From b54a1a3ef1d7b7566395cd5d7604ca7c93012f98 Mon Sep 17 00:00:00 2001
From: Harutaka Kawamura <hkawamura0130@gmail.com>
Date: Tue, 12 Dec 2023 09:55:23 +0900
Subject: [PATCH] docs[patch]: Fix embeddings example for Databricks (#14576)

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Fix `from langchain.llms import DatabricksEmbeddings` to `from
langchain.embeddings import DatabricksEmbeddings`.

Signed-off-by: harupy <17039389+harupy@users.noreply.github.com>
---
 docs/docs/integrations/providers/databricks.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/docs/integrations/providers/databricks.md b/docs/docs/integrations/providers/databricks.md
index d3db485ae0..3ac64692cb 100644
--- a/docs/docs/integrations/providers/databricks.md
+++ b/docs/docs/integrations/providers/databricks.md
@@ -66,7 +66,7 @@ Databricks Foundation Model APIs
 [Databricks Foundation Model APIs](https://docs.databricks.com/machine-learning/foundation-models/index.html) allow you to access and query state-of-the-art open source models from dedicated serving endpoints. With Foundation Model APIs, developers can quickly and easily build applications that leverage a high-quality generative AI model without maintaining their own model deployment. The following example uses the `databricks-bge-large-en` endpoint to generate embeddings from  text:
 
 ```python
-from langchain.llms import DatabricksEmbeddings
+from langchain.embeddings import DatabricksEmbeddings
 
 
 embeddings = DatabricksEmbeddings(endpoint="databricks-bge-large-en")