docs(product): add brief documentation for LangChain support (#7061)

* docs(product): add brief documentation for Langchain support

* Update

---------

Co-authored-by: Igor Lukanin <[email protected]>

Author: hassankhan

docs/docs-new/pages/product/configuration/visualization-tools.mdx

@@ -88,7 +88,7 @@ to other BIs or visualization tools not listed here.
   />
 </Grid>
 
-## Custom data experiences
+## AI- & LLM-based experiences
 
 The following tools deliver data insights closer to end users, e.g., by
 providing a conversational interface for semantic layer:
@@ -99,6 +99,11 @@ providing a conversational interface for semantic layer:
     imageUrl="https://static.cube.dev/icons/delphi.svg"
     title="Delphi"
   />
+  <GridItem
+    url="visualization-tools/langchain"
+    imageUrl="https://static.cube.dev/icons/langchain.svg"
+    title="LangChain"
+  />
 </Grid>
 
 ## Low-code tools & internal tool builders

docs/docs-new/pages/product/configuration/visualization-tools/langchain.mdx

@@ -0,0 +1,42 @@
+# LangChain
+
+[LangChain][langchain] is a framework designed to simplify the creation of
+applications using large language models.
+
+To get started with LangChain, follow the [instructions][langchain-docs-getting-started].
+
+## Document loader
+
+Cube's integration with LangChain comes as the [document loader][langchain-docs-cube]
+that is intended to be used to populate a vector database with embeddings derived
+from the data model. Later, this vector database can be queried to find best-matching
+entities of the semantic layer. This is useful to match free-form input, e.g., queries
+in a natural language, with the views and their members in the data model.
+
+<Diagram src="https://ucarecdn.com/32e98c8b-a920-4620-a8d2-05d57618db8e/" />
+
+We're also providing an chat-based demo application (see source code on GitHub) with example OpenAI prompts for constructing queries to Cube's SQL API. If you wish to create an AI-powered conversational interface for the semantic layer, functioning similar to Delphi, these prompts can be a good starting point.
+
+## Configuring the connection to Cube
+
+The document loader connects to Cube using the [REST API][ref-rest-api], and will need a
+[JWT][ref-jwt] to authenticate.
+
+If you're using Cube Cloud, you can retrieve these details from a deployment's
+<Btn>Overview</Btn> page.
+
+## Querying Cube
+
+Please refer to the [blog post](https://cube-blog-preview.vercel.app/blog/introducing-the-langchain-integration)
+for details on querying Cube and building a complete AI-based application.
+
+Also, please feel free to review a chat-based demo application [source code](https://github.com/cube-js/cube/tree/master/examples/langchain)
+on GitHub.
+
+[langchain]: https://python.langchain.com/
+[langchain-docs-getting-started]:
+  https://python.langchain.com/docs/get_started/installation.html
+[langchain-docs-cube]:
+  https://python.langchain.com/docs/integrations/document_loaders/cube_semantic#example
+[ref-rest-api]: /product/apis-integrations/rest-api
+[ref-jwt]: /product/auth#generating-json-web-tokens-jwt

docs/docs-new/pages/product/introduction.mdx

@@ -18,7 +18,7 @@ to your customers and driving intelligence back into the business.
 
 With Cube, you can build a data model, manage access control and caching, and expose your data to every application
 via REST, GraphQL, and SQL APIs. With these APIs, you can use any charting library to build custom UI,
-connect existing dashboarding and reporting tools, and build AI agents with frameworks like Langchain.
+connect existing dashboarding and reporting tools, and build AI agents with frameworks like LangChain.
 
 
 ## Code-first