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fix(examples): correct WebBaseLoader typo in LangChain QA examples #576

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fix(examples): correct WebBaseLoader typo in LangChain QA examples #576

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2 changes: 1 addition & 1 deletion examples/gemini/python/langchain/Gemini_LangChain_QA_Chroma_WebLoad.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -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"
]
},
{
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2 changes: 1 addition & 1 deletion examples/gemini/python/langchain/Gemini_LangChain_QA_Pinecone_WebLoad.ipynb
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Expand Up @@ -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."
]
},
{
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