app: description: Routes each question to a specialised retrieval and prompt path. Answers only from the Jira knowledge base. icon: 🤖 icon_background: '#FFEAD5' icon_type: emoji mode: advanced-chat name: 'Exercise 4: Advanced RAG' use_icon_as_answer_icon: false dependencies: - current_identifier: null type: marketplace value: marketplace_plugin_unique_identifier: langgenius/azure_openai:0.0.69@c744f6bf60699c296307a4420094a7f88b4f6e7fdf353b67b2b88ddb455074b2 version: null kind: app version: 0.7.0 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: 'Hi! I''m your AI testing assistant for the Atlassian REST project. I can: - explain a Jira issue ("What is REST-266 about?") - summarize or compare several issues - suggest what to test for a risk - write Gherkin test cases from issues or requirements Ask your question below.' retriever_resource: enabled: true sensitive_word_avoidance: enabled: false speech_to_text: enabled: false suggested_questions: - What is REST-266 about? - Summarize REST-266 and REST-265 - What should we test for backward compatibility? - Generate test cases for REST-266 suggested_questions_after_answer: enabled: true text_to_speech: enabled: false language: '' voice: '' graph: edges: - data: isInIteration: false isInLoop: false sourceType: llm targetType: answer id: llm-answer selected: false source: llm sourceHandle: source target: answer targetHandle: target type: custom zIndex: 0 - data: isInIteration: false isInLoop: false sourceType: start targetType: question-classifier id: 1748687003240-source-1748756681684-target source: '1748687003240' sourceHandle: source target: '1748756681684' targetHandle: target type: custom zIndex: 0 - data: isInIteration: false isInLoop: false sourceType: question-classifier targetType: parameter-extractor id: 1748756681684-1-1748719005808-target source: '1748756681684' sourceHandle: '1' target: '1748719005808' targetHandle: target type: custom zIndex: 0 - data: isInIteration: false isInLoop: false sourceType: question-classifier targetType: knowledge-retrieval id: 1748756681684-1748767162845-1748767429892-target source: '1748756681684' sourceHandle: '1748767162845' target: '1748767429892' targetHandle: target type: custom zIndex: 0 - data: isInIteration: false isInLoop: false sourceType: knowledge-retrieval targetType: llm id: 1748767429892-source-1748767447593-target source: '1748767429892' sourceHandle: source target: '1748767447593' targetHandle: target type: custom zIndex: 0 - data: isInIteration: false isInLoop: false sourceType: llm targetType: answer id: 1748767447593-source-1748767451380-target source: '1748767447593' sourceHandle: source target: '1748767451380' targetHandle: target type: custom zIndex: 0 - data: isInIteration: false isInLoop: false sourceType: question-classifier targetType: parameter-extractor id: 1748756681684-2-1748767570289-target source: '1748756681684' sourceHandle: '2' target: '1748767570289' targetHandle: target type: custom zIndex: 0 - data: isInIteration: false isInLoop: false sourceType: llm targetType: answer id: 1748783029512-source-1748783041984-target source: '1748783029512' sourceHandle: source target: '1748783041984' targetHandle: target type: custom zIndex: 0 - data: isInIteration: false isInLoop: false sourceType: parameter-extractor targetType: iteration id: 1748767570289-source-1748783526414-target source: '1748767570289' sourceHandle: source target: '1748783526414' targetHandle: target type: custom zIndex: 0 - data: isInIteration: true isInLoop: false iteration_id: '1748783526414' sourceType: iteration-start targetType: knowledge-retrieval id: 1748783526414start-source-1748783571565-target source: 1748783526414start sourceHandle: source target: '1748783571565' targetHandle: target type: custom zIndex: 1001 - data: isInIteration: false isInLoop: false sourceType: question-classifier targetType: knowledge-retrieval id: 1748756681684-1748767109717-1748784602400-target source: '1748756681684' sourceHandle: '1748767109717' target: '1748784602400' targetHandle: target type: custom zIndex: 0 - data: isInIteration: false isInLoop: false sourceType: knowledge-retrieval targetType: llm id: 1748784602400-source-1748784677999-target source: '1748784602400' sourceHandle: source target: '1748784677999' targetHandle: target type: custom zIndex: 0 - data: isInIteration: false isInLoop: false