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Fix docs links (#2572)

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Fix broken links in documentation.
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Venky 2023-04-08 17:33:28 +02:00 committed by GitHub
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docs/use_cases/question_answering.md
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@ -51,8 +51,8 @@ chain.run(input_documents=docs, question=query)
``` ```
The following resources exist: The following resources exist:
- [Question Answering Notebook](/modules/indexes/chain_examples/question_answering.ipynb): A notebook walking through how to accomplish this task. - [Question Answering Notebook](../modules/chains/index_examples/question_answering.ipynb): A notebook walking through how to accomplish this task.
- [VectorDB Question Answering Notebook](/modules/indexes/chain_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. - [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.
## Adding in sources ## Adding in sources
@ -67,8 +67,8 @@ chain({"input_documents": docs, "question": query}, return_only_outputs=True)
``` ```
The following resources exist: The following resources exist:
- [QA With Sources Notebook](/modules/indexes/chain_examples/qa_with_sources.ipynb): A notebook walking through how to accomplish this task. - [QA With Sources Notebook](../modules/chains/index_examples/qa_with_sources.ipynb): A notebook walking through how to accomplish this task.
- [VectorDB QA With Sources Notebook](/modules/indexes/chain_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. - [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.
## Additional Related Resources ## Additional Related Resources