๐ฆWeaviate
Weaviate
In case of weaviate you would have to install weaviate with docker-compose and then use that component in the GenAI Stack.
Compulsory Arguments:
class_name => The name of the index under which documents are stored
fields:
url => Url of the weaviate node
text_key => The column against which to do the vector embedding search
auth_config: (Optional)
api_key => api_key of the weaviate cluster if you are using weaviate cloud .
Prerequisites:
Here the docker-compose configurations:
This is a sample docker-compose file
version: '3.4'
services:
weaviate:
image: semitechnologies/weaviate:1.20.5
restart: on-failure:0
ports:
- "8080:8080"
environment:
QUERY_DEFAULTS_LIMIT: 20
AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED: 'true'
PERSISTENCE_DATA_PATH: '/var/lib/weaviate'
DEFAULT_VECTORIZER_MODULE: text2vec-transformers
ENABLE_MODULES: text2vec-transformers
TRANSFORMERS_INFERENCE_API: http://t2v-transformers:8080
CLUSTER_HOSTNAME: 'node1'
volumes:
- weaviate_data:/var/lib/weaviate
t2v-transformers:
image: semitechnologies/transformers-inference:sentence-transformers-multi-qa-MiniLM-L6-cos-v1
environment:
ENABLE_CUDA: 0
volumes:
weaviate_data:
This docker compose file uses sentence transformers for embedding for more embeddings and other options refer this doc.
GenAI Stack Configurations for Weaviate:
=> Sample vectordb configuration for weaviate
"vectordb": {
"name": "weaviate",
"class_name": "LegalDocs",
"fields": {
"url": "http://localhost:9999/",
"text_key": "clause_text"
}
}
Note: Weaviate expects class_name in PascalCase otherwise it might lead to weird index not found errors.
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