Ok I added the mebedding model, the process now takes much more time but still no collection in the store at the end. when I create an index with the code the index seems to be empty too by looking at it. something is odd
# here we set the file_path to become no part of the embedding, it's not for this use case # also we check if a doc has zero content then we don't try to embedd it as it would result in an error docs = [] for doc in reader.iter_data(): if len(doc) > 1: print('ok') doc[0].excluded_llm_metadata_keys.append("file_path") doc[0].excluded_embed_metadata_keys.append("file_path") if doc[0].text != '': docs = docs + [doc[0]]
i think it was even more bad... the qdrant dashboard does not a realtime search... you have to reload the page to get the full list of collections. so its all my fault beginning at missing to add the embedding model to the pipeline