Merged in feature/active-rates (pull request #1007)
Feature/active rates * amendment intent tag * prompt update * Merge remote-tracking branch 'origin/dev' into feature/active-rates * Merge remote-tracking branch 'origin/dev' into feature/active-rates * Merge branch 'dev' into feature/active-rates * exhibit standardization updates * use AARETE_DERIVED_EXHIBIT_TITLE for intent * active rates stuff * Merge branch 'dev' into feature/active-rates * prompt update * amendment intent types * active rates logic update * unit tests * Merged dev into feature/active-rates * logic updates * Merge remote-tracking branch 'origin/dev' into feature/active-rates * Merge remote-tracking branch 'origin/dev' into feature/active-rates * updated instrumentation cost logs * cost loging * caching updates * Merge remote-tracking branch 'origin/dev' into feature/active-rates * Merge remote-tracking branch 'origin/dev' into feature/active-rates * logging fix * null check issue fixes * caching fix Approved-by: Praneel Panchigar Approved-by: Siddhant Medar
This commit is contained in:
Karan Desai
committed by
Siddhant Medar
Siddhant Medar
parent
384f0444f7
commit
868cd200c6
@@ -199,9 +199,13 @@ def add_active_rate_flags(df: pd.DataFrame) -> pd.DataFrame:
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# by service term rather than exhibit title.
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if "AMENDMENT_INTENT_LANGUAGE" in df.columns and "AMENDMENT_INTENT" in df.columns:
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from src.pipelines.shared.postprocessing.postprocessing_funcs import (
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_language_is_na,
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)
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_files_no_lang = set(
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df.groupby("FILE_NAME")["AMENDMENT_INTENT_LANGUAGE"]
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.apply(lambda s: s.isna().all())
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.apply(lambda s: s.apply(_language_is_na).all())
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.pipe(lambda x: x[x].index)
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)
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@@ -751,6 +751,37 @@ _VALID_INTENT_TAGS = {
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"UNCLEAR_REVIEW",
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}
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_NA_LANGUAGE_STRINGS = {"", "n/a", "nan"}
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def _language_is_na(val) -> bool:
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"""Return True when val carries no actionable amendment language.
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Handles four storage forms that appear in the DataFrame:
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- None / float NaN / pd.NA / pd.NaT (any pandas/Python missing value)
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- Python list, e.g. ["1. N/A"] (parsed from LLM JSON response)
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- JSON-array string, e.g. '["N/A"]' (serialised form)
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- Plain string, e.g. "N/A"
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"""
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if isinstance(val, list):
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if not val:
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return True
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# Strip leading enumeration ("1. ", "2. ") before checking each item.
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return all(
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re.sub(r"^\d+\.\s*", "", str(item)).strip().lower() in _NA_LANGUAGE_STRINGS
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for item in val
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)
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# pd.isna covers None, float NaN, pd.NA, and pd.NaT for scalar values.
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# Must come after the list branch because pd.isna on a list returns an array.
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if pd.isna(val):
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return True
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s = str(val).strip()
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# JSON-array strings like '["N/A"]' or '["1. N/A"]'
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if s.startswith("[") and s.endswith("]"):
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inner = re.sub(r"^\d+\.\s*", "", s[1:-1].strip().strip("\"'")).strip()
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return inner.lower() in _NA_LANGUAGE_STRINGS
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return s.lower() in _NA_LANGUAGE_STRINGS
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def add_amendment_intent(df: pd.DataFrame) -> pd.DataFrame:
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"""
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@@ -814,29 +845,35 @@ def add_amendment_intent(df: pd.DataFrame) -> pd.DataFrame:
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candidate_languages = df.loc[title_mask, "AMENDMENT_INTENT_LANGUAGE"]
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candidate_languages = candidate_languages[
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~candidate_languages.astype(str).str.strip().isin({"", "N/A", "n/a", "nan"})
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& ~candidate_languages.isna()
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~candidate_languages.apply(_language_is_na)
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]
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_raw = candidate_languages.iloc[0] if len(candidate_languages) > 0 else ""
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amendment_language = (
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candidate_languages.iloc[0] if len(candidate_languages) > 0 else ""
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"\n".join(str(item) for item in _raw)
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if isinstance(_raw, list)
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else str(_raw)
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)
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if string_utils.is_empty(str(amendment_language)) or str(
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amendment_language
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).strip() in {"N/A", "n/a", "nan"}:
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if string_utils.is_empty(amendment_language) or amendment_language.strip() in {
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"N/A",
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"n/a",
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"nan",
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}:
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intent_map[map_key] = "N/A"
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reason_map[map_key] = "no_language"
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continue
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try:
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prompt = prompt_templates.AMENDMENT_INTENT(
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str(exhibit_title), str(amendment_language)
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str(exhibit_title), amendment_language
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)
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raw = llm_utils.invoke_claude(
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prompt,
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"sonnet_latest",
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filename,
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file_name if has_file else "",
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max_tokens=150,
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cache=True,
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instruction=prompt_templates.AMENDMENT_INTENT_INSTRUCTION(),
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usage_label="AMENDMENT_INTENT",
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)
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parsed = json_utils.parse_json_dict(raw)
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@@ -3105,11 +3105,7 @@ def AMENDMENT_INTENT_INSTRUCTION() -> str:
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def AMENDMENT_INTENT(exhibit_title: str, amendment_language: str) -> str:
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return (
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f"Amendment changes:\n{amendment_language}\n\n"
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f"Exhibit: {exhibit_title}\n\n"
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f"{AMENDMENT_INTENT_INSTRUCTION()}"
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)
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return f"Amendment changes:\n{amendment_language}\n\nExhibit: {exhibit_title}"
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#####################################################################################
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@@ -282,6 +282,18 @@ class TestAmendmentLanguageNormalisation(unittest.TestCase):
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# ["N/A"] is treated as no language → fallback OVERLAY_UPDATE applied
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self.assertEqual(result["AMENDMENT_INTENT"].iloc[0], "OVERLAY_UPDATE")
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def test_placeholder_list_language_becomes_overlay_update(self):
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# Python list ["1. N/A"] is placeholder language — not a real null, but
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# _language_is_na should treat it as no language so fallback applies.
