Skip to main content

Vector databases

Vector databases store high-dimensional vectors and search them by similarity. SnoutData supports Pinecone: connect with an API key, see your indexes in the explorer, and query them with SQL.

Connect to Pinecone​

  1. Open the connections sidebar and choose New connection.
  2. Pick the Pinecone driver.
  3. Enter an environment label and your API key. There is no host or port to configure; SnoutData discovers your index hosts from Pinecone.
  4. Click Test, then Save.

Your API key is stored encrypted by your operating system's keychain, the same as any other connection secret. See Security.

Browse indexes​

Expand the connection to list your project's indexes, shown like tables. Each index exposes columns for the record id, the similarity score, the stored values (the vector), and the metadata fields found on its records.

Query with SQL​

Query an index with SQL. The WHERE clause becomes a Pinecone metadata filter and LIMIT sets how many matches to return (topK):

SELECT id, score, title
FROM articles
WHERE category = 'news'
LIMIT 10;

Supported WHERE operators include =, !=, comparisons, IN / NOT IN, and IS [NOT] NULL (which maps to a field-exists check). JOIN, GROUP BY, ORDER BY, OFFSET, and LIKE are not supported on a vector query.

On the roadmap​

Two vector features are not available yet:

  • Semantic search from plain text ("find records similar to this sentence"). This needs an embedding model to turn your text into a query vector, which is coming.
  • A native vector-query editor (the equivalent of the MongoDB pipeline editor). For now, query indexes with SQL.

Because a vector query needs an actual query vector, there is no AI SQL-to-vector translation: SQL on a Pinecone connection always uses the offline compiler.