Per-document cluster assignments are not on this object. They are documents in
the output collections listed in output_collection_ids: a member document
(is_centroid=false) carries cluster_id, cluster_label and source_document_id.
With enrich_source_collection=true they are fields on your source documents.
Mixpeek API key, sent as Authorization: Bearer mxp_sk_.... Create one in Studio under Settings → API Keys, or with an admin key via POST /v1/organizations/users/{user_email}/api-keys. A missing, malformed, unknown, revoked, inactive or expired key returns 401; a key without the permission, scope or plan an operation needs returns 403.
Namespace id (ns_...), not the namespace name. This scopes the request rather than authenticating it, and it is required on every operation marked x-mixpeek-namespace-scoped.
Set on a composite cluster: the clusters whose run centroids it groups. Executing it without a mode runs mode 'composite' over the newest completed run of each source cluster, one per partition.
Automatically learned feature weights (when multi_feature_strategy='weighted'). Keys are feature URIs, values are learned weights. Only populated after clustering execution completes.
Clustering quality score from weight learning (e.g., silhouette score). Only populated when multi_feature_strategy='weighted' and weights were learned.
Stored post-HDBSCAN face-identity merge config. Populated from vector_config.face_cluster_merge at cluster creation and replayed into ClusteringConfig on every execute. Only applies to vector clustering.
Stored per-execution document cap. Populated from vector_config.sample_size at cluster creation and replayed into ClusteringConfig on every POST /v1/clusters/{id}/execute so re-runs stay consistent with the original config. When None, the export is uncapped (bounded by the engine's 100,000 safety limit). Only applies to vector clustering.
Stored layout-stability mode from vector_config.layout_stability. Replayed into ClusteringConfig on every execute. When unset, executions default to 'align' (keep the map stable across runs via post-hoc registration).
Collection IDs where cluster documents are stored. Each holds one centroid document (is_centroid=true) per cluster and one member document (is_centroid=false) per clustered source document. A member carries cluster_id, cluster_label and source_document_id, the ID of the document you clustered, so filter on source_document_id to find a source document's cluster. For single output: list with one collection ID. For per-feature output: list with one collection ID per feature. With enrich_source_collection=true no output collection is created: this lists the source collections, and each source document carries its assignment as fields.
Clustering quality metrics (silhouette_score, davies_bouldin_score, calinski_harabasz_score, etc.). Open diagnostic bag like ClusteringResult.metrics: mostly numeric, but the three-state degenerate flag (False | reason string) and its prose degenerate_detail are non-numeric, so the value type is Any. A float-only type 400'd every GET on a cluster whose run flagged a degenerate result ('s read-path sibling: the write side persisted the string, this read side then rejected it).
List of errors encountered during LLM labeling (if any). Stored in MongoDB cluster metadata only, NOT in vector store cluster documents. Used to track LLM failures while allowing fallback labels to work.
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Replayed into ClusteringConfig on every execute.","uniqueKey":"preprocessing_steps.oneOf.1","typeLabel":"null"}],"uniqueKey":"preprocessing_steps","typeLabel":"Preprocessing Steps · object[] | null"},"hierarchical_vector":{"oneOf":[{"type":"boolean","title":"Hierarchical Vector","description":"Whether recursive sub-clustering is enabled for vector clustering.","uniqueKey":"hierarchical_vector.oneOf.0","typeLabel":"boolean"},{"type":"null","title":"Hierarchical Vector","description":"Whether recursive sub-clustering is enabled for vector clustering.","uniqueKey":"hierarchical_vector.oneOf.1","typeLabel":"null"}],"uniqueKey":"hierarchical_vector","typeLabel":"boolean | null"},"max_hierarchy_depth":{"oneOf":[{"type":"integer","title":"Max Hierarchy Depth","description":"Maximum recursion depth for hierarchical sub-clustering.","uniqueKey":"max_hierarchy_depth.oneOf.0","typeLabel":"integer"},{"type":"null","title":"Max Hierarchy Depth","description":"Maximum recursion depth for hierarchical sub-clustering.","uniqueKey":"max_hierarchy_depth.oneOf.1","typeLabel":"null"}],"uniqueKey":"max_hierarchy_depth","typeLabel":"integer | null"},"vis_n_components":{"oneOf":[{"type":"integer","title":"Vis N Components","description":"Stored visualization dimensionality (2D or 3D). Replayed into ClusteringConfig on every execute as the default.","enum":[2,3],"uniqueKey":"vis_n_components.oneOf.0","typeLabel":"enum\u003cinteger>"},{"type":"null","title":"Vis N Components","description":"Stored visualization dimensionality (2D or 3D). Replayed into ClusteringConfig on every execute as the default.","uniqueKey":"vis_n_components.oneOf.1","typeLabel":"null"}],"uniqueKey":"vis_n_components","typeLabel":"enum\u003cinteger> | null"},"layout_stability":{"oneOf":[{"type":"string","title":"Layout Stability","description":"Stored layout-stability mode from vector_config.layout_stability. Replayed into ClusteringConfig on every execute. When unset, executions default to 'align' (keep the map stable across runs via post-hoc registration).","enum":["none","transform","align"],"uniqueKey":"layout_stability.oneOf.0","typeLabel":"enum\u003cstring>"},{"type":"null","title":"Layout Stability","description":"Stored layout-stability mode from vector_config.layout_stability. Replayed into ClusteringConfig on every execute. When unset, executions default to 'align' (keep the map stable across runs via post-hoc registration).","uniqueKey":"layout_stability.oneOf.1","typeLabel":"null"}],"uniqueKey":"layout_stability","typeLabel":"enum\u003cstring> | null"},"clustered_attributes":{"oneOf":[{"type":"array","items":{"type":"string","uniqueKey":"clustered_attributes.oneOf.0.items.[INDEX]","typeLabel":"string"},"title":"Clustered Attributes","description":"Attribute field names that were clustered. Only for attribute clustering.","uniqueKey":"clustered_attributes.oneOf.0","typeLabel":"string[]"},{"type":"null","title":"Clustered Attributes","description":"Attribute field names that were clustered. Only for attribute clustering.","uniqueKey":"clustered_attributes.oneOf.1","typeLabel":"null"}],"uniqueKey":"clustered_attributes","typeLabel":"string[] | null"},"hierarchical_grouping":{"oneOf":[{"type":"boolean","title":"Hierarchical Grouping","description":"Whether hierarchical clustering was used. Only for attribute clustering.","uniqueKey":"hierarchical_grouping.oneOf.0","typeLabel":"boolean"},{"type":"null","title":"Hierarchical Grouping","description":"Whether hierarchical clustering was used. Only for attribute clustering.","uniqueKey":"hierarchical_grouping.oneOf.1","typeLabel":"null"}],"uniqueKey":"hierarchical_grouping","typeLabel":"boolean | null"},"aggregation_method":{"oneOf":[{"type":"string","title":"Aggregation Method","description":"Method for aggregating attributes (most_frequent/first/last). Only for attribute clustering.","uniqueKey":"aggregation_method.oneOf.0","typeLabel":"string"},{"type":"null","title":"Aggregation Method","description":"Method for aggregating attributes (most_frequent/first/last). Only for attribute clustering.","uniqueKey":"aggregation_method.oneOf.1","typeLabel":"null"}],"uniqueKey":"aggregation_method","typeLabel":"string | null"},"output_collection_ids":{"items":{"type":"string","uniqueKey":"output_collection_ids.items.[INDEX]","typeLabel":"string"},"type":"array","title":"Output Collection Ids","description":"Collection IDs where cluster documents are stored. Each holds one centroid document (is_centroid=true) per cluster and one member document (is_centroid=false) per clustered source document. A member carries cluster_id, cluster_label and source_document_id, the ID of the document you clustered, so filter on source_document_id to find a source document's cluster. For single output: list with one collection ID. For per-feature output: list with one collection ID per feature. With enrich_source_collection=true no output collection is created: this lists the source collections, and each source document carries its assignment as fields.","uniqueKey":"output_collection_ids","typeLabel":"string[]"},"output_collection_names":{"items":{"type":"string","uniqueKey":"output_collection_names.items.