black format

src/codes/code_funcs.py

@@ -141,9 +141,9 @@ def code_category(service, proc_category, hcpcs_level2_mapping, filename):
             for key, value in hcpcs_level2_mapping.items()
             if key.startswith(category)
         ]
-    
+
     prompt, _parser = prompt_templates.CODE_CATEGORY(service, matching_values)
-    
+
     llm_answer_raw = llm_utils.invoke_claude(
         prompt,
         "sonnet_latest",
@@ -151,9 +151,7 @@ def code_category(service, proc_category, hcpcs_level2_mapping, filename):
         cache=True,
         instruction=prompt_templates.CODE_CATEGORY_INSTRUCTION(),
     )
-    llm_answer_final = _parser(
+    llm_answer_final = _parser(llm_answer_raw)  # Returns list
-        llm_answer_raw
-    )  # Returns list
 
     code_answer_dict = {"PROCEDURE_CD": [], "PROCEDURE_CD_DESC": []}
     for answer in llm_answer_final:
@@ -355,8 +353,10 @@ def code_implicit_rag(service, implicit_run_dict, filename, constants):
             )
         )
 
-        prompt, _parser = prompt_templates.CODE_IMPLICIT(service, level_dict["match_list"])
+        prompt, _parser = prompt_templates.CODE_IMPLICIT(
-        
+            service, level_dict["match_list"]
+        )
+
         # Run Prompt
         llm_answer_raw = llm_utils.invoke_claude(
             prompt,
@@ -458,7 +458,6 @@ def code_last_check(service, filename):
         return "Generic"
 
 
-
 def fill_bill_type(
     service,
     answer_dict,
@@ -488,7 +487,6 @@ def fill_bill_type(
         service, valid_bill_type, reimb_term=reimb_term, exhibit_text=None
     )
 
-
     # First attempt: Service term + reimbursement term
     llm_answer_raw = llm_utils.invoke_claude(
         prompt,

src/document_classification/main.py

@@ -314,7 +314,7 @@ if __name__ == "__main__":
         level=logging.INFO,
         format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
     )
-    
+
     # Suppress AWS SDK logging
     logging.getLogger("botocore").setLevel(logging.WARNING)
     logging.getLogger("boto3").setLevel(logging.WARNING)

src/pipelines/clients/bcbs_promise/prompts/prompt_calls.py

@@ -5,7 +5,9 @@ import src.prompts.prompt_templates as prompt_templates
 from src.constants.constants import Constants
 from src.prompts.fieldset import Field, FieldSet
 from src.utils import llm_utils, string_utils, timing_utils, rag_utils
-from src.pipelines.shared.preprocessing.hybrid_smart_chunking_funcs import format_chunks_for_llm
+from src.pipelines.shared.preprocessing.hybrid_smart_chunking_funcs import (
+    format_chunks_for_llm,
+)
 import json
 
 
@@ -74,7 +76,9 @@ def prompt_exhibit_level_breakout(
 def prompt_dynamic_primary(
     exhibit_text: str, field: Field, constants: Constants, filename: str, TEMPLATE
 ):
-    prompt, _parser = TEMPLATE(exhibit_text, field.field_name, field.get_prompt(constants))
+    prompt, _parser = TEMPLATE(
+        exhibit_text, field.field_name, field.get_prompt(constants)
+    )
     logging.debug(f"Dynamic primary prompt for {filename}; {field}: {prompt}")
     llm_answer_raw = llm_utils.invoke_claude(
         prompt,
@@ -284,7 +288,9 @@ def prompt_lob_relationship(
         f"LOB: {answer_dict.get("AARETE_DERIVED_LOB")}\nPROGRAM: {programs}"
     )
 
-    prompt, _parser = prompt_templates.LOB_RELATIONSHIP(exhibit_text, dynamic_primary_values)
+    prompt, _parser = prompt_templates.LOB_RELATIONSHIP(
+        exhibit_text, dynamic_primary_values
+    )
     llm_answer_raw = llm_utils.invoke_claude(
         prompt,
         model_id="sonnet_latest",
@@ -292,9 +298,7 @@ def prompt_lob_relationship(
         cache=True,
         instruction=prompt_templates.LOB_RELATIONSHIP_INSTRUCTION(),
     )
-    llm_answer_final = _parser(
+    llm_answer_final = _parser(llm_answer_raw)
-        llm_answer_raw
-    )
     return llm_answer_final
 
 
@@ -320,7 +324,7 @@ def prompt_special_case_assignment(
             ]
         )
         return answer_dict
-    
+
     prompt, _parser = prompt_templates.SPECIAL_CASE_ASSIGNMENT(
         exhibit_text, reimbursement_dict, special_case_dicts, special_case_term
     )
@@ -334,9 +338,7 @@ def prompt_special_case_assignment(
         instruction=prompt_templates.SPECIAL_CASE_ASSIGNMENT_INSTRUCTION(),
         usage_label="SPECIAL_CASE_ASSIGNMENT",
     )
-    index_answer = _parser(
+    index_answer = _parser(llm_answer_raw)
-        llm_answer_raw
-    )
     try:
         if not string_utils.is_empty(index_answer):
             return special_case_dicts[int(index_answer)]
@@ -405,7 +407,9 @@ def validate_reimbursements_for_llm(answer_dict: dict[str, str], filename: str)
         return False
 
     service_term, reimb_term = answer_dict["SERVICE_TERM"], answer_dict["REIMB_TERM"]
-    prompt, _parser = prompt_templates.VALIDATE_REIMBURSEMENTS_PROMPT(service_term, reimb_term)
+    prompt, _parser = prompt_templates.VALIDATE_REIMBURSEMENTS_PROMPT(
+        service_term, reimb_term
+    )
     logging.debug(f"Prompt for reimbursement validation in {filename}:\n{prompt}")
 
     llm_response = llm_utils.invoke_claude(
@@ -464,9 +468,7 @@ def prompt_exhibit_linkage(page_header, previous_header, filename):
     """
     prompt, _parser = prompt_templates.EXHIBIT_LINKAGE(page_header, previous_header)
     claude_answer_raw = llm_utils.invoke_claude(prompt, "legacy_sonnet", filename)
-    exhibit_is_different = _parser(
+    exhibit_is_different = _parser(claude_answer_raw)
-        claude_answer_raw
-    )
     return exhibit_is_different
 
 
@@ -494,9 +496,7 @@ def prompt_exhibit_header(page_content, filename):
         instruction=prompt_templates.EXHIBIT_HEADER_INSTRUCTION(),
         usage_label="EXHIBIT_HEADER",
     )
-    llm_answer_extracted = _parser(
+    llm_answer_extracted = _parser(llm_answer_raw)
-        llm_answer_raw
-    )
     return llm_answer_extracted
 
 
@@ -665,9 +665,7 @@ def prompt_lesser_of_distribution(
         )
         return reimb_term  # Return original term unchanged
     else:
-        llm_answer_final = _parser(
+        llm_answer_final = _parser(llm_answer_raw)
-            llm_answer_raw
-        )
 
         logging.debug(
             f"Applied lesser-of to '{service_term}' on page {page_num}: "
@@ -702,7 +700,9 @@ def prompt_lesser_of_check(
         f"LESSER_OF_CHECK input: service='{service_term[:50]}...', reimb_term='{reimb_term[:100]}...'"
     )
 
-    prompt, _parser = prompt_templates.LESSER_OF_CHECK(service_term, reimb_term, exhibit_title)
+    prompt, _parser = prompt_templates.LESSER_OF_CHECK(
+        service_term, reimb_term, exhibit_title
+    )
     llm_answer_raw = llm_utils.invoke_claude(
         prompt,
         "sonnet_latest",
@@ -714,9 +714,7 @@ def prompt_lesser_of_check(
 
     try:
         # Extract JSON from pipes
-        llm_answer_final = _parser(
+        llm_answer_final = _parser(llm_answer_raw)
-            llm_answer_raw
-        )
 
         logging.debug(f"LESSER_OF_CHECK extracted from pipes: {llm_answer_final}")
 
@@ -790,9 +788,7 @@ def prompt_exhibit_title_match(
     llm_answer_raw = llm_utils.invoke_claude(prompt, "sonnet_latest", filename)
 
     try:
-        llm_answer_final = _parser(
+        llm_answer_final = _parser(llm_answer_raw)
-            llm_answer_raw
-        )
         return llm_answer_final.strip().upper()  # Return "YES" or "NO" as string
     except:
         return "NO"  # Default to "NO" string
@@ -811,9 +807,11 @@ def provider_name_match_check(
         return False
 
