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Feat: Use data pipeline to visualize the parsing configuration of the knowledge base (#10423)
### What problem does this PR solve? #9869 ### Type of change - [x] New Feature (non-breaking change which adds functionality) --------- Signed-off-by: dependabot[bot] <support@github.com> Signed-off-by: jinhai <haijin.chn@gmail.com> Signed-off-by: Jin Hai <haijin.chn@gmail.com> Co-authored-by: chanx <1243304602@qq.com> Co-authored-by: balibabu <cike8899@users.noreply.github.com> Co-authored-by: Lynn <lynn_inf@hotmail.com> Co-authored-by: 纷繁下的无奈 <zhileihuang@126.com> Co-authored-by: huangzl <huangzl@shinemo.com> Co-authored-by: writinwaters <93570324+writinwaters@users.noreply.github.com> Co-authored-by: Wilmer <33392318@qq.com> Co-authored-by: Adrian Weidig <adrianweidig@gmx.net> Co-authored-by: Zhichang Yu <yuzhichang@gmail.com> Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> Co-authored-by: Yongteng Lei <yongtengrey@outlook.com> Co-authored-by: Liu An <asiro@qq.com> Co-authored-by: buua436 <66937541+buua436@users.noreply.github.com> Co-authored-by: BadwomanCraZY <511528396@qq.com> Co-authored-by: cucusenok <31804608+cucusenok@users.noreply.github.com> Co-authored-by: Russell Valentine <russ@coldstonelabs.org> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Billy Bao <newyorkupperbay@gmail.com> Co-authored-by: Zhedong Cen <cenzhedong2@126.com> Co-authored-by: TensorNull <129579691+TensorNull@users.noreply.github.com> Co-authored-by: TensorNull <tensor.null@gmail.com> Co-authored-by: TeslaZY <TeslaZY@outlook.com> Co-authored-by: Ajay <160579663+aybanda@users.noreply.github.com> Co-authored-by: AB <aj@Ajays-MacBook-Air.local> Co-authored-by: 天海蒼灆 <huangaoqin@tecpie.com> Co-authored-by: He Wang <wanghechn@qq.com> Co-authored-by: Atsushi Hatakeyama <atu729@icloud.com> Co-authored-by: Jin Hai <haijin.chn@gmail.com> Co-authored-by: Mohamed Mathari <155896313+melmathari@users.noreply.github.com> Co-authored-by: Mohamed Mathari <nocodeventure@Mac-mini-van-Mohamed.fritz.box> Co-authored-by: Stephen Hu <stephenhu@seismic.com> Co-authored-by: Shaun Zhang <zhangwfjh@users.noreply.github.com> Co-authored-by: zhimeng123 <60221886+zhimeng123@users.noreply.github.com> Co-authored-by: mxc <mxc@example.com> Co-authored-by: Dominik Novotný <50611433+SgtMarmite@users.noreply.github.com> Co-authored-by: EVGENY M <168018528+rjohny55@users.noreply.github.com> Co-authored-by: mcoder6425 <mcoder64@gmail.com> Co-authored-by: lemsn <lemsn@msn.com> Co-authored-by: lemsn <lemsn@126.com> Co-authored-by: Adrian Gora <47756404+adagora@users.noreply.github.com> Co-authored-by: Womsxd <45663319+Womsxd@users.noreply.github.com> Co-authored-by: FatMii <39074672+FatMii@users.noreply.github.com>
This commit is contained in:
Kevin Hu
@ -102,16 +102,24 @@ export default {
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noMoreData: `That's all. Nothing more.`,
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},
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knowledgeDetails: {
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fileSize: 'File Size',
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fileType: 'File Type',
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uploadedBy: 'Uploaded by',
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notGenerated: 'Not generated',
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generatedOn: 'Generated on',
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subbarFiles: 'Files',
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generateKnowledgeGraph:
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'This will extract entities and relationships from all your documents in this dataset. The process may take a while to complete.',
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generateRaptor:
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'This will extract entities and relationships from all your documents in this dataset. The process may take a while to complete.',
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generate: 'Generate',
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raptor: 'Raptor',
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knowledgeGraph: 'Knowledge Graph',
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processingType: 'Processing Type',
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dataPipeline: 'Data Pipeline',
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operations: 'Operations',
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taskId: 'Task ID',
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duration: 'Duration',
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details: 'Details',
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status: 'Status',
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task: 'Task',
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startDate: 'Start Date',
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@ -123,7 +131,7 @@ export default {
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success: 'Success',
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failed: 'Failed',
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completed: 'Completed',
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processLog: 'Process Log',
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datasetLog: 'Dataset Log',
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created: 'Created',
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learnMore: 'Learn More',
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general: 'General',
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@ -138,12 +146,12 @@ export default {
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testing: 'Retrieval testing',
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files: 'files',
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configuration: 'Configuration',
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knowledgeGraph: 'Knowledge graph',
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knowledgeGraph: 'Knowledge Graph',
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name: 'Name',
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namePlaceholder: 'Please input name!',
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doc: 'Docs',
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datasetDescription:
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'😉 Please wait for your files to finish parsing before starting an AI-powered chat.',
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'Please wait for your files to finish parsing before starting an AI-powered chat.',
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addFile: 'Add file',
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searchFiles: 'Search your files',
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localFiles: 'Local files',
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@ -261,13 +269,30 @@ export default {
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reRankModelWaring: 'Re-rank model is very time consuming.',
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},
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knowledgeConfiguration: {