[INDEX]","typeLabel":"string"},"type":"array","title":"Output Collection Names","description":"Names of output collections. Corresponds to output_collection_ids.","uniqueKey":"output_collection_names","typeLabel":"string[]"},"algorithm":{"oneOf":[{"type":"string","title":"Algorithm","description":"Clustering algorithm used (hdbscan, kmeans, attribute_based, etc.)","uniqueKey":"algorithm.oneOf.0","typeLabel":"string"},{"type":"null","title":"Algorithm","description":"Clustering algorithm used (hdbscan, kmeans, attribute_based, etc.)","uniqueKey":"algorithm.oneOf.1","typeLabel":"null"}],"uniqueKey":"algorithm","typeLabel":"string | null"},"algorithm_params":{"oneOf":[{"type":"object","title":"Algorithm Params","description":"Algorithm-specific parameters (not used for attribute_based)","additionalProperties":true,"uniqueKey":"algorithm_params.oneOf.0","typeLabel":"Algorithm Params · object"},{"type":"null","title":"Algorithm Params","description":"Algorithm-specific parameters (not used for attribute_based)","uniqueKey":"algorithm_params.oneOf.1","typeLabel":"null"}],"uniqueKey":"algorithm_params","typeLabel":"Algorithm Params · object | null"},"enrich_source":{"type":"boolean","title":"Enrich Source","description":"Whether source documents were enriched with cluster_id","default":false,"uniqueKey":"enrich_source","typeLabel":"boolean"},"source_enrichment_config":{"oneOf":[{"type":"object","title":"SourceEnrichmentConfig","description":"Configuration for source enrichment (if enrich_source=True)","example":{"field_mappings":[{"source_field":"cluster_id","target_field":"category_id"},{"source_field":"cluster_label","target_field":"category_name"},{"source_field":"distance_to_centroid","target_field":"category_confidence"}]},"properties":{"field_mappings":{"items":{"properties":{"source_field":{"type":"string","title":"Source Field","description":"Field from cluster results to include. Available fields: cluster_id, cluster_label, distance_to_centroid, member_count, keywords, x, y, z (visualization coords), metadata.*. cluster_id is the run's cluster index (cl_0, cl_1, ...); an execution with a filters override writes it qualified by that partition (\u003cpartition>:cl_0), so partitions of one cluster never share a value.","isRequired":true,"uniqueKey":"source_enrichment_config.oneOf.0.field_mappings.items.[INDEX].source_field","typeLabel":"string"},"target_field":{"type":"string","title":"Target Field","description":"Target field name in enriched document. Example: 'category_id' for cluster_id, 'product_category' for cluster_label","isRequired":true,"uniqueKey":"source_enrichment_config.oneOf.0.field_mappings.items.[INDEX].target_field","typeLabel":"string"}},"type":"object","required":["source_field","target_field"],"title":"EnrichmentFieldMapping","description":"Maps a cluster result field to a document enrichment field.\n\nSimilar to InputMapping pattern used throughout Mixpeek.","uniqueKey":"source_enrichment_config.oneOf.0.field_mappings.items.[INDEX]","typeLabel":"EnrichmentFieldMapping · object"},"type":"array","title":"Field Mappings","description":"List of field mappings from cluster results to document fields. Default includes cluster_id and cluster_label. Can include: distance_to_centroid, member_count, keywords, visualization coords (x, y, z), etc.","uniqueKey":"source_enrichment_config.oneOf.0.field_mappings","typeLabel":"EnrichmentFieldMapping · object[]"}},"uniqueKey":"source_enrichment_config.oneOf.0","typeLabel":"SourceEnrichmentConfig · object"},{"type":"null","description":"Configuration for source enrichment (if enrich_source=True)","uniqueKey":"source_enrichment_config.oneOf.1","typeLabel":"null"}],"uniqueKey":"source_enrichment_config","typeLabel":"SourceEnrichmentConfig · object | null"},"llm_labeling":{"oneOf":[{"type":"object","title":"LLMLabeling","description":"Configuration for LLM-based cluster labeling (applies to all cluster types)","example":{"enabled":true,"include_keywords":true,"include_summary":true,"labeling_inputs":{"input_mappings":[{"input_key":"title","path":"title","source_type":"payload"},{"input_key":"description","path":"description","source_type":"payload"},{"input_key":"text","path":"text","source_type":"payload"}]},"model_name":"gpt-4o-mini-2024-07-18","provider":"openai"},"properties":{"enabled":{"type":"boolean","title":"Enabled","description":"Whether to generate labels for clusters using LLM. When enabled, clusters will have semantic labels like 'High-Performance Laptops' instead of generic labels like 'Cluster 0'.","default":false,"uniqueKey":"llm_labeling.oneOf.0.enabled","typeLabel":"boolean"},"labeling_inputs":{"oneOf":[{"type":"object","title":"LLMLabelingInput","description":"Input configuration for LLM labeling. Supports flexible input mappings for multimodal inputs (text, images, videos, audio). Use input_mappings for advanced multimodal labeling with providers like Gemini. If not provided (null/undefined), the full document payload is serialized as JSON and sent to the LLM — WARNING: in practice this produces schema-metadata labels (e.g. 'Mid-Timeline Video Events' with keywords like ['start_time', 'end_time']) because the LLM describes whatever field names it sees. For meaningful labels, set input_mappings that point at semantic fields (title, description, thumbnail_url, document_blobs[].url for image/video content) rather than relying on the default.","required":["input_mappings"],"properties":{"input_mappings":{"items":{"properties":{"input_key":{"type":"string","title":"Input Key","description":"Key used in the constructed inputs payload.","isRequired":true,"uniqueKey":"llm_labeling.oneOf.0.labeling_inputs.oneOf.0.input_mappings.items.[INDEX].input_key","typeLabel":"string"},"source_type":{"oneOf":[{"type":"string","title":"InputSourceType","description":"Source of the value (payload, literal, vector).","enum":["payload","literal","vector","blob"],"uniqueKey":"llm_labeling.oneOf.0.labeling_inputs.oneOf.0.input_mappings.items.[INDEX].source_type.oneOf.0","typeLabel":"enum\u003cstring>"},{"type":"null","description":"Source of the value (payload, literal, vector).","uniqueKey":"llm_labeling.oneOf.0.labeling_inputs.oneOf.0.input_mappings.items.[INDEX].source_type.oneOf.1","typeLabel":"null"}],"uniqueKey":"llm_labeling.oneOf.0.labeling_inputs.oneOf.0.input_mappings.items.[INDEX].source_type","typeLabel":"enum\u003cstring> | null"},"path":{"oneOf":[{"type":"string","title":"Path","description":"Dot-notation path when source_type is PAYLOAD or VECTOR. PAYLOAD paths resolve from the document's ROOT fields — each document/source_document dict IS the payload (e.g. path 'text' reads {'text': ...}); a {'payload': {...}} envelope is also accepted as a fallback.","uniqueKey":"llm_labeling.oneOf.0.labeling_inputs.oneOf.0.input_mappings.items.[INDEX].path.oneOf.0","typeLabel":"string"},{"type":"null","title":"Path","description":"Dot-notation path when source_type is PAYLOAD or VECTOR. PAYLOAD paths resolve from the document's ROOT fields — each document/source_document dict IS the payload (e.g. path 'text' reads {'text': ...}); a {'payload': {...}} envelope is also accepted as a fallback.","uniqueKey":"llm_labeling.oneOf.0.labeling_inputs.oneOf.0.input_mappings.items.[INDEX].path.oneOf.1","typeLabel":"null"}],"uniqueKey":"llm_labeling.oneOf.0.labeling_inputs.oneOf.0.input_mappings.items.[INDEX].path","typeLabel":"string | null"},"override":{"oneOf":[{"title":"Override","description":"Static value used when source_type is LITERAL. Overrides any path.","uniqueKey":"llm_labeling.oneOf.0.labeling_inputs.oneOf.0.input_mappings.items.[INDEX].override.oneOf.0","typeLabel":"any"},{"type":"null","title":"Override","description":"Static value used when source_type is LITERAL. Overrides any path.","uniqueKey":"llm_labeling.oneOf.0.labeling_inputs.oneOf.0.input_mappings.items.[INDEX].override.oneOf.1","typeLabel":"null"}],"uniqueKey":"llm_labeling.oneOf.0.labeling_inputs.oneOf.0.input_mappings.items.[INDEX].override","typeLabel":"any | null"}},"type":"object","required":["input_key"],"title":"InputMapping","description":"Declarative mapping for building inputs from various sources.\n\n- input_key: The key used in the constructed inputs payload\n- source_type: Where to fetch the value (payload, literal, vector)\n- path: Dot-notation path when source_type is PAYLOAD or VECTOR\n- override: Static value when source_type is LITERAL","examples":[{"input_key":"query_text","path":"content.title","source_type":"payload"},{"input_key":"lang","override":"en","source_type":"literal"},{"input_key":"image_vector","path":"features.clip_vit_l_14","source_type":"vector"}],"uniqueKey":"llm_labeling.oneOf.0.labeling_inputs.oneOf.0.input_mappings.items.[INDEX]","typeLabel":"InputMapping · object"},"type":"array","minItems":1,"title":"Input Mappings","description":"Flexible input mappings for constructing LLM context. Supports multimodal inputs (text, image_url, video_url, audio_url). Each mapping specifies how to extract data from document payloads. At least one input mapping is required.","isRequired":true,"uniqueKey":"llm_labeling.oneOf.0.labeling_inputs.oneOf.0.input_mappings","typeLabel":"InputMapping · object[]"}},"uniqueKey":"llm_labeling.oneOf.0.labeling_inputs.oneOf.0","typeLabel":"LLMLabelingInput · object"},{"type":"null","description":"Input configuration for LLM labeling. Supports flexible input mappings for multimodal inputs (text, images, videos, audio). Use input_mappings for advanced multimodal labeling with providers like Gemini. If not provided (null/undefined), the full document payload is serialized as JSON and sent to the LLM — WARNING: in practice this produces schema-metadata labels (e.g. 