forked from Archives/langchain
You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
33 lines
1.6 KiB
ReStructuredText
33 lines
1.6 KiB
ReStructuredText
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.
|
|
|
|
|
|
.. toctree::
|
|
:maxdepth: 1
|
|
:glob:
|
|
:hidden:
|
|
|
|
data_augmented_generation/*
|