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deleteGenerateModalContent: `
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<p>Deleting the generated <strong class='text-text-primary'>{{type}}</strong> results
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will remove all derived entities and relationships from this dataset.
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Your original files will remain intact.<p>
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<br/>
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Do you want to continue?
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`,
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extractRaptor: 'Extract Raptor',
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extractKnowledgeGraph: 'Extract Knowledge Graph',
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filterPlaceholder: 'please input filter',
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fileFilterTip: '',
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fileFilter: 'File Filter',
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setDefaultTip: '',
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setDefault: 'Set as Default',
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eidtLinkDataPipeline: 'Edit Data Pipeline',
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linkPipelineSetTip: 'Manage data pipeline linkage with this dataset',
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default: 'Default',
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dataPipeline: 'Data Pipeline',
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linkDataPipeline: 'Link Data Pipeline',
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enableAutoGenerate: 'Enable Auto Generate',
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teamPlaceholder: 'Please select a team.',
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dataFlowPlaceholder: 'Please select a data flow.',
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dataFlowPlaceholder: 'Please select a pipeline.',
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buildItFromScratch: 'Build it from scratch',
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useRAPTORToEnhanceRetrieval: 'Use RAPTOR to Enhance Retrieval',
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extractKnowledgeGraph: 'Extract Knowledge Graph',
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dataFlow: 'Data Flow',
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dataFlow: 'Pipeline',
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parseType: 'Parse Type',
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manualSetup: 'Manual Setup',
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builtIn: 'Built-in',
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@ -395,7 +420,7 @@ export default {
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<p>In a Tag column, <b>comma</b> is used to separate tags.</p>
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<i>Lines of texts that fail to follow the above rules will be ignored.</i>
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`,
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useRaptor: 'Use RAPTOR to enhance retrieval',
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useRaptor: 'RAPTOR',
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useRaptorTip:
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'Enable RAPTOR for multi-hop question-answering tasks. See https://ragflow.io/docs/dev/enable_raptor for details.',
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prompt: 'Prompt',
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@ -441,7 +466,7 @@ This auto-tagging feature enhances retrieval by adding another layer of domain-s
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topnTags: 'Top-N Tags',
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tags: 'Tags',
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addTag: 'Add tag',
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useGraphRag: 'Extract knowledge graph',
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useGraphRag: 'Knowledge graph',
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useGraphRagTip:
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'Construct a knowledge graph over file chunks of the current knowledge base to enhance multi-hop question-answering involving nested logic. See https://ragflow.io/docs/dev/construct_knowledge_graph for details.',
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graphRagMethod: 'Method',
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@ -449,7 +474,7 @@ This auto-tagging feature enhances retrieval by adding another layer of domain-s
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General: Use prompts provided by github.com/microsoft/graphrag to extract entities and relationships`,
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resolution: 'Entity resolution',
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resolutionTip: `An entity deduplication switch. When enabled, the LLM will combine similar entities - e.g., '2025' and 'the year of 2025', or 'IT' and 'Information Technology' - to construct a more accurate graph`,
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community: 'Community reports generation',
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community: 'Community reports',
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communityTip:
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'In a knowledge graph, a community is a cluster of entities linked by relationships. You can have the LLM generate an abstract for each community, known as a community report. See here for more information: https://www.microsoft.com/en-us/research/blog/graphrag-improving-global-search-via-dynamic-community-selection/',
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theDocumentBeingParsedCannotBeDeleted:
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@ -1040,7 +1065,7 @@ This auto-tagging feature enhances retrieval by adding another layer of domain-s
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{input}
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The above is the content you need to summarize.`,
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createGraph: 'Create agent',
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createFromTemplates: 'Create from templates',
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createFromTemplates: 'Create from template',
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retrieval: 'Retrieval',
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generate: 'Generate',
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answer: 'Interact',
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@ -1560,6 +1585,13 @@ This delimiter is used to split the input text into several text pieces echo of
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sqlStatementTip:
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'Write your SQL query here. You can use variables, raw SQL, or mix both using variable syntax.',
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frameworkPrompts: 'Framework',
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release: 'Publish',
