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Merged in feature/lesser-table-caching-refactor-hybrid (pull request #847)

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Feature/lesser table caching refactor hybrid

* chore: Remove unused duplicate main.py from shared pipeline

* fix: Correct crosswalk paths in aarete_derived.py

* chore: Remove unused documentation files from fieldExtraction

* docs: Add documentation files to documentation folder

* docs: Update README with uv setup, expanded project structure, and branching conventions

* docs: Add uv installation steps with Ubuntu/WSL emphasis

* Enable prompt caching for all remaining LLM calls

- Add _INSTRUCTION() functions for: EXHIBIT_HEADER, EXHIBIT_LINKAGE,
  EXHIBIT_TITLE_MATCH, DATE_FIX, DERIVED_TERM_DATE, CHECK_PROVIDER_NAME_MATCH,
  SPECIAL_CASE_ASSIGNMENT
- Update all invoke_claude() calls in saas and clover pipelines to use
  cache=True with corresponding _INSTRUCTION() functions
- Add new instructions to get_cacheable_instructions() for cache warming
- Update tests for new instruction functions

Functions now using caching:
- prompt_exhibit_level
- prompt_exhibit_lesser (EXHIBIT_LEVEL_LESSER_OF)
- prompt_fee_schedule_breakout
- prompt_grouper_breakout
- prompt_special_case_assignment
- prompt_exhibit_linkage
- prompt_exhibit_header
- prompt_smart_chunked (ONE_TO_ONE templates)
- prompt_date_fix
- prompt_derived_term_date
- prompt_exhibit_title_match
- provider_name_match_check

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

* Reorder

* feat: Add bcbs_promise client pipeline with OFFSET_TERM extraction

- Add new bcbs_promise client with HSC-based OFFSET_TERM field extraction
- Extract full paragraph text of offset/recoupment provisions from contracts
- Derive OFFSET_INDICATOR (Y/N) from OFFSET_TERM presence
- Fix reorder_columns to preserve extra columns not in COLUMN_ORDER
- Update QC/QA output path to outputs/qc_qa/

* fix: Update dev deps and test assertions for QC/QA output path

- Add pytest/pytest-mock to dev dependencies for mypy type checking
- Update test assertions to expect outputs/qc_qa instead of qa_qc_output

* style: Apply black formatting to prompt_templates.py

* Merge main, move scripts

* Archive some scripts

* update py version

* remove .py version file

* Remove ASCII characters

* Restore testbed code

* restore tracking

* Update testbed metrics

* Enable prompt caching for CODE_LAST_CHECK, FILL_BILL_TYPE, DUAL_LOB_CHECK, and GROUPER_BREAKOUT

- Add CODE_LAST_CHECK_INSTRUCTION() for service specificity classification
- Add FILL_BILL_TYPE_INSTRUCTION() for bill type code determination
- Add DUAL_LOB_CHECK_INSTRUCTION() for Medicare/Medicaid classification
- Update code_funcs.py to use caching for CODE_LAST_CHECK, FILL_BILL_TYPE, GROUPER_BREAKOUT
- Update postprocessing_funcs.py to use caching for DUAL_LOB_CHECK
- Add new instructions to get_cacheable_instructions() for cache warming
- Add unit tests for new instruction functions