'Mid-Timeline Video Events' with keywords like ['start_time', 'end_time']) because the LLM describes whatever field names it sees. For meaningful labels, set input_mappings that point at semantic fields (title, description, thumbnail_url, document_blobs[].url for image/video content) rather than relying on the default.","uniqueKey":"llm_labeling.oneOf.0.labeling_inputs.oneOf.1","typeLabel":"null"}],"uniqueKey":"llm_labeling.oneOf.0.labeling_inputs","typeLabel":"LLMLabelingInput · object | null"},"provider":{"oneOf":[{"type":"string","title":"LLMProvider","description":"LLM provider to use for labeling. Supported providers:\n- openai: GPT models (GPT-4o, GPT-4o-mini, GPT-4.1, O3-mini)\n- google: Gemini models (Gemini 2.5 Flash, Gemini 1.5 Flash)\n- anthropic: Claude models (Claude 3.5 Sonnet, Claude 3.5 Haiku)\n- typesafe: Jev (jev-latest). Picks each cluster's label from candidate_labels with a calibrated probability instead of writing one. Text only: it reads the representative documents' text fields.\n\nIf not specified, automatically inferred from model_name.","enum":["openai","google","anthropic","typesafe"],"example":"openai","uniqueKey":"llm_labeling.oneOf.0.provider.oneOf.0","typeLabel":"enum\u003cstring>"},{"type":"null","description":"LLM provider to use for labeling. Supported providers:\n- openai: GPT models (GPT-4o, GPT-4o-mini, GPT-4.1, O3-mini)\n- google: Gemini models (Gemini 2.5 Flash, Gemini 1.5 Flash)\n- anthropic: Claude models (Claude 3.5 Sonnet, Claude 3.5 Haiku)\n- typesafe: Jev (jev-latest). Picks each cluster's label from candidate_labels with a calibrated probability instead of writing one. Text only: it reads the representative documents' text fields.\n\nIf not specified, automatically inferred from model_name.","example":"openai","uniqueKey":"llm_labeling.oneOf.0.provider.oneOf.1","typeLabel":"null"}],"uniqueKey":"llm_labeling.oneOf.0.provider","typeLabel":"enum\u003cstring> | null"},"model_name":{"oneOf":[{"type":"string","title":"OpenAIModel","description":"OpenAI model identifiers for LLM generation.\n\nModels listed in order of capability and cost (highest to lowest).\nAll models support vision (images) except O3-mini.\n\nValues:\n GPT_4O: Latest GPT-4 Omni model (2024-08-06)\n - Use for: Production, highest quality generation\n - Context: 128K tokens\n - Vision: Yes\n - Cost: $2.50/1M input, $10/1M output\n - Performance: 200-500ms per request\n - When to use: Need best quality, willing to pay premium\n\n GPT_41: GPT-4.1 (2025-04-14)\n - Use for: Future-proofed pipelines\n - Context: 128K tokens\n - Vision: Yes\n - Cost: TBD (expected similar to GPT-4o)\n - When to use: Want latest model features\n\n GPT_4O_MINI: Smaller, faster GPT-4 Omni (2024-07-18)\n - Use for: High-volume, cost-sensitive workloads\n - Context: 128K tokens\n - Vision: Yes\n - Cost: $0.15/1M input, $0.60/1M output\n - Performance: 100-200ms per request\n - When to use: Good balance of quality and cost\n\n GPT_41_MINI: Smaller GPT-4.1 (2025-04-14)\n - Use for: Future cost-optimized pipelines\n - Context: 128K tokens\n - Vision: Yes\n - Cost: TBD (expected similar to GPT-4o-mini)\n - When to use: Want latest features at lower cost\n\n O3_MINI: Reasoning-optimized model (2025-01-31)\n - Use for: Complex reasoning, math, code\n - Context: 200K tokens\n - Vision: No\n - Cost: TBD\n - When to use: Need advanced reasoning capabilities\n\nExamples:\n - Use GPT_4O for caption generation with images (best quality)\n - Use GPT_4O_MINI for high-volume video scene summarization (cost-effective)\n - Use O3_MINI for complex entity extraction requiring reasoning","enum":["gpt-4o-2024-08-06","gpt-4o-mini-2024-07-18","gpt-4.1-2025-04-14","gpt-4.1-mini-2025-04-14","o3-mini-2025-01-31"],"example":"gemini-3.5-flash-lite","uniqueKey":"llm_labeling.oneOf.0.model_name.oneOf.0","typeLabel":"enum\u003cstring>"},{"type":"string","title":"GoogleModel","description":"Google Gemini model identifiers for LLM generation.\n\nGemini models excel at multimodal understanding with best-in-class video support.\nAll models support text, images, video, audio, and PDFs.\n\nValues:\n GEMINI_3_5_FLASH_LITE: Gemini 3.5 Flash Lite model (recommended, the default)\n - Use for: Fastest generation, cost-effective multimodal\n - Context: 1M tokens\n - Multimodal: Text, images, video, audio, PDFs\n - When to use: Default choice for all Gemini use cases\n\n GEMINI_2_5_FLASH_LITE: Gemini 2.5 Flash Lite model\n - Kept so stored configs that name it still validate. The Gemini\n developer API no longer serves it to new keys; Vertex does.\n\n GEMINI_2_5_PRO: Gemini 2.5 Pro model\n - Use for: Higher quality reasoning, complex tasks\n - Context: 1M tokens\n\n GEMINI_2_5_FLASH: Gemini 2.5 Flash model\n - Kept for backward compatibility.\n\n GEMINI_3_1_FLASH_LITE: Gemini 3.1 Flash-Lite model\n - Use for: Fast, low-cost generation on the 3.1 generation","enum":["gemini-2.5-flash-lite","gemini-2.5-flash","gemini-2.5-pro","gemini-3.1-flash-lite","gemini-3.5-flash-lite"],"example":"gemini-3.5-flash-lite","uniqueKey":"llm_labeling.oneOf.0.model_name.oneOf.1","typeLabel":"enum\u003cstring>"},{"type":"string","title":"AnthropicModel","description":"Anthropic Claude model identifiers for LLM generation.\n\nClaude models excel at long context, complex reasoning, and safety.\nAll models support text and images.\n\nValues:\n CLAUDE_3_5_SONNET: Most capable Claude model\n - Use for: Complex reasoning, long documents, safety-critical\n - Context: 200K tokens\n - Vision: Yes\n - Cost: $3/1M input, $15/1M output\n - Performance: 300-800ms per request\n - When to use: Need best reasoning, safety, or long context\n\n CLAUDE_3_5_HAIKU: Fast, cost-effective Claude model\n - Use for: High-volume, quick summaries\n - Context: 200K tokens\n - Vision: Yes\n - Cost: $0.25/1M input, $1.25/1M output\n - Performance: 100-300ms per request\n - When to use: Good balance of quality and cost\n\nExamples:\n - Use CLAUDE_3_5_SONNET for complex entity extraction from contracts (best reasoning)\n - Use CLAUDE_3_5_HAIKU for high-volume content moderation (cost-effective)","enum":["claude-sonnet-4-5-20250929","claude-haiku-4-5-20251001","claude-3-5-sonnet-20241022","claude-3-5-haiku-20241022"],"example":"gemini-3.5-flash-lite","uniqueKey":"llm_labeling.oneOf.0.model_name.oneOf.2","typeLabel":"enum\u003cstring>"},{"type":"string","title":"TypeSafeModel","description":"TypeSafe AI Jev model identifiers.\n\nJev answers typed questions (choice, score, yes/no) with calibrated\nprobabilities. It reads text only and does not generate text, so it fits\ndecision steps: classification, filtering, relevance scoring, and choosing\na label or cluster branch from a fixed set.\n\nValues:\n JEV_LATEST: Most recent stable Jev release (moves on new releases)\n JEV_PREVIEW: Most recent Jev release, stable or not\n JEV_1_13: Pinned Jev 1.13; use when you tuned thresholds on it","enum":["jev-latest","jev-preview","jev-1.13.0"],"example":"gemini-3.5-flash-lite","uniqueKey":"llm_labeling.oneOf.0.model_name.oneOf.3","typeLabel":"enum\u003cstring>"},{"type":"null","title":"Model Name","description":"REQUIRED when enabled=True. Specific LLM model to use for cluster labeling. All models are defined as enums for type safety.\n\nOpenAI Models (provider='openai'):\n- gpt-4o-2024-08-06: Highest quality, best for production ($2.50/$10 per 1M tokens)\n- gpt-4o-mini-2024-07-18: Cost-effective, recommended for most use cases ($0.15/$0.60 per 1M tokens)\n- gpt-4.1-2025-04-14: Latest model, future-proofed\n- gpt-4.1-mini-2025-04-14: Latest cost-optimized model\n- o3-mini-2025-01-31: Advanced reasoning, best for complex clustering\n\nGoogle Models (provider='google'):\n- gemini-3.5-flash-lite: Fastest, latest multimodal model, recommended ($0.30/$2.50 per 1M tokens)\n\nAnthropic Models (provider='anthropic'):\n- claude-3-5-sonnet-20241022: Best reasoning, 200K context ($3/$15 per 1M tokens)\n- claude-3-5-haiku-20241022: Fast, cost-effective ($0.25/$1.25 per 1M tokens)\n\nRecommendation:\n- Use gemini-3.5-flash-lite (DEFAULT) - multimodal support\n- Use gpt-4o-mini-2024-07-18 for OpenAI compatibility\n- Use gpt-4o-2024-08-06 for highest quality when cost is not a concern","example":"gemini-3.5-flash-lite","uniqueKey":"llm_labeling.oneOf.0.model_name.oneOf.4","typeLabel":"null"}],"uniqueKey":"llm_labeling.oneOf.0.model_name","typeLabel":"enum\u003cstring> · enum\u003cstring> · enum\u003cstring> · enum\u003cstring> · null"},"include_summary":{"type":"boolean","title":"Include Summary","description":"Whether to generate cluster summaries","default":true,"uniqueKey":"llm_labeling.oneOf.0.include_summary","typeLabel":"boolean"},"include_keywords":{"type":"boolean","title":"Include Keywords","description":"Whether to extract keywords for clusters","default":true,"uniqueKey":"llm_labeling.oneOf.0.include_keywords","typeLabel":"boolean"},"max_samples_per_cluster":{"oneOf":[{"type":"integer","title":"Max Samples Per Cluster","description":"Maximum representative documents to send to LLM per cluster for semantic analysis. When null (default), automatically scales based on cluster size and spread — smaller/tighter clusters get fewer samples, larger/sparser clusters get more (range 3-20). Set explicitly to override with a fixed value.","maximum":20,"minimum":1,"uniqueKey":"llm_labeling.oneOf.0.max_samples_per_cluster.oneOf.0","typeLabel":"integer"},{"type":"null","title":"Max Samples Per Cluster","description":"Maximum representative documents to send to LLM per cluster for semantic analysis. When null (default), automatically scales based on cluster size and spread — smaller/tighter clusters get fewer samples, larger/sparser clusters get more (range 3-20). Set explicitly to override with a fixed value.","uniqueKey":"llm_labeling.oneOf.0.max_samples_per_cluster.oneOf.1","typeLabel":"null"}],"uniqueKey":"llm_labeling.oneOf.0.max_samples_per_cluster","typeLabel":"integer | null"},"sample_text_max_length":{"type":"integer","maximum":500,"minimum":50,"title":"Sample Text Max Length","description":"Maximum characters per document sample text","default":200,"uniqueKey":"llm_labeling.oneOf.0.sample_text_max_length","typeLabel":"integer"},"sample_selection_strategy":{"type":"string","enum":["nearest","representative"],"title":"Sample Selection Strategy","description":"How representative documents are chosen for the labeling LLM. 