Hi!
I am trying to use a local setup of Weaviate without a specified vectorizer as I would prefer to generate my own embeddings. I am using the v4 Python client and running into an issue. Here is my reproducible example:
import weaviate
from sentence_transformers import SentenceTransformer
client_weaviate = weaviate.connect_to_local()
print(client_weaviate.is_ready()) # True
print(client_weaviate.is_live()) # True
print(client_weaviate.is_connected()) # True
model = SentenceTransformer('all-MiniLM-L6-v2')
test_string = "test_string"
emb = model.encode([test_string])
In this code chunk I am unable to retrieve the model (I get stuck on the line with model = …). However, if I move the model chunk above the weaviate client chunk, it works as intended. I am not sure what could be the issue here and I would appreciate some help.
Below you can also see my Docker file:
version: '3.4'
services:
weaviate:
command:
- --host
- 0.0.0.0
- --port
- '8080'
- --scheme
- http
image: cr.weaviate.io/semitechnologies/weaviate:1.24.5
ports:
- 8080:8080
- 50051:50051
volumes:
- weaviate_data:/var/lib/weaviate
restart: on-failure:0
environment:
QUERY_DEFAULTS_LIMIT: 25
AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED: 'true'
PERSISTENCE_DATA_PATH: '/var/lib/weaviate'
DEFAULT_VECTORIZER_MODULE: 'none'
CLUSTER_HOSTNAME: 'node1'
volumes:
weaviate_data:
Thanks for your help!