Data Augmented Generation
=========================

The walkthroughs here are related to data augmented generation.
They cover either how to work with the components of data augmented generation (documents, embeddings, and vectorstores), or are end-to-end examples for using these components.

**Components**

`Text Splitters <data_augmented_generation/textsplitter.ipynb>`_: A walkthrough of how to split large documents up into smaller, more manageable pieces of text.

`Embeddings & VectorStores <data_augmented_generation/embeddings.ipynb>`_: A walkthrough of the different embedding and vectorstore functionalies that LangChain supports.


**Examples**

`Question Answering <data_augmented_generation/question_answering.ipynb>`_: A walkthrough of how to use LangChain for question answering over specific documents.

`Question Answering with Sources <data_augmented_generation/qa_with_sources.ipynb>`_: A walkthrough of how to use LangChain for question answering (with sources) over specific documents.

`Summarization <data_augmented_generation/summarize.ipynb>`_: A walkthrough of how to use LangChain for summarization over specific documents.

`Vector DB Question Answering <data_augmented_generation/vector_db_qa.ipynb>`_: A walkthrough of how to use LangChain for question answering over a vector database.

`Vector DB Question Answering with Sources <data_augmented_generation/vector_db_qa_with_sources.ipynb>`_: A walkthrough of how to use LangChain for question answering (with sources) over a vector database.


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   data_augmented_generation/*