'nearest' (default): the N members closest to the cluster centroid — maximally prototypical, but adjacent clusters can yield near-identical sample sets and therefore near-identical labels. 'representative': a mixed panel of ~40% nearest-to-centroid, ~40% diversity picks (farthest-point coverage of the cluster's extent), and ~20% high-density examples — differentiates similar clusters at the same sample count and LLM cost.","default":"nearest","uniqueKey":"llm_labeling.oneOf.0.sample_selection_strategy","typeLabel":"enum\u003cstring>"},"use_embedding_dedup":{"type":"boolean","title":"Use Embedding Dedup","description":"Enable embedding-based label deduplication to prevent near-duplicate labels (requires sentence-transformers)","default":true,"uniqueKey":"llm_labeling.oneOf.0.use_embedding_dedup","typeLabel":"boolean"},"embedding_similarity_threshold":{"type":"number","maximum":1,"minimum":0.5,"title":"Embedding Similarity Threshold","description":"Cosine similarity threshold for duplicate label detection (labels above this are considered duplicates)","default":0.8,"uniqueKey":"llm_labeling.oneOf.0.embedding_similarity_threshold","typeLabel":"number"},"cache_ttl_seconds":{"type":"integer","maximum":2592000,"minimum":0,"title":"Cache Ttl Seconds","description":"Time-to-live for cached labels in seconds. Labels for clusters with identical representative documents will be reused within this TTL window, reducing LLM API costs. Default: 604800 (7 days). Set to 0 to disable caching.","default":604800,"uniqueKey":"llm_labeling.oneOf.0.cache_ttl_seconds","typeLabel":"integer"},"labeling_context":{"oneOf":[{"type":"string","title":"Labeling Context","description":"OPTIONAL. Freeform domain context about the data being clustered, injected into the labeling prompt as a clearly-delimited 'Domain context provided by the user' block. Unlike custom_prompt, this does NOT replace the default prompt — it grounds the default labeler so labels use the right domain vocabulary. Example: 'These are scenes from pharmaceutical TV ads' turns generic labels like 'People Talking Outdoors' into 'Patient Testimonial Scenes'. Max 2000 characters.","maxLength":2000,"example":"These are scenes from pharmaceutical TV advertisements","uniqueKey":"llm_labeling.oneOf.0.labeling_context.oneOf.0","typeLabel":"string"},{"type":"null","title":"Labeling Context","description":"OPTIONAL. Freeform domain context about the data being clustered, injected into the labeling prompt as a clearly-delimited 'Domain context provided by the user' block. Unlike custom_prompt, this does NOT replace the default prompt — it grounds the default labeler so labels use the right domain vocabulary. Example: 'These are scenes from pharmaceutical TV ads' turns generic labels like 'People Talking Outdoors' into 'Patient Testimonial Scenes'. Max 2000 characters.","example":"These are scenes from pharmaceutical TV advertisements","uniqueKey":"llm_labeling.oneOf.0.labeling_context.oneOf.1","typeLabel":"null"}],"uniqueKey":"llm_labeling.oneOf.0.labeling_context","typeLabel":"string | null"},"custom_prompt":{"oneOf":[{"type":"string","title":"Custom Prompt","description":"OPTIONAL. Custom prompt template for LLM labeling. NOT REQUIRED - uses default discriminative prompt if not provided. When provided, completely replaces the default prompt. Your custom prompt receives cluster information but you must format it yourself. Use when: - Need domain-specific labeling (e.g., medical, legal, technical) - Want different label format (e.g., emoji labels, code names) - Require specific output structure - Have custom business logic for categorization Default prompt includes: cluster document samples, forbidden labels for uniqueness, and JSON response format. See engine/clusters/labeling/prompts.py for reference. Example: 'Analyze these product clusters and generate SHORT category names (2-3 words max) focusing on product type and price range. Return JSON: [{\"cluster_id\": \"cl_0\", \"label\": \"...\"}]'","example":"Analyze these document clusters and generate technical labels (2-3 words). Focus on programming languages and frameworks mentioned. Return JSON: [{'cluster_id': 'cl_0', 'label': '...', 'keywords': [...]}]","uniqueKey":"llm_labeling.oneOf.0.custom_prompt.oneOf.0","typeLabel":"string"},{"type":"null","title":"Custom Prompt","description":"OPTIONAL. Custom prompt template for LLM labeling. NOT REQUIRED - uses default discriminative prompt if not provided. When provided, completely replaces the default prompt. Your custom prompt receives cluster information but you must format it yourself. Use when: - Need domain-specific labeling (e.g., medical, legal, technical) - Want different label format (e.g., emoji labels, code names) - Require specific output structure - Have custom business logic for categorization Default prompt includes: cluster document samples, forbidden labels for uniqueness, and JSON response format. See engine/clusters/labeling/prompts.py for reference. Example: 'Analyze these product clusters and generate SHORT category names (2-3 words max) focusing on product type and price range. Return JSON: [{\"cluster_id\": \"cl_0\", \"label\": \"...\"}]'","example":"Analyze these document clusters and generate technical labels (2-3 words). Focus on programming languages and frameworks mentioned. Return JSON: [{'cluster_id': 'cl_0', 'label': '...', 'keywords': [...]}]","uniqueKey":"llm_labeling.oneOf.0.custom_prompt.oneOf.1","typeLabel":"null"}],"uniqueKey":"llm_labeling.oneOf.0.custom_prompt","typeLabel":"string | null"},"response_shape":{"oneOf":[{"type":"string","title":"Response Shape","description":"OPTIONAL. Define custom structured output for LLM labeling. NOT REQUIRED - uses default structure (label, summary, keywords) if not provided. When provided, LLM output will match this structure and be stored in cluster documents. \n\nTwo modes supported:\n1. Natural language prompt (string): Describe desired output in plain English\n - Service automatically infers JSON schema from your description\n - Example: 'Extract cluster category, confidence score (0-1), and top 3 representative terms'\n - Auto-generates schema with appropriate types (string, number, array, etc.)\n\n2. Explicit JSON schema (dict): Provide complete JSON schema for output structure\n - Full control over output structure, types, and constraints\n - Example: {'type': 'object', 'properties': {'category': {'type': 'string'}, ...}}\n\n\nUse when:\n - Need custom metadata fields (confidence scores, sentiment, complexity)\n - Want domain-specific structure (taxonomy hierarchies, entity extractions)\n - Require specific data types (arrays, nested objects, enums)\n - Have downstream schema requirements\n\n\nOutput fields are automatically added to cluster collection schema and stored in metadata.\nDefault behavior (if not provided): label (string), summary (string), keywords (array of strings)\n","example":"Extract cluster category, confidence score between 0 and 1, and top 3 representative keywords","uniqueKey":"llm_labeling.oneOf.0.response_shape.oneOf.0","typeLabel":"string"},{"type":"object","title":"Response Shape","description":"OPTIONAL. Define custom structured output for LLM labeling. NOT REQUIRED - uses default structure (label, summary, keywords) if not provided. When provided, LLM output will match this structure and be stored in cluster documents. \n\nTwo modes supported:\n1. Natural language prompt (string): Describe desired output in plain English\n - Service automatically infers JSON schema from your description\n - Example: 'Extract cluster category, confidence score (0-1), and top 3 representative terms'\n - Auto-generates schema with appropriate types (string, number, array, etc.)\n\n2. Explicit JSON schema (dict): Provide complete JSON schema for output structure\n - Full control over output structure, types, and constraints\n - Example: {'type': 'object', 'properties': {'category': {'type': 'string'}, ...}}\n\n\nUse when:\n - Need custom metadata fields (confidence scores, sentiment, complexity)\n - Want domain-specific structure (taxonomy hierarchies, entity extractions)\n - Require specific data types (arrays, nested objects, enums)\n - Have downstream schema requirements\n\n\nOutput fields are automatically added to cluster collection schema and stored in metadata.\nDefault behavior (if not provided): label (string), summary (string), keywords (array of strings)\n","example":"Extract cluster category, confidence score between 0 and 1, and top 3 representative keywords","additionalProperties":true,"uniqueKey":"llm_labeling.oneOf.0.response_shape.oneOf.1","typeLabel":"Response Shape · object"},{"type":"null","title":"Response Shape","description":"OPTIONAL. Define custom structured output for LLM labeling. NOT REQUIRED - uses default structure (label, summary, keywords) if not provided. When provided, LLM output will match this structure and be stored in cluster documents. \n\nTwo modes supported:\n1. Natural language prompt (string): Describe desired output in plain English\n - Service automatically infers JSON schema from your description\n - Example: 'Extract cluster category, confidence score (0-1), and top 3 representative terms'\n - Auto-generates schema with appropriate types (string, number, array, etc.)