forked from Archives/langchain
parent
f5afb60116
commit
7a4e1b72a8
@ -51,8 +51,8 @@ chain.run(input_documents=docs, question=query)
|
||||
```
|
||||
|
||||
The following resources exist:
|
||||
- [Question Answering Notebook](/modules/indexes/chain_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.
|
||||
- [Question Answering Notebook](../modules/chains/index_examples/question_answering.ipynb): A notebook walking through how to accomplish this task.
|
||||
- [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
|
||||
|
||||
@ -67,8 +67,8 @@ chain({"input_documents": docs, "question": query}, return_only_outputs=True)
|
||||
```
|
||||
|
||||
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.
|
||||
- [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.
|
||||
- [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/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
|
||||
|
||||
|
Loading…
Reference in New Issue
Block a user