From 33d445550e649b5de25bb2600b9b86c4b3de1b76 Mon Sep 17 00:00:00 2001 From: sifatj <26035630+sifatj@users.noreply.github.com> Date: Fri, 1 Nov 2024 15:44:26 +0000 Subject: [PATCH] docs: update VectorStore api reference url in retrievers.ipynb (#27814) **Description:** Update outdated `VectorStore` api reference url in Vector store subsection of `retrievers.ipynb` --- docs/docs/tutorials/retrievers.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/docs/tutorials/retrievers.ipynb b/docs/docs/tutorials/retrievers.ipynb index 5dd6db59ff805..9c9bd12e04741 100644 --- a/docs/docs/tutorials/retrievers.ipynb +++ b/docs/docs/tutorials/retrievers.ipynb @@ -123,7 +123,7 @@ "\n", "Vector search is a common way to store and search over unstructured data (such as unstructured text). The idea is to store numeric vectors that are associated with the text. Given a query, we can [embed](/docs/concepts/embedding_models) it as a vector of the same dimension and use vector similarity metrics to identify related data in the store.\n", "\n", - "LangChain [VectorStore](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.VectorStore.html) objects contain methods for adding text and `Document` objects to the store, and querying them using various similarity metrics. They are often initialized with [embedding](/docs/how_to/embed_text) models, which determine how text data is translated to numeric vectors.\n", + "LangChain [VectorStore](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.base.VectorStore.html) objects contain methods for adding text and `Document` objects to the store, and querying them using various similarity metrics. They are often initialized with [embedding](/docs/how_to/embed_text) models, which determine how text data is translated to numeric vectors.\n", "\n", "LangChain includes a suite of [integrations](/docs/integrations/vectorstores) with different vector store technologies. Some vector stores are hosted by a provider (e.g., various cloud providers) and require specific credentials to use; some (such as [Postgres](/docs/integrations/vectorstores/pgvector)) run in separate infrastructure that can be run locally or via a third-party; others can run in-memory for lightweight workloads. Here we will demonstrate usage of LangChain VectorStores using [Chroma](/docs/integrations/vectorstores/chroma), which includes an in-memory implementation.\n", "\n",