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Question Answering over Docs
 Contents 
Document Question Answering
Adding in sources
Additional Related Resources
End-to-end examples
Question Answering over Docs#
Conceptual Guide
Question answering in this context refers to question answering over your document data.
For question answering over other types of data, please see other sources documentation like SQL database Question Answering or Interacting with APIs.
For question answering over many documents, you almost always want to create an index over the data.
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).
See this notebook for a more detailed introduction to this, but for a super quick start the steps involved are:
Load Your Documents
from langchain.document_loaders import TextLoader
loader = TextLoader('../state_of_the_union.txt')
See here for more information on how to get started with document loading.
Create Your Index
from langchain.indexes import VectorstoreIndexCreator
index = VectorstoreIndexCreator().from_loaders([loader])
The best and most popular index by far at the moment is the VectorStore index.
Query Your Index
query = "What did the president say about Ketanji Brown Jackson"
index.query(query)
Alternatively, use query_with_sources to also get back the sources involved
query = "What did the president say about Ketanji Brown Jackson"
index.query_with_sources(query)
Again, these high level interfaces obfuscate a lot of what is going on under the hood, so please see this notebook for a lower level walkthrough.
Document Question Answering#
Question answering involves fetching multiple documents, and then asking a question of them.
The LLM response will contain the answer to your question, based on the content of the documents.
rtdocs_stable/api.python.langchain.com/en/stable/use_cases/question_answering.html