> ## Documentation Index > Fetch the complete documentation index at: https://docs.mixpeek.com/docs/llms.txt > Use this file to discover all available pages before exploring further. # Execute Clustering > Execute clustering on a specific cluster. This endpoint: 1. Validates the cluster exists 2. Queues clustering job via Celery 3. Returns task_id immediately (non-blocking) 4. Celery prepares data and submits to Engine 5. Monitor progress via GET /v1/tasks/{task_id} Flow: - API: Receives request - Celery: Fetches documents, creates parquet, uploads to S3 - Engine: Runs Ray job on parquet data - Status: Automatically updates cluster when complete Use GET /v1/clusters/{id}/executions to retrieve results. Optional body (`ExecuteClusterByIdRequest`) accepts a `filters` override that applies to this execution only and is not persisted on the cluster. ## OpenAPI ````yaml post /v1/clusters/{cluster_id}/execute openapi: 3.1.0 info: title: Mixpeek API description: >- This is the Mixpeek API, providing access to various endpoints for data processing and retrieval. termsOfService: https://mixpeek.com/terms contact: name: Mixpeek Support url: https://mixpeek.com/contact email: info@mixpeek.com version: '0.82' servers: - url: https://api.mixpeek.com description: Production security: [] paths: /v1/clusters/{cluster_id}/execute: post: tags: - Clusters summary: Execute Clustering description: |- Execute clustering on a specific cluster. This endpoint: 1. Validates the cluster exists 2. Queues clustering job via Celery 3. Returns task_id immediately (non-blocking) 4. Celery prepares data and submits to Engine 5. Monitor progress via GET /v1/tasks/{task_id} Flow: - API: Receives request - Celery: Fetches documents, creates parquet, uploads to S3 - Engine: Runs Ray job on parquet data - Status: Automatically updates cluster when complete Use GET /v1/clusters/{id}/executions to retrieve results. Optional body (`ExecuteClusterByIdRequest`) accepts a `filters` override that applies to this execution only and is not persisted on the cluster. operationId: execute_clustering_v1_clusters__cluster_id__execute_post parameters: - name: cluster_id in: path required: true schema: type: string description: Cluster ID to execute title: Cluster Id description: Cluster ID to execute requestBody: content: application/json: schema: $ref: '#/components/schemas/ExecuteClusterByIdRequest' description: Optional per-execution overrides (e.g. filter override). responses: '200': description: Successful Response content: application/json: schema: $ref: '#/components/schemas/TaskResponse' '400': description: Bad Request content: application/json: schema: $ref: '#/components/schemas/ErrorResponse' '401': description: Unauthorized content: application/json: schema: $ref: '#/components/schemas/ErrorResponse' '403': description: Forbidden content: application/json: schema: $ref: '#/components/schemas/ErrorResponse' '404': description: Not Found content: application/json: schema: $ref: '#/components/schemas/ErrorResponse' '422': description: Validation Error content: application/json: schema: $ref: '#/components/schemas/HTTPValidationError' '500': description: Internal Server Error content: application/json: schema: $ref: '#/components/schemas/ErrorResponse' components: schemas: ExecuteClusterByIdRequest: properties: mode: type: string enum: - full - assign - composite title: Mode description: >- Execution mode: - full: Run complete clustering pipeline (default) - assign: Incremental assignment — assign new documents to existing cluster centroids without re-clustering. Requires a previous successful execution with centroids. - composite: Cluster centroids from multiple prior executions to create cross-execution cluster groupings. Requires source_execution_ids pointing to completed runs with centroids. default: full source_execution_ids: anyOf: - items: type: string type: array - type: 'null' title: Source Execution Ids description: >- List of clustering execution run_ids whose centroids will be clustered together (only used when mode='composite'). Each referenced execution must have status='completed' and non-empty centroids. Minimum 2 executions required. assignment_threshold: anyOf: - type: number maximum: 1 minimum: 0 - type: 'null' title: Assignment Threshold description: >- Minimum cosine similarity to assign a document to a cluster (only used when mode='assign'). Documents below this threshold are marked as noise (cluster_id=-1). Range: 0.0-1.0. default: 0.5 filters: anyOf: - $ref: '#/components/schemas/LogicalOperator-Input' - type: 'null' description: >- Optional filter override. When provided, this filter is used for this execution instead of the cluster's stored filters. The override is not persisted on the cluster. sample_size: anyOf: - type: integer maximum: 1000000 - type: 'null' title: Sample Size description: >- Optional per-execution document cap. When provided, overrides the cluster's stored sample_size for this execution only. The override is not persisted on the cluster. KMeans supports up to 1M; O(N²) algorithms (HDBSCAN, Spectral, Agglomerative) are capped at 50K by the engine. vis_n_components: anyOf: - type: integer enum: - 2 - 3 - type: 'null' title: Vis N Components description: >- Number of dimensions for visualization coordinates (2 or 3). When 3, the z coordinate is populated for depth-based rendering. defer_visualization: anyOf: - type: boolean - type: 'null' title: Defer Visualization description: >- When True, skip inline UMAP during clustering and compute the scatter coordinates lazily at /visualization time. UMAP is ~90% of the clustering critical path at scale, so this makes large runs complete much faster; the first scatter load pays a one-time bounded cost. The override is not persisted on the cluster. llm_labeling: anyOf: - $ref: '#/components/schemas/LLMLabeling-Input' - type: 'null' description: >- Optional inline LLM labeling override. When provided, this configuration is used for this execution instead of the cluster's stored llm_labeling. Use this to enable labeling for a single run without mutating the cluster definition. Set ``enabled: true`` to actually run labeling — passing the object alone is insufficient. The override is not persisted on the cluster. type: object title: ExecuteClusterByIdRequest description: |- Optional body for POST /v1/clusters/{cluster_id}/execute. Lets callers override the cluster's stored filters for a single execution without mutating the cluster definition — useful for per-partition re-runs (e.g., re-clustering one brand without disturbing other partitions). TaskResponse: properties: task_id: type: string title: Task Id description: >- Unique identifier for the task. REQUIRED. Used to poll task status via GET /v1/tasks/{task_id}. This ID is also stored on parent resources (batches, clusters, etc.) for cross-referencing. Format: UUID v4 or custom string identifier. examples: - task_abc123def456 - 550e8400-e29b-41d4-a716-446655440000 task_type: $ref: '#/components/schemas/TaskType' description: >- Type of operation this task represents. REQUIRED. Identifies the specific async operation being performed. Used for filtering and categorizing tasks. Common types: api_buckets_batches_process, engine_cluster_build, api_taxonomies_execute. See TaskType enum for complete list of supported operations. examples: - api_buckets_batches_process - engine_cluster_build - api_taxonomies_execute status: $ref: '#/components/schemas/TaskStatusEnum' description: >- Current status of the task. REQUIRED. Indicates the current state of the async operation. Terminal statuses (COMPLETED, FAILED, CANCELED) indicate the task has finished and will not change. Active statuses (PENDING, IN_PROGRESS, PROCESSING) indicate the task is still running and should be polled. Use this field to determine when to stop polling. examples: - PENDING - PROCESSING - COMPLETED - FAILED inputs: anyOf: - items: anyOf: - type: string - additionalProperties: true type: object type: array - type: 'null' title: Inputs description: >- Input parameters or data used to start the task. OPTIONAL. May include IDs, configuration objects, or file references. Useful for debugging and understanding what data the task processed. Format: List of strings (IDs) or objects (configuration). Example: ['batch_id_123'] or [{'bucket_id': 'bkt_abc', 'config': {...}}] examples: - - batch_xyz789 - - obj_123 - obj_456 - obj_789 - - bucket_id: bkt_abc collection_ids: - col_1 - col_2 outputs: anyOf: - items: anyOf: - type: string - additionalProperties: true type: object type: array - type: 'null' title: Outputs description: >- Output results produced by the task. OPTIONAL. Populated when task completes successfully. May include processed file IDs, result metrics, or status summaries. Check this field after task reaches COMPLETED status to get results. Format: List of strings (output IDs) or objects (result data). examples: - - document_123 - document_456 - - failed_count: 2 processed_count: 100 success_rate: 0.98 - - cluster_id: cl_abc123 num_clusters: 5 additional_data: anyOf: - additionalProperties: true type: object - type: 'null' title: Additional Data description: >- Additional metadata and context for the task. OPTIONAL. Contains job IDs, error details, progress info, and other task-specific metadata. Common fields (all task types): - 'error': Error message if task failed - 'job_id': Ray job ID for engine tasks - 'from_mongodb': True if retrieved from MongoDB fallback (not Redis) Batch-specific fields (task_type=api_buckets_batches_process): - 