diff --git a/examples/gemini/python/langchain/Gemini_LangChain_QA_Chroma_WebLoad.ipynb b/examples/gemini/python/langchain/Gemini_LangChain_QA_Chroma_WebLoad.ipynb
index 6d2342a40..d55deeb7b 100644
--- a/examples/gemini/python/langchain/Gemini_LangChain_QA_Chroma_WebLoad.ipynb
+++ b/examples/gemini/python/langchain/Gemini_LangChain_QA_Chroma_WebLoad.ipynb
@@ -403,7 +403,7 @@
         "\n",
         "To create a Chroma vector database from the website data, you will use the `from_documents` function of `Chroma`. Under the hood, this function creates embeddings from the documents created by the document loader of LangChain using any specified embedding model and stores them in a Chroma vector database.  \n",
         "\n",
-        "You have to specify the `docs` you created from the website data using LangChain's `WebBasedLoader` and the `gemini_embeddings` as the embedding model when invoking the `from_documents` function to create the vector database from the website data. You can also specify a directory in the `persist_directory` argument to store the vector store on the disk. If you don't specify a directory, the data will be ephemeral in-memory.\n"
+        "You have to specify the `docs` you created from the website data using LangChain's `WebBaseLoader` and the `gemini_embeddings` as the embedding model when invoking the `from_documents` function to create the vector database from the website data. You can also specify a directory in the `persist_directory` argument to store the vector store on the disk. If you don't specify a directory, the data will be ephemeral in-memory.\n"
       ]
     },
     {
diff --git a/examples/gemini/python/langchain/Gemini_LangChain_QA_Pinecone_WebLoad.ipynb b/examples/gemini/python/langchain/Gemini_LangChain_QA_Pinecone_WebLoad.ipynb
index 6090249d6..5b109eb8e 100644
--- a/examples/gemini/python/langchain/Gemini_LangChain_QA_Pinecone_WebLoad.ipynb
+++ b/examples/gemini/python/langchain/Gemini_LangChain_QA_Pinecone_WebLoad.ipynb
@@ -449,7 +449,7 @@
         "\n",
         "Next, you'll insert the documents you extracted earlier from the website data into the newly created index using LangChain's `Pinecone.from_documents`. Under the hood, this function creates embeddings from the documents created by the document loader of LangChain using any specified embedding model and inserts them into the specified index in a Pinecone vector database.  \n",
         "\n",
-        "You have to specify the `docs` you created from the website data using LangChain's `WebBasedLoader` and the `gemini_embeddings` as the embedding model when invoking the `from_documents` function to create the vector database from the website data."
+        "You have to specify the `docs` you created from the website data using LangChain's `WebBaseLoader` and the `gemini_embeddings` as the embedding model when invoking the `from_documents` function to create the vector database from the website data."
       ]
     },
     {