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createFromBlank: 'Create from blank',
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createFromTemplate: 'Create from template',
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importJsonFile: 'Import JSON file',
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ceateAgent: 'Agent flow',
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createPipeline: 'Data pipeline',
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chooseAgentType: 'Choose Agent Type',
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},
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llmTools: {
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bad_calculator: {
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@ -1583,6 +1615,9 @@ This delimiter is used to split the input text into several text pieces echo of
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serverType: 'Server Type',
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addMCP: 'Add MCP',
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editMCP: 'Edit MCP',
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toolsAvailable: 'tools available',
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mcpServers: 'MCP Servers',
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customizeTheListOfMcpServers: 'Customize the list of MCP servers',
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},
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search: {
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searchApps: 'Search Apps',
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@ -1630,14 +1665,138 @@ This delimiter is used to split the input text into several text pieces echo of
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parseSummaryTip: 'Parser：deepdoc',
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rerunFromCurrentStep: 'Rerun From Current Step',
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rerunFromCurrentStepTip: 'Changes detected. Click to re-run.',
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confirmRerun: 'Confirm Rerun Process',
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confirmRerunModalContent: `
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<p class="text-sm text-text-disabled font-medium mb-2">
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You are about to rerun the process starting from the <strong class="text-text-primary">{{step}}</strong> step.
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</p>
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<p class="text-sm mb-3 text-text-secondary">This will:</p>
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<ul class="list-disc list-inside space-y-1 text-sm text-text-secondary">
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<li>Overwrite existing results from the current step onwards</li>
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<li>Create a new log entry for tracking</li>
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<li>Previous steps will remain unchanged</li>
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</ul>`,
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changeStepModalTitle: 'Step Switch Warning',
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changeStepModalContent: `
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<p>You are currently editing the results of this stage.</p>
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<p>If you switch to a later stage, your changes will be lost. </p>
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<p>To keep them, please click Rerun to re-run the current stage.</p> `,
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changeStepModalConfirmText: 'Switch Anyway',
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changeStepModalCancelText: 'Cancel',
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unlinkPipelineModalTitle: 'Unlink data pipeline',
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unlinkPipelineModalContent: `
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<p>Once unlinked, this Dataset will no longer be connected to the current Data Pipeline.</p>
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<p>Files that are already being parsed will continue until completion</p>
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<p>Files that are not yet parsed will no longer be processed</p> <br/>
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<p>Are you sure you want to proceed?</p> `,
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unlinkPipelineModalConfirmText: 'Unlink',
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},
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dataflow: {
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parser: 'Parser',
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parserDescription: 'Parser',
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chunker: 'Chunker',
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chunkerDescription: 'Chunker',
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parserDescription:
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'Extracts raw text and structure from files for downstream processing.',
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tokenizer: 'Tokenizer',
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tokenizerDescription: 'Tokenizer',
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tokenizerDescription:
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'Transforms text into the required data structure (e.g., vector embeddings for Embedding Search) depending on the chosen search method.',
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splitter: 'Token Splitter',
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splitterDescription:
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'Split text into chunks by token length with optional delimiters and overlap.',
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hierarchicalMergerDescription:
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'Split documents into sections by title hierarchy with regex rules for finer control.',
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hierarchicalMerger: 'Title Splitter',
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extractor: 'Context Generator',
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extractorDescription:
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'Use an LLM to extract structured insights from document chunks—such as summaries, classifications, etc.',
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outputFormat: 'Output format',
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lang: 'Language',
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fileFormats: 'File formats',
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fields: 'Fields',
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addParser: 'Add Parser',
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hierarchy: 'Hierarchy',
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regularExpressions: 'Regular Expressions',
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overlappedPercent: 'Overlapped percent',