\n\n2. Explicit JSON schema (dict): Provide complete JSON schema for output structure\n - Full control over output structure, types, and constraints\n - Example: {'type': 'object', 'properties': {'category': {'type': 'string'}, ...}}\n\n\nUse when:\n - Need custom metadata fields (confidence scores, sentiment, complexity)\n - Want domain-specific structure (taxonomy hierarchies, entity extractions)\n - Require specific data types (arrays, nested objects, enums)\n - Have downstream schema requirements\n\n\nOutput fields are automatically added to cluster collection schema and stored in metadata.\nDefault behavior (if not provided): label (string), summary (string), keywords (array of strings)\n","example":"Extract cluster category, confidence score between 0 and 1, and top 3 representative keywords","uniqueKey":"llm_labeling.oneOf.0.response_shape.oneOf.2","typeLabel":"null"}],"uniqueKey":"llm_labeling.oneOf.0.response_shape","typeLabel":"string | Response Shape · object | null"},"parameters":{"additionalProperties":true,"type":"object","title":"Parameters","description":"Provider-specific parameters forwarded to the LLM service. For OpenAI: temperature, max_tokens, top_p, json_output, etc. For Google: temperature, top_k, max_output_tokens, json_output, etc.","uniqueKey":"llm_labeling.oneOf.0.parameters","typeLabel":"Parameters · object"},"candidate_labels":{"oneOf":[{"type":"array","items":{"type":"string","uniqueKey":"llm_labeling.oneOf.0.candidate_labels.oneOf.0.items.[INDEX]","typeLabel":"string"},"title":"Candidate Labels","description":"OPTIONAL. A fixed vocabulary to label clusters from. Each cluster gets the candidate that best fits its representative documents, instead of a freshly written name. Pass a list of labels, or a map of label to a one-line description of what belongs under it. Up to 255 labels. REQUIRED when provider is 'typesafe' (Jev) unless response_shape is an explicit schema of closed-set fields, because Jev picks from options and does not write text. The picked label's probability is stored as label_confidence. Useful for mapping clusters onto an existing taxonomy level, and for labeling every level of a hierarchical run with a consistent vocabulary.","example":["Skateboarding","Surfing","Snowboarding","Cooking"],"uniqueKey":"llm_labeling.oneOf.0.candidate_labels.oneOf.0","typeLabel":"string[]"},{"type":"object","title":"Candidate Labels","description":"OPTIONAL. A fixed vocabulary to label clusters from. Each cluster gets the candidate that best fits its representative documents, instead of a freshly written name. Pass a list of labels, or a map of label to a one-line description of what belongs under it. Up to 255 labels. REQUIRED when provider is 'typesafe' (Jev) unless response_shape is an explicit schema of closed-set fields, because Jev picks from options and does not write text. The picked label's probability is stored as label_confidence. Useful for mapping clusters onto an existing taxonomy level, and for labeling every level of a hierarchical run with a consistent vocabulary.","example":["Skateboarding","Surfing","Snowboarding","Cooking"],"additionalProperties":{"oneOf":[{"type":"string","uniqueKey":"llm_labeling.oneOf.0.candidate_labels.oneOf.1.additionalProperties.oneOf.0","typeLabel":"string"},{"type":"null","uniqueKey":"llm_labeling.oneOf.0.candidate_labels.oneOf.1.additionalProperties.oneOf.1","typeLabel":"null"}],"uniqueKey":"llm_labeling.oneOf.0.candidate_labels.oneOf.1.additionalProperties","typeLabel":"string | null"},"uniqueKey":"llm_labeling.oneOf.0.candidate_labels.oneOf.1","typeLabel":"Candidate Labels · object"},{"type":"null","title":"Candidate Labels","description":"OPTIONAL. A fixed vocabulary to label clusters from. Each cluster gets the candidate that best fits its representative documents, instead of a freshly written name. Pass a list of labels, or a map of label to a one-line description of what belongs under it. Up to 255 labels. REQUIRED when provider is 'typesafe' (Jev) unless response_shape is an explicit schema of closed-set fields, because Jev picks from options and does not write text. The picked label's probability is stored as label_confidence. Useful for mapping clusters onto an existing taxonomy level, and for labeling every level of a hierarchical run with a consistent vocabulary.","example":["Skateboarding","Surfing","Snowboarding","Cooking"],"uniqueKey":"llm_labeling.oneOf.0.candidate_labels.oneOf.2","typeLabel":"null"}],"uniqueKey":"llm_labeling.oneOf.0.candidate_labels","typeLabel":"string[] | Candidate Labels · object | null"}},"uniqueKey":"llm_labeling.oneOf.0","typeLabel":"LLMLabeling · object"},{"type":"null","description":"Configuration for LLM-based cluster labeling (applies to all cluster types)","uniqueKey":"llm_labeling.oneOf.1","typeLabel":"null"}],"uniqueKey":"llm_labeling","typeLabel":"LLMLabeling · object | null"},"num_clusters":{"oneOf":[{"type":"integer","title":"Num Clusters","description":"Number of clusters found (excludes noise/outliers, populated after execution)","uniqueKey":"num_clusters.oneOf.0","typeLabel":"integer"},{"type":"null","title":"Num Clusters","description":"Number of clusters found (excludes noise/outliers, populated after execution)","uniqueKey":"num_clusters.oneOf.1","typeLabel":"null"}],"uniqueKey":"num_clusters","typeLabel":"integer | null"},"num_documents_clustered":{"oneOf":[{"type":"integer","title":"Num Documents Clustered","description":"Total documents processed","uniqueKey":"num_documents_clustered.oneOf.0","typeLabel":"integer"},{"type":"null","title":"Num Documents Clustered","description":"Total documents processed","uniqueKey":"num_documents_clustered.oneOf.1","typeLabel":"null"}],"uniqueKey":"num_documents_clustered","typeLabel":"integer | null"},"execution_time_seconds":{"oneOf":[{"type":"number","title":"Execution Time Seconds","description":"Time taken to complete clustering","uniqueKey":"execution_time_seconds.oneOf.0","typeLabel":"number"},{"type":"null","title":"Execution Time Seconds","description":"Time taken to complete clustering","uniqueKey":"execution_time_seconds.oneOf.1","typeLabel":"null"}],"uniqueKey":"execution_time_seconds","typeLabel":"number | null"},"quality_metrics":{"oneOf":[{"type":"object","title":"Quality Metrics","description":"Clustering quality metrics (silhouette_score, davies_bouldin_score, calinski_harabasz_score, etc.). Open diagnostic bag like ClusteringResult.metrics: mostly numeric, but the three-state degenerate flag (False | reason string) and its prose degenerate_detail are non-numeric, so the value type is Any. A float-only type 400'd every GET on a cluster whose run flagged a degenerate result ('s read-path sibling: the write side persisted the string, this read side then rejected it).","additionalProperties":true,"uniqueKey":"quality_metrics.oneOf.0","typeLabel":"Quality Metrics · object"},{"type":"null","title":"Quality Metrics","description":"Clustering quality metrics (silhouette_score, davies_bouldin_score, calinski_harabasz_score, etc.). Open diagnostic bag like ClusteringResult.metrics: mostly numeric, but the three-state degenerate flag (False | reason string) and its prose degenerate_detail are non-numeric, so the value type is Any. A float-only type 400'd every GET on a cluster whose run flagged a degenerate result ('s read-path sibling: the write side persisted the string, this read side then rejected it).","uniqueKey":"quality_metrics.oneOf.1","typeLabel":"null"}],"uniqueKey":"quality_metrics","typeLabel":"Quality Metrics · object | null"},"hierarchy_detected":{"type":"boolean","title":"Hierarchy Detected","description":"Whether implicit hierarchy was detected (multi-feature independent) or created (hierarchical attributes)","default":false,"uniqueKey":"hierarchy_detected","typeLabel":"boolean"},"parent_cluster_id":{"oneOf":[{"type":"string","title":"Parent Cluster Id","description":"For child clusters in hierarchy","uniqueKey":"parent_cluster_id.oneOf.0","typeLabel":"string"},{"type":"null","title":"Parent Cluster Id","description":"For child clusters in hierarchy","uniqueKey":"parent_cluster_id.oneOf.1","typeLabel":"null"}],"uniqueKey":"parent_cluster_id","typeLabel":"string | null"},"child_cluster_ids":{"oneOf":[{"type":"array","items":{"type":"string","uniqueKey":"child_cluster_ids.oneOf.0.items.[INDEX]","typeLabel":"string"},"title":"Child Cluster Ids","description":"For parent clusters","uniqueKey":"child_cluster_ids.oneOf.0","typeLabel":"string[]"},{"type":"null","title":"Child Cluster Ids","description":"For parent clusters","uniqueKey":"child_cluster_ids.oneOf.1","typeLabel":"null"}],"uniqueKey":"child_cluster_ids","typeLabel":"string[] | null"},"hierarchy_relationships":{"oneOf":[{"type":"array","items":{"additionalProperties":true,"type":"object","uniqueKey":"hierarchy_relationships.oneOf.0.items.