Distance between entrypoint and query node

mklobucaric · December 20, 2024, 1:23pm

Description

I have created a sanbox in Weaviate Cloud to test functionality before putting the vectorstore in production. I am testing openai embeddings:
“text-embedding-3-small” and “text-embedding-3-large”.

I tried to change dimensions of “text-embedding-3-large” to 256, to see how sould retrieval go, but I got error below. “distance between entrypoint and query node: 256 vs 1536”

Can I change dimensions in Sandbox? Or do I have to setup something additional when I am doing hybrid or smilarity search?

This is my setup:

    client = weaviate.connect_to_weaviate_cloud(
        cluster_url=weaviate_url,
        auth_credentials=Auth.api_key(weaviate_api_key),
        headers={"X-OpenAI-Api-Key": openai_api_key},
        # headers={"X-Google-Studio-Api-Key": google_studio_api_key},
    )

        vectorizer_config = Configure.Vectorizer.text2vec_openai(
            model=model,
            model_version="3",
            dimensions=dimensions,
        )

        client.collections.create(
            name=collection_name,
            vectorizer_config=vectorizer_config,
            vector_index_config=Configure.VectorIndex.hnsw(
                distance_metric=VectorDistances.COSINE
            ),
            properties=properties,
        )

I have changed now everything to model: str = “text-embedding-3-small”, and dimensions: int = 1536, and now retrieval works fine…

Second somehow related question: Does, Google’s “text-embedding-004” embedding works in sandbox? I had earlier some trouble setting it up…

Third question, are this integrations necessary (since this integrations don’t support Google’s embeddings):

    # integrations = [
    #     Integrations.openai(
    #         api_key=openai_api_key,
    #         requests_per_minute_embeddings=3000,
    #         tokens_per_minute_embeddings=1000000,
    #     ),
    # ]
    # client.integrations.configure(integrations)

Server Setup Information

Weaviate Server Version:
Deployment Method: sanbox in Weaviate Cloud
Multi Node? Number of Running Nodes: no
Client Language and Version: Python v4
Multitenancy?: No

Any additional Information

"StatusCode.UNKNOWN\n\tdetails = “explorer: get class: vector search: object vector search at index …: vector search: knn search: distance between entrypoint and query node: 256 vs 1536: vector lengths don't match”\n\tdebug_error_string = "UNKNOWN:Error received from peer "

DudaNogueira · December 20, 2024, 1:59pm

hi @mklobucaric !!

Welcome to our community !!

This seems related to this bug we have recently discovered:

github.com/weaviate/weaviate

Hybrid search falling back to default vectorizer confs when dimensions is set

opened 08:15PM - 18 Dec 24 UTC

dudanogueira

bug

### How to reproduce this bug? Here is a reproducible code: ```python impor…t os import weaviate from weaviate import classes as wvc import weaviate.error_msgs client = weaviate.connect_to_local( headers={ "X-OpenAI-Api-Key": os.getenv("OPENAI_APIKEY", "CHANGE_ME"), } ) print(f"Client: {weaviate.__version__}, Server: {client.get_meta().get('version')}") client.collections.delete("Test") collection = client.collections.create( name="Test", vectorizer_config=wvc.config.Configure.Vectorizer.text2vec_openai( model="text-embedding-3-large", dimensions=1024, #type_="text", vectorize_collection_name=False ), properties=[ wvc.config.Property( name="text", data_type=wvc.config.DataType.TEXT, tokenization=wvc.config.Tokenization.WORD ) ] ) # Create a single object response = collection.data.insert( properties={ "text": "COVID-19 has many symptoms." } ) # objects indeed has 1024 dimensions response = collection.query.fetch_objects( limit=5, include_vector=True ) for obj in response.objects: print( f"fetch_objects: {obj.uuid} ({len(obj.vector['default'])}) | Properties: {obj.properties}") # you can perform a neartext response = collection.query.near_text( query="hybrid query with 1024 dimensions", #alpha=0.75, limit=5, include_vector=True ) for obj in response.objects: print( f"near text query: {obj.uuid} ({len(obj.vector['default'])}) | Properties: {obj.properties}") #but it fails to hybrid try: response = collection.query.hybrid( query="hybrid query with 1024 dimensions", alpha=0.75, limit=5, include_vector=True ) for obj in response.objects: print( f"hybrid query: {obj.uuid} ({len(obj.vector['default'])}) | Properties: {obj.properties}") except Exception as e: print("ERROR!!!", e) # if we close the client client.close() # and point it to a catch endpoint client = weaviate.connect_to_local( headers={ "X-OpenAI-Api-Key": os.getenv("OPENAI_APIKEY", "CHANGE_ME"), "X-OpenAI-BaseUrl": "https://webhook.site/beef60de-4d45-4c61-9928-b20fa619f91e", } ) collection = client.collections.get("Test") response = collection.query.hybrid( query="hybrid query with 1024 dimensions", #alpha=0.75, limit=5, include_vector=True ) for obj in response.objects: print( f"hybrid query: {obj.uuid} ({len(obj.vector['default'])}) | Properties: {obj.properties}") # we get this payload payload = { "input": [ "hybrid query with 1024 dimensions" ], "model": "text-embedding-3-small", "dimensions": 1536 } ``` ### What is the expected behavior? The hybrid search should work. It should generate the query vectorization payload as: ```json { "input": [ "hybrid query with 1024 dimensions" ], "model": "text-embedding-3-large", "dimensions": 1024 } ``` ### What is the actual behavior? The generated payload to vectorize a hybrid query is passing the wrong model and dimension as the payload: ```json { "input": [ "hybrid query with 1024 dimensions" ], "model": "text-embedding-3-small", "dimensions": 1536 } ``` ### Supporting information Client: 4.10.2, Server: 1.28.1 ### Server Version 1.28.1 ### Weaviate Setup Single Node ### Nodes count 1 ### Code of Conduct - [X] I have read and agree to the Weaviate's [Contributor Guide](https://weaviate.io/developers/contributor-guide) and [Code of Conduct](https://weaviate.io/service/code-of-conduct)

Can you confirm that near_text will work, but the failing query is a hybrid one?

Thanks!

mklobucaric · December 20, 2024, 3:16pm

Thank you!

Yes, you are right, nearText works but hybrid not…

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Distance between entrypoint and query node

Description

Server Setup Information

Any additional Information

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