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SMN
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Joined September 25, 2024
I am implementing an application where I want to use Qdrant as a persistent database. However, when I use an automerging method to split the documents, I am then unable to properly save the data. When I load the database and perform queries, I get an error: 'raise ValueError(f"doc_id {doc_id} not found.") ValueError: doc_id 199bc490-2969-4b2f-be85-71c0d29a078b not found.' The issue is that in 'auto_merging_retriever.py', line 154, in _try_merging nodes, is_changed_1 = self._get_parents_and_merge(nodes)'. Any suggestion on how i can properly use Qdrant? thanks
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hello, is there an example, where the LLM can be integrated to generate the filtering on metadata values? The only example i see is where the user pass the values to the filter eg:
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from llama_index.vector_stores.types import ExactMatchFilter, MetadataFilters

filters = MetadataFilters(
    filters=[ExactMatchFilter(key="type", value="fruit")]
)
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