From 55643b5a0429fb8afb933c7260cc3bf90cfb92b4 Mon Sep 17 00:00:00 2001 From: Siddhant Medar Date: Wed, 5 Nov 2025 15:20:33 +0000 Subject: [PATCH] Merged in feature/saas-hybrid-smart-chunking (pull request #760) Feature/saas hybrid smart chunking * WIP: Initial work on SaaS HSC integration * clean up hsc funcs script * fix merging dict issue * Merge remote-tracking branch 'origin/main' into feature/saas-hybrid-smart-chunking * added changes as per feedback comments * add tests and remove old smart chunking funcs * add more tests Approved-by: Katon Minhas --- fieldExtraction/poetry.lock | 5308 +++++++++-------- fieldExtraction/src/config.py | 2 + .../src/investment/file_processing.py | 27 +- .../investment/hybrid_smart_chunking_funcs.py | 322 + .../hybrid_smart_chunking_preprocessing.py | 304 + .../src/investment/one_to_one_funcs.py | 71 - fieldExtraction/src/investment/preprocess.py | 1 - .../src/investment/smart_chunking_funcs.py | 505 -- fieldExtraction/src/prompts/fieldset.py | 6 + .../src/prompts/investment_prompts.json | 193 +- fieldExtraction/src/utils/rag_utils.py | 169 + .../tests/test_hybrid_smart_chunking_funcs.py | 450 ++ ...est_hybrid_smart_chunking_preprocessing.py | 353 ++ fieldExtraction/tests/test_rag_utils.py | 332 ++ .../tests/test_smart_chunking_funcs.py | 72 - 15 files changed, 5016 insertions(+), 3099 deletions(-) create mode 100644 fieldExtraction/src/investment/hybrid_smart_chunking_funcs.py create mode 100644 fieldExtraction/src/investment/hybrid_smart_chunking_preprocessing.py delete mode 100644 fieldExtraction/src/investment/smart_chunking_funcs.py create mode 100644 fieldExtraction/src/utils/rag_utils.py create mode 100644 fieldExtraction/tests/test_hybrid_smart_chunking_funcs.py create mode 100644 fieldExtraction/tests/test_hybrid_smart_chunking_preprocessing.py create mode 100644 fieldExtraction/tests/test_rag_utils.py delete mode 100644 fieldExtraction/tests/test_smart_chunking_funcs.py diff --git a/fieldExtraction/poetry.lock b/fieldExtraction/poetry.lock index 5286d01..5380e5d 100644 --- a/fieldExtraction/poetry.lock +++ b/fieldExtraction/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 2.0.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.1.1 and should not be changed by hand. 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multi-runtime failover FIELDS = get_arg_value("fields", "all") # Valid: one_to_one, one_to_n, all FIELD_JSON_PATH = "src/prompts/investment_prompts.json" +RAG_RETRIEVAL_QUESTIONS_PATH = "src/prompts/rag_retrieval_questions.json" + # Client-specific args STATE = [s.strip() for s in get_arg_value("state", "None").upper().split("-") if s] CLIENT = [s.strip() for s in get_arg_value("client", "None").split("-") if s] diff --git a/fieldExtraction/src/investment/file_processing.py b/fieldExtraction/src/investment/file_processing.py index 1427cd7..c720030 100644 --- a/fieldExtraction/src/investment/file_processing.py +++ b/fieldExtraction/src/investment/file_processing.py @@ -9,6 +9,7 @@ import src.investment.one_to_one_funcs as one_to_one_funcs import src.investment.postprocess as postprocess import src.investment.postprocessing_funcs as postprocessing_funcs import src.investment.preprocess as preprocess +import src.investment.hybrid_smart_chunking_funcs as hybrid_smart_chunking_funcs import src.investment.row_funcs as row_funcs import src.investment.tin_npi_funcs as tin_npi_funcs import src.utils.io_utils as io_utils @@ -132,24 +133,32 @@ def run_one_to_one_prompts( one_to_one_results, one_to_one_fields = tin_npi_funcs.run_provider_info_fields( contract_text, one_to_one_fields, text_dict, filename ) - - ################## RUN SMART CHUNKED PROMPTS ################## - smart_chunked_answers_dict, one_to_one_fields = one_to_one_funcs.run_smart_chunked_fields( - one_to_one_fields, constants, contract_text, filename + + ################## RUN HYBRID SMART CHUNKED PROMPTS ################## + # RAG function loads retrieval questions internally and matches with investment_prompts.json + hybrid_smart_chunked_answers_dict = hybrid_smart_chunking_funcs.run_hybrid_smart_chunked_fields( + one_to_one_fields, constants, contract_text, filename, text_dict ) - + ################## RUN FULL CONTEXT PROMPTS ################## full_context_answers_dict = one_to_one_funcs.run_full_context_fields( one_to_one_fields, contract_text, constants, filename ) - ################## COMBINE ANSWERS ################ - one_to_one_results.update(smart_chunked_answers_dict) - one_to_one_results.update(full_context_answers_dict) + # Prefer HSC answers over full_context when HSC value is not N/A + for key, value in full_context_answers_dict.items(): + one_to_one_results[key] = value + + for key, value in hybrid_smart_chunked_answers_dict.items(): + if not string_utils.is_empty(value): + one_to_one_results[key] = value + elif key not in one_to_one_results: + # Add HSC's N/A if field doesn't exist yet + one_to_one_results[key] = value ################## ADD AD FIELDS ################ one_to_one_results = one_to_one_funcs.get_aarete_derived_dates(one_to_one_results, text_dict, filename) - + ################## Crosswalk Fields ################## one_to_one_results = aarete_derived.get_crosswalk_fields( [one_to_one_results], constants diff --git a/fieldExtraction/src/investment/hybrid_smart_chunking_funcs.py b/fieldExtraction/src/investment/hybrid_smart_chunking_funcs.py new file mode 100644 index 0000000..9c3c468 --- /dev/null +++ b/fieldExtraction/src/investment/hybrid_smart_chunking_funcs.py @@ -0,0 +1,322 @@ +""" +RAG-Based Field Extraction Functions + +This module provides RAG (Retrieval-Augmented Generation) based field extraction +for contract processing using hybrid retrieval (BM25 + semantic search) with reranking. +""" + +import gc +import logging +import re +from typing import List, Tuple, Dict + +from langchain.schema import Document +from langchain_text_splitters import RecursiveCharacterTextSplitter + +import src.utils.llm_utils as llm_utils +import src.utils.string_utils as string_utils +import src.investment.preprocessing_funcs as preprocessing_funcs +import src.investment.hybrid_smart_chunking_preprocessing as hybrid_smart_chunking_preprocessing +from constants.constants import Constants +from constants.delimiters import Delimiter +from src import config +from src.prompts import prompt_templates +from src.prompts.fieldset import FieldSet, Field +from src.investment.one_to_one_funcs import check_and_update_effective_date +from src.utils.rag_utils import ( + RAGConfig, + HSC_CONFIG, + get_bedrock_client, + get_reranker_client, + get_embeddings, + create_vectorstore, + create_hybrid_retriever, + rerank_documents, +) + + +def clip_context_by_tokens(context: str, max_tokens: int = 150000) -> str: + """ + Clip context text if it exceeds max_tokens. + Uses closed formula: char_count / 3.5 for Claude tokenization. + + Args: + context: Text to potentially clip + max_tokens: Maximum token limit (default 150k for Claude 200k window) + + Returns: + Clipped or original context text + """ + if not context: + return "" + + # Estimate tokens: char_count / 3.5 (accurate for Claude) + token_count = int(len(context) / 3.5) + + # Only clip if exceeds max_tokens + if token_count > max_tokens: + approx_chars = int(max_tokens * 3.5) + clipped_context = context[:approx_chars] + logging.info(f"Context clipped from ~{token_count} to ~{max_tokens} tokens") + return clipped_context + else: + logging.info(f"Context size ~{token_count} tokens - no clipping needed") + return context + + +def prepare_documents_for_chunking( + sections: dict, filename: str = "contract" +) -> List[Document]: + """Prepare contract sections as documents for chunking.""" + documents = [] + for section, section_data in sections.items(): + section_header = section + page_numbers = section_data["pages"] + section_text = section_data["content"] + doc = Document( + page_content=section_text, + metadata={ + "section_header": section_header, + "section_length": len(section_text), + "pages": page_numbers, + "filename": filename, + }, + ) + documents.append(doc) + + return documents + + +def format_chunks_for_llm(text_dict: Dict, docs: List[Document]) -> str: + """Format retrieved documents for LLM context.""" + if not docs: + return "" + + pages = set() + + for doc in docs: + if "pages" in doc.metadata: + pages.update(doc.metadata["pages"]) + + pages = set(map(str, pages)) + + # Filter out None values and pages that don't exist in text_dict + context = "\n\n".join( + [ + text_dict[page] + for page in pages + if page in text_dict and text_dict[page] is not None + ] + ) + + return clip_context_by_tokens(context) + + +def add_page_numbers_to_chunks(sections): + prev = None + updated_sections = {} + for section, section_data in sections.items(): + matches = re.findall(r"Start of Page No\.?\s*=\s*(\d+)", section_data) + prev_last_page = int(prev[-1]) if prev else None + matches = [int(m) for m in matches] + all_matches = ( + matches + [prev_last_page] if prev_last_page is not None else matches + ) + all_matches = sorted(set(all_matches)) + updated_sections[section] = {"content": section_data, "pages": all_matches} + prev = all_matches + return updated_sections + + +def preprocessing_pipeline(contract_text): + cleaned_text = preprocessing_funcs.clean_newlines(contract_text) + cleaned_text = preprocessing_funcs.clean_law_symbols(cleaned_text) + cleaned_text = re.sub( + r"^(.*?)(Start of Page No\.?\s*=\s*1)", r"\2", cleaned_text, flags=re.DOTALL + ) + sections, original_content, line_ending, verification_result = ( + hybrid_smart_chunking_preprocessing.split_contract_text_into_sections( + cleaned_text + ) + ) + is_preserved = verification_result[0] # get status + + if not is_preserved: + logging.info( + "Failed to preserve content during section split, reverting to full contract" + ) + sections = {} + chunk_size = 9000 # keep bigger chunks to have enough coverage as it would be split further by text splitter + for i in range(0, len(cleaned_text), chunk_size): + section_name = f"Section {i // chunk_size}" + sections[section_name] = cleaned_text[i : i + chunk_size] + + sections_with_page_numbers = add_page_numbers_to_chunks(sections) + return sections_with_page_numbers, is_preserved + + +def run_hybrid_smart_chunked_fields( + one_to_one_fields: FieldSet, + constants, + contract_text: str, + filename: str, + text_dict: dict, + rag_config: RAGConfig = HSC_CONFIG, +) -> dict[str, str]: + """Process fields using RAG: hybrid retrieval (BM25+semantic) + reranking. + + Uses retrieval questions from the field's retrieval_question attribute for chunk retrieval, + then actual LLM prompts from the field's prompt attribute for field extraction. + """ + + logging.info(f"Starting RAG extraction for {filename}") + + # Collect fields that have retrieval questions defined + # Only process fields that have the retrieval_question attribute set + fields_to_process = [ + field for field in one_to_one_fields.fields + if field.retrieval_question is not None + ] + + if not fields_to_process: + logging.info(f"No fields with retrieval questions found | Filename: {filename}") + return {} + + try: + # Setup: clients, splitter, chunks + reranker = get_reranker_client(rag_config.RERANKER) + embeddings = get_embeddings( + rag_config.EMBEDDING_MODEL_ID, rag_config.BEDROCK_REGION + ) + splitter = RecursiveCharacterTextSplitter( + chunk_size=rag_config.CHUNK_SIZE, + chunk_overlap=rag_config.CHUNK_OVERLAP, + add_start_index=True, + strip_whitespace=True, + separators=["\n\n", "\n", " ", ""], + ) + + sections, is_preserved = preprocessing_pipeline(contract_text) + docs = prepare_documents_for_chunking(sections, filename) + chunks = splitter.split_documents(docs) + del docs + gc.collect() + + if not chunks: + return {} + + # Build retriever + vectorstore = create_vectorstore(chunks, embeddings) + retriever = create_hybrid_retriever(chunks, vectorstore, rag_config) + + # Process fields + answers_dict = {} + for field in fields_to_process: + try: + # Get retrieval question from field attribute + retrieval_query = field.retrieval_question + + if not retrieval_query: + continue + # Retrieve + rerank using retrieval question + retrieved = retriever.invoke(retrieval_query)[ + : rag_config.ENSEMBLE_TOP_K + ] + reranked = rerank_documents( + retrieval_query, + retrieved, + reranker, + rag_config.RERANKER_TOP_K, + rag_config.RERANKER, + ) + + # Build context and use actual LLM prompt for extraction + context = format_chunks_for_llm(text_dict, reranked) + + if len(context) == 0: + # Fallback: mark field for full_context processing + field.field_type = "full_context" + logging.warning( + f"No context retrieved for {field.field_name}, marking as full_context" + ) + continue + + llm_prompt = field.get_prompt( + constants + ) # Actual extraction prompt from investment_prompts.json + prompt = prompt_templates.ONE_TO_ONE_SINGLE_FIELD_TEMPLATE( + context, llm_prompt + ) + + logging.info(f"Field: {field.field_name}") + logging.info(f"Retrieval question: {retrieval_query}") + logging.info(f"LLM prompt: {llm_prompt}") + + # LLM call + raw = llm_utils.invoke_claude( + prompt, "sonnet_latest", filename, max_tokens=8192 + ) + + parsed = string_utils.extract_text_from_delimiters( + raw, Delimiter.PIPE + ) + + answers_dict[field.field_name] = parsed if parsed else "N/A" + + if answers_dict[field.field_name] == "N/A": + field.field_type = "full_context" + logging.warning( + f"Obtained N/A for {field.field_name}, marking as full_context" + ) + + except Exception as e: + # Mark field for full_context retry and preserve error info + field.field_type = "full_context" + logging.error( + f"Error on {field.field_name}: {type(e).__name__}: {str(e)}, marking as full_context" + ) + answers_dict[field.field_name] = "N/A" + + del retriever, vectorstore, chunks + gc.collect() + logging.info(f"RAG extraction done: {len(answers_dict)} fields") + + answers_dict = check_and_update_effective_date( + answers_dict, text_dict, filename + ) + + # even after running the vision api, if the effective date is still N/A, we need to run the prompt again with full context + if "AARETE_DERIVED_EFFECTIVE_DT" in answers_dict and string_utils.is_empty( + answers_dict.get("AARETE_DERIVED_EFFECTIVE_DT") + ): + # if the effective date is N/A, we need to run the prompt again with full context + one_to_one_fields.update_field_attr( + field_name="AARETE_DERIVED_EFFECTIVE_DT", + attr="field_type", + value="full_context", + ) + # if the termination date is N/A, we need to run the prompt again with full context + if "TERMINATION_DT" in answers_dict and string_utils.is_empty( + answers_dict.get("TERMINATION_DT") + ): + # if the termination date is N/A, we need to run the prompt again with full context + one_to_one_fields.update_field_attr( + field_name="TERMINATION_DT", + attr="field_type", + value="full_context", + ) + + # Normalize PROVIDER_STATE and PAYER_STATE to standardized two-letter abbreviation + for state_field in ["PROVIDER_STATE", "PAYER_STATE"]: + if state_field in answers_dict: + answers_dict[state_field] = ( + string_utils.normalize_state_to_abbreviation( + answers_dict[state_field] + ) + ) + + return answers_dict + + except Exception as e: + logging.error(f"RAG pipeline failed: {e}") + return {} diff --git a/fieldExtraction/src/investment/hybrid_smart_chunking_preprocessing.py b/fieldExtraction/src/investment/hybrid_smart_chunking_preprocessing.py new file mode 100644 index 0000000..de327f8 --- /dev/null +++ b/fieldExtraction/src/investment/hybrid_smart_chunking_preprocessing.py @@ -0,0 +1,304 @@ +import re + + +def split_contract_text_into_sections(text_content): + """ + Splits a text document into sections based on common heading patterns, + ensuring 100% text preservation by tracking every single character. + + Args: + text_content (str): The text content to split into sections + + Returns: + tuple: (sections_dict, original_content, line_ending, verification_result) + - sections_dict: Dictionary with section titles as keys and content as values + - original_content: The original text content (for verification) + - line_ending: The line ending type detected ('\n', '\r\n', or '') + - verification_result: Tuple (is_preserved, message) indicating if content was preserved + """ + + original_content = text_content + + # Split into lines but preserve the original line endings + if "\r\n" in original_content: + raw_lines = original_content.split("\r\n") + line_ending = "\r\n" + elif "\n" in original_content: + raw_lines = original_content.split("\n") + line_ending = "\n" + else: + raw_lines = [original_content] + line_ending = "" + + # --- Define all heading patterns --- + main_arabic_heading_pattern = re.compile( + r"^\s*(\d{1})\.?