diff --git a/open_notebook/graphs/chat.py b/open_notebook/graphs/chat.py
index 5e3b4ca..7342ca3 100644
--- a/open_notebook/graphs/chat.py
+++ b/open_notebook/graphs/chat.py
@@ -25,7 +25,12 @@ class ThreadState(TypedDict):
 def call_model_with_messages(state: ThreadState, config: RunnableConfig) -> dict:
     system_prompt = Prompter(prompt_template="chat").render(data=state)
     payload = [system_prompt] + state.get("messages", [])
-    model = provision_langchain_model(str(payload), config, "chat", max_tokens=2000)
+    model = provision_langchain_model(
+        str(payload),
+        config.get("configurable", {}).get("model_id"),
+        "chat",
+        max_tokens=2000,
+    )
     ai_message = model.invoke(payload)
     return {"messages": ai_message}
 
diff --git a/open_notebook/graphs/tools.py b/open_notebook/graphs/tools.py
index 620fac4..9c3df13 100644
--- a/open_notebook/graphs/tools.py
+++ b/open_notebook/graphs/tools.py
@@ -1,10 +1,7 @@
 from datetime import datetime
-from typing import List
 
 from langchain.tools import tool
 
-from open_notebook.domain.notebook import hybrid_search
-
 
 # todo: turn this into a system prompt variable
 @tool
@@ -14,14 +11,3 @@ def get_current_timestamp() -> str:
     Returns the current timestamp in the format YYYYMMDDHHmmss.
     """
     return datetime.now().strftime("%Y%m%d%H%M%S")
-
-
-@tool
-def repository_search(keyword_searches: List[str], vector_searches: List[str]) -> str:
-    """
-    name: repository_search
-    Makes a search in the content repository for the given query.
-    keyword_searches: List[str] - A list of search terms to search for using keyword search.
-    vector_searches: List[str] - A list of search terms to search for using vector search.
-    """
-    return hybrid_search(keyword_searches, vector_searches, 20)
diff --git a/open_notebook/graphs/utils.py b/open_notebook/graphs/utils.py
index 07365ea..3c79d85 100644
--- a/open_notebook/graphs/utils.py
+++ b/open_notebook/graphs/utils.py
@@ -8,7 +8,9 @@ from open_notebook.prompter import Prompter
 from open_notebook.utils import token_count
 
 
-def provision_langchain_model(content, config, default_type, **kwargs) -> BaseChatModel:
+def provision_langchain_model(
+    content, model_id, default_type, **kwargs
+) -> BaseChatModel:
     """
     Returns the best model to use based on the context size and on whether there is a specific model being requested in Config.
     If context > 105_000, returns the large_context_model
@@ -22,10 +24,8 @@ def provision_langchain_model(content, config, default_type, **kwargs) -> BaseCh
             f"Using large context model because the content has {tokens} tokens"
         )
         model = model_manager.get_default_model("large_context", **kwargs)
-    elif config.get("configurable", {}).get("model_id"):
-        model = model_manager.get_model(
-            config.get("configurable", {}).get("model_id"), **kwargs
-        )
+    elif model_id:
+        model = model_manager.get_model(model_id, **kwargs)
     else:
         model = model_manager.get_default_model(default_type, **kwargs)
 
@@ -45,7 +45,9 @@ def run_pattern(
         data=state
     )
     payload = [system_prompt] + messages
-    chain = provision_langchain_model(str(payload), config, "transformation")
+    chain = provision_langchain_model(
+        str(payload), config.get("configurable", {}).get("model_id"), "transformation"
+    )
 
     response = chain.invoke(payload)