Store File in Vector Database

Parse File

Use this endpoint to add a PDF, CSV, TXT or DOC/DOCX document to the Qolaba AI database. The API will parse the document and store it in the vector database, returning a unique ID that can be used to retrieve information from the document using the Large Language Model (LLM).

Please note the following guidelines when indexing Document:

• The PDF, DOC/DOCX file should not exceed 200 pages.

• The CSV file should not contain more than 30 columns and 500 rows.

• When uploading a CSV file to the API, the first row must contain the column names. This helps the Large Language Model (LLM) better understand the values in each row of the CSV file.

• Ensure that the document does not contain any sensitive or confidential information.

The unique ID returned after indexing the document can be used in subsequent requests to the Chatbot API to retrieve relevant information from the document, after ensuring enable_tool and search_doc to be true.

POST /pdfVectorStore

Headers

Name	Value
Content-Type	application/json
Authorization	Bearer <token>

Body

Name	Type	Description
url	string	The url parameter specifies the URL of the document to be indexed.

Response

200

{
  "output": null, 
  "error": null, 
  "error_data": null
}

500

{
  "output": null, 
  "error": string, 
  "error_data": string
}

Upon successfully indexing a document, the API will return a response with the unique identifier of the indexed document in output parameter.

You can use the unique identifier in subsequent requests to the Chat API to retrieve information from the indexed PDF.

Run the API

To test this API, please use the following link:

https://app.theneo.io/api-runner/qolaba/ml-apis/api-reference/store-file-in-vector-database