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

* Fix postprocessing_funcs to remove invalid columns

* Merge branch 'main' into feature/lesser-table-caching-refactor-hybrid

* Revert prompt caching changes from aed1b73c

* update formatting

* Update imports


Approved-by: Sha Brown
Approved-by: Praneel Panchigar
This commit is contained in:
Katon Minhas
2026-01-26 16:52:55 +00:00
parent dcec3f4b7a
commit afb6d5185d
584 changed files with 17548 additions and 33208 deletions
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src/utils/string_utils.py
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import json
import ast
import logging
import re
import traceback
import warnings
from datetime import datetime
from typing import Literal, overload
import numpy as np
import pandas as pd
from src.constants.delimiters import Delimiter
from src.constants.regex_patterns import (
BACKTICK_PATTERN,
PIPE_PATTERN,
TRIPLE_BACKTICK_PATTERN,
)
def extract_text_from_delimiters(
raw_output: str, delimiter: Delimiter, match_index: int = -1
) -> str:
"""
Extracts a match enclosed in specified delimiters from the raw output.
This function searches for text enclosed by a specified delimiter in the given raw output string.
It returns the match specified by the `which_match` index. If no match is found, it returns "N/A".
- raw_output (str): The raw output string to search within.
- which_match (int, optional): The index of the match to return. Defaults to -1, which returns the last match. If the index is out of range, the last match is returned.
- str: The extracted text or "N/A" if no match is found.
Raises:
- TypeError: If `raw_output` is not a string or `delimiter` is not a Delimiter enum value.
- ValueError: If an unsupported delimiter is provided.
Returns:
- str: The extracted answer or "N/A" if no match is found.
"""
if not isinstance(raw_output, str):
raise TypeError(
"Expected a string for raw_output, got {0}.".format(
type(raw_output).__name__
)
)
if not isinstance(delimiter, Delimiter):
raise TypeError(
"Expected a Delimiter enum value, got {0}.".format(type(delimiter).__name__)
)
# Define a pattern based on the delimiter type
if delimiter == Delimiter.PIPE:
pattern = PIPE_PATTERN
elif delimiter == Delimiter.BACKTICK:
pattern = BACKTICK_PATTERN
elif delimiter == Delimiter.TRIPLE_BACKTICK:
pattern = TRIPLE_BACKTICK_PATTERN
else:
raise ValueError("Unsupported delimiter. Use one of the Delimiter enum values.")
# Find all matches based on the pattern
matches = re.findall(pattern=pattern, string=raw_output)
if len(matches) == 0:
return "N/A"
# Adjust for negative indices
if match_index < 0:
match_index += len(matches)
if not 0 <= match_index < len(matches):
warnings.warn("Index out of range. Returning the last match by default.")
match_index = -1
return matches[match_index]
def page_key_sort(page_key: str) -> tuple[int, float | str]:
"""Sorts page keys, prioritizing numeric keys first.
Args:
page_key (str): The page key to sort.
Returns:
tuple[int, float|str]: A tuple where the first element is 0 for numeric keys and 1 for non-numeric keys, and the second element is the numeric value for numeric keys or the original string for non-numeric keys.
Example:
>>> page_key_sort("1")
(0, 1.0)
>>> page_key_sort("A")
(1, "A")
Example Usage with mixed types:
>>> L = ["10", "2", "A", "1"]
>>> sort(L, key=page_key_sort)
>>> print(L)
['1', '2', '10', 'A']
"""
try:
return (0, float(page_key)) # Numeric pages first, in numerical order
except ValueError:
return (1, str(page_key)) # Non-numeric pages after, in alphabetical order
def json_parsing_search(response_text: str, field_list: list[str]) -> dict[str, str]:
"""
Parses a JSON-like string to extract values for fields.
This function attempts to clean and format the input string to resemble a valid JSON structure.
It then searches for the specified fields within the string and extracts their corresponding values.
Parameters:
response_text (str): The JSON-like string to parse.
field_list (list[str]): A list of field names to search for in the response_text.
Returns:
dict[str, str]: A dictionary where keys are field names and values are the extracted values from the response_text.
"""
try:
if response_text.split("{", 1)[1].strip()[0] == '"':
response_text = "{" + response_text.split("{", 1)[1]
else:
