Partially Update Cluster

Headers

Authorization

string

required

REQUIRED: Bearer token authentication using your API key. Format: 'Bearer sk_xxxxxxxxxxxxx'. You can create API keys in the Mixpeek dashboard under Organization Settings.

X-Namespace

string

required

REQUIRED: Namespace identifier for scoping this request. All resources (collections, buckets, taxonomies, etc.) are scoped to a namespace. You can provide either the namespace name or namespace ID. Format: ns_xxxxxxxxxxxxx (ID) or a custom name like 'my-namespace'

Path Parameters

cluster_identifier

string

required

Cluster ID or name

Body

application/json

Request model for partially updating a cluster (PATCH operation).

cluster_name

string | null

Updated name for the cluster

description

string | null

Updated description for the cluster

metadata

Metadata · object

Updated metadata for the cluster

Response

Successful Response

Cluster metadata stored in MongoDB.

collection_ids

string[] | null

Collections to cluster together

Minimum array length: 1

cluster_name

string | null

Optional human-friendly name for the clustering job

cluster_type

enum<string>

default:vector

Vector or attribute clustering

Available options:

vector,

attribute

vector_config

VectorBasedConfig · object

Required when cluster_type is 'vector'

Show child attributes

vector_config.clustering_method

enum<string>

required

Clustering algorithm to use

Available options:

kmeans,

dbscan,

hdbscan,

agglomerative,

spectral,

gaussian_mixture,

mean_shift,

optics,

attribute_based

vector_config.feature_uri

string | null

DEPRECATED: Use feature_uris instead. Canonical feature URI for the vector embedding to cluster. Format: 'mixpeek://{extractor}@{version}/{output}'. For multi-feature clustering, use feature_uris (plural) instead.

Example:

"mixpeek://multimodal_extractor@v1/multimodal_embedding"

vector_config.feature_uris

string[] | null

RECOMMENDED. List of feature URIs to cluster. Format: 'mixpeek://{extractor}@{version}/{output}'. For single-feature clustering, provide a list with one element. For multi-feature clustering, provide multiple feature URIs. Each feature must exist in all input collections.

Minimum array length: 1

Example:

["mixpeek://text_extractor@v1/embedding"]

vector_config.sample_size

integer | null

Number of samples to use for clustering

vector_config.kmeans_parameters

KMeansParams · object

Parameters for K-means clustering (deprecated, use algorithm_params)

KMeansParams
Kmeans Parameters

Show child attributes

vector_config.kmeans_parameters.n_clusters

integer

default:8

Number of clusters to form

Required range: 2 <= x <= 1000

vector_config.kmeans_parameters.max_iter

integer

default:300

Maximum number of iterations

Required range: 1 <= x <= 10000

vector_config.kmeans_parameters.random_state

integer | null

default:42

Random seed for reproducibility

vector_config.kmeans_parameters.n_init

integer

default:10

Number of times k-means will run with different centroid seeds

Required range: x >= 1

vector_config.kmeans_parameters.tol

number

default:0.0001

Tolerance for convergence

vector_config.kmeans_parameters.init

string

default:k-means++

Method for initialization ('k-means++' or 'random')

vector_config.kmeans_parameters.verbose

integer

default:0

Verbosity mode

Required range: x >= 0

vector_config.kmeans_parameters.copy_x

boolean

default:true

If True, the original data is not modified

vector_config.kmeans_parameters.algorithm

string

default:lloyd

K-means algorithm to use ('lloyd', 'elkan', or 'auto')

vector_config.dbscan_parameters

DBSCANParams · object

Parameters for DBSCAN clustering (deprecated, use algorithm_params)

DBSCANParams
Dbscan Parameters

Show child attributes

vector_config.dbscan_parameters.eps

number

default:0.5

Maximum distance between two samples for one to be considered in the neighborhood of the other

vector_config.dbscan_parameters.min_samples

integer

default:5

Number of samples in a neighborhood for a point to be considered a core point

Required range: x >= 1

vector_config.dbscan_parameters.metric

string

default:euclidean

Metric to use for distance computation

vector_config.dbscan_parameters.metric_params

Metric Params · object

Additional keyword arguments for the metric function

vector_config.dbscan_parameters.algorithm

string

default:auto

Algorithm to compute pointwise distances and find nearest neighbors ('auto', 'ball_tree', 'kd_tree', 'brute')

vector_config.dbscan_parameters.leaf_size

integer

default:30

Leaf size passed to BallTree or KDTree

Required range: x >= 1

vector_config.dbscan_parameters.p

number

default:2

The power of the Minkowski metric to be used to calculate distance between points

vector_config.dbscan_parameters.n_jobs

integer

default:1

The number of parallel jobs to run (-1 means using all processors)

vector_config.hdbscan_parameters

HDBSCANParams · object

Parameters for HDBSCAN clustering (deprecated, use algorithm_params)

