compass.scripts.process.process_jurisdictions_with_openai#

async process_jurisdictions_with_openai(out_dir, tech, jurisdiction_fp, model='gpt-4o-mini', num_urls_to_check_per_jurisdiction=5, max_num_concurrent_browsers=10, max_num_concurrent_website_searches=10, max_num_concurrent_jurisdictions=25, url_ignore_substrings=None, known_doc_urls=None, file_loader_kwargs=None, search_engines=None, pytesseract_exe_fp=None, td_kwargs=None, tpe_kwargs=None, ppe_kwargs=None, log_dir=None, clean_dir=None, ordinance_file_dir=None, jurisdiction_dbs_dir=None, perform_website_search=True, llm_costs=None, log_level='INFO', keep_async_logs=False)[source]#

Download and extract ordinances for a list of jurisdictions

This function scrapes ordinance documents (PDFs or HTML text) for a list of specified jurisdictions and processes them using one or more LLM models. Output files, logs, and intermediate artifacts are stored in configurable directories.

Parameters:

out_dir (path-like) – Path to the output directory. If it does not exist, it will be created. This directory will contain the structured ordinance CSV file, all downloaded ordinance documents (PDFs and HTML), usage metadata, and default subdirectories for logs and intermediate outputs (unless otherwise specified).
tech ({"wind", "solar"}) – Label indicating which technology type is being processed.
jurisdiction_fp (path-like) – Path to a CSV file specifying the jurisdictions to process. The CSV must contain at least two columns: “County” and “State”, which specify the county and state names, respectively. If you would like to process a subdivision with a county, you must also include “Subdivision” and “Jurisdiction Type” columns. The “Subdivision” should be the name of the subdivision, and the “Jurisdiction Type” should be a string identifying the type of subdivision (e.g., “City”, “Township”, etc.)
model (str or list of dict, optional) – LLM model(s) to use for scraping and parsing ordinance documents. If a string is provided, it is assumed to be the name of the default model (e.g., “gpt-4o”), and environment variables are used for authentication.

If a list is provided, it should contain dictionaries of arguments that can initialize instances of OpenAIConfig. Each dictionary can specify the model name, client type, and initialization arguments.

