Revised version of slides originally presented March 27, 2024
.docx
$ pandoc -t plain -o eastville-code.txt eastville-code.docx
chicago = Jurisdiction( name="Chicago", hierarchy={ "title": r"TITLE \d+", "chapter": r"CHAPTER \d+-\d+", "article": r"ARTICLE [IVX]+\\.", "section": r"\d+-\d+-\d+", }, source_local="../data/chicago/chicago.txt", ) chicago_tree = chicago.parse()
los_angeles = Jurisdiction( name="Los Angeles", hierarchy={ "chapter": r"CHAPTER [IVX]+[A-Z]?", "article": r"ARTICLE \d+", "section": r"SEC. \d+\.\d+", "subsection": r"[A-Z]\.", }, source_local="../data/los-angeles/los-angeles.txt", ) los_angeles_tree = los_angeles.parse()
with conn.cursor as cursor: cursor.execute( """ CREATE TABLE IF NOT EXISTS muni ( id SERIAL PRIMARY KEY, jurisdiction TEXT, L1_ref TEXT, L1_heading TEXT, L2_ref TEXT, L2_heading TEXT, L3_ref TEXT, L3_heading TEXT, L4_ref TEXT, L4_heading TEXT, segment INTEGER, text TEXT, embedding VECTOR(%s) ); """, (EMBEDDING_LENGTH,))
system_prompt = """ You will be given a text block, which may provide context about other parts of the document (a municipal code or ordinance) from which it was taken. Your task is to classify the text block. Your response should be a single word from the following list, choosing the best fit according to the explanation provided on each line: 'rule': Statement of a rule, obligation, or prohibition, 'penalty': Penalties for violations of rules specified in other parts of the code, 'definition': Definition of terms used in other parts of the code, 'interpretation': Guidance about interpretation of language or rules, other than definitions, 'date': Effective dates of rules in the document or enacting legistlation, or termination dates, 'other': Any other type of context not covered by the above categories, """
[Passed to LLM API call ]
pgvector
from openai import OpenAI EMBEDDING_MODEL = "text-embedding-3-small" def create_embedding(text: str, model: str=EMBEDDING_MODEL) -> list[float]: """Create an embedding for a block of text.""" client = OpenAI() response = client.embeddings.create(input=[text], model=model) return response.data[0].embedding
[.453, .765, .023, .114, ...] 1536 dimensions
[.453, .765, .023, .114, ...]
WITH semantic_search AS ( SELECT id, L4_heading, RANK () OVER (ORDER BY embedding <=> %(embedding)s) AS rank FROM muni ORDER BY embedding <=> %(embedding)s LIMIT 20 ), keyword_search AS ( SELECT id, L4_heading, RANK () OVER (ORDER BY ts_rank_cd(textsearchable, query) DESC) FROM muni, plainto_tsquery('english', %(query)s) query WHERE textsearchable @@ query ORDER BY ts_rank_cd(textsearchable, query) DESC LIMIT 20 ) SELECT COALESCE(semantic_search.id, keyword_search.id) AS id, COALESCE(1.0 / (%(k)s + semantic_search.rank), 0.0) + COALESCE(1.0 / (%(k)s + keyword_search.rank), 0.0) AS score, COALESCE(semantic_search.L4_heading, keyword_search.L4_heading) AS L4_heading FROM semantic_search FULL OUTER JOIN keyword_search ON semantic_search.id = keyword_search.id ORDER BY score DESC LIMIT %(limit)s; .
def is_relevant(text: str, query: str, threshold: int = 4, model: str = LANGUAGE_MODEL) -> bool: client = OpenAI() system_prompt = """ You will be given a text block and a query. Your task is to determine whether the text block is relevant to the query. Please respond with a single integer from 1 to 5 (inclusive), where 1 means 'the text is not related to, and does not help to answer, the query', 5 means 'the text is clearly and related to, and helps to directly answer, the query', and intermediate values represent degrees of relevance in between. """ prompt = f""" Text block: {text} Query: {query} Your response (1, 2, 3, 4, or 5): """ ... .
... response = client.chat.completions.create( model=model, messages=[ {"role": "system", "content": system_prompt}, {"role": "user", "content": prompt} ], temperature=0.7, max_tokens=2, top_p=1 ) response = response.choices[0].message.content if response is not None: try: if int(response) >= threshold: return True except ValueError as e: warn(f'WARNING: cannot convert response {response} to integer. Error: {e}') return False .
SEC. 11.00. PROVISIONS APPLICABLE TO CODE. (l) In addition to any other remedy or penalty provided by this Code, any violation of any provision of this Code is declared to be a public nuisance and may be abated by the City or by the City Attorney on behalf of the people of the State of California as a nuisance by means of a restraining order, injunction or any other order or judgment in law or equity issued by a court of competent jurisdiction ... SEC. 45.00 DEFINITIONS (a) "BOARD" means the Board of Police Commissioners unless otherwise designated. ... SEC. 45.19. DISPLAY OF DRUG PARAPHERNALIA TO MINORS C. Definitions. 1. “Drug paraphernalia,” including but not limited to one or more of those items identified in that list set forth ... .
Based on the provided municipal code sections, below are the penalties for business owners who fail to provide a sign stating that minors are not permitted to enter an establishment selling drug paraphernalia: - **Public Nuisance Declaration**: The violation is declared a public nuisance, which can be abated by the City or the City Attorney through legal means such as restraining orders, or injunctions (SEC. 11.00 (l)). - **Injunctive Relief**: The City or City Attorney may seek injunctive relief in court to enforce compliance with the requirement to post the mandated sign (SEC. 11.00 (l)). - **Civil Penalties**: Every day the violation continues is considered a new offense, subject to a maximum civil penalty of $2,500 for each offense (SEC. 11.00 (l)). - **Performance Bond Requirement**: During civil action, the court may demand posting of a performance bond to ensure future compliance with the municipal code and any court orders or judgments (SEC. 11.00 (l)). These penalties are designed to compel business owners to adhere to the regulations concerning the display and sale of drug paraphernalia, especially in relation to minors, as prescribed in SEC. 45.19. .