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Frequently asked questions
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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