[LLM-app] Cleaning old templates (#7262)

GitOrigin-RevId: 76882172dcce6dde64af646cc8ab0d828ddf6c60

README.md

@@ -26,10 +26,10 @@ The application templates provided in this repo scale up to **millions of pages
 | [`Question-Answering RAG App`](examples/pipelines/demo-question-answering/)    | Basic end-to-end RAG app. A question-answering pipeline that uses the GPT model of choice to provide answers to queries to your documents (PDF, DOCX,...) on a live connected data source (files, Google Drive, Sharepoint,...). You can also try out a [demo REST endpoint](https://pathway.com/solutions/rag-pipelines#try-it-out).              |
 | [`Live Document Indexing (Vector Store / Retriever)`](examples/pipelines/demo-document-indexing/)     | A real-time document indexing pipeline for RAG that acts as a vector store service. It performs live indexing on your documents (PDF, DOCX,...) from a connected data source (files, Google Drive, Sharepoint,...). It can be used with any frontend, or integrated as a retriever backend for a [Langchain](https://pathway.com/developers/templates/langchain-integration) or [Llamaindex](https://pathway.com/developers/templates/llamaindex-pathway) application. You can also try out a [demo REST endpoint](https://pathway.com/solutions/ai-contract-management#try-it-out).         |
 | [`Multimodal RAG pipeline with GPT4o`](examples/pipelines/gpt_4o_multimodal_rag/) | Multimodal RAG using GPT-4o in the parsing stage to index PDFs and other documents from a connected data source files, Google Drive, Sharepoint,...). It is perfect for extracting information from unstructured financial documents in your folders (including charts and tables), updating results as documents change or new ones arrive.|
-| [`Adaptive RAG App`](examples/pipelines/adaptive-rag/) | A RAG application using Adaptive RAG, a technique developed by Pathway to reduce token cost in RAG up to 4x while maintaining accuracy. |
-| [`Private RAG App with Mistral and Ollama`](examples/pipelines/private-rag/) |  A fully private (local) version of the `demo-question-answering` RAG pipeline using Pathway, Mistral, and Ollama. |
 | [`Unstructured-to-SQL pipeline + SQL question-answering`](examples/pipelines/unstructured_to_sql_on_the_fly/) | A RAG example which connects to unstructured financial data sources (financial report PDFs), structures the data into SQL, and loads it into a PostgreSQL table. It also answers natural language user queries to these financial documents by translating them into SQL using an LLM and executing the query on the PostgreSQL table. |
 | [`Alerting when answers change on Google Drive`](examples/pipelines/drive_alert/)                                        | Ask questions about your private data (docs), and tell the app to alert you whenever responses change. The app is always connected to your Google Docs folder and listening for changes. Whenever new relevant information is added to the data sources, the LLM decides if there is a substantial difference in response and notifies the user with a Slack message.|
+| [`Adaptive RAG App`](examples/pipelines/adaptive-rag/) | A RAG application using Adaptive RAG, a technique developed by Pathway to reduce token cost in RAG up to 4x while maintaining accuracy. |
+| [`Private RAG App with Mistral and Ollama`](examples/pipelines/private-rag/) |  A fully private (local) version of the `demo-question-answering` RAG pipeline using Pathway, Mistral, and Ollama. |
 
 
 ## How do these LLM Apps work?