diff --git a/src/khoj/processor/conversation/gpt.py b/src/khoj/processor/conversation/gpt.py
index 986ffc17..bcc37db8 100644
--- a/src/khoj/processor/conversation/gpt.py
+++ b/src/khoj/processor/conversation/gpt.py
@@ -78,6 +78,107 @@ Summarize the notes in second person perspective:"""
     return str(story).replace("\n\n", "")
 
 
+def extract_questions(text, model="text-davinci-003", conversation_log={}, api_key=None, temperature=0, max_tokens=100):
+    """
+    Infer search queries to retrieve relevant notes to answer user query
+    """
+    # Initialize Variables
+    openai.api_key = api_key or os.getenv("OPENAI_API_KEY")
+
+    # Extract Past User Message and Inferred Questions from Conversation Log
+    chat_history = "".join(
+        [
+            f'Q: {chat["intent"]["query"]}\n\n{chat["intent"].get("inferred-queries") or list([chat["intent"]["query"]])}\n\n{chat["message"]}\n\n'
+            for chat in conversation_log.get("chat", [])[-4:]
+            if chat["by"] == "khoj"
+        ]
+    )
+
+    # Get dates relative to today for prompt creation
+    today = datetime.today()
+    current_new_year = today.replace(month=1, day=1)
+    last_new_year = current_new_year.replace(year=today.year - 1)
+
+    prompt = f"""
+You are Khoj, an extremely smart and helpful search assistant with the ability to retrieve information from the users notes.
+- The user will provide their questions and answers to you for context.
+- Add as much context from the previous questions and answers as required into your search queries.
+- Break messages into multiple search queries when required to retrieve the relevant information.
+- Add date filters to your search queries from questions and answers when required to retrieve the relevant information.
+
+What searches, if any, will you need to perform to answer the users question?
+Provide search queries as a JSON list of strings
+Current Date: {today.strftime("%A, %Y-%m-%d")}
+
+Q: How was my trip to Cambodia?
+
+["How was my trip to Cambodia?"]
+
+A: The trip was amazing. I went to the Angkor Wat temple and it was beautiful.
+
+Q: Who did i visit that temple with?
+
+["Who did I visit the Angkor Wat Temple in Cambodia with?"]
+
+A: You visited the Angkor Wat Temple in Cambodia with Pablo, Namita and Xi.
+
+Q: What national parks did I go to last year?
+
+["National park I visited in {last_new_year.strftime("%Y")} dt>=\\"{last_new_year.strftime("%Y-%m-%d")}\\" dt<\\"{current_new_year.strftime("%Y-%m-%d")}\\""]
+
+A: You visited the Grand Canyon and Yellowstone National Park in {last_new_year.strftime("%Y")}.
+
+Q: How are you feeling today?
+
+[]
+
+A: I'm feeling a little bored. Helping you will hopefully make me feel better!
+
+Q: How many tennis balls fit in the back of a 2002 Honda Civic?
+
+["What is the size of a tennis ball?", "What is the trunk size of a 2002 Honda Civic?"]
+
+A: 1085 tennis balls will fit in the trunk of a Honda Civic
+
+Q: Is Bob older than Tom?
+
+["When was Bob born?", "What is Tom's age?"]
+
+A: Yes, Bob is older than Tom. As Bob was born on 1984-01-01 and Tom is 30 years old.
+
+Q: What is their age difference?
+
+["What is Bob's age?", "What is Tom's age?"]
+
+A: Bob is {current_new_year.year - 1984 - 30} years older than Tom. As Bob is {current_new_year.year - 1984} years old and Tom is 30 years old.
+
+{chat_history}
+Q: {text}
+
+"""
+
+    # Get Response from GPT
+    response = openai.Completion.create(
+        prompt=prompt, model=model, temperature=temperature, max_tokens=max_tokens, stop=["A: ", "\n"]
+    )
+
+    # Extract, Clean Message from GPT's Response
+    response_text = response["choices"][0]["text"]
+    try:
+        questions = json.loads(
+            # Clean response to increase likelihood of valid JSON. E.g replace ' with " to enclose strings
+            response_text.strip(empty_escape_sequences)
+            .replace("['", '["')
+            .replace("']", '"]')
+            .replace("', '", '", "')
+        )
+    except json.decoder.JSONDecodeError:
+        logger.warn(f"GPT returned invalid JSON. Falling back to using user message as search query.\n{response_text}")
+        questions = [text]
+    logger.debug(f"Extracted Questions by GPT: {questions}")
+    return questions
+
+
 def extract_search_type(text, model, api_key=None, temperature=0.5, max_tokens=100, verbose=0):
     """
     Extract search type from user query using OpenAI's GPT
diff --git a/src/khoj/routers/api.py b/src/khoj/routers/api.py
index 4839df48..dd39f0d3 100644
--- a/src/khoj/routers/api.py
+++ b/src/khoj/routers/api.py
@@ -10,7 +10,7 @@ from fastapi import HTTPException
 
