Hi,
I setup weaviate with follow docker compose:
version: "3.4"
services:
weaviate:
image: semitechnologies/weaviate:1.25.10
ports:
- "8088:8080"
- "50051:50051"
volumes:
- ./data:/var/lib/weaviate
restart: on-failure:0
networks:
- weaviate_default
environment:
TRANSFORMERS_INFERENCE_API: 'http://t2v-transformers:8080'
RERANKER_INFERENCE_API: 'http://reranker-transformers:8080'
QUERY_DEFAULTS_LIMIT: 25
AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED: 'true'
PERSISTENCE_DATA_PATH: '/var/lib/weaviate'
DEFAULT_VECTORIZER_MODULE: 'text2vec-transformers'
ENABLE_MODULES: 'text2vec-transformers,reranker-transformers'
CLUSTER_HOSTNAME: 'node1'
t2v-transformers:
image: semitechnologies/transformers-inference:baai-bge-m3-onnx
networks:
- weaviate_default
environment:
ENABLE_CUDA: 0 # set to 1 to enable
reranker-transformers:
build:
context: reranker-transformers-1.1.1
dockerfile: Dockerfile
args:
HF_ENDPOINT: "https://hf-mirror.com"
MODEL_NAME: "BAAI/bge-reranker-large"
image: weaviate-reranker-transformers:latest
networks:
- weaviate_default
environment:
ENABLE_CUDA: '0'
networks:
weaviate_default:
driver: bridge
And I connet it via weaviate client, which version is 4.7.1, with following code
client = weaviate.connect_to_custom(
http_host=os.getenv('WEAVIATE_HOST'),
http_port=int(os.getenv('WEAVIATE_HTTP_PORT').strip()),
http_secure=False,
grpc_host=os.getenv('WEAVIATE_HOST'),
grpc_port=int(os.getenv('WEAVIATE_GRPC_PORT').strip()),
grpc_secure=False,
additional_config=AdditionalConfig(
timeout=Timeout(init=30, query=60, insert=120) # Values in seconds
),
)
When I use following code to create a collection, got a Error with status code 422, and response body {âerrorâ: [{âmessageâ: âmodule âtext2vec-transformersâ: invalid combination of propertiesâ}]}.
client.collections.create(
name=index_name,
properties=[
wvc.config.Property(name='j_key', data_type=wvc.config.DataType.INT,
index_filterable=True,
index_searchable=False,
skip_vectorization=True,
vectorize_property_name=False,
),
],
vectorizer_config=wvc.config.Configure.Vectorizer.text2vec_transformers(
vectorize_collection_name=False,
inference_url='http://t2v-transformers:8080',
),
)
And the client.get_meta() ,return
{
"hostname": "http://[::]:8080",
"modules": {
"reranker-transformers": {
"model": {
"_name_or_path": "./models/model",
"add_cross_attention": false,
"architectures": [
"XLMRobertaForSequenceClassification"
],
"attention_probs_dropout_prob": 0.1,
"bad_words_ids": null,
"begin_suppress_tokens": null,
"bos_token_id": 0,
"chunk_size_feed_forward": 0,
"classifier_dropout": null,
"cross_attention_hidden_size": null,
"decoder_start_token_id": null,
"diversity_penalty": 0,
"do_sample": false,
"early_stopping": false,
"encoder_no_repeat_ngram_size": 0,
"eos_token_id": 2,
"exponential_decay_length_penalty": null,
"finetuning_task": null,
"forced_bos_token_id": null,
"forced_eos_token_id": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 1024,
"id2label": {
"0": "LABEL_0"
},
"initializer_range": 0.02,
"intermediate_size": 4096,
"is_decoder": false,
"is_encoder_decoder": false,
"label2id": {
"LABEL_0": 0
},
"layer_norm_eps": 1e-05,
"length_penalty": 1,
"max_length": 20,
"max_position_embeddings": 514,
"min_length": 0,
"model_type": "xlm-roberta",
"no_repeat_ngram_size": 0,
"num_attention_heads": 16,
"num_beam_groups": 1,
"num_beams": 1,
"num_hidden_layers": 24,
"num_return_sequences": 1,
"output_attentions": false,
"output_hidden_states": false,
"output_past": true,
"output_scores": false,
"pad_token_id": 1,
"position_embedding_type": "absolute",
"prefix": null,
"problem_type": null,
"pruned_heads": {},
"remove_invalid_values": false,
"repetition_penalty": 1,
"return_dict": true,
"return_dict_in_generate": false,
"sep_token_id": null,
"suppress_tokens": null,
"task_specific_params": null,
"temperature": 1,
"tf_legacy_loss": false,
"tie_encoder_decoder": false,
"tie_word_embeddings": true,
"tokenizer_class": null,
"top_k": 50,
"top_p": 1,
"torch_dtype": "float32",
"torchscript": false,
"transformers_version": "4.41.2",
"type_vocab_size": 1,
"typical_p": 1,
"use_bfloat16": false,
"use_cache": true,
"vocab_size": 250002
}
},
"text2vec-transformers": {
"model": {
"_name_or_path": "./models/model",
"add_cross_attention": false,
"architectures": [
"XLMRobertaModel"
],
"attention_probs_dropout_prob": 0.1,
"bad_words_ids": null,
"begin_suppress_tokens": null,
"bos_token_id": 0,
"chunk_size_feed_forward": 0,
"classifier_dropout": null,
"cross_attention_hidden_size": null,
"decoder_start_token_id": null,
"diversity_penalty": 0,
"do_sample": false,
"early_stopping": false,
"encoder_no_repeat_ngram_size": 0,
"eos_token_id": 2,
"exponential_decay_length_penalty": null,
"finetuning_task": null,
"forced_bos_token_id": null,
"forced_eos_token_id": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 1024,
"id2label": {
"0": "LABEL_0",
"1": "LABEL_1"
},
"initializer_range": 0.02,
"intermediate_size": 4096,
"is_decoder": false,
"is_encoder_decoder": false,
"label2id": {
"LABEL_0": 0,
"LABEL_1": 1
},
"layer_norm_eps": 1e-05,
"length_penalty": 1,
"max_length": 20,
"max_position_embeddings": 8194,
"min_length": 0,
"model_type": "xlm-roberta",
"no_repeat_ngram_size": 0,
"num_attention_heads": 16,
"num_beam_groups": 1,
"num_beams": 1,
"num_hidden_layers": 24,
"num_return_sequences": 1,
"output_attentions": false,
"output_hidden_states": false,
"output_past": true,
"output_scores": false,
"pad_token_id": 1,
"position_embedding_type": "absolute",
"prefix": null,
"problem_type": null,
"pruned_heads": {},
"remove_invalid_values": false,
"repetition_penalty": 1,
"return_dict": true,
"return_dict_in_generate": false,
"sep_token_id": null,
"suppress_tokens": null,
"task_specific_params": null,
"temperature": 1,
"tf_legacy_loss": false,
"tie_encoder_decoder": false,
"tie_word_embeddings": true,
"tokenizer_class": null,
"top_k": 50,
"top_p": 1,
"torch_dtype": "float32",
"torchscript": false,
"transformers_version": "4.39.3",
"type_vocab_size": 1,
"typical_p": 1,
"use_bfloat16": false,
"use_cache": true,
"vocab_size": 250002
}
}
},
"version": "1.25.10"
}
Please help.
Thanks.