     # Create prompt for LLM
-    CHECK_PROVIDER_NAME_MATCH_PROMPT, _parser = prompt_templates.CHECK_PROVIDER_NAME_MATCH_PROMPT(
+    CHECK_PROVIDER_NAME_MATCH_PROMPT, _parser = (
-        provider_name=provider_name,
+        prompt_templates.CHECK_PROVIDER_NAME_MATCH_PROMPT(
-        hybrid_smart_chunking_provider_group_name=hybrid_smart_chunking_provider_group_name,
+            provider_name=provider_name,
+            hybrid_smart_chunking_provider_group_name=hybrid_smart_chunking_provider_group_name,
+        )
     )
 
     try:

src/pipelines/clients/clover/prompts/prompt_calls.py

@@ -5,7 +5,9 @@ import src.prompts.prompt_templates as prompt_templates
 from src.constants.constants import Constants
 from src.prompts.fieldset import Field, FieldSet
 from src.utils import llm_utils, string_utils, timing_utils, rag_utils
-from src.pipelines.shared.preprocessing.hybrid_smart_chunking_funcs import format_chunks_for_llm
+from src.pipelines.shared.preprocessing.hybrid_smart_chunking_funcs import (
+    format_chunks_for_llm,
+)
 import json
 
 
@@ -81,7 +83,9 @@ def prompt_exhibit_level_breakout(
 def prompt_dynamic_primary(
     exhibit_text: str, field: Field, constants: Constants, filename: str, TEMPLATE
 ):
-    prompt, _parser = TEMPLATE(exhibit_text, field.field_name, field.get_prompt(constants))
+    prompt, _parser = TEMPLATE(
+        exhibit_text, field.field_name, field.get_prompt(constants)
+    )
     logging.debug(f"Dynamic primary prompt for {filename}; {field}: {prompt}")
     llm_answer_raw = llm_utils.invoke_claude(
         prompt,
@@ -91,9 +95,7 @@ def prompt_dynamic_primary(
         instruction=prompt_templates.DYNAMIC_PRIMARY_INSTRUCTION(),
     )
     logging.debug(f"Claude answer for {filename}; {field}: {llm_answer_raw}")
-    llm_answer_final = _parser(
+    llm_answer_final = _parser(llm_answer_raw)
-        llm_answer_raw
-    )
     return llm_answer_final
 
 
@@ -245,9 +247,7 @@ def prompt_carveout_check(
         usage_label="CARVEOUT_CHECK",
     )
 
-    carveout_answer = _parser(
+    carveout_answer = _parser(llm_answer_raw)
-        llm_answer_raw
-    )
     return carveout_answer
 
 
@@ -295,7 +295,9 @@ def prompt_lob_relationship(
         f"LOB: {answer_dict.get("AARETE_DERIVED_LOB")}\nPROGRAM: {programs}"
     )
 
-    prompt, _parser = prompt_templates.LOB_RELATIONSHIP(exhibit_text, dynamic_primary_values)
+    prompt, _parser = prompt_templates.LOB_RELATIONSHIP(
+        exhibit_text, dynamic_primary_values
+    )
     llm_answer_raw = llm_utils.invoke_claude(
         prompt,
         model_id="sonnet_latest",
@@ -303,9 +305,7 @@ def prompt_lob_relationship(
         cache=True,
         instruction=prompt_templates.LOB_RELATIONSHIP_INSTRUCTION(),
     )
-    llm_answer_final = _parser(
+    llm_answer_final = _parser(llm_answer_raw)
-        llm_answer_raw
-    )
     return llm_answer_final
 
 
@@ -331,7 +331,7 @@ def prompt_special_case_assignment(
             ]
         )
         return answer_dict
-    
+
     prompt, _parser = prompt_templates.SPECIAL_CASE_ASSIGNMENT(
         exhibit_text, reimbursement_dict, special_case_dicts, special_case_term
     )
@@ -345,9 +345,7 @@ def prompt_special_case_assignment(
         instruction=prompt_templates.SPECIAL_CASE_ASSIGNMENT_INSTRUCTION(),
         usage_label="SPECIAL_CASE_ASSIGNMENT",
     )
-    index_answer = _parser(
+    index_answer = _parser(llm_answer_raw)
-        llm_answer_raw
-    )
     try:
         if not string_utils.is_empty(index_answer):
             return special_case_dicts[int(index_answer)]
@@ -435,7 +433,9 @@ def validate_reimbursements_for_llm(answer_dict: dict[str, str], filename: str)
         return False
 
     service_term, reimb_term = answer_dict["SERVICE_TERM"], answer_dict["REIMB_TERM"]
-    prompt, _parser = prompt_templates.VALIDATE_REIMBURSEMENTS_PROMPT(service_term, reimb_term)
+    prompt, _parser = prompt_templates.VALIDATE_REIMBURSEMENTS_PROMPT(
+        service_term, reimb_term
+    )
     logging.debug(f"Prompt for reimbursement validation in {filename}:\n{prompt}")
 
     llm_response = llm_utils.invoke_claude(
@@ -448,11 +448,7 @@ def validate_reimbursements_for_llm(answer_dict: dict[str, str], filename: str)
     logging.debug(
         f"LLM response for reimbursement validation in {filename}:\n{llm_response}"
     )
-    final_answer = (
+    final_answer = _parser(llm_response).strip().upper()
-        _parser(llm_response)
-        .strip()
-        .upper()
-    )
     return final_answer == "YES"
 
 
@@ -505,9 +501,7 @@ def prompt_exhibit_linkage(page_header, previous_header, filename):
         instruction=prompt_templates.EXHIBIT_LINKAGE_INSTRUCTION(),
         usage_label="EXHIBIT_LINKAGE",
     )
-    exhibit_is_different = _parser(
+    exhibit_is_different = _parser(claude_answer_raw)
-        claude_answer_raw
-    )
     return exhibit_is_different
 
 
@@ -535,9 +529,7 @@ def prompt_exhibit_header(page_content, filename):
         instruction=prompt_templates.EXHIBIT_HEADER_INSTRUCTION(),
         usage_label="EXHIBIT_HEADER",
     )
-    llm_answer_extracted = _parser(
+    llm_answer_extracted = _parser(llm_answer_raw)
-        llm_answer_raw
-    )
     return llm_answer_extracted
 
 
@@ -713,9 +705,7 @@ def prompt_lesser_of_distribution(
         )
         return reimb_term  # Return original term unchanged
     else:
-        llm_answer_final = _parser(
+        llm_answer_final = _parser(llm_answer_raw)
-            llm_answer_raw
-        )
 
         logging.debug(
             f"Applied lesser-of to '{service_term}' on page {page_num}: "
@@ -750,7 +740,9 @@ def prompt_lesser_of_check(
         f"LESSER_OF_CHECK input: service='{service_term[:50]}...', reimb_term='{reimb_term[:100]}...'"
     )
 
-    prompt, _parser = prompt_templates.LESSER_OF_CHECK(service_term, reimb_term, exhibit_title)
+    prompt, _parser = prompt_templates.LESSER_OF_CHECK(
+        service_term, reimb_term, exhibit_title
+    )
     llm_answer_raw = llm_utils.invoke_claude(
         prompt,
         "sonnet_latest",
@@ -761,9 +753,7 @@ def prompt_lesser_of_check(
     )
     try:
         # Extract JSON from pipes
-        llm_answer_final = _parser(
+        llm_answer_final = _parser(llm_answer_raw)
-            llm_answer_raw
-        )
 
         logging.debug(f"LESSER_OF_CHECK extracted from pipes: {llm_answer_final}")
 
@@ -844,9 +834,7 @@ def prompt_exhibit_title_match(
     )
 
     try:
-        llm_answer_final = _parser(
+        llm_answer_final = _parser(llm_answer_raw)
-            llm_answer_raw
-        )
         return llm_answer_final.strip().upper()  # Return "YES" or "NO" as string
     except:
         return "NO"  # Default to "NO" string
@@ -865,9 +853,11 @@ def provider_name_match_check(
         return False
 
