New data indexing docs page (#8013)

GitOrigin-RevId: d4af088f50bb592678fe5fd782139c1b938495da

examples/pipelines/demo-question-answering/README.md

@@ -28,7 +28,7 @@ This demo allows you to:
 - Get an executive outlook for a question on different files to easily access available knowledge in your documents;
 
 
-Note: This app relies on [Pathway Vector store](https://pathway.com/developers/api-docs/pathway-xpacks-llm/vectorstore) to learn more, you can check out [this blog post](https://pathway.com/developers/user-guide/llm-xpack/vectorstore_pipeline/).
+Note: This app relies on [Document Store](https://pathway.com/developers/api-docs/pathway-xpacks-llm/document_store) to learn more, you can check out [this blog post](https://pathway.com/developers/user-guide/llm-xpack/docs-indexing/).
 
 ## Table of contents
 - [Summary of available endpoints](#Summary-of-available-endpoints)

examples/pipelines/gpt_4o_multimodal_rag/README.md

@@ -51,7 +51,7 @@ The architecture of this multimodal RAG application involves several key compone
 
 - **Data Ingestion**: Ingests data from various sources like local folders, Google Drive, or SharePoint.
 - **Document Parsing and Embedding**: Utilizes `OpenParse` for parsing documents and `OpenAIEmbedder` for embedding text. This includes handling and processing images within PDFs.
-- **Vector Store**: The `VectorStoreServer` indexes parsed documents and retrieves relevant chunks for answering questions.
+- **Document Store**: The `DocumentStoreServer` indexes parsed documents and retrieves relevant chunks for answering questions.
 - **Question Answering**: Uses the `BaseRAGQuestionAnswerer` class to call `GPT-4o` for generating responses based on the retrieved context.
 - **Server Setup**: Sets up a REST endpoint to serve the RAG application.
 
@@ -266,7 +266,6 @@ Let's discuss how we can help you build a powerful, customized RAG application.
 - [Discord Community of Pathway](https://discord.gg/pathway)
 - [Pathway Issue Tracker](https://github.com/pathwaycom/pathway/issues)
 - [End-to-end dynamic RAG pipeline with Pathway](https://github.com/pathwaycom/llm-app/tree/main/examples/pipelines/demo-question-answering)
-- [Using Pathway as a vector store with Langchain](https://python.langchain.com/v0.2/docs/integrations/vectorstores/pathway/) 
 - [Using Pathway as a retriever with LlamaIndex](https://docs.llamaindex.ai/en/stable/examples/retrievers/pathway_retriever/) 
 
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