sourceType: llm targetType: answer id: 1748784677999-source-1748784661836-target source: '1748784677999' sourceHandle: source target: '1748784661836' targetHandle: target type: custom zIndex: 0 - data: isInIteration: false isInLoop: false sourceType: question-classifier targetType: parameter-extractor id: 1748756681684-1748774938291-17487850509710-target source: '1748756681684' sourceHandle: '1748774938291' target: '17487850509710' targetHandle: target type: custom zIndex: 0 - data: isInIteration: true isInLoop: false iteration_id: '17487852863990' sourceType: iteration-start targetType: knowledge-retrieval id: 1748785286399start-source-1748785286399017487852864000-target source: 1748785286399start sourceHandle: source target: '1748785286399017487852864000' targetHandle: target type: custom zIndex: 1001 - data: isInIteration: false isInLoop: false sourceType: parameter-extractor targetType: if-else id: 17487850509710-source-1748785479296-target source: '17487850509710' sourceHandle: source target: '1748785479296' targetHandle: target type: custom zIndex: 0 - data: isInIteration: false isInLoop: false sourceType: if-else targetType: iteration id: 1748785479296-true-17487852863990-target source: '1748785479296' sourceHandle: 'true' target: '17487852863990' targetHandle: target type: custom zIndex: 0 - data: isInIteration: false isInLoop: false sourceType: if-else targetType: llm id: 1748785479296-false-1748785532843-target source: '1748785479296' sourceHandle: 'false' target: '1748785532843' targetHandle: target type: custom zIndex: 0 - data: isInIteration: false isInLoop: false sourceType: llm targetType: answer id: 1748785557565-source-1748785836978-target source: '1748785557565' sourceHandle: source target: '1748785836978' targetHandle: target type: custom zIndex: 0 - data: isInIteration: false isInLoop: false sourceType: llm targetType: answer id: 1748785532843-source-1748786521492-target source: '1748785532843' sourceHandle: source target: '1748786521492' targetHandle: target type: custom zIndex: 0 - data: isInIteration: false isInLoop: false sourceType: question-classifier targetType: llm id: 1748756681684-1748785172818-1748786653855-target source: '1748756681684' sourceHandle: '1748785172818' target: '1748786653855' targetHandle: target type: custom zIndex: 0 - data: isInIteration: false isInLoop: false sourceType: llm targetType: answer id: 1748786653855-source-1748785149607-target source: '1748786653855' sourceHandle: source target: '1748785149607' targetHandle: target type: custom zIndex: 0 - data: isInIteration: false isInLoop: false sourceType: iteration targetType: code id: 1748783526414-source-1762727584347-target source: '1748783526414' sourceHandle: source target: '1762727584347' targetHandle: target type: custom zIndex: 0 - data: isInIteration: false isInLoop: false sourceType: code targetType: llm id: 1762727584347-source-1748783029512-target source: '1762727584347' sourceHandle: source target: '1748783029512' targetHandle: target type: custom zIndex: 0 - data: isInIteration: false isInLoop: false sourceType: iteration targetType: code id: 17487852863990-source-1762728060345-target source: '17487852863990' sourceHandle: source target: '1762728060345' targetHandle: target type: custom zIndex: 0 - data: isInIteration: false isInLoop: false sourceType: code targetType: llm id: 1762728060345-source-1748785557565-target source: '1762728060345' sourceHandle: source target: '1748785557565' targetHandle: target type: custom zIndex: 0 - data: isInIteration: false isInLoop: false sourceType: knowledge-retrieval targetType: if-else id: 1748687775954-source-issue_found-target source: '1748687775954' sourceHandle: source target: issue_found targetHandle: target type: custom zIndex: 0 - data: isInIteration: false isInLoop: false sourceType: if-else targetType: llm id: issue_found-true-llm-target source: issue_found sourceHandle: 'true' target: llm targetHandle: target type: custom zIndex: 0 - data: isInIteration: false isInLoop: false sourceType: if-else targetType: answer id: issue_found-false-answer_not_found-target source: issue_found sourceHandle: 'false' target: answer_not_found targetHandle: target type: custom zIndex: 0 - data: isInIteration: false isInLoop: false sourceType: parameter-extractor targetType: if-else id: 1748719005808-source-has_key-target source: '1748719005808' sourceHandle: source target: has_key targetHandle: target type: custom zIndex: 0 - data: isInIteration: false isInLoop: false sourceType: if-else targetType: knowledge-retrieval id: has_key-true-1748687775954-target source: has_key sourceHandle: 'true' target: '1748687775954' targetHandle: target type: custom zIndex: 0 - data: isInIteration: false isInLoop: false sourceType: if-else targetType: knowledge-retrieval id: has_key-false-1748784602400-target source: has_key sourceHandle: 'false' target: '1748784602400' targetHandle: target type: custom zIndex: 0 nodes: - data: desc: '' selected: false title: User Input type: start variables: [] height: 73 id: '1748687003240' position: x: -1083.666487928184 y: 288.65445462792803 positionAbsolute: x: -1083.666487928184 y: 288.65445462792803 selected: false sourcePosition: right targetPosition: left type: custom width: 242 zIndex: 0 - data: context: enabled: true variable_selector: - '1748687775954' - result desc: '' model: completion_params: temperature: 0.3 mode: chat name: gpt-35-turbo-16k provider: langgenius/azure_openai/azure_openai prompt_template: - id: 5c286fbf-33f9-4150-8c64-696789577558 role: system text: 'You are a software testing assistant helping engineers understand Jira issues in the Atlassian REST project. Answer only from the context below. Never use outside knowledge. The context was filtered to the Jira issue the user asked about. If it is empty or does not mention that issue, reply exactly: "I''m sorry, I can''t find details about that issue in the project documentation." Your reply should: - Start with the issue key and its summary - List the type, status and assignee - Explain in two or three sentences what the issue changes or fixes - End with "What to test": two to four concrete test ideas based on the description Context: {{#context#}} User question: {{#sys.query#}} ' selected: false title: LLM - Specific Issue type: llm variables: [] vision: enabled: false height: 88 id: llm position: x: 1500 y: 33.57027864430731 positionAbsolute: x: 1500 y: 33.57027864430731 selected: false sourcePosition: right targetPosition: left type: custom width: 242 zIndex: 0 - data: answer: '{{#llm.text#}}' desc: '' selected: false title: Answer - Specific Issue type: answer variables: [] height: 103 id: answer position: x: 1840 y: 33.57027864430731 positionAbsolute: x: 1840 y: 33.57027864430731 selected: false sourcePosition: right targetPosition: left type: custom width: 242 zIndex: 0 - data: dataset_ids: - W1plFG6wPRupvQB0T6LXXARA+GkduRuNFrUPXMZHtprMHDIwKWFC/Xl2XDIWt2EY desc: '' metadata_filtering_conditions: conditions: - comparison_operator: is id: 2cc78fcc-d7fd-49e1-a66d-e71af312b069 name: issue_key value: '{{#1748719005808.issue_key#}}' logical_operator: and metadata_filtering_mode: manual multiple_retrieval_config: reranking_enable: false reranking_mode: weighted_score reranking_model: model: '' provider: '' score_threshold: null top_k: 4 weights: keyword_setting: keyword_weight: 0 vector_setting: embedding_model_name: text-embedding-3-large embedding_provider_name: langgenius/azure_openai/azure_openai vector_weight: 1 weight_type: customized query_variable_selector: - '1748687003240' - sys.query retrieval_mode: multiple selected: false title: KR - Single Issue type: knowledge-retrieval height: 90 id: '1748687775954' position: x: 456.25 y: 33.57027864430731 positionAbsolute: x: 456.25 y: 33.57027864430731 selected: false sourcePosition: right targetPosition: left type: custom width: 242 zIndex: 0 - data: desc: '' instruction: 'Extract the one Jira issue key the user is asking about. A Jira issue key is an uppercase project prefix, a hyphen and a number, for example REST-266. Normalize other spellings to that format. This project''s prefix is REST, so "rest 266", "REST266", "issue 266" and "ticket 266" all become REST-266. Return only the key. ' model: completion_params: temperature: 0 mode: chat name: gpt-35-turbo-16k provider: langgenius/azure_openai/azure_openai parameters: - description: The Jira issue key the user asks about, for example REST-266. name: issue_key required: false type: string query: - sys - query reasoning_mode: prompt selected: false title: Issue Key Extractor type: parameter-extractor variables: [] vision: enabled: false height: 88 id: '1748719005808' position: x: -284.44013680327953 y: 33.57027864430731 