Vectara · Schema

Vectara Corpus

A Vectara corpus is a container of indexed documents used for retrieval and grounded generation.

Artificial IntelligenceAgentsCorporaEmbeddingsEnterprise SearchGenerative AIGrounded GenerationHallucination DetectionLLMMCPRAGRetrievalSearchSemantic SearchVector Search

Properties

Name Type Description
key string Unique key identifying the corpus.
name string Human-readable corpus name.
description string Description of the corpus and its intended contents.
queries_are_answers boolean Whether stored queries should be treated as answers (question-answer corpora).
documents_are_questions boolean Whether stored documents should be treated as questions.
encoder_id string Identifier of the embedding encoder used by the corpus.
filter_attributes array
custom_dimensions array
enabled boolean Whether the corpus accepts indexing and queries.
created_at string
View JSON Schema on GitHub

JSON Schema

vectara-corpus-schema.json Raw ↑
{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "$id": "https://raw.githubusercontent.com/api-evangelist/vectara/main/json-schema/vectara-corpus-schema.json",
  "title": "Vectara Corpus",
  "description": "A Vectara corpus is a container of indexed documents used for retrieval and grounded generation.",
  "type": "object",
  "required": ["key", "name"],
  "properties": {
    "key": {
      "type": "string",
      "description": "Unique key identifying the corpus."
    },
    "name": {
      "type": "string",
      "description": "Human-readable corpus name."
    },
    "description": {
      "type": "string",
      "description": "Description of the corpus and its intended contents."
    },
    "queries_are_answers": {
      "type": "boolean",
      "description": "Whether stored queries should be treated as answers (question-answer corpora)."
    },
    "documents_are_questions": {
      "type": "boolean",
      "description": "Whether stored documents should be treated as questions."
    },
    "encoder_id": {
      "type": "string",
      "description": "Identifier of the embedding encoder used by the corpus."
    },
    "filter_attributes": {
      "type": "array",
      "items": { "$ref": "#/$defs/FilterAttribute" }
    },
    "custom_dimensions": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "name": { "type": "string" },
          "description": { "type": "string" }
        }
      }
    },
    "enabled": {
      "type": "boolean",
      "description": "Whether the corpus accepts indexing and queries."
    },
    "created_at": {
      "type": "string",
      "format": "date-time"
    }
  },
  "$defs": {
    "FilterAttribute": {
      "type": "object",
      "properties": {
        "name": { "type": "string" },
        "level": { "type": "string", "enum": ["document", "document_part"] },
        "indexed": { "type": "boolean" },
        "type": { "type": "string", "enum": ["text", "integer", "real", "boolean"] }
      }
    }
  }
}

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