Ingest Document From Embedding

curl --request POST \
  --url https://api.fieldwise.ai/api/v1/documents/ingest/embedding \
  --header 'Content-Type: application/json' \
  --header 'X-API-Key: <api-key>' \
  --data '
{
  "content": "This is the text content that was used to generate the embedding vector.",
  "embedding": [
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  ],
  "external_id": "emb_doc_123",
  "metadata": {
    "author": "John Doe",
    "category": "research",
    "tags": [
      "machine-learning",
      "embeddings"
    ]
  }
}
'

{
  "data": {
    "task_identifier": "<string>",
    "file_path": "<string>",
    "external_id": "<string>"
  },
  "success": true,
  "error": {
    "error_code": "RESOURCE_NOT_FOUND",
    "message": "Resource not found"
  },
  "meta": {
    "timestamp": "2023-11-07T05:31:56Z",
    "version": "1.0"
  }
}

Documents

Ingest Document From Embedding

Ingest a document directly from a pre-computed embedding vector.

This endpoint allows you to create documents directly from embedding vectors, bypassing the file upload and text extraction process. This is useful when:

You already have pre-computed embeddings
You want to ingest structured data or custom content
You need to create documents from API responses or generated content
You want to maintain embedding consistency across systems

The embedding vector must be exactly 1536 dimensions (OpenAI’s text-embedding-ada-002 format).

Performance Benefits:

No file processing overhead
No text extraction or embedding generation
Immediate availability for search
Synchronous operation (completed immediately)

Retrieval:

Documents can be retrieved using the returned task_identifier via GET /task/
External ID retrieval also works via GET /external/ if provided

Best Practices:

Ensure embedding vector quality matches your use case
Include meaningful content text for human readability
Use consistent metadata structure for filtering
Consider embedding source and generation method in metadata

POST

api

documents

ingest

embedding

Ingest Document From Embedding

curl --request POST \
  --url https://api.fieldwise.ai/api/v1/documents/ingest/embedding \
  --header 'Content-Type: application/json' \
  --header 'X-API-Key: <api-key>' \
  --data '
{
  "content": "This is the text content that was used to generate the embedding vector.",
  "embedding": [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
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1,
1,
1,
1,
1,
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1,
1,
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  ],
  "external_id": "emb_doc_123",
  "metadata": {
    "author": "John Doe",
    "category": "research",
    "tags": [
      "machine-learning",
      "embeddings"
    ]
  }
}
'

{
  "data": {
    "task_identifier": "<string>",
    "file_path": "<string>",
    "external_id": "<string>"
  },
  "success": true,
  "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 ingesting documents via embedding vectors.

embedding

number[]

required

Pre-computed embedding vector (1536 dimensions)

content

string

required

The text content associated with this embedding

Maximum string length: 3000

metadata

Metadata · object

Document metadata (must be primitive types, arrays, or nested objects)

external_id

string | null

External identifier

Maximum string length: 255

Response

Successful Response

data

UploadResponseData · object

required

Response data

Show child attributes

success

boolean

default:true

error

ErrorDetail · object

Schema for API error responses.

Show child attributes

Example:

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