positionAbsolute: x: -284.44013680327953 y: 33.57027864430731 selected: false sourcePosition: right targetPosition: left type: custom width: 242 zIndex: 0 - data: classes: - id: '1' name: specific_issue - id: '2' name: list_issues - id: '1748767109717' name: general_project - id: '1748767162845' name: test_related - id: '1748774938291' name: test_generation - id: '1748785172818' name: unrelated_or_invalid desc: '' instruction: 'You classify questions sent to a software testing assistant for the Atlassian REST Jira project. Choose exactly ONE category: - specific_issue: asks about one Jira issue that it names by key or number. Examples: "What does REST-266 fix?", "Who is assigned to issue 259?" - list_issues: mentions two or more Jira issue keys and asks to summarize, compare or relate them. Example: "Summarize REST-266 and REST-265" - general_project: asks about the project as a whole (scope, modules, features, contributors), about a group of issues, or which issue matches a description, without naming a key. Examples: "What is this project about?", "Which issues relate to Jackson?", "Which issue reports that CORS preflight requests are not supported?" - test_related: asks about test strategy, risk, regression or coverage, without asking to write test cases. Example: "What should we test for backward compatibility?" - test_generation: asks to write, generate or create test cases or Gherkin scenarios, with or without issue keys. Examples: "Generate test cases for REST-266", "Create test scenarios for a password reset feature" - unrelated_or_invalid: not about this project, its Jira issues, its documentation or testing. Examples: "Write me a poem", "Why is the sky blue?" If the user asks to write or generate tests, choose test_generation even when the question contains issue keys. ' instructions: '' model: completion_params: temperature: 0 mode: chat name: gpt-35-turbo-16k provider: langgenius/azure_openai/azure_openai query_variable_selector: - '1748687003240' - sys.query selected: false title: Question Classifier topics: [] type: question-classifier vision: enabled: false height: 346 id: '1748756681684' position: x: -694.8206093064899 y: 288.65445462792803 positionAbsolute: x: -694.8206093064899 y: 288.65445462792803 selected: false sourcePosition: right targetPosition: left type: custom width: 242 zIndex: 0 - data: dataset_ids: - W1plFG6wPRupvQB0T6LXXARA+GkduRuNFrUPXMZHtprMHDIwKWFC/Xl2XDIWt2EY desc: '' multiple_retrieval_config: reranking_enable: false reranking_mode: weighted_score score_threshold: null top_k: 4 weights: keyword_setting: keyword_weight: 0 vector_setting: embedding_model_name: text-embedding-3-large embedding_provider_name: langgenius/azure_openai/azure_openai vector_weight: 1 weight_type: customized query_variable_selector: - '1748687003240' - sys.query retrieval_mode: multiple selected: false title: KR - Test Related type: knowledge-retrieval height: 90 id: '1748767429892' position: x: -284.44013680327953 y: 610.4803953947226 positionAbsolute: x: -284.44013680327953 y: 610.4803953947226 selected: false sourcePosition: right targetPosition: left type: custom width: 242 zIndex: 0 - data: context: enabled: true variable_selector: - '1748767429892' - result desc: '' model: completion_params: temperature: 0.3 mode: chat name: gpt-35-turbo-16k provider: langgenius/azure_openai/azure_openai prompt_template: - id: 8c741de7-7353-402c-bfcb-a1e6b6798e35 role: system text: 'You are a senior test engineer advising a team that works on the Atlassian REST project. Answer only from the Jira issues in the context below. Never use outside knowledge. If the context does not help answer the question, reply exactly: "I''m sorry, I can''t find relevant testing information in the project documentation to answer that." Your reply should: - Name the Jira issues that create the risk or need testing, with their keys - Explain each risk in one sentence - Recommend what to test: the test level (unit, integration, regression, security) and concrete checks - Order the recommendations by risk, highest first Context: {{#context#}} User question: {{#sys.query#}} ' selected: false title: LLM - Test Related type: llm variables: [] vision: enabled: false height: 88 id: '1748767447593' position: x: 89.0778811944802 y: 610.4803953947226 positionAbsolute: x: 89.0778811944802 y: 610.4803953947226 