Search Document Pages

curl --request POST \
  --url https://api.fieldwise.ai/api/v1/documents/search/pages \
  --header 'Content-Type: application/json' \
  --header 'X-API-Key: <api-key>' \
  --data '{
  "query": "<string>",
  "embedding": [
    123
  ],
  "metadata_filter": {},
  "created_at_filter": {},
  "updated_at_filter": {},
  "top_k": 10,
  "min_relevance": 0,
  "optimize_metadata": false,
  "optimize_query": false
}'

{
  "success": true,
  "data": {
    "results": [
      {
        "id": 123,
        "document_id": 123,
        "page_number": 123,
        "content": "<string>",
        "relevance": 123
      }
    ],
    "optimized_query": "<string>",
    "optimized_metadata": {}
  },
  "error": {
    "error_code": "RESOURCE_NOT_FOUND",
    "message": "Resource not found"
  },
  "meta": {
    "timestamp": "2023-11-07T05:31:56Z",
    "version": "1.0"
  }
}

Documents

Search Document Pages

Search through document pages using semantic similarity.

This endpoint performs a semantic search at the page level, which is best for:

Finding broad context about a topic
Getting an overview of document sections
When you need more context around the matches

Performance Considerations:

OPTIMIZED: No longer materializes document IDs in memory
Fastest search performance using compound indexes
Most efficient memory usage
Best for initial broad searches
Recommended for large document collections

Best Practices:

Use for initial exploratory searches
Combine with metadata filters for better performance
Start with this before using more granular searches
Ideal for queries needing broader context
Use min_relevance to filter out low-quality matches

The search uses embeddings to find the most semantically similar pages to your query, regardless of exact keyword matches.

You can provide either:

A text query (which will be converted to embeddings)
A pre-computed embedding vector (1536 dimensions)

Filter options:

min_relevance: Set a threshold (0-1) to only return results above a certain relevance score

POST

api

documents

pages

Search Document Pages

curl --request POST \
  --url https://api.fieldwise.ai/api/v1/documents/search/pages \
  --header 'Content-Type: application/json' \
  --header 'X-API-Key: <api-key>' \
  --data '{
  "query": "<string>",
  "embedding": [
    123
  ],
  "metadata_filter": {},
  "created_at_filter": {},
  "updated_at_filter": {},
  "top_k": 10,
  "min_relevance": 0,
  "optimize_metadata": false,
  "optimize_query": false
}'

{
  "success": true,
  "data": {
    "results": [
      {
        "id": 123,
        "document_id": 123,
        "page_number": 123,
        "content": "<string>",
        "relevance": 123
      }
    ],
    "optimized_query": "<string>",
    "optimized_metadata": {}
  },
  "error": {
    "error_code": "RESOURCE_NOT_FOUND",
    "message": "Resource not found"
  },
  "meta": {
    "timestamp": "2023-11-07T05:31:56Z",
    "version": "1.0"
  }
}

Authorizations

X-API-Key

string

header

required

Headers

X-API-Key

string | null

Body

application/json

Request model for searching pages.

query

string | null

The search query (required if embedding not provided)

embedding

number[] | null

Pre-computed embedding vector for search (required if query not provided)

metadata_filter

object | null

Optional metadata filters using MongoDB-like query syntax

created_at_filter

object | null

Filter by created_at database field. Supports operators: $eq, $ne, $gt, $lt, $gte, $lte, $in, $nin. Use ISO date format (e.g., '2024-01-01T00:00:00')

updated_at_filter

object | null

Filter by updated_at database field. Supports operators: $eq, $ne, $gt, $lt, $gte, $lte, $in, $nin. Use ISO date format (e.g., '2024-01-01T00:00:00')

top_k

integer

default:10

Number of results to return

Required range: 1 <= x <= 1000

min_relevance

number

default:0

Minimum relevance score (0-1) to filter results. Only results above this threshold will be returned.

Required range: 0 <= x <= 1

optimize_metadata

boolean

default:false

Whether to optimize metadata filter (only works with query, not embedding)

optimize_query

boolean

default:false

Whether to optimize search query (only works with query, not embedding)

Response

Successful Response

Response model for page search.

data

object

required

Response data

Show child attributes

success

boolean

default:true

error

object | null

Schema for API error responses.

Show child attributes

Example:

{
  "error_code": "RESOURCE_NOT_FOUND",
  "message": "Resource not found"
}

Documents

Extractions

Tasks

Search Document Pages

Authorizations

Headers

Body

Response