\s+([A-Za-z0-9\s&\/-]+?)\.?\s*$", re.IGNORECASE + ) + sub_arabic_heading_pattern = re.compile( + r"^\s*(\d+(?:\.\d+)+)\s+([A-Za-z0-9\s&\/-]+?)\.?\s*$", re.IGNORECASE + ) + main_roman_heading_pattern = re.compile( + r"^\s*([IVXLCDM]+)\.?\s+([A-Za-z0-9\s&\/-]+?)\.?\s*$", re.IGNORECASE + ) + keyword_colon_pattern = re.compile(r"^\s*(RECITALS|AGREEMENT):\s*$", re.IGNORECASE) + standalone_keyword_pattern = re.compile( + r"^\s*(RECITALS|AGREEMENT)\s*$", re.IGNORECASE + ) + article_roman_pattern = re.compile( + r"^\s*ARTICLE\s+([IVXLCDM]+)\.?\s+([A-Za-z0-9\s&\/-]+?)\.?\s*$", re.IGNORECASE + ) + exhibit_pattern = re.compile(r"^\s*EXHIBIT\s+([A-Z])(?:\s+(.*))?$", re.IGNORECASE) + attachment_pattern = re.compile( + r"^\s*Attachment\s+([A-Za-z0-9\s&\/.-]+?)\s*$", re.IGNORECASE + ) + in_witness_whereof_pattern = re.compile( + r"^\s*IN\s+WITNESS\s+WHEREOF,?$", re.IGNORECASE + ) + signatory_name_pattern = re.compile(r"^\s*([A-Z][A-Z0-9\s&\/.-]+)$") + specific_signatory_pattern = re.compile(r"^\s*(PROVIDER|ICM):\s*$", re.IGNORECASE) + + # --- Identify all potential heading lines --- + heading_events = [] + + i = 0 + while i < len(raw_lines): + line_stripped = raw_lines[i].strip() + next_line_raw = raw_lines[i + 1] if i + 1 < len(raw_lines) else "" + next_line_stripped = next_line_raw.strip() + + current_heading_title = None + heading_start_line = i + heading_end_line = i + lines_consumed = 1 + + # Check for multi-line heading prefixes + is_numeric_prefix_only = re.fullmatch(r"^\s*(\d{1})\.?\s*$", line_stripped) + is_roman_prefix_only = re.fullmatch(r"^\s*([IVXLCDM]+)\.?\s*$", line_stripped) + is_exhibit_prefix_only = re.fullmatch( + r"^\s*EXHIBIT\s+([A-Z])\s*$", line_stripped, re.IGNORECASE + ) + + # Check if next line is a primary heading + primary_heading_patterns = [ + sub_arabic_heading_pattern, + main_arabic_heading_pattern, + main_roman_heading_pattern, + keyword_colon_pattern, + standalone_keyword_pattern, + article_roman_pattern, + exhibit_pattern, + attachment_pattern, + in_witness_whereof_pattern, + specific_signatory_pattern, + ] + + is_next_line_a_primary_heading = any( + pattern.match(next_line_stripped) for pattern in primary_heading_patterns + ) + + # Check if next line is a signatory block + if ( + signatory_name_pattern.match(next_line_stripped) + and len(next_line_stripped.split()) > 1 + and i + 2 < len(raw_lines) + ): + line_after_next_stripped = raw_lines[i + 2].strip() + if re.match( + r"^\s*By:", line_after_next_stripped, re.IGNORECASE + ) or re.match(r"^\s*Name:", line_after_next_stripped, re.IGNORECASE): + is_next_line_a_primary_heading = True + + # Multi-line heading combination logic + if ( + (is_numeric_prefix_only or is_roman_prefix_only) + and next_line_stripped + and not is_next_line_a_primary_heading + ): + current_heading_title = f"{line_stripped} {next_line_stripped}" + heading_end_line = i + 1 + lines_consumed = 2 + elif ( + is_exhibit_prefix_only + and next_line_stripped + and not is_next_line_a_primary_heading + ): + current_heading_title = f"{line_stripped} {next_line_stripped}" + heading_end_line = i + 1 + lines_consumed = 2 + else: + # Single line heading checks + if article_roman_pattern.match(line_stripped): + match = article_roman_pattern.match(line_stripped) + current_heading_title = ( + f"ARTICLE {match.group(1)} {match.group(2).strip().rstrip('.')}" + ) + elif keyword_colon_pattern.match(line_stripped): + match = keyword_colon_pattern.match(line_stripped) + current_heading_title = match.group(1).strip().upper() + elif standalone_keyword_pattern.match(line_stripped): + match = standalone_keyword_pattern.match(line_stripped) + current_heading_title = match.group(1).strip().upper() + elif exhibit_pattern.match(line_stripped): + match = exhibit_pattern.match(line_stripped) + exhibit_title = f"EXHIBIT {match.group(1).strip()}" + if match.group(2): + exhibit_title += f" {match.group(2).strip()}" + current_heading_title = exhibit_title + elif attachment_pattern.match(line_stripped): + match = attachment_pattern.match(line_stripped) + current_heading_title = f"Attachment {match.group(1).strip()}" + elif in_witness_whereof_pattern.match(line_stripped): + current_heading_title = "IN WITNESS WHEREOF" + elif sub_arabic_heading_pattern.match(line_stripped): + match = sub_arabic_heading_pattern.match(line_stripped) + current_heading_title = ( + f"{match.group(1)} {match.group(2).strip().rstrip('.')}" + ) + elif main_arabic_heading_pattern.match(line_stripped): + match = main_arabic_heading_pattern.match(line_stripped) + current_heading_title = ( + f"{match.group(1)} {match.group(2).strip().rstrip('.')}" + ) + elif main_roman_heading_pattern.match(line_stripped): + match = main_roman_heading_pattern.match(line_stripped) + current_heading_title = ( + f"{match.group(1)} {match.group(2).strip().rstrip('.')}" + ) + elif specific_signatory_pattern.match(line_stripped): + match = specific_signatory_pattern.match(line_stripped) + current_heading_title = match.group(1).strip().upper() + else: + # Check for signatory block + if ( + signatory_name_pattern.match(line_stripped) + and len(line_stripped.split()) > 1 + ): + for j in range(1, min(5, len(raw_lines) - i)): + next_line = raw_lines[i + j].strip() + if re.match(r"^\s*By:", next_line, re.IGNORECASE) or re.match( + r"^\s*Name:", next_line, re.IGNORECASE + ): + current_heading_title = line_stripped + break + + if current_heading_title: + heading_events.append( + (heading_start_line, heading_end_line, current_heading_title) + ) + + i += lines_consumed + + # Sort heading events by their start line index + heading_events.sort(key=lambda x: x[0]) + + # --- SECTION EXTRACTION --- + # This ensures every single line is accounted for and handles duplicate section titles + + sections = {} + section_counter = {} # Track occurrences of each title + current_start_line = 0 + current_section_title = "Preamble" + + for start_line, end_line, title in heading_events: + # Add content before this heading to current section + if current_start_line < start_line: + section_lines = raw_lines[current_start_line:start_line] + section_content = line_ending.join(section_lines) + if section_content: # Only add non-empty sections + sections[current_section_title] = section_content + + # Make section title unique if we've seen it before + if title in section_counter: + section_counter[title] += 1 + unique_title = f"{title} ({section_counter[title]})" + else: + section_counter[title] = 1 + unique_title = title + + # Start new section with the heading itself + current_section_title = unique_title + current_start_line = start_line + + # Add the final section (from last heading to end of file) + if current_start_line < len(raw_lines): + section_lines = raw_lines[current_start_line:] + section_content = line_ending.join(section_lines) + if section_content: + sections[current_section_title] = section_content + elif not sections and raw_lines: + # Edge case: no headings found + sections["Preamble"] = line_ending.join(raw_lines) + + # Verify preservation + verification_result = verify_preservation(original_content, sections, line_ending) + + return sections, original_content, line_ending, verification_result + + +def verify_preservation(original_content, sections, line_ending): + """ + Verification that ensures 100% text preservation. + + Returns: + tuple: (is_preserved, message) where is_preserved is True if content is perfectly preserved + """ + # Reconstruct the content exactly as it was, preserving order + reconstructed_content = line_ending.join(sections.values()) + + # Character-by-character comparison + if original_content == reconstructed_content: + return True, "Perfect preservation: All content preserved exactly." + + # Find the exact difference + original_len = len(original_content) + reconstructed_len = len(reconstructed_content) + + # Find first difference + min_len = min(original_len, reconstructed_len) + for i in range(min_len): + if original_content[i] != reconstructed_content[i]: + context_start = max(0, i - 50) + context_end = min(original_len, i + 50) + return False, ( + f"First difference at position {i}:\n" + f"Original: {repr(original_content[context_start:context_end])}\n" + f"Reconstructed: {repr(reconstructed_content[context_start:context_end])}" + ) + + # Same content but different lengths + if original_len != reconstructed_len: + shorter, longer = ( + (original_content, reconstructed_content) + if original_len < reconstructed_len + else (reconstructed_content, original_content) + ) + return ( + False, + f"Length mismatch. Extra content in longer version: {repr(longer[min_len:min_len+50])}", + ) + + return False, "Unknown difference detected." + + +def get_section_info(sections): + """ + Helper function to get information about the extracted sections. + + Returns: + dict: Information about sections including counts and character counts + """ + info = { + "total_sections": len(sections), + "section_details": {}, + "total_characters": 0, + } + + for title, content in sections.items(): + char_count = len(content) + info["section_details"][title] = { + "character_count": char_count, + "line_count": content.count("\n") + 1 if content else 0, + } + info["total_characters"] += char_count + + return info \ No newline at end of file diff --git a/fieldExtraction/src/investment/one_to_one_funcs.py b/fieldExtraction/src/investment/one_to_one_funcs.py index ebea8e2..df85209 100644 --- a/fieldExtraction/src/investment/one_to_one_funcs.py +++ b/fieldExtraction/src/investment/one_to_one_funcs.py @@ -11,11 +11,6 @@ from constants.constants import Constants from constants.delimiters import Delimiter from src import config from src.investment import postprocessing_funcs -from src.investment.smart_chunking_funcs import ( - field_context, - group_fields, - stitch_chunks, -) from src.investment.vision_funcs import get_image_array_based_answer from src.prompts.fieldset import Field, FieldSet @@ -63,72 +58,6 @@ def extract_global_lesser_of(contract_text: str, filename: str) -> dict: or "N/A" } - -def run_smart_chunked_fields( - one_to_one_fields: FieldSet, - constants, - contract_text: str, - filename: str, -) -> tuple[dict[str, str], FieldSet]: - """Process fields that require smart chunking and return the extracted answers. - - Args: - one_to_one_fields (FieldSet): FieldSet containing the fields to be processed. - contract_text (str): Full text of contract. - filename (str): Name of the file being processed. - - Raises: ValueError: If the field group has no defined fields. - - Returns: - dict[str, str]: Dictionary mapping field names to extracted answers. - FieldSet: FieldSet containing the 1:1 fields - """ - - one_to_one_fields, splits = field_context( - one_to_one_fields, constants, contract_text, filename - ) - - smart_chunked_fields = one_to_one_fields.filter( - field_type="smart_chunked" - ) # Only get the ones that are still smart_chunked after getting context - groups = group_fields(smart_chunked_fields) - - # create new field groups and corresponding chunks - new_field_groups = {} - one_to_one_chunks = {} - for idx, group in enumerate(groups, 1): - # convert dictionary of fields to list of fields - group_list = list(group) - new_field_groups[f"Group {idx}"] = {"fields": group_list} - - # combine chunks of fields of one group into one list - retrieved_text_list = [field.retrieved_text for field in group_list] - - # Flatten list of lists into one list - retrieved_text_list = [ - item for internal_list in retrieved_text_list for item in internal_list - ] - retrieved_text_list = sorted( - set( - item - for sublist in retrieved_text_list - for item in (sublist if isinstance(sublist, (list, set)) else [sublist]) - ) - ) - - # join relevant chunks of all fields of the group into one text - one_to_one_chunks[f"Group {idx}"] = stitch_chunks(retrieved_text_list) - - field_groups = [field_group for field_group in new_field_groups.keys()] - - answers_dict = {} - for field_group in field_groups: - answers_dict = prompt_calls.prompt_smart_chunked(answers_dict, new_field_groups, field_group, one_to_one_chunks, filename) - - one_to_one_fields = pass_smart_chunked_to_full_context(answers_dict, one_to_one_fields) - - return answers_dict, one_to_one_fields - def pass_smart_chunked_to_full_context(answers_dict, one_to_one_fields): # even after running the vision api, if the effective date is still N/A, we need to run the prompt again with full context if "EFFECTIVE_DT" in answers_dict and string_utils.is_empty( diff --git a/fieldExtraction/src/investment/preprocess.py b/fieldExtraction/src/investment/preprocess.py index 850e0a1..d6969f4 100644 --- a/fieldExtraction/src/investment/preprocess.py +++ b/fieldExtraction/src/investment/preprocess.py @@ -1,6 +1,5 @@ import logging -import src.investment.smart_chunking_funcs as smart_chunking_funcs import src.investment.table_funcs as table_funcs from src import config from src.investment import preprocessing_funcs diff --git a/fieldExtraction/src/investment/smart_chunking_funcs.py b/fieldExtraction/src/investment/smart_chunking_funcs.py deleted file mode 100644 index 1d1433f..0000000 --- a/fieldExtraction/src/investment/smart_chunking_funcs.py +++ /dev/null @@ -1,505 +0,0 @@ -""" -Smart Chunking Functions for Contract Processing - -This module provides intelligent text chunking and semantic search capabilities -for contract processing. It includes: - -- Lightweight in-memory vectorstore operations using FAISS -- Multiple chunking strategies (hierarchical, OR, AND, regex) -- Semantic and keyword-based chunk retrieval -- Optimized for ephemeral, per-contract vectorstore usage - -Key Components: -- field_context(): Main function for semantic chunk retrieval using FAISS -- group_fields(): Groups fields based on retrieved text overlap -- Various chunk_*() functions: Different keyword matching strategies - -Performance Notes: -- Uses FAISS for fast, in-memory similarity search -- No persistent storage - vectorstores are created and destroyed per contract -- Designed for concurrent processing without resource leaks -- Much lower memory footprint than persistent vectorstore solutions -""" - -import json -import os -import re -from collections import defaultdict -from itertools import combinations -from typing import Any, Dict, List, Optional, Set, Tuple, Union - -import nltk -from constants.constants import Constants -from langchain_aws.embeddings.bedrock import BedrockEmbeddings -from langchain_community.retrievers import BM25Retriever -from langchain_community.vectorstores import FAISS -from langchain_text_splitters import RecursiveCharacterTextSplitter -from nltk.tokenize import word_tokenize -from src import config -from src.prompts.fieldset import FieldSet - -# Append path of NLTK data directory (fieldExtraction/nltk_data) -nltk.data.path.append( - os.path.join( - os.path.dirname(os.path.dirname(os.path.dirname(__file__))), "nltk_data" - ) -) - - -# Setup bedrock -embeddings_model = BedrockEmbeddings( - client=config.BEDROCK_RUNTIME, model_id="amazon.titan-embed-text-v1" -) - -# =================================================================== -# CONFIGURATION CONSTANTS -# =================================================================== - -# Text chunking parameters -CHUNK_PREFIX_LENGTH = 11 # "chunk 001: " length -CHUNK_OVERLAP = 500 -CHUNK_SIZE = 2000 -TOP_K = 8 - -# Regular expressions -DATE_PATTERN = r"(?i)\bday of\b|\b(?:jan(?:uary)?