'batch_id': Batch identifier (REQUIRED) - 'bucket_id': Source bucket identifier (REQUIRED) - 'namespace_id': Namespace identifier (REQUIRED) - 'current_tier': Currently processing tier number, 0-indexed (OPTIONAL, None if not started) - 'total_tiers': Total number of tiers in the batch pipeline (REQUIRED) - 'collection_ids': Array of ALL collection IDs across all tiers (REQUIRED) - 'object_count': Number of objects being processed (REQUIRED) - 'sample_object_ids': First 5 object IDs for debugging/display (OPTIONAL) Performance Note: Full object_ids array is NOT stored in task metadata to avoid bloating task documents (batches with 10k+ objects would add 200KB+ per task). For full object list, query the batch directly via GET /v1/buckets/{bucket_id}/batches/{batch_id}. Note: For detailed per-tier status, use GET /v1/buckets/{bucket_id}/batches/{batch_id} to access the tier_tasks[] array which contains individual tier statuses, collection_ids, and timestamps for each tier. examples: - batch_id: btch_xyz789 bucket_id: bkt_products collection_ids: - col_tier0 - col_tier1 - col_tier2 current_tier: 1 job_id: ray_job_123 namespace_id: ns_abc123 object_count: 10000 sample_object_ids: - obj_001 - obj_002 - obj_003 - obj_004 - obj_005 total_tiers: 3 - error: 'Failed to process object: Invalid file format' job_id: '123' - cluster_id: cl_abc collection_ids: - col_1 from_mongodb: true error: anyOf: - type: string - type: 'null' title: Error description: >- Flattened error message for convenient error handling. OPTIONAL. Automatically populated from additional_data['error'] when the task has FAILED status. This is a convenience field - the full error details are always available in additional_data['error']. Use this field for displaying errors to users or logging. Will be None if task has not failed or if no error details are available. Serialized as 'error' in API responses for backward compatibility. examples: - 'Failed to process batch: Object not found' - 'Invalid file format: Expected PDF, got PNG' - 'Clustering failed: Insufficient data points' - null queue_position: anyOf: - type: integer - type: 'null' title: Queue Position description: >- 1-based position in the Ray processing waitlist. None if the batch was dispatched immediately (no queue). Position 1 means this batch will be processed next. estimated_wait_minutes: anyOf: - type: number - type: 'null' title: Estimated Wait Minutes description: >- Estimated minutes until this batch starts processing, based on queue position and average batch duration. None if the batch was dispatched immediately. type: object required: - task_id - task_type - status title: TaskResponse description: |- Task response model returned by the API. Extends TaskModel with additional convenience fields for API responses. This is the model returned when you GET /v1/tasks/{task_id}. Additional Fields: error_message: Convenience field that surfaces errors from additional_data for easier error handling in client code. Inheritance: Inherits all fields and documentation from TaskModel, including: - task_id: Unique identifier - task_type: Operation type - status: Current status - inputs: Input parameters - outputs: Output results - additional_data: Metadata and context Storage Architecture: Same as TaskModel - stored in Redis (24hr TTL) with MongoDB fallback. Usage: This model is automatically returned by task API endpoints. You don't need to construct it manually - just call GET /v1/tasks/{task_id}. Error Handling: Check the error_message field for a user-friendly error string, or additional_data['error'] for the full error details. Example Response: { "task_id": "task_abc123", "task_type": "api_buckets_batches_process", "status": "FAILED", "inputs": ["batch_xyz"], "outputs": null, "additional_data": { "error": "Failed to process batch: Object not found", "batch_id": "batch_xyz" }, "error_message": "Failed to process batch: Object not found" } examples: - additional_data: batch_id: btch_xyz789 bucket_id: bkt_products collection_ids: - col_tier0 - col_tier1 - col_tier2 current_tier: 1 job_id: ray_job_123 namespace_id: ns_abc123 object_count: 10000 sample_object_ids: - obj_001 - obj_002 - obj_003 - obj_004 - obj_005 total_tiers: 3 description: >- Multi-tier batch processing task in progress (tier 1 of 3) with 10k objects inputs: - batch_xyz789 status: IN_PROGRESS task_id: 2d322a05-3178-4eca-aac6-b82b0a0313aa task_type: api_buckets_batches_process - additional_data: cluster_id: cl_abc123 job_id: ray_job_456 description: Completed clustering task with results inputs: - collection_ids: - col_products config: algorithm: kmeans k: 5 outputs: - cluster_id: cl_abc123 num_clusters: 5 silhouette_score: 0.78 status: COMPLETED task_id: task_cluster_789 task_type: engine_cluster_build - additional_data: bucket_id: bkt_test error: 'Invalid file format: Expected PDF, got PNG' object_id: obj_123 description: Failed object creation task with error inputs: - bucket_id: bkt_test object_id: obj_123 status: FAILED task_id: task_failed_123 task_type: api_buckets_objects_create - additional_data: batch_id: batch_old_123 from_mongodb: true note: Retrieved from persistent storage after 24hr Redis expiry description: Task retrieved from MongoDB fallback (Redis expired) inputs: - batch_old_123 outputs: - Processed 500 objects status: COMPLETED task_id: taYOUR_OLD_API_KEY task_type: api_buckets_batches_process ErrorResponse: properties: success: type: boolean title: Success description: Always false for error responses default: false status: type: integer title: Status description: HTTP status code for this error error: $ref: '#/components/schemas/ErrorDetail' description: Error details payload type: object required: - status - error title: ErrorResponse description: Error response model. examples: - error: details: id: ns_123 resource: namespace message: Namespace not found type: NotFoundError status: 404 success: false HTTPValidationError: properties: detail: items: $ref: '#/components/schemas/ValidationError' type: array title: Detail type: object title: HTTPValidationError LogicalOperator-Input: properties: AND: anyOf: - items: anyOf: - $ref: '#/components/schemas/LogicalOperator-Input' - $ref: '#/components/schemas/FilterCondition' type: array - type: 'null' title: And description: Logical AND operation - all conditions must be true example: - field: name operator: eq value: John - field: age operator: gte value: 30 OR: anyOf: - items: anyOf: - $ref: '#/components/schemas/LogicalOperator-Input' - $ref: '#/components/schemas/FilterCondition' type: array - type: 'null' title: Or description: Logical OR operation - at least one condition must be true example: - field: status operator: eq value: active - field: role operator: eq value: admin NOT: anyOf: - items: anyOf: - $ref: '#/components/schemas/LogicalOperator-Input' - $ref: '#/components/schemas/FilterCondition' type: array - type: 'null' title: Not description: Logical NOT operation - all conditions must be false example: - field: department operator: eq value: HR - field: location operator: eq value: remote case_sensitive: anyOf: - type: boolean - type: 'null' title: Case Sensitive description: Whether to perform case-sensitive matching default: false example: true additionalProperties: true type: object title: LogicalOperator description: >- Represents a logical operation (AND, OR, NOT) on filter conditions. Allows nesting with a defined depth limit. Also supports shorthand syntax where field names can be passed directly as key-value pairs for equality filtering (e.g., {"metadata.title": "value"}). LLMLabeling-Input: 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 labeling_inputs: anyOf: - $ref: '#/components/schemas/LLMLabelingInput-Input' - 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. provider: anyOf: - $ref: '#/components/schemas/LLMProvider' - type: 'null' description: |- LLM provider to use for labeling. Supported providers: - openai: GPT models (GPT-4o, GPT-4o-mini, GPT-4.1, O3-mini) - google: Gemini models (Gemini 2.5 Flash, Gemini 1.5 Flash) - anthropic: Claude models (Claude 3.5 Sonnet, Claude 3.5 Haiku) If not specified, automatically inferred from model_name. examples: - openai - google - anthropic model_name: anyOf: - $ref: '#/components/schemas/OpenAIModel' - $ref: '#/components/schemas/GoogleModel' - $ref: '#/components/schemas/AnthropicModel' - 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. OpenAI Models (provider='openai'): - gpt-4o-2024-08-06: Highest quality, best for production ($2.50/$10 per 1M tokens) - gpt-4o-mini-2024-07-18: Cost-effective, recommended for most use cases ($0.15/$0.60 per 1M tokens) - gpt-4.1-2025-04-14: Latest model, future-proofed - gpt-4.1-mini-2025-04-14: Latest cost-optimized model - o3-mini-2025-01-31: Advanced reasoning, best for complex clustering Google Models (provider='google'): - gemini-2.5-flash-lite: Fastest, latest multimodal model, recommended ($0.15/$0.60 per 1M tokens) Anthropic Models (provider='anthropic'): - claude-3-5-sonnet-20241022: Best reasoning, 200K context ($3/$15 per 1M tokens) - claude-3-5-haiku-20241022: Fast, cost-effective ($0.25/$1.25 per 1M tokens) Recommendation: - Use gemini-2.5-flash-lite (DEFAULT) - multimodal support - Use gpt-4o-mini-2024-07-18 for OpenAI compatibility - Use gpt-4o-2024-08-06 for highest quality when cost is not a concern examples: - gemini-2.5-flash-lite - gemini-2.5-pro - gpt-4o-mini-2024-07-18 - gpt-4o-2024-08-06 - claude-sonnet-4-5-20250929 - claude-haiku-4-5-20251001 - gpt-4.1-2025-04-14 - gpt-4.1-mini-2025-04-14 - o3-mini-2025-01-31 include_summary: type: boolean title: Include Summary description: Whether to generate cluster summaries default: true include_keywords: type: boolean title: Include Keywords description: Whether to extract keywords for clusters default: true max_samples_per_cluster: anyOf: - type: integer maximum: 20 minimum: 1 - 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. sample_text_max_length: type: integer maximum: 500 minimum: 50 title: Sample Text Max Length description: Maximum characters per document sample text default: 200 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 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 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 custom_prompt: anyOf: - type: 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": "..."}]' examples: - >- 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': [...]}] - >- Generate emoji-based labels for these clusters. Use 1-2 emojis that represent the main theme. Return JSON: [{'cluster_id': 'cl_0', 'label': '🚀 Tech Innovation'}] - null response_shape: anyOf: - type: string - additionalProperties: true type: 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. Two modes supported: 1. Natural language prompt (string): Describe desired output in plain English - Service automatically infers JSON schema from your description - Example: 'Extract cluster category, confidence score (0-1), and top 3 representative terms' - Auto-generates schema with appropriate types (string, number, array, etc.) 2. Explicit JSON schema (dict): Provide complete JSON schema for output structure - Full control over output structure, types, and constraints - Example: {'type': 'object', 'properties': {'category': {'type': 'string'}, ...}} Use when: - Need custom metadata fields (confidence scores, sentiment, complexity) - Want domain-specific structure (taxonomy hierarchies, entity extractions) - Require specific data types (arrays, nested objects, enums) - Have downstream schema requirements Output fields are automatically added to cluster collection schema and stored in metadata. Default behavior (if not provided): label (string), summary (string), keywords (array of strings) examples: - >- Extract cluster category, confidence score between 0 and 1, and top 3 representative keywords - >- Generate cluster theme, sentiment (positive/negative/neutral), and list of key entities - properties: category: description: Main category type: string subcategory: description: Subcategory if applicable type: string confidence: maximum: 1 minimum: 0 type: number keywords: items: type: string maxItems: 5 type: array required: - category - confidence type: 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. type: object title: LLMLabeling description: |- Configuration for LLM-based cluster labeling. Supports multiple LLM providers with comprehensive model selection: - OpenAI: GPT-4o, GPT-4o-mini, GPT-4.1, O3-mini (best for quality) - Google: Gemini 2.5 Flash, Gemini 1.5 Flash (best for speed and cost) - Anthropic: Claude 3.5 Sonnet, Claude 3.5 Haiku (best for reasoning) All models are defined as enums and validated at API level. examples: - description: Text-only labeling with multiple fields 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 - description: Multimodal labeling with images (Gemini) enabled: true include_keywords: true include_summary: true labeling_inputs: input_mappings: - input_key: text path: headline source_type: payload - input_key: image_url path: thumbnail_url source_type: payload model_name: gemini-2.5-flash-lite provider: google - description: Multimodal labeling with video (Gemini) enabled: true include_keywords: true include_summary: true labeling_inputs: input_mappings: - input_key: text path: description source_type: payload - input_key: video_url path: video_url source_type: payload model_name: gemini-2.5-flash-lite provider: google - description: OpenAI GPT-4o (highest quality, text-only) enabled: true include_keywords: true include_summary: true labeling_inputs: input_mappings: - input_key: text path: text source_type: payload model_name: gpt-4o-2024-08-06 provider: openai - description: Anthropic Claude 3.5 Sonnet (best reasoning) enabled: true include_keywords: true include_summary: true labeling_inputs: input_mappings: - input_key: text path: text source_type: payload model_name: claude-sonnet-4-5-20250929 provider: anthropic - description: 'Minimal configuration (uses defaults: text field from payload)' enabled: true - custom_prompt: >- You are a medical document classifier. Analyze the following patient record clusters and generate HIPAA-compliant category labels (2-3 words) that describe the medical condition or treatment type. DO NOT include patient names or identifiers. Return JSON array: [{"cluster_id": "cl_0", "label": "...", "keywords": [...]