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77 lines
2.1 KiB
Python
77 lines
2.1 KiB
Python
from models.databases.repository import Repository
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class Vector(Repository):
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def __init__(self, supabase_client):
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self.db = supabase_client
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def get_vectors_by_file_name(self, file_name):
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response = (
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self.db.table("vectors")
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.select(
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"metadata->>file_name, metadata->>file_size, metadata->>file_extension, metadata->>file_url",
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"content",
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"brains_vectors(brain_id,vector_id)",
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)
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.match({"metadata->>file_name": file_name})
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.execute()
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)
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return response
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def get_vectors_by_file_sha1(self, file_sha1):
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response = (
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self.db.table("vectors")
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.select("id")
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.filter("file_sha1", "eq", file_sha1)
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.execute()
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)
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return response
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# TODO: remove duplicate similarity_search in supabase vector store
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def similarity_search(self, query_embedding, table, k, threshold):
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response = self.db.rpc(
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table,
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{
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"query_embedding": query_embedding,
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"match_count": k,
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"match_threshold": threshold,
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},
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).execute()
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return response
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def update_summary(self, document_id, summary_id):
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return (
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self.db.table("summaries")
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.update({"document_id": document_id})
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.match({"id": summary_id})
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.execute()
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)
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def get_vectors_by_batch(self, batch_id):
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response = (
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self.db.table("vectors")
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.select(
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"name:metadata->>file_name, size:metadata->>file_size",
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count="exact",
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)
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.eq("id", batch_id)
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.execute()
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)
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return response
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def get_vectors_in_batch(self, batch_ids):
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response = (
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self.db.table("vectors")
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.select(
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"name:metadata->>file_name, size:metadata->>file_size",
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count="exact",
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)
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.in_("id", batch_ids)
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.execute()
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)
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return response
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