|feb(?:ruary)?|mar(?:ch)?|apr(?:il)?|may|jun(?:e)?|jul(?:y)?|aug(?:ust)?|sep(?:tember)?|oct(?:ober)?|nov(?:ember)?|dec(?:ember)?)\b|\b(?:\d{1,2}[\/\.-]\d{1,2}[\/\.-](?:\d{2}|\d{4})|\d{4}[\/\.-]\d{1,2}[\/\.-]\d{1,2}|\d{8}|\d{2}\d{2}|\d{4}[\/\.-]\d{1,2}[\/\.-]\d{1,2}|\d{4}[\.]\d{1,2}[\.]\d{1,2})\b" - - -def parse_chunk(chunk: str) -> tuple[int, str]: - """Extract chunk number and content from a chunk string - - Args: - chunk (str): Text chunk, prefixed with "Chunk NNN: " - - Returns: - tuple[int, str]: Tuple of (chunk_number, content) - """ - if not chunk.startswith("chunk ") or len(chunk) < CHUNK_PREFIX_LENGTH: - raise ValueError("Invalid chunk format") - try: - chunk_number = int(chunk[6:9]) # Extract chunk number from "chunk NNN: " - except ValueError: - raise ValueError("Invalid chunk number format") - content = chunk[CHUNK_PREFIX_LENGTH:] - return chunk_number, content - - -def find_overlap(prev_chunk, next_chunk, min_overlap=3): - """ - Finds the maximum overlap between the end of prev_chunk and the start of next_chunk. - Returns the number of overlapping characters. - """ - max_overlap = min(len(prev_chunk), len(next_chunk)) - for i in range(max_overlap, min_overlap - 1, -1): - if prev_chunk[-i:] == next_chunk[:i]: - return i - return 0 # No overlap found - - -def stitch_chunks(chunks: list[str], overlap_size: int = CHUNK_OVERLAP) -> str: - """Stitch together a list of chunks, with an overlap of `overlap_size` characters - - Args: - chunks (list[str]): List of chunk strings, each prefixed with "chunk NNN: " - overlap_size (int, optional): Overlap size between chunks. Defaults to CHUNK_OVERLAP. - - Returns: - str: Combined text with properly handled overlaps - """ - if not chunks: - return "" - - if overlap_size < 0: - raise ValueError("Overlap size must be non-negative") - - # Parse and sort chunks - parsed_chunks = [parse_chunk(chunk) for chunk in chunks] - sorted_chunks = sorted(parsed_chunks, key=lambda x: x[0]) - - # Start with the first chunk's content - result = sorted_chunks[0][1] - - # Process subsequent chunks - for i in range(1, len(sorted_chunks)): - current_chunk = sorted_chunks[i][1] - # if chunks are contiguous - if sorted_chunks[i][0] == sorted_chunks[i - 1][0] + 1: - # Check for overlap - overlap = find_overlap(result, current_chunk) - if ( - overlap > 0 and len(result) >= overlap and len(current_chunk) >= overlap - ): # if overlap is enabled and there is enough text to overlap - result = result[:-overlap] + current_chunk - else: - result += "\n" + current_chunk - else: # if chunks are not contiguous, just append them - result += "\n" + current_chunk - - return result - - -def field_context( - one_to_one_fields: FieldSet, constants: Constants, contract_text: str, filename: str -) -> Tuple[FieldSet, List[str]]: - """Lightweight field context extraction using FAISS (in-memory only).""" - - # Convert contract_text to lowercase for case-insensitive processing - contract_text = contract_text.lower() - - text_splitter = RecursiveCharacterTextSplitter( - chunk_size=CHUNK_SIZE, chunk_overlap=CHUNK_OVERLAP - ) - splits = text_splitter.split_text(contract_text) - - # add chunk number starting from 1 - splits = [ - "chunk " + str(idx + 1).zfill(3) + ": " + str(split) - for idx, split in enumerate(splits) - ] - - # Create lightweight FAISS vectorstore (in-memory only) - vectorstore = FAISS.from_texts(texts=splits, embedding=embeddings_model) - - vectorstore_retriever = vectorstore.as_retriever(search_kwargs={"k": TOP_K}) - keyword_retriever = BM25Retriever.from_texts( - splits, k=TOP_K, preprocess_func=word_tokenize - ) - - for field in one_to_one_fields.fields: - if field.field_type == "smart_chunked": - questions = {} - questions[field.field_name] = field.get_prompt(constants) - question = json.dumps(questions) - - # Convert field keywords to lowercase for case-insensitive processing - field_keywords = [keyword.lower() for keyword in field.keywords] - - # get relevant chunks based on semantic search and keyword search and merge them - semantic_relevant_docs = vectorstore_retriever.invoke(question) - keyword_relevant_docs = keyword_retriever.invoke(" ;".join(field.keywords)) - semantic_relevant_chunks = [ - doc.page_content for doc in semantic_relevant_docs - ] - keyword_relevant_chunks = [ - doc.page_content for doc in keyword_relevant_docs - ] - if field.field_name == "CONTRACT_TITLE": - keyword_relevant_chunks = keyword_relevant_chunks + splits[:5] - field.retrieved_text = sorted( - list(set(semantic_relevant_chunks + keyword_relevant_chunks)) - ) - - # Fallback: if no context or all context, treat as full_context - if len(field.retrieved_text) == 0 or len(field.retrieved_text) == len( - splits - ): - field.field_type = "full_context" - - return one_to_one_fields, splits - - -# Function to group fields based on overlap threshold -def group_fields(field_set: FieldSet, threshold=3) -> List[Set]: - """ - Groups fields in the given FieldSet based on the overlap of their retrieved text. - Args: - field_set (FieldSet): A FieldSet object containing fields to be grouped. - threshold (int, optional): The minimum overlap required to group fields together. Defaults to 3. - Returns: - List[Set[Field]]: A list of sets, where each set contains fields that have sufficient overlap. - """ - overlap_matrix = { - (f1, f2): len(set(f1.retrieved_text) & set(f2.retrieved_text)) - for f1, f2 in combinations(field_set.fields, 2) - if (overlap := len(set(f1.retrieved_text) & set(f2.retrieved_text))) - >= threshold - } - - groups = [] - visited = set() # store field objects, not names - - for field in field_set.fields: - if field not in visited: # check if field is already in a group - group = {field} - stack = [field] - while stack: - curr = stack.pop() - visited.add(curr) # add field to visited set - for (f1, f2), overlap in overlap_matrix.items(): - if f1 == curr and f2 not in visited and overlap >= threshold: - group.add(f2) - stack.append(f2) - elif f2 == curr and f1 not in visited and overlap >= threshold: - group.add(f1) - stack.append(f1) - if group: # only add non-empty groups - groups.append(group) - return groups - - -def chunk_hierarchical( - text_dict: dict[str, str], - keywords: list[str], - regex: str = None, - case_sensitive: bool = False, -) -> list[str]: - """Look for the keywords, one-by-one, and take the first that satisfies the keyword. - We pad responsive pages with a one-page buffer (before and after) when possible. - - Args: - text_dict (dict[str, str]): A dictionary keyed by string page number and valued - by string page contents - keywords (list[str]): A list of string keywords - case_sensitive(bool): A flag for converting all keywords and text to lower case, - in order to ignore case altogether. - - Returns: - list[str]: The sorted list of string page numbers responsive to the first - keyword found - """ - if not case_sensitive: - keywords = [keyword.lower() for keyword in keywords] - text_dict = {key: value.lower() for key, value in text_dict.items()} - - page_list = [] - for keyword in keywords: - if len(page_list) == 0: - for page in text_dict.keys(): - if keyword in text_dict[page]: - if str(int(page) - 1) in text_dict.keys(): - page_list.append(str(int(page) - 1)) - - page_list.append(page) - - if str(int(page) + 1) in text_dict.keys(): - page_list.append(str(int(page) + 1)) - - page_list_sorted = sorted([int(page_str) for page_str in set(page_list)]) - page_list_sorted_str = list(map(str, page_list_sorted)) - return page_list_sorted_str - - -def chunk_or( - text_dict: dict[str, str], - keywords: list[str], - regex: str = None, - case_sensitive: bool = False, -) -> list[str]: - """Look for any of the keywords and add any pages with any keywords to the chunk. - We pad responsive pages with a one-page buffer (before and after) when possible. - - Args: - text_dict (dict[str, str]): A dictionary keyed by string page number and valued - by string page contents - keywords (list[str]): A list of string keywords - case_sensitive(bool): A flag for converting all keywords and text to lower case, - in order to ignore case altogether. - - Returns: - list[str]: The sorted list of string page numbers responsive to the first - keyword found - """ - if not case_sensitive: - keywords = [keyword.lower() for keyword in keywords] - text_dict = {key: value.lower() for key, value in text_dict.items()} - - page_list = [] - for page in text_dict.keys(): - for keyword in keywords: - if keyword in text_dict[page]: - # Add buffer of previous and next page, if we're not at the beginning nor end - # If we are at the beginning (or the end) just take the next (or previous) page - if str(int(page) - 1) in text_dict.keys(): - page_list.append(str(int(page) - 1)) - - page_list.append(page) - - if str(int(page) + 1) in text_dict.keys(): - page_list.append(str(int(page) + 1)) - - page_list_sorted = sorted([int(page_str) for page_str in set(page_list)]) - page_list_sorted_str = list(map(str, page_list_sorted)) - return page_list_sorted_str - - -# And: run each keyword and include a page if all are present -def chunk_and( - text_dict: dict[str, str], - keywords: list[str], - regex: str = None, - case_sensitive: bool = False, -) -> list[str]: - """Look for all of the keywords and add any pages with all keywords to the chunk. - We pad responsive pages with a one-page buffer (before and after) when possible. - - Args: - text_dict (dict[str, str]): A dictionary keyed by string page number and valued - by string page contents - keywords (list[str]): A list of string keywords - case_sensitive(bool): A flag for converting all keywords and text to lower case, - in order to ignore case altogether. - - Returns: - list[str]: The sorted list of string page numbers responsive to the first - keyword found - """ - if not case_sensitive: - keywords = [keyword.lower() for keyword in keywords] - text_dict = {key: value.lower() for key, value in text_dict.items()} - - page_list = [] - for page in text_dict.keys(): - if all([keyword in text_dict[page] for keyword in keywords]): - # Add buffer of previous and next page, if we're not at the beginning nor end - # If we are at the beginning (or the end) just take the next (or previous) page - if str(int(page) - 1) in text_dict.keys(): - page_list.append(str(int(page) - 1)) - - page_list.append(page) - - if str(int(page) + 1) in text_dict.keys(): - page_list.append(str(int(page) + 1)) - - page_list_sorted = sorted([int(page_str) for page_str in set(page_list)]) - page_list_sorted_str = list(map(str, page_list_sorted)) - return page_list_sorted_str - - -# Regex: Check for a regex and include a page if the regex matches within the page -def chunk_regex( - text_dict: dict[str, str], keywords: list[str], regex: str, case_sensitive: bool -) -> list[str]: - """Look for regex match and add any pages with regex match to the chunk. - We pad responsive pages with a one-page buffer (before and after) when possible. - - Args: - text_dict (dict[str, str]): A dictionary keyed by string page number and valued - by string page contents - regex (str): A regex expression - - Returns: - list[str]: The sorted list of string page numbers responsive to the first - keyword found - """ - - page_list = [] - for page in text_dict.keys(): - if re.search(regex, text_dict[page]): - # Add buffer of previous and next page, if we're not at the beginning nor end - # If we are at the beginning (or the end) just take the next (or previous) page - if str(int(page) - 1) in text_dict.keys(): - page_list.append(str(int(page) - 1)) - - page_list.append(page) - - if str(int(page) + 1) in text_dict.keys(): - page_list.append(str(int(page) + 1)) - - page_list_sorted = sorted([int(page_str) for page_str in set(page_list)]) - page_list_sorted_str = list(map(str, page_list_sorted)) - return page_list_sorted_str - - -def keyword_search( - text_dict: dict[str, str], - keywords: list[str], - case_sensitive: bool = False, - regex: bool = False, -) -> dict[str, list]: - """Look for keyword/regex match and add any pages with keyword/regex match to the chunk. - - Args: - text_dict (dict[str, str]): A dictionary keyed by string page number and valued - by string page contents - keywords (list[str]): A list of string keywords/regex - case_sensitive(bool): A flag for converting all keywords and text to lower case, - in order to ignore case altogether. - regex (bool): A flag to identify if the passed keyword list is regex list or string list - - Returns: - list[str]: The dictionary containing keyword as keys and sorted list of string page numbers responsive to the - keyword found - """ - if not case_sensitive and not regex: - keywords = [keyword.lower() for keyword in keywords] - text_dict = {key: value.lower() for key, value in text_dict.items()} - - page_dict = {} - for keyword in keywords: - page_dict[keyword] = [] - - for page in text_dict.keys(): - if (not regex and keyword in text_dict[page]) or ( - regex and re.search(keyword, text_dict[page]) - ): - page_dict[keyword].append(page) - - page_dict_sorted = {} - for keyword in page_dict: - page_dict_sorted[keyword] = sorted( - [int(page_str) for page_str in set(page_dict[keyword])] - ) - - return page_dict_sorted - - -def smart_chunk_one_to_one( - text_dict: dict, contract_text: str, one_to_one_fields: FieldSet -) -> dict: - """Performs a keyword search on textract outputs to retrieve relevant chunks for a given field. - - Args: - text_dict (dict): Dictionary keyed by string page-number and valued by actual textract output page - contract_text(str): Contract Text - - Raises: - ValueError: When the `key_dict` from keyword_mappings has a methodology that is not implemented. - Current options are `or` and `hierarchy`. - - Returns: - dict: A dictionary keyed by field (defined by the keys in keyword_mappings) valued by - corresponding chunks of text (input strings delimited by newlines). - """ - - smart_chunk_fields = one_to_one_fields.filter(field_type="smart_chunked") - - chunks = defaultdict(str) - - for field in smart_chunk_fields.fields: - # Determine chunking function based on methodology - if field.methodology == "or": - chunk_func = chunk_or - elif field.methodology == "hierarchy": - chunk_func = chunk_hierarchical - elif field.methodology == "and": - chunk_func = chunk_and - elif field.methodology == "regex": - chunk_func = chunk_regex - else: - chunk_func = None - - if chunk_func: - page_list = chunk_func( - text_dict, field.keywords, field.regex_pattern, field.case_sensitive - ) - keyword_page_dict = keyword_search( - text_dict, field.keywords, field.case_sensitive, field.regex_pattern - ) - else: - page_list = list(text_dict.keys()) - keyword_page_dict = {"All pages": list(text_dict.keys())} - raise ValueError( - "methodology must be one of 'hierarchy', 'or', 'and', 'regex', 'include_exclude' - using entire contract as chunk" - ) - - if len(page_list) == len(text_dict.keys()): - chunks[field.field_name] = contract_text - else: - chunks[field.field_name] = "\n".join( - [text_dict[str(page_num)] for page_num in page_list] - ) - - chunks[field.field_name + "_pages"] = keyword_page_dict - chunks[field.field_name + "_methodology"] = field.methodology - chunks[field.field_name + "_case"] = field.case_sensitive - return chunks diff --git a/fieldExtraction/src/prompts/fieldset.py b/fieldExtraction/src/prompts/fieldset.py index dbb15b6..8565b4c 100644 --- a/fieldExtraction/src/prompts/fieldset.py +++ b/fieldExtraction/src/prompts/fieldset.py @@ -75,6 +75,12 @@ class Field: if "breakout_template" in field_dict else None ) + # RAG Retrieval Attributes + self.retrieval_question = ( + field_dict["retrieval_question"] + if "retrieval_question" in field_dict + else None + ) def __repr__(self): """Returns a string representation of the Field object.""" diff --git a/fieldExtraction/src/prompts/investment_prompts.json b/fieldExtraction/src/prompts/investment_prompts.json index df30418..5ae2a1b 100644 --- a/fieldExtraction/src/prompts/investment_prompts.json +++ b/fieldExtraction/src/prompts/investment_prompts.json @@ -2,14 +2,14 @@ { "field_name": "SERVICE_TERM", "relationship": "one_to_n", - "field_type": "reimbursement_level", + "field_type": "reimbursement_level", "prompt": "What is the healthcare Service that is being reimbursed? Include the full Service, including any distinguishing context. Include any codes that are part of the service, including revenue codes and procedure codes. Use only exact text from the document.