Each dictionary must also include a tasks key, which maps to a string or list of strings indicating the tasks that instance should handle. Exactly one of the instances must include “default” as a task, which will be used when no specific task is matched. For example:
```
"model": [
    {
        "model": "gpt-4o-mini",
        "llm_call_kwargs": {
            "temperature": 0,
            "timeout": 300,
        },
        "client_kwargs": {
            "api_key": "<your_api_key>",
            "api_version": "<your_api_version>",
            "azure_endpoint": "<your_azure_endpoint>",
        },
        "tasks": ["default", "date_extraction"],
    },
    {
        "model": "gpt-4o",
        "client_type": "openai",
        "tasks": ["ordinance_text_extraction"],
    }
]
```
By default, "gpt-4o".
num_urls_to_check_per_jurisdiction (int, optional) – Number of unique Google search result URLs to check for each jurisdiction when attempting to locate ordinance documents. By default, 5.
max_num_concurrent_browsers (int, optional) – Maximum number of browser instances to launch concurrently for retrieving information from the web. Increasing this value too much may lead to timeouts or performance issues on machines with limited resources. By default, 10.
max_num_concurrent_website_searches (int, optional) – Maximum number of website searches allowed to run simultaneously. Increasing this value can speed up searches, but may lead to timeouts or performance issues on machines with limited resources. By default, 10.
max_num_concurrent_jurisdictions (int, default 25) – Maximum number of jurisdictions to process concurrently. Limiting this can help manage memory usage when dealing with a large number of documents. By default 25.
url_ignore_substrings (list of str, optional) – A list of substrings that, if found in any URL, will cause the URL to be excluded from consideration. This can be used to specify particular websites or entire domains to ignore. For example:
```
url_ignore_substrings = [
    "wikipedia",
    "nrel.gov",
    "www.co.delaware.in.us/documents/1649699794_0382.pdf",
]
```
The above configuration would ignore all wikipedia articles, all websites on the NREL domain, and the specific file located at www.co.delaware.in.us/documents/1649699794_0382.pdf. By default, None.
known_doc_urls (dict | str, optional) – A dictionary where keys are the jurisdiction codes (as strings) and values are a string or list of strings representing known URL’s to check for those jurisdictions. If provided, these URL’s will be checked first and if an ordinance document is found, no further scraping will be performed. This input can also be a path to a JSON file containing the dictionary of code-to-url mappings. By default, None.
file_loader_kwargs (dict, optional) – Dictionary of keyword arguments pairs to initialize elm.web.file_loader.AsyncFileLoader. If found, the “pw_launch_kwargs” key in these will also be used to initialize the elm.web.search.google.PlaywrightGoogleLinkSearch used for the google URL search. By default, None.
search_engines (list, optional) – A list of dictionaries, where each dictionary contains information about a search engine class that should be used for the document retrieval process. Each dictionary should contain at least the key "se_name", which should correspond to one of the search engine class names from elm.web.search.run.SEARCH_ENGINE_OPTIONS. The rest of the keys in the dictionary should contain keyword-value pairs to be used as parameters to initialize the search engine class (things like API keys and configuration options; see the ELM documentation for details on search engine class parameters). The list should be ordered by search engine preference - the first search engine parameters will be used to submit the queries initially, then any subsequent search engine listings will be used as fallback (in order that they appear). Do not repeat search engines - only the last config dictionary will be used to initialize the search engine if you do. If None, then all default configurations for the search engines (along with the fallback order) are used. By default, None.
pytesseract_exe_fp (path-like, optional) – Path to the pytesseract executable. If specified, OCR will be used to extract text from scanned PDFs using Google’s Tesseract. By default None.
td_kwargs (dict, optional) – Additional keyword arguments to pass to tempfile.TemporaryDirectory. The temporary directory is used to store documents which have not yet been confirmed to contain relevant information. By default, None.
tpe_kwargs (dict, optional) – Additional keyword arguments to pass to concurrent.futures.ThreadPoolExecutor, used for I/O-bound tasks such as logging. By default, None.
ppe_kwargs (dict, optional) – Additional keyword arguments to pass to concurrent.futures.ProcessPoolExecutor, used for CPU-bound tasks such as PDF loading and parsing. By default, None.
log_dir (path-like, optional) – Path to the directory for storing log files. If not provided, a logs subdirectory will be created inside out_dir. By default, None.
clean_dir (path-like, optional) – Path to the directory for storing cleaned ordinance text output. If not provided, a cleaned_text subdirectory will be created inside out_dir. By default, None.
ordinance_file_dir (path-like, optional) – Path to the directory where downloaded ordinance files (PDFs or HTML) for each jurisdiction are stored. If not provided, a ordinance_files subdirectory will be created inside out_dir. By default, None.
jurisdiction_dbs_dir (path-like, optional) – Path to the directory where parsed ordinance database files are stored for each jurisdiction. If not provided, a jurisdiction_dbs subdirectory will be created inside out_dir. By default, None.
perform_website_search (bool, default True) – Option to fallback to a jurisdiction website crawl-based search for ordinance documents if the search engine approach fails to recover any relevant documents. By default, True.
llm_costs (dict, optional) – Dictionary mapping model names to their token costs, used to track the estimated total cost of LLM usage during the run. The structure should be:
```
{"model_name": {"prompt": float, "response": float}}
```
Costs are specified in dollars per million tokens. For example:
```
"llm_costs": {"my_gpt": {"prompt": 1.5, "response": 3.7}}
```
registers a model named “my_gpt” with a cost of $1.5 per million input (prompt) tokens and $3.7 per million output (response) tokens for the current processing run.

Note

The displayed total cost does not track cached tokens, so treat it like an estimate. Your final API costs may vary.

If set to None, no custom model costs are recorded, and cost tracking may be unavailable in the progress bar. By default, None.
log_level (str, optional) – Logging level for ordinance scraping and parsing (e.g., “TRACE”, “DEBUG”, “INFO”, “WARNING”, or “ERROR”). By default, "INFO".
keep_async_logs (bool, default False) – Option to store the full asynchronous log record to a file. This is only useful if you intend to monitor overall processing progress from a file instead of from the terminal. If True, all of the unordered records are written to a “all.log” file in the log_dir directory. By default, False.

Returns:

seconds_elapsed (float) – Total time taken to complete the processing, in seconds.
cost (float) – Total estimated cost for the LLM calls made during this run. If no cost info is provided, this will be 0.
output_directory (path-like) – Path to output directory containing data.