Remove cache-augmented generation documentation

concepts/metadata-filtering.mdx

@@ -3,7 +3,7 @@ title: "Metadata Filtering"
 description: "Canonical reference for Morphik’s metadata filter DSL and typed comparisons."
 ---
 
-Morphik lets you filter documents and chunks directly in the database using a concise JSON filter syntax. The same structure powers the REST API, Python SDK (sync + async), folder helpers, `UserScope`, caches, and knowledge-graph builders, so you can define a filter once and reuse it everywhere.
+Morphik lets you filter documents and chunks directly in the database using a concise JSON filter syntax. The same structure powers the REST API, Python SDK (sync + async), folder helpers, `UserScope`, and knowledge-graph builders, so you can define a filter once and reuse it everywhere.
 
 <Note>
   Prefer server-side filters over client-side post-processing. You’ll reduce bandwidth, improve performance, and keep behavior consistent between endpoints.
@@ -14,7 +14,7 @@ Morphik lets you filter documents and chunks directly in the database using a co
 You can pass `filters` (or `document_filters`) to:
 
 - Retrieval endpoints: [`retrieve_chunks`](/python-sdk/retrieve_chunks), [`retrieve_docs`](/python-sdk/retrieve_docs), [`query`](/python-sdk/query), [`query_document`](/python-sdk/query_document) ingestion options.
-- Listing/management: [`list_documents`](/python-sdk/list_documents), document/folder analytics, graph create/update, caches, chat history, and anywhere an SDK method exposes a `filters` argument.
+- Listing/management: [`list_documents`](/python-sdk/list_documents), document/folder analytics, graph create/update, chat history, and anywhere an SDK method exposes a `filters` argument.
 
 ## Quick Start
 

docs.json

@@ -117,7 +117,6 @@
               "blogs/memory-vibe-coding",
               "blogs/llm-science-battle",
               "blogs/gpt-vs-morphik-multimodal",
-              "blogs/cache-augmented-generation",
               "blogs/stop-parsing-docs"
             ]
           }
@@ -213,13 +212,6 @@
               "python-sdk/check_workflow_status"
             ]
           },
-          {
-            "group": "Cache Management",
-            "pages": [
-              "python-sdk/create_cache",
-              "python-sdk/get_cache"
-            ]
-          },
           {
             "group": "Chat & Conversation Management",
             "pages": [

introduction.mdx

@@ -8,7 +8,6 @@ Morphik is a database that makes it easy to create fast, versatile, and producti
 Key features include:
 
 - **First class support for Unstructured Data**: Unlike traditional databases, Morphik allows users to directly ingest unstructured data of all forms - including (but not limited to) videos and PDFs. We've built research-driven custom algorithms to ensure state-of-the-art retrieval accuracy.
-- **Persistent KV-caching**: For documents you reference often, you can process them once and *freeze* the LLM's internal state such that you can use it again later. This helps drastically reduce compute costs as well speed up model responses.
 - **Out of the box MCP support**: Morphik has [built in support](/using-morphik/mcp) for [Model Context Protocol](https://modelcontextprotocol.io/introduction) - so you can integrate your knowledge with any MCP client in a single click.
 - **User and Folder Scoping**: Organize and isolate your data with multi-user and folder-based access controls. Create logical boundaries for different projects or user groups while maintaining a unified database.
 - **Completely source-available**: You can check out Morphik core code [here!](https://github.com/morphik-org/morphik-core/).

knowledge-base/how-do-i-set-up-rag.mdx

@@ -21,18 +21,15 @@ db = Morphik("morphik://owner_id:token@api.morphik.ai")
 # 1. Ingest a document
 doc = db.ingest_file("document.pdf")
 
-# 2. Create a cache for quick retrieval
-db.create_cache(name="docs-cache", model="openai_gpt4o", gguf_file="model.gguf", docs=[doc.id])
-
-# 3 & 4. Retrieve and generate with context
+# 2. Retrieve and generate with context
 response = db.query("What topics are covered?", k=4)
 print(response.text)
 ```
 
 ### Related questions
 
-- **Q:** What are the steps to set up a RAG pipeline with Morphik?  
-  **A:** The key steps are: (1) Initialize the Morphik client, (2) Ingest your documents using `ingest_file()`, (3) Create a cache with `create_cache()`, and (4) Query using `query()` with your question and desired number of results.
+- **Q:** What are the steps to set up a RAG pipeline with Morphik?
+  **A:** The key steps are: (1) Initialize the Morphik client, (2) Ingest your documents using `ingest_file()`, and (3) Query using `query()` with your question and desired number of results.
 
 - **Q:** How can I quickly build a retrieval-augmented generation workflow?  
   **A:** Use Morphik's built-in RAG capabilities by following the code example above. The `query()` method handles both retrieval and generation in one step when you provide a question and set `k` for the number of relevant chunks to retrieve.