 # Internal Packages
 from khoj.configure import configure_processor, configure_search
-from khoj.processor.conversation.gpt import converse
+from khoj.processor.conversation.gpt import converse, extract_questions
 from khoj.processor.conversation.utils import message_to_log, message_to_prompt
 from khoj.search_type import image_search, text_search
 from khoj.utils.helpers import timer
@@ -191,6 +191,7 @@ def update(t: Optional[SearchType] = None, force: Optional[bool] = False):
 def chat(q: Optional[str] = None):
     # Initialize Variables
     api_key = state.processor_config.conversation.openai_api_key
+    model = state.processor_config.conversation.model
 
     # Load Conversation History
     chat_session = state.processor_config.conversation.chat_session
@@ -203,10 +204,14 @@ def chat(q: Optional[str] = None):
         else:
             return {"status": "ok", "response": []}
 
-    # Collate context for GPT
-    result_list = search(q, n=2, r=True, score_threshold=0, dedupe=False)
-    collated_result = "\n\n".join([f"# {item.additional['compiled']}" for item in result_list])
-    logger.debug(f"Reference Context:\n{collated_result}")
+    # Infer search queries from user message
+    inferred_queries = extract_questions(q, model=model, api_key=api_key, conversation_log=meta_log)
+
+    # Collate search results as context for GPT
+    result_list = []
+    for query in inferred_queries:
+        result_list.extend(search(query, n=5, r=True, score_threshold=-5.0, dedupe=False))
+    collated_result = "\n\n".join({f"# {item.additional['compiled']}" for item in result_list})
 
     try:
         gpt_response = converse(collated_result, q, meta_log, api_key=api_key)
@@ -218,7 +223,10 @@ def chat(q: Optional[str] = None):
     # Update Conversation History
     state.processor_config.conversation.chat_session = message_to_prompt(q, chat_session, gpt_message=gpt_response)
     state.processor_config.conversation.meta_log["chat"] = message_to_log(
-        q, gpt_response, khoj_message_metadata={"context": collated_result}, conversation_log=meta_log.get("chat", [])
+        q,
+        gpt_response,
+        khoj_message_metadata={"context": collated_result, "intent": {"inferred-queries": inferred_queries}},
+        conversation_log=meta_log.get("chat", []),
     )
 
     return {"status": status, "response": gpt_response, "context": collated_result}
diff --git a/tests/test_chat_actors.py b/tests/test_chat_actors.py
index 365643cc..009ff54e 100644
--- a/tests/test_chat_actors.py
+++ b/tests/test_chat_actors.py
@@ -4,9 +4,10 @@ from datetime import datetime
 
 # External Packages
 import pytest
+from freezegun import freeze_time
 
 # Internal Packages
-from khoj.processor.conversation.gpt import converse
+from khoj.processor.conversation.gpt import converse, extract_questions
 from khoj.processor.conversation.utils import message_to_log
 
 
@@ -20,6 +21,164 @@ if api_key is None:
 