HDBSCANParams
Hdbscan Parameters

Show child attributes

vector_config.hdbscan_parameters.min_cluster_size

integer

default:5

Minimum number of samples in a cluster

Required range: x >= 2

vector_config.hdbscan_parameters.min_samples

integer | null

Number of samples in a neighborhood for a point to be considered a core point. Defaults to min_cluster_size if None

Required range: x >= 1

vector_config.hdbscan_parameters.cluster_selection_epsilon

number

default:0

A distance threshold for cluster selection. Clusters below this value will be merged

Required range: x >= 0

vector_config.hdbscan_parameters.max_cluster_size

integer | null

Maximum number of samples in a cluster. Clusters above this size will be split

Required range: x >= 1

vector_config.hdbscan_parameters.metric

string

default:euclidean

Metric to use for distance computation

vector_config.hdbscan_parameters.alpha

number

default:1

A distance scaling parameter

vector_config.hdbscan_parameters.cluster_selection_method

string

default:eom

Method to select clusters from the condensed tree ('eom' or 'leaf')

vector_config.hdbscan_parameters.allow_single_cluster

boolean

default:false

Allow HDBSCAN to find only a single cluster

vector_config.hdbscan_parameters.prediction_data

boolean

default:false

Whether to generate extra data for predicting cluster membership

vector_config.hdbscan_parameters.match_reference_implementation

boolean

default:false

Whether to match the reference implementation exactly

vector_config.algorithm_params

KMeansParams · object

Algorithm-specific parameters

Show child attributes

vector_config.algorithm_params.n_clusters

integer

default:8

Number of clusters to form

Required range: 2 <= x <= 1000

vector_config.algorithm_params.max_iter

integer

default:300

Maximum number of iterations

Required range: 1 <= x <= 10000

vector_config.algorithm_params.random_state

integer | null

default:42

Random seed for reproducibility

vector_config.algorithm_params.n_init

integer

default:10

Number of times k-means will run with different centroid seeds

Required range: x >= 1

vector_config.algorithm_params.tol

number

default:0.0001

Tolerance for convergence

vector_config.algorithm_params.init

string

default:k-means++

Method for initialization ('k-means++' or 'random')

vector_config.algorithm_params.verbose

integer

default:0

Verbosity mode

Required range: x >= 0

vector_config.algorithm_params.copy_x

boolean

default:true

If True, the original data is not modified

vector_config.algorithm_params.algorithm

string

default:lloyd

K-means algorithm to use ('lloyd', 'elkan', or 'auto')

vector_config.multi_feature_strategy

enum<string>

default:concatenate

Strategy for handling multiple feature vectors:

concatenate: Combine embeddings into one vector, single clustering
independent: Run separate clustering per feature
weighted: Learn optimal feature weights

Available options:

concatenate,

independent,

weighted

vector_config.normalize_features

boolean

default:true

Apply L2 normalization to each feature block before concatenation. Prevents feature dominance when combining different modalities. Only applies when multi_feature_strategy='concatenate'.

vector_config.feature_weights

Feature Weights · object

Optional per-feature weights (0.0-1.0) for concatenation strategy. Keys are feature URIs, values are weights. Example: {'mixpeek://text@v1/emb': 0.7, 'mixpeek://image@v1/emb': 0.3}. Defaults to equal weights (1.0) if not specified. Only applies when multi_feature_strategy='concatenate'. If multi_feature_strategy='weighted' and this is None, weights are learned automatically using weight_learning_config.

