One post tagged with "gemini"

We've released a new StackQL provider for the Google Gemini API:

• gemini - the Generative Language API surface (generativelanguage.googleapis.com): inference (content generation, embeddings, token counting, batches) plus the API's own management surface - models, tuned models and permissions, files, cached contents, corpora, file search stores and long-running operations (10 services, 32 resources, 75 operations)

Everything reachable with a GEMINI_API_KEY ships in one provider. Authentication is handled automatically, LIMIT is pushed down to the wire, and paginated lists are walked for you.

Inference as a result set​

Asking Gemini a question is a SELECT. The reply comes back as a row, with the text in the candidates array and the token spend in usageMetadata:

SELECT
  JSON_EXTRACT(candidates, '$[0].content.parts[0].text') AS reply,
  JSON_EXTRACT(usageMetadata, '$.totalTokenCount') AS total_tokens
FROM gemini.models.content
WHERE modelsId = 'gemini-2.5-flash'
AND contents = '[{"parts":[{"text":"Name the four Galilean moons of Jupiter, comma separated."}]}]';

Token counting works the same way via gemini.models.token_counts - free of charge, so a prompt can be priced before it is sent. Embeddings come back as rows too:

SELECT JSON_EXTRACT(embedding, '$.values[0]') AS first_dimension
FROM gemini.models.embeddings
WHERE modelsId = 'gemini-embedding-001'
AND content = '{"parts":[{"text":"stackql lets you query cloud APIs with SQL"}]}';

Which model can do what​

The provider ships a convenience view that fans each model's supported generation methods and limits out into flat columns:

SELECT name, display_name, input_token_limit, thinking, supports_generate_content
FROM gemini.models.vw_model_capabilities
ORDER BY name;

One row per model with token limits, default sampling parameters, and boolean flags for content generation, token counting, embedding, batching and caching support - useful for picking a model programmatically instead of reading release notes.

The management surface as SQL​

Files, cached contents, corpora, file search stores and tuned models are managed with the corresponding SQL verbs:

-- create
INSERT INTO gemini.corpora.corpora (displayName)
SELECT 'my-knowledge-base'
RETURNING name, displayName;

-- read
SELECT name, displayName, mimeType, sizeBytes, state
FROM gemini.files.files;

-- update
UPDATE gemini.cached_contents.cached_contents
SET ttl = '600s'
WHERE cachedContentsId = 'my-cache-id';

-- delete
DELETE FROM gemini.cached_contents.cached_contents
WHERE cachedContentsId = 'my-cache-id';

Batches are long-running operations and follow the async job pattern: SELECT polls them, EXEC runs the lifecycle ops:

SELECT name, done, JSON_EXTRACT(metadata, '$.state') AS state
FROM gemini.batches.batches;

EXEC gemini.batches.batches.cancel @batchesId = 'my-batch-id';

Tuned model permissions are a full CRUD surface as well, so sharing a tuned model is an INSERT and auditing who can see it is a SELECT.

One credential, one boundary​

The provider covers the Gemini API surface under a GEMINI_API_KEY. Billing, quota, API key management and IAM are GCP control-plane services (cloudbilling, serviceusage, apikeys, iam) that authenticate with OAuth/ADC - they live in the google provider. StackQL does cross-provider joins, so the two compose; worked examples are at gemini-provider.stackql.io/google-control-plane.

Authentication​

Set a single environment variable:

export GEMINI_API_KEY=...

Keys are created in Google AI Studio. The key is only ever sent as a request header (x-goog-api-key) - never in the URL query string.

Get started​

Pull the provider from the public registry:

registry pull gemini

Provider docs are at gemini-provider.stackql.io. Let us know what you build. Star us on GitHub.