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POST
/
v1
/
artifacts
/
ingest
curl --request POST \ --url https://{base_url}/gpt/v1/artifacts/ingest \ --header 'Content-Type: application/json' \ --data ' { "input": { "type": "file", "value": "JVBERi0xLjQKJaqrrK0KMS..." }, "artifact": "quarterly_report" } '
{
  "object": "list",
  "model": "private-gpt",
  "data": [
    {
      "object": "ingest.document",
      "artifact": "quarterly_report",
      "doc_metadata": {
        "file_name": "Q3_Report.pdf",
        "page_number": 1,
        "total_pages": 10
      }
    }
  ]
}

Body

application/json

JSON request body supporting multiple input types

Request body for ingesting content into the system for AI context.

​
artifact
string
required

Unique identifier for the text being ingested within the collection

Required string length: 1 - 255
Examples:

"user_manual_chapter_1"

"policy_document_2024"

​
input
FileArtifact · object
required

Input for base64 encoded files.

​
collection
string
default:pgpt_collection

Collection name to group related documents for better organization and filtering

Required string length: 1 - 255
Examples:

"corporate_docs"

"user_manuals"

"financial_reports"

​
metadata
Metadata · object

Optional metadata dictionary containing additional document information. If provided, must include 'file_name' with a valid file extension

Example:
{
  "author": "John Doe",
  "department": "HR",
  "file_name": "company_policy.txt"
}

Response

Content successfully ingested and processed

Response model for successful document ingestion operations.

​
object
string
required

Response object type, always 'list' for ingestion responses

Allowed value: "list"
​
model
string
required

Model identifier, always 'private-gpt'

Allowed value: "private-gpt"
​
data
IngestedDoc · object[]
required

List of ingested documents with their metadata and processing information