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40 lines
2.7 KiB
Markdown
40 lines
2.7 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 recommended way to get started using a question answering chain is:
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```python
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from langchain.chains.question_answering import load_qa_chain
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chain = load_qa_chain(llm, chain_type="stuff")
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chain.run(input_documents=docs, question=query)
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```
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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 recommended way to get started using a question answering with sources chain is:
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```python
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from langchain.chains.qa_with_sources import load_qa_with_sources_chain
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chain = load_qa_with_sources_chain(llm, chain_type="stuff")
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chain({"input_documents": docs, "question": query}, return_only_outputs=True)
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```
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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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