diff --git a/citation_fuzzy_match.py b/citation_fuzzy_match.py
new file mode 100644
index 0000000..070a26a
--- /dev/null
+++ b/citation_fuzzy_match.py
@@ -0,0 +1,129 @@
+import openai
+from typing import List
+from pydantic import Field, BaseModel
+from openai_function_call import OpenAISchema
+
+
+class Fact(BaseModel):
+    """
+    Class representing single statement.
+    Each fact has a body and a list of sources.
+    If there are multiple facts make sure to break them apart such that each one only uses a set of sources that are relevant to it.
+    """
+
+    fact: str = Field(..., description="Body of the sentence, as part of a response")
+    substring_quote: List[str] = Field(
+        ...,
+        description="Each source should be a direct quote from the context, as a substring of the original content",
+    )
+
+    def _get_span(self, quote, context, errs=100):
+        import regex
+
+        minor = quote
+        major = context
+
+        errs_ = 0
+        s = regex.search(f"({minor}){{e<={errs_}}}", major)
+        while s is None and errs_ <= errs:
+            errs_ += 1
+            s = regex.search(f"({minor}){{e<={errs_}}}", major)
+
+        if s is not None:
+            yield from s.spans()
+
+    def get_spans(self, context):
+        for quote in self.substring_quote:
+            yield from self._get_span(quote, context)
+
+
+class QuestionAnswer(OpenAISchema):
+    """
+    Class representing a question and its answer as a list of facts each one should have a soruce.
+    each sentence contains a body and a list of sources."""
+
+    question: str = Field(..., description="Question that was asked")
+    answer: List[Fact] = Field(
+        ...,
+        description="Body of the answer, each fact should be its seperate object with a body and a list of sources",
+    )
+
+
+def ask_ai(question: str, context: str) -> QuestionAnswer:
+    """
+    Function to ask AI a question and get back an Answer object.
+    but should be updated to use the actual method for making a request to the AI.
+
+    Args:
+        question (str): The question to ask the AI.
+        context (str): The context for the question.
+
+    Returns:
+        Answer: The Answer object.
+    """
+
+    # Making a request to the hypothetical 'openai' module
+    completion = openai.ChatCompletion.create(
+        model="gpt-3.5-turbo-0613",
+        temperature=0.2,
+        max_tokens=1000,
+        functions=[QuestionAnswer.openai_schema],
+        function_call={"name": QuestionAnswer.openai_schema["name"]},
+        messages=[
+            {
+                "role": "system",
+                "content": f"You are a world class algorithm to answer questions with correct and exact citations. ",
+            },
+            {"role": "user", "content": f"Answer question using the following context"},
+            {"role": "user", "content": f"{context}"},
+            {"role": "user", "content": f"Question: {question}"},
+            {
+                "role": "user",
+                "content": f"Tips: Make sure to cite your sources, and use the exact words from the context.",
+            },
+        ],
+    )
+
+    # Creating an Answer object from the completion response
+    return QuestionAnswer.from_response(completion)
+
+
+question = "What did the author do during college?"
+context = """
+My name is Jason Liu, and I grew up in Toronto Canada but I was born in China.
+I went to an arts highschool but in university I studied Computational Mathematics and physics. 
+As part of coop I worked at many companies including Stitchfix, Facebook.
+I also started the Data Science club at the University of Waterloo and I was the president of the club for 2 years.
+"""
+
+
+def highlight(text, span):
+    return (
+        "..."
+        + text[span[0] - 20 : span[0]]
+        + "*"
+        + "\033[91m"
+        + text[span[0] : span[1]]
+        + "\033[0m"
+        + "*"
+        + text[span[1] : span[1] + 20]
+        + "..."
+    )
+
+
+answer = ask_ai(question, context)
+
+print("Question:", question)
+print()
+for fact in answer.answer:
+    print("Statement:", fact.fact)
+    for span in fact.get_spans(context):
+        print("Citation:", highlight(context, span))
+    print()
+# >>> Question: What did the author do during college?
+# >>> Statement: In university, the author studied Computational Mathematics and physics.
+# >>> Citation: ...arts highschool but *in university I studied Computational
+# >>> Mathematics and physics*. As part of coop I ...
+# >>> Statement: The author started the Data Science club at the University of Waterloo and was the president of the club for 2 years.
+# >>> Citation: ...x, Facebook. I also *started the Data Science club at the University of Waterloo* and I was the presi...
+# >>> Citation: ...erloo and I was the *president of the club for 2 years*. ...