Rename unstructured pipeline in llm-app (#6547)

GitOrigin-RevId: 5537fcbe81ee0883af45d631f5e70082dabfc824

README.md

@@ -92,7 +92,7 @@ Pick one that is closest to your needs.
 | [`contextless`](examples/pipelines/contextless/app.py)                            | This simple example calls OpenAI ChatGPT API but does not use an index when processing queries. It relies solely on the given user query. We recommend it to start your Pathway LLM journey.                                                                                                                                                                        |
 | [`contextful`](examples/pipelines/contextful/app.py)                              | This default example of the app will index the jsonlines documents located in the [`data/pathway-docs`](examples/data/pathway-docs) directory. These indexed documents are then taken into account when processing queries. |
 | [`contextful-s3`](examples/pipelines/contextful_s3/app.py)                        | This example operates similarly to the contextful mode. The main difference is that the documents are stored and indexed from an S3 bucket, allowing the handling of a larger volume of documents. This can be more suitable for production environments.                                                                                                           |
-| [`unstructured`](examples/pipelines/unstructured/app.py)                          | Process unstructured documents such as PDF, HTML, DOCX, PPTX, and more. Visit [unstructured-io](https://unstructured-io.github.io/unstructured/) for the full list of supported formats.                                                                                                                                                                            |
+| [`contextful-parsing`](examples/pipelines/contextful_parsing/app.py)                          | Process unstructured documents such as PDF, HTML, DOCX, PPTX, and more. Visit [unstructured-io](https://unstructured-io.github.io/unstructured/) for the full list of supported formats.                                                                                                                                                                            |
 | [`local`](examples/pipelines/local/app.py)                                        | This example runs the application using Huggingface Transformers, which eliminates the need for the data to leave the machine. It provides a convenient way to use state-of-the-art NLP models locally.                                                                                                                                                             |
 | [`unstructured-to-sql`](examples/pipelines/unstructured_to_sql_on_the_fly/app.py) | This example extracts the data from unstructured files and stores it into a PostgreSQL table. It also transforms the user query into an SQL query which is then executed on the PostgreSQL table.                                                                                                                                                                   |
 | [`alert`](examples/pipelines/alert/app.py)                                        | Ask questions, get alerted whenever response changes. Pathway is always listening for changes, whenever new relevant information is added to the stream (local files in this example), LLM decides if there is a substantial difference in response and notifies the user with a Slack message.                                                                     |
@@ -168,7 +168,7 @@ If you are unfamiliar with how to contribute to GitHub projects, here is a [Get
 
 ## ☁️ Hosted Version ☁️
 
-Please see <a href="https://cloud.pathway.com/">cloud.pathway.com</a> for hosted services. You can quickly set up variants of the `unstructured` app, which connect live data sources on Google Drive and Sharepoint to your Gen AI app.
+Please see <a href="https://cloud.pathway.com/">cloud.pathway.com</a> for hosted services. You can quickly set up variants of the `demo-document-indexing` app, which connect live data sources on Google Drive and Sharepoint to your Gen AI app.
 
 ## Need help?
 

examples/pipelines/unstructured/README.md → examples/pipelines/contextful_parsing/README.md

@@ -1,6 +1,6 @@
-# Unstructured Pipeline
+# Contextful Parsing Pipeline
 
-This example implements a RAG pipeline, similarly to [contextful pipeline](). It uses, however, [Unstructured](https://unstructured.io/) library for parsing documents, which are then split into smaller chunks. 
+This example implements a RAG pipeline, similarly to [contextful pipeline](). It uses, however, [Unstructured](https://unstructured.io/) library for parsing documents, e.g. PDFs, which are then split into smaller chunks. 
 
 ## How to run the project