\n\nEXCLUDE the following non-healthcare services:\n- Administrative fees (copying, records requests, processing fees)\n- Operational costs (supplies, equipment rental)\n- Legal/compliance costs (audits, investigations)\n- General business expenses\n- General payment process or claim processing language (e.g. Clean Claims)\n\nOnly extract services that involve direct patient care, medical procedures, or clinical services. If no healthcare service is being reimbursed, return 'N/A'." }, { - "field_name" : "REIMB_TERM", - "relationship" : "one_to_n", - "field_type" : "reimbursement_level", - "prompt" : "Write the full sentence or paragraph in the text describing the reimbursement methodology, including any lesser of language, rates, unit_of_measure and other important information." + "field_name": "REIMB_TERM", + "relationship": "one_to_n", + "field_type": "reimbursement_level", + "prompt": "Write the full sentence or paragraph in the text describing the reimbursement methodology, including any lesser of language, rates, unit_of_measure and other important information." }, { "field_name": "LESSER_OF_IND", @@ -38,7 +38,7 @@ }, { "field_name": "UNIT_OF_MEASURE", - "relationship": "one_to_n", + "relationship": "one_to_n", "field_type": "methodology_breakout", "prompt": "Extract the unit of measure for flat rate or fee schedule reimbursements. Look for specific units like 'per day', 'per visit', 'per procedure', 'per ASA unit', etc. If a specific unit of measure is found and matches the valid values list, return that exact match. If a unit is mentioned but not in the valid values list, return 'Per Unit'. For flat rate methodologies where no specific unit is mentioned, default to 'Per Unit' regardless of whether procedure codes are present. For non-flat rate methodologies (percentages, bundled payments, etc.) where no unit is explicitly stated, return 'N/A'. Valid values are: {valid_values}. Don't assume units based on context; only extract explicitly stated units, except for the flat rate default rule.", "valid_values": "VALID_UNIT_OF_MEASURE" @@ -53,7 +53,7 @@ "field_name": "REIMB_PCT_RATE", "relationship": "one_to_n", "field_type": "methodology_breakout", - "prompt":"What is the percentage rate of reimbursement? Extract the numeric percentage value (without % symbol).\n\nLook for percentages in both numeric form (e.g., '50%', '100%') and written form (e.g., 'fifty percent', 'one hundred percent').\n\nConvert written percentages to numbers (fifty = 50, one hundred = 100, etc.).\n\nIf the text contains 'plus' followed by a percentage, add that percentage to 100 (e.g., 'plus 5%' = 105).\n\nIf no percentage is found AND no specific dollar amount is mentioned in the text, return 100 as default.\n\nIf a specific dollar amount is mentioned, return 'N/A'." + "prompt": "What is the percentage rate of reimbursement? Extract the numeric percentage value (without % symbol).\n\nLook for percentages in both numeric form (e.g., '50%', '100%') and written form (e.g., 'fifty percent', 'one hundred percent').\n\nConvert written percentages to numbers (fifty = 50, one hundred = 100, etc.).\n\nIf the text contains 'plus' followed by a percentage, add that percentage to 100 (e.g., 'plus 5%' = 105).\n\nIf no percentage is found AND no specific dollar amount is mentioned in the text, return 100 as default.\n\nIf a specific dollar amount is mentioned, return 'N/A'." }, { "field_name": "REIMB_CONVERSION_FACTOR", @@ -79,7 +79,7 @@ "relationship": "one_to_n", "field_type": "fee_schedule_breakout", "prompt": "What type of fee schedule is it? Choose from the following: {valid_values}. In case of Medicare, return 'CMS'. In case of Medicaid, return 'State'. Return 'Proprietary' when the payer has defined the fee schedule (e.g. 'payor’s Medicaid fee schedule', 'payor’s Medicare fee schedule', 'health plan fee schedule'). If fee schedule is not mentioned, write 'N/A'.", - "valid_values" : "VALID_AARETE_DERIVED_FEE_SCHEDULE" + "valid_values": "VALID_AARETE_DERIVED_FEE_SCHEDULE" }, { "field_name": "FEE_SCHEDULE_VERSION", @@ -92,87 +92,87 @@ "relationship": "one_to_n", "field_type": "fee_schedule_breakout", "prompt": "Identify the version of the fee schedule by following these steps in order:\n\n1. Look for explicit version indicators:\n • Version numbers (e.g., 'Version 5.2')\n • Specific years (e.g., '2023')\n • Specific dates (e.g., 'January 2023')\n • Descriptive terms (e.g., 'prevailing', 'current')\n\n2. If found, return the exact version indicator as it appears in the text.\n\n3. If multiple indicators are found, prioritize them in this order:\n a. Explicit version numbers\n b. Years or dates\n c. Descriptive terms\n\n4. If no explicit version is found, but descriptive terms such as 'prevailing', 'in effect on the date of service', 'then-current', 'most current', 'unspecified', or 'current' are present, return 'current'.\n\n5. If no version indicators are found, choose ONLY from the following standardized values: {valid_values}.\n\n6. If nothing related to version is mentioned, return 'N/A'.\n\nExamples:\n• '2023 Medicare Fee Schedule' → return '2023'\n• 'prevailing CMS rates' → return 'current'\n• 'Version 5.2 Fee Schedule' → return '5.2'", - "valid_values" : "VALID_AARETE_DERIVED_FEE_SCHEDULE_VERSION" + "valid_values": "VALID_AARETE_DERIVED_FEE_SCHEDULE_VERSION" }, { - "field_name" : "LOB", - "relationship" : "one_to_n", - "field_type" : "dynamic_primary", - "prompt" : "Extract ONLY LOBs that are **explicitly mentioned** in the text. Do NOT extract based on Programs, Products, context, implications, or any other form of inference. Choose LOBs ONLY from the following values: {valid_values}. ", + "field_name": "LOB", + "relationship": "one_to_n", + "field_type": "dynamic_primary", + "prompt": "Extract ONLY LOBs that are **explicitly mentioned** in the text. Do NOT extract based on Programs, Products, context, implications, or any other form of inference. Choose LOBs ONLY from the following values: {valid_values}. ", "reimb_level_prompt": "What LOB applies specifically to this service-reimbursement pair? Extract only LOBs that are directly associated with this particular service or methodology. Choose ONLY from: {valid_values}. If no LOB is specifically associated with this service-reimbursement pair, return 'N/A'.", - "valid_values" : "VALID_LOBS" + "valid_values": "VALID_LOBS" }, { "field_name": "AARETE_DERIVED_LOB", "relationship": "one_to_n", "field_type": "crosswalk", "prompt": "TBD", - "crosswalk" : "CROSSWALK_LOB", - "base_field" : "LOB" + "crosswalk": "CROSSWALK_LOB", + "base_field": "LOB" }, { - "field_name" : "NETWORK", - "relationship" : "one_to_n", - "field_type" : "dynamic_primary", - "prompt" : "List any Networks explicitly stated in the text. Choose ONLY from the following: {valid_values}.", + "field_name": "NETWORK", + "relationship": "one_to_n", + "field_type": "dynamic_primary", + "prompt": "List any Networks explicitly stated in the text. Choose ONLY from the following: {valid_values}.", "reimb_level_prompt": "What Network applies specifically to this service-reimbursement pair? Look for network references that are directly associated with this particular service or methodology. Choose ONLY from: {valid_values}. If no network is specifically associated with this service-reimbursement pair, return 'N/A'.", - "valid_values" : "VALID_NETWORKS" + "valid_values": "VALID_NETWORKS" }, { "field_name": "AARETE_DERIVED_NETWORK", "relationship": "one_to_n", "field_type": "crosswalk", "prompt": "TBD", - "crosswalk" : "CROSSWALK_NETWORK", - "base_field" : "NETWORK" + "crosswalk": "CROSSWALK_NETWORK", + "base_field": "NETWORK" }, { - "field_name" : "PRODUCT", - "relationship" : "one_to_n", - "field_type" : "dynamic_primary", - "prompt" : "List any Products explicitly stated in the text. Look for the brand name of a health plan or product. Do not include the name of the entire healthplan (such as 'Molina', 'Centene' etc) - rather, look for the Product that these healthplans offer. Valid products include, but are not limited to, the following: {valid_values}. If any of the values in this list are mentioned, return them exactly as written in the list. Use reasonable judgment for obvious synonyms and equivalent terms.", - "reimb_level_prompt" : "What Product applies specifically to this service-reimbursement pair? Look for product references that are directly associated with this particular service or methodology. Choose ONLY from: {valid_values}. If no product is specifically associated with this service-reimbursement pair, return 'N/A'.", - "valid_values" : "VALID_PRODUCTS" + "field_name": "PRODUCT", + "relationship": "one_to_n", + "field_type": "dynamic_primary", + "prompt": "List any Products explicitly stated in the text. Look for the brand name of a health plan or product. Do not include the name of the entire healthplan (such as 'Molina', 'Centene' etc) - rather, look for the Product that these healthplans offer. Valid products include, but are not limited to, the following: {valid_values}. If any of the values in this list are mentioned, return them exactly as written in the list. Use reasonable judgment for obvious synonyms and equivalent terms.", + "reimb_level_prompt": "What Product applies specifically to this service-reimbursement pair? Look for product references that are directly associated with this particular service or methodology. Choose ONLY from: {valid_values}. If no product is specifically associated with this service-reimbursement pair, return 'N/A'.", + "valid_values": "VALID_PRODUCTS" }, { "field_name": "AARETE_DERIVED_PRODUCT", "relationship": "one_to_n", "field_type": "TBD", "prompt": "TBD", - "base_field" : "PRODUCT" + "base_field": "PRODUCT" }, { - "field_name" : "PROGRAM", - "relationship" : "one_to_n", - "field_type" : "dynamic_primary", - "prompt" : "List any Programs explicitly stated in the text. Choose ONLY from the following: {valid_values}. Note that 'Medicare' does NOT mean 'Medicare Advantage'. If only 'Medicare' is seen, do not write 'Medicare Advantage'. Choose the answer from the list that most closely matches what is found in the text. Use reasonable judgment for obvious synonyms and equivalent terms (e.g., 'Medicare-Medicaid Program' matches 'Medicare-Medicaid Plan').", - "reimb_level_prompt" : "What Program applies specifically to this service-reimbursement pair? Look for program references that are directly associated with this particular service or reimbursement methodology. Choose ONLY from: {valid_values}. Note: 'Medicare' alone does NOT mean 'Medicare Advantage'. Use reasonable judgment for synonyms (e.g., 'Medicare-Medicaid Program' matches 'Medicare-Medicaid Plan'). If no program is specifically associated with this service-reimbursement pair, return 'N/A'.", - "valid_values" : "VALID_PROGRAMS" + "field_name": "PROGRAM", + "relationship": "one_to_n", + "field_type": "dynamic_primary", + "prompt": "List any Programs explicitly stated in the text. Choose ONLY from the following: {valid_values}. Note that 'Medicare' does NOT mean 'Medicare Advantage'. If only 'Medicare' is seen, do not write 'Medicare Advantage'. Choose the answer from the list that most closely matches what is found in the text. Use reasonable judgment for obvious synonyms and equivalent terms (e.g., 'Medicare-Medicaid Program' matches 'Medicare-Medicaid Plan').", + "reimb_level_prompt": "What Program applies specifically to this service-reimbursement pair? Look for program references that are directly associated with this particular service or reimbursement methodology. Choose ONLY from: {valid_values}. Note: 'Medicare' alone does NOT mean 'Medicare Advantage'. Use reasonable judgment for synonyms (e.g., 'Medicare-Medicaid Program' matches 'Medicare-Medicaid Plan'). If no program is specifically associated with this service-reimbursement pair, return 'N/A'.", + "valid_values": "VALID_PROGRAMS" }, { "field_name": "AARETE_DERIVED_PROGRAM", "relationship": "one_to_n", "field_type": "crosswalk", "prompt": "TBD", - "crosswalk" : "CROSSWALK_PROGRAM", - "base_field" : "PROGRAM" + "crosswalk": "CROSSWALK_PROGRAM", + "base_field": "PROGRAM" }, { - "field_name" : "CLAIM_TYPE_CD", - "relationship" : "one_to_n", - "field_type" : "exhibit_level", - "prompt": "What is the Claim Type of the contract, exhibit, or services mentioned in the text? Choose ONLY from the following: {valid_values}.\n\nGuidelines:\n- Return 'Professional' if the text refers to services rendered by individual healthcare providers or provider groups (e.g., physician services, office visits, outpatient procedures).\n- Return 'Institutional' if the text refers to services provided by hospitals, facilities, or institutions (e.g., inpatient, outpatient facility, or surgery centers).\n- Return 'Ancillary' if the text refers to services such as Home Health, Durable Medical Equipment (DME), Laboratory, Radiology, Dialysis, Diagnostic Imaging, Genetic Testing, Blood Testing, Sleep Lab Services, Telemedicine, Behavioral or Mental Health, or other ancillary settings.\n- If multiple claim types are mentioned, return the one most closely associated with the compensation or service description in the context.\n- If no claim type is mentioned or cannot be determined, return 'N/A'.\nDo not infer or guess.", - "valid_values" : "VALID_CLAIM_TYPE", - "keywords" : [], - "smart_chunking_methodology" : "TBD", - "allow_na" : true + "field_name": "CLAIM_TYPE_CD", + "relationship": "one_to_n", + "field_type": "exhibit_level", + "prompt": "What is the Claim Type of the contract, exhibit, or services mentioned in the text? Choose ONLY from the following: {valid_values}.\n\nGuidelines:\n- Return 'Professional' if the text refers to services rendered by individual healthcare providers or provider groups (e.g., physician services, office visits, outpatient procedures).\n- Return 'Institutional' if the text refers to services provided by hospitals, facilities, or institutions (e.g., inpatient, outpatient facility, or surgery centers).\n- Return 'Ancillary' if the text refers to services such as Home Health, Durable Medical Equipment (DME), Laboratory, Radiology, Dialysis, Diagnostic Imaging, Genetic Testing, Blood Testing, Sleep Lab Services, Telemedicine, Behavioral or Mental Health, or other ancillary settings.\n- If multiple claim types are mentioned, return the one most closely associated with the compensation or service description in the context.\n- If no claim type is mentioned or cannot be determined, return 'N/A'.\nDo not infer or guess.", + "valid_values": "VALID_CLAIM_TYPE", + "keywords": [], + "smart_chunking_methodology": "TBD", + "allow_na": true }, { "field_name": "AARETE_DERIVED_CLAIM_TYPE_CD", "base_field": "CLAIM_TYPE_CD", "relationship": "one_to_n", "field_type": "crosswalk", - "valid_values":"TBD", + "valid_values": "TBD", "prompt": "", "crosswalk": "CROSSWALK_CLAIM_TYPE_CD" }, @@ -181,33 +181,48 @@ "relationship": "one_to_one", "field_type": "smart_chunked", "prompt": "What is the complete title of the contract or the document? It is generally written in CAPITAL LETTERS and is generally found at the beginning of the document before a paragraph containing provider and payer name. It typically contains the words AGREEMENT, AMENDMENT, CONTRACT, ADDENDUM, MEMORANDUM, LETTER, PROVIDER or REQUEST. In case of agreement, typical structure is the provider name, followed by the phrase with agreement, followed potentially by a number signifying a version of the contract. In case of amendment, typical structure is word amendment followed by number, followed by the phrase with agreement. Extract the complete title of the contract or document as accurately as possible. Ensure: All leading characters are included for each word (e.g. 