diff --git a/examples/langgraph-agent/agent.ipynb b/examples/langgraph-agent/agent.ipynb
index 54fe4b20..f04fb573 100644
--- a/examples/langgraph-agent/agent.ipynb
+++ b/examples/langgraph-agent/agent.ipynb
@@ -22,10 +22,10 @@
    ]
   },
   {
-   "metadata": {},
    "cell_type": "code",
-   "outputs": [],
    "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "import asyncio\n",
     "import json\n",
@@ -47,10 +47,10 @@
    ]
   },
   {
-   "metadata": {},
    "cell_type": "code",
-   "outputs": [],
    "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "def setup_logging():\n",
     "    logger = logging.getLogger()\n",
@@ -67,8 +67,8 @@
    ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## LangSmith integration (Optional)\n",
     "\n",
@@ -78,18 +78,18 @@
    ]
   },
   {
-   "metadata": {},
    "cell_type": "code",
-   "outputs": [],
    "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "os.environ['LANGCHAIN_TRACING_V2'] = 'false'\n",
     "os.environ['LANGCHAIN_PROJECT'] = 'Graphiti LangGraph Tutorial'"
    ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Configure Graphiti\n",
     "\n",
@@ -103,10 +103,10 @@
    ]
   },
   {
-   "metadata": {},
    "cell_type": "code",
-   "outputs": [],
    "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Configure Graphiti\n",
     "\n",
@@ -127,8 +127,8 @@
    ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Generating a database schema \n",
     "\n",
@@ -138,10 +138,10 @@
    ]
   },
   {
-   "metadata": {},
    "cell_type": "code",
-   "outputs": [],
    "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Note: This will clear the database\n",
     "await clear_data(client.driver)\n",
@@ -149,8 +149,8 @@
    ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Load Shoe Data into the Graph\n",
     "\n",
@@ -161,10 +161,10 @@
    ]
   },
   {
-   "metadata": {},
    "cell_type": "code",
-   "outputs": [],
    "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "async def ingest_products_data(client: Graphiti):\n",
     "    script_dir = Path.cwd().parent\n",
@@ -187,8 +187,8 @@
    ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Create a user node in the Graphiti graph\n",
     "\n",
@@ -196,10 +196,10 @@
    ]
   },
   {
-   "metadata": {},
    "cell_type": "code",
-   "outputs": [],
    "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from graphiti_core.search.search_config_recipes import NODE_HYBRID_SEARCH_EPISODE_MENTIONS\n",
     "\n",
@@ -224,20 +224,20 @@
    ]
   },
   {
-   "metadata": {},
    "cell_type": "code",
-   "outputs": [],
    "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "def edges_to_facts_string(entities: list[EntityEdge]):\n",
     "    return '-' + '\\n- '.join([edge.fact for edge in entities])"
    ]
   },
   {
-   "metadata": {},
    "cell_type": "code",
-   "outputs": [],
    "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from langchain_core.messages import AIMessage, SystemMessage\n",
     "from langchain_core.tools import tool\n",
@@ -248,8 +248,8 @@
    ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## `get_shoe_data` Tool\n",
     "\n",
@@ -257,10 +257,10 @@
    ]
   },
   {
-   "metadata": {},
    "cell_type": "code",
-   "outputs": [],
    "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "@tool\n",
     "async def get_shoe_data(query: str) -> str:\n",
@@ -278,25 +278,27 @@
    ]
   },
   {
-   "metadata": {},
    "cell_type": "code",
-   "outputs": [],
    "execution_count": null,
-   "source": "llm = ChatOpenAI(model='gpt-4o-mini', temperature=0).bind_tools(tools)"
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm = ChatOpenAI(model='gpt-4o-mini', temperature=0).bind_tools(tools)"
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "code",
-   "outputs": [],
    "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Test the tool node\n",
     "await tool_node.ainvoke({'messages': [await llm.ainvoke('wool shoes')]})"
    ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Chatbot Function Explanation\n",
     "\n",
@@ -312,10 +314,10 @@
    ]
   },
   {
-   "metadata": {},
    "cell_type": "code",
-   "outputs": [],
    "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "class State(TypedDict):\n",
     "    messages: Annotated[list, add_messages]\n",
@@ -372,8 +374,8 @@
    ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Setting up the Agent\n",
     "\n",
@@ -387,10 +389,10 @@
    ]
   },
   {
-   "metadata": {},
    "cell_type": "code",
-   "outputs": [],
    "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "graph_builder = StateGraph(State)\n",
     "\n",
@@ -420,23 +422,23 @@
    ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "metadata": {},
    "source": "Our LangGraph agent graph is illustrated below."
   },
   {
-   "metadata": {},
    "cell_type": "code",
-   "outputs": [],
    "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "with suppress(Exception):\n",
     "    display(Image(graph.get_graph().draw_mermaid_png()))"
    ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Running the Agent\n",
     "\n",
@@ -444,10 +446,10 @@
    ]
   },
   {
-   "metadata": {},
    "cell_type": "code",
-   "outputs": [],
    "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "await graph.ainvoke(\n",
     "    {\n",
@@ -465,8 +467,8 @@
    ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Viewing the Graph\n",
     "\n",
@@ -474,15 +476,17 @@
    ]
   },
   {
-   "metadata": {},
    "cell_type": "code",
-   "outputs": [],
    "execution_count": null,
-   "source": "display(Image(filename='tinybirds-jess.png', width=850))"
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "display(Image(filename='tinybirds-jess.png', width=850))"
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Running the Agent interactively\n",
     "\n",
@@ -490,10 +494,10 @@
    ]
   },
   {
-   "metadata": {},
    "cell_type": "code",
-   "outputs": [],
    "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "conversation_output = widgets.Output()\n",
     "config = {'configurable': {'thread_id': uuid.uuid4().hex}}\n",
@@ -512,14 +516,14 @@
     "\n",
     "    try:\n",
     "        async for event in graph.astream(\n",
-    "                graph_state,\n",
-    "                config=config,\n",
+    "            graph_state,\n",
+    "            config=config,\n",
     "        ):\n",
     "            for value in event.values():\n",
     "                if 'messages' in value:\n",
     "                    last_message = value['messages'][-1]\n",
     "                    if isinstance(last_message, AIMessage) and isinstance(\n",
-    "                            last_message.content, str\n",
+    "                        last_message.content, str\n",
     "                    ):\n",
     "                        conversation_output.append_stdout(last_message.content)\n",
     "    except Exception as e:\n",
diff --git a/graphiti_core/llm_client/client.py b/graphiti_core/llm_client/client.py
index ac01bed7..0c28e895 100644
--- a/graphiti_core/llm_client/client.py
+++ b/graphiti_core/llm_client/client.py
@@ -56,7 +56,6 @@ class LLMClient(ABC):
         self.cache_enabled = cache
         self.cache_dir = Cache(DEFAULT_CACHE_DIR)  # Create a cache directory
 
