Updating old links in READMEs (#8035)

GitOrigin-RevId: e33326ac1c2d427c572b2365d6290a0393ca1830

README.md

@@ -20,7 +20,7 @@ The application templates provided in this repo scale up to **millions of pages
 | Application (template)                                                                           | Description                                                                                                                                                                                                                                                                                                                                                         |
 | --------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | [`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).         |
+| [`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/blog/langchain-integration) or [Llamaindex](https://pathway.com/blog/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.|
 | [`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. |
 | [`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. |

examples/pipelines/adaptive-rag/README.md

@@ -36,7 +36,7 @@ We encourage you to check the implementation of `answer_with_geometric_rag_strat
 
 ## Customizing the pipeline
 
-The code can be modified by changing the `app.yaml` configuration file. To read more about YAML files used in Pathway templates, read [our guide](https://pathway.com/developers/user-guide/llm-xpack/yaml-templates).
+The code can be modified by changing the `app.yaml` configuration file. To read more about YAML files used in Pathway templates, read [our guide](https://pathway.com/developers/ai-pipelines/configure-yaml).
 
 In the `app.yaml` file we define:
 - input connectors

examples/pipelines/demo-document-indexing/README.md

@@ -50,7 +50,7 @@ This folder contains several objects:
 
 ## Customizing the pipeline
 
-The code can be modified by changing the `app.yaml` configuration file. To read more about YAML files used in Pathway templates, read [our guide](https://pathway.com/developers/user-guide/llm-xpack/yaml-templates).
+The code can be modified by changing the `app.yaml` configuration file. To read more about YAML files used in Pathway templates, read [our guide](https://pathway.com/developers/ai-pipelines/configure-yaml).
 
 In the `app.yaml` file we define:
 - input connectors

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

@@ -95,7 +95,7 @@ Default LLM provider in this template is OpenAI, so, unless you change the confi
 
 ## Customizing the pipeline
 
-The code can be modified by changing the `app.yaml` configuration file. To read more about YAML files used in Pathway templates, read [our guide](https://pathway.com/developers/user-guide/llm-xpack/yaml-templates).
+The code can be modified by changing the `app.yaml` configuration file. To read more about YAML files used in Pathway templates, read [our guide](https://pathway.com/developers/ai-pipelines/configure-yaml/).
 
 In the `app.yaml` file we define:
 - input connectors

examples/pipelines/gpt_4o_multimodal_rag/README.md

@@ -256,7 +256,7 @@ RAG applications are most effective when tailored to your specific use case. Her
 
 Ready to Get Started? 
 
-Let's discuss how we can help you build a powerful, customized RAG application. [Reach us here to talk or request a demo!](https://pathway.com/solutions/enterprise-generative-ai?modal=requestdemo)
+Let's discuss how we can help you build a powerful, customized RAG application. [Reach us here to talk or request a demo!](https://pathway.com/solutions/slides-ai-search?modal=requestdemo)
 
 
 ## Quick Links:

examples/pipelines/private-rag/README.md

@@ -49,7 +49,7 @@ The architecture consists of two connected technology bricks, which will run as
 
 ## Customizing the pipeline
 
-The code can be modified by changing the `app.yaml` configuration file. To read more about YAML files used in Pathway templates, read [our guide](https://pathway.com/developers/user-guide/llm-xpack/yaml-templates).
+The code can be modified by changing the `app.yaml` configuration file. To read more about YAML files used in Pathway templates, read [our guide](https://pathway.com/developers/ai-pipelines/configure-yaml/).
 
 In the `app.yaml` file we define:
 - input connectors

examples/pipelines/slides_ai_search/README.md

@@ -338,7 +338,7 @@ python app.py
 
 ## Not sure how to get started? 
 
-Let's discuss how we can help you build a powerful, customized RAG application. [Reach us here to talk or request a demo!](https://pathway.com/solutions/enterprise-generative-ai?modal=requestdemo)
+Let's discuss how we can help you build a powerful, customized RAG application. [Reach us here to talk or request a demo!](https://pathway.com/solutions/slides-ai-search?modal=requestdemo)
 
 
 ## FAQ