Cluster Documents
POST /api/discovery/enhanced-search/cluster-documents
POST
/api/discovery/enhanced-search/cluster-documents
Cluster documents into semantic groups using K-means or community detection.
Request Body:
num_clusters: Number of clusters to create (2-20, default: 5)method: Clustering method (“kmeans” or “community”, default: “kmeans”)model_name: Embedding model reported in the response (default: “multilingual-e5-large-instruct”)
Returns:
clusters: Dictionary mapping cluster IDs to document ID listscluster_stats: Statistics for each cluster:document_count: Number of documents in clusterdocuments: Preview of first 5 document IDs
Raises:
- 404: No document embeddings found (reindex required)
- 500: Clustering operation failed
Request Body required
Section titled “Request Body required ”Responses
Section titled “ Responses ”Successful Response
ClusterResult
Document clustering result response model.
Provides results from document clustering operations including cluster assignments and cluster statistics for semantic document grouping and organization analysis.
Fields:
clusters: Dictionary mapping cluster IDs to lists of document IDs assigned to each clustercluster_stats: Dictionary containing cluster statistics including:document_count: Number of documents in clusterdocuments: Preview list of document IDs in cluster
Usage: POST /api/discovery/enhanced-search/cluster-documents returns this response model.
JSON Example:
{
"clusters": {
"cluster_0": ["doc_1", "doc_2", "doc_3"],
"cluster_1": ["doc_4", "doc_5"]
},
"clusterStats": {
"cluster_0": {
"documentCount": 3,
"documents": ["doc_1", "doc_2", "doc_3"]
},
"cluster_1": {
"documentCount": 2,
"documents": ["doc_4", "doc_5"]
}
}
}Validation Error
HTTPValidationError
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