id: 989185b9-19fb-486e-b430-b9c379b532b9
role: user
text: "<Task>\nDo a general overview style summary to the following text\
\ in Chinese and in markdown format, please output the url as the reference\
\ if have. \n<Text to be summarized>\n{{#1719840940932.output#}}\n<Summary>"
selected: false
title: LLM 4
type: llm
variables: []
vision:
enabled: false
height: 90
id: '1719932888279'
position:
x: 3315
y: 388.5
positionAbsolute:
x: 3315
y: 388.5
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 244
- data:
code: "def main(arg2):\n urls = []\n for item in arg2: # 直接迭代arg2中的每个元素（这里假设arg2是一个列表的列表）\n\
\ if \"url\" in item:\n urls.append(item[\"url\"])\n \
\ return {\n \"result\": urls[:10] # 返回最多前10个URL\n }\n"
code_language: python3
desc: ''
outputs:
result:
children: null
type: array[string]
selected: false
title: URL 收集器
type: code
variables:
- value_selector:
- '1719840515773'
- output
variable: arg2
height: 54
id: '1719933634159'
position:
x: 1379
y: 388.5
positionAbsolute:
x: 1379
y: 388.5
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 244
- data:
desc: ''
isInIteration: true
isIterationStart: true
iteration_id: '1719840515773'
provider_id: searxng
provider_name: searxng
provider_type: builtin
selected: false
title: SearXNG 搜索
tool_configurations:
num_results: 1
result_type: link
search_type: general
tool_label: SearXNG 搜索
tool_name: searxng_search
tool_parameters:
query:
type: mixed
value: '{{#1719840515773.item#}}'
type: tool
extent: parent
height: 142
id: '1720362263119'
parentId: '1719840515773'
position:
x: 117
y: 87
positionAbsolute:
x: 1060.691577672443
y: 475.5
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 244
zIndex: 1001
- data:
desc: ''
isInIteration: true
selected: false
title: ''
type: iteration-start
draggable: false
height: 48
id: 1719840515773start0
parentId: '1719840515773'
position:
x: 24
y: 68
positionAbsolute:
x: 967.6915776724429
y: 456.5
selectable: false
sourcePosition: right
targetPosition: left
type: custom-iteration-start
width: 44
zIndex: 1002
- data:
desc: ''
isInIteration: true
selected: false
title: ''
type: iteration-start
draggable: false
height: 48
id: 1719878483248start1
parentId: '1719878483248'
position:
x: 24
y: 68
positionAbsolute:
x: 1601.4861397281613
y: 412.5358915534005
selectable: false
sourcePosition: right
targetPosition: left
type: custom-iteration-start
width: 44
zIndex: 1002
- data:
authorization:
config: null
type: no-auth
body:
data: []
type: none
desc: ''
headers: ''
isInIteration: true
iteration_id: '1719878483248'
method: get
params: ''
retry_config:
max_retries: 3
retry_enabled: true
retry_interval: 100
selected: true
timeout:
max_connect_timeout: 0
max_read_timeout: 0
max_write_timeout: 0
title: HTTP 请求 2
type: http-request
url: https://r.jina.ai/{{#1719878483248.item#}}
variables: []
extent: parent
height: 139
id: '1735721206584'
parentId: '1719878483248'
position:
x: 245.97478863893912
y: 150.75067962958838
positionAbsolute:
x: 1823.4609283671005
y: 495.2865711829889
selected: true
sourcePosition: right
targetPosition: left
type: custom
width: 244
zIndex: 1002
- data:
context:
enabled: false
variable_selector: []
desc: ''
isInIteration: true
iteration_id: '1719878483248'
model:
completion_params: {}
mode: chat
name: Qwen/Qwen2.5-7B-Instruct
provider: langgenius/siliconflow/siliconflow
prompt_template:
- id: ee1d16e1-8e25-4a62-a37b-737bd96ed261
role: system
text: 'Please provide a comprehensive summary of the given content {{#1735721206584.body#}},
focusing on:
1. Main ideas and key points
2. Supporting evidence and examples
3. Important conclusions
4. Practical implications
Format requirements:
- Keep it concise and clear
- Use bullet points where appropriate
- Highlight critical information
- Maintain logical flow
Please limit the summary to [number] words/paragraphs.'
selected: false
title: LLM 3
type: llm
variables: []
vision:
enabled: false
extent: parent
height: 96
id: '1735721255006'
parentId: '1719878483248'
position:
x: 762.2202360862525
y: 175.6293151651082
positionAbsolute:
x: 2339.706375814414
y: 520.1652067185087
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 244
zIndex: 1002
viewport:
x: -4576.567611286098
y: -240.70003116203407
zoom: 1.251020478825474最后，比如我们搜索智汇技术网站
等待片刻即可：
等待片刻后可以看到他进行了2次总结，虽然结果不怎么样，但是这一套工作流的组合还是可以学习下，主要涉及到：
1、llm配置 2、循环 3、searxng 4、python代码 5、jina转换 6、聚合
整个一套工作流的每一个组件学习完之后差不多就能对dify工作流的使用非常熟练了。
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