response_text = "{" + response_text
except:
response_text = "{" + response_text
if len(response_text.split("}", 1)) > 1:
if response_text.rsplit("}", 1)[0].strip()[-1] == '"':
response_text = response_text.rsplit("}", 1)[0] + "}"
elif response_text.rsplit("}", 1)[0].strip()[-1] == "}":
response_text = response_text.rsplit("}", 1)[0]
else:
response_text = response_text.rstrip(",") + "}"
field_l = []
answer_l = []
position_dict = {}
for f in field_list:
location = response_text.find('"' + f + '"')
if location != -1:
position_dict[location] = f
field_list = list(dict(sorted(position_dict.items())).values())
for f in field_list:
if f in response_text:
field_l.append(f)
value = response_text.split('"' + f + '"')[0]
response_text = response_text.split('"' + f + '"')[1]
if f != field_list[0] and f != field_list[-1]:
answer_l.append(
value.strip("\n")
.strip('"')
.strip(":")
.strip(" ")
.strip("\n")
.strip(" ")
.strip(",")
.strip('"')
)
elif f == field_list[-1]:
answer_l.append(
value.strip("\n")
.strip('"')
.strip(":")
.strip(" ")
.strip("\n")
.strip(" ")
.strip(",")
.strip('"')
)
answer_l.append(
response_text.strip("\n")
.strip('"')
.strip(":")
.strip(" ")
.strip("}")
.strip("\n")
.strip(" ")
.strip('"')
)
return dict(zip(field_l, answer_l))
def universal_json_load(s: str):
"""
Extract and parse all highest-level valid JSON objects from a string.
Returns the last valid top-level JSON object found (dict, list, etc).
Raises ValueError if no valid JSON objects are found. Does not clean up syntax.
"""
if not isinstance(s, str):
raise TypeError(f"Expected string input, got {type(s).__name__}")
found = []
i = 0
while i < len(s):
if s[i] in "{[":
start = i
stack = [s[i]]
j = i + 1
while j < len(s):
# Skip over string literals to avoid counting brackets inside strings
if s[j] == '"':
j += 1
while j < len(s):
if s[j] == "\\" and j + 1 < len(s):
j += 2 # Skip escaped character
continue
if s[j] == '"':
break
j += 1
elif s[j] in "{[":
stack.append(s[j])
elif s[j] in "}]":
if not stack:
break
open_bracket = stack.pop()
if (open_bracket == "{" and s[j] != "}") or (
open_bracket == "[" and s[j] != "]"
):
break
if not stack:
candidate = s[start : j + 1]
# Only consider top-level objects (not nested)
# Check if start is not inside another object
# This is guaranteed by stack being empty only at top-level
try:
obj = json.loads(candidate)
found.append(obj)
except Exception:
pass
i = j # Move past this top-level object
break
j += 1
else:
pass
i += 1
if found:
return found[-1]
raise ValueError(f"No valid JSON objects found in string: {s[:100]}...")
reimbursement_strings = [ # These are for `method='keyword'`
"%",
"$",
"percent",
"billed charges",
"compensation",
"reimbursement",
"fee schedule",
]
reimb_regex = r"(?<![$%])(?:\$\d+|\d+[$%])(?![$%])"
# TODO: check should this always be checking just page 1? Bc in file_processing and table_utils it is but in preprocessing_funcs it's checking specific pages
def contains_reimbursement(text, page="1", method="keyword"):
"""
Checks if the given text contains any reimbursement-related keywords or patterns.
Args:
text (str or dict): The text to check. If a dictionary is provided, it should have page numbers as keys.
page (str, optional): The page number to check in the dictionary. Defaults to "1".
method (str, optional): The method to use for checking. Options are "keyword"
and "regex". Defaults to "keyword".
Returns:
bool: True if any reimbursement-related keyword or pattern is found, False otherwise.
"""
if method == "keyword":
if isinstance(text, dict):
text_to_check = text.get(page, "").lower()
elif isinstance(text, str):
text_to_check = text.lower()
else:
logging.warning("contains_reimbursement - Invalid data type")
return False
return any(keyword in text_to_check for keyword in reimbursement_strings)
elif method == "regex":
return bool(re.search(reimb_regex, text))
else:
raise ValueError("Invalid method. Choose 'keyword' or 'regex'.")
def count_reimbursements_in_exhibit(exhibit_text: str) -> int: # JUST by regex
"""Counts reimbursements in an exhibit text. Reimbursements are detected by a regex
search as defined by `reimb_regex`.
Args:
exhibit_text (str): Input exhibit text
Returns:
int: Number of reimbursements detected
"""
return len(re.findall(reimb_regex, exhibit_text))
# Overloads for type checking
@overload