Show child attributes

vector_config.feature_weights.{key}

number

Example:

{
  "mixpeek://image_extractor@v1/embedding": 0.3,
  "mixpeek://text_extractor@v1/embedding": 0.7
}

vector_config.weight_learning_config

WeightLearningConfig · object

Configuration for automatic feature weight learning. Only used when multi_feature_strategy='weighted' and feature_weights is None. If feature_weights is provided, manual weights are used instead of learning. If this is None when learning is needed, default WeightLearningConfig is used.

Show child attributes

vector_config.weight_learning_config.method

enum<string>

default:bayesian

Weight learning method:

bayesian: Gaussian process optimization (recommended, scales to 5+ features)
grid_search: Exhaustive search (limited to 2-3 features, simpler but slower)

Available options:

grid_search,

bayesian

vector_config.weight_learning_config.max_iterations

integer

default:20

Maximum optimization iterations:

grid_search: Number of values to try per feature (total: max_iterations^n_features)
bayesian: Number of weight combinations to evaluate Recommended: 20 for bayesian, 5 for grid_search

Required range: 5 <= x <= 100

vector_config.weight_learning_config.metric

enum<string>

default:silhouette

Clustering quality metric to optimize:

silhouette: Measures how similar points are to their cluster vs other clusters (range: [-1, 1], higher is better)
davies_bouldin: Ratio of within-cluster to between-cluster distances (range: [0, ∞], lower is better)
calinski_harabasz: Ratio of between-cluster to within-cluster variance (range: [0, ∞], higher is better) Recommended: silhouette (most general-purpose)

Available options:

silhouette,

davies_bouldin,

calinski_harabasz

vector_config.weight_learning_config.sample_size

integer | null

Optional: Learn weights on a random sample (speeds up large datasets). If provided and dataset has more documents, weights are learned on sample_size random documents, then applied to full dataset. Recommended: 5000 for datasets >10k documents

Required range: x >= 100

Example:

5000

vector_config.weight_learning_config.random_state

integer

default:42

Random seed for reproducibility of weight learning

Example:

{
  "max_iterations": 20,
  "method": "bayesian",
  "metric": "silhouette",
  "random_state": 42,
  "sample_size": 5000
}

vector_config.output_strategy

enum<string>

default:single

Output collection creation strategy:

single: Create one collection with all feature vectors
per_feature: Create separate collections for each feature (for hierarchical taxonomies)

Available options:

single,

per_feature

vector_config.effective_feature_method

enum<string>

default:mean

Method for calculating cluster centroids:

mean: Average of all vectors in cluster
median: Median vector (robust to outliers)
medoid: Actual cluster member closest to centroid

Available options:

mean,

median,

medoid

vector_config.enrich_source

boolean

default:false

Whether to enrich source documents with cluster_id

Example:

{
  "algorithm_params": { "min_cluster_size": 10, "min_samples": 5 },
  "clustering_method": "hdbscan",
  "description": "HDBSCAN clustering with multimodal embeddings",
  "feature_uri": "mixpeek://multimodal_extractor@v1/multimodal_embedding",
  "sample_size": 1000
}

attribute_config

AttributeBasedConfig · object

Required when cluster_type is 'attribute'

Show child attributes

attribute_config.attributes

string[]

required

List of attribute field names to use for clustering. Documents will be grouped by unique combinations of these attribute values. Supports dot-notation for nested fields (e.g., 'metadata.category'). Order matters for hierarchical grouping: first attribute is top-level, subsequent are nested.

Minimum array length: 1

attribute_config.hierarchical_grouping

boolean

default:false

Whether to create hierarchical clusters based on attribute order. When True: Creates parent clusters for each unique value of the first attribute, then child clusters for subsequent attributes within each parent. When False: Creates flat clusters for each unique combination of all attributes. Example with ['category', 'brand']: hierarchical=True → 'Electronics' (parent) → 'Apple', 'Samsung' (children). hierarchical=False → 'Electronics_Apple', 'Electronics_Samsung' (flat).

attribute_config.aggregation_method

string | null

Method for aggregating attribute values when creating cluster centroids. Options: 'most_frequent' (default), 'first', 'last'. Most use cases should use the default.

Example:

"most_frequent"

Example:

{
  "attributes": ["category"],
  "description": "Simple category clustering",
  "hierarchical_grouping": false
}

filters

LogicalOperator · object

Optional filters to pre-filter documents before clustering (same format as list documents). Applied during Qdrant scroll before parquet export. Useful for clustering subsets like: status='active', category='electronics', etc.

