Refactor documentation structure and enhance SDK content (#26)

- Removed outdated sections on knowledge graphs and related images.
- Updated Python SDK documentation to include new methods for app management, such as `create_app`, `delete_app`, and `rename_app`.
- Enhanced existing sections with clearer descriptions and examples, particularly for folder management and user scoping.
- Adjusted references to deprecated methods and improved overall clarity in usage instructions.
- Added new methods for document and folder summary management, including `upsert_document_summary` and `upsert_folder_summary`.
- Improved the organization of the Python SDK documentation to better reflect current capabilities and usage patterns.

Author: Adityav369

assets/med_data_k_graph.png

@@ -15,7 +15,7 @@ This separation keeps your application management in the UI while all document d
 | Component | Primary role | Typical hosting |
 | --- | --- | --- |
 | Cloud UI | Auth, orgs, billing, app metadata, dashboards | Vercel (or your web host) |
-| Morphik Core | Ingestion, storage, retrieval, search, graphs, chat | EC2 or Kubernetes |
+| Morphik Core | Ingestion, storage, retrieval, search, chat | EC2 or Kubernetes |
 | Embedding GPU (optional) | Multimodal embeddings (ColPali API mode) | Lambda GPU, on-prem GPU |
 | Postgres + pgvector | Documents, embeddings, app isolation | Neon or any Postgres |
 | Object storage | Raw files and chunk payloads | S3 or local disk |
@@ -102,4 +102,3 @@ Agent mode runs in a server route (Cloud UI) so it can call your LLM provider se
 - The UI calls `/api/agent/chat` on the Cloud UI.
 - The server route calls Morphik Core for retrieval (using the app token).
 - The server route streams the LLM response back to the browser.
-

concepts/cloud-architecture.mdx

@@ -5,7 +5,7 @@ description: 'Using Late-interaction and Contrastive learning to achieve state-o
 
 ## Introduction
 
-Upto now, we've seen RAG techniques that **i)** parse a given document, **ii)** convert it to text, and **iii)** embed the text for retrieval. These techniques have been particularly text-heavy. Embedding models expect text in, knowledge graphs expect text in, and parsers break down when provided with documents that aren't text-dominant. This motivates the question:
+Upto now, we've seen RAG techniques that **i)** parse a given document, **ii)** convert it to text, and **iii)** embed the text for retrieval. These techniques have been particularly text-heavy. Embedding models expect text in, and parsers break down when provided with documents that aren't text-dominant. This motivates the question:
 
 > When was the last time you looked at a document and only saw text?
 
@@ -57,7 +57,7 @@ from morphik import Morphik
 
 db = Morphik("YOUR-URI-HERE")
 
-db.ingest_file("report_with_images_and_graphs.pdf", use_colpali=True)
+db.ingest_file("report_with_images_and_charts.pdf", use_colpali=True)
 ```
 
 Here is an example query pathway:
@@ -109,5 +109,3 @@ If you're experiencing context limit issues with image-based retrieval, it may b
 
 
 
-
-