}] description: Custom prompt for domain-specific labeling enabled: true labeling_inputs: input_mappings: - input_key: text path: text source_type: payload model_name: gpt-4o-mini-2024-07-18 provider: openai TaskType: type: string enum: - api_namespaces_create - api_namespaces_delete - api_namespaces_snapshot_create - api_namespaces_snapshot_restore - api_namespaces_migrations_run - api_buckets_objects_create - api_buckets_delete - api_buckets_batches_process - api_buckets_batches_submit - api_buckets_uploads_create - api_buckets_uploads_confirm - api_buckets_uploads_batch_confirm - api_collections_documents_create - api_collections_extraction_artifacts - api_taxonomies_create - api_taxonomies_execute - api_taxonomies_materialize - api_evaluations_run - api_evaluations_dataset_create - api_retrievers_publish - api_collections_export - api_collections_trigger - engine_feature_extractor_run - engine_inference_run - engine_object_processing - engine_cluster_build - thumbnail - video_segment - audio_segment - converted_video - materialize - plugin_custom - model_custom title: TaskType description: >- Types of asynchronous tasks that can be performed in the system. Task types identify the specific operation being performed. This helps with tracking, debugging, and filtering tasks by operation type. Categories: API Tasks: User-initiated operations via API endpoints Engine Tasks: Background processing tasks Inference Tasks: Specialized inference operations API Task Types: API_NAMESPACES_CREATE: Creating a new namespace API_NAMESPACES_MIGRATIONS_RUN: Running a namespace migration API_BUCKETS_OBJECTS_CREATE: Creating objects in a bucket API_BUCKETS_DELETE: Deleting a bucket and its contents API_BUCKETS_BATCHES_PROCESS: Processing a batch of objects API_BUCKETS_BATCHES_SUBMIT: Submitting a batch for processing API_BUCKETS_UPLOADS_CREATE: Creating an upload session API_BUCKETS_UPLOADS_CONFIRM: Confirming an upload completion API_BUCKETS_UPLOADS_BATCH_CONFIRM: Confirming batch upload completion API_TAXONOMIES_CREATE: Creating a new taxonomy API_TAXONOMIES_EXECUTE: Executing taxonomy classification API_TAXONOMIES_MATERIALIZE: Materializing taxonomy results API_RETRIEVERS_PUBLISH: Publishing retriever assets (OG images, etc.) Engine Task Types: ENGINE_FEATURE_EXTRACTOR_RUN: Running feature extraction on data ENGINE_INFERENCE_RUN: Running inference operations ENGINE_OBJECT_PROCESSING: Processing object data ENGINE_CLUSTER_BUILD: Building clusters from data Inference Task Types: THUMBNAIL: Generating thumbnails MATERIALIZE: Materializing processed data Usage: Task types are automatically assigned when tasks are created. You can filter tasks by type when listing or searching for specific operations. TaskStatusEnum: type: string enum: - PENDING - QUEUED - IN_PROGRESS - PROCESSING - COMPLETED - COMPLETED_WITH_ERRORS - FAILED - CANCELED - INTERRUPTED - UNKNOWN - SKIPPED - DRAFT - ACTIVE - ARCHIVED - SUSPENDED title: TaskStatusEnum description: |- Enumeration of task statuses for tracking asynchronous operations. Task statuses indicate the current state of asynchronous operations like batch processing, object ingestion, clustering, and taxonomy execution. Status Categories: Operation Statuses: Track progress of async operations Lifecycle Statuses: Track entity state (buckets, collections, namespaces) Values: PENDING: Task is queued but has not started processing yet IN_PROGRESS: Task is currently being executed PROCESSING: Task is actively processing data (similar to IN_PROGRESS) COMPLETED: Task finished successfully with no errors COMPLETED_WITH_ERRORS: Task finished but some items failed (partial success) FAILED: Task encountered an error and could not complete CANCELED: Task was manually canceled by a user or system UNKNOWN: Task status could not be determined SKIPPED: Task was intentionally skipped DRAFT: Task is in draft state and not yet submitted ACTIVE: Entity is active and operational (for buckets, collections, etc.) ARCHIVED: Entity has been archived SUSPENDED: Entity has been temporarily suspended Terminal Statuses: COMPLETED, COMPLETED_WITH_ERRORS, FAILED, CANCELED are terminal statuses. Once a task reaches these states, it will not transition to another state. Partial Success Handling: COMPLETED_WITH_ERRORS indicates that the operation completed but some documents/items failed. The task result includes: - List of successful items - List of failed items with error details - Success rate percentage This allows clients to handle partial success scenarios appropriately. Polling Guidance: - Poll tasks in PENDING, QUEUED, IN_PROGRESS, or PROCESSING states - Stop polling when task reaches COMPLETED, COMPLETED_WITH_ERRORS, FAILED, or