 
 # Test
+# ----------------------------------------------------------------------------------------------------
+@pytest.mark.chatquality
+@freeze_time("1984-04-02")
+def test_extract_question_with_date_filter_from_relative_day():
+    # Act
+    response = extract_questions("Where did I go for dinner yesterday?")
+
+    # Assert
+    expected_responses = [
+        ('dt="1984-04-01"', ""),
+        ('dt>="1984-04-01"', 'dt<"1984-04-02"'),
+        ('dt>"1984-03-31"', 'dt<"1984-04-02"'),
+    ]
+    assert len(response) == 1
+    assert any([start in response[0] and end in response[0] for start, end in expected_responses]), (
+        "Expected date filter to limit to 1st April 1984 in response but got: " + response[0]
+    )
+
+
+# ----------------------------------------------------------------------------------------------------
+@pytest.mark.chatquality
+@freeze_time("1984-04-02")
+def test_extract_question_with_date_filter_from_relative_month():
+    # Act
+    response = extract_questions("Which countries did I visit last month?")
+
+    # Assert
+    expected_responses = [('dt>="1984-03-01"', 'dt<"1984-04-01"'), ('dt>="1984-03-01"', 'dt<="1984-03-31"')]
+    assert len(response) == 1
+    assert any([start in response[0] and end in response[0] for start, end in expected_responses]), (
+        "Expected date filter to limit to March 1984 in response but got: " + response[0]
+    )
+
+
+# ----------------------------------------------------------------------------------------------------
+@pytest.mark.chatquality
+@freeze_time("1984-04-02")
+def test_extract_question_with_date_filter_from_relative_year():
+    # Act
+    response = extract_questions("Which countries have I visited this year?")
+
+    # Assert
+    expected_responses = [
+        ('dt>="1984-01-01"', ""),
+        ('dt>="1984-01-01"', 'dt<"1985-01-01"'),
+        ('dt>="1984-01-01"', 'dt<="1984-12-31"'),
+    ]
+    assert len(response) == 1
+    assert any([start in response[0] and end in response[0] for start, end in expected_responses]), (
+        "Expected date filter to limit to 1984 in response but got: " + response[0]
+    )
+
+
+# ----------------------------------------------------------------------------------------------------
+@pytest.mark.chatquality
+def test_extract_multiple_explicit_questions_from_message():
+    # Act
+    response = extract_questions("What is the Sun? What is the Moon?")
+
+    # Assert
+    expected_responses = [
+        ("sun", "moon"),
+    ]
+    assert len(response) == 2
+    assert any([start in response[0].lower() and end in response[1].lower() for start, end in expected_responses]), (
+        "Expected two search queries in response but got: " + response[0]
+    )
+
+
+# ----------------------------------------------------------------------------------------------------
+@pytest.mark.chatquality
+def test_extract_multiple_implicit_questions_from_message():
+    # Act
+    response = extract_questions("Is Morpheus taller than Neo?")
+
+    # Assert
+    expected_responses = [
+        ("morpheus", "neo"),
+    ]
+    assert len(response) == 2
+    assert any([start in response[0].lower() and end in response[1].lower() for start, end in expected_responses]), (
+        "Expected two search queries in response but got: " + response[0]
+    )
+
+
+# ----------------------------------------------------------------------------------------------------
+@pytest.mark.chatquality
+def test_generate_search_query_using_question_from_chat_history():
+    # Arrange
+    message_list = [
+        ("What is the name of Mr. Vaders daughter?", "Princess Leia", ""),
+    ]
+
+    # Act
+    response = extract_questions("Does he have any sons?", conversation_log=populate_chat_history(message_list))
+
+    # Assert
+    assert len(response) == 1
+    assert "Vader" in response[0]
+
+
+# ----------------------------------------------------------------------------------------------------
+@pytest.mark.chatquality
+def test_generate_search_query_using_answer_from_chat_history():
+    # Arrange
+    message_list = [
+        ("What is the name of Mr. Vaders daughter?", "Princess Leia", ""),
+    ]
+
+    # Act
+    response = extract_questions("Is she a Jedi?", conversation_log=populate_chat_history(message_list))
+
+    # Assert
+    assert len(response) == 1
+    assert "Leia" in response[0]
+
+
+# ----------------------------------------------------------------------------------------------------
+@pytest.mark.chatquality
+def test_generate_search_query_using_question_and_answer_from_chat_history():
+    # Arrange
+    message_list = [
+        ("Does Luke Skywalker have any Siblings?", "Yes, Princess Leia", ""),
+    ]
+
+    # Act
+    response = extract_questions("Who is their father?", conversation_log=populate_chat_history(message_list))
+
+    # Assert
+    assert len(response) == 1
+    assert "Leia" in response[0] and "Luke" in response[0]
+
+
+# ----------------------------------------------------------------------------------------------------
+@pytest.mark.chatquality
+def test_generate_search_query_with_date_and_context_from_chat_history():
+    # Arrange
+    message_list = [
+        ("When did I visit Masai Mara?", "You visited Masai Mara in April 2000", ""),
+    ]
+
+    # Act
+    response = extract_questions(
+        "What was the Pizza place we ate at over there?", conversation_log=populate_chat_history(message_list)
+    )
+
+    # Assert
+    expected_responses = [
+        ('dt>="2000-04-01"', 'dt<"2000-05-01"'),
+        ('dt>="2000-04-01"', 'dt<="2000-04-31"'),
+    ]
+    assert len(response) == 1
+    assert "Masai Mara" in response[0]
+    assert any([start in response[0] and end in response[0] for start, end in expected_responses]), (
+        "Expected date filter to limit to April 2000 in response but got: " + response[0]
+    )
+
+
 # ----------------------------------------------------------------------------------------------------
 @pytest.mark.chatquality
 def test_chat_with_no_chat_history_or_retrieved_content():
@@ -34,7 +193,7 @@ def test_chat_with_no_chat_history_or_retrieved_content():
     expected_responses = ["Khoj", "khoj"]
     assert len(response) > 0
     assert any([expected_response in response for expected_response in expected_responses]), (
-        "Expected assistants name, [K|k]hoj, in response but got" + response
+        "Expected assistants name, [K|k]hoj, in response but got: " + response
     )
 