'NETWORK' instead of 'ETWORK'). Spaces between words are preserved in phrases (e.g., 'PROVIDER COLLABORATION' instead of 'PROVIDERCOLLABORATION'). If text appears incomplete or lacks spacing, use logical patterns like capitalization or common formatting rules to infer and correct the output. Replace new lines and line breaks with space.", - "keywords" : ["AGREEMENT", "AMENDMENT", "ADDENDUM", "PROVIDER", "CONTRACT", "MEMORANDUM", "LETTER", "REQUEST", "EFFECTIVE"], - "methodology" : "hierarchy", - "case_sensitive" : "False" + "keywords": [ + "AGREEMENT", + "AMENDMENT", + "ADDENDUM", + "PROVIDER", + "CONTRACT", + "MEMORANDUM", + "LETTER", + "REQUEST", + "EFFECTIVE" + ], + "methodology": "hierarchy", + "case_sensitive": "False", + "retrieval_question": "What is the title or name of this contract, agreement, amendment, addendum, memorandum, or document? Contract title in capital letters at the beginning. Provider agreement, amendment to agreement, contract title, document title, addendum title." }, { "field_name": "CONTRACT_AMENDMENT_NUM", "relationship": "one_to_one", "field_type": "full_context", - "prompt": "Extract the amendment number if this document is an amendment to a contract. Follow these guidelines:\n1. Look for phrases like 'Amendment No. X', 'X Amendment', 'Amendment X to the Agreement', 'Xth Amendment' in the document title, preamble, or header sections.\n2. Return only the numeric value (e.g., for 'Amendment No. 3' return '3').\n3. If the amendment number is written as a word (e.g., 'First Amendment'), convert it to a number (e.g., '1').\n4. If the amendment number is an alphanumeric value, return the whole value (e.g., for '2A' return '2A').\n5. If the amendment number uses Roman numerals (e.g., 'Amendment IV'), convert it to a number (e.g., '4').\n6. If the amendment number includes decimal points (e.g., 'Amendment 2.1'), return the full numeric value (e.g., '2.1').\n7. If multiple amendment numbers appear, return the one most clearly associated with the current document.\n8. If the document is an amendment but the number cannot be determined, return 'UNKNOWN'.\n9. If the document is not an amendment, return 'N/A'.\n10. If the Amendment number contains any leading zeroes(e.g. '001'), Drop all leading zeros (e.g. '1').\n11. If the document is an amendment but the number cannot be determined, return '1'." + "prompt": "Extract the amendment number if this document is an amendment to a contract. Follow these guidelines:\n1. Look for phrases like 'Amendment No. X', 'X Amendment', 'Amendment X to the Agreement', 'Xth Amendment' in the document title, preamble, or header sections.\n2. Return only the numeric value (e.g., for 'Amendment No. 3' return '3').\n3. If the amendment number is written as a word (e.g., 'First Amendment'), convert it to a number (e.g., '1').\n4. If the amendment number is an alphanumeric value, return the whole value (e.g., for '2A' return '2A').\n5. If the amendment number uses Roman numerals (e.g., 'Amendment IV'), convert it to a number (e.g., '4').\n6. If the amendment number includes decimal points (e.g., 'Amendment 2.1'), return the full numeric value (e.g., '2.1').\n7. If multiple amendment numbers appear, return the one most clearly associated with the current document.\n8. If the document is an amendment but the number cannot be determined, return 'UNKNOWN'.\n9. If the document is not an amendment, return 'N/A'.\n10. If the Amendment number contains any leading zeroes(e.g. '001'), Drop all leading zeros (e.g. '1').\n11. If the document is an amendment but the number cannot be determined, return '1'.", + "retrieval_question": "What is the amendment number of this document? Amendment No., Amendment Number, ordinal amendment, numbered amendment? Amendment to agreement, amendment to contract." }, { "field_name": "PROVIDER_STATE", "relationship": "one_to_one", "field_type": "full_context", - "prompt": "What US state is the provider based in or where does this contract apply? Look for:\n\n1. Provider address information in the contract header, preamble, or signature sections\n2. State names or abbreviations in provider contact information\n3. Language indicating the governing state or jurisdiction (e.g., 'governed by the laws of [State]')\n4. Service area or coverage area specifications\n5. Provider licensing information that mentions a specific state\n\nReturn the full state name (e.g., 'California', 'Texas', 'New York') or standard 2-letter abbreviation (e.g., 'CA', 'TX', 'NY') as it appears in the document.\n\nIf multiple states are mentioned, prioritize:\n1. The state in the provider's primary address\n2. The state mentioned in governing law clauses\n3. The state most frequently referenced in the contract\n\nIf no state can be determined, return 'N/A'." + "prompt": "What US state is the provider based in or where does this contract apply? Look for:\n\n1. Provider address information in the contract header, preamble, or signature sections\n2. State names or abbreviations in provider contact information\n3. Language indicating the governing state or jurisdiction (e.g., 'governed by the laws of [State]')\n4. Service area or coverage area specifications\n5. Provider licensing information that mentions a specific state\n\nReturn the full state name (e.g., 'California', 'Texas', 'New York') or standard 2-letter abbreviation (e.g., 'CA', 'TX', 'NY') as it appears in the document.\n\nIf multiple states are mentioned, prioritize:\n1. The state in the provider's primary address\n2. The state mentioned in governing law clauses\n3. The state most frequently referenced in the contract\n\nIf no state can be determined, return 'N/A'.", + "retrieval_question": "What US state is the provider based in or located in? What state does this contract apply to? What state is mentioned in the provider's address, service area, or licensing information?" }, { "field_name": "PAYER_STATE", "relationship": "one_to_one", "field_type": "full_context", - "prompt": "What US state is the payer based in or incorporated in? Look for:\n\n1. Payer address information in the contract header, preamble, or signature sections\n2. State names or abbreviations in payer contact information\n3. State of incorporation or principal place of business\n4. Payer headquarters location\n5. Payer licensing information that mentions a specific state\n\nReturn the full state name (e.g., 'California', 'Texas', 'New York') or standard 2-letter abbreviation (e.g., 'CA', 'TX', 'NY') as it appears in the document.\n\nIf multiple states are mentioned, prioritize:\n1. The state in the payer's primary/headquarters address\n2. The state of incorporation\n3. The state most frequently referenced for the payer organization\n\nIf no state can be determined, return 'N/A'." + "prompt": "What US state is the payer based in or incorporated in? Look for:\n\n1. Payer address information in the contract header, preamble, or signature sections\n2. State names or abbreviations in payer contact information\n3. State of incorporation or principal place of business\n4. Payer headquarters location\n5. Payer licensing information that mentions a specific state\n\nReturn the full state name (e.g., 'California', 'Texas', 'New York') or standard 2-letter abbreviation (e.g., 'CA', 'TX', 'NY') as it appears in the document.\n\nIf multiple states are mentioned, prioritize:\n1. The state in the payer's primary/headquarters address\n2. The state of incorporation\n3. The state most frequently referenced for the payer organization\n\nIf no state can be determined, return 'N/A'.", + "retrieval_question": "What US state is the payer based in, incorporated in, or headquartered in? What state is mentioned in the payer's address or principal place of business?" }, { "field_name": "PAYER_NAME", "relationship": "one_to_one", "field_type": "full_context", - "prompt": "What is the name of the payer that is a party to the contract as stated in the Preamble?" + "prompt": "What is the name of the payer that is a party to the contract as stated in the Preamble?", + "retrieval_question": "What is the name of the payer, health plan, insurance company, or payor that is a party to this contract? Payer name in the preamble, parties section, or beginning of agreement." }, { "field_name": "TIN", @@ -256,8 +271,16 @@ "relationship": "one_to_one", "field_type": "smart_chunked", "prompt": "Please analyze the following contract and extract ONLY the effective date of the CURRENT contract or CURRENT amendment. THE DATE MAY OR MAY NOT BE EXPLICITLY LABELED AS AN EFFECTIVE DATE. Follow these retrieval rules in order:\n\n[RETRIEVAL RULES]\nLOOK FOR dates that appear after these common phrases but not limited to (case-insensitive matching):\n'Effective Date:' FOLLOWED BY a date\n'is entered into as of' FOLLOWED BY a date\n'is made and entered into as of the' FOLLOWED BY a date\n'effective as of' FOLLOWED BY a date\n'shall begin on' FOLLOWED BY a date\n'Effective Date of Agreement:' FOLLOWED BY a date\n'Effective Date of Amendment' FOLLOWED BY a date\n'made this' FOLLOWED BY a date\n'made and entered into' FOLLOWED BY a date\n\nALSO LOOK FOR dates in phrases that semantically indicate an effective date (may or may not be explictly labelled as effective date), even if they do not exactly match the phrases above.\nExamples include but ARE NOT LIMITED TO:\nPhrases indicating when a contract or amendment begins or takes effect, Phrases specifying the start date of the contract or amendment, Phrases defining when the contract or amendment becomes operational, or Any phrase indicating contract or amendment creation or execution date.\n\nIt is possible that the contract will specify that the effective date is derived from elsewhere in the contract. One specific case is outlined:\n\nSIGNATURE DATE HANDLING:\nPRIMARY RULE: DO NOT USE signature dates unless STRICTLY meeting the criteria below\nWHEN TO USE a signature date: ONLY IF that SPECIFIC signature date has an EXPLICIT statement marking IT as the effective date\nHere are some example scenarios:\n✓ USE: 'Effective Date: 1/13/2024' + 'Date: [blank]' below\n✓ USE: 'Effective Date of Agreement: 2/15/2016' + 'Date: [blank]' below\n\nWHEN NOT TO USE a signature date:\n- Even if there is an empty effective date section or field nearby\nHere are some example scenarios but not limited to:\n✗ DO NOT USE: 'Date: 1/1/2024' + 'Effective Date: [blank]' below\n✗ DO NOT USE: 'Effective Date: [blank]' + 'Date: 1/1/2024' below\n\nSECONDARY RULE – UNREFERENCED DATE NEAR “EFFECTIVE DATE:” LABEL:\nIf no explicit effective date is found in the contract, and the “Effective Date:” label appears with a blank or empty field, you MAY consider a nearby date as the effective date ONLY IF ALL of the following conditions are true:\n- The nearby date is NOT referenced by any signature, title, or labeled field (i.e., it is unassigned or free-floating)\n- Other nearby dates ARE explicitly labeled (e.g., under “Date” near signature lines or roles)\n- The unassigned date appears directly under or beside the “Effective Date:” label\n- The document does not provide a clearer effective date elsewhere\n\nIn such cases, treat the unreferenced date near the “Effective Date:” label as the effective date. Preserve the date's exact format as it appears.\n\n[OUTPUT DATE HANDLING]\nCRITICAL - DATE FORMAT PRESERVATION:\n- You MUST preserve the EXACT format of the date AS IT APPEARS in the document\n- DO NOT modify, standardize, or reformat the date in ANY way\n- Preserve ALL original: Spacing (including multiple spaces), punctuation, separators (commas, hyphens, forward slashes, etc.), leading/trailing spaces, or partial or incomplete dates\n\nExamples of EXACT DATE preservation:\n✓ If document shows: '3-1 , , 03' → return |3-1 , , 03|\n✓ If document shows: 'June 1st,2010' → return |June 1st,2010|\n✓ If document shows: 'Feb 1st day of , 10' → return |Feb 1st day of , 10|\n✓ If document shows: '6 /1/ 2010' → return |6 /1/ 2010|\n✓ If document shows: '12-1-20' → return |12-1-20|\n✓ If document shows: 'made this day of Jan, 2009' → return |day of Jan, 2009|\n✓ If document shows: 'to be effective as of , 20 ( the 'Effective Date').' → return |, 20|\n\nIt is POSSIBLE that the intended effective date could be blank (left blank, not filled), empty, incomplete, or partial. DO NOT ASSUME/INFER anything about the date or its components.\nReturn N/A only if NO valid effective date is found (e.g. cases with blank/empty/incomplete dates/placeholder values).\n\nIF MULTIPLE matches are retrieved:\n- VERIFY that you have PRECISELY followed the retrieval rules\n- If multiple matches are present, PRIORITIZE and SELECT the LATEST date as the answer\n- MAINTAIN the EXACT FORMAT of the selected date as it appears in the document\n\nBefore providing the final answer, SHOW your reasoning:\n- LIST ALL dates found that match patterns in the retrieval rules\n- For EACH date found:\n* EXPLAIN why it is considered for effective date or not\n* Include the EXACT text snippet where the date was found\n- JUSTIFY your final date selection or why N/A is being returned\n\nFinal Answer Format:\n- Your answer MUST be ENCLOSED in |pipes|\n- The text between the pipes MUST be an EXACT COPY of how the date appears in the document\n- DO NOT add or remove ANY characters, spaces, or formatting", - "keywords": ["effective date", "effective date:","entered into", "shall be effective", "will become effective", "signature page"], - "format": "date" + "keywords": [ + "effective date", + "effective date:", + "entered into", + "shall be effective", + "will become effective", + "signature page" + ], + "format": "date", + "retrieval_question": "What is the effective date of this contract or amendment? When does this agreement take effect, commence, begin, or become operational? Effective date, entered into as of, made this, made and entered into, shall begin on, commences on, commencement date, start date." }, { "field_name": "AARETE_DERIVED_EFFECTIVE_DT", @@ -271,8 +294,13 @@ "relationship": "one_to_one", "field_type": "smart_chunked", "prompt": "What is the initial contract or amendment termination date? Look within the Term or Term and Termination section or wherever applicable. Extract the specific end date of the initial term before any renewals or extensions. \nOutput Format Rules: \n- If an exact specific date is mentioned: Output the date (e.g., '12/31/2024') \n- If described using terminology like duration or recurring period: Output the exact terminology (e.g., 'three years from the contract effective date', 'year to year') \n- If multiple dates or conditions are mentioned, prioritize the initial termination date before any automatic renewals. \n - Do not modify, transform, or calculate dates. Example Outputs: - For specific date: '12/31/2014' - For duration: 'three years from the contract effective date' - For recurring period: 'year to year' - For conditional termination: 'upon execution of new FPA or 12/31/2014, whichever is earlier'", - "keywords" : ["term and termination", "Term", "Initial Term"], - "format": "date" + "keywords": [ + "term and termination", + "Term", + "Initial Term" + ], + "format": "date", + "retrieval_question": "What is the initial contract or amendment termination date? What is the end date, expiration date, or date when the initial term concludes? When does the contract terminate before any renewals?" }, { "field_name": "AARETE_DERIVED_TERMINATION_DT", @@ -280,27 +308,51 @@ "relationship": "one_to_one", "field_type": "date_inferred", "prompt": "TBD", - "keywords" : ["TERM AND TERMINATION", "Term and Termination", "Term and termination", "term and termination", "Term"], - "methodology" : "or", - "case_sensitive" : "True" + "keywords": [ + "TERM AND TERMINATION", + "Term and Termination", + "Term and termination", + "term and termination", + "Term" + ], + "methodology": "or", + "case_sensitive": "True" }, { "field_name": "AUTO_RENEWAL_IND", "relationship": "one_to_one", "field_type": "smart_chunked", "prompt": "This pertains to the term and termination section of the contract. If this contract automatically renews, answer Y. If this contract will remain in effect unless terminated, answer Y. Otherwise, answer N.", - "keywords" : ["Renew", "renew", "Auto renew", "auto renew", "automatically renew", "evergreen", "Evergreen"], - "methodology" : "or", - "case_sensitive" : "True" + "keywords": [ + "Renew", + "renew", + "Auto renew", + "auto renew", + "automatically renew", + "evergreen", + "Evergreen" + ], + "methodology": "or", + "case_sensitive": "True", + "retrieval_question": "Does this contract automatically renew, auto-renew, continue automatically, or remain in effect unless terminated by either party? Is this an evergreen contract or does it renew for successive terms or renewal periods?" }, { "field_name": "AUTO_RENEWAL_TERM", "relationship": "one_to_one", "field_type": "smart_chunked", "prompt": "If the contract automatically renews, what is the length of the renewal term? This pertains to the term and termination section of the contract. The response should be a numerical representation of the renewal period with no punctuation. Acceptable formats are '1 year' or '12 months' (both representing the same duration). 