CANCELED - Use exponential backoff (1s → 30s) when polling ErrorDetail: properties: message: type: string title: Message description: Human-readable error message type: type: string title: Type description: Stable error type identifier (machine-readable) code: anyOf: - type: string - type: 'null' title: Code description: >- Fine-grained error code for programmatic handling (e.g., namespace_name_taken, feature_extractor_not_found). Present only when consumers may need to branch on a specific error condition. details: anyOf: - additionalProperties: true type: object - type: 'null' title: Details description: >- Optional structured details to help debugging (validation errors, IDs, etc.) type: object required: - message - type title: ErrorDetail description: Error detail model. ValidationError: properties: loc: items: anyOf: - type: string - type: integer type: array title: Location msg: type: string title: Message type: type: string title: Error Type type: object required: - loc - msg - type title: ValidationError FilterCondition: properties: field: type: string title: Field description: Field name to filter on operator: $ref: '#/components/schemas/FilterOperator' description: Comparison operator default: eq value: anyOf: - $ref: '#/components/schemas/DynamicValue' - {} title: Value description: Value to compare against type: object required: - field - value title: FilterCondition description: |- Represents a single filter condition. Attributes: field: The field to filter on operator: The comparison operator value: The value to compare against LLMLabelingInput-Input: properties: input_mappings: items: $ref: '#/components/schemas/InputMapping' 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. type: object required: - input_mappings title: LLMLabelingInput description: |- Input configuration for LLM-based cluster labeling. Supports flexible input mappings similar to retrievers and buckets, allowing multimodal inputs (text, images, videos, audio) for providers like Gemini that support native multimodal understanding. Examples: # Text-only labeling: LLMLabelingInput(input_mappings=[ InputMapping(input_key="headline", source_type="payload", path="headline"), InputMapping(input_key="description", source_type="payload", path="description") ]) # Multimodal labeling with images: LLMLabelingInput(input_mappings=[ InputMapping(input_key="text", source_type="payload", path="headline"), InputMapping(input_key="image_url", source_type="payload", path="thumbnail_url") ]) # Multimodal with video (for Gemini): LLMLabelingInput(input_mappings=[ InputMapping(input_key="text", source_type="payload", path="description"), InputMapping(input_key="video_url", source_type="payload", path="video_url") ]) LLMProvider: type: string enum: - openai - google - anthropic title: LLMProvider description: >- Supported LLM providers for content generation. Each provider has different strengths, pricing, and multimodal capabilities. Choose based on your use case, performance requirements, and budget. Values: OPENAI: OpenAI GPT models (GPT-4o, GPT-4.1, O3-mini) - Best for: General purpose, vision tasks, structured outputs - Multimodal: Text, images - Performance: Fast (100-500ms), reliable - Cost: Moderate to high ($0.15-$10 per 1M tokens) - Use when: Need high-quality generation with vision support GOOGLE: Google Gemini models (Gemini 3.1 Flash Lite, Gemini 2.5 Pro) - Best for: Fast generation, video understanding, cost-efficiency - Multimodal: Text, images, video, audio, PDFs - Performance: Very fast (50-200ms) - Cost: Low to moderate ($0.075-$0.40 per 1M tokens) - Use when: Need video/audio/PDF support or cost-efficiency ANTHROPIC: Anthropic Claude models (Claude 3.5 Sonnet, Claude 3.5 Haiku) - Best for: Long context, complex reasoning, safety - Multimodal: Text, images - Performance: Moderate (200-800ms) - Cost: Moderate to high ($0.25-$15 per 1M tokens) - Use when: Need long context or complex reasoning Examples: - Use OPENAI for production with structured JSON outputs - Use GOOGLE for video summarization and cost-sensitive workloads - Use ANTHROPIC for complex reasoning with long documents OpenAIModel: type: string 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 title: OpenAIModel description: |- OpenAI model identifiers for LLM generation. Models listed in order of capability and cost (highest to lowest). All models support vision (images) except O3-mini. Values: GPT_4O: Latest GPT-4 Omni model (2024-08-06) - Use for: Production, highest quality generation - Context: 128K tokens - Vision: Yes - Cost: $2.50/1M input, $10/1M output - Performance: 200-500ms per request - When to use: Need best quality, willing to pay premium GPT_41: GPT-4.1 (2025-04-14) - Use for: Future-proofed pipelines - Context: 128K tokens - Vision: Yes - Cost: TBD (expected similar to GPT-4o) - When to use: Want latest model features GPT_4O_MINI: Smaller, faster GPT-4 Omni (2024-07-18) - Use for: High-volume, cost-sensitive workloads - Context: 128K tokens - Vision: Yes - Cost: $0.15/1M input, $0.60/1M output - Performance: 100-200ms per request - When to use: Good balance of quality and cost GPT_41_MINI: Smaller GPT-4.1 (2025-04-14) - Use for: Future cost-optimized pipelines - Context: 128K tokens - Vision: Yes - Cost: TBD (expected similar to GPT-4o-mini) - When to use: Want latest features at lower cost O3_MINI: Reasoning-optimized model (2025-01-31) - Use for: Complex reasoning, math, code - Context: 200K tokens - Vision: No - Cost: TBD - When to use: Need advanced reasoning capabilities Examples: - Use GPT_4O for caption generation with images (best quality) - Use GPT_4O_MINI for high-volume video scene summarization (cost-effective) - Use O3_MINI for complex entity extraction requiring reasoning GoogleModel: type: string enum: - gemini-2.5-flash-lite - gemini-2.5-flash - gemini-2.5-pro - gemini-3.1-flash-lite title: GoogleModel description: >- Google Gemini model identifiers for LLM generation. Gemini models excel at multimodal understanding with best-in-class video support. All models support text, images, video, audio, and PDFs. Values: GEMINI_2_5_FLASH_LITE: Gemini 2.5 Flash Lite model (recommended, stable GA) - Use for: Fastest generation, cost-effective multimodal - Context: 1M tokens - Multimodal: Text, images, video, audio, PDFs - When to use: Default choice for all Gemini use cases GEMINI_2_5_PRO: Gemini 2.5 Pro model - Use for: Higher quality reasoning, complex tasks - Context: 1M tokens GEMINI_2_5_FLASH: Gemini 2.5 Flash model - Kept for backward compatibility. GEMINI_3_1_FLASH_LITE: Alias for gemini-2.5-flash-lite (backwards compat) - Note: gemini-3.1-flash-lite does NOT exist in Google's API. This value is mapped to gemini-2.5-flash-lite at runtime. AnthropicModel: type: string enum: - claude-sonnet-4-5-20250929 - claude-haiku-4-5-20251001 - claude-3-5-sonnet-20241022 - claude-3-5-haiku-20241022 title: AnthropicModel description: |- Anthropic Claude model identifiers for LLM generation. Claude models excel at long context, complex reasoning, and safety. All models support text and images. Values: CLAUDE_3_5_SONNET: Most capable Claude model - Use for: Complex reasoning, long documents, safety-critical - Context: 200K tokens - Vision: Yes - Cost: $3/1M input, $15/1M output - Performance: 300-800ms per request - When to use: Need best reasoning, safety, or long context CLAUDE_3_5_HAIKU: Fast, cost-effective Claude model - Use for: High-volume, quick summaries - Context: 200K tokens - Vision: Yes - Cost: $0.25/1M input, $1.25/1M output - Performance: 100-300ms per request - When to use: Good balance of quality and cost Examples: - Use CLAUDE_3_5_SONNET for complex entity extraction from contracts (best reasoning) - Use CLAUDE_3_5_HAIKU for high-volume content moderation (cost-effective) FilterOperator: type: string enum: - eq - ne - gt - lt - gte - lte - in - nin - contains - starts_with - ends_with - regex - exists - is_null - text - phrase title: FilterOperator description: Supported filter operators across database implementations. DynamicValue: properties: type: type: string const: dynamic title: Type default: dynamic field: type: string title: Field description: >- The dot-notation path to the value in the runtime query request, e.g., 'inputs.user_id' examples: - inputs.query_text - filters.AND[0].value type: object required: - field title: DynamicValue description: A value that should be dynamically resolved from the query request. InputMapping: properties: input_key: type: string title: Input Key description: Key used in the constructed inputs payload. source_type: anyOf: - $ref: '#/components/schemas/InputSourceType' - type: 'null' description: Source of the value (payload, literal, vector). path: anyOf: - type: string - type: 'null' title: Path description: >- Dot-notation path inside payload/vector when source_type is PAYLOAD or VECTOR. override: anyOf: - {} - type: 'null' title: Override description: Static value used when source_type is LITERAL. Overrides any path. type: object required: - input_key title: InputMapping description: |- Declarative mapping for building inputs from various sources. - input_key: The key used in the constructed inputs payload - source_type: Where to fetch the value (payload, literal, vector) - path: Dot-notation path when source_type is PAYLOAD or VECTOR - 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 InputSourceType: type: string enum: - payload - literal - vector - blob title: InputSourceType description: Where the value for an input should be retrieved from. ````