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Hey @ywancit! I'm here to assist you with any issues you may be experiencing. I can help troubleshoot bugs, answer questions, and guide you through contributing. Let's tackle this together! To read a Milvus index that already contains your data and add data to it, you need to ensure that the metadata dictionary contains the necessary fields when converting the metadata back to a node. Specifically, the Here's how you can handle this:
By ensuring that the |
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I can only find documnets about milvus like
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex([Document(text="The number that is being searched for is ten.")], storage_context=storage_context)
But I want to just read the milvus index I created before without passing [Document(text="The number that is being searched for is ten.")], so I use from_vector_store
index = VectorStoreIndex.from_vector_store(vector_store=vector_store)
query_engine = index.as_query_engine()
response = query_engine.query("The number that is being searched for is?")
print(response)
It works when my index is created by llama_index, but when my milvus index created by my own code and data with fields other than
doc_id_field
embedding_field
but also desc , source ,
the query raise error
ValueError: Node content not found in metadata dict.
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