def is_empty(value: str, pd_mask: bool = True) -> bool: ...
@overload
def is_empty(value: list, pd_mask: bool = True) -> bool: ...
@overload
def is_empty(value: pd.Series, pd_mask: Literal[True] = True) -> pd.Series: ...
@overload
def is_empty(value: pd.Series, pd_mask: Literal[False]) -> bool: ...
def is_empty(value: str | list | pd.Series, pd_mask: bool = True) -> bool | pd.Series:
"""
Checks if a value is considered empty or invalid.
Args:
value: The value to check.
Can be a string, list, or pandas Series.
pd_mask (bool): Only relevant for Series inputs.
If True, returns a mask for empty values in a pandas Series.
Otherwise, the function returns True iff the entire Series is empty.
Returns:
bool | pd.Series:
- When the input is a string, returns True if the value is empty or invalid, False otherwise.
- When the input is a list, returns True if the list is empty or all elements are empty/invalid.
- When the input is a pandas Series:
- If `pd_mask` is True, returns a boolean mask indicating which elements are empty or invalid.
- If `pd_mask` is False, returns True iff all elements are empty/invalid, or if the entire Series is empty.
"""
empty_values = [None, "", "N/A", "NA", "null", "none", "NaN", np.nan, "nan", "None"]
if isinstance(value, list): # Handle list inputs
return not value or all(is_empty(v) for v in value)
# if it's a pd.Series, return the mask (True for empty values)
if isinstance(value, pd.Series):
if pd_mask:
return (
value.isna()
| (value.isin(empty_values))
| (value.astype(str).str.strip() == "")
| (
value.astype(str)
.str.lower()
.str.strip()
.isin(["n/a", "na", "null", "none", "nan"])
)
)
else:
# If the Series is empty, it's considered empty
if value.empty:
return True
# Use the string-version check for each element in the Series
return all(is_empty(str(v)) for v in value)
if pd.isna(value):
return True
if isinstance(value, str):
if not value or value.isspace():
return True
lower_stripped = value.lower().strip()
if lower_stripped in [
"n/a",
"na",
"null",
"none",
"nan",
"no_identifiers_found",
]:
return True
return value in empty_values
def datetime_str():
return datetime.now().strftime("[%Y-%m-%d %H:%M:%S]")
def extract_signature_page(text_dict: dict, filename: str) -> dict:
"""Extracts the signature page(s) from a contract text dictionary and returns it as a dictionary.
First tries to match the textract marker "this page has N signature(s)", then falls back to looking
for the word "signature" if no matches are found. "this page has N signature(s)" is Textract's marker for signatures
Args:
text_dict (dict): A dictionary containing text data, organized by pages.
filename (str): The name of the file being processed.
Returns:
dict: A dictionary containing the extracted signature page(s).
"""
signature_pages = {}
# This regex pattern matches the Textract marker for signatures
# It will look for both numeric and a few textual representations of numbers (one-ten)
textract_pattern = re.compile(
r"this page has (?:\d+|one|two|three|four|five|six|seven|eight|nine|ten) signature",
re.IGNORECASE,
)
keywords = [
"signature and information",
"signature authorization",
"in witness whereof",
]
# First pass: Try to match the full textract pattern
for page_num, page_text in text_dict.items():
if textract_pattern.search(page_text):
signature_pages[page_num] = page_text
# If no pages found with textract pattern, try with keyword
if not signature_pages:
# Iterate through each page in the text dictionary and check for keywords
for page_num, page_text in text_dict.items():
lower_text = page_text.lower()
if any(keyword in lower_text for keyword in keywords):
signature_pages[page_num] = page_text
# If no signature pages found, try checking for the word "signature page follows" and return the next page only if it exists
if not signature_pages:
for page_num, page_text in text_dict.items():
if "signature page follows" in page_text.lower():
next_page_num = str(int(page_num) + 1)
if next_page_num in text_dict:
signature_pages[next_page_num] = text_dict[next_page_num]
return signature_pages
def extract_effective_date_pages(text_dict: dict, filename: str) -> dict:
"""
Extracts the effective date page(s) from a contract text dictionary and returns it as a dictionary.
This function identifies pages that either contain the Textract marker
