compass.extraction.solar.ordinance.SolarOrdinanceTextExtractor#

class SolarOrdinanceTextExtractor(llm_service, usage_tracker=None, **kwargs)[source]#

Bases: PromptBasedTextExtractor

Extract succinct ordinance text from input

Parameters:

llm_service (Service) – LLM service used for queries.
usage_tracker (UsageTracker, optional) – Optional tracker instance to monitor token usage during LLM calls. By default, None.
**kwargs –
Keyword arguments to be passed to the underlying service processing function (i.e. llm_service.call(**kwargs)). Should not contain the following keys:
- usage_sub_label
- messages
These arguments are provided by this caller object.

Methods

call(sys_msg, content[, usage_sub_label])

Call LLM

Attributes

`FORMATTING_PROMPT`	Prompt component instructing model to preserve text structure
`IN_LABEL`	Identifier for collected text ingested by this class
`OUTPUT_PROMPT`	Prompt component instructing model output guidelines
`OUT_LABEL`
`PROMPTS`	Dicts defining the prompts for ordinance text extraction
`SYSTEM_MESSAGE`	System message for text extraction LLM calls
`TASK_DESCRIPTION`	Task description to show in progress bar
`TASK_ID`	ID to use for this extraction for linking with LLM configs
`parsers`	Iterable of parsers provided by this extractor

IN_LABEL = 'relevant_text'#: Identifier for collected text ingested by this class

FORMATTING_PROMPT = '## Formatting & Structure ##:\n- **Preserve _all_ section titles, headers, and numberings** for reference.\n- **Maintain the original wording, formatting, and structure** to ensure accuracy.'#: Prompt component instructing model to preserve text structure

OUTPUT_PROMPT = "## Output Handling ##:\n- This is a strict extraction task — act like a text filter, **not** a summarizer or writer.\n- Do not add, explain, reword, or summarize anything.\n- The output must be a **copy-paste** of the original excerpt. **Absolutely no paraphrasing or rewriting.**\n- The output must consist **only** of contiguous or discontiguous verbatim blocks copied from the input.\n- The only allowed change is to remove irrelevant sections of text. You can remove irrelevant text from within sections, but you cannot add any new text or modify the text you keep in any way.\n- If **no relevant text** is found, return the response: 'No relevant text.'"#: Prompt component instructing model output guidelines

SYSTEM_MESSAGE = 'You are a text extraction assistant. Your job is to extract only verbatim, **unmodified** excerpts from the provided text. Do not interpret or paraphrase. Do not summarize. Only return exactly copied segments that match the specified scope. If the relevant content appears within a table, return the entire table, including headers and footers, exactly as formatted.'#: System message for text extraction LLM calls

async call(sys_msg, content, usage_sub_label=LLMUsageCategory.DEFAULT)#

Call LLM

Parameters:

sys_msg (str) – The LLM system message.
content (str) – Your chat message for the LLM.
usage_sub_label (str, optional) – Label to store token usage under. By default, "default".

Returns:

str or None – The LLM response, as a string, or None if something went wrong during the call.

property parsers#

Iterable of parsers provided by this extractor

Yields:

name (str) – Name describing the type of text output by the parser.
parser (callable()) – Async function that takes a text_chunks input and outputs parsed text.

TASK_DESCRIPTION = 'Extracting solar ordinance text'#: Task description to show in progress bar

TASK_ID = 'ordinance_text_extraction'#: ID to use for this extraction for linking with LLM configs

PROMPTS = [{'key': 'cleaned_text_for_extraction', 'out_fn': '{jurisdiction} Utility Scale Solar Ordinance.txt', 'prompt': '# CONTEXT #\nWe want to reduce the provided excerpt to only contain information about **solar energy systems**. The extracted text will be used for structured data extraction, so it must be both **comprehensive** (retaining all relevant details) and **focused** (excluding unrelated content), with **zero rewriting or paraphrasing**. Ensure that all retained information is **directly applicable to solar energy systems** while preserving full context and accuracy.\n\n# OBJECTIVE #\nExtract all text **pertaining to solar energy systems** from the provided excerpt.\n\n# RESPONSE #\nFollow these guidelines carefully:\n\n1. ## Scope of Extraction ##:\n- Include **all** text that pertains to** solar energy systems**, even if they are referred to by different names such as: Solar panels, solar energy conversion systems (secs), solar energy facilities (sef), solar energy farms (sef), solar farms (sf), utility-scale solar energy systems (uses), commercial solar energy systems (cses), ground-mounted solar energy systems (gses), alternate energy systems (aes), commercial energy production systems (cepcs), or similar\n- Explicitly include any text related to **bans or prohibitions** on solar energy systems.\n- Explicitly include any text related to the adoption or enactment date of the ordinance (if any).\n\n2. ## Exclusions ##:\n- Do **not** include text that does not pertain to solar energy systems.\n\n3. {FORMATTING_PROMPT}\n\n4. {OUTPUT_PROMPT}'}]#: Dicts defining the prompts for ordinance text extraction