POST /v1/search

Performs a basic vector similarity search across one or more datasets.

Request Body

datasets (array of strings, Optional): Names of the dataset components to perform the similarity search on. Each dataset must have exactly one column augmented with an embedding. If None, all available datasets are used.
text (string): Query plaintext used to retrieve similar rows from the underlying datasets listed in the from request key.
limit (integer): The number of rows to return, per from dataset. Default: 3.
where (string): An SQL filter predicate to apply within the search.
additional_columns (array of strings): Additional columns, from the datasets, to return in the response (under .matches[*].metadata).

Example

Spicepod

embeddings:
  - name: embedding_maker
    from: openai
datasets:
  - name: app_messages
    from: file://my.csv
    embeddings:
      - column: document_text
        use: embedding_maker

Request

curl -XPOST http://localhost:3000/v1/search \
  -d '{
    "datasets": ["app_messages"],
    "text": "Tokyo plane tickets",
    "where": 'user=1234321',
    "additional_columns": ["timestamp"],
    "limit": 3
  }'

Response

{
  "matches": [{
    "value": "I booked use some tickets",
    "dataset": "app_messages",
    "primary_key": {"id": "6fd5a215-0881-421d-ace0-b293b83452b5"},
    "metadata": {"timestamp": 1724716542}
   },
   {
    "value": "direct to Narata",
    "dataset": "app_messages",
    "primary_key": {"id": "8a25595f-99fb-4404-8c82-e1046d8f4c4b"},
    "metadata": {"timestamp": 1724715881}
   },
   {
    "value": "Yes, we're sitting together",
    "dataset": "app_messages",
    "primary_key": {"id": "8421ed84-b86d-4b10-b4da-7a432e8912c0"},
    "metadata": {"timestamp": 1724716123}
   }],
  "duration_ms": 42,
}

The v1/search endpoint supports chunked embedding columns.

Example​

Example