[INDEX]","typeLabel":"object"},"title":"Hierarchy Relationships","description":"Parent-child relationships detected from cluster membership overlap","uniqueKey":"hierarchy_relationships.oneOf.0","typeLabel":"Hierarchy Relationships · object[]"},{"type":"null","title":"Hierarchy Relationships","description":"Parent-child relationships detected from cluster membership overlap","uniqueKey":"hierarchy_relationships.oneOf.1","typeLabel":"null"}],"uniqueKey":"hierarchy_relationships","typeLabel":"Hierarchy Relationships · object[] | null"},"status":{"type":"string","enum":["PENDING","QUEUED","IN_PROGRESS","PROCESSING","COMPLETED","COMPLETED_WITH_ERRORS","FAILED","CANCELED","INTERRUPTED","UNKNOWN","SKIPPED","DRAFT","ACTIVE","ARCHIVED","SUSPENDED","DEACTIVATED"],"title":"TaskStatusEnum","description":"Cluster job status (propagated from TaskService)","default":"PENDING","uniqueKey":"status","typeLabel":"enum\u003cstring>"},"error":{"oneOf":[{"type":"string","title":"Error","description":"Error message if cluster execution failed. Propagated from TaskService.","uniqueKey":"error.oneOf.0","typeLabel":"string"},{"type":"null","title":"Error","description":"Error message if cluster execution failed. Propagated from TaskService.","uniqueKey":"error.oneOf.1","typeLabel":"null"}],"uniqueKey":"error","typeLabel":"string | null"},"failure_category":{"oneOf":[{"type":"string","title":"FailureCategory","description":"Machine-readable failure classification. Same enum as batch failure_category.","enum":["timeout","infrastructure","orphaned","pipeline","mvs_durability","validation","unknown"],"uniqueKey":"failure_category.oneOf.0","typeLabel":"enum\u003cstring>"},{"type":"null","description":"Machine-readable failure classification. Same enum as batch failure_category.","uniqueKey":"failure_category.oneOf.1","typeLabel":"null"}],"uniqueKey":"failure_category","typeLabel":"enum\u003cstring> | null"},"last_execution_task_id":{"oneOf":[{"type":"string","title":"Last Execution Task Id","description":"Most recent task ID for this cluster","uniqueKey":"last_execution_task_id.oneOf.0","typeLabel":"string"},{"type":"null","title":"Last Execution Task Id","description":"Most recent task ID for this cluster","uniqueKey":"last_execution_task_id.oneOf.1","typeLabel":"null"}],"uniqueKey":"last_execution_task_id","typeLabel":"string | null"},"last_run_id":{"oneOf":[{"type":"string","title":"Last Run Id","description":"Most recent execution run ID","uniqueKey":"last_run_id.oneOf.0","typeLabel":"string"},{"type":"null","title":"Last Run Id","description":"Most recent execution run ID","uniqueKey":"last_run_id.oneOf.1","typeLabel":"null"}],"uniqueKey":"last_run_id","typeLabel":"string | null"},"created_at":{"type":"string","format":"date-time","title":"Created At","description":"When cluster was created","uniqueKey":"created_at","typeLabel":"string\u003cdate-time>"},"updated_at":{"type":"string","format":"date-time","title":"Updated At","description":"When cluster was last updated","uniqueKey":"updated_at","typeLabel":"string\u003cdate-time>"},"last_executed_at":{"oneOf":[{"type":"string","title":"Last Executed At","description":"Last execution timestamp","format":"date-time","uniqueKey":"last_executed_at.oneOf.0","typeLabel":"string\u003cdate-time>"},{"type":"null","title":"Last Executed At","description":"Last execution timestamp","uniqueKey":"last_executed_at.oneOf.1","typeLabel":"null"}],"uniqueKey":"last_executed_at","typeLabel":"string\u003cdate-time> | null"},"completed_at":{"oneOf":[{"type":"string","title":"Completed At","description":"When clustering completed successfully","format":"date-time","uniqueKey":"completed_at.oneOf.0","typeLabel":"string\u003cdate-time>"},{"type":"null","title":"Completed At","description":"When clustering completed successfully","uniqueKey":"completed_at.oneOf.1","typeLabel":"null"}],"uniqueKey":"completed_at","typeLabel":"string\u003cdate-time> | null"},"llm_labeling_errors":{"oneOf":[{"type":"array","items":{"type":"string","uniqueKey":"llm_labeling_errors.oneOf.0.items.[INDEX]","typeLabel":"string"},"title":"Llm Labeling Errors","description":"List of errors encountered during LLM labeling (if any). Stored in MongoDB cluster metadata only, NOT in vector store cluster documents. Used to track LLM failures while allowing fallback labels to work.","uniqueKey":"llm_labeling_errors.oneOf.0","typeLabel":"string[]"},{"type":"null","title":"Llm Labeling Errors","description":"List of errors encountered during LLM labeling (if any). Stored in MongoDB cluster metadata only, NOT in vector store cluster documents. Used to track LLM failures while allowing fallback labels to work.","uniqueKey":"llm_labeling_errors.oneOf.1","typeLabel":"null"}],"uniqueKey":"llm_labeling_errors","typeLabel":"string[] | null"},"metadata":{"additionalProperties":true,"type":"object","title":"Metadata","description":"Additional user-defined metadata","uniqueKey":"metadata","typeLabel":"Metadata · object"}},"type":"object","required":["cluster_name","namespace_id","input_collections","cluster_type"],"title":"ClusterMetadata","description":"Cluster job metadata stored in MongoDB clusters collection.\n\nThis is separate from cluster documents themselves. Tracks job-level\nconfiguration, status, and summary statistics.\n\nSupports both vector and attribute clustering with appropriate metadata.","uniqueKey":"","typeLabel":"ClusterMetadata · object"},"examples":{"Example":{"title":"Example","value":{"cluster_name":"\u003cstring>","namespace_id":"\u003cstring>","input_collections":["\u003cstring>"],"cluster_type":"vector","cluster_id":"\u003cstring>","description":"\u003cstring>","source_cluster_ids":["\u003cstring>"],"source_bucket_ids":["\u003cstring>"],"filters":{},"feature_uris":["\u003cstring>"],"multi_feature_strategy":"\u003cstring>","learned_weights":{},"learning_quality_score":123,"effective_feature_method":"\u003cstring>","face_cluster_merge":{"enabled":true,"centroid_cosine_threshold":0.55,"bbox_iou_threshold":0.4,"scene_jaccard_threshold":0.3,"bbox_field":"bbox","frame_field":"frame_number","scene_field":"scene_id"},"sample_size":123,"preprocessing_steps":[{}],"hierarchical_vector":true,"max_hierarchy_depth":123,"vis_n_components":2,"layout_stability":"none","clustered_attributes":["\u003cstring>"],"hierarchical_grouping":true,"aggregation_method":"\u003cstring>","output_collection_ids":["\u003cstring>"],"output_collection_names":["\u003cstring>"],"algorithm":"\u003cstring>","algorithm_params":{},"enrich_source":false,"source_enrichment_config":{"field_mappings":[{"source_field":"cluster_id","target_field":"category_id"},{"source_field":"cluster_label","target_field":"category_name"},{"source_field":"distance_to_centroid","target_field":"category_confidence"}]},"llm_labeling":{"enabled":true,"include_keywords":true,"include_summary":true,"labeling_inputs":{"input_mappings":[{"input_key":"title","path":"title","source_type":"payload"},{"input_key":"description","path":"description","source_type":"payload"},{"input_key":"text","path":"text","source_type":"payload"}]},"model_name":"gpt-4o-mini-2024-07-18","provider":"openai"},"num_clusters":123,"num_documents_clustered":123,"execution_time_seconds":123,"quality_metrics":{},"hierarchy_detected":false,"parent_cluster_id":"\u003cstring>","child_cluster_ids":["\u003cstring>"],"hierarchy_relationships":[{}],"status":"PENDING","error":"\u003cstring>","failure_category":"timeout","last_execution_task_id":"\u003cstring>","last_run_id":"\u003cstring>","created_at":"2023-11-07T05:31:56Z","updated_at":"2023-11-07T05:31:56Z","last_executed_at":"2023-11-07T05:31:56Z","completed_at":"2023-11-07T05:31:56Z","llm_labeling_errors":["\u003cstring>"],"metadata":{}}}},"exampleType":"generated"}},"exampleType":"generated"},"11a12f8e-4767-4e62-bb76-6be6b6a16c24":{"description":"Bad Request","content":{"application/json":{"schema":{"properties":{"status":{"type":"integer","title":"Status","description":"HTTP status code for this error","isRequired":true,"uniqueKey":"status","typeLabel":"integer"},"error":{"properties":{"message":{"type":"string","title":"Message","description":"Human-readable error message","isRequired":true,"uniqueKey":"error.message","typeLabel":"string"},"type":{"type":"string","title":"Type","description":"Stable error type identifier (machine-readable)","isRequired":true,"uniqueKey":"error.type","typeLabel":"string"},"code":{"oneOf":[{"type":"string","title":"Code","description":"Fine-grained error code for programmatic handling (e.g., namespace_name_taken, feature_extractor_not_found). 