     # Create prompt for LLM
-    CHECK_PROVIDER_NAME_MATCH_PROMPT, _parser = prompt_templates.CHECK_PROVIDER_NAME_MATCH_PROMPT(
+    CHECK_PROVIDER_NAME_MATCH_PROMPT, _parser = (
-        provider_name=provider_name,
+        prompt_templates.CHECK_PROVIDER_NAME_MATCH_PROMPT(
-        hybrid_smart_chunking_provider_group_name=hybrid_smart_chunking_provider_group_name,
+            provider_name=provider_name,
+            hybrid_smart_chunking_provider_group_name=hybrid_smart_chunking_provider_group_name,
+        )
     )
 
     try:
@@ -905,4 +895,4 @@ def provider_name_match_check(
         logging.error(
             f"[is_provider_name_match_llm] Error calling LLM: {e}, defaulting to False"
         )
-        return False
+        return False

src/pipelines/saas/prompts/prompt_calls.py

@@ -5,7 +5,9 @@ import src.prompts.prompt_templates as prompt_templates
 from src.constants.constants import Constants
 from src.prompts.fieldset import Field, FieldSet
 from src.utils import llm_utils, string_utils, timing_utils, rag_utils
-from src.pipelines.shared.preprocessing.hybrid_smart_chunking_funcs import format_chunks_for_llm
+from src.pipelines.shared.preprocessing.hybrid_smart_chunking_funcs import (
+    format_chunks_for_llm,
+)
 import json
 
 
@@ -80,7 +82,9 @@ def prompt_exhibit_level_breakout(
 def prompt_dynamic_primary(
     exhibit_text: str, field: Field, constants: Constants, filename: str, TEMPLATE
 ):
-    prompt, _parser = TEMPLATE(exhibit_text, field.field_name, field.get_prompt(constants))
+    prompt, _parser = TEMPLATE(
+        exhibit_text, field.field_name, field.get_prompt(constants)
+    )
     logging.debug(f"Dynamic primary prompt for {filename}; {field}: {prompt}")
     llm_answer_raw = llm_utils.invoke_claude(
         prompt,
@@ -91,7 +95,7 @@ def prompt_dynamic_primary(
     )
     logging.debug(f"Claude answer for {filename}; {field}: {llm_answer_raw}")
     llm_answer_final = _parser(llm_answer_raw)
-    
+
     return llm_answer_final
 
 
@@ -291,7 +295,9 @@ def prompt_lob_relationship(
         f"LOB: {answer_dict.get("AARETE_DERIVED_LOB")}\nPROGRAM: {programs}"
     )
 
-    prompt, _parser = prompt_templates.LOB_RELATIONSHIP(exhibit_text, dynamic_primary_values)
+    prompt, _parser = prompt_templates.LOB_RELATIONSHIP(
+        exhibit_text, dynamic_primary_values
+    )
     llm_answer_raw = llm_utils.invoke_claude(
         prompt,
         model_id="sonnet_latest",
@@ -325,7 +331,7 @@ def prompt_special_case_assignment(
             ]
         )
         return answer_dict
-    
+
     prompt, _parser = prompt_templates.SPECIAL_CASE_ASSIGNMENT(
         exhibit_text, reimbursement_dict, special_case_dicts, special_case_term
     )
@@ -407,7 +413,9 @@ def validate_reimbursements_for_llm(answer_dict: dict[str, str], filename: str)
         return False
 
     service_term, reimb_term = answer_dict["SERVICE_TERM"], answer_dict["REIMB_TERM"]
-    prompt, _parser = prompt_templates.VALIDATE_REIMBURSEMENTS_PROMPT(service_term, reimb_term)
+    prompt, _parser = prompt_templates.VALIDATE_REIMBURSEMENTS_PROMPT(
+        service_term, reimb_term
+    )
     logging.debug(f"Prompt for reimbursement validation in {filename}:\n{prompt}")
 
     llm_response = llm_utils.invoke_claude(
@@ -474,7 +482,7 @@ def prompt_exhibit_linkage(page_header, previous_header, filename):
         usage_label="EXHIBIT_LINKAGE",
     )
     exhibit_is_different = _parser(claude_answer_raw)
-    
+
     return exhibit_is_different
 
 
@@ -713,7 +721,9 @@ def prompt_lesser_of_check(
         f"LESSER_OF_CHECK input: service='{service_term}...', reimb_term='{reimb_term}...'"
     )
 
-    prompt, _parser = prompt_templates.LESSER_OF_CHECK(service_term, reimb_term, exhibit_title)
+    prompt, _parser = prompt_templates.LESSER_OF_CHECK(
+        service_term, reimb_term, exhibit_title
+    )
     llm_answer_raw = llm_utils.invoke_claude(
         prompt,
         "sonnet_latest",
@@ -820,9 +830,11 @@ def provider_name_match_check(
         return False
 
     # Create prompt for LLM
-    CHECK_PROVIDER_NAME_MATCH_PROMPT, _parser = prompt_templates.CHECK_PROVIDER_NAME_MATCH_PROMPT(
+    CHECK_PROVIDER_NAME_MATCH_PROMPT, _parser = (
-        provider_name=provider_name,
+        prompt_templates.CHECK_PROVIDER_NAME_MATCH_PROMPT(
-        hybrid_smart_chunking_provider_group_name=hybrid_smart_chunking_provider_group_name,
+            provider_name=provider_name,
+            hybrid_smart_chunking_provider_group_name=hybrid_smart_chunking_provider_group_name,
+        )
     )
 
     try:
@@ -862,6 +874,7 @@ def provider_name_match_check(
         )
         return False
 
+
 def prompt_split_reimb_dates(date_range: str, filename: str) -> dict:
     prompt, _parser = prompt_templates.SPLIT_REIMB_DATES(date_range)
 
@@ -879,4 +892,3 @@ def prompt_split_reimb_dates(date_range: str, filename: str) -> dict:
     # Extract the effective and termination dates from the LLM output
     llm_answer_final = _parser(llm_answer_raw)
     return llm_answer_final
-

src/pipelines/saas/test.py

@@ -1,4 +1,3 @@
-
 from src.pipelines.shared.postprocessing import aarete_derived
 from src.constants.constants import Constants
 from src.utils import io_utils
@@ -9,10 +8,12 @@ from src.pipelines.shared.extraction.one_to_n_funcs import reimbursement_level
 
 service_term = "Covered Services provided to KIDSfirst Members"
 reimb_term = "One hundred percent (100%) of the prevailing yearly and current Medicaid fee schedule for the State of Texas or the Participating Ancillary Services Provider's usual and customary charge, whichever is less."
-exhibit_title = 'Attachment B SECTION 1 - PCHP MEDICAID STAR HMO SERVICES'
+exhibit_title = "Attachment B SECTION 1 - PCHP MEDICAID STAR HMO SERVICES"
 filename = ""
 
 
-lesser_of_answer_dict = prompt_calls.prompt_lesser_of_check(service_term, reimb_term, exhibit_title, filename)
+lesser_of_answer_dict = prompt_calls.prompt_lesser_of_check(
+    service_term, reimb_term, exhibit_title, filename
+)
 
 print(lesser_of_answer_dict)

src/pipelines/shared/extraction/dynamic_funcs.py

@@ -64,14 +64,13 @@ def dynamic_primary(
                 if has_medicare and has_medicaid:
                     if "duals" not in values_lower:
                         if exhibit_text_answer:
-                            exhibit_text_answer.append("Duals") 
+                            exhibit_text_answer.append("Duals")
                         else:
                             exhibit_text_answer = ["Duals"]
                         logging.debug(
                             f"Added 'Duals' to LOB valid values because both Medicare and Medicaid were detected"
                         )
 
-
             field.update_valid_values(exhibit_text_answer + ["N/A"])
             dynamic_reimbursement_fields.add_field(field)
             dynamic_primary_fields.remove_field(field.field_name)

src/pipelines/shared/extraction/one_to_n_funcs.py

@@ -97,7 +97,7 @@ def reimbursement_level(
         reimbursement_primary_answers = clean_reimbursement_primary(
             reimbursement_primary_answers, constants, filename
         )
-    
+
     return reimbursement_primary_answers
 
