Expanding use-cases docs: Q&A and chatbots

docs/understanding/putting_it_all_together/q_and_a.md

 # Q&A patterns
 
+(Semantic-search)=
+
 ## Semantic Search
 
 The most basic example usage of LlamaIndex is through semantic search. We provide a simple in-memory vector store for you to get started, but you can also choose to use any one of our [vector store integrations](/community/integrations/vector_stores.md):
@@ -23,6 +25,8 @@ print(response)
 
 - [Example](/examples/vector_stores/SimpleIndexDemo.ipynb) ([Notebook](https://github.com/run-llama/llama_index/tree/main/docs/examples/vector_stores/SimpleIndexDemo.ipynb))
 
+(Summarization)=
+
 ## Summarization
 
 A summarization query requires the LLM to iterate through many if not most documents in order to synthesize an answer.
@@ -57,6 +61,8 @@ Here are some relevant resources:
 - [SQL Guide (Core)](/examples/index_structs/struct_indices/SQLIndexDemo.ipynb) ([Notebook](https://github.com/jerryjliu/llama_index/blob/main/docs/examples/index_structs/struct_indices/SQLIndexDemo.ipynb))
 - [Pandas Demo](/examples/query_engine/pandas_query_engine.ipynb) ([Notebook](https://github.com/jerryjliu/llama_index/blob/main/docs/examples/query_engine/pandas_query_engine.ipynb))
 
+(Combine-multiple-sources)=
+
 ## Synthesis over Heterogeneous Data
 
 LlamaIndex supports synthesizing across heterogeneous data sources. This can be done by composing a graph over your existing data.
@@ -81,6 +87,8 @@ response = query_engine.query("<query_str>")
 
 - [City Analysis](/examples/composable_indices/city_analysis/PineconeDemo-CityAnalysis.ipynb) ([Notebook](https://github.com/jerryjliu/llama_index/blob/main/docs/examples/composable_indices/city_analysis/PineconeDemo-CityAnalysis.ipynb))
 
+(Route-across-multiple-sources)=
+
 ## Routing over Heterogeneous Data
 
 LlamaIndex also supports routing over heterogeneous data sources with `RouterQueryEngine` - for instance, if you want to "route" a query to an
@@ -153,6 +161,8 @@ This module will help break down a complex query into a simpler one over your ex
 
 You can also rely on the LLM to _infer_ whether to perform compare/contrast queries (see Multi-Document Queries below).
 
+(Multi-document-queries)=
+
 ## Multi-Document Queries
 
 Besides the explicit synthesis/routing flows described above, LlamaIndex can support more general multi-document queries as well.

docs/use_cases/chatbots.md

@@ -6,6 +6,12 @@ LlamaIndex gives you the tools to build knowledge-augmented chatbots and agents.
 
 Here's some relevant resources:
 
-- [Building a chatbot](/understanding/putting_it_all_together/chatbots/building_a_chatbot.md)
-- [How to build a chatbot](/examples/agent/Chatbot_SEC.ipynb) tutorial
-- [Using with a LangChain Agent](/community/integrations/using_with_langchain.md)
+- [Building a chatbot](/understanding/putting_it_all_together/chatbots/building_a_chatbot.md) tutorial
+- [create-llama](https://blog.llamaindex.ai/create-llama-a-command-line-tool-to-generate-llamaindex-apps-8f7683021191), a command line tool that generates a full-stack chatbot application for you
+- [SECinsights.ai](https://www.secinsights.ai/), an open-source application that uses LlamaIndex to build a chatbot that answers questions about SEC filings
+- [RAGs](https://blog.llamaindex.ai/introducing-rags-your-personalized-chatgpt-experience-over-your-data-2b9d140769b1), a project inspired by OpenAI's GPTs that lets you build a low-code chatbot over your data using Streamlit
+- Our [OpenAI agents](/module_guides/deploying/agents/modules.md) are all chat bots in nature
+
+## External sources
+
+- [Building a chatbot with Streamlit](https://blog.streamlit.io/build-a-chatbot-with-custom-data-sources-powered-by-llamaindex/)

docs/use_cases/q_and_a.md

@@ -9,19 +9,28 @@ Q&A has all sorts of sub-types, such as:
 ### What to do
 
 - **Semantic search**: finding data that matches not just your query terms, but your intent and the meaning behind your question. This is sometimes known as "top k" search.
+  - [Example of semantic search](semantic-search)
 - **Summarization**: condensing a large amount of data into a short summary relevant to your current question
+  - [Example of summarization](summarization)
 
 ### Where to search
 
 - **Over documents**: LlamaIndex can pull in unstructured text, PDFs, Notion and Slack documents and more and index the data within them.
+  - [Example of search over documents](combine-multiple-sources)
+  - [Building a multi-document agent over the LlamaIndex docs](/examples/agent/multi_document_agents-v1.ipynb)
 - **Over structured data**: if your data already exists in a SQL database, as JSON or as any number of other structured formats, LlamaIndex can query the data in these sources.
+  - [Searching Pandas tables](/examples/query_engine/pandas_query_engine.md)
+  - [Text to SQL](/examples/index_structs/struct_indices/SQLIndexDemo.md)
 
 ### How to search
 
 - **Combine multiple sources**: is some of your data in Slack, some in PDFs, some in unstructured text? LlamaIndex can combine queries across an arbitrary number of sources and combine them.
+  - [Example of combining multiple sources](combine-multiple-sources)
 - **Route across multiple sources**: given multiple data sources, your application can first pick the best source and then "route" the question to that source.
+  - [Example of routing across multiple sources](route-across-multiple-sources)
 - **Multi-document queries**: some questions have partial answers in multiple data sources which need to be questioned separately before they can be combined
+  - [Example of multi-document queries](multi-document-queries)
 
-## Examples
+## Further examples
 
-For examples of all of these types of Q&A, check out [Q&A](/understanding/putting_it_all_together/q_and_a.md) under "Putting it all together".
+For further examples of Q&A use cases, see our [Q&A section in Putting it All Together](/understanding/putting_it_all_together/q_and_a.html).