Show child attributes

filters.AND

FilterCondition · object[] | null

Logical AND operation - all conditions must be true

Represents a single filter condition.

Attributes: field: The field to filter on operator: The comparison operator value: The value to compare against

Show child attributes

filters.AND.field

string

required

Field name to filter on

filters.AND.value

required

Value to compare against

Show child attributes

filters.AND.value.field

string

required

The dot-notation path to the value in the runtime query request, e.g., 'inputs.user_id'

filters.AND.value.type

string

default:dynamic

Allowed value: "dynamic"

filters.AND.operator

enum<string>

default:eq

Comparison operator

Available options:

eq,

ne,

gt,

lt,

gte,

lte,

in,

nin,

contains,

starts_with,

ends_with,

regex,

exists,

is_null,

text

Example:

[
  {
    "field": "name",
    "operator": "eq",
    "value": "John"
  },
  {
    "field": "age",
    "operator": "gte",
    "value": 30
  }
]

filters.OR

FilterCondition · object[] | null

Logical OR operation - at least one condition must be true

Represents a single filter condition.

Attributes: field: The field to filter on operator: The comparison operator value: The value to compare against

Show child attributes

filters.OR.field

string

required

Field name to filter on

filters.OR.value

required

Value to compare against

Show child attributes

filters.OR.value.field

string

required

The dot-notation path to the value in the runtime query request, e.g., 'inputs.user_id'

filters.OR.value.type

string

default:dynamic

Allowed value: "dynamic"

filters.OR.operator

enum<string>

default:eq

Comparison operator

Available options:

eq,

ne,

gt,

lt,

gte,

lte,

in,

nin,

contains,

starts_with,

ends_with,

regex,

exists,

is_null,

text

Example:

[
  {
    "field": "status",
    "operator": "eq",
    "value": "active"
  },
  {
    "field": "role",
    "operator": "eq",
    "value": "admin"
  }
]

filters.NOT

FilterCondition · object[] | null

Logical NOT operation - all conditions must be false

Represents a single filter condition.

Attributes: field: The field to filter on operator: The comparison operator value: The value to compare against

Show child attributes

filters.NOT.field

string

required

Field name to filter on

filters.NOT.value

required

Value to compare against

Show child attributes

filters.NOT.value.field

string

required

The dot-notation path to the value in the runtime query request, e.g., 'inputs.user_id'

filters.NOT.value.type

string

default:dynamic

Allowed value: "dynamic"

filters.NOT.operator

enum<string>

default:eq

Comparison operator

Available options:

eq,

ne,

gt,

lt,

gte,

lte,

in,

nin,

contains,

starts_with,

ends_with,

regex,

exists,

is_null,

text

Example:

[
  {
    "field": "department",
    "operator": "eq",
    "value": "HR"
  },
  {
    "field": "location",
    "operator": "eq",
    "value": "remote"
  }
]

filters.case_sensitive

boolean | null

default:false

Whether to perform case-sensitive matching

Example:

true

llm_labeling

LLMLabeling · object

Optional configuration for LLM-based cluster labeling. When provided with enabled=True, clusters will have semantic labels generated by LLM instead of generic labels like 'Cluster 0'. When not provided or enabled=False, uses fallback labels.

Show child attributes

llm_labeling.enabled

boolean

default:false

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'.

llm_labeling.labeling_inputs

LLMLabelingInput · object

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 will be serialized as JSON and sent to the LLM, providing complete context for semantic labeling.

Show child attributes

llm_labeling.labeling_inputs.input_mappings

InputMapping · object[]

required

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.

Minimum array length: 1

Show child attributes

llm_labeling.labeling_inputs.input_mappings.input_key

string

required

Key used in the constructed inputs payload.

llm_labeling.labeling_inputs.input_mappings.source_type

enum<string> | null

Source of the value (payload, literal, vector).

Available options:

payload,

literal,

vector

llm_labeling.labeling_inputs.input_mappings.path

string | null

Dot-notation path inside payload/vector when source_type is PAYLOAD or VECTOR.

llm_labeling.labeling_inputs.input_mappings.override

any | null

Static value used when source_type is LITERAL. Overrides any path.

llm_labeling.provider

enum<string> | null

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.

