{
  "opencollection": "1.0.0",
  "info": {
    "name": "QuantCDN AI Agents AI Vector Database API",
    "version": "4.15.8"
  },
  "request": {
    "auth": {
      "type": "bearer",
      "token": "{{bearerToken}}"
    }
  },
  "items": [
    {
      "info": {
        "name": "AI Vector Database",
        "type": "folder"
      },
      "items": [
        {
          "info": {
            "name": "List Vector Database Collections",
            "type": "http"
          },
          "http": {
            "method": "GET",
            "url": "https://dashboard.quantcdn.io/api/v3/organizations/:organisation/ai/vector-db/collections",
            "params": [
              {
                "name": "organisation",
                "value": "",
                "type": "path",
                "description": "The organisation ID"
              }
            ]
          },
          "docs": "Lists all vector database collections (knowledge bases) for an organization."
        },
        {
          "info": {
            "name": "Create Vector Database Collection",
            "type": "http"
          },
          "http": {
            "method": "POST",
            "url": "https://dashboard.quantcdn.io/api/v3/organizations/:organisation/ai/vector-db/collections",
            "params": [
              {
                "name": "organisation",
                "value": "",
                "type": "path",
                "description": "The organisation ID"
              }
            ],
            "body": {
              "type": "json",
              "data": "{}"
            }
          },
          "docs": "Creates a new vector database collection (knowledge base category) for semantic search. Collections store documents with embeddings for RAG (Retrieval Augmented Generation).\n     *\n     * **Use Cases:**\n     * - Product documentation ('docs')\n     * - Company policies ('policies')\n     * - Support knowledge base ('support')\n     * - Technical specifications ('specs')"
        },
        {
          "info": {
            "name": "Get Collection Details",
            "type": "http"
          },
          "http": {
            "method": "GET",
            "url": "https://dashboard.quantcdn.io/api/v3/organizations/:organisation/ai/vector-db/collections/:collectionId",
            "params": [
              {
                "name": "organisation",
                "value": "",
                "type": "path",
                "description": "The organisation ID"
              },
              {
                "name": "collectionId",
                "value": "",
                "type": "path",
                "description": "The collection ID"
              }
            ]
          },
          "docs": "Get detailed information about a specific vector database collection."
        },
        {
          "info": {
            "name": "Delete Collection",
            "type": "http"
          },
          "http": {
            "method": "DELETE",
            "url": "https://dashboard.quantcdn.io/api/v3/organizations/:organisation/ai/vector-db/collections/:collectionId",
            "params": [
              {
                "name": "organisation",
                "value": "",
                "type": "path",
                "description": "The organisation ID"
              },
              {
                "name": "collectionId",
                "value": "",
                "type": "path",
                "description": "The collection ID"
              }
            ]
          },
          "docs": "Deletes a vector database collection and all its documents. This action cannot be undone."
        },
        {
          "info": {
            "name": "List Documents in Collection",
            "type": "http"
          },
          "http": {
            "method": "GET",
            "url": "https://dashboard.quantcdn.io/api/v3/organizations/:organisation/ai/vector-db/collections/:collectionId/documents",
            "params": [
              {
                "name": "organisation",
                "value": "",
                "type": "path"
              },
              {
                "name": "collectionId",
                "value": "",
                "type": "path"
              },
              {
                "name": "key",
                "value": "",
                "type": "query",
                "description": "Filter by document key"
              },
              {
                "name": "limit",
                "value": "",
                "type": "query"
              },
              {
                "name": "offset",
                "value": "",
                "type": "query"
              }
            ]
          },
          "docs": "Lists documents in a collection with pagination. Supports filtering by document key."
        },
        {
          "info": {
            "name": "Upload Documents to Collection",
            "type": "http"
          },
          "http": {
            "method": "POST",
            "url": "https://dashboard.quantcdn.io/api/v3/organizations/:organisation/ai/vector-db/collections/:collectionId/documents",
            "params": [
              {
                "name": "organisation",
                "value": "",
                "type": "path",
                "description": "The organisation ID"
              },
              {
                "name": "collectionId",
                "value": "",
                "type": "path",
                "description": "The collection ID"
              }
            ],
            "body": {
              "type": "json",
              "data": "{}"
            }
          },
          "docs": "Uploads documents to a vector database collection with automatic embedding generation. Documents are chunked (if needed), embedded using the collection's embedding model, and stored.\n     *\n     * **Supported Content:**\n     * - Plain text content\n     * - URLs to fetch content from\n     * - Markdown documents\n     *\n     * **Metadata:**\n     * Each document can include metadata (title, source_url, section, tags) that is returned with search results."
        },
        {
          "info": {
            "name": "Delete Documents from Collection",
            "type": "http"
          },
          "http": {
            "method": "DELETE",
            "url": "https://dashboard.quantcdn.io/api/v3/organizations/:organisation/ai/vector-db/collections/:collectionId/documents",
            "params": [
              {
                "name": "organisation",
                "value": "",
                "type": "path",
                "description": "Organisation machine name"
              },
              {
                "name": "collectionId",
                "value": "",
                "type": "path",
                "description": "Collection UUID"
              }
            ],
            "body": {
              "type": "json",
              "data": "{}"
            }
          },
          "docs": "Delete documents from a collection. Supports three deletion modes:\n     *\n     * 1. **Purge All** - Set `purgeAll: true` to delete ALL documents in the collection\n     *\n     * 2. **By Document IDs** - Provide `documentIds` array with specific document UUIDs\n     *\n     * 3. **By Metadata** - Provide `metadata` object with `field` and `values` to delete documents where the metadata field matches any of the values\n     *\n     * **Drupal Integration:**\n     * When using with Drupal AI Search, use "
        },
        {
          "info": {
            "name": "Semantic Search Query",
            "type": "http"
          },
          "http": {
            "method": "POST",
            "url": "https://dashboard.quantcdn.io/api/v3/organizations/:organisation/ai/vector-db/collections/:collectionId/query",
            "params": [
              {
                "name": "organisation",
                "value": "",
                "type": "path",
                "description": "The organisation ID"
              },
              {
                "name": "collectionId",
                "value": "",
                "type": "path",
                "description": "The collection ID"
              }
            ],
            "body": {
              "type": "json",
              "data": "{}"
            }
          },
          "docs": "Performs semantic search on a collection using vector similarity. Returns the most relevant documents based on meaning, not keyword matching.\n     *\n     * **Three Search Modes:**\n     *\n     * 1. **Text Query** - Provide `query` string, server generates embedding\n     *    - Query text is embedded using the collection's embedding model\n     *    - Embeddings are cached for repeated queries\n     *\n     * 2. **Vector Query** - Provide pre-computed `vector` array\n     *    - Skip embedding generat"
        }
      ]
    }
  ],
  "bundled": true
}