-    
     def _clean_input(self, input: str) -> str:
         """Clean input string of invalid unicode and control characters.
 
diff --git a/graphiti_core/search/search_utils.py b/graphiti_core/search/search_utils.py
index 726ff46b..e271dc01 100644
--- a/graphiti_core/search/search_utils.py
+++ b/graphiti_core/search/search_utils.py
@@ -18,6 +18,7 @@ import asyncio
 import logging
 from collections import defaultdict
 from time import time
+from typing import Any
 
 import numpy as np
 from neo4j import AsyncDriver, Query
@@ -191,12 +192,27 @@ async def edge_similarity_search(
         'CYPHER runtime = parallel parallelRuntimeSupport=all\n' if USE_PARALLEL_RUNTIME else ''
     )
 
-    query: LiteralString = """
-                MATCH (n:Entity)-[r:RELATES_TO]->(m:Entity)
-                WHERE ($group_ids IS NULL OR r.group_id IN $group_ids)
-                AND ($source_uuid IS NULL OR n.uuid IN [$source_uuid, $target_uuid])
-                AND ($target_uuid IS NULL OR m.uuid IN [$source_uuid, $target_uuid])
-                WITH DISTINCT r, vector.similarity.cosine(r.fact_embedding, $search_vector) AS score
+    query_params: dict[str, Any] = {}
+
+    group_filter_query: LiteralString = ''
+    if group_ids is not None:
+        group_filter_query += 'WHERE r.group_id IN $group_ids'
+        query_params['group_ids'] = group_ids
+        query_params['source_node_uuid'] = source_node_uuid
+        query_params['target_node_uuid'] = target_node_uuid
+
+        if source_node_uuid is not None:
+            group_filter_query += '\nAND (n.uuid IN [$source_uuid, $target_uuid])'
+
+        if target_node_uuid is not None:
+            group_filter_query += '\nAND (m.uuid IN [$source_uuid, $target_uuid])'
+
+    query: LiteralString = (
+        """
+                        MATCH (n:Entity)-[r:RELATES_TO]->(m:Entity)
+                        """
+        + group_filter_query
+        + """\nWITH DISTINCT r, vector.similarity.cosine(r.fact_embedding, $search_vector) AS score
                 WHERE score > $min_score
                 RETURN
                     r.uuid AS uuid,
@@ -214,9 +230,11 @@ async def edge_similarity_search(
                 ORDER BY score DESC
                 LIMIT $limit
         """
+    )
 
     records, _, _ = await driver.execute_query(
         runtime_query + query,
+        query_params,
         search_vector=search_vector,
         source_uuid=source_node_uuid,
         target_uuid=target_node_uuid,
@@ -325,11 +343,20 @@ async def node_similarity_search(
         'CYPHER runtime = parallel parallelRuntimeSupport=all\n' if USE_PARALLEL_RUNTIME else ''
     )
 
+    query_params: dict[str, Any] = {}
+
+    group_filter_query: LiteralString = ''
+    if group_ids is not None:
+        group_filter_query += 'WHERE n.group_id IN $group_ids'
+        query_params['group_ids'] = group_ids
+
     records, _, _ = await driver.execute_query(
         runtime_query
         + """
             MATCH (n:Entity)
-            WHERE $group_ids IS NULL OR n.group_id IN $group_ids
+            """
+        + group_filter_query
+        + """
             WITH n, vector.similarity.cosine(n.name_embedding, $search_vector) AS score
             WHERE score > $min_score
             RETURN
@@ -342,6 +369,7 @@ async def node_similarity_search(
             ORDER BY score DESC
             LIMIT $limit
             """,
+        query_params,
         search_vector=search_vector,
         group_ids=group_ids,
         limit=limit,
@@ -436,11 +464,20 @@ async def community_similarity_search(
         'CYPHER runtime = parallel parallelRuntimeSupport=all\n' if USE_PARALLEL_RUNTIME else ''
     )
 
+    query_params: dict[str, Any] = {}
+
+    group_filter_query: LiteralString = ''
+    if group_ids is not None:
+        group_filter_query += 'WHERE comm.group_id IN $group_ids'
+        query_params['group_ids'] = group_ids
+
     records, _, _ = await driver.execute_query(
         runtime_query
         + """
            MATCH (comm:Community)
-           WHERE ($group_ids IS NULL OR comm.group_id IN $group_ids)
+           """
+        + group_filter_query
+        + """
            WITH comm, vector.similarity.cosine(comm.name_embedding, $search_vector) AS score
            WHERE score > $min_score
            RETURN