'Year to year' and 'year' should be interpreted as '1 year'. If the contract automatically renews but the length of the renewal term is not specified, default to '1 year'. Only return the length of time for the renewal with no additional context.", - "keywords" : ["Renew", "renew", "Auto renew", "auto renew", "automatically renew", "evergreen", "Evergreen"], - "methodology" : "or", - "case_sensitive" : "True" + "keywords": [ + "Renew", + "renew", + "Auto renew", + "auto renew", + "automatically renew", + "evergreen", + "Evergreen" + ], + "methodology": "or", + "case_sensitive": "True", + "retrieval_question": "What is the length, duration, or period of the automatic renewal term, successive renewal term, or additional renewal period? How long is each renewal period or renewal term in years or months?" }, { "field_name": "PROV_TAXONOMY_CD", @@ -412,7 +464,7 @@ "field_name": "GROUPER_CD", "relationship": "one_to_n", "field_type": "code_primary_breakout", - "prompt": "Identify grouper codes (DRGs) in the text: - Basic format: 3 digits (like 123) - May include severity/risk indicators (like 123-1) - May include decimal points (like 123.4) - Maximum length of 5 characters." + "prompt": "Identify grouper codes (DRGs) in the text: - Basic format: 3 digits (like 123) - May include severity/risk indicators (like 123-1) - May include decimal points (like 123.4) - Maximum length of 5 characters." }, { "field_name": "GROUPER_CD_DESC", @@ -497,7 +549,7 @@ "relationship": "one_to_n", "field_type": "special_case_primary", "prompt": "Identify and extract outlier payment terms. Outlier payments are payment adjustments that provide additional supplemental reimbursement on top of base payments when claims exceed predetermined cost, day, or resource thresholds for unusually high-cost or high-resource cases. These outlier payments apply only to inpatient services and never apply to outpatient services. Extract the exact language that defines conditions and thresholds for outlier payments for inpatient services. Look for specific dollar amounts, day counts, percentages, or other metrics that trigger payments for outlier payments. Also look for any services, codes, or conditions excluded from outlier payments. Look for terms like 'outlier payments', 'outlier threshold', 'unusually high-cost cases', 'extended stay supplements', 'exceptional circumstances payments', 'high-resource case adjustments'. Only extract terms that are explicitly described as outlier payments. Return the exact contract language or 'N/A'.", - "breakout_template" : "OUTLIER_BREAKOUT" + "breakout_template": "OUTLIER_BREAKOUT" }, { "field_name": "OUTLIER_FIRST_DOLLAR_IND", @@ -614,13 +666,12 @@ "field_type": "stop_loss_breakout", "prompt": "For each code identified in STOP_LOSS_EXCLUSION_CD, extract the specific description of that code as provided in the contract. For example, if a contract excludes CPT 12345 and describes it as 'Experimental surgical procedure', return 'Experimental surgical procedure' as the description. Only include descriptions that are directly associated with excluded codes. If the contract lists excluded codes without descriptions, write 'N/A'." }, - { "field_name": "SEQUESTRATION_TERM", "relationship": "one_to_n", "field_type": "special_case_primary", "prompt": "Describes a government-mandated reduction in federal spending for a period of time. Look for the term 'Sequestration'.", - "breakout_template" : "SEQUESTRATION_BREAKOUT" + "breakout_template": "SEQUESTRATION_BREAKOUT" }, { "field_name": "SEQUESTRATION_START_DT", diff --git a/fieldExtraction/src/utils/rag_utils.py b/fieldExtraction/src/utils/rag_utils.py new file mode 100644 index 0000000..fff6540 --- /dev/null +++ b/fieldExtraction/src/utils/rag_utils.py @@ -0,0 +1,169 @@ +""" +RAG (Retrieval-Augmented Generation) Utilities + +This module provides configuration and utility functions for RAG-based +field extraction using hybrid retrieval (BM25 + semantic search) with reranking. +""" + +import json +import logging +from dataclasses import dataclass +from typing import Any, List, Dict + +import boto3 +from langchain.retrievers import EnsembleRetriever +from langchain.schema import Document +from langchain_aws import BedrockEmbeddings +from langchain_community.retrievers import BM25Retriever +from langchain_community.vectorstores import FAISS +from langchain_community.vectorstores.utils import DistanceStrategy + + +@dataclass +class RAGConfig: + """Configuration for RAG pipeline""" + + CHUNK_SIZE: int = 2000 + CHUNK_OVERLAP: int = 200 + SEMANTIC_TOP_K: int = 35 + KEYWORD_TOP_K: int = 25 + ENSEMBLE_TOP_K: int = 40 + RERANKER_TOP_K: int = 15 + BM25_WEIGHT: float = 0.40 + VECTOR_WEIGHT: float = 0.60 + RERANKER: str = "amazon" # "cohere" or "amazon" + EMBEDDING_MODEL_ID: str = "amazon.titan-embed-text-v2:0" + BEDROCK_REGION: str = "us-east-1" + + +# Default configuration instance +HSC_CONFIG = RAGConfig() + +# Reranker model configurations +RERANKER_CONFIGS = { + "amazon": { + "model_id": "amazon.rerank-v1:0", + "name": "Amazon Rerank 1.0", + "region": "us-west-2", + }, + "cohere": { + "model_id": "cohere.rerank-v3-5:0", + "name": "Cohere Rerank 3.5", + "region": "us-east-1", + }, +} + +# Initialize AWS Bedrock clients at module level +_bedrock_client = None +_reranker_clients = {} # Cache per region +_embeddings = None + + +def get_bedrock_client(region_name: str = HSC_CONFIG.BEDROCK_REGION): + """Get or create the Bedrock client.""" + global _bedrock_client + if _bedrock_client is None: + _bedrock_client = boto3.client("bedrock-runtime", region_name=region_name) + return _bedrock_client + + +def get_reranker_client(model: str = HSC_CONFIG.RERANKER): + """Get or create the reranker client for specific model.""" + global _reranker_clients + cfg = RERANKER_CONFIGS.get(model, RERANKER_CONFIGS["amazon"]) + region = cfg["region"] + + if region not in _reranker_clients: + _reranker_clients[region] = boto3.client("bedrock-runtime", region_name=region) + return _reranker_clients[region] + + +def get_embeddings( + model_id: str = HSC_CONFIG.EMBEDDING_MODEL_ID, + region_name: str = HSC_CONFIG.BEDROCK_REGION, +): + """Get or create the embeddings model.""" + global _embeddings + if _embeddings is None: + client = get_bedrock_client(region_name) + _embeddings = BedrockEmbeddings(model_id=model_id, client=client) + return _embeddings + + +def create_vectorstore(chunks: List[Document], embeddings: BedrockEmbeddings) -> FAISS: + """Create FAISS vectorstore from chunks.""" + return FAISS.from_documents( + chunks, embeddings, distance_strategy=DistanceStrategy.COSINE + ) + + +def create_hybrid_retriever( + chunks: List[Document], vectorstore: FAISS, cfg: RAGConfig = HSC_CONFIG +) -> EnsembleRetriever: + """Create hybrid BM25 + semantic retriever.""" + bm25 = BM25Retriever.from_documents(chunks, k1=1.6, b=0.75) + bm25.k = cfg.KEYWORD_TOP_K + vector = vectorstore.as_retriever( + search_type="similarity", search_kwargs={"k": cfg.SEMANTIC_TOP_K} + ) + return EnsembleRetriever( + retrievers=[vector, bm25], weights=[cfg.VECTOR_WEIGHT, cfg.BM25_WEIGHT] + ) + + +def rerank_documents( + query: str, + docs: List[Document], + client: Any, + top_k: int = HSC_CONFIG.RERANKER_TOP_K, + model: str = HSC_CONFIG.RERANKER, +) -> List[Document]: + """Rerank documents using AWS Bedrock (Cohere/Amazon).""" + if not docs: + return docs + + try: + cfg = RERANKER_CONFIGS.get(model, RERANKER_CONFIGS["cohere"]) + + payload: Dict[str, Any] = ( + {"query": query, "documents": [{"text": d.page_content} for d in docs]} + if model == "amazon" + else { + "query": query, + "documents": [d.page_content for d in docs], + "top_n": min(top_k, len(docs)), + "api_version": 2, + } + ) + + result = json.loads( + client.invoke_model(modelId=cfg["model_id"], body=json.dumps(payload))[ + "body" + ].read() + ) + + scored = sorted( + [ + {"score": item["relevance_score"], "doc": docs[item["index"]]} + for item in result["results"] + ], + key=lambda x: x["score"], + reverse=True, + )[:top_k] + + return [ + Document( + page_content=item["doc"].page_content, + metadata={ + **item["doc"].metadata, + "rerank_score": item["score"], + "rerank_model": cfg["name"], + }, + ) + for item in scored + ] + except Exception as e: + logging.error( + f"Reranking failed: {e} | Selecting {top_k} from base retrieved docs" + ) + return docs[:top_k] diff --git a/fieldExtraction/tests/test_hybrid_smart_chunking_funcs.py b/fieldExtraction/tests/test_hybrid_smart_chunking_funcs.py new file mode 100644 index 0000000..706cf87 --- /dev/null +++ b/fieldExtraction/tests/test_hybrid_smart_chunking_funcs.py @@ -0,0 +1,450 @@ +""" +Unit tests for src.investment.hybrid_smart_chunking_funcs module + +Tests hybrid smart chunking functions including: +- Context clipping by token count +- Document preparation for chunking +- Chunk formatting for LLM +- Page number extraction +- Preprocessing pipeline +""" + +import pytest +from unittest.mock import Mock, patch, MagicMock +from langchain.schema import Document +from src.investment.hybrid_smart_chunking_funcs import ( + clip_context_by_tokens, + prepare_documents_for_chunking, + format_chunks_for_llm, + add_page_numbers_to_chunks, + preprocessing_pipeline, +) + + +class TestClipContextByTokens: + """Test clip_context_by_tokens function""" + + def test_context_under_limit(self): + """Test context that doesn't need clipping""" + context = "Short text" * 100 # ~1100 chars = ~314 tokens + max_tokens = 1000 + + result = clip_context_by_tokens(context, max_tokens) + + assert result == context + assert len(result) == len(context) + + def test_context_over_limit(self): + """Test context that needs clipping""" + context = "A" * 350000 # ~100k tokens + max_tokens = 10000 # Clip to 10k tokens + + result = clip_context_by_tokens(context, max_tokens) + + # Should be clipped to approximately 10k tokens (35k chars) + assert len(result) < len(context) + assert len(result) <= int(max_tokens * 3.5) + + def test_empty_context(self): + """Test empty context""" + result = clip_context_by_tokens("") + + assert result == "" + + def test_none_context(self): + """Test None context""" + result = clip_context_by_tokens(None) + + assert result == "" + + def test_exact_limit(self): + """Test context exactly at the limit""" + max_tokens = 1000 + context = "A" * int(max_tokens * 3.5) # Exactly at limit + + result = clip_context_by_tokens(context, max_tokens) + + assert result == context + + def test_custom_max_tokens(self): + """Test with custom max_tokens parameter""" + context = "A" * 100000 + max_tokens = 5000 + + result = clip_context_by_tokens(context, max_tokens) + + estimated_tokens = int(len(result) / 3.5) + assert estimated_tokens <= max_tokens + + def test_token_estimation_accuracy(self): + """Test that token estimation formula is applied correctly""" + # 3500 chars should be ~1000 tokens + context = "A" * 3500 + max_tokens = 1000 + + result = clip_context_by_tokens(context, max_tokens) + + # Should not clip since it's right at the limit + assert len(result) == 3500 + + +class TestPrepareDocumentsForChunking: + """Test prepare_documents_for_chunking function""" + + def test_single_section(self): + """Test preparing a single section""" + sections = { + "Section 1": { + "content": "Section content here", + "pages": [1, 2] + } + } + filename = "test_contract.pdf" + + docs = prepare_documents_for_chunking(sections, filename) + + assert len(docs) == 1 + assert docs[0].page_content == "Section content here" + assert docs[0].metadata["section_header"] == "Section 1" + assert docs[0].metadata["pages"] == [1, 2] + assert docs[0].metadata["filename"] == filename + + def test_multiple_sections(self): + """Test preparing multiple sections""" + sections = { + "Section 1": {"content": "Content 1", "pages": [1]}, + "Section 2": {"content": "Content 2", "pages": [2, 3]}, + "Section 3": {"content": "Content 3", "pages": [4]} + } + + docs = prepare_documents_for_chunking(sections) + + assert len(docs) == 3 + assert all(isinstance(doc, Document) for doc in docs) + + def test_empty_sections(self): + """Test with empty sections dictionary""" + sections = {} + + docs = prepare_documents_for_chunking(sections) + + assert docs == [] + + def test_section_metadata(self): + """Test that metadata is correctly set""" + sections = { + "Test Section": { + "content": "This is test content", + "pages": [5, 6, 7] + } + } + filename = "contract.pdf" + + docs = prepare_documents_for_chunking(sections, filename) + + metadata = docs[0].metadata + assert metadata["section_header"] == "Test Section" + assert metadata["section_length"] == len("This is test content") + assert metadata["pages"] == [5, 6, 7] + assert metadata["filename"] == filename + + def test_default_filename(self): + """Test default filename when not provided""" + sections = { + "Section": {"content": "Content", "pages": [1]} + } + + docs = prepare_documents_for_chunking(sections) + + assert docs[0].metadata["filename"] == "contract" + + +class TestFormatChunksForLLM: + """Test format_chunks_for_llm function""" + + def test_empty_docs(self): + """Test with empty document list""" + text_dict = {"1": "Page 1 content"} + + result = format_chunks_for_llm(text_dict, []) + + assert result == "" + + def test_single_document(self): + """Test with single document""" + text_dict = { + "1": "Page 1 content", + "2": "Page 2 content" + } + docs = [ + Document( + page_content="chunk1", + metadata={"pages": [1]} + ) + ] + + result = format_chunks_for_llm(text_dict, docs) + + assert "Page 1 content" in result + assert "Page 2 content" not in result + + def test_multiple_documents_different_pages(self): + """Test with documents from different pages""" + text_dict = { + "1": "Page 1 content", + "2": "Page 2 content", + "3": "Page 3 content" + } + docs = [ + Document(page_content="chunk1", metadata={"pages": [1]}), + Document(page_content="chunk2", metadata={"pages": [3]}) + ] + + result = format_chunks_for_llm(text_dict, docs) + + assert "Page 1 content" in result + assert "Page 3 content" in result + assert "Page 2 content" not in result + + def test_overlapping_pages(self): + """Test with documents that reference overlapping pages""" + text_dict = { + "1": "Page 1 content", + "2": "Page 2 content" + } + docs = [ + Document(page_content="chunk1", metadata={"pages": [1, 2]}), + Document(page_content="chunk2", metadata={"pages": [2]}) + ] + + result = format_chunks_for_llm(text_dict, docs) + + # Both pages should appear only once (set deduplication) + assert "Page 1 content" in result + assert "Page 2 content" in result + + def