# Polars Semantic Search — One-Line Vector Search for DataFrames

> Add semantic search to Polars DataFrames in one line of Python. Local-first, Rust-powered, zero network calls.

Canonical: https://omna.dev/polars-semantic-search

## The one-line API

```python
import polars as pl, omna
df = pl.read_parquet("clinical_notes.parquet")
df.omna.search("chest pain radiating to left arm", top_k=3)
```

## Benchmarks

- ~12ms p50 latency on 1M rows (M2 MacBook, MiniLM embeddings)
- 0 network calls — runs in your Python process
- HIPAA-compatible by design — no egress, no BAA required

## vs Pinecone / Weaviate / pgvector

Hosted vector DBs are designed for distributed, multi-tenant production search. For analytical Polars pipelines — notebooks, ETL, ad-hoc exploration — they are operationally heavy. Omna trades infrastructure for an in-process Rust kernel.