From d2447d7a59ffae0075dad0beb0ddbacdd01a93f4 Mon Sep 17 00:00:00 2001 From: Rahul Lashkari Date: Sat, 19 Apr 2025 23:08:34 +0530 Subject: [PATCH] fix(examples): correct WebBaseLoader typo in LangChain QA examples --- .../python/langchain/Gemini_LangChain_QA_Chroma_WebLoad.ipynb | 2 +- .../python/langchain/Gemini_LangChain_QA_Pinecone_WebLoad.ipynb | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) 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." ] }, {