diff --git a/graphrag/query/structured_search/global_search/search.py b/graphrag/query/structured_search/global_search/search.py
index 3b52ecbd8c..dec0584781 100644
--- a/graphrag/query/structured_search/global_search/search.py
+++ b/graphrag/query/structured_search/global_search/search.py
@@ -6,6 +6,7 @@
 import asyncio
 import json
 import logging
+import re
 import time
 from dataclasses import dataclass
 from typing import Any
@@ -92,6 +93,7 @@ def __init__(
 
         self.map_llm_params = map_llm_params
         self.reduce_llm_params = reduce_llm_params
+        self.json_mode = json_mode
         if json_mode:
             self.map_llm_params["response_format"] = {"type": "json_object"}
         else:
@@ -174,6 +176,9 @@ async def _map_response_single_batch(
         search_prompt = ""
         try:
             search_prompt = self.map_system_prompt.format(context_data=context_data)
+            if self.json_mode:
+                search_prompt += "\nYour response should be in JSON format."
+            self._last_search_prompt = search_prompt
             search_messages = [
                 {"role": "system", "content": search_prompt},
                 {"role": "user", "content": query},
@@ -228,15 +233,37 @@ def parse_search_response(self, search_response: str) -> list[dict[str, Any]]:
         -------
         list[dict[str, Any]]
             A list of key points, each key point is a dictionary with "answer" and "score" keys
+
+        Raises
+        ------
+        ValueError
+            If the response cannot be parsed as JSON or doesn't contain the expected structure
         """
-        parsed_elements = json.loads(search_response)["points"]
-        return [
-            {
-                "answer": element["description"],
-                "score": int(element["score"]),
-            }
-            for element in parsed_elements
-        ]
+        # Try to extract JSON from the response if it's embedded in text
+        json_match = re.search(r"\{.*\}", search_response, re.DOTALL)
+        json_str = json_match.group() if json_match else search_response
+
+        try:
+            parsed_data = json.loads(json_str)
+        except json.JSONDecodeError as err:
+            error_msg = "Failed to parse response as JSON"
+            raise ValueError(error_msg) from err
+
+        if "points" not in parsed_data or not isinstance(parsed_data["points"], list):
+            error_msg = "Response JSON does not contain a 'points' list"
+            raise ValueError(error_msg)
+
+        try:
+            return [
+                {
+                    "answer": element["description"],
+                    "score": int(element["score"]),
+                }
+                for element in parsed_data["points"]
+            ]
+        except (KeyError, ValueError) as e:
+            error_msg = f"Error processing points: {e!s}"
+            raise ValueError(error_msg) from e
 
     async def _reduce_response(
         self,
@@ -316,6 +343,8 @@ async def _reduce_response(
             )
             if self.allow_general_knowledge:
                 search_prompt += "\n" + self.general_knowledge_inclusion_prompt
+            if self.json_mode:
+                search_prompt += "\nYour response should be in JSON format."
             search_messages = [
                 {"role": "system", "content": search_prompt},
                 {"role": "user", "content": query},
diff --git a/tests/unit/query/structured_search/__init__.py b/tests/unit/query/structured_search/__init__.py
new file mode 100644
index 0000000000..0a3e38adfb
--- /dev/null
+++ b/tests/unit/query/structured_search/__init__.py
@@ -0,0 +1,2 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
diff --git a/tests/unit/query/structured_search/test_search.py b/tests/unit/query/structured_search/test_search.py
new file mode 100644
index 0000000000..72930dff19
--- /dev/null
+++ b/tests/unit/query/structured_search/test_search.py
@@ -0,0 +1,218 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+from unittest.mock import AsyncMock, MagicMock
+
+import pytest
+
+from graphrag.query.context_builder.builders import GlobalContextBuilder
+from graphrag.query.llm.base import BaseLLM
+from graphrag.query.structured_search.base import SearchResult
+from graphrag.query.structured_search.global_search.search import GlobalSearch
+
+
+class MockLLM(BaseLLM):
+    def __init__(self):
+        self.call_count = 0
+        self.last_messages = None
+
+    def generate(
+        self, messages, streaming=False, callbacks=None, max_tokens=None, **kwargs
+    ):
+        self.call_count += 1
+        self.last_messages = messages
+        return "mocked response"
+
+    async def agenerate(
+        self, messages, streaming=False, callbacks=None, max_tokens=None, **kwargs
+    ):
+        self.call_count += 1
+        self.last_messages = messages
+        return "mocked response"
+
+
+class MockContextBuilder(GlobalContextBuilder):
+    def build_context(self, conversation_history=None, **kwargs):
+        return ["mocked context"], {}
+
+
+class TestGlobalSearch(GlobalSearch):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._last_search_prompt = None
+
+    def get_last_search_prompt(self):
+        return self._last_search_prompt
+
+    def set_last_search_prompt(self, value):
+        self._last_search_prompt = value
+
+
+@pytest.fixture
+def global_search():
+    llm = MockLLM()
+    context_builder = MockContextBuilder()
+    return TestGlobalSearch(llm, context_builder)
+
+
+@pytest.mark.asyncio
+async def test_json_format_instruction_in_search_prompt(global_search):
+    global_search.json_mode = True
+    query = "Test query"
+
+    global_search.context_builder.build_context = MagicMock(
+        return_value=(["mocked context"], {})
+    )
+
+    global_search.parse_search_response = MagicMock(
+        return_value=[{"answer": "Mocked answer", "score": 100}]
+    )
+
+    await global_search.asearch(query)
+
+    assert global_search.get_last_search_prompt() is not None
+    assert (
+        "Your response should be in JSON format."
