Superduper
Superduper is a framework for building flexible, compositional AI applications which may be applied directly to databases using a declarative programming model. These applications declare and maintain a desired state of the database, and use the database directly to store outputs of AI components, meta-data about the components and data pertaining to the state of the system.
Qdrant is available as a vector search provider in Superduper.
Installation
pip install superduper-framework
Setup
- To use Qdrant for vector search layer, create a
settings.yamlfile with the following config.
settings.yaml
cluster:
vector_search:
type: qdrant
vector_search_kwargs:
url: "http://localhost:6333"
api_key: "<YOUR_API_KEY>
# Supports all parameters of qdrant_client.QdrantClient
- Set the
SUPERDUPER_CONFIGenv value to the path of the config file.
export SUPERDUPER_CONFIG=path/to/settings.yaml
That’s all. You can now use Superduper backed by Qdrant.
Example
Here’s an example to run vector search using the configured Qdrant index.
import json
import requests
from superduper import superduper, Document
from superduper.ext.sentence_transformers import SentenceTransformer
r = requests.get('https://superduperdb-public-demo.s3.amazonaws.com/text.json')
with open('text.json', 'wb') as f:
f.write(r.content)
with open('text.json', 'r') as f:
data = json.load(f)
db = superduper('mongomock://test')
_ = db['documents'].insert_many([Document({'txt': txt}) for txt in data]).execute()
model = SentenceTransformer(
identifier="test",
predict_kwargs={"show_progress_bar": True},
model="all-MiniLM-L6-v2",
device="cpu",
postprocess=lambda x: x.tolist(),
)
vector_index = model.to_vector_index(select=db['documents'].find(), key='txt')
db.apply(vector_index)
query = db['documents'].like({'txt': 'Tell me about vector-search'}, vector_index=vector_index.identifier, n=3).find()
cursor = query.execute()
for r in cursor:
print('=' * 100)
print(r.unpack()['txt'])
print('=' * 100)