 
@@ -675,7 +675,9 @@ def split_reimb_dates(one_to_n_results: list, filename: str) -> list:
         date_range = str(record["REIMB_DATES"]).strip()
 
         try:
-            llm_answer_final = prompt_calls.prompt_split_reimb_dates(date_range, filename)
+            llm_answer_final = prompt_calls.prompt_split_reimb_dates(
+                date_range, filename
+            )
             # Update the specific record with parsed dates
             if llm_answer_final and isinstance(llm_answer_final, dict):
                 if "start_date" in llm_answer_final:
@@ -862,7 +864,9 @@ def lesser_of_distribution(
 
             # STANDALONE: Complete rate - ADD to results
             elif scope == "STANDALONE":
-                logging.debug(f"STANDALONE rate from page {page_num} - ADDED to results")
+                logging.debug(
+                    f"STANDALONE rate from page {page_num} - ADDED to results"
+                )
                 final_reimbursement_level_answers.append(
                     answer_dict
                 )  # ← Only this gets added

src/pipelines/shared/extraction/tin_npi_funcs.py

@@ -203,6 +203,7 @@ def clean_provider_info(provider_info: list[dict]) -> list[dict]:
         cleaned_providers.append(cleaned_provider)
     return cleaned_providers
 
+
 def merge_provider_info_with_hybrid_smart_chunking(one_to_one_results, filename):
     """
     Merges provider_info into one_to_one_results.
@@ -223,8 +224,10 @@ def merge_provider_info_with_hybrid_smart_chunking(one_to_one_results, filename)
     other_npis = []
     other_names = []
 
-    provider_name = one_to_one_results.get("PROVIDER_NAME") # Returns List
+    provider_name = one_to_one_results.get("PROVIDER_NAME")  # Returns List
-    provider_info = one_to_one_results["PROV_INFO_JSON"] # Returns string representation of list
+    provider_info = one_to_one_results[
+        "PROV_INFO_JSON"
+    ]  # Returns string representation of list
     prov_info_json_list = (
         json.loads(provider_info) if isinstance(provider_info, str) else provider_info
     )
@@ -234,7 +237,7 @@ def merge_provider_info_with_hybrid_smart_chunking(one_to_one_results, filename)
     for provider in prov_info_json_list:
         name_matches = prompt_calls.provider_name_match_check(
             provider_name, provider.get("NAME", "UNKNOWN"), filename
-        ) # Returns Boolean
+        )  # Returns Boolean
 
         if name_matches:
             found_match = True

src/pipelines/shared/postprocessing/aarete_derived.py

@@ -1,4 +1,4 @@
-
+import logging
 import src.config as config
 import src.utils.string_utils as string_utils
 from src.constants.constants import Constants
@@ -33,7 +33,7 @@ def fill_na_mapping(answer_dicts):
 
         # Get existing AARETE_DERIVED_LOB values (if any)
         existing_lob_list = answer_dict.get("AARETE_DERIVED_LOB", "")
-        
+
         if not string_utils.is_empty(existing_lob_list):
             for lob_val in existing_lob_list:
                 if "|" in existing_lob_list:
@@ -55,7 +55,8 @@ def fill_na_mapping(answer_dicts):
                 "src/constants/mappings/crosswalk_program_lob.json",
             )
             if (
-                not string_utils.is_empty(program_filled_value_list) and "N/A" not in program_filled_value_list
+                not string_utils.is_empty(program_filled_value_list)
+                and "N/A" not in program_filled_value_list
             ):
                 for program_lob_val in program_filled_value_list:
                     if "|" in program_lob_val:
@@ -64,10 +65,7 @@ def fill_na_mapping(answer_dicts):
                             for v in program_lob_val.split("|")
                             if v.strip() and v.strip() != "N/A"
                         )
-                    elif (
+                    elif program_lob_val.strip() and program_lob_val.strip() != "N/A":
-                        program_lob_val.strip()
-                        and program_lob_val.strip() != "N/A"
-                    ):
                         program_lob_values = {program_lob_val.strip()}
                     all_lob_values.update(program_lob_values)
 
@@ -91,16 +89,13 @@ def fill_na_mapping(answer_dicts):
                             for v in product_filled_value_list.split("|")
                             if v.strip() and v.strip() != "N/A"
                         )
-                    elif (
+                    elif product_lob_val.strip() and product_lob_val.strip() != "N/A":
-                        product_lob_val.strip()
-                        and product_lob_val.strip() != "N/A"
-                    ):
                         product_lob_values = {product_lob_val.strip()}
                     all_lob_values.update(product_lob_values)
 
         # Set the merged, deduplicated value
         if all_lob_values:
-            answer_dict["AARETE_DERIVED_LOB"] = "|".join(sorted(all_lob_values))
+            answer_dict["AARETE_DERIVED_LOB"] = sorted(all_lob_values)
             # Set relationship flags if values came from PROGRAM or PRODUCT
             # Only set if field is empty or "N/A" (preserve LLM-determined Inclusive/Exclusive)
             if program_lob_values:
@@ -145,9 +140,9 @@ def get_crosswalk_fields(answer_dicts: list, constants: Constants):
                 to_field_value = answer_dict.get(to_field_name)
                 from_field_value = answer_dict.get(from_field_name)
                 # If from_field_value is populated and to_field_value is not populated, perform mapping
-                if not string_utils.is_empty(
+                if not string_utils.is_empty(from_field_value) and (
-                    from_field_value
+                    string_utils.is_empty(to_field_value) or "N/A" in to_field_value
-                ) and (string_utils.is_empty(to_field_value) or "N/A" in to_field_value):
+                ):
                     if isinstance(from_field_value, list):
                         from_field_value_list = from_field_value
                     else:
@@ -176,4 +171,8 @@ def get_crosswalk_fields(answer_dicts: list, constants: Constants):
                                     )
                                 )
                     answer_dict[to_field_name] = to_field_answer_list
+                    # update from field value to list if not already
+                    if not isinstance(from_field_value, list):
+                        answer_dict[from_field_name] = from_field_value_list
+
     return answer_dicts

src/pipelines/shared/preprocessing/hybrid_smart_chunking_funcs.py

@@ -328,6 +328,7 @@ def extract_amendment_num_from_filename(answer_dict: dict, filename: str) -> dic
 
     return answer_dict
 
+
 def prompt_hsc_single_field(
     field, retriever, reranker, rag_config, text_dict, constants, filename
 ):
@@ -365,7 +366,9 @@ def prompt_hsc_single_field(
         llm_prompt = field.get_prompt_dict(
             constants
         )  # Returns dict of {field_name : prompt}
-        prompt, _parser = prompt_templates.ONE_TO_ONE_SINGLE_FIELD_TEMPLATE(context, llm_prompt)
+        prompt, _parser = prompt_templates.ONE_TO_ONE_SINGLE_FIELD_TEMPLATE(
+            context, llm_prompt
+        )
 
         logging.debug(f"Field: {field.field_name}")
         logging.debug(f"Retrieval question: {retrieval_query}")
@@ -384,7 +387,7 @@ def prompt_hsc_single_field(
             field_value = llm_answer_final.get(field.field_name)
             if not isinstance(field_value, list):
                 field_value = [field_value]
-            
+
             if field_value[0] == "N/A":
                 field.field_type = "full_context"
                 if field.field_name == "PAYER_NAME":
@@ -412,4 +415,3 @@ def prompt_hsc_single_field(
             f"Error on {field.field_name}: {type(e).__name__}: {str(e)}, marking as full_context"
         )
         return (field.field_name, "N/A", field)
-

src/prompts/prompt_templates.py

@@ -138,9 +138,6 @@ def get_cacheable_instructions():
     if config.FIELDS in ["all", "one_to_n"]:
         try:
             cacheable_instructions["DYNAMIC_PRIMARY"] = DYNAMIC_PRIMARY_INSTRUCTION()
-            cacheable_instructions["DYNAMIC_PRIMARY_TEXT"] = (
-                DYNAMIC_PRIMARY_TEXT_INSTRUCTION()
-            )
             cacheable_instructions["CARVEOUT_CHECK"] = CARVEOUT_CHECK_INSTRUCTION()
             cacheable_instructions["DYNAMIC_ASSIGNMENT"] = (
                 DYNAMIC_ASSIGNMENT_INSTRUCTION()
@@ -240,7 +237,7 @@ Briefly explain your answer, then put the final answer in the properly-formatted
 def EXHIBIT_LEVEL(context, fields) -> Tuple[str, Callable[[str], dict]]:
     """Returns ONLY dynamic content for exhibit level extraction.
     Call EXHIBIT_LEVEL_INSTRUCTION() separately for the cached instruction.
-    
+
     Returns:
         Tuple of (prompt_string, parser_function) where parser expects JSON dict output.
     """
@@ -251,7 +248,7 @@ Here are the attributes to be included in the dictionary, and instructions on ho
 [CONTEXT]
 Here is the text to analyze:
 {context.replace('"', "'")}"""
-    
+
     return (prompt, _json_dict_parser)
 
