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Merge pull request #18 from sangyh/patch-1
updated readme with working documentation urls
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README.md

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

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