perf: improve transaction performance in vector db
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@@ -30,19 +30,13 @@ class FaissVecDB(BaseVecDB):
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async def initialize(self):
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await self.document_storage.initialize()
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async def insert(
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self,
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content: str,
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metadata: dict = None,
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id: str = None,
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) -> int:
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async def insert(self, content: str, metadata: dict = None, id: str = None) -> int:
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"""
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插入一条文本和其对应向量,自动生成 ID 并保持一致性。
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"""
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metadata = metadata or {}
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str_id = id or str(uuid.uuid4()) # 使用 UUID 作为原始 ID
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# 获取向量
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vector = await self.embedding_provider.get_embedding(content)
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vector = np.array(vector, dtype=np.float32)
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async with self.document_storage.connection.cursor() as cursor:
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@@ -54,9 +48,9 @@ class FaissVecDB(BaseVecDB):
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result = await self.document_storage.get_document_by_doc_id(str_id)
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int_id = result["id"]
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# 插入向量到 FAISS
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await self.embedding_storage.insert(vector, int_id)
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return int_id
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# 插入向量到 FAISS
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await self.embedding_storage.insert(vector, int_id)
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return int_id
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async def retrieve(
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self, query: str, k: int = 5, fetch_k: int = 20, metadata_filters: dict = None
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