 
@@ -273,10 +270,12 @@ Briefly explain your answer before putting the final answer in a properly-format
 {JSON_LIST_FORMAT_INSTRUCTIONS}"""
 
 
-def DYNAMIC_PRIMARY(context, field_name, field_prompt) -> Tuple[str, Callable[[str], list]]:
+def DYNAMIC_PRIMARY(
+    context, field_name, field_prompt
+) -> Tuple[str, Callable[[str], list]]:
     """Returns ONLY dynamic content for text-based dynamic primary extraction.
     Call DYNAMIC_PRIMARY_TEXT_INSTRUCTION() separately for the cached instruction.
-    
+
     Returns:
         Tuple of (prompt_string, parser_function) where parser expects JSON list output.
     """
@@ -286,7 +285,7 @@ def DYNAMIC_PRIMARY(context, field_name, field_prompt) -> Tuple[str, Callable[[s
 [CONTEXT]
 Here is the text to analyze:
 {context.replace('"', "'")}"""
-    
+
     return (prompt, _json_list_parser)
 
 
@@ -369,7 +368,7 @@ def REIMB_DATES_ASSIGNMENT(
 ) -> Tuple[str, Callable[[str], dict]]:
     """Returns ONLY dynamic content for REIMB_DATES assignment.
     Call REIMB_DATES_ASSIGNMENT_INSTRUCTION() separately for the cached instruction.
-    
+
     Returns:
         Tuple of (prompt_string, parser_function) where parser expects JSON dict output.
     """
@@ -386,7 +385,7 @@ Here is the section of the contract. Examine this section closely and identify w
 This is the term you need to find the date range for. It appears on page {page_num} of the text.
 Service Term: "{service_term}"
 Reimbursement Term: "{reimb_term}" """
-    
+
     return (prompt, _json_dict_parser)
 
 
@@ -459,7 +458,7 @@ def DYNAMIC_ASSIGNMENT(
     """
     Returns prompt for dynamic assignment extraction.
     Call DYNAMIC_ASSIGNMENT_INSTRUCTION() separately for the cached instruction.
-    
+
     Returns:
         Tuple of (prompt_string, parser_function) where parser expects JSON dict output.
     """
@@ -477,7 +476,7 @@ Reimbursement Term: "{reimb_term}"
 [{field_name} INFORMATION]
 Here is the definition and valid values. Use this information to help you find the right answer.
 {field_name} : {field_prompt}"""
-    
+
     return (prompt, _json_dict_parser)
 
 
@@ -485,7 +484,7 @@ def REIMBURSEMENT_PRIMARY(context) -> Tuple[str, Callable[[str], list]]:
     """
     Returns prompt for reimbursement primary extraction.
     Call REIMBURSEMENT_PRIMARY_INSTRUCTION() separately for the cached instruction.
-    
+
     Returns:
         Tuple of (prompt_string, parser_function) where parser expects JSON list output.
     """
@@ -496,7 +495,7 @@ def REIMBURSEMENT_PRIMARY(context) -> Tuple[str, Callable[[str], list]]:
 
 Briefly talk through your reasoning. Then return a properly formatted JSON list of dictionaries with all extracted SERVICE_TERM AND REIMB_TERM values.
 """
-    
+
     return (prompt, _json_list_parser)
 
 
@@ -618,7 +617,7 @@ def LESSER_OF_DISTRIBUTION(
     """
     Returns prompt for lesser-of distribution.
     Call LESSER_OF_DISTRIBUTION_INSTRUCTION() separately for the cached instruction.
-    
+
     Returns:
         Tuple of (prompt_string, parser_function) where parser expects JSON list output.
     """
@@ -650,7 +649,7 @@ EXHIBIT TEXT:
 
 Analyze the inputs above. Briefly explain your reasoning, then provide your final answer as a JSON list.
 """
-    
+
     return (prompt, _json_list_parser)
 
 
@@ -738,18 +737,20 @@ Briefly explain your reasoning, then return a properly-formatted JSON dictionary
 {JSON_DICT_FORMAT_INSTRUCTIONS}"""
 
 
-def LESSER_OF_CHECK(service_term: str, reimb_term: str, exhibit_title: str) -> Tuple[str, Callable[[str], dict]]:
+def LESSER_OF_CHECK(
+    service_term: str, reimb_term: str, exhibit_title: str
+) -> Tuple[str, Callable[[str], dict]]:
     """
     Returns prompt for lesser-of check classification.
     Call LESSER_OF_CHECK_INSTRUCTION() separately for the cached instruction.
-    
+
     Returns:
         Tuple of (prompt_string, parser_function) where parser expects JSON dict output.
     """
     prompt = f"""**Current Exhibit:** {exhibit_title}
 **Service:** {service_term}
 **Reimbursement:** {reimb_term}"""
-    
+
     return (prompt, _json_dict_parser)
 
 
@@ -774,17 +775,19 @@ Examples: ["YES"] or ["NO"]
 {JSON_LIST_FORMAT_INSTRUCTIONS}"""
 
 
-def EXHIBIT_TITLE_MATCH(current_exhibit_title: str, target_exhibit_reference: str) -> Tuple[str, Callable[[str], list]]:
+def EXHIBIT_TITLE_MATCH(
+    current_exhibit_title: str, target_exhibit_reference: str
+) -> Tuple[str, Callable[[str], list]]:
     """Returns ONLY dynamic content for exhibit title matching.
     Call EXHIBIT_TITLE_MATCH_INSTRUCTION() separately for the cached instruction.
-    
+
     Returns:
         Tuple of (prompt_string, parser_function) where parser expects JSON list output.
     """
     prompt = f"""[CONTEXT]
 Current Exhibit Title: "{current_exhibit_title}"
 Target Exhibit Reference: "{target_exhibit_reference}" """
-    
+
     return (prompt, _json_list_parser)
 
 
@@ -793,14 +796,16 @@ Target Exhibit Reference: "{target_exhibit_reference}" """
 #####################################################################################
 
 
-def METHODOLOGY_BREAKOUT(service_term: str, reimb_term: str) -> Tuple[str, Callable[[str], list]]:
+def METHODOLOGY_BREAKOUT(
+    service_term: str, reimb_term: str
+) -> Tuple[str, Callable[[str], list]]:
     """
     Returns ONLY dynamic content for methodology breakout extraction.
     Call METHODOLOGY_BREAKOUT_INSTRUCTION() separately for the cached instruction.
 
     All fields run by this prompt will end up becoming their own row, rather than being distributed across the rows of the exhibit.
     Note: questions/fields are now included in METHODOLOGY_BREAKOUT_INSTRUCTION() for caching.
-    
+
     Returns:
         Tuple of (prompt_string, parser_function) where parser expects JSON list output.
     """
@@ -809,7 +814,7 @@ Service Term: {service_term}
 Reimbursement Term: {reimb_term}
 
 Explain your reasoning as you work through the context. After your explanation, include the heading "FINAL REIMBURSEMENT METHODOLOGY:" followed by a properly formatted JSON list with your final output."""
-    
+
     return (prompt, _json_list_parser)
 
 
@@ -872,18 +877,20 @@ Populate a JSON dictionary with the following fields:
 Briefly explain your reasoning, then write your JSON dictionary."""
 
