forked from Archives/langchain
985496f4be
Big docs refactor! Motivation is to make it easier for people to find resources they are looking for. To accomplish this, there are now three main sections: - Getting Started: steps for getting started, walking through most core functionality - Modules: these are different modules of functionality that langchain provides. Each part here has a "getting started", "how to", "key concepts" and "reference" section (except in a few select cases where it didnt easily fit). - Use Cases: this is to separate use cases (like summarization, question answering, evaluation, etc) from the modules, and provide a different entry point to the code base. There is also a full reference section, as well as extra resources (glossary, gallery, etc) Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>
24 lines
2.2 KiB
Markdown
24 lines
2.2 KiB
Markdown
# Question Answering
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Question answering involves fetching multiple documents, and then asking a question of them.
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The LLM response will contain the answer to your question, based on the content of the documents.
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The following resources exist:
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- [Question Answering Notebook](/modules/chains/combine_docs_examples/question_answering.ipynb): A notebook walking through how to accomplish this task.
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- [VectorDB Question Answering Notebook](/modules/chains/combine_docs_examples/vector_db_qa.ipynb): A notebook walking through how to do question answering over a vector database. This can often be useful for when you have a LOT of documents, and you don't want to pass them all to the LLM, but rather first want to do some semantic search over embeddings.
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### Adding in sources
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There is also a variant of this, where in addition to responding with the answer the language model will also cite its sources (eg which of the documents passed in it used).
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The following resources exist:
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- [QA With Sources Notebook](/modules/chains/combine_docs_examples/qa_with_sources.ipynb): A notebook walking through how to accomplish this task.
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- [VectorDB QA With Sources Notebook](/modules/chains/combine_docs_examples/vector_db_qa_with_sources.ipynb): A notebook walking through how to do question answering with sources over a vector database. This can often be useful for when you have a LOT of documents, and you don't want to pass them all to the LLM, but rather first want to do some semantic search over embeddings.
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### Additional Related Resources
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Additional related resources include:
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- [Utilities for working with Documents](/modules/utils/how_to_guides.rst): Guides on how to use several of the utilities which will prove helpful for this task, including Text Splitters (for splitting up long documents) and Embeddings & Vectorstores (useful for the above Vector DB example).
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- [CombineDocuments Chains](/modules/chains/combine_docs.md): A conceptual overview of specific types of chains by which you can accomplish this task.
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- [Data Augmented Generation](combine_docs.md): An overview of data augmented generation, which is the general concept of combining external data with LLMs (of which this is a subset).
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