bump version to 0.0.95 (#1324)

docs/use_cases/question_answering.md

@@ -1,5 +1,41 @@
 # Question Answering
 
+Question answering in this context refers to question answering over your document data. 
+For question answering over other types of data, like [SQL databases](../modules/chains/examples/sqlite.html) or [APIs](../modules/chains/examples/api.html), please see [here](../modules/chains/utility_how_to.html)
+
+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](../modules/indexes/getting_started.ipynb) for a more detailed introduction to this, but for a super quick start the steps involved are:
+
+**Load Your Documents**
+```python
+from langchain.document_loaders import TextLoader
+loader = TextLoader('../state_of_the_union.txt')
+```
+See [here](../modules/document_loaders/how_to_guides.rst) for more information on how to get started with document loading.
+
+**Create Your Index**
+```python
+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**
+```python
+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
+```python
+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](../modules/indexes/getting_started.ipynb) 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.
 
@@ -15,7 +51,7 @@ 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.
 
-### Adding in sources
+## 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).
 
@@ -31,7 +67,7 @@ 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.
 
-### Additional Related Resources
+## 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).