 
-def FEE_SCHEDULE_BREAKOUT(methodology, fee_schedule) -> Tuple[str, Callable[[str], dict]]:
+def FEE_SCHEDULE_BREAKOUT(
+    methodology, fee_schedule
+) -> Tuple[str, Callable[[str], dict]]:
     """Returns ONLY dynamic content for fee schedule breakout.
     Call FEE_SCHEDULE_BREAKOUT_INSTRUCTION() separately for the cached instruction.
     Note: Field definitions are now included in FEE_SCHEDULE_BREAKOUT_INSTRUCTION() for caching.
-    
+
     Returns:
         Tuple of (prompt_string, parser_function) where parser expects JSON dict output.
     """
     prompt = f"""[CONTEXT]
 Analyze and respond to the following text, specifically for {fee_schedule} Fee Schedule:
 Methodology: {methodology.replace('"', "'")}"""
-    
+
     return (prompt, _json_dict_parser)
 
 
@@ -918,7 +925,7 @@ def GROUPER_BREAKOUT(service, term) -> Tuple[str, Callable[[str], dict]]:
     """Returns ONLY dynamic content for grouper breakout.
     Call GROUPER_BREAKOUT_INSTRUCTION() separately for the cached instruction.
     Note: Field definitions are now included in GROUPER_BREAKOUT_INSTRUCTION() for caching.
-    
+
     Returns:
         Tuple of (prompt_string, parser_function) where parser expects JSON dict output.
     """
@@ -926,14 +933,14 @@ def GROUPER_BREAKOUT(service, term) -> Tuple[str, Callable[[str], dict]]:
 Here are the service and reimbursement terms to analyze:
 SERVICE: {service}
 REIMBURSEMENT TERM: {term}"""
-    
+
     return (prompt, _json_dict_parser)
 
 
 def SPECIAL_CASE_BREAKOUT(term, questions) -> Tuple[str, Callable[[str], dict]]:
     """
     Generic prompt for special case breakout fields when no additional descriptions are needed.
-    
+
     Returns:
         Tuple of (prompt_string, parser_function) where parser expects JSON dict output.
     """
@@ -951,7 +958,7 @@ Here is the term to analyze:
 [OUTPUT FORMAT]
 Briefly explain your answer, then put your final answer in JSON dictionary format. 
 """
-    
+
     return (prompt, _json_dict_parser)
 
 
@@ -975,7 +982,7 @@ def RATE_ESCALATOR_BREAKOUT(term) -> Tuple[str, Callable[[str], dict]]:
 def TRIGGER_CAP_BREAKOUT(term) -> Tuple[str, Callable[[str], dict]]:
     """
     Returns prompt and parser for trigger cap breakout.
-    
+
     Returns:
         Tuple of (prompt_string, parser_function) where parser expects JSON dict output.
     """
@@ -1016,21 +1023,21 @@ def OUTLIER_BREAKOUT(term) -> Tuple[str, Callable[[str], dict]]:
     Returns ONLY dynamic content for outlier breakout extraction.
     Call OUTLIER_BREAKOUT_INSTRUCTION() separately for the cached instruction.
     Note: Field definitions are now included in OUTLIER_BREAKOUT_INSTRUCTION() for caching.
-    
+
     Returns:
         Tuple of (prompt_string, parser_function) where parser expects JSON dict output.
     """
     prompt = f"""[CONTEXT]
 Here is the text to analyze and respond to:
 {term}"""
-    
+
     return (prompt, _json_dict_parser)
 
 
 def FACILITY_ADJUSTMENT_BREAKOUT(term) -> Tuple[str, Callable[[str], dict]]:
     """
     Returns prompt and parser for facility adjustment breakout.
-    
+
     Returns:
         Tuple of (prompt_string, parser_function) where parser expects JSON dict output.
     """
@@ -1043,7 +1050,7 @@ def FACILITY_ADJUSTMENT_BREAKOUT(term) -> Tuple[str, Callable[[str], dict]]:
 def SEQUESTRATION_BREAKOUT(term) -> Tuple[str, Callable[[str], dict]]:
     """
     Returns prompt and parser for sequestration breakout.
-    
+
     Returns:
         Tuple of (prompt_string, parser_function) where parser expects JSON dict output.
     """
@@ -1056,7 +1063,7 @@ def SEQUESTRATION_BREAKOUT(term) -> Tuple[str, Callable[[str], dict]]:
 def DISCOUNT_BREAKOUT(term) -> Tuple[str, Callable[[str], dict]]:
     """
     Returns prompt and parser for discount breakout.
-    
+
     Returns:
         Tuple of (prompt_string, parser_function) where parser expects JSON dict output.
     """
@@ -1069,7 +1076,7 @@ def DISCOUNT_BREAKOUT(term) -> Tuple[str, Callable[[str], dict]]:
 def PREMIUM_BREAKOUT(term) -> Tuple[str, Callable[[str], dict]]:
     """
     Returns prompt and parser for premium breakout.
-    
+
     Returns:
         Tuple of (prompt_string, parser_function) where parser expects JSON dict output.
     """
@@ -1082,7 +1089,7 @@ def PREMIUM_BREAKOUT(term) -> Tuple[str, Callable[[str], dict]]:
 def ADDITION_BREAKOUT(term) -> Tuple[str, Callable[[str], dict]]:
     """
     Returns prompt and parser for addition breakout.
-    
+
     Returns:
         Tuple of (prompt_string, parser_function) where parser expects JSON dict output.
     """
@@ -1095,7 +1102,7 @@ def ADDITION_BREAKOUT(term) -> Tuple[str, Callable[[str], dict]]:
 def STOP_LOSS_BREAKOUT(term) -> Tuple[str, Callable[[str], dict]]:
     """
     Returns prompt and parser for stop loss breakout.
-    
+
     Returns:
         Tuple of (prompt_string, parser_function) where parser expects JSON dict output.
     """
@@ -1143,7 +1150,7 @@ def CODE_EXPLICIT(service, methodology) -> Tuple[str, Callable[[str], dict]]:
     Returns ONLY dynamic content for code explicit extraction.
     Call CODE_EXPLICIT_INSTRUCTION() separately for the cached instruction.
     Note: Field definitions are now included in CODE_EXPLICIT_INSTRUCTION() for caching.
-    
+
     Returns:
         Tuple of (prompt_string, parser_function) where parser expects JSON dict output.
     """
@@ -1151,7 +1158,7 @@ def CODE_EXPLICIT(service, methodology) -> Tuple[str, Callable[[str], dict]]:
 Here is the Service and Methodology to analyze:
 Service: {service.replace('"', "'")}
 Methodology: {methodology.replace('"', "'")}"""
-    
+
     return (prompt, _json_dict_parser)
 
 
@@ -1174,7 +1181,7 @@ Explain your answer in 1-2 sentences. Write your final answer in a properly-form
 
 def CODE_CATEGORY(service, choices) -> Tuple[str, Callable[[str], list]]:
     """Call CODE_CATEGORY_INSTRUCTION() separately for the cached instruction.
-    
+
     Returns:
         Tuple of (prompt_string, parser_function) where parser expects JSON list output.
     """
@@ -1185,7 +1192,7 @@ Here are the descriptions to choose from:
 [CONTEXT]
 Here is the Service Term to analyze:
 {service.replace('"', "'")}"""
-    
+
     return (prompt, _json_list_parser)
 
 
@@ -1210,14 +1217,14 @@ Examples: ["Drugs"], ["Vaccines"], ["PT/OT/ST"], ["PT"], ["Surgery"], ["N/A"]
 
 def CODE_IMPLICIT_SPECIAL(service) -> Tuple[str, Callable[[str], list]]:
     """Call CODE_IMPLICIT_SPECIAL_INSTRUCTION() separately for the cached instruction.
-    
+
     Returns:
         Tuple of (prompt_string, parser_function) where parser expects JSON list output.
     """
     prompt = f"""[CONTEXT]
 Here is the Service to analyze:
 {service}"""
-    
+
     return (prompt, _json_list_parser)
 
 
@@ -1247,7 +1254,7 @@ Explain your answer, ensuring each point in the instructions is addressed. Then
 
 def CODE_IMPLICIT(service, choices) -> Tuple[str, Callable[[str], list]]:
     """Call CODE_IMPLICIT_INSTRUCTION() separately for the cached instruction.
-    
+
     Returns:
         Tuple of (prompt_string, parser_function) where parser expects JSON list output.
     """
@@ -1258,7 +1265,7 @@ Here are the descriptions to choose from:
 [CONTEXT]
 Here is the Service Term to analyze:
 {service.replace('"', "'")}"""
-    
+
     return (prompt, _json_list_parser)
 
 
@@ -1283,13 +1290,13 @@ Examples: ["Specific"] or ["Generic"]
 
 def CODE_LAST_CHECK(service) -> Tuple[str, Callable[[str], list]]:
     """Call CODE_LAST_CHECK_INSTRUCTION() separately for the cached instruction.
-    
+
     Returns:
         Tuple of (prompt_string, parser_function) where parser expects JSON list output.
     """
     prompt = f"""[SERVICE TO ANALYZE]
 {service}"""
-    
+
     return (prompt, _json_list_parser)
 
