How to load existing db to similarity search?

[DudaNogueira]

Hi @MartinMin !!

O believe you are facing the same issue from this thread:

Langchain WeaviateHybridSearchRetriever with filters?

Nevermind fixed it. Hey, thank you for the quick replies. I have tried your example but sadly it does not work. When initialising the db, I get an “list index out of range error”. Here is my code: from langchain_cohere import CohereEmbeddings import weaviate from weaviate.classes.init import Auth from weaviate.classes.query import Filter embeddings = CohereEmbeddings(model=EMBEDDINGS_MODEL, cohere_api_key=COHERE_API_KEY) headers = { "X-Cohere-Api-Key": COHERE_API_KEY, } client = weavia…

This error seems to come from hugging face interface at langchain:

langchain_community/embeddings/huggingface.py", line 331,

can you try this?

db = WeaviateVectorStore.from_documents(embeddings=embedding_model, client=weaviate_client, index_name='test')

My guess is that the huggingface interface in langchain is not checking when the documents passed as parameter is an empty list.