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ibl-data-manager (4.84.1-ai-plus)

API for iblai

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https://docs.ibl.ai/_mock/apis/ibl/
https://base.manager.iblai.app/

ai-account

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ai-analytics

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ai-finetuning

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ai-index

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ai_index_orgs_users_documents_document_from_pool_create

Request

Train a document from the document pool

Sample request:

{"document_id": 3, "to_pathway": "7c43ec09-3d37-489c-a461-8d73df1d91c7"}
Security
PlatformApiKeyAuthentication
Path
orgstringrequired
user_idstringrequired
Bodyapplication/jsonrequired
to_pathwaystringrequired

Pathway for document to be trained in.

document_idintegerrequired

The document id from pool.

curl -i -X POST \
  'https://docs.ibl.ai/_mock/apis/ibl/api/ai-index/orgs/{org}/users/{user_id}/documents/document-from-pool/' \
  -H 'Authorization: YOUR_API_KEY_HERE' \
  -H 'Content-Type: application/json' \
  -d '{
    "to_pathway": "string",
    "document_id": 0
  }'

Responses

Bodyapplication/json
document_idstring

Document id for document in training

task_idstring

Task id for document in training

messagestringrequired

Message of the training

errorstring
Response
application/json
{
  "document_id": "string",
  "task_id": "string",
  "message": "string",
  "error": "string"
}

ai_index_orgs_users_documents_pathways_list

Request

Description: Retrieves a list of document embeddings for a specific pathway with optional search and pagination.

Methods:

  • GET: Retrieves document embeddings that match the specified search criteria and are paginated according to offset and limit.

Parameters:

  • search (str): Search query to filter document names or URLs.
  • offset (int): Offset number for pagination.
  • limit (int): Limit number for pagination.

Returns:

  • GET: A paginated list of document embeddings with their details. { "count": 10, "next": "http://api.example.com/retriever_documents/?offset=10&limit=2", "previous": "http://api.example.com/retriever_documents/?offset=0&limit=2", "results": [ { "document_name": "Document2", "platform_key": "example_platform", "pathway": "example_pathway" } ] }

Error Responses:

  • 400 Bad Request: Invalid query parameters.
  • 404 Not Found: No document embeddings found for the specified criteria.
Security
PlatformApiKeyAuthentication
Path
orgstringrequired
pathwaystringrequired
user_idstringrequired
Query
limitinteger

limit number

offsetinteger

Offset number

searchstringnon-empty

Search query

curl -i -X GET \
  'https://docs.ibl.ai/_mock/apis/ibl/api/ai-index/orgs/{org}/users/{user_id}/documents/pathways/{pathway}/?limit=0&offset=0&search=string' \
  -H 'Authorization: YOUR_API_KEY_HERE'

Responses

Bodyapplication/json
countintegerrequired
Example: 123
nextstring or null(uri)
Example: "http://api.example.org/accounts/?offset=400&limit=100"
previousstring or null(uri)
Example: "http://api.example.org/accounts/?offset=200&limit=100"
resultsArray of objects(RetrieverDocumentEmbedding)required
results[].​idintegerread-onlyrequired
results[].​metadataany or null
results[].​document_namestring or null
results[].​document_typestring or null
results[].​usernamestringrequired
results[].​training_status(TrainingStatusEnum (string or null)) or (BlankEnum (any or null)) or (NullEnum (any or null))
One of:
  • trained - Trained
  • failed - Failed
  • pending - Pending
  • untrained - Untrained
string or null(TrainingStatusEnum)
Enum"trained""failed""pending""untrained"
results[].​pathwaystringrequired
results[].​urlstring or null
results[].​tokensinteger or null(int64)[ -9223372036854776000 .. 9223372036854776000 ]
results[].​platform_keystringrequired
results[].​is_trainedboolean
results[].​access(AccessEnum (string or null)) or (BlankEnum (any or null)) or (NullEnum (any or null))
One of:
  • public - Public
  • private - Private
string or null(AccessEnum)
Enum"public""private"
results[].​crawler_max_depthinteger or null[ -2147483648 .. 2147483647 ]
results[].​crawler_max_pages_limitinteger or null[ -2147483648 .. 2147483647 ]
results[].​crawler_max_concurrencyinteger or null[ -2147483648 .. 2147483647 ]
results[].​crawler_match_patternsany

List of patterns that the crawler should use to match urls. Patterns may be a glob pattern or a full regex pattern. Indicate the specified type in crawler_pattern_type.

results[].​crawler_pattern_typeCrawlerPatternTypeEnum (string) or BlankEnum (any)
One of:
  • glob - Glob
  • regex - Regex
string(CrawlerPatternTypeEnum)
Enum"glob""regex"
results[].​last_trained_atstring or null(date-time)read-onlyrequired
Response
application/json
{
  "count": 123,
  "next": "http://api.example.org/accounts/?offset=400&limit=100",
  "previous": "http://api.example.org/accounts/?offset=200&limit=100",
  "results": [
    [ … ]
  ]
}

ai_index_orgs_users_documents_search_create

Request

Retrieve resource documents similar to the given query.

This endpoint performs a semantic search to find documents that are relevant to the provided query within the specified pathway.

Args: request: The HTTP request containing the search query. org: Organization key identifier.

Returns: Response: A list of documents relevant to the search query.

Raises: ValidationError: If the request data is invalid.

Security
PlatformApiKeyAuthentication
Path
orgstringrequired
user_idstringrequired
Body
required
querystringrequired

The query for similarity search

pathwaystringrequired

The pathway of the docs

curl -i -X POST \
  'https://docs.ibl.ai/_mock/apis/ibl/api/ai-index/orgs/{org}/users/{user_id}/documents/search/' \
  -H 'Authorization: YOUR_API_KEY_HERE' \
  -H 'Content-Type: application/json' \
  -d '{
    "query": "Computational thinking",
    "pathway": "test-pathway"
  }'

Responses

Bodyapplication/json
resultsArray of objects(PageContent)required

Docs from similarity search

results[].​page_contentstringrequired

The page content

Response
application/json
{
  "results": [
    { … },
    { … }
  ]
}

ai-marketing

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credentials

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media

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