Martin Holecek

> > After inserting data into Milvus, you can use this code to connect with Milvus, where "collection_name" is the collection you have already inserted into Milvus, and "index_params" is...

Hi @fabmeyer, You can use constructor if you already have collection. ```py # Get existing collection from Milvus with changing index parameters vector_db: Milvus = Milvus( embedding_function=embeddings, collection_name=MILVUS_COLLECTION_NAME, index_params={ "metric_type":...

Thank you for your question @fabmeyer. In the context of working with Milvus, it's important to note that embeddings play a crucial role. The embeddings are used to convert your...

Continuing from my previous point, @fabmeyer, while you can technically use 'fake' embeddings from this [link](https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/fake), they lack the semantic value necessary for effective operations like similarity searches in Milvus....

I had the same issue before. This is probably caused by having the embeddings with different dimensions already stored inside the chroma db. I fixed that by removing the chroma...

Did you try to change the tile size?

Did you try `--fp32` flag? It might be caused by floating-point precision. I believe that the model is trained using half-precision (FP16) arithmetic. It could be that the platform you...

Same problem here. Any idea how to fix it?

I was able to successfully implement the feature by downgrading the codemirror library to version ^5.65.13. Here's how I did it: In your package.json file, adjust the dependencies to include...