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df = _df(
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QC_RANK=["1.1"],
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QC_FLAG=["Child"],
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AMENDMENT_INTENT=["N/A"],
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AMENDMENT_INTENT_LANGUAGE=[["1. N/A"]],
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)
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result = add_active_rate_flags(df)
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self.assertEqual(result["AMENDMENT_INTENT"].iloc[0], "OVERLAY_UPDATE")
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# ── add_amendment_intent ──────────────────────────────────────────────────────
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@@ -363,6 +375,23 @@ class TestAddAmendmentIntent(unittest.TestCase):
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mock_invoke.assert_not_called()
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self.assertEqual(result["AMENDMENT_INTENT"].iloc[0], "N/A")
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@patch(
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"src.pipelines.shared.postprocessing.postprocessing_funcs.llm_utils.invoke_claude"
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)
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def test_pd_na_language_yields_na_intent_without_llm(self, mock_invoke):
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# pd.NA must be treated as missing — not stringified to "<NA>" and sent to LLM.
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df = pd.DataFrame(
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{
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"FILE_NAME": ["f1.pdf"],
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"AARETE_DERIVED_EXHIBIT_TITLE": ["Ex A"],
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"AMENDMENT_INTENT_LANGUAGE": [pd.NA],
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"QC_FLAG": ["Child"],
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}
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)
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result = add_amendment_intent(df)
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mock_invoke.assert_not_called()
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self.assertEqual(result["AMENDMENT_INTENT"].iloc[0], "N/A")
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@patch(
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"src.pipelines.shared.postprocessing.postprocessing_funcs.llm_utils.invoke_claude"
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)
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@@ -91,6 +91,9 @@ USAGE_LABEL_TO_SEGMENT: dict[str, str] = {
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"prompt_provider_info": "one_to_one",
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"CHECK_PROVIDER_NAME_MATCH": "one_to_one", # prompt_calls.py:1042
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"EXTRACT_AMENDMENT_NUM_FROM_FILENAME": "one_to_one", # hybrid_smart_chunking_funcs.py:404
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# AMENDMENT_INTENT_LANGUAGE: per-document extraction of verbatim amendment change list.
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# Fires from prompt_templates.AMENDMENT_INTENT_LANGUAGE (prompt_templates.py:3020).
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"AMENDMENT_INTENT_LANGUAGE": "one_to_one",
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# DATE_FIX / DERIVED_TERM_DATE run inside run_one_to_one_prompts, deriving
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# AARETE_DERIVED_EFFECTIVE_DT / AARETE_DERIVED_TERMINATION_DT from raw dates.
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# Classified by *when* they fire (one_to_one phase), not their postprocess-like role.
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@@ -109,6 +112,15 @@ USAGE_LABEL_TO_SEGMENT: dict[str, str] = {
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# --- postprocess (runs after all per-contract extraction completes) ---
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"AARETE_DERIVED_PAYER_NAME": "postprocess", # runner.py:290 (post-run clustering)
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"AARETE_DERIVED_PROVIDER_NAME": "postprocess", # runner.py:300 (post-run clustering)
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# --- active_rates (batch-level, runs after parent-child mapping) ---
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# exhibit_title_standardization: 2-pass LLM taxonomy derivation + mapping.
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# Fires only from active_rates._standardize_and_derive_intent (exhibit_title_standardization.py:68).
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"exhibit_title_standardization": "active_rates",
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# AMENDMENT_INTENT: per-(file, exhibit_title) LLM classification into
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# FULL_REPLACEMENT / PARTIAL_REPLACEMENT / ADDITIVE / UNCLEAR_REVIEW.
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# Fires from active_rates._standardize_and_derive_intent (postprocessing_funcs.py:829)
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# and also from postprocess.run_postprocessing (postprocess.py:131) per-contract.
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"AMENDMENT_INTENT": "active_rates",
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}
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@@ -632,10 +632,9 @@ def invoke_claude(
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segment=segment,
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model=model_id,
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prompt_chars=len(prompt) if prompt else 0,
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context_chars=(
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len(context_for_caching) if context_for_caching else 0
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),
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has_context_cache=bool(context_for_caching),
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context_chars=len(context_for_caching or instruction or ""),
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has_context_cache=cache
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and bool(context_for_caching or instruction),
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cache_hit_inmem=True,
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**_ctx_minus_explicit_keys(
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dict(ctx),
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@@ -659,8 +658,8 @@ def invoke_claude(
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try:
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with instr_scope(
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_usage_label=usage_label,
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_has_context_cache=bool(context_for_caching),
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_context_chars=len(context_for_caching) if context_for_caching else 0,
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_has_context_cache=cache and bool(context_for_caching or instruction),
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_context_chars=len(context_for_caching or instruction or ""),
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_prompt_chars=len(prompt) if prompt else 0,
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_cache_key=cache_key,
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):
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