Search document chunks (v2)

curl --request GET \
  --url https://rest-api.captide.co/api/v2/rag/chunks \
  --header 'X-API-Key: <api-key>'

{
  "relevantChunks": [
    {
      "pageNumber": 2,
      "documentMetadata": {
        "id": "2dfd0c2a-fb6d-486a-ad62-3b1830eb30bf",
        "title": "ASML 2024 Annual Report",
        "documentCategory": "annual-financial-report",
        "date": "2024-12-31",
        "fiscalQuarter": 4,
        "fiscalYear": 2024,
        "companyName": "ASML HOLDING NV",
        "tickers": [
          "ASML",
          "ASML.AS"
        ]
      }
    }
  ]
}

Agentic RAG

Chunk Retrieval

Search for relevant document chunks based on a query. Under the hood, Captide’s RAG system uses a combination of LLMs and vector databases to find the most relevant chunks of text from corporate disclosures that are most relevant to the query. This endpoint returns chunks of text from corporate disclosures that are most relevant to the query, along with metadata about their source documents. This endpoint is useful for building search interfaces or retrieving specific content from documents.

This v2 endpoint supports newer document categories in source mappings (e.g., earnings-presentation, investor-event-presentation, shareholder-meeting-circular, etc.). Agent response quality is exactly the same compared to v1.

GET

api

rag

chunks

Search document chunks (v2)

curl --request GET \
  --url https://rest-api.captide.co/api/v2/rag/chunks \
  --header 'X-API-Key: <api-key>'

{
  "relevantChunks": [
    {
      "pageNumber": 2,
      "documentMetadata": {
        "id": "2dfd0c2a-fb6d-486a-ad62-3b1830eb30bf",
        "title": "ASML 2024 Annual Report",
        "documentCategory": "annual-financial-report",
        "date": "2024-12-31",
        "fiscalQuarter": 4,
        "fiscalYear": 2024,
        "companyName": "ASML HOLDING NV",
        "tickers": [
          "ASML",
          "ASML.AS"
        ]
      }
    }
  ]
}

Authorizations

X-API-Key

string

header

required

Query Parameters

query

string

required

The natural language query

Minimum string length: 1

Example:

"What is Zscaler's net dollar retention rate?"

document_ids

string | null

Comma-separated string of document file IDs. If provided and non-empty, skips document and company selection and goes directly to retrieval.

Response

Chunks found successfully

Response model for chunk search results.

relevantChunks

Chunk · object[]

required

List of relevant document chunks

Show child attributes

Company Retrieval Agent Response

API documentation

Companies

Documents

Agentic RAG

Chunk Retrieval

Authorizations

Query Parameters

Response