Free
Semantic Search
Search your own text by meaning, not keywords.
Gemini embeddingsSimilarity rankingBring your own textFree
Build the index first to enable search.
Frequently asked questions
How is this different from keyword search?
It matches meaning, not exact words. A query like "how do I get a refund" can surface a passage about "returning a purchase" even with no shared keywords.
How does it work under the hood?
Each line or paragraph becomes a row in a Pixeltable table with a Gemini embedding index. Your query is embedded and ranked with .similarity() — the same pattern that powers RAG.
Is my text stored?
Your corpus is scoped to your browser session and is not shared with other visitors.
Built with Pixeltable
Run this as tables and an API on Pixeltable Cloud.
Fact sheet
Last updated
| Modality | Text |
|---|---|
| Inputs | Your notes plus a meaning query |
| Outputs | Passages ranked by similarity |
| Computed column | Gemini embed_content index; query with column.similarity(string=...) |
| What updates | A new or changed row recomputes only that row. Unchanged rows stay cached. |
| Canonical doc | gemini.embed_content |
Runnable sample
import pixeltable as pxt
from pixeltable.functions.gemini import embed_content
TableModel = pxt.model_base()
text_embed = embed_content.using(model='gemini-embedding-001')
class Notes(TableModel, name='notes'):
text: pxt.String
__indexes__ = [
pxt.EmbeddingIndex(text, embedding=text_embed),
]
# Query with Notes.text.similarity(string='how do I get a refund')Related tools
- Chat with PDF
Modality: Document
Computed column: extract_document_text, then Groq chat_completions. No embedding index
Docs - Document Summarizer
Modality: Document
Computed column: extract_document_text, then Groq chat_completions for the summary
Docs