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/v1/collections/{collection_identifier}","title":"Get Collection","href":"/api-reference/collections/get-collection"},{"openapi":"patch /v1/collections/{collection_identifier}","title":"Update Collection","href":"/api-reference/collections/update-collection"},{"openapi":"get /v1/collections/{collection_identifier}/features","title":"Describe collection features","href":"/api-reference/collections/describe-collection-features"},{"openapi":"post /v1/collections/list","title":"List Collections","href":"/api-reference/collections/list-collections"},{"openapi":"post /v1/collections/{collection_identifier}/export","title":"Export Collection","href":"/api-reference/collections/export-collection"},{"openapi":"post /v1/collections/{collection_identifier}/trigger","title":"Trigger Collection Processing","href":"/api-reference/collections/trigger-collection-processing"},{"openapi":"delete /v1/collections/{collection_identifier}","title":"Delete 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ID.","href":"/api-reference/collection-documents/get-a-document-by-id"},{"openapi":"get /v1/collections/{collection_identifier}/documents/{document_id}/download","title":"Download frame-accurate scene media for this document.","href":"/api-reference/collection-documents/download-document-media"},{"openapi":"put /v1/collections/{collection_identifier}/documents/{document_id}","title":"Update Document","href":"/api-reference/collection-documents/update-document"},{"openapi":"patch /v1/collections/{collection_identifier}/documents/{document_id}","title":"Patch Document","href":"/api-reference/collection-documents/patch-document"},{"openapi":"patch /v1/collections/{collection_identifier}/documents/{document_id}/acl","title":"Update a document's access control list (ACL).","href":"/api-reference/collection-documents/update-a-documents-access-control-list-acl"},{"openapi":"post /v1/collections/{collection_identifier}/documents/list","title":"List documents.","href":"/api-reference/collection-documents/list-documents"},{"openapi":"post /v1/collections/{collection_identifier}/documents/aggregate","title":"Aggregate Documents","href":"/api-reference/collection-documents/aggregate-documents"},{"openapi":"post /v1/collections/{collection_identifier}/documents/distinct","title":"Return distinct values for a single field.","href":"/api-reference/collection-documents/return-distinct-values-for-a-single-field"},{"openapi":"patch /v1/collections/{collection_identifier}/documents/bulk","title":"Bulk Update Documents","href":"/api-reference/collection-documents/bulk-update-documents"},{"openapi":"post /v1/collections/{collection_identifier}/documents/batch","title":"Batch Update Documents","href":"/api-reference/collection-documents/batch-update-documents"},{"openapi":"delete /v1/collections/{collection_identifier}/documents/batch","title":"Batch Delete 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/v1/collections/features","title":"Discover Features","href":"/api-reference/features/discover-features"}]},{"group":"Feature Extractors","pages":[{"openapi":"get /v1/collections/features/extractors","title":"List Feature Extractors","href":"/api-reference/feature-extractors/list-feature-extractors"},{"openapi":"get /v1/collections/features/extractors/{feature_extractor_id}","title":"Get Feature Extractor by Name","href":"/api-reference/feature-extractors/get-feature-extractor-by-name"},{"group":"Marketplace","pages":[{"openapi":"get /v1/public/extractors/","title":"List Public Extractors","href":"/api-reference/public-extractor-marketplace/list-public-extractors"},{"openapi":"get /v1/public/extractors/{public_name}","title":"Get Public Extractor","href":"/api-reference/public-extractor-marketplace/get-public-extractor"}]},{"group":"Publishing","pages":[{"openapi":"post /v1/extractors/{extractor_id}/publish","title":"Publish an extractor to the marketplace","href":"/api-reference/extractor-publishing/publish-extractor-marketplace"},{"openapi":"get /v1/extractors/{extractor_id}/publish","title":"Get published extractor status","href":"/api-reference/extractor-publishing/get-published-extractor-status"},{"openapi":"patch /v1/extractors/{extractor_id}/publish","title":"Update published extractor","href":"/api-reference/extractor-publishing/update-published-extractor"},{"openapi":"delete /v1/extractors/{extractor_id}/publish","title":"Unpublish an extractor","href":"/api-reference/extractor-publishing/unpublish-extractor"},{"openapi":"post /v1/extractors/install","title":"Install a public extractor","href":"/api-reference/extractor-publishing/install-public-extractor"}]},{"group":"Submissions","pages":[{"openapi":"get /v1/extractors","title":"List all extractors available to organization","href":"/api-reference/extractor-submissions/list-all-extractors-available-organization"},{"openapi":"post /v1/extractors/submissions/uploads","title":"Get presigned URL for extractor submission","href":"/api-reference/extractor-submissions/get-presigned-url-extractor-submission"},{"openapi":"post /v1/extractors/submissions/uploads/{submission_id}/confirm","title":"Confirm extractor submission upload","href":"/api-reference/extractor-submissions/confirm-extractor-submission-upload"},{"openapi":"get /v1/extractors/submissions","title":"List extractor submissions","href":"/api-reference/extractor-submissions/list-extractor-submissions"},{"openapi":"get /v1/extractors/submissions/{submission_id}","title":"Get submission details","href":"/api-reference/extractor-submissions/get-submission-details"}]}]},{"group":"Documents","pages":[{"openapi":"get /v1/documents/{document_id}","title":"Get a document by ID (namespace-scoped).","href":"/api-reference/documents/get-a-document-by-id-namespace-scoped"},{"openapi":"put /v1/documents/{document_id}","title":"Update a document by ID (namespace-scoped).","href":"/api-reference/documents/update-a-document-by-id-namespace-scoped"},{"openapi":"patch /v1/documents/{document_id}","title":"Partially update a document by ID (namespace-scoped).","href":"/api-reference/documents/partially-update-a-document-by-id-namespace-scoped"},{"openapi":"post /v1/documents/list","title":"List documents across all collections (namespace-scoped).","href":"/api-reference/documents/list-documents-across-all-collections-namespace-scoped"},{"openapi":"post /v1/documents/batch-get","title":"Batch get documents by IDs","href":"/api-reference/documents/batch-get-documents-by-ids"},{"openapi":"post /v1/documents/list/batch","title":"Run multiple list-documents queries concurrently.","href":"/api-reference/documents/run-multiple-list-documents-queries-concurrently"}]},{"group":"Retrievers","pages":[{"openapi":"post /v1/retrievers","title":"Create Retriever","href":"/api-reference/retrievers/create-retriever"},{"openapi":"post /v1/retrievers/list","title":"List Retrievers","href":"/api-reference/retrievers/list-retrievers"},{"openapi":"get /v1/retrievers/{retriever_id}","title":"Get Retriever","href":"/api-reference/retrievers/get-retriever"},{"openapi":"patch /v1/retrievers/{retriever_id}","title":"Patch Retriever","href":"/api-reference/retrievers/patch-retriever"},{"openapi":"post /v1/retrievers/{retriever_id}/clone","title":"Clone Retriever","href":"/api-reference/retrievers/clone-retriever"},{"openapi":"delete /v1/retrievers/{retriever_id}","title":"Delete Retriever","href":"/api-reference/retrievers/delete-retriever"},{"openapi":"post /v1/retrievers/{retriever_id}/execute","title":"Execute Retriever (Auto-Optimized)","href":"/api-reference/retrievers/execute-retriever-auto-optimized"},{"openapi":"post /v1/retrievers/{retriever_id}/execute/batch","title":"Batch Execute Retriever","href":"/api-reference/retrievers/batch-execute-retriever"},{"openapi":"post /v1/retrievers/{retriever_id}/executions/list","title":"List Executions","href":"/api-reference/retrievers/list-executions"},{"openapi":"get /v1/retrievers/{retriever_id}/executions/{execution_id}","title":"Get Execution","href":"/api-reference/retrievers/get-execution"},{"openapi":"get /v1/retrievers/{retriever_id}/executions/{execution_id}/status","title":"Get Live Execution Status","href":"/api-reference/retrievers/get-live-execution-status"},{"openapi":"post /v1/retrievers/{retriever_id}/execute/explain","title":"Explain Retriever Execution Plan","href":"/api-reference/retrievers/explain-retriever-execution-plan"},{"openapi":"get /v1/retrievers/{retriever_id}/executions/{execution_id}/profile","title":"Get Execution Query Profile (explain-on-execute read path)","href":"/api-reference/retrievers/get-execution-profile"},{"openapi":"get /v1/retrievers/typeahead","title":"Typeahead","href":"/api-reference/retrievers/typeahead"},{"group":"Stages","pages":[{"openapi":"get /v1/retrievers/stages","title":"List Available Retriever Stages","href":"/api-reference/retriever-stages/list-available-retriever-stages"}]},{"group":"Interactions","pages":[{"openapi":"post /v1/retrievers/interactions","title":"Create Interaction","href":"/api-reference/retriever-interactions/create-interaction"},{"openapi":"post /v1/retrievers/interactions/list","title":"List Interactions","href":"/api-reference/retriever-interactions/list-interactions"},{"openapi":"get /v1/retrievers/interactions/{interaction_id}","title":"Get Interaction","href":"/api-reference/retriever-interactions/get-interaction"},{"openapi":"delete /v1/retrievers/interactions/{interaction_id}","title":"Delete Interaction","href":"/api-reference/retriever-interactions/delete-interaction"},{"openapi":"post /v1/retrievers/interactions/batch","title":"Create Interactions Bulk","href":"/api-reference/retriever-interactions/create-interactions-bulk"}]},{"group":"Evaluations","pages":[{"openapi":"get /v1/retrievers/evaluations/datasets","title":"List evaluation datasets","href":"/api-reference/retriever-evaluations/list-evaluation-datasets"},{"openapi":"post /v1/retrievers/evaluations/datasets","title":"Create evaluation dataset","href":"/api-reference/retriever-evaluations/create-evaluation-dataset"},{"openapi":"get /v1/retrievers/evaluations/datasets/{dataset_identifier}","title":"Get evaluation dataset","href":"/api-reference/retriever-evaluations/get-evaluation-dataset"},{"openapi":"get /v1/retrievers/{retriever_id}/evaluations","title":"List evaluations","href":"/api-reference/retriever-evaluations/list-evaluations"},{"openapi":"post /v1/retrievers/{retriever_id}/evaluations","title":"Run evaluation","href":"/api-reference/retriever-evaluations/run-evaluation"},{"openapi":"get /v1/retrievers/{retriever_id}/evaluations/{evaluation_id}","title":"Get