 
@@ -1313,9 +1320,11 @@ Briefly explain your answer, then return your final answer in a properly-formatt
 {JSON_LIST_FORMAT_INSTRUCTIONS}"""
 
 
-def FILL_BILL_TYPE(service, choices, reimb_term=None, exhibit_text=None) -> Tuple[str, Callable[[str], list]]:
+def FILL_BILL_TYPE(
+    service, choices, reimb_term=None, exhibit_text=None
+) -> Tuple[str, Callable[[str], list]]:
     """Call FILL_BILL_TYPE_INSTRUCTION() separately for the cached instruction.
-    
+
     Returns:
         Tuple of (prompt_string, parser_function) where parser expects JSON list output.
     """
@@ -1344,7 +1353,7 @@ Here are the descriptions to choose from:
 [SERVICE TERM TO ANALYZE]
 {service.replace('"', "'")}
 {reimb_term_section}{exhibit_context_section}"""
-    
+
     return (prompt, _json_list_parser)
 
 
@@ -1353,10 +1362,12 @@ Here are the descriptions to choose from:
 #####################################################################################
 
 
-def ONE_TO_ONE_SINGLE_FIELD_TEMPLATE(context: str, field_prompt: dict[str, str]) -> Tuple[str, Callable[[str], dict]]:
+def ONE_TO_ONE_SINGLE_FIELD_TEMPLATE(
+    context: str, field_prompt: dict[str, str]
+) -> Tuple[str, Callable[[str], dict]]:
     """
     Returns prompt and parser for one-to-one single field extraction.
-    
+
     Returns:
         Tuple of (prompt_string, parser_function) where parser expects JSON dict output.
     """
@@ -1369,7 +1380,7 @@ Answer the following question: {field_prompt}
 
 Briefly explain your answer, then put the final answer in a properly-formatted JSON dictionary, where the key is the field name and value is the answer. If there is no valid answer, return "N/A".
 """
-    
+
     return (prompt, _json_dict_parser)
 
 
@@ -1437,11 +1448,13 @@ FINAL PROVIDER INFO:
 Contract text:"""
 
 
-def TIN_NPI_TEMPLATE(context, questions, payer_name) -> Tuple[str, Callable[[str], list]]:
+def TIN_NPI_TEMPLATE(
+    context, questions, payer_name
+) -> Tuple[str, Callable[[str], list]]:
     """
     Returns prompt for TIN/NPI extraction.
     Call TIN_NPI_TEMPLATE_INSTRUCTION() separately for the cached instruction.
-    
+
     Returns:
         Tuple of (prompt_string, parser_function) where parser expects JSON list output (list of dicts).
     """
@@ -1458,7 +1471,7 @@ def TIN_NPI_TEMPLATE(context, questions, payer_name) -> Tuple[str, Callable[[str
 Contract text:
 {context.replace('"', "'" )}
 """
-    
+
     return (prompt, _json_list_parser)
 
 
@@ -1527,7 +1540,7 @@ def CHECK_PROVIDER_NAME_MATCH_PROMPT(
 ) -> Tuple[str, Callable[[str], list]]:
     """Returns ONLY dynamic content for provider name matching.
     Call CHECK_PROVIDER_NAME_MATCH_INSTRUCTION() separately for the cached instruction.
-    
+
     Returns:
         Tuple of (prompt_string, parser_function) where parser expects JSON list output.
     """
@@ -1536,14 +1549,16 @@ PROVIDER NAME A: {provider_name}
 PROVIDER NAME B: {hybrid_smart_chunking_provider_group_name}
 
 Note: PROVIDER NAME B is typically a single name but may contain multiple names. Determine if PROVIDER NAME A matches PROVIDER NAME B (or ANY name if multiple are present)."""
-    
+
     return (prompt, _json_list_parser)
 
 
-def ONE_TO_ONE_MULTI_FIELD_TEMPLATE(context, questions: dict[str, str]) -> Tuple[str, Callable[[str], dict]]:
+def ONE_TO_ONE_MULTI_FIELD_TEMPLATE(
+    context, questions: dict[str, str]
+) -> Tuple[str, Callable[[str], dict]]:
     """
     Returns prompt and parser for one-to-one multi-field extraction.
-    
+
     Returns:
         Tuple of (prompt_string, parser_function) where parser expects JSON dict output.
     """
@@ -1587,12 +1602,12 @@ def ONE_TO_ONE_MULTI_FIELD_INSTRUCTION() -> str:
 def EXTRACT_AMENDMENT_NUM_FROM_FILENAME(filename) -> Tuple[str, Callable[[str], dict]]:
     """
     Returns prompt and parser for extracting amendment number from filename.
-    
+
     Returns:
         Tuple of (prompt_string, parser_function) where parser expects JSON dict output.
     """
     prompt = f"""Filename: {filename}"""
-    
+
     return (prompt, _json_dict_parser)
 
 
@@ -1627,7 +1642,7 @@ def EXHIBIT_HEADER_INSTRUCTION() -> str:
     EXHIBIT_HEADER_MARKERS = constants.EXHIBIT_HEADER_MARKERS
 
     markers_text = "\n* ".join(EXHIBIT_HEADER_MARKERS)
-    
+
     return f"""[OBJECTIVE]
 Analyze contract excerpts and extract the full exhibit/attachment header found.
 Capture the complete hierarchy of document identifiers present in the text.
@@ -1665,14 +1680,14 @@ def EXHIBIT_HEADER(context) -> Tuple[str, Callable[[str], list]]:
     """Returns ONLY dynamic content for exhibit header extraction.
     Call EXHIBIT_HEADER_INSTRUCTION() separately for the cached instruction.
     Note: Header markers are now included in EXHIBIT_HEADER_INSTRUCTION() for caching.
-    
+
     Returns:
         Tuple of (prompt_string, parser_function) where parser expects JSON list output.
     """
     prompt = f"""[CONTEXT]
 Here is the text to analyze:
 {context.replace('"', "'")}"""
-    
+
     return (prompt, _json_list_parser)
 
 
@@ -1700,7 +1715,7 @@ Examples: ["Y"] or ["N"]
 def EXHIBIT_LINKAGE(header1, header2) -> Tuple[str, Callable[[str], list]]:
     """Returns ONLY dynamic content for exhibit linkage comparison.
     Call EXHIBIT_LINKAGE_INSTRUCTION() separately for the cached instruction.
-    
+
     Returns:
         Tuple of (prompt_string, parser_function) where parser expects JSON list output.
     """
@@ -1710,7 +1725,7 @@ Header 1:
 
 Header 2:
 "{header2}" """
-    
+
     return (prompt, _json_list_parser)
 
 
@@ -1788,10 +1803,13 @@ Briefly explain your reasoning following the three conditions above, then return
 Examples: ["YES"] or ["NO"]
 {JSON_LIST_FORMAT_INSTRUCTIONS}"""
 
-def VALIDATE_REIMBURSEMENTS_PROMPT(service_term: str, reimb_term: str) -> Tuple[str, Callable[[str], list]]:
+
+def VALIDATE_REIMBURSEMENTS_PROMPT(
+    service_term: str, reimb_term: str
+) -> Tuple[str, Callable[[str], list]]:
     """Returns ONLY dynamic content for validation.
     Call VALIDATE_REIMBURSEMENTS_INSTRUCTION() separately for the cached instruction.
-    
+
     Returns:
         Tuple of (prompt_string, parser_function) where parser expects JSON list output.
     """
@@ -1808,7 +1826,7 @@ REIMBURSEMENT TERM: {reimb_term}
 
 If 1=yes, 2=yes, 3=no → return ["YES"]
 Otherwise → return ["NO"]"""
-    
+
     return (prompt, _json_list_parser)
 
 
@@ -1859,14 +1877,14 @@ Output: ["N/A"]
 def DATE_FIX_PROMPT(context: str) -> Tuple[str, Callable[[str], list]]:
     """Returns ONLY dynamic content for date fixing.
     Call DATE_FIX_INSTRUCTION() separately for the cached instruction.
-    
+
     Returns:
         Tuple of (prompt_string, parser_function) where parser expects JSON list output.
     """
     prompt = f"""[CONTEXT]
 Please analyze this date:
 {context}"""
-    
+
     return (prompt, _json_list_parser)
 
 
@@ -1896,7 +1914,7 @@ def CARVEOUT_CHECK_INSTRUCTION() -> str:
     special_case_text = "\n\n".join(
         [f"{name} : {desc}" for name, desc in special_case_definitions.items()]
     )
-    
+
     return f"""[OBJECTIVE]
 Examine the Reimbursement Term and identify which case it falls under.
 
