https://github.com/phact/fastastra

A bit of extra usability for astradb (modeled after fastlite)
https://github.com/phact/fastastra

Last synced: 3 months ago
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A bit of extra usability for astradb (modeled after fastlite)

• Host: GitHub
• URL: https://github.com/phact/fastastra
• Owner: phact
• Created: 2024-08-02T04:39:43.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2025-06-03T20:11:37.000Z (about 1 year ago)
• Last Synced: 2025-09-14T15:11:43.948Z (9 months ago)
• Language: Python
• Size: 750 KB
• Stars: 2
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# fastastra

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fastastra is modeled after [fastlite](https://github.com/AnswerDotAI/fastlite) and it allows you to use [FastHTML](https://github.com/AnswerDotAI/fasthtml) with AstraDB (Cassandra). 

## Installation

    poetry add fastastra

or 

    pip install fastastra

## To connect:

    from fastastra.fastastra import AstraDatabase

    db = AstraDatabase(token, dbid) # get your token and dbid from https://astra.datastax.com

## Basic usage

### List tables

    db.t

    

### Create a table

    cats = db.t.cats

    if cats not in db.t:

        cats.create(cat_id=uuid.uuid1, name=str, partition_keys='cat_id')

### Insert a row

    cat_timeuuid = uuid.uuid1()

    cats.update(cat_id=cat_timeuuid, name="fluffy")

### List all rows

    rows = cats()

### ANN / vector search

    db = AstraDatabase(token, dbid, embedding_model="embed-english-v3.0") # supports all embedding models in LiteLLM using env vars

    dogs = db.t.dogs

    if dogs not in db.t:

        #dogs.create(id=int, name=str, good_boy=bool, embedding=(list[float], 2), pk='id') # specify dimensions in create

        dogs.create(id=int, name=str, good_boy=bool, embedding=list[float], pk='id') # infer dimensions from db.embedding_model

        dogs.c.good_boy.index()

        dogs.c.embedding.index()

    dogs.insert(id=2, good_boy=True, name="spike", embedding=[0.1, 0.2])

    index_lookukp = dogs.xtra(good_boy=True)

    ann_matches = dogs.xtra(embedding=[0.2, 0.2])

### Get dataclass and pydantic model

    dataclass = cats.dataclass()

    model = cats.pydantic_model()

### Get a row

    print(cats[cat_timeuuid])

# Delete a row

    cats.delete(cat_timeuuid)

 or

    cats.delete(str(cat_timeuuid))

## Run a FastHTML example:

This example was taken almost verbatim from [the FastHTML examples repo](https://github.com/AnswerDotAI/fasthtml-example). The only change was the dependency, the db connection string, and changing the id from `int` to `uuid1`.

    uv sync

    uv run python examples/todo.py