From f7d1b1fbb191a6d043a03d3ba6114d0b9a92486f Mon Sep 17 00:00:00 2001 From: Kanav Bansal <13186335+bansalkanav@users.noreply.github.com> Date: Sun, 20 Jul 2025 02:57:31 +0530 Subject: [PATCH] docs(docs): update RAG tutorials link to point to correct path (#32113) --- .../docs/integrations/text_embedding/google_generative_ai.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/docs/integrations/text_embedding/google_generative_ai.ipynb b/docs/docs/integrations/text_embedding/google_generative_ai.ipynb index 5e743279a23..26a022c8f77 100644 --- a/docs/docs/integrations/text_embedding/google_generative_ai.ipynb +++ b/docs/docs/integrations/text_embedding/google_generative_ai.ipynb @@ -173,7 +173,7 @@ "source": [ "## Indexing and Retrieval\n", "\n", - "Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our [RAG tutorials](/docs/tutorials/).\n", + "Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our [RAG tutorials](/docs/tutorials/rag).\n", "\n", "Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`." ]