VectorFin works with your existing data stack

Query VectorFin embeddings and signals from a Python notebook via the REST API. Works on every plan, including Free, with no SDK and no infrastructure.

1. 01
   Install requests and set your API key. No SDK required. The REST API uses a single header (X-API-Key). Free-tier keys are issued instantly when you sign up at app.vectorfinancials.com. Everything below runs in a plain Jupyter / Colab notebook cell.
2. 02
   List available tickers. Fetch the tickers your plan can access. In beta the universe is the S&P 500 (~500 tickers); Free returns the top 100. The 5,000+ ticker universe is the Starter (and above) plan limit at GA.
3. 03
   Fetch transcript embeddings. Pull 768-dim embeddings (Google gemini-embedding-2-preview) for a ticker and fiscal period. fiscal_period is required (it scopes the partition scan); limit caps at 100 per call.
4. 04
   Fetch quant signals. Pull the signal feed for a ticker. Free tier is capped at a 90-day lookback and a 30-row cap; paid tiers go back to the start of data history.
5. 05
   Local cosine search over fetched embeddings. There is no server-side vector search on the REST API, so fetch the chunks for the tickers you care about and run similarity locally. For corpus-wide search, upgrade to Pro and query the shared Iceberg tables directly (see the BigQuery and Snowflake guides, or the shared bucket).