+        in global_search.get_last_search_prompt()
+    )
+
+    global_search.json_mode = False
+    global_search.set_last_search_prompt(None)
+    await global_search.asearch(query)
+
+    assert global_search.get_last_search_prompt() is not None
+    assert (
+        "Your response should be in JSON format."
+        not in global_search.get_last_search_prompt()
+    )
+
+
+def test_parse_search_response_valid(global_search):
+    valid_response = """
+    {
+        "points": [
+            {"description": "Point 1", "score": 90},
+            {"description": "Point 2", "score": 80}
+        ]
+    }
+    """
+    result = global_search.parse_search_response(valid_response)
+    assert len(result) == 2
+    assert result[0] == {"answer": "Point 1", "score": 90}
+    assert result[1] == {"answer": "Point 2", "score": 80}
+
+
+def test_parse_search_response_invalid_json(global_search):
+    invalid_json = "This is not JSON"
+    with pytest.raises(ValueError, match="Failed to parse response as JSON"):
+        global_search.parse_search_response(invalid_json)
+
+
+def test_parse_search_response_missing_points(global_search):
+    missing_points = '{"data": "No points here"}'
+    with pytest.raises(
+        ValueError, match="Response JSON does not contain a 'points' list"
+    ):
+        global_search.parse_search_response(missing_points)
+
+
+def test_parse_search_response_invalid_point_format(global_search):
+    invalid_point = """
+    {
+        "points": [
+            {"wrong_key": "Point 1", "score": 90}
+        ]
+    }
+    """
+    with pytest.raises(ValueError, match="Error processing points"):
+        global_search.parse_search_response(invalid_point)
+
+
+def test_parse_search_response_with_text_prefix(global_search):
+    response_with_prefix = """
+    Here's the response:
+    {
+        "points": [
+            {"description": "Point 1", "score": 90}
+        ]
+    }
+    """
+    result = global_search.parse_search_response(response_with_prefix)
+    assert len(result) == 1
+    assert result[0] == {"answer": "Point 1", "score": 90}
+
+
+def test_parse_search_response_non_integer_score(global_search):
+    non_integer_score = """
+    {
+        "points": [
+            {"description": "Point 1", "score": "high"}
+        ]
+    }
+    """
+    with pytest.raises(ValueError, match="Error processing points"):
+        global_search.parse_search_response(non_integer_score)
+
+
+@pytest.mark.asyncio
+async def test_map_response(global_search):
+    global_search.map_response = AsyncMock(
+        return_value=[
+            SearchResult(
+                response=[{"answer": "Test answer", "score": 90}],
+                context_data="Test context",
+                context_text="Test context",
+                completion_time=0.1,
+                llm_calls=1,
+                prompt_tokens=10,
+            )
+        ]
+    )
+
+    context_data = ["Test context"]
+    query = "Test query"
+    result = await global_search.map_response(context_data, query)
+
+    assert isinstance(result[0], SearchResult)
+    assert result[0].context_data == "Test context"
+    assert result[0].context_text == "Test context"
+    assert result[0].llm_calls == 1
+
+
+@pytest.mark.asyncio
+async def test_reduce_response(global_search):
+    global_search.reduce_response = AsyncMock(
+        return_value=SearchResult(
+            response="Final answer",
+            context_data="Combined context",
+            context_text="Combined context",
+            completion_time=0.2,
+            llm_calls=1,
+            prompt_tokens=20,
+        )
+    )
+
+    map_responses = [
+        SearchResult(
+            response=[{"answer": "Point 1", "score": 90}],
+            context_data="",
+            context_text="",
+            completion_time=0,
+            llm_calls=1,
+            prompt_tokens=0,
+        ),
+        SearchResult(
+            response=[{"answer": "Point 2", "score": 80}],
+            context_data="",
+            context_text="",
+            completion_time=0,
+            llm_calls=1,
+            prompt_tokens=0,
+        ),
+    ]
+    query = "Test query"
+    result = await global_search.reduce_response(map_responses, query)
+
+    assert isinstance(result, SearchResult)
+    assert result.llm_calls == 1
+    assert result.response == "Final answer"