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86 lines
4.1 KiB
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86 lines
4.1 KiB
Plaintext
# Question answering over documents
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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](/docs/use_cases/tabular.html) or [Interacting with APIs](/docs/use_cases/apis.html).
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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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**Load Your Documents**
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```python
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from langchain.document_loaders import TextLoader
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loader = TextLoader('../../modules/state_of_the_union.txt')
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```
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See [here](/docs/modules/data_connection/document_loaders/) 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](/docs/modules/data_connection/) for a more thorough introduction to data modules.
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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](/docs/modules/chains/additional/question_answering.html): A notebook walking through how to accomplish this task.
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- [VectorDB Question Answering Notebook](/docs/modules/chains/popular/vector_db_qa.html): 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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## Additional Related Resources
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Additional related resources include:
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- [Building blocks for working with Documents](/docs/modules/data_connection/): 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](/docs/modules/chains/document/): 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](/docs/use_cases/question_answering/semantic-search-over-chat.html): A notebook that semantically searches over a group chat conversation.
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- [Document context aware text splitting and QA](/docs/use_cases/question_answering/document-context-aware-QA.html): A notebook that shows context aware splitting on markdown files and SelfQueryRetriever for QA using the resulting metadata.
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