selected: false sourcePosition: right targetPosition: left type: custom width: 242 zIndex: 0 - data: answer: '{{#1748767447593.text#}}' desc: '' selected: false title: Answer - Test Related type: answer variables: [] height: 103 id: '1748767451380' position: x: 456.2543663027028 y: 610.4803953947226 positionAbsolute: x: 456.2543663027028 y: 610.4803953947226 selected: false sourcePosition: right targetPosition: left type: custom width: 242 zIndex: 0 - data: desc: '' instruction: 'Extract every Jira issue key mentioned in the user''s question, in the order they appear. A Jira issue key is an uppercase project prefix, a hyphen and a number, for example REST-266. Normalize other spellings to that format. This project''s prefix is REST, so "rest 266", "REST266", "issue 266" and "ticket 266" all become REST-266. If the question contains no issue key, return an empty list. ' model: completion_params: temperature: 0 mode: chat name: gpt-35-turbo-16k provider: langgenius/azure_openai/azure_openai parameters: - description: Every Jira issue key in the question, for example ["REST-266", "REST-265"]. name: issue_keys required: true type: array[string] query: - sys - query reasoning_mode: prompt selected: false title: Issue Keys Extractor type: parameter-extractor variables: [] vision: configs: detail: high variable_selector: - sys - files enabled: false height: 88 id: '1748767570289' position: x: -284.44013680327953 y: 211.5747334168887 positionAbsolute: x: -284.44013680327953 y: 211.5747334168887 selected: false sourcePosition: right targetPosition: left type: custom width: 242 zIndex: 0 - data: context: enabled: true variable_selector: - '1762727584347' - flat_context desc: '' model: completion_params: temperature: 0.3 mode: chat name: gpt-35-turbo-16k provider: langgenius/azure_openai/azure_openai prompt_template: - id: 41d8a4b2-979b-4166-a924-d9b9e7b724da role: system text: 'You are a software testing assistant helping engineers understand Jira issues in the Atlassian REST project. Answer only from the context below. Never use outside knowledge. The context holds the Jira issues retrieved for the keys the user asked about. Batch status: {{#1762727584347.batch_status#}} If the batch status starts with "Partial result" or "No requested Jira issues", begin your reply with that status and name the missing keys. Never describe an issue that is not in the context. For each issue found, in the order the user asked: - A heading with the issue key and summary - Type, status and assignee - One or two sentences on what it changes - What to test: two or three concrete ideas If the issues share a component, risk or theme, finish with a short "How they relate" section. Context: {{#context#}} User question: {{#sys.query#}} ' selected: false title: LLM - Multiple Issues type: llm variables: [] vision: enabled: false height: 88 id: '1748783029512' position: x: 1092.5667995574531 y: 211.5747334168887 positionAbsolute: x: 1092.5667995574531 y: 211.5747334168887 selected: false sourcePosition: right targetPosition: left type: custom width: 242 zIndex: 0 - data: answer: '{{#1748783029512.text#}}' desc: '' selected: false title: Answer - Multiple Issues type: answer variables: [] height: 103 id: '1748783041984' position: x: 1436.1121280862612 y: 211.5747334168887 positionAbsolute: x: 1436.1121280862612 y: 211.5747334168887 selected: false sourcePosition: right targetPosition: left type: custom width: 242 zIndex: 0 - data: desc: '' error_handle_mode: remove-abnormal-output height: 187 is_parallel: true iterator_input_type: array[string] iterator_selector: - '1748767570289' - issue_keys output_selector: - '1748783571565' - result output_type: array[object] parallel_nums: 10 selected: false start_node_id: 1748783526414start title: Iteration - Issues type: iteration width: 618 height: 187 id: '1748783526414' position: x: 89.0778811944802 y: 211.5747334168887 positionAbsolute: x: 89.0778811944802 y: 211.5747334168887 selected: false sourcePosition: right targetPosition: left type: custom width: 618 zIndex: 0 - data: desc: '' isInIteration: true selected: false title: '' type: iteration-start draggable: false height: 48 id: 1748783526414start parentId: '1748783526414' position: x: 24 y: 68 positionAbsolute: x: 113.0778811944802 y: 279.5747334168887 selectable: false selected: false sourcePosition: right targetPosition: left type: custom-iteration-start width: 44 zIndex: 1001 - data: dataset_ids: - W1plFG6wPRupvQB0T6LXXARA+GkduRuNFrUPXMZHtprMHDIwKWFC/Xl2XDIWt2EY desc: '' isInIteration: true isInLoop: false iteration_id: '1748783526414' metadata_filtering_conditions: conditions: - comparison_operator: is id: 2cc78fcc-d7fd-49e1-a66d-e71af312b069 name: issue_key value: '{{#1748783526414.item#}}' logical_operator: and metadata_filtering_mode: manual multiple_retrieval_config: reranking_enable: false reranking_mode: weighted_score reranking_model: model: '' provider: '' score_threshold: null top_k: 4 weights: keyword_setting: keyword_weight: 0 vector_setting: embedding_model_name: text-embedding-3-large embedding_provider_name: langgenius/azure_openai/azure_openai vector_weight: 1 weight_type: customized query_variable_selector: - '1748783526414' - item retrieval_mode: multiple selected: false title: KR - Each Issue type: knowledge-retrieval height: 90 id: '1748783571565' parentId: '1748783526414' position: x: 213.39028285937735 y: 65 positionAbsolute: x: 302.46816405385755 y: 276.5747334168887 selected: false sourcePosition: right targetPosition: left type: custom width: 242 zIndex: 1001 - data: dataset_ids: - W1plFG6wPRupvQB0T6LXXARA+GkduRuNFrUPXMZHtprMHDIwKWFC/Xl2XDIWt2EY desc: '' multiple_retrieval_config: reranking_enable: false reranking_mode: weighted_score reranking_model: model: '' provider: '' score_threshold: null top_k: 4 weights: keyword_setting: keyword_weight: 0 vector_setting: embedding_model_name: text-embedding-3-large embedding_provider_name: langgenius/azure_openai/azure_openai vector_weight: 1 weight_type: customized query_variable_selector: - sys - query retrieval_mode: multiple selected: false title: KR - General Project type: knowledge-retrieval height: 90 id: '1748784602400' position: x: -284.44013680327953 y: 464.40811576196575 positionAbsolute: x: -284.44013680327953 y: 464.40811576196575 selected: false sourcePosition: right targetPosition: left type: custom width: 242 zIndex: 0 - data: answer: '{{#1748784677999.text#}}' desc: '' selected: false title: Answer - General Project type: answer variables: [] height: 103 id: '1748784661836' position: x: 456.2543663027028 y: 464.40811576196575 positionAbsolute: x: 456.2543663027028 y: 464.40811576196575 selected: false sourcePosition: right targetPosition: left type: custom width: 242 zIndex: 0 - data: context: enabled: true variable_selector: - '1748784602400' - result desc: '' model: completion_params: temperature: 0.3 mode: chat name: gpt-35-turbo-16k provider: langgenius/azure_openai/azure_openai prompt_template: - id: b3c07274-69ce-4bb7-843b-e6db08c42df1 role: system text: 'You are a software testing assistant helping engineers understand the Atlassian REST project: its scope, modules, features and open work. Answer only from the context below. Never use outside knowledge. The context contains several Jira issues; combine them into one answer. If the context does not help answer the question, reply exactly: "I''m sorry, I can''t find relevant project information in the documentation to answer that." Your reply should: - Answer the question directly in the first sentence - Support it with the Jira issues in the context, citing their keys (for example REST-320) - Point out what matters for testing when it is relevant Context: {{#context#}} User question: {{#sys.query#}} ' selected: false structured_output_enabled: false title: LLM - General Project type: llm variables: [] vision: enabled: false height: 88 id: '1748784677999' position: x: 89.0778811944802 y: 464.40811576196575 positionAbsolute: x: 89.0778811944802 y: 464.40811576196575 selected: false sourcePosition: right targetPosition: left type: custom width: 242 zIndex: 0 - data: desc: '' instruction: 'Extract every Jira issue key mentioned in the user''s question, in the order they appear. A Jira issue key is an uppercase project prefix, a hyphen and a number, for example REST-266. Normalize other spellings to that format. This project''s prefix is REST, so "rest 266", "REST266", "issue 266" and "ticket 266" all become REST-266. If the question contains no issue key, return an empty list. ' model: completion_params: temperature: 0 mode: chat name: gpt-35-turbo-16k provider: langgenius/azure_openai/azure_openai parameters: - description: Every Jira issue key in the