evaluation results","href":"/api-reference/retriever-evaluations/get-evaluation-results"},{"openapi":"post /v1/evaluations/score","title":"Score predictions vs ground truth (stateless)","href":"/api-reference/retriever-evaluations/score-predictions"},{"openapi":"get /v1/evaluations","title":"Discover evaluation endpoints","href":"/api-reference/retriever-evaluations/discover-evaluation-endpoints"},{"openapi":"post /v1/retrievers/{retriever_id}/evaluations/generate-from-interactions","title":"Generate evaluation dataset from interactions","href":"/api-reference/retriever-evaluations/generate-from-interactions"}]},{"group":"Benchmarks","pages":[{"openapi":"get /v1/retrievers/benchmarks","title":"List benchmarks","href":"/api-reference/retriever-benchmarks/list-benchmarks"},{"openapi":"post /v1/retrievers/benchmarks","title":"Create benchmark","href":"/api-reference/retriever-benchmarks/create-benchmark"},{"openapi":"get /v1/retrievers/benchmarks/{benchmark_id}","title":"Get benchmark","href":"/api-reference/retriever-benchmarks/get-benchmark"},{"openapi":"delete /v1/retrievers/benchmarks/{benchmark_id}","title":"Delete benchmark","href":"/api-reference/retriever-benchmarks/delete-benchmark"}]},{"group":"API Keys","pages":[{"openapi":"post /v1/retrievers/{retriever_id}/api-keys","title":"Create Retriever API Key","href":"/api-reference/retriever-api-keys/create-retriever-api-key"},{"openapi":"get /v1/retrievers/{retriever_id}/api-keys","title":"List Retriever API Keys","href":"/api-reference/retriever-api-keys/list-retriever-api-keys"},{"openapi":"delete /v1/retrievers/{retriever_id}/api-keys/{key_id}","title":"Revoke Retriever API Key","href":"/api-reference/retriever-api-keys/revoke-retriever-api-key"}]},{"group":"Adhoc","pages":[{"openapi":"post /v1/retrievers/execute","title":"Execute Adhoc Retriever","href":"/api-reference/adhoc-retrievers/execute-adhoc-retriever"},{"openapi":"post /v1/retrievers/executions/list","title":"List Adhoc Executions","href":"/api-reference/adhoc-retrievers/list-adhoc-executions"},{"openapi":"get /v1/retrievers/executions/{execution_id}","title":"Get Adhoc Execution","href":"/api-reference/adhoc-retrievers/get-adhoc-execution"}]},{"group":"Publishing","pages":[{"openapi":"post /v1/retrievers/{retriever_id}/publish","title":"Publish Retriever","href":"/api-reference/published-retrievers/publish-retriever"},{"openapi":"get /v1/retrievers/published","title":"List Published Retrievers","href":"/api-reference/published-retrievers/list-published-retrievers"},{"openapi":"get /v1/retrievers/{retriever_id}/publish","title":"Get Published Retriever","href":"/api-reference/published-retrievers/get-published-retriever"},{"openapi":"patch /v1/retrievers/{retriever_id}/publish","title":"Update Published Retriever","href":"/api-reference/published-retrievers/update-published-retriever"},{"openapi":"delete /v1/retrievers/{retriever_id}/publish","title":"Unpublish Retriever","href":"/api-reference/published-retrievers/unpublish-retriever"},{"openapi":"get /v1/retrievers/{retriever_id}/publish/availability","title":"Check Name Availability","href":"/api-reference/published-retrievers/check-name-availability"},{"openapi":"get /v1/retrievers/publish/stats","title":"Get Organization Publish Stats","href":"/api-reference/published-retrievers/get-organization-publish-stats"},{"openapi":"post /v1/retrievers/{retriever_id}/publish/reconcile","title":"Reconcile Publish State","href":"/api-reference/published-retrievers/reconcile-publish-state"}]},{"group":"Public Retriever API","pages":[{"openapi":"get /v1/public/retrievers/","title":"List Public Retrievers","href":"/api-reference/public-retriever-api/list-public-retrievers"},{"openapi":"get /v1/public/retrievers/{public_name}/config","title":"Get Public Retriever Config","href":"/api-reference/public-retriever-api/get-public-retriever-config"},{"openapi":"get /v1/public/retrievers/{public_name}/template","title":"Get Public Retriever Template","href":"/api-reference/public-retriever-api/get-public-retriever-template"},{"openapi":"post /v1/public/retrievers/{public_name}/execute","title":"Execute Public Retriever","href":"/api-reference/public-retriever-api/execute-public-retriever"},{"openapi":"post /v1/public/retrievers/{public_name}/verify","title":"Verify Password","href":"/api-reference/public-retriever-api/verify-password"},{"openapi":"post /v1/public/retrievers/{public_name}/interactions","title":"Track Interaction","href":"/api-reference/public-retriever-api/track-interaction"},{"openapi":"post /v1/public/retrievers/{public_name}/interactions/batch","title":"Track Interaction Batch","href":"/api-reference/public-retriever-api/track-interaction-batch"}]},{"group":"Feature search","pages":[{"openapi":"post /v1/features/search","title":"Feature Search","href":"/api-reference/feature-search/feature-search"}]}]},{"group":"Enrich \u0026 Improve","pages":[{"group":"Taxonomies","pages":[{"openapi":"post /v1/taxonomies","title":"Create Taxonomy","href":"/api-reference/taxonomies/create-taxonomy"},{"openapi":"get /v1/taxonomies/{taxonomy_identifier}","title":"Get Taxonomy","href":"/api-reference/taxonomies/get-taxonomy"},{"openapi":"patch /v1/taxonomies/{taxonomy_identifier}","title":"Partially Update Taxonomy","href":"/api-reference/taxonomies/partially-update-taxonomy"},{"openapi":"post /v1/taxonomies/list","title":"List Taxonomies","href":"/api-reference/taxonomies/list-taxonomies"},{"openapi":"post /v1/taxonomies/execute/{taxonomy_identifier}","title":"Test taxonomy configuration (validation only) — DEPRECATED path","href":"/api-reference/taxonomies/test-taxonomy-configuration-validation-only"},{"openapi":"get /v1/taxonomies/{taxonomy_id}/versions","title":"List Taxonomy Versions","href":"/api-reference/taxonomies/list-taxonomy-versions"},{"openapi":"post /v1/taxonomies/{taxonomy_id}/versions","title":"Create Taxonomy 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Creates cluster metadata\n 2. Creates output collection for cluster documents\n 3. Returns cluster metadata with output_collection_id\n\n The cluster can then be executed via POST /v1/clusters/{id}/execute","href":"/api-reference/clusters/create-cluster"},{"openapi":"get /v1/clusters/{cluster_identifier}","title":"Get Cluster","href":"/api-reference/clusters/get-cluster"},{"openapi":"patch /v1/clusters/{cluster_identifier}","title":"Partially Update Cluster","description":"This endpoint partially updates a cluster (PATCH operation).\n Only provided fields will be updated. At minimum, metadata can always be updated.\n Immutable fields like cluster_id, status, and computed fields cannot be modified.","href":"/api-reference/clusters/partially-update-cluster"},{"openapi":"post /v1/clusters/list","title":"List Clusters","href":"/api-reference/clusters/list-clusters"},{"openapi":"post /v1/clusters/{cluster_id}/execute","title":"Execute Clustering","href":"/api-reference/clusters/execute-clustering"},{"openapi":"get /v1/clusters/{cluster_id}/groups","title":"List Cluster Groups","href":"/api-reference/clusters/list-cluster-groups"},{"openapi":"delete /v1/clusters/{cluster_id}","title":"Delete Cluster","href":"/api-reference/clusters/delete-cluster"},{"group":"Triggers","pages":[{"openapi":"post /v1/clusters/triggers","title":"Create Cluster Trigger","href":"/api-reference/cluster-triggers/create-cluster-trigger"},{"openapi":"post /v1/clusters/triggers/list","title":"List Cluster Triggers","href":"/api-reference/cluster-triggers/list-cluster-triggers"},{"openapi":"get /v1/clusters/triggers/{trigger_id}","title":"Get Cluster Trigger","href":"/api-reference/cluster-triggers/get-cluster-trigger"},{"openapi":"patch /v1/clusters/triggers/{trigger_id}","title":"Update Cluster Trigger","href":"/api-reference/cluster-triggers/update-cluster-trigger"},{"openapi":"post /v1/clusters/triggers/{trigger_id}/pause","title":"Pause Cluster Trigger","href":"/api-reference/cluster-triggers/pause-cluster-trigger"},{"openapi":"post /v1/clusters/triggers/{trigger_id}/resume","title":"Resume Cluster Trigger","href":"/api-reference/cluster-triggers/resume-cluster-trigger"},{"openapi":"post /v1/clusters/triggers/{trigger_id}/history","title":"Get Trigger Execution History","href":"/api-reference/cluster-triggers/get-trigger-execution-history"},{"openapi":"delete /v1/clusters/triggers/{trigger_id}","title":"Delete Cluster Trigger","href":"/api-reference/cluster-triggers/delete-cluster-trigger"}]},{"group":"Executions","pages":[{"openapi":"get /v1/clusters/{cluster_id}/executions","title":"Get Latest Cluster Execution","href":"/api-reference/cluster-executions/get-latest-cluster-execution"},{"openapi":"get /v1/clusters/{cluster_id}/executions/{run_id}","title":"Get Specific Cluster Execution","href":"/api-reference/cluster-executions/get-specific-cluster-execution"},{"openapi":"post /v1/clusters/{cluster_id}/executions/list","title":"List Cluster Execution History","href":"/api-reference/cluster-executions/list-cluster-execution-history"},{"openapi":"post /v1/clusters/{cluster_id}/executions/{run_id}/cancel","title":"Cancel Execution","href":"/api-reference/cluster-executions/cancel-execution"},{"openapi":"get /v1/clusters/{cluster_id}/executions/{run_id}/export","title":"Export Cluster Run Data (CSV / Parquet)","href":"/api-reference/cluster-executions/export-cluster-run-data"},{"openapi":"patch 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