"this page has N signature(s)" or the keyword 'effective date:'.
Additionally, it ensures that the first page (page 1) is always included
in the result, as it often contains effective date.
Args:
text_dict (dict): A dictionary containing text data, organized by pages.
filename (str): The name of the file being processed.
Returns:
dict: A dictionary containing the extracted effective date page(s),
where keys are page numbers and values are the corresponding page text.
"""
effective_date_pages = {}
# Regex pattern to match the Textract marker for signature pages which often has effective dates
# This pattern accounts for both numeric and textual representations of numbers (one-ten).
textract_pattern = re.compile(
r"this page has (?:\d+|one|two|three|four|five|six|seven|eight|nine|ten) signature",
re.IGNORECASE,
)
# Iterate through each page in the text dictionary.
for page_num, page_text in text_dict.items():
# page_num = int(float(page_num))
# Check if the page contains the Textract marker or the keyword 'effective date:'.
if textract_pattern.search(page_text) or "effective date:" in page_text.lower():
effective_date_pages[page_num] = page_text
# Always include the first page (page 1) as it often contains effective date.
elif page_num == 1 or page_num == "1" or page_num == "1.0":
effective_date_pages[page_num] = page_text
return effective_date_pages
def normalize_state_to_abbreviation(state_value: str) -> str:
"""Convert state names or abbreviations to standardized two-letter abbreviations.
Handles various formats of state names and abbreviations, including full names,
common abbreviations, and variations. Returns the standardized two-letter abbreviation
or the original value if no match is found.
Args:
state_value (str): State name or abbreviation from extraction
Returns:
str: Two-letter state abbreviation or original value if no match found
"""
if not state_value or state_value in ["N/A", "UNKNOWN"]:
return state_value
# Dictionary mapping full state names to abbreviations
state_mapping = {
"alabama": "AL",
"alaska": "AK",
"arizona": "AZ",
"arkansas": "AR",
"california": "CA",
"colorado": "CO",
"connecticut": "CT",
"delaware": "DE",
"florida": "FL",
"georgia": "GA",
"hawaii": "HI",
"idaho": "ID",
"illinois": "IL",
"indiana": "IN",
"iowa": "IA",
"kansas": "KS",
"kentucky": "KY",
"louisiana": "LA",
"maine": "ME",
"maryland": "MD",
"massachusetts": "MA",
"michigan": "MI",
"minnesota": "MN",
"mississippi": "MS",
"missouri": "MO",
"montana": "MT",
"nebraska": "NE",
"nevada": "NV",
"new hampshire": "NH",
"new jersey": "NJ",
"new mexico": "NM",
"new york": "NY",
"north carolina": "NC",
"north dakota": "ND",
"ohio": "OH",
"oklahoma": "OK",
"oregon": "OR",
"pennsylvania": "PA",
"rhode island": "RI",
"south carolina": "SC",
"south dakota": "SD",
"tennessee": "TN",
"texas": "TX",
"utah": "UT",
"vermont": "VT",
"virginia": "VA",
"washington": "WA",
"west virginia": "WV",
"wisconsin": "WI",
"wyoming": "WY",
"district of columbia": "DC",
}
# Clean and normalize the input
cleaned_state = state_value.strip().lower()
# Check if it's already a valid 2-letter abbreviation
if len(cleaned_state) == 2 and cleaned_state.upper() in state_mapping.values():
return cleaned_state.upper()
# Check if it matches a full state name
if cleaned_state in state_mapping:
return state_mapping[cleaned_state]
# Handle common variations
variations = {
"calif": "CA",
"cal": "CA",
"fla": "FL",
"florida": "FL",
"ny": "NY",
"n.y.": "NY",
"tx": "TX",
"tex": "TX",
"penn": "PA",
"pa": "PA",
"mass": "MA",
"massachusetts": "MA",
"wash": "WA",
"washington state": "WA",
"d.c.": "DC",
"dc": "DC",
"washington dc": "DC",
}
if cleaned_state in variations:
return variations[cleaned_state]
# If no match found, return original value
return state_value
def flatten_to_strings(items) -> list[str]:
"""Helper function to flatten and stringify any nested structure
Args:
items (Any): The input items; can be a list, tuple, or single value
Returns:
list[str]: A flattened list of stringified items
"""
result = []
if isinstance(items, (list, tuple)):
for item in items:
if isinstance(item, (list, tuple)):
result.extend(flatten_to_strings(item))
else:
result.append(str(item).strip())
else:
result.append(str(items).strip())
return result
def parse_state_field_to_list(raw_value):
"""
Convert PROVIDER_STATE raw input into a clean list of state strings.
