rename files

examples/citation_with_fuzzy_matching/citation_fuzzy_match.py

@@ -1,134 +0,0 @@
-from typing import List
-
-import openai
-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] - 50 : span[0]].replace("\n", "")
-        + "\033[91m"
-        + "<"
-        + text[span[0] : span[1]].replace("\n", "")
-        + "> "
-        + "\033[0m"
-        + text[span[1] : span[1] + 20].replace("\n", "")
-        + "..."
-    )
-
-
-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: The author studied Computational Mathematics and physics in university.
-    Citation: ...s born in China.I went to an 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: ...y companies including Stitchfix, Facebook.I also <started the Data Science club at the University of Waterloo>  and I was the presi...
-    Citation: ... club at the University of Waterloo and I was the <president of the club for 2 years> ...
-    """

examples/citation_with_fuzzy_matching/diagram.py

@@ -1,6 +0,0 @@
-import erdantic as erd
-
-from citation_fuzzy_match import QuestionAnswer
-
-diagram = erd.create(QuestionAnswer)
-diagram.draw("examples/citation_fuzzy_match/schema.png")