test_missing_page_in_text_dict(self): + """Test when document references page not in text_dict""" + text_dict = { + "1": "Page 1 content" + } + docs = [ + Document(page_content="chunk", metadata={"pages": [1, 99]}) + ] + + result = format_chunks_for_llm(text_dict, docs) + + # Should only include page 1, skip page 99 + assert "Page 1 content" in result + + def test_none_values_in_text_dict(self): + """Test handling of None values in text_dict""" + text_dict = { + "1": "Page 1 content", + "2": None, + "3": "Page 3 content" + } + docs = [ + Document(page_content="chunk", metadata={"pages": [1, 2, 3]}) + ] + + result = format_chunks_for_llm(text_dict, docs) + + # Should skip None value for page 2 + assert "Page 1 content" in result + assert "Page 3 content" in result + + def test_document_without_pages_metadata(self): + """Test document without pages in metadata""" + text_dict = {"1": "Page 1 content"} + docs = [ + Document(page_content="chunk", metadata={}) + ] + + result = format_chunks_for_llm(text_dict, docs) + + # Should return empty or handle gracefully + assert isinstance(result, str) + + @patch('src.investment.hybrid_smart_chunking_funcs.clip_context_by_tokens') + def test_calls_clip_context(self, mock_clip): + """Test that format_chunks_for_llm calls clip_context_by_tokens""" + mock_clip.return_value = "clipped context" + text_dict = {"1": "Page 1 content"} + docs = [Document(page_content="chunk", metadata={"pages": [1]})] + + result = format_chunks_for_llm(text_dict, docs) + + mock_clip.assert_called_once() + assert result == "clipped context" + + def test_integer_page_numbers(self): + """Test handling of integer page numbers (converted to strings)""" + text_dict = { + "1": "Page 1 content", + "2": "Page 2 content" + } + docs = [ + Document(page_content="chunk", metadata={"pages": [1, 2]}) # Integers + ] + + result = format_chunks_for_llm(text_dict, docs) + + assert "Page 1 content" in result + assert "Page 2 content" in result + + +class TestAddPageNumbersToChunks: + """Test add_page_numbers_to_chunks function""" + + def test_single_section_with_page_markers(self): + """Test extracting page numbers from section with markers""" + sections = { + "Section 1": "Start of Page No. = 1\nContent here\nStart of Page No. = 2\nMore content" + } + + result = add_page_numbers_to_chunks(sections) + + assert "Section 1" in result + assert result["Section 1"]["content"] == sections["Section 1"] + assert 1 in result["Section 1"]["pages"] + assert 2 in result["Section 1"]["pages"] + + def test_multiple_sections(self): + """Test multiple sections with page markers""" + sections = { + "Section 1": "Start of Page No. = 5\nContent", + "Section 2": "Start of Page No. = 6\nMore content\nStart of Page No. = 7\nEven more" + } + + result = add_page_numbers_to_chunks(sections) + + assert 5 in result["Section 1"]["pages"] + assert 6 in result["Section 2"]["pages"] + assert 7 in result["Section 2"]["pages"] + + def test_section_without_markers(self): + """Test section without page markers""" + sections = { + "Section 1": "Start of Page No. = 1\nContent", + "Section 2": "Content without markers" + } + + result = add_page_numbers_to_chunks(sections) + + # Section 2 should inherit last page from Section 1 + assert result["Section 2"]["pages"] == [1] + + def test_empty_sections(self): + """Test with empty sections dictionary""" + sections = {} + + result = add_page_numbers_to_chunks(sections) + + assert result == {} + + def test_page_number_deduplication(self): + """Test that duplicate page numbers are removed""" + sections = { + "Section 1": "Start of Page No. = 3\nContent\nStart of Page No. = 3\nMore" + } + + result = add_page_numbers_to_chunks(sections) + + # Should have unique page numbers + assert result["Section 1"]["pages"].count(3) == 1 + + def test_page_numbers_are_sorted(self): + """Test that page numbers are sorted""" + sections = { + "Section 1": "Start of Page No. = 5\nStart of Page No. = 2\nStart of Page No. = 8" + } + + result = add_page_numbers_to_chunks(sections) + + pages = result["Section 1"]["pages"] + assert pages == sorted(pages) + + def test_alternative_marker_format(self): + """Test alternative page marker format without period""" + sections = { + "Section 1": "Start of Page No = 10\nContent" + } + + result = add_page_numbers_to_chunks(sections) + + assert 10 in result["Section 1"]["pages"] + + +class TestPreprocessingPipeline: + """Test preprocessing_pipeline function""" + + @patch('src.investment.hybrid_smart_chunking_funcs.hybrid_smart_chunking_preprocessing.split_contract_text_into_sections') + @patch('src.investment.hybrid_smart_chunking_funcs.add_page_numbers_to_chunks') + def test_pipeline_calls_functions(self, mock_add_pages, mock_split): + """Test that pipeline calls the necessary functions""" + contract_text = "Test contract text" + + # Mock split_contract_text_into_sections return + mock_sections = {"Section 1": "content"} + mock_verification = (True, "Perfect preservation") + mock_split.return_value = (mock_sections, contract_text, "\n", mock_verification) + + # Mock add_page_numbers_to_chunks return + mock_sections_with_pages = { + "Section 1": {"content": "content", "pages": [1]} + } + mock_add_pages.return_value = mock_sections_with_pages + + sections, is_preserved = preprocessing_pipeline(contract_text) + + mock_split.assert_called_once_with(contract_text) + mock_add_pages.assert_called_once() + assert is_preserved is True + assert sections == mock_sections_with_pages + + @patch('src.investment.hybrid_smart_chunking_funcs.hybrid_smart_chunking_preprocessing.split_contract_text_into_sections') + @patch('src.investment.hybrid_smart_chunking_funcs.add_page_numbers_to_chunks') + def test_pipeline_returns_preservation_status(self, mock_add_pages, mock_split): + """Test that pipeline returns preservation status""" + # Test successful preservation + mock_split.return_value = ({}, "text", "\n", (True, "Success")) + mock_add_pages.return_value = {} + + sections, is_preserved = preprocessing_pipeline("text") + assert is_preserved is True + + # Test failed preservation + mock_split.return_value = ({}, "text", "\n", (False, "Failed")) + mock_add_pages.return_value = {} + + sections, is_preserved = preprocessing_pipeline("text") + assert is_preserved is False + + @patch('src.investment.hybrid_smart_chunking_funcs.hybrid_smart_chunking_preprocessing.split_contract_text_into_sections') + @patch('src.investment.hybrid_smart_chunking_funcs.add_page_numbers_to_chunks') + def test_pipeline_with_real_text(self, mock_add_pages, mock_split): + """Test pipeline with realistic contract text""" + contract_text = """1 Definitions +Definition content +2 Terms +Terms content""" + + mock_sections = { + "1 Definitions": "Definition content", + "2 Terms": "Terms content" + } + mock_split.return_value = (mock_sections, contract_text, "\n", (True, "OK")) + + mock_sections_with_pages = { + "1 Definitions": {"content": "Definition content", "pages": [1]}, + "2 Terms": {"content": "Terms content", "pages": [2]} + } + mock_add_pages.return_value = mock_sections_with_pages + + sections, is_preserved = preprocessing_pipeline(contract_text) + + assert len(sections) == 2 + assert "1 Definitions" in sections + assert "2 Terms" in sections + assert is_preserved is True diff --git a/fieldExtraction/tests/test_hybrid_smart_chunking_preprocessing.py b/fieldExtraction/tests/test_hybrid_smart_chunking_preprocessing.py new file mode 100644 index 0000000..ba33686 --- /dev/null +++ b/fieldExtraction/tests/test_hybrid_smart_chunking_preprocessing.py @@ -0,0 +1,353 @@ +""" +Unit tests for src.investment.hybrid_smart_chunking_preprocessing module + +Tests contract text preprocessing and section splitting functionality including: +- Section identification based on various heading patterns +- Text preservation verification +- Section metadata extraction +""" + +import pytest +from src.investment.hybrid_smart_chunking_preprocessing import ( + split_contract_text_into_sections, + verify_preservation, + get_section_info, +) + + +class TestSplitContractTextIntoSections: + """Test split_contract_text_into_sections function""" + + def test_simple_text_no_headings(self): + """Test text with no recognizable headings""" + text = "This is a simple contract text.\nWith no headings at all." + sections, original, line_ending, verification = split_contract_text_into_sections(text) + + assert "Preamble" in sections + assert sections["Preamble"] == text + assert original == text + assert line_ending == "\n" + assert verification[0] is True + + def test_arabic_numbered_headings(self): + """Test single-digit arabic numbered headings (1. Title)""" + text = "Preamble text\n1 Definitions\nDefinition content\n2 Terms\nTerms content" + sections, original, line_ending, verification = split_contract_text_into_sections(text) + + assert "Preamble" in sections + assert "1 Definitions" in sections + assert "2 Terms" in sections + assert verification[0] is True + + def test_sub_arabic_numbered_headings(self): + """Test multi-level arabic numbered headings (1.1, 1.2, etc.)""" + text = "1.1 First Section\nContent A\n1.2 Second Section\nContent B" + sections, original, line_ending, verification = split_contract_text_into_sections(text) + + assert "1.1 First Section" in sections + assert "1.2 Second Section" in sections + assert verification[0] is True + + def test_roman_numeral_headings(self): + """Test roman numeral headings (I. Title, II. Title)""" + text = "I Introduction\nIntro content\nII Background\nBackground content" + sections, original, line_ending, verification = split_contract_text_into_sections(text) + + assert "I Introduction" in sections + assert "II Background" in sections + assert verification[0] is True + + def test_article_headings(self): + """Test ARTICLE headings (ARTICLE I Title)""" + text = "ARTICLE I Definitions\nDef content\nARTICLE II Terms\nTerms content" + sections, original, line_ending, verification = split_contract_text_into_sections(text) + + assert "ARTICLE I Definitions" in sections + assert "ARTICLE II Terms" in sections + assert verification[0] is True + + def test_exhibit_headings(self): + """Test EXHIBIT headings (EXHIBIT A, EXHIBIT B Title)""" + text = "Main text\nEXHIBIT A\nExhibit A content\nEXHIBIT B Schedule\nExhibit B content" + sections, original, line_ending, verification = split_contract_text_into_sections(text) + + assert "EXHIBIT A" in sections + assert "EXHIBIT B Schedule" in sections + assert verification[0] is True + + def test_attachment_headings(self): + """Test Attachment headings""" + text = "Contract text\nAttachment 1\nAttachment content" + sections, original, line_ending, verification = split_contract_text_into_sections(text) + + assert "Attachment 1" in sections + assert verification[0] is True + + def test_recitals_and_agreement_keywords(self): + """Test RECITALS and AGREEMENT keyword headings""" + text = "RECITALS:\nRecital text\nAGREEMENT\nAgreement text" + sections, original, line_ending, verification = split_contract_text_into_sections(text) + + assert "RECITALS" in sections + assert "AGREEMENT" in sections + assert verification[0] is True + + def test_in_witness_whereof_heading(self): + """Test IN WITNESS WHEREOF heading""" + text = "Contract body\nIN WITNESS WHEREOF\nSignature section" + sections, original, line_ending, verification = split_contract_text_into_sections(text) + + assert "IN WITNESS WHEREOF" in sections + assert verification[0] is True + + def test_signatory_headings(self): + """Test signatory block headings (PROVIDER:, ICM:)""" + text = "Agreement text\nPROVIDER:\nBy: John Doe\nICM:\nBy: Jane Smith" + sections, original, line_ending, verification = split_contract_text_into_sections(text) + + assert "PROVIDER" in sections or "ICM" in sections + assert verification[0] is True + + def test_multi_line_headings(self): + """Test headings that span multiple lines""" + text = "1\nDefinitions and Terms\nContent here\n2\nOther Section\nMore content" + sections, original, line_ending, verification = split_contract_text_into_sections(text) + + # Should combine multi-line headings + assert any("Definitions" in key for key in sections.keys()) + assert verification[0] is True + + def test_duplicate_section_titles(self): + """Test handling of duplicate section titles""" + text = "1 Terms\nFirst terms\n1 Terms\nSecond terms" + sections, original, line_ending, verification = split_contract_text_into_sections(text) + + # Should have unique keys (second occurrence gets suffix) + assert "1 Terms" in sections + assert "1 Terms (2)" in sections + assert verification[0] is True + + def test_windows_line_endings(self): + """Test text with Windows line endings (\\r\\n)""" + text = "Section 1\r\nContent A\r\nSection 2\r\nContent B" + sections, original, line_ending, verification = split_contract_text_into_sections(text) + + assert line_ending == "\r\n" + assert verification[0] is True + + def test_unix_line_endings(self): + """Test text with Unix line endings (\\n)""" + text = "Section 1\nContent A\nSection 2\nContent B" + sections, original, line_ending, verification = split_contract_text_into_sections(text) + + assert line_ending == "\n" + assert verification[0] is True + + def test_no_line_endings(self): + """Test text with no line endings""" + text = "Single line of text with no breaks" + sections, original, line_ending, verification = split_contract_text_into_sections(text) + + assert line_ending == "" + assert "Preamble" in sections + assert verification[0] is True + + def test_empty_text(self): + """Test empty text input""" + text = "" + sections, original, line_ending, verification = split_contract_text_into_sections(text) + + # Should handle empty text gracefully + assert isinstance(sections, dict) + assert original == "" + + def test_mixed_heading_types(self): + """Test document with multiple types of headings""" + text = """RECITALS: +Whereas clause +1 Definitions +Term definitions +ARTICLE I Scope +Scope content +EXHIBIT A +Exhibit content""" + sections, original, line_ending, verification = split_contract_text_into_sections(text) + + assert "RECITALS" in sections + assert "1 Definitions" in sections + assert "ARTICLE I Scope" in sections + # EXHIBIT A might combine with next line, so check for both possibilities + assert "EXHIBIT A" in sections or "EXHIBIT A Exhibit content" in sections + assert verification[0] is True + + +class TestVerifyPreservation: + """Test verify_preservation function""" + + def test_perfect_preservation(self): + """Test when content is perfectly preserved""" + original = "Line 1\nLine 2\nLine 3" + sections = {"Section1": "Line 1\nLine 2", "Section2": "Line 3"} + line_ending = "\n" + + is_preserved, message = verify_preservation(original, sections, line_ending) + + assert is_preserved is True + assert "Perfect preservation" in message + + def test_content_mismatch(self): + """Test when content differs""" + original = "Line 1\nLine 2\nLine 3" + sections = {"Section1": "Line 1\nLine X"} # Different content + line_ending = "\n" + + is_preserved, message = verify_preservation(original, sections, line_ending) + + assert is_preserved is False + assert "First difference at position" in message or "Length mismatch" in message + + def test_length_mismatch(self): + """Test when reconstructed content has different length""" + original = "Short text" + sections = {"Section1": "Much longer text than original"} + line_ending = "\n" + + is_preserved, message = verify_preservation(original, sections, line_ending) + + assert is_preserved is False + + def test_empty_sections(self): + """Test with empty sections dict""" + original = "Some content" + sections = {} + line_ending = "\n" + + is_preserved, message = verify_preservation(original, sections, line_ending) + + assert is_preserved is False + + def test_windows_line_endings_preservation(self): + """Test preservation with Windows line endings""" + original = "Line 1\r\nLine 2\r\nLine 3" + sections = {"Section1": "Line 1\r\nLine 2", "Section2": "Line 3"} + line_ending = "\r\n" + + is_preserved, message = verify_preservation(original, sections, line_ending) + + assert is_preserved is True + + +class TestGetSectionInfo: + """Test get_section_info function""" + + def test_basic_section_info(self): + """Test extracting basic section information""" + sections = { + "Section 1": "This is section one content", + "Section 2": "This is section two\nWith multiple lines" + } + + info = get_section_info(sections) + + assert info["total_sections"] == 2 + assert "Section 1" in info["section_details"] + assert "Section 2" in info["section_details"] + assert info["section_details"]["Section 1"]["character_count"] == 27 + assert info["section_details"]["Section 2"]["line_count"] == 2 + + def test_empty_sections(self): + """Test with empty sections dict""" + sections = {} + + info = get_section_info(sections) + + assert info["total_sections"] == 0 + assert info["section_details"] == {} + assert info["total_characters"] == 0 + + def test_section_with_empty_content(self): + """Test section with empty string content""" + sections = {"Empty Section": ""} + + info = get_section_info(sections) + + assert info["total_sections"] == 1 + assert info["section_details"]["Empty Section"]["character_count"] == 0 + + def test_total_character_count(self): + """Test total character count calculation""" + sections = { + "Section 1": "12345", # 5 chars + "Section 2": "67890", # 5 chars + "Section 3": "ABCDE" # 5 chars + } + + info = get_section_info(sections) + + assert info["total_characters"] == 15 + + def test_line_count_calculation(self): + """Test line count is calculated correctly""" + sections = { + "Single Line": "One line only", + "Multi Line": "Line 1\nLine 2\nLine 3" + } + + info = get_section_info(sections) + + assert info["section_details"]["Single Line"]["line_count"] == 1 + assert info["section_details"]["Multi Line"]["line_count"] == 3 + + +class TestIntegration: + """Integration tests for the full preprocessing workflow""" + + def test_realistic_contract_structure(self): + """Test with a realistic contract structure""" + contract_text = """PROVIDER AGREEMENT + +RECITALS: +This Agreement is made between the parties. + +AGREEMENT + +1 Definitions +For purposes of this Agreement, the following terms shall have the meanings set forth below. + +1.1 Provider +Provider means the healthcare provider. + +1.2 Services +Services means healthcare services. + +2 Terms and Conditions +The parties agree to the following terms. + +EXHIBIT A Payment Schedule +Payment details here. + +IN WITNESS WHEREOF +The parties have executed this Agreement. + +PROVIDER: +By: _______________ +Name: John Doe +""" + + sections, original, line_ending, verification = split_contract_text_into_sections(contract_text) + + # Verify major sections are identified + assert "RECITALS" in sections + assert "AGREEMENT" in sections + assert any("Definitions" in key for key in sections.keys()) + assert "EXHIBIT A Payment Schedule" in sections + assert "IN WITNESS WHEREOF" in sections + + # Verify preservation + assert verification[0] is True + + # Verify section info + info = get_section_info(sections) + assert info["total_sections"] > 0 + # Total characters should match (allow for slight differences in section joining) + assert abs(info["total_characters"] - len(contract_text)) < 20 diff --git a/fieldExtraction/tests/test_rag_utils.py b/fieldExtraction/tests/test_rag_utils.py new file mode 100644 index 0000000..738697d --- /dev/null +++ b/fieldExtraction/tests/test_rag_utils.py @@ -0,0 +1,332 @@ +""" +Unit tests for src.utils.rag_utils module + +Tests RAG (Retrieval-Augmented Generation) utility functions including: +- RAGConfig configuration +- Client initialization and caching +- Vectorstore creation +- Hybrid retriever creation +- Document reranking +""" + +import pytest +from unittest.mock import Mock, patch, MagicMock +from langchain.schema import Document +from src.utils.rag_utils import ( + RAGConfig, + HSC_CONFIG, + RERANKER_CONFIGS, + get_bedrock_client, + get_reranker_client, + get_embeddings, + create_vectorstore, + create_hybrid_retriever, + rerank_documents, +) + + +class TestRAGConfig: + """Test RAGConfig dataclass""" + + def test_default_config(self): + """Test that HSC_CONFIG has expected default values""" + assert HSC_CONFIG.CHUNK_SIZE == 2000 + assert HSC_CONFIG.CHUNK_OVERLAP == 200 + assert HSC_CONFIG.SEMANTIC_TOP_K == 35 + assert HSC_CONFIG.KEYWORD_TOP_K == 25 + assert HSC_CONFIG.ENSEMBLE_TOP_K == 40 + assert HSC_CONFIG.RERANKER_TOP_K == 15 + assert HSC_CONFIG.BM25_WEIGHT == 0.40 + assert HSC_CONFIG.VECTOR_WEIGHT == 0.60 + assert HSC_CONFIG.RERANKER == "amazon" + assert HSC_CONFIG.EMBEDDING_MODEL_ID == "amazon.titan-embed-text-v2:0" + assert HSC_CONFIG.BEDROCK_REGION == "us-east-1" + + def test_custom_config(self): + """Test creating custom RAGConfig""" + custom_config = RAGConfig( + CHUNK_SIZE=1000, + CHUNK_OVERLAP=100, + RERANKER="cohere" + ) + assert custom_config.CHUNK_SIZE == 1000 + assert custom_config.CHUNK_OVERLAP == 100 + assert custom_config.RERANKER == "cohere" + # Defaults should still apply + assert custom_config.SEMANTIC_TOP_K == 35 + + def test_reranker_configs(self): + """Test RERANKER_CONFIGS dictionary structure""" + assert "amazon" in RERANKER_CONFIGS + assert "cohere" in RERANKER_CONFIGS + + # Amazon config + assert RERANKER_CONFIGS["amazon"]["model_id"] == "amazon.rerank-v1:0" + assert RERANKER_CONFIGS["amazon"]["name"] == "Amazon Rerank 1.0" + assert RERANKER_CONFIGS["amazon"]["region"] == "us-west-2" + + # Cohere config + assert RERANKER_CONFIGS["cohere"]["model_id"] == "cohere.rerank-v3-5:0" + assert RERANKER_CONFIGS["cohere"]["name"] == "Cohere Rerank 3.5" + assert RERANKER_CONFIGS["cohere"]["region"] == "us-east-1" + + +class TestClientFunctions: + """Test client initialization and caching functions""" + + @patch('src.utils.rag_utils.boto3.client') + def test_get_bedrock_client(self, mock_boto_client): + """Test Bedrock client creation and caching""" + # Reset module-level cache + import src.utils.rag_utils as rag_utils + rag_utils._bedrock_client = None + + mock_client = Mock() + mock_boto_client.return_value = mock_client + + # First call should create client + client1 = get_bedrock_client() + assert client1 == mock_client + mock_boto_client.assert_called_once_with("bedrock-runtime", region_name="us-east-1") + + # Second call should return cached client + client2 = get_bedrock_client() + assert client2 == mock_client + assert mock_boto_client.call_count == 1 # Should not create new client + + @patch('src.utils.rag_utils.boto3.client') + def test_get_reranker_client_amazon(self, mock_boto_client): + """Test Amazon reranker client creation""" + # Reset module-level cache + import src.utils.rag_utils as rag_utils + rag_utils._reranker_clients = {} + + mock_client = Mock() + mock_boto_client.return_value = mock_client + + client = get_reranker_client("amazon") + assert client == mock_client + mock_boto_client.assert_called_with("bedrock-runtime", region_name="us-west-2") + + @patch('src.utils.rag_utils.boto3.client') + def test_get_reranker_client_cohere(self, mock_boto_client): + """Test Cohere reranker client creation""" + # Reset module-level cache + import src.utils.rag_utils as rag_utils + rag_utils._reranker_clients = {} + + mock_client = Mock() + mock_boto_client.return_value = mock_client + + client = get_reranker_client("cohere") + assert client == mock_client + mock_boto_client.assert_called_with("bedrock-runtime", region_name="us-east-1") + + @patch('src.utils.rag_utils.get_bedrock_client') + @patch('src.utils.rag_utils.BedrockEmbeddings') + def test_get_embeddings(self, mock_embeddings_class, mock_get_client): + """Test embeddings model creation and caching""" + # Reset module-level cache + import src.utils.rag_utils as rag_utils + rag_utils._embeddings = None + + mock_client = Mock() + mock_get_client.return_value = mock_client + mock_embeddings = Mock() + mock_embeddings_class.return_value = mock_embeddings + + # First call should create embeddings + embeddings1 = get_embeddings() + assert embeddings1 == mock_embeddings + mock_embeddings_class.assert_called_once_with( + model_id="amazon.titan-embed-text-v2:0", + client=mock_client + ) + + # Second call should return cached embeddings + embeddings2 = get_embeddings() + assert embeddings2 == mock_embeddings + assert mock_embeddings_class.call_count == 1 + + +class TestVectorstoreFunctions: + """Test vectorstore and retriever creation functions""" + + @patch('src.utils.rag_utils.FAISS.from_documents') + def test_create_vectorstore(self, mock_faiss): + """Test FAISS vectorstore creation""" + mock_vectorstore = Mock() + mock_faiss.return_value = mock_vectorstore + mock_embeddings = Mock() + + chunks = [ + Document(page_content="test content 1"), + Document(page_content="test content 2") + ] + + result = create_vectorstore(chunks, mock_embeddings) + + assert result == mock_vectorstore + mock_faiss.assert_called_once() + # Verify chunks and embeddings were passed + call_args = mock_faiss.call_args + assert call_args[0][0] == chunks + assert call_args[0][1] == mock_embeddings + + @patch('src.utils.rag_utils.BM25Retriever.from_documents') + @patch('src.utils.rag_utils.EnsembleRetriever') + def test_create_hybrid_retriever(self, mock_ensemble, mock_bm25): + """Test hybrid retriever creation with BM25 and semantic search""" + # Setup mocks + mock_bm25_retriever = Mock() + mock_bm25.return_value = mock_bm25_retriever + mock_ensemble_retriever = Mock() + mock_ensemble.return_value = mock_ensemble_retriever + + mock_vectorstore = Mock() + mock_vector_retriever = Mock() + mock_vectorstore.as_retriever.return_value = mock_vector_retriever + + chunks = [Document(page_content="test")] + config = RAGConfig() + + result = create_hybrid_retriever(chunks, mock_vectorstore, config) + + assert result == mock_ensemble_retriever + # Verify BM25 was created + mock_bm25.assert_called_once() + # Verify vectorstore retriever was created + mock_vectorstore.as_retriever.assert_called_once() + # Verify ensemble was created with correct weights + call_args = mock_ensemble.call_args[1] + assert call_args['weights'] == [config.VECTOR_WEIGHT, config.BM25_WEIGHT] + + +class TestRerankDocuments: + """Test document reranking functionality""" + + def test_rerank_empty_docs(self): + """Test reranking with empty document list""" + result = rerank_documents("query", [], Mock()) + assert result == [] + + @patch('src.utils.rag_utils.json.loads') + def test_rerank_amazon_model(self, mock_json_loads): + """Test reranking with Amazon model""" + # Setup + mock_client = Mock() + mock_response = { + "body": Mock(read=Mock(return_value=b'{}')) + } + mock_client.invoke_model.return_value = mock_response + mock_json_loads.return_value = { + "results": [ + {"index": 0, "relevance_score": 0.9}, + {"index": 1, "relevance_score": 0.7} + ] + } + + docs = [ + Document(page_content="doc1", metadata={"source": "test"}), + Document(page_content="doc2", metadata={"source": "test"}) + ] + + result = rerank_documents( + query="test query", + docs=docs, + client=mock_client, + top_k=2, + model="amazon" + ) + + # Verify results + assert len(result) == 2 + assert result[0].page_content == "doc1" + assert result[0].metadata["rerank_score"] == 0.9 + assert result[1].page_content == "doc2" + assert result[1].metadata["rerank_score"] == 0.7 + + @patch('src.utils.rag_utils.json.loads') + def test_rerank_cohere_model(self, mock_json_loads): + """Test reranking with Cohere model""" + mock_client = Mock() + mock_response = { + "body": Mock(read=Mock(return_value=b'{}')) + } + mock_client.invoke_model.return_value = mock_response + mock_json_loads.return_value = { + "results": [ + {"index": 1, "relevance_score": 0.8}, + {"index": 0, "relevance_score": 0.6} + ] + } + + docs = [ + Document(page_content="doc1"), + Document(page_content="doc2") + ] + + result = rerank_documents( + query="test query", + docs=docs, + client=mock_client, + top_k=2, + model="cohere" + ) + + # Verify reranked order (doc2 should be first due to higher score) + assert len(result) == 2 + assert result[0].page_content == "doc2" + assert result[1].page_content == "doc1" + + @patch('src.utils.rag_utils.json.loads', side_effect=Exception("API Error")) + @patch('src.utils.rag_utils.logging') + def test_rerank_error_handling(self, mock_logging, mock_json_loads): + """Test that reranking failures return top_k documents""" + docs = [ + Document(page_content=f"doc{i}") for i in range(10) + ] + mock_client = Mock() + + result = rerank_documents( + query="test", + docs=docs, + client=mock_client, + top_k=3, + model="amazon" + ) + + # Should return first top_k docs on error + assert len(result) == 3 + assert result[0].page_content == "doc0" + # Verify error was logged + mock_logging.error.assert_called_once() + + def test_rerank_top_k_limit(self): + """Test that reranking respects top_k limit""" + mock_client = Mock() + mock_response = { + "body": Mock(read=Mock(return_value=b'{"results": [{"index": 0, "relevance_score": 0.9}, {"index": 1, "relevance_score": 0.8}, {"index": 2, "relevance_score": 0.7}]}')) + } + mock_client.invoke_model.return_value = mock_response + + docs = [Document(page_content=f"doc{i}") for i in range(3)] + + with patch('src.utils.rag_utils.json.loads') as mock_json: + mock_json.return_value = { + "results": [ + {"index": 0, "relevance_score": 0.9}, + {"index": 1, "relevance_score": 0.8}, + {"index": 2, "relevance_score": 0.7} + ] + } + + result = rerank_documents( + query="test", + docs=docs, + client=mock_client, + top_k=2, + model="amazon" + ) + + assert len(result) == 2 diff --git a/fieldExtraction/tests/test_smart_chunking_funcs.py b/fieldExtraction/tests/test_smart_chunking_funcs.py deleted file mode 100644 index d3b3ef4..0000000 --- a/fieldExtraction/tests/test_smart_chunking_funcs.py +++ /dev/null @@ -1,72 +0,0 @@ -import pytest -from src.investment.smart_chunking_funcs import parse_chunk, stitch_chunks - - -def test_parse_chunk_valid(): - chunk = "chunk 001: This is a test chunk." - chunk_number, content = parse_chunk(chunk) - assert chunk_number == 1 - assert content == "This is a test chunk." - - -def test_parse_chunk_valid_with_large_number(): - chunk = "chunk 123: Another test chunk with a larger number." - chunk_number, content = parse_chunk(chunk) - assert chunk_number == 123 - assert content == "Another test chunk with a larger number." - - -def test_parse_chunk_empty_content(): - chunk = "chunk 005: " - chunk_number, content = parse_chunk(chunk) - assert chunk_number == 5 - assert content == "" - - -def test_parse_chunk_invalid_format(): - chunk = "invalid chunk format" - with pytest.raises(ValueError): - parse_chunk(chunk) - - -def test_parse_chunk_short_prefix(): - chunk = "chunk 01: Short prefix" - with pytest.raises(ValueError): - parse_chunk(chunk) - - -def test_stitch_chunks_empty_list(): - assert stitch_chunks([]) == "" - - -def test_stitch_chunks_single_chunk(): - assert stitch_chunks(["chunk 001: hello world"]) == "hello world" - - -def test_stitch_chunks_two_chunks(): - chunks = ["chunk 001: hello world", "chunk 002: world and more"] - assert stitch_chunks(chunks, overlap_size=5) == "hello world and more" - - -def test_stitch_chunks_out_of_order(): - chunks = ["chunk 002: world", "chunk 001: hello"] - assert stitch_chunks(chunks, overlap_size=0) in ["helloworld", "hello\nworld"] - - -def test_stitch_noncontiguous_chunks(): - chunks = ["chunk 001: hello", "chunk 003: world"] - assert stitch_chunks(chunks, overlap_size=10) in [ - "helloworld", - "hello\nworld", - ] # the overlap size will be ignored in the noncontiguous case - - -def test_stitch_long_overlap(): # not enough text to overlap, so we just append them - chunks = ["chunk 001: hello", "chunk 002: world"] - assert stitch_chunks(chunks, overlap_size=10) in ["helloworld", "hello\nworld"] - - -def test_stitch_chunks_negative_overlap(): - chunks = ["chunk 001: hello", "chunk 002: world"] - with pytest.raises(ValueError): - stitch_chunks(chunks, overlap_size=-1)