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searchMethod: 'Search method',
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begin: 'File',
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parserMethod: 'Parser method',
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systemPrompt: 'System Prompt',
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systemPromptPlaceholder:
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'Enter system prompt for image analysis, if empty the system default value will be used',
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exportJson: 'Export JSON',
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viewResult: 'View Result',
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running: 'Running',
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summary: 'Augmented Context',
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keywords: 'Keywords',
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questions: 'Questions',
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metadata: 'Metadata',
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fieldName: 'Result Destination',
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prompts: {
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system: {
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keywords: `Role
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You are a text analyzer.
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Task
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Extract the most important keywords/phrases of a given piece of text content.
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Requirements
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- Summarize the text content, and give the top 5 important keywords/phrases.
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- The keywords MUST be in the same language as the given piece of text content.
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- The keywords are delimited by ENGLISH COMMA.
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- Output keywords ONLY.`,
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questions: `Role
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You are a text analyzer.
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Task
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Propose 3 questions about a given piece of text content.
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Requirements
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- Understand and summarize the text content, and propose the top 3 important questions.
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- The questions SHOULD NOT have overlapping meanings.
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- The questions SHOULD cover the main content of the text as much as possible.
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- The questions MUST be in the same language as the given piece of text content.
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- One question per line.
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- Output questions ONLY.`,
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summary: `Act as a precise summarizer. Your task is to create a summary of the provided content that is both concise and faithful to the original.
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Key Instructions:
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1. Accuracy: Strictly base the summary on the information given. Do not introduce any new facts, conclusions, or interpretations that are not explicitly stated.
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2. Language: Write the summary in the same language as the source text.
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3. Objectivity: Present the key points without bias, preserving the original intent and tone of the content. Do not editorialize.
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4. Conciseness: Focus on the most important ideas, omitting minor details and fluff.`,
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metadata: `Extract important structured information from the given content. Output ONLY a valid JSON string with no additional text. If no important structured information is found, output an empty JSON object: {}.
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Important structured information may include: names, dates, locations, events, key facts, numerical data, or other extractable entities.`,
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},
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user: {
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keywords: `Text Content
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[Insert text here]`,
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questions: `Text Content
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[Insert text here]`,
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summary: `Text to Summarize:
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[Insert text here]`,
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metadata: `Content: [INSERT CONTENT HERE]`,
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},
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},
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cancel: 'Cancel',
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swicthPromptMessage:
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'The prompt word will change. Please confirm whether to abandon the existing prompt word?',
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tokenizerSearchMethodOptions: {
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full_text: 'Full-text',
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embedding: 'Embedding',
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},
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filenameEmbeddingWeight: 'Filename embedding weight',
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tokenizerFieldsOptions: {
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text: 'Text',
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keywords: 'Keywords',
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questions: 'Questions',
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summary: 'Augmented Context',
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},
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},
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datasetOverview: {
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downloadTip: 'Files being downloaded from data sources. ',
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processingTip: 'Files being processed by data flows.',
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totalFiles: 'Total Files',
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downloading: 'Downloading',
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processing: 'Processing',
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},
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},
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};
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