Accepts:
- list → returned as-is
- JSON list string → parsed to list
- comma-separated string → split to list
- single string → wrapped in list
- other types → converted to string → wrapped in list
Returns:
list[str]: Clean list of state strings
"""
if raw_value is None:
return []
# Case 1: Already a list
if isinstance(raw_value, list):
return raw_value
# Case 2: String input
if isinstance(raw_value, str):
stripped = raw_value.strip()
# JSON list string
if stripped.startswith("[") and stripped.endswith("]"):
try:
parsed = json.loads(stripped)
if isinstance(parsed, list):
return parsed
else:
return [parsed]
except Exception as e:
logging.debug(
f"Failed JSON parse for state list: {e}. Falling back to comma split."
)
# fallback: treat as comma-separated
# Comma-separated list
if "," in stripped:
return [s.strip() for s in stripped.split(",") if s.strip()]
# Single state string
return [stripped]
# Case 3: Any other type (int, dict, etc.)
try:
return [str(raw_value).strip()]
except Exception:
logging.error(f"Could not convert value to string: {raw_value!r}")
return []
def normalize_state_field(answers_dict: dict, field_name: str = "PROVIDER_STATE"):
if field_name not in answers_dict or not answers_dict[field_name]:
answers_dict[field_name] = []
return answers_dict
raw_value = answers_dict[field_name]
# ---- Step 1: Parse into list ----
state_list = parse_raw_state_value(raw_value)
# ---- Step 2: Normalize each state ----
normalized_states = []
for state in state_list:
try:
normalized_states.append(normalize_state_to_abbreviation(state))
except Exception as e:
logging.warning(
f"Failed to normalize state '{state}': {e}. Keeping original."
)
normalized_states.append(state)
answers_dict[field_name] = normalized_states
return answers_dict
def parse_raw_state_value(raw_value):
"""
Parses various input formats into a flat list of state strings.
Handles: strings, lists, nested lists, and various delimiters.
"""
if not raw_value:
return []
# If it's a string that looks like a list, try to parse it
if isinstance(raw_value, str):
stripped = raw_value.strip()
# Try to parse as Python literal (handles "[['NC', 'WA'], 'CA|UT']")
if stripped.startswith("["):
try:
parsed = ast.literal_eval(stripped)
return parse_raw_state_value(parsed) # Recursive call with parsed list
except (ValueError, SyntaxError):
pass # Not valid Python literal, continue with string parsing
# Regular string splitting on delimiters (not whitespace - state names can have spaces like "West Virginia")
states = re.split(r"[|,;]+", stripped)
return [s.strip() for s in states if s.strip()]
if isinstance(raw_value, list):
result = []
for item in raw_value:
result.extend(parse_raw_state_value(item)) # Recursive call
return result
return [str(raw_value)]
def normalize_to_json_list(val):
"""
Normalizes a value to a JSON list format.
Handles: pipe-delimited strings, string representations of Python lists,
actual lists with pipe-delimited items, and single values.
"""
# Handle None and empty values first
if is_empty(val):
return val
# Handle pandas NA/NaN - check for scalar first
try:
if pd.isna(val):
return val
except (ValueError, TypeError):
# pd.isna fails on arrays/lists, which is fine - we'll handle them below
pass
if isinstance(val, str):
# Check if it's a string representation of a Python list (e.g., "['item1', 'item2']")
if val.startswith("[") and val.endswith("]"):
try:
# Try JSON parse first
parsed = json.loads(val)
except json.JSONDecodeError:
# If JSON fails, try ast.literal_eval for Python list syntax
try:
parsed = ast.literal_eval(val)
except (ValueError, SyntaxError):
return val
if isinstance(parsed, list):
expanded = []
for item in parsed:
if isinstance(item, str) and "|" in item:
expanded.extend(item.split("|"))
else:
expanded.append(item)
result = json.dumps(expanded)
return result
return val
# Pipe-delimited format - convert to JSON list
if "|" in val:
result = json.dumps(val.split("|"))
return result
# Single value - convert to JSON list
result = json.dumps([val])
return result
if isinstance(val, list):
# Check if list contains pipe-delimited strings and expand them
expanded = []
for item in val:
if isinstance(item, str) and "|" in item:
expanded.extend(item.split("|"))
else:
expanded.append(item)
result = json.dumps(expanded)
return result
return val