diff --git a/README.md b/README.md
index 888d39010..2f46a576d 100644
--- a/README.md
+++ b/README.md
@@ -31,10 +31,10 @@ Ingestion has the following steps:
 
 1. Pull html from documentation site
 2. Load html with LangChain's [ReadTheDocs Loader](https://langchain.readthedocs.io/en/latest/modules/document_loaders/examples/readthedocs_documentation.html)
-3. Split documents with LangChain's [TextSplitter](https://langchain.readthedocs.io/en/latest/modules/utils/combine_docs_examples/textsplitter.html)
-4. Create a vectorstore of embeddings, using LangChain's [vectorstore wrapper](https://langchain.readthedocs.io/en/latest/modules/utils/combine_docs_examples/vectorstores.html) (with OpenAI's embeddings and FAISS vectorstore).
+3. Split documents with LangChain's [TextSplitter](https://langchain.readthedocs.io/en/latest/reference/modules/text_splitter.html)
+4. Create a vectorstore of embeddings, using LangChain's [vectorstore wrapper](https://langchain.readthedocs.io/en/latest/reference/modules/vectorstore.html) (with OpenAI's embeddings and FAISS vectorstore).
 
-Question-Answering has the following steps, all handled by [ChatVectorDBChain](https://langchain.readthedocs.io/en/latest/modules/chains/combine_docs_examples/chat_vector_db.html):
+Question-Answering has the following steps, all handled by [ChatVectorDBChain](https://langchain.readthedocs.io/en/latest/modules/indexes/chain_examples/chat_vector_db.html):
 
 1. Given the chat history and new user input, determine what a standalone question would be (using GPT-3).
 2. Given that standalone question, look up relevant documents from the vectorstore.