@@ -1919,11 +1937,13 @@ Example: ["OUTLIER_TERM"] or ["RATE_ESCALATOR"]
 {JSON_LIST_FORMAT_INSTRUCTIONS}"""
 
 
-def CARVEOUT_CHECK(service_term: str, reimb_term: str) -> Tuple[str, Callable[[str], list]]:
+def CARVEOUT_CHECK(
+    service_term: str, reimb_term: str
+) -> Tuple[str, Callable[[str], list]]:
     """Returns ONLY dynamic content for carveout check.
     Call CARVEOUT_CHECK_INSTRUCTION() separately for the cached instruction.
     Note: Case definitions are now included in CARVEOUT_CHECK_INSTRUCTION() for caching.
-    
+
     Returns:
         Tuple of (prompt_string, parser_function) where parser expects JSON list output.
     """
@@ -1931,14 +1951,14 @@ def CARVEOUT_CHECK(service_term: str, reimb_term: str) -> Tuple[str, Callable[[s
 Here is the Reimbursement Term to analyze:
 Service: {service_term}
 Reimbursement Term: {reimb_term.replace('"', "'")}"""
-    
+
     return (prompt, _json_list_parser)
 
 
 def EFFECTIVE_DATE_FIX_PROMPT() -> Tuple[str, Callable[[str], dict]]:
     """
     Returns prompt and parser for effective date extraction.
-    
+
     Returns:
         Tuple of (prompt_string, parser_function) where parser expects JSON dict output.
     """
@@ -1950,7 +1970,7 @@ def EFFECTIVE_DATE_FIX_PROMPT() -> Tuple[str, Callable[[str], dict]]:
     4. Any day values with spaces or leading zeros (e.g., '0 1' or '01') are interpreted as single-digit days
     
     Return N/A only for date if NO valid effective date is found. give the answer in the format: {{\"AARETE_DERIVED_EFFECTIVE_DT\": \"YYYY/MM/DD\"}}.  nothing else."""
-    
+
     return (prompt, _json_dict_parser)
 
 
@@ -1995,13 +2015,13 @@ Note: Use "N/A" for dates that cannot be determined or extracted from the input
 
 def SPLIT_REIMB_DATES(date_range: str) -> Tuple[str, Callable[[str], dict]]:
     """Call SPLIT_REIMB_DATES_INSTRUCTION() separately for the cached instruction.
-    
+
     Returns:
         Tuple of (prompt_string, parser_function) where parser expects JSON dict output.
     """
     prompt = f"""[DATE RANGE TO EXTRACT]
 {date_range}"""
-    
+
     return (prompt, _json_dict_parser)
 
 
@@ -2034,10 +2054,12 @@ Your response MUST include the JSON list with your final answer.
 {JSON_LIST_FORMAT_INSTRUCTIONS}"""
 
 
-def LOB_RELATIONSHIP(exhibit_text, dynamic_primary_values) -> Tuple[str, Callable[[str], list]]:
+def LOB_RELATIONSHIP(
+    exhibit_text, dynamic_primary_values
+) -> Tuple[str, Callable[[str], list]]:
     """Returns ONLY dynamic content for LOB relationship analysis.
     Call LOB_RELATIONSHIP_INSTRUCTION() separately for the cached instruction.
-    
+
     Returns:
         Tuple of (prompt_string, parser_function) where parser expects JSON list output.
     """
@@ -2048,7 +2070,7 @@ It was previously identified that the Exhibit contains the following LOB and Pro
 [CONTEXT]
 Here is the Exhibit to be analyzed:
 {exhibit_text.replace('"', "'")}"""
-    
+
     return (prompt, _json_list_parser)
 
 
@@ -2078,14 +2100,14 @@ def DERIVED_TERM_DATE_PROMPT(
 ) -> Tuple[str, Callable[[str], list]]:
     """Returns ONLY dynamic content for derived term date calculation.
     Call DERIVED_TERM_DATE_INSTRUCTION() separately for the cached instruction.
-    
+
     Returns:
         Tuple of (prompt_string, parser_function) where parser expects JSON list output.
     """
     prompt = f"""[CONTEXT]
 Effective Date: {effective_date}
 Termination Information: {termination_information}"""
-    
+
     return (prompt, _json_list_parser)
 
 
@@ -2119,7 +2141,7 @@ def SPECIAL_CASE_ASSIGNMENT(
 ) -> Tuple[str, Callable[[str], list]]:
     """Returns ONLY dynamic content for special case assignment.
     Call SPECIAL_CASE_ASSIGNMENT_INSTRUCTION() separately for the cached instruction.
-    
+
     Returns:
         Tuple of (prompt_string, parser_function) where parser expects JSON list output.
     """
@@ -2139,5 +2161,5 @@ Reimbursement: {reimbursement_dict["REIMB_TERM"]}
 {special_case_list}
 
 Return the integer of the {special_case_term} that is most associated with the REIMBURSEMENT TERM."""
-    
+
-    return (prompt, _json_list_parser)
+    return (prompt, _json_list_parser)

src/tests/test_json_parsers.py

@@ -53,12 +53,12 @@ class TestParseJsonDict:
 
     def test_dict_with_newlines(self):
         """Test parsing dict with newlines in the JSON."""
-        output = '''
+        output = """
         {
             "field": "value",
             "count": 42
         }
-        '''
+        """
         result = parse_json_dict(output)
         assert result == {"field": "value", "count": 42}
 
@@ -181,13 +181,13 @@ class TestParseJsonList:
 
     def test_list_with_newlines(self):
         """Test parsing list with newlines in the JSON."""
-        output = '''
+        output = """
         [
             "item1",
             "item2",
             "item3"
         ]
-        '''
+        """
         result = parse_json_list(output)
         assert result == ["item1", "item2", "item3"]
 
@@ -317,11 +317,11 @@ class TestEdgeCases:
 
     def test_json_in_markdown_code_block(self):
         """Test parsing JSON within markdown code blocks."""
-        output = '''
+        output = """
         ```json
         {"field": "value"}
         ```
-        '''
+        """
         result = parse_json_dict(output)
         assert result == {"field": "value"}
 

src/utils/json_utils.py

@@ -43,9 +43,7 @@ def parse_json_dict(raw_llm_output: str) -> dict[str, Any]:
         {'key': 'value'}
     """
     if not isinstance(raw_llm_output, str):
-        raise TypeError(
+        raise TypeError(f"Expected string input, got {type(raw_llm_output).__name__}")
-            f"Expected string input, got {type(raw_llm_output).__name__}"
-        )
 
     found_dicts = []
     i = 0
@@ -130,9 +128,7 @@ def parse_json_list(raw_llm_output: str) -> list[Any]:
         ['item1', 'item2']
     """
     if not isinstance(raw_llm_output, str):
-        raise TypeError(
+        raise TypeError(f"Expected string input, got {type(raw_llm_output).__name__}")
-            f"Expected string input, got {type(raw_llm_output).__name__}"
-        )
 
     found_lists = []
     i = 0
@@ -207,9 +203,7 @@ def parse_json_dict_or_list(raw_llm_output: str) -> dict[str, Any] | list[Any]:
         TypeError: If raw_llm_output is not a string.
     """
     if not isinstance(raw_llm_output, str):
-        raise TypeError(
+        raise TypeError(f"Expected string input, got {type(raw_llm_output).__name__}")
-            f"Expected string input, got {type(raw_llm_output).__name__}"
-        )
 
     found_objects = []
     i = 0

src/utils/logging_utils.py

@@ -218,7 +218,7 @@ def setup_per_file_logging() -> None:
     ]
     for logger_name in noisy_loggers:
         logging.getLogger(logger_name).setLevel(logging.WARNING)
-    
+
     # Specifically suppress bedrock-related loggers
     logging.getLogger("botocore.handlers").setLevel(logging.WARNING)
     logging.getLogger("botocore.endpoint").setLevel(logging.WARNING)