Merged in bugfix/multi-tin-npi-fix (pull request #699)
Bugfix/multi tin npi fix * Add debug logging for OCR candidates and provider info extraction * Update TIN and NPI extraction instructions to handle multiple values in a single record * Add validation layer to cross-check TINs and NPIs with regex findings in provider info extraction * Enhance exact match extraction by normalizing identifiers and validating lengths for TIN and NPI Approved-by: Katon Minhas
This commit is contained in:
Alex Galarce
@@ -46,15 +46,25 @@ def get_all_matches_with_ocr(
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results = []
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seen_values = set()
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# Get exact matches
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# Get exact matches and normalize them
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exact_matches = get_all_matches(text, exact_pattern)
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for match in exact_matches:
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results.append((match, "EXACT"))
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seen_values.add(match)
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# Normalize: remove hyphens/dots, keep only digits
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normalized = "".join(c for c in match if c.isdigit())
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# Validate length based on identifier type
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if identifier_type == "TIN":
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expected_length = 9
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elif identifier_type == "NPI":
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expected_length = 10
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if len(normalized) == expected_length and normalized not in seen_values:
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results.append((normalized, "EXACT"))
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seen_values.add(normalized)
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# Get OCR-correctable candidates (even if we found exact matches)
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ocr_candidates = get_all_matches(text, ocr_pattern)
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for candidate in ocr_candidates:
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logging.debug(f"OCR candidate for {identifier_type}: {candidate}")
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corrected, is_valid = attempt_ocr_correction(candidate, identifier_type)
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if is_valid and corrected not in seen_values:
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results.append((corrected, "OCR_CORRECTED"))
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@@ -478,9 +488,14 @@ def get_provider_info(text_dict: dict, page_num: str, filename: str) -> list:
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prompt, "legacy_sonnet", filename, max_tokens=8192
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) # Sometimes rosters can have 50+ provider
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logging.debug(
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f"Raw LLM response for provider info on page {page_num}: {claude_answer_raw}"
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)
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# Defensive programming for any json parsing errors
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try:
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providers = string_utils.universal_json_load(claude_answer_raw)
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logging.debug(f"Parsed provider info on page {page_num}: {providers}")
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# Ensure we have a list of providers (not a dict or other type)
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if isinstance(providers, dict):
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# Single provider returned as dict - wrap in list
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@@ -499,6 +514,56 @@ def get_provider_info(text_dict: dict, page_num: str, filename: str) -> list:
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}
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]
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# VALIDATION LAYER - Cross-check with regex findings
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page_text = text_dict[page_num]
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# Get all TINs and NPIs found via regex on this page
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regex_tins = [
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tin
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for tin, _ in get_all_matches_with_ocr(
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page_text,
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regex_patterns.TIN_PATTERN,
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regex_patterns.TIN_OCR_PATTERN,
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identifier_type="TIN",
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)
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]
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regex_npis = [
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npi
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for npi, _ in get_all_matches_with_ocr(
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page_text,
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regex_patterns.NPI_PATTERN,
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regex_patterns.NPI_OCR_PATTERN,
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identifier_type="NPI",
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)
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]
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# Collect all TINs/NPIs that LLM found
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llm_found_tins = []
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llm_found_npis = []
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for provider in providers:
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if provider.get("TIN") and provider["TIN"] != "UNKNOWN":
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# Normalize for comparison (remove hyphens/dots)
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raw_tins = [tin.strip() for tin in provider["TIN"].split("|")]
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normalized_tins = [
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tin.replace("-", "").replace(".", "") for tin in raw_tins
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]
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llm_found_tins.extend(normalized_tins)
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if provider.get("NPI") and provider["NPI"] != "UNKNOWN":
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raw_npis = [npi.strip() for npi in provider["NPI"].split("|")]
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normalized_npis = [
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npi.replace("-", "").replace(".", "") for npi in raw_npis
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]
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llm_found_npis.extend(normalized_npis)
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# Note: regex_tins and regex_npis are already normalized in get_all_matches_with_ocr
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missed_tins = [tin for tin in regex_tins if tin not in llm_found_tins]
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missed_npis = [npi for npi in regex_npis if npi not in llm_found_npis]
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if missed_tins or missed_npis:
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# WARN ONLY if there are missed identifiers
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logging.warning(
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f"LLM missed identifiers on page {page_num} of {filename}. Missed TINs: {missed_tins}, Missed NPIs: {missed_npis}"
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)
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# Add IS_GROUP flag based on ON_SIGNATURE_PAGE and page_num
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for provider in providers:
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if float(page_num) <= 2.0 or provider.get("ON_SIGNATURE_PAGE", "N") == "Y":
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@@ -553,7 +618,7 @@ def run_provider_info_fields(
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npi_matches = get_all_matches_with_ocr(
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contract_text,
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regex_patterns.NPI_PATTERN,
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regex_patterns.NPI_OCR_PATTERN, # no OCR correction for NPI right now
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regex_patterns.NPI_OCR_PATTERN,
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identifier_type="NPI",
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)
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@@ -561,6 +626,9 @@ def run_provider_info_fields(
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all_tins = [tin for tin, quality in tin_matches]
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all_npis = [npi for npi, quality in npi_matches]
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logging.debug(f"Extracted TINs: {all_tins}")
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logging.debug(f"Extracted NPIs: {all_npis}")
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# Log OCR corrections for monitoring
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exact_tins = [tin for tin, quality in tin_matches if quality == "EXACT"]
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ocr_corrected_tins = [
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@@ -2,7 +2,6 @@ import json
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from functools import cache
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import pandas as pd
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import src.utils.llm_utils as llm_utils
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from constants.delimiters import Delimiter
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from src.utils.string_utils import extract_text_from_delimiters
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@@ -840,6 +839,12 @@ IMPORTANT DISTINCTION:
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For EACH provider entity found (group or individual practitioner), extract:
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{questions}
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CRITICAL: If a provider has multiple TINs or NPIs, include ALL of them separated by pipes (|) in the same provider record. Do NOT create separate records for the same provider entity.
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Examples:
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- If you see "NPI: 1083087266 and 1396117412", return "NPI": "1083087266|1396117412"
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- If you see "Tax ID: 12-3456789, also 98-7654321", return "TIN": "123456789|987654321"
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Briefly explain your reasoning. After your explanation, include the heading "FINAL PROVIDER INFO:" followed by a properly formatted JSON array with your final output.
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[EXTRACTION AND FORMATTING INSTRUCTIONS]
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@@ -1206,8 +1211,3 @@ Here is the term to analyze:
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Briefly explain your answer, then put your final answer in JSON dictionary format.
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"""
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