diff --git a/Makefile b/Makefile
deleted file mode 100644
index b076eb0da..000000000
--- a/Makefile
+++ /dev/null
@@ -1,33 +0,0 @@
-ROOT_DIR := $(shell dirname $(realpath $(firstword $(MAKEFILE_LIST))))
-OS := linux
-ifeq ($(shell uname -s), Darwin)
-	OS = osx
-endif
-
-PYTHON_VERSION = ${shell python --version | grep -Eo '[0-9]\.[0-9]+'}
-PYTHON_VERSIONS := 3.10 3.11
-
-build-dev:
-	uv sync --extra dev --extra test
-	uv pip install -e .
-	. .venv/bin/activate
-	uv pip install sqlite-vec chardet datasets sentence_transformers pypdf
-
-build-ollama: fix-line-endings
-	llama stack build --template ollama --image-type venv
-
-fix-line-endings:
-	sed -i '' 's/\r$$//' llama_stack/distribution/common.sh
-	sed -i '' 's/\r$$//' llama_stack/distribution/build_venv.sh
-
-test-sqlite-vec:
-	pytest llama_stack/providers/tests/vector_io/test_sqlite_vec.py \
-	-v -s --tb=short --disable-warnings --asyncio-mode=auto
-
-test-ollama-vector-integration:
-	INFERENCE_MODEL=llama3.2:3b-instruct-fp16 LLAMA_STACK_CONFIG=ollama \
-	pytest -s -v tests/client-sdk/vector_io/test_vector_io.py
-
-
-make serve-ollama:
-	ollama run llama3.2:3b-instruct-fp16 --keepalive 24h
diff --git a/rfcs/RFC-0004-triage-role.md b/rfcs/RFC-0004-triage-role.md
index 51263f0b0..d91a268e7 100644
--- a/rfcs/RFC-0004-triage-role.md
+++ b/rfcs/RFC-0004-triage-role.md
@@ -42,23 +42,6 @@ And other important items outlined more in depth in the [GitHub documentation](h
 ## Nomination Process for Triage-role
 The process for nomination for the triage role should be simple and at the discretion of the maintainers.
 
-## Example
-
-We tested this functionality using the @feast-dev repository and have provided screenshots outlining how to make this change.
-
-Step 1:
-![Figure 1: Select Repository Settings](./_static/triage-role-config-1.png)
-
-Step 2:
-![Figure 2: Invite Outside Collaborator](./_static/triage-role-config-2.png)
-
-Step 3:
-![Figure 3: Select Triage Role](./_static/triage-role-config-3.png)
-
-Step 4:
-![Figure 4: User Receives Triage Role](./_static/triage-role-config-4.png)
-
-
 ## Thank you
 
 Thank you in advance for your feedback and support and we look forward to collaborating on this great project!
diff --git a/sqlite_vec_test.py b/sqlite_vec_test.py
deleted file mode 100644
index 7aeff3577..000000000
--- a/sqlite_vec_test.py
+++ /dev/null
@@ -1,112 +0,0 @@
-#!/usr/bin/env python3
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the terms described in the LICENSE file in
-# the root directory of this source tree.
-
-import os
-import uuid
-
-from termcolor import cprint
-
-# Set environment variables
-os.environ["INFERENCE_MODEL"] = "llama3.2:3b-instruct-fp16"
-os.environ["LLAMA_STACK_CONFIG"] = "ollama"
-
-# Import libraries after setting environment variables
-from llama_stack_client.lib.agents.agent import Agent
-from llama_stack_client.lib.agents.event_logger import EventLogger
-from llama_stack_client.types import Document
-from llama_stack_client.types.agent_create_params import AgentConfig
-
-from llama_stack.distribution.library_client import LlamaStackAsLibraryClient
-
-
-def main():
-    # Initialize the client
-    client = LlamaStackAsLibraryClient("ollama")
-    vector_db_id = f"test-vector-db-{uuid.uuid4().hex}"
-
-    _ = client.initialize()
-
-    model_id = "llama3.2:3b-instruct-fp16"
-
-    # Define the list of document URLs and create Document objects
-    urls = [
-        "chat.rst",
-        "llama3.rst",
-        "memory_optimizations.rst",
-        "lora_finetune.rst",
-    ]
-    documents = [
-        Document(
-            document_id=f"num-{i}",
-            content=f"https://raw.githubusercontent.com/pytorch/torchtune/main/docs/source/tutorials/{url}",
-            mime_type="text/plain",
-            metadata={},
-        )
-        for i, url in enumerate(urls)
-    ]
-    # (Optional) Use the documents as needed with your client here
-
-    client.vector_dbs.register(
-        provider_id="sqlite_vec",
-        vector_db_id=vector_db_id,
-        embedding_model="all-MiniLM-L6-v2",
-        embedding_dimension=384,
-    )
-
-    client.tool_runtime.rag_tool.insert(
-        documents=documents,
-        vector_db_id=vector_db_id,
-        chunk_size_in_tokens=512,
-    )
-    # Create agent configuration
-    agent_config = AgentConfig(
-        model=model_id,
-        instructions="You are a helpful assistant",
-        enable_session_persistence=False,
-        toolgroups=[
-            {
-                "name": "builtin::rag",
-                "args": {
-                    "vector_db_ids": [vector_db_id],
-                },
-            }
-        ],
-    )
-
-    # Instantiate the Agent
-    agent = Agent(client, agent_config)
-
-    # List of user prompts
-    user_prompts = [
-        "What are the top 5 topics that were explained in the documentation? Only list succinct bullet points.",
-        "Was anything related to 'Llama3' discussed, if so what?",
-        "Tell me how to use LoRA",
-        "What about Quantization?",
-    ]
-
-    # Create a session for the agent
-    session_id = agent.create_session("test-session")
-
-    # Process each prompt and display the output
-    for prompt in user_prompts:
-        cprint(f"User> {prompt}", "green")
-        response = agent.create_turn(
-            messages=[
-                {
-                    "role": "user",
-                    "content": prompt,
-                }
-            ],
-            session_id=session_id,
-        )
-        # Log and print events from the response
-        for log in EventLogger().log(response):
-            log.print()
-
-
-if __name__ == "__main__":
-    main()