question, for example ["REST-266", "REST-265"]. name: issue_keys required: false type: array[string] query: - sys - query reasoning_mode: prompt selected: false title: Issue Keys Extractor (Tests) type: parameter-extractor variables: [] vision: configs: detail: high variable_selector: - sys - files enabled: false height: 88 id: '17487850509710' position: x: -284.44013680327953 y: 754.9250298061089 positionAbsolute: x: -284.44013680327953 y: 754.9250298061089 selected: false sourcePosition: right targetPosition: left type: custom width: 242 zIndex: 0 - data: answer: '{{#1748786653855.text#}}' desc: '' selected: false title: Answer - Polite Redirect type: answer variables: [] height: 103 id: '1748785149607' position: x: 89.0778811944802 y: 1011.2915512785755 positionAbsolute: x: 89.0778811944802 y: 1011.2915512785755 selected: false sourcePosition: right targetPosition: left type: custom width: 242 zIndex: 0 - data: desc: '' error_handle_mode: remove-abnormal-output height: 187 is_parallel: true iterator_input_type: array[string] iterator_selector: - '17487850509710' - issue_keys output_selector: - '1748785286399017487852864000' - result output_type: array[object] parallel_nums: 10 selected: false start_node_id: 1748785286399start title: Iteration - Tests type: iteration width: 618 height: 187 id: '17487852863990' position: x: 456.2543663027028 y: 743.564737158242 positionAbsolute: x: 456.2543663027028 y: 743.564737158242 selected: false sourcePosition: right targetPosition: left type: custom width: 618 zIndex: 0 - data: dataset_ids: - W1plFG6wPRupvQB0T6LXXARA+GkduRuNFrUPXMZHtprMHDIwKWFC/Xl2XDIWt2EY desc: '' isInIteration: true isInLoop: false iteration_id: '17487852863990' metadata_filtering_conditions: conditions: - comparison_operator: is id: 2cc78fcc-d7fd-49e1-a66d-e71af312b069 name: issue_key value: '{{#17487852863990.item#}}' logical_operator: and metadata_filtering_mode: manual multiple_retrieval_config: reranking_enable: false reranking_mode: weighted_score reranking_model: model: '' provider: '' score_threshold: null top_k: 4 weights: keyword_setting: keyword_weight: 0 vector_setting: embedding_model_name: text-embedding-3-large embedding_provider_name: langgenius/azure_openai/azure_openai vector_weight: 1 weight_type: customized query_variable_selector: - '17487852863990' - item retrieval_mode: multiple selected: false title: KR - Issue for Tests type: knowledge-retrieval height: 90 id: '1748785286399017487852864000' parentId: '17487852863990' position: x: 213.39028285937735 y: 65 positionAbsolute: x: 669.6446491620802 y: 808.564737158242 selected: false sourcePosition: right targetPosition: left type: custom width: 242 zIndex: 1001 - data: desc: '' isInIteration: true selected: false title: '' type: iteration-start draggable: false height: 48 id: 1748785286399start parentId: '17487852863990' position: x: 24 y: 68 positionAbsolute: x: 480.2543663027028 y: 811.564737158242 selectable: false selected: false sourcePosition: right targetPosition: left type: custom-iteration-start width: 44 zIndex: 1001 - data: cases: - case_id: 'true' conditions: - comparison_operator: not empty id: 1f00de1d-b3c7-4f97-8409-165cb5d04016 value: '' varType: array[string] variable_selector: - '17487850509710' - issue_keys id: 'true' logical_operator: and desc: '' selected: false title: Has Issue Keys? type: if-else height: 140 id: '1748785479296' position: x: 89.0778811944802 y: 754.9250298061089 positionAbsolute: x: 89.0778811944802 y: 754.9250298061089 selected: false sourcePosition: right targetPosition: left type: custom width: 242 zIndex: 0 - data: context: enabled: false variable_selector: [] desc: '' model: completion_params: temperature: 0.3 mode: chat name: gpt-35-turbo-16k provider: langgenius/azure_openai/azure_openai prompt_template: - id: f1e90d8d-abc0-478c-8604-f2805bc0c2f1 role: system text: 'You are an experienced test designer. The user describes a requirement in their own words, without a Jira issue key. Write Gherkin test scenarios for that requirement: - One gherkin code block with a Feature and three to five Scenarios - Cover the main flow, invalid input and at least one edge case - Keep every step concrete and testable; leave out steps that check nothing - If the requirement is too vague to test, ask one clarifying question instead of guessing User requirement: {{#sys.query#}} ' selected: false title: LLM - Gherkin from Requirement type: llm variables: [] vision: enabled: false height: 88 id: '1748785532843' position: x: 456.2543663027028 y: 986.9215676569963 positionAbsolute: x: 456.2543663027028 y: 986.9215676569963 selected: false sourcePosition: right targetPosition: left type: custom width: 242 zIndex: 0 - data: context: enabled: true variable_selector: - '1762728060345' - flat_context desc: '' model: completion_params: temperature: 0.3 mode: chat name: gpt-35-turbo-16k provider: langgenius/azure_openai/azure_openai prompt_template: - id: 94d687e6-c23d-43e3-977c-65cb9c4a8cc1 role: system text: 'You are an experienced test designer. Write Gherkin test scenarios for the Jira issues in the context below. Use only the behaviour described in the context. Do not invent endpoints, fields or error messages. Batch status: {{#1762728060345.batch_status#}} If the batch status starts with "Partial result" or "No requested Jira issues", begin your reply with that status and name the missing keys. Only write scenarios for issues that are in the context. For each issue: - A heading "### : " - One gherkin code block with a Feature and two or three Scenarios: the main behaviour, an edge case, and a regression check when relevant - Concrete, testable Given/When/Then steps Context: {{#context#}} User request: {{#sys.query#}} ' selected: false title: LLM - Gherkin from Issues type: llm variables: [] vision: enabled: false height: 88 id: '1748785557565' position: x: 1434.2543663027027 y: 743.564737158242 positionAbsolute: x: 1434.2543663027027 y: 743.564737158242 selected: false sourcePosition: right targetPosition: left type: custom width: 242 zIndex: 0 - data: answer: '{{#1748785557565.text#}}' desc: '' selected: false title: Answer - Gherkin from Issues type: answer variables: [] height: 103 id: '1748785836978' position: x: 1738.2543663027027 y: 743.564737158242 positionAbsolute: x: 1738.2543663027027 y: 743.564737158242 selected: false sourcePosition: right targetPosition: left type: custom width: 242 zIndex: 0 - data: answer: '{{#1748785532843.text#}}' desc: '' selected: false title: Answer - Gherkin from Requirement type: answer variables: [] height: 103 id: '1748786521492' position: x: 760.2543663027028 y: 986.9215676569963 positionAbsolute: x: 760.2543663027028 y: 986.9215676569963 selected: false sourcePosition: right targetPosition: left type: custom width: 242 zIndex: 0 - data: context: enabled: false variable_selector: [] desc: '' model: completion_params: temperature: 0.3 mode: chat name: gpt-35-turbo-16k provider: langgenius/azure_openai/azure_openai prompt_template: - id: 3c1b3adc-6c0e-4224-a8e1-18c5f6c6fb6d role: system text: 'You are Testus Patronus, an assistant that only helps with software testing for the Atlassian REST Jira project. The user''s message is outside that scope. Do not answer it, not even partially. Start your reply with exactly: "I''m sorry, I can''t help with that: it isn''t covered by the project documentation." Then add one friendly sentence: say what you can help with (Jira issues, project scope, test strategy and Gherkin test cases) and suggest one example question that doesn''t name a specific issue, such as "What is this project about?". User message: {{#sys.query#}} ' selected: false title: LLM - Polite Redirect type: llm variables: [] vision: enabled: false height: 88 id: '1748786653855' position: x: -284.44013680327953 y: 1011.2915512785755 positionAbsolute: x: -284.44013680327953 y: 1011.2915512785755 selected: false sourcePosition: right targetPosition: left type: custom width: 242 zIndex: 0 - data: code: "import re\n\n\ndef main(inputs: list, expected_keys: list) -> dict:\n\ \ flat_context = []\n for item in inputs:\n if isinstance(item,\ \ list):\n flat_context.extend(value for value in item if isinstance(value,\ \ dict))\n elif isinstance(item, dict):\n flat_context.append(item)\n\ \n expected = [str(key).upper() for key in expected_keys if key]\n \ \ retrieved_keys = set(re.findall(\n r\"(? dict:\n\ \ flat_context = []\n for item in inputs:\n if isinstance(item,\ \ list):\n flat_context.extend(value for value in item if isinstance(value,\ \ dict))\n elif isinstance(item, dict):\n flat_context.append(item)\n\ \n expected = [str(key).upper() for key in expected_keys if key]\n \ \ retrieved_keys = set(re.findall(\n r\"(?