 
@@ -42,20 +201,16 @@ def test_chat_with_no_chat_history_or_retrieved_content():
 @pytest.mark.chatquality
 def test_answer_from_chat_history_and_no_content():
     # Arrange
-    conversation_log = {"chat": []}
     message_list = [
         ("Hello, my name is Testatron. Who are you?", "Hi, I am Khoj, a personal assistant. How can I help?", ""),
         ("When was I born?", "You were born on 1st April 1984.", ""),
     ]
-    # Generate conversation logs
-    for user_message, gpt_message, _ in message_list:
-        conversation_log["chat"] += message_to_log(user_message, gpt_message)
 
     # Act
     response = converse(
         text="",  # Assume no context retrieved from notes for the user_query
         user_query="What is my name?",
-        conversation_log=conversation_log,
+        conversation_log=populate_chat_history(message_list),
         api_key=api_key,
     )
 
@@ -63,7 +218,7 @@ def test_answer_from_chat_history_and_no_content():
     expected_responses = ["Testatron", "testatron"]
     assert len(response) > 0
     assert any([expected_response in response for expected_response in expected_responses]), (
-        "Expected [T|t]estatron in response but got" + response
+        "Expected [T|t]estatron in response but got: " + response
     )
 
 
@@ -72,20 +227,16 @@ def test_answer_from_chat_history_and_no_content():
 def test_answer_from_chat_history_and_previously_retrieved_content():
     "Chat actor needs to use context in previous notes and chat history to answer question"
     # Arrange
-    conversation_log = {"chat": []}
     message_list = [
         ("Hello, my name is Testatron. Who are you?", "Hi, I am Khoj, a personal assistant. How can I help?", ""),
         ("When was I born?", "You were born on 1st April 1984.", "Testatron was born on 1st April 1984 in Testville."),
     ]
-    # Generate conversation logs
-    for user_message, gpt_message, context in message_list:
-        conversation_log["chat"] += message_to_log(user_message, gpt_message, {"context": context})
 
     # Act
     response = converse(
         text="",  # Assume no context retrieved from notes for the user_query
         user_query="Where was I born?",
-        conversation_log=conversation_log,
+        conversation_log=populate_chat_history(message_list),
         api_key=api_key,
     )
 
@@ -100,20 +251,16 @@ def test_answer_from_chat_history_and_previously_retrieved_content():
 def test_answer_from_chat_history_and_currently_retrieved_content():
     "Chat actor needs to use context across currently retrieved notes and chat history to answer question"
     # Arrange
-    conversation_log = {"chat": []}
     message_list = [
         ("Hello, my name is Testatron. Who are you?", "Hi, I am Khoj, a personal assistant. How can I help?", ""),
         ("When was I born?", "You were born on 1st April 1984.", ""),
     ]
-    # Generate conversation logs
-    for user_message, gpt_message, context in message_list:
-        conversation_log["chat"] += message_to_log(user_message, gpt_message, {"context": context})
 
     # Act
     response = converse(
         text="Testatron was born on 1st April 1984 in Testville.",  # Assume context retrieved from notes for the user_query
         user_query="Where was I born?",
-        conversation_log=conversation_log,
+        conversation_log=populate_chat_history(message_list),
         api_key=api_key,
     )
 
