Relyt

Table( self.collection_name, Base.metadata, Column("id", TEXT, primary_key=True, default=uuid.uuid4), Column("embedding", Vector(self.embedding_dimension)), Column("document", String, nullable=True), Column("metadata", JSON, nullable=True), extend_existing=True, )

Run more texts through the embeddings and add to the vectorstore.

Run similarity search with AnalyticDB with distance.

Return docs most similar to query.

Return docs most similar to embedding vector.

Delete by vector IDs.

Return VectorStore initialized from texts and embeddings. Postgres Connection string is required Either pass it as a parameter or set the PG_CONNECTION_STRING environment variable.

Return VectorStore initialized from documents and embeddings. Postgres Connection string is required Either pass it as a parameter or set the PG_CONNECTION_STRING environment variable.

Return connection string from database parameters.