forked from Archives/langchain
24 lines
2.2 KiB
Markdown
24 lines
2.2 KiB
Markdown
|
# 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.
|
||
|
|
|
||
|
|
The following resources exist:
|
||
|
|
- [Question Answering Notebook](/modules/chains/combine_docs_examples/question_answering.ipynb): A notebook walking through how to accomplish this task.
|
||
|
|
- [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.
|
||
|
|
|
||
|
|
### Adding in sources
|
||
|
|
|
||
|
|
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).
|
||
|
|
|
||
|
|
The following resources exist:
|
||
|
|
- [QA With Sources Notebook](/modules/chains/combine_docs_examples/qa_with_sources.ipynb): A notebook walking through how to accomplish this task.
|
||
|
|
- [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.
|
||
|
|
|
||
|
|
### Additional Related Resources
|
||
|
|
|
||
|
|
Additional related resources include:
|
||
|
|
- [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).
|
||
|
|
- [CombineDocuments Chains](/modules/chains/combine_docs.md): A conceptual overview of specific types of chains by which you can accomplish this task.
|
||
|
|
- [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).
|