mirror of
https://github.com/dogkeeper886/ollama-k80-lab.git
synced 2025-12-09 23:37:07 +00:00
525 lines
16 KiB
YAML
525 lines
16 KiB
YAML
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- data:
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code: "import re\n\n\ndef atlassian_to_markdown(text: str) -> str:\n \"\
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\"\"\n Converts Atlassian wiki-style markup to standard Markdown.\n \
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\ \"\"\"\n\n # Normalize line breaks\n text = text.replace('\\\\\
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n', '\\n').replace('\\r\\n', '\\n')\n\n # Bold text: +*text*+ → **text**\n\
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\ text = re.sub(r'\\+\\*(.+?)\\*\\+', r'**\\1**', text)\n\n # Headings:\
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\ h1. → #, h2. → ##, etc.\n text = re.sub(r'^h([1-6])\\.\\s+', lambda\
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\ m: '#' *\n int(m.group(1)) + ' ', text, flags=re.MULTILINE)\n\
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\n # Blockquotes: > lines\n text = re.sub(r'^\\s*>', '>', text, flags=re.MULTILINE)\n\
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\n # Image conversion: !URL|params! → \n text = re.sub(r'!([^\\\
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|!]+)\\|[^!]*!', r'', text)\n\n # Escaped dividers to markdown\
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\ horizontal rules\n text = re.sub(r'\\\\[-]+', '---', text)\n\n #\
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\ Remove extra Unicode whitespace characters (e.g., non-breaking spaces)\n\
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\ # note: the space before \\t is a non-breaking space\n text = re.sub(r'[ \\\
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t]+', ' ', text)\n\n # Collapse multiple blank lines to a maximum of\
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\ 2\n text = re.sub(r'\\n{3,}', '\\n\\n', text)\n\n # Strip trailing\
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\ spaces\n text = '\\n'.join(line.rstrip() for line in text.splitlines())\n\
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\n return text.strip()\n\n\ndef format_comments_display(comments: list)\
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\ -> str:\n \"\"\"\n Format a list of comments to simple markdown\
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\ with display name and converted body.\n \"\"\"\n output = []\n \
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\ for comment in comments:\n name = comment.get(\"author\", {}).get(\"\
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displayName\", \"Unknown Author\")\n body_raw = comment.get(\"body\"\
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, \"\")\n body_md = atlassian_to_markdown(body_raw)\n output.append(f\"\
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### {name}\\n\\n{body_md}\\n\")\n return \"\\n---\\n\".join(output)\n\
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\n\ndef main(jira_response: list) -> dict:\n \"\"\"Formats JSON data\
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\ into a Jira-style ticket string (simplified format).\"\"\"\n issue\
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\ = jira_response[0][\"issue\"]\n jira_ticket = issue[\"key\"]\n root_cause\
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\ = atlassian_to_markdown(issue[\"fields\"][\"customfield_10205\"])\n \
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\ description = atlassian_to_markdown(issue[\"fields\"][\"description\"\
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])\n comments = format_comments_display(issue[\"fields\"][\"comment\"\
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][\"comments\"])\n summary = issue[\"fields\"][\"summary\"]\n ticket\
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\ = f\"\"\"\n**Jira Ticket** {jira_ticket}\n\n**Summary:*** {summary}\n\n\
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**Root Cause:**\n{root_cause}\n\n**Description:**\n\n{description}\n\n**Comment:**\n\
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\n{comments}\n\"\"\"\n\n return {\n \"result\": ticket\n }\n"
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code_language: python3
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desc: ''
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outputs:
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result:
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children: null
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type: string
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selected: false
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title: FORMAT RAW DATA
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type: code
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variables:
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- value_selector:
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- '1747883690530'
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- json
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variable: jira_response
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type: custom
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width: 243
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- data:
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desc: ''
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is_team_authorization: true
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output_schema: null
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paramSchemas:
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- auto_generate: null
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default: null
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form: llm
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human_description:
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en_US: The Key of the Jira issue to retrieve details for (e.g., 'PROJ-123').
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ja_JP: The Key of the Jira issue to retrieve details for (e.g., 'PROJ-123').
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pt_BR: O ID ou a chave da issue a ser recuperada.
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zh_Hans: 要获取详情的 Jira 问题的 Key (例如 'PROJ-123')。
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label:
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en_US: Issue Key
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ja_JP: Issue Key
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pt_BR: ID ou Chave da Issue
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zh_Hans: 问题 Key
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llm_description: The unique key (e.g., 'PROJ-123') for the Jira issue for
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which details are requested. This field is mandatory.
