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58 lines
1.6 KiB
Python
58 lines
1.6 KiB
Python
from typing import Any, List
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from langchain.docstore.document import Document
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from langchain.embeddings.base import Embeddings
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from langchain.vectorstores import SupabaseVectorStore
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from supabase.client import Client
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class CustomSupabaseVectorStore(SupabaseVectorStore):
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"""A custom vector store that uses the match_vectors table instead of the vectors table."""
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brain_id: str = "none"
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def __init__(
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self,
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client: Client,
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embedding: Embeddings,
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table_name: str,
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brain_id: str = "none",
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):
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super().__init__(client, embedding, table_name)
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self.brain_id = brain_id
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def similarity_search(
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self,
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query: str,
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k: int = 6,
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table: str = "match_vectors",
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threshold: float = 0.5,
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**kwargs: Any
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) -> List[Document]:
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vectors = self._embedding.embed_documents([query])
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query_embedding = vectors[0]
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res = self._client.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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"p_brain_id": str(self.brain_id),
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},
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).execute()
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match_result = [
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(
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Document(
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metadata=search.get("metadata", {}), # type: ignore
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page_content=search.get("content", ""),
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),
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search.get("similarity", 0.0),
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)
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for search in res.data
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if search.get("content")
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]
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documents = [doc for doc, _ in match_result]
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return documents
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