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94 lines
4.6 KiB
Markdown
94 lines
4.6 KiB
Markdown
# Question Answering over Docs
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> [Conceptual Guide](https://docs.langchain.com/docs/use-cases/qa-docs)
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Question answering in this context refers to question answering over your document data.
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For question answering over other types of data, please see other sources documentation like [SQL database Question Answering](./tabular.md) or [Interacting with APIs](./apis.md).
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For question answering over many documents, you almost always want to create an index over the data.
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This can be used to smartly access the most relevant documents for a given question, allowing you to avoid having to pass all the documents to the LLM (saving you time and money).
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See [this notebook](../modules/indexes/getting_started.ipynb) for a more detailed introduction to this, but for a super quick start the steps involved are:
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**Load Your Documents**
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```python
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from langchain.document_loaders import TextLoader
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loader = TextLoader('../state_of_the_union.txt')
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```
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See [here](../modules/indexes/document_loaders.rst) for more information on how to get started with document loading.
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**Create Your Index**
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```python
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from langchain.indexes import VectorstoreIndexCreator
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index = VectorstoreIndexCreator().from_loaders([loader])
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```
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The best and most popular index by far at the moment is the VectorStore index.
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**Query Your Index**
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```python
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query = "What did the president say about Ketanji Brown Jackson"
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index.query(query)
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```
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Alternatively, use `query_with_sources` to also get back the sources involved
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```python
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query = "What did the president say about Ketanji Brown Jackson"
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index.query_with_sources(query)
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```
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Again, these high level interfaces obfuscate a lot of what is going on under the hood, so please see [this notebook](../modules/indexes/getting_started.ipynb) for a lower level walkthrough.
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## Document 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/index_examples/question_answering.ipynb): A notebook walking through how to accomplish this task.
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- [VectorDB Question Answering Notebook](../modules/chains/index_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/index_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/index_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/indexes/combine_docs.md): A conceptual overview of specific types of chains by which you can accomplish this task.
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## End-to-end examples
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For examples to this done in an end-to-end manner, please see the following resources:
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- [Semantic search over a group chat with Sources Notebook](question_answering/semantic-search-over-chat.ipynb): A notebook that semantically searches over a group chat conversation.
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