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improve documentation on how to pass in custom prompts (#561)

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Harrison Chase
2023-01-08 19:20:13 -08:00
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16
docs/use_cases/question_answering.md
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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 recommended way to get started using a question answering chain is:
```python
from langchain.chains.question_answering import load_qa_chain
chain = load_qa_chain(llm, chain_type="stuff")
chain.run(input_documents=docs, question=query)
```
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.
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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).
The recommended way to get started using a question answering with sources chain is:
```python
from langchain.chains.qa_with_sources import load_qa_with_sources_chain
chain = load_qa_with_sources_chain(llm, chain_type="stuff")
chain({"input_documents": docs, "question": query}, return_only_outputs=True)
```
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.

10
docs/use_cases/summarization.md
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# Summarization
Summarization involves creating a smaller summary of multiple longer documents.
This can be useful for distilling long documents into the core pieces of information
This can be useful for distilling long documents into the core pieces of information.
The recommended way to get started using a summarization chain is:
```python
from langchain.chains.summarize import load_summarize_chain
chain = load_summarize_chain(llm, chain_type="map_reduce")
chain.run(docs)
```
The following resources exist:
- [Summarization Notebook](/modules/chains/combine_docs_examples/summarize.ipynb): A notebook walking through how to accomplish this task.