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max: null
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min: null
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name: issue_key
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options: []
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placeholder: null
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precision: null
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required: true
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scope: null
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template: null
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type: string
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params:
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issue_key: ''
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provider_id: langgenius/jira/jira
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provider_name: langgenius/jira/jira
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provider_type: builtin
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selected: false
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title: Get Jira Issue Details
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tool_configurations: {}
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tool_description: Retrieves details for a specific Jira issue.
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tool_label: Get Jira Issue Details
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tool_name: get_issue
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tool_parameters:
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issue_key:
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type: mixed
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value: '{{#1747817616323.key#}}'
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desc: ''
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model:
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completion_params:
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num_ctx: 8192
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temperature: 0
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mode: chat
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name: gemma3:12b
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provider: langgenius/ollama/ollama
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prompt_template:
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role: system
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text: Summary a customer issue. Response summary only, no opening words.
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- id: e02c0dbc-6515-4667-8b6a-870455d2f15c
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role: user
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text: 'Customer issue: {{#1747819131717.result#}}'
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selected: false
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title: SUMMARY CUSTOMER ISSUE
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type: llm
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variables: []
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vision:
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enabled: false
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height: 89
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context:
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desc: ''
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model:
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completion_params:
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temperature: 0
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mode: chat
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name: gemma3:12b
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provider: langgenius/ollama/ollama
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prompt_template:
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- id: 66955ec2-2239-4caa-8133-c7e8473d075c
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role: system
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text: You use customer issue summary to create keywords which will be used
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to retrieve or search the knowledge base. Response keywords only, no opening
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words.
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- id: 355705f6-4ca4-432f-ad34-d7224c9bd71e
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role: user
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text: 'Customer issue summary: {{#1747895800944.text#}}'
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selected: false
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title: Generate search keywords
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type: llm
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variables: []
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vision:
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enabled: false
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height: 89
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id: '1747901440984'
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type: custom
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- data:
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context:
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enabled: true
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variable_selector:
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- result
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desc: ''
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model:
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completion_params:
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temperature: 0
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mode: chat
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name: gemma3:12b
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provider: langgenius/ollama/ollama
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prompt_template:
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- id: d6a06595-cb43-4025-b87b-9a1e35528ae0
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role: system
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text: "You use customer issue summary and information from knowledge as\
|
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\ reference to create test cases. \n\nThe test cases should contain a\
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\ test case name, a precondition, test steps in table format(step number,\
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\ action, expected result).\n\nResponse test cases only, no opening words."
|
||
- id: 443b66c2-7ece-4851-a498-472948c1b2ce
|
||
role: user
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text: 'Knowledge: {{#context#}}
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|
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Summary: {{#1747895800944.text#}}'
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selected: false
|
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structured_output_enabled: false
|
||
title: Create test case
|
||
type: llm
|
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variables: []
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vision:
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enabled: false
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height: 89
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sourcePosition: right
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targetPosition: left
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type: custom
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width: 243
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- data:
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dataset_ids:
|
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- bV6r/AjJvrfKH9xVPmTMOCUnavWq1VX11N8GOBmLZ7+12qiBCmyR4xhjofhketH4
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desc: ''
|
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multiple_retrieval_config:
|
||
reranking_enable: true
|
||
reranking_mode: weighted_score
|
||
top_k: 4
|
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weights:
|
||
keyword_setting:
|
||
keyword_weight: 0.3
|
||
vector_setting:
|
||
embedding_model_name: nomic-embed-text
|
||
embedding_provider_name: langgenius/ollama/ollama
|
||
vector_weight: 0.7
|
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query_variable_selector:
|
||
- '1747901440984'
|
||
- text
|
||
retrieval_mode: multiple
|
||
selected: false
|
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title: Knowledge Retrieval
|
||
type: knowledge-retrieval
|
||
height: 91
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position:
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sourcePosition: right
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targetPosition: left
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type: custom
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width: 243
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- data:
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code: "\ndef main(customer_issue_summary: str, test_cases: str) -> dict:\n\
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||
\ return {\n \"result\": customer_issue_summary + \"\\n\" + test_cases,\n\
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||
\ }\n"
|
||
code_language: python3
|
||
desc: ''
|
||
outputs:
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result:
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children: null
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type: string
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selected: false
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title: Format output
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type: code
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variables:
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- value_selector:
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- '1747895800944'
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- text
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variable: customer_issue_summary
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- value_selector:
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- '1747901628308'
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- text
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variable: test_cases
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height: 53
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targetPosition: left
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y: 241.51914411649688
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zoom: 0.5839836973065593
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