From 0996272aee9fef15854e0f66cd0a1942759c506c Mon Sep 17 00:00:00 2001 From: dishaprakash <57954147+dishaprakash@users.noreply.github.com> Date: Wed, 17 Sep 2025 23:15:58 +0530 Subject: [PATCH] chore: update PGVectorStore How to guide --- examples/pg_vectorstore_how_to.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/pg_vectorstore_how_to.ipynb b/examples/pg_vectorstore_how_to.ipynb index 7809554..2c5e75a 100644 --- a/examples/pg_vectorstore_how_to.ipynb +++ b/examples/pg_vectorstore_how_to.ipynb @@ -692,7 +692,7 @@ "source": [ "# Hybrid Search with PGVectorStore\n", "\n", - "A Hybrid Search combines multiple lookup strategies to provide more comprehensive and relevant search results. Specifically, it leverages both dense embedding vector search (for semantic similarity) and TSV (Text Search Vector) based keyword search (for lexical matching). This approach is particularly powerful for applications requiring efficient searching through customized text and metadata, especially when a specialized embedding model isn't feasible or necessary.\n", + "A Hybrid Search combines multiple lookup strategies to provide more comprehensive and relevant search results. Specifically, it leverages both dense embedding vector search as the primary search (for semantic similarity) and TSV (Text Search Vector) based keyword search as the secondary search (for lexical matching). This approach is particularly powerful for applications requiring efficient searching through customized text and metadata, especially when a specialized embedding model isn't feasible or necessary.\n", "\n", "By integrating both semantic and lexical capabilities, hybrid search helps overcome the limitations of each individual method:\n", "* **Semantic Search**: Excellent for understanding the meaning of a query, even if the exact keywords aren't present. However, it can sometimes miss highly relevant documents that contain the precise keywords but have a slightly different semantic context.\n",