Available options:

openai,

google,

anthropic

Example:

"openai"

llm_labeling.model_name

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: Fastest, latest multimodal model, recommended ($0.10/$0.40 per 1M tokens)
gemini-1.5-flash-001: Fast, cost-effective, 1M token context ($0.075/$0.30 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 gpt-4o-mini-2024-07-18 for balanced quality and cost
Use gemini-2.5-flash for fastest generation with multimodal support
Use gpt-4o-2024-08-06 for highest quality when cost is not a concern

Available options:

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:

"gpt-4o-mini-2024-07-18"

llm_labeling.include_summary

boolean

default:true

Whether to generate cluster summaries

llm_labeling.include_keywords

boolean

default:true

Whether to extract keywords for clusters

llm_labeling.max_samples_per_cluster

integer

default:10

Maximum representative documents to send to LLM per cluster for semantic analysis

Required range: 1 <= x <= 20

llm_labeling.sample_text_max_length

integer

default:200

Maximum characters per document sample text

Required range: 50 <= x <= 500

llm_labeling.use_embedding_dedup

boolean

default:false

Enable embedding-based label deduplication to prevent near-duplicate labels (requires sentence-transformers)

llm_labeling.embedding_similarity_threshold

number

default:0.8

Cosine similarity threshold for duplicate label detection (labels above this are considered duplicates)

Required range: 0.5 <= x <= 1

llm_labeling.cache_ttl_seconds

integer

default:604800

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.

Required range: 0 <= x <= 2592000

llm_labeling.custom_prompt

string | null

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': [...]}]"

llm_labeling.response_shape

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:

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.)
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)

Example:

"Extract cluster category, confidence score between 0 and 1, and top 3 representative keywords"

llm_labeling.parameters

Parameters · object

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.

Example:

{
  "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"
}

enrich_source_collection

boolean

default:false

If True, cluster results are written back to source collection(s) in-place instead of creating new output collections. Documents will be enriched with cluster_id, cluster_label, distance_to_centroid, and optionally other metadata. Similar to taxonomy enrichment pattern.

source_enrichment_config

SourceEnrichmentConfig · object

Configuration for source collection enrichment (only used if enrich_source_collection=True). Controls which fields are added to source documents and field naming conventions.

Show child attributes

source_enrichment_config.field_mappings

EnrichmentFieldMapping · object[]

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.

Show child attributes

source_enrichment_config.field_mappings.source_field

string

required

Field from cluster results to include. Available fields: cluster_id, cluster_label, distance_to_centroid, member_count, keywords, x, y, z (visualization coords), metadata.*

source_enrichment_config.field_mappings.target_field

string

required

Target field name in enriched document. Example: 'category_id' for cluster_id, 'product_category' for cluster_label

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"
    }
  ]
}

cluster_id

string

Unique cluster identifier

parquet_path

string | null

S3 path to parquet files with cluster data

members_key

string | null

S3 key to members.parquet (if saved)

num_clusters

integer | null

Number of clusters found

cluster_stats

ClusterStats · object

Clustering quality metrics

Show child attributes

cluster_stats.num_clusters

integer

required

cluster_stats.noise_points

integer | null

Number of noise points (for DBSCAN, etc.)

cluster_stats.silhouette_score

number | null

Silhouette score (-1 to 1, higher is better)

cluster_stats.extra

Extra · object

status

enum<string>

default:PENDING

Clustering job status

Available options:

PENDING,

IN_PROGRESS,

PROCESSING,

COMPLETED,

COMPLETED_WITH_ERRORS,

FAILED,

CANCELED,

UNKNOWN,

SKIPPED,

DRAFT,

ACTIVE,

ARCHIVED,

SUSPENDED

task_id

string | null

Associated task ID for clustering job

last_run_id

string | null

Run ID of the most recent successful clustering execution. Used to retrieve execution results.

created_at

string<date-time>

When the cluster was created

updated_at

string<date-time>

When the cluster was last updated

metadata

Metadata · object

Additional user-defined metadata for the cluster

Health

Organizations

Namespaces

Buckets

Feature Extractors

Collections

Retrievers

Taxonomies

Clusters

Templates

Analytics

Resource Search

Inference

Agent Sessions

Tasks

Webhooks

Notifications

Triggers

Partially Update Cluster

Headers

Path Parameters

Body

Response