34 lines
No EOL
1.2 KiB
Python
34 lines
No EOL
1.2 KiB
Python
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from cognitive_architecture.infrastructure.databases.vector.get_vector_database import get_vector_database
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async def adapted_qdrant_batch_search(results_to_check,vector_client):
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search_results_list = []
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for result in results_to_check:
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id = result[0]
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embedding = result[1]
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node_id = result[2]
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target = result[3]
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b= result[4]
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# Assuming each result in results_to_check contains a single embedding
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limits = [3] * len(embedding) # Set a limit of 3 results for this embedding
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try:
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#Perform the batch search for this id with its embedding
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#Assuming qdrant_batch_search function accepts a single embedding and a list of limits
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#qdrant_batch_search
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id_search_results = await vector_client.batch_search(collection_name = id, embeddings= embedding, with_vectors=limits)
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search_results_list.append((id, id_search_results, node_id, target))
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except Exception as e:
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print(f"Error during batch search for ID {id}: {e}")
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continue
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return search_results_list
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if __name__ == '__main__':
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client = get_vector_database()
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adapted_qdrant_batch_search() |