@@ -127,20 +274,16 @@ def test_answer_from_chat_history_and_currently_retrieved_content():
 def test_no_answer_in_chat_history_or_retrieved_content():
     "Chat actor should say don't know as not enough contexts in chat history or retrieved to answer question"
     # Arrange
-    conversation_log = {"chat": []}
     message_list = [
         ("Hello, my name is Testatron. Who are you?", "Hi, I am Khoj, a personal assistant. How can I help?", ""),
         ("When was I born?", "You were born on 1st April 1984.", ""),
     ]
-    # Generate conversation logs
-    for user_message, gpt_message, context in message_list:
-        conversation_log["chat"] += message_to_log(user_message, gpt_message, {"context": context})
 
     # Act
     response = converse(
         text="",  # Assume no context retrieved from notes for the user_query
         user_query="Where was I born?",
-        conversation_log=conversation_log,
+        conversation_log=populate_chat_history(message_list),
         api_key=api_key,
     )
 
@@ -222,21 +365,17 @@ def test_answer_requires_date_aware_aggregation_across_provided_notes():
 def test_answer_general_question_not_in_chat_history_or_retrieved_content():
     "Chat actor should be able to answer general questions not requiring looking at chat history or notes"
     # Arrange
-    conversation_log = {"chat": []}
     message_list = [
         ("Hello, my name is Testatron. Who are you?", "Hi, I am Khoj, a personal assistant. How can I help?", ""),
         ("When was I born?", "You were born on 1st April 1984.", ""),
         ("Where was I born?", "You were born Testville.", ""),
     ]
-    # Generate conversation logs
-    for user_message, gpt_message, context in message_list:
-        conversation_log["chat"] += message_to_log(user_message, gpt_message, {"context": context})
 
     # Act
     response = converse(
         text="",  # Assume no context retrieved from notes for the user_query
-        user_query="Write a haiku about unit testing",
-        conversation_log=conversation_log,
+        user_query="Write a haiku about unit testing in 3 lines",
+        conversation_log=populate_chat_history(message_list),
         api_key=api_key,
     )
 
@@ -277,3 +416,17 @@ def test_ask_for_clarification_if_not_enough_context_in_question():
     assert any([expected_response in response for expected_response in expected_responses]), (
         "Expected chat actor to ask for clarification in response, but got: " + response
     )
+
+
+# Helpers
+# ----------------------------------------------------------------------------------------------------
+def populate_chat_history(message_list):
+    # Generate conversation logs
+    conversation_log = {"chat": []}
+    for user_message, gpt_message, context in message_list:
+        conversation_log["chat"] += message_to_log(
+            user_message,
+            gpt_message,
+            {"context": context, "intent": {"query": user_message, "inferred-queries": f'["{user_message}"]'}},
+        )
+    return conversation_log
diff --git a/tests/test_chat_director.py b/tests/test_chat_director.py
index 35e08c88..99261a72 100644
--- a/tests/test_chat_director.py
+++ b/tests/test_chat_director.py
@@ -25,7 +25,11 @@ def populate_chat_history(message_list):
     # Generate conversation logs
     conversation_log = {"chat": []}
     for user_message, gpt_message, context in message_list:
-        conversation_log["chat"] += message_to_log(user_message, gpt_message, {"context": context})
+        conversation_log["chat"] += message_to_log(
+            user_message,
+            gpt_message,
+            {"context": context, "intent": {"query": user_message, "inferred-queries": f'["{user_message}"]'}},
+        )
 
     # Update Conversation Metadata Logs in Application State
     state.processor_config.conversation.meta_log = conversation_log
@@ -175,7 +179,6 @@ def test_answer_requires_current_date_awareness(chat_client):
 
 
 # ----------------------------------------------------------------------------------------------------
-@pytest.mark.xfail(AssertionError, reason="Chat director not capable of answering time aware questions yet")
 @pytest.mark.chatquality
 @freeze_time("2023-04-01")
 def test_answer_requires_date_aware_aggregation_across_provided_notes(chat_client):
@@ -259,7 +262,6 @@ def test_answer_in_chat_history_beyond_lookback_window(chat_client):
 
 
 # ----------------------------------------------------------------------------------------------------
-@pytest.mark.xfail(reason="Chat director not capable of answering this question yet")
 @pytest.mark.chatquality
 def test_answer_requires_multiple_independent_searches(chat_client):
     "Chat director should be able to answer by doing multiple independent searches for required information"