From 67d47d42fad3ccb448acf7604e05ff05cee4f643 Mon Sep 17 00:00:00 2001 From: Mohammed-Balkhair-hub Date: Fri, 23 Jan 2026 20:50:47 +0300 Subject: [PATCH 1/4] build rag + extract controls chunk all frameworks files to build rag + extract controls from chosen framewirk files. --- services/ai-service/.gitignore | 36 + services/{test => ai-service}/.python-version | 0 services/ai-service/README.md | 111 + services/ai-service/main.py | 309 +++ services/ai-service/pyproject.toml | 20 + services/ai-service/src/ARCHITECTURE.md | 544 +++++ services/ai-service/src/__init__.py | 53 + services/ai-service/src/core/__init__.py | 13 + services/ai-service/src/core/evaluator.py | 95 + .../src/core/framework_consolidator.py | 199 ++ .../src/core/framework_extractor.py | 123 + .../src/core/framework_organizer.py | 329 +++ .../ai-service/src/embeddings/__init__.py | 20 + .../src/embeddings/gemma_embedder.py | 149 ++ .../src/embeddings/haystack_retriever.py | 142 ++ .../src/embeddings/qdrant_manager.py | 254 ++ .../ai-service/src/processing/__init__.py | 8 + .../ai-service/src/processing/pdf_parser.py | 43 + .../ai-service/src/processing/text_chunker.py | 235 ++ services/ai-service/src/prompts/__init__.py | 3 + .../src/prompts/prompt_generator.py | 77 + services/ai-service/src/utils/__init__.py | 15 + .../ai-service/src/utils/framework_utils.py | 179 ++ services/ai-service/uv.lock | 2167 +++++++++++++++++ services/test/README.md | 0 services/test/main.py | 6 - services/test/pyproject.toml | 7 - 27 files changed, 5124 insertions(+), 13 deletions(-) create mode 100644 services/ai-service/.gitignore rename services/{test => ai-service}/.python-version (100%) create mode 100644 services/ai-service/README.md create mode 100644 services/ai-service/main.py create mode 100644 services/ai-service/pyproject.toml create mode 100644 services/ai-service/src/ARCHITECTURE.md create mode 100644 services/ai-service/src/__init__.py create mode 100644 services/ai-service/src/core/__init__.py create mode 100644 services/ai-service/src/core/evaluator.py create mode 100644 services/ai-service/src/core/framework_consolidator.py create mode 100644 services/ai-service/src/core/framework_extractor.py create mode 100644 services/ai-service/src/core/framework_organizer.py create mode 100644 services/ai-service/src/embeddings/__init__.py create mode 100644 services/ai-service/src/embeddings/gemma_embedder.py create mode 100644 services/ai-service/src/embeddings/haystack_retriever.py create mode 100644 services/ai-service/src/embeddings/qdrant_manager.py create mode 100644 services/ai-service/src/processing/__init__.py create mode 100644 services/ai-service/src/processing/pdf_parser.py create mode 100644 services/ai-service/src/processing/text_chunker.py create mode 100644 services/ai-service/src/prompts/__init__.py create mode 100644 services/ai-service/src/prompts/prompt_generator.py create mode 100644 services/ai-service/src/utils/__init__.py create mode 100644 services/ai-service/src/utils/framework_utils.py create mode 100644 services/ai-service/uv.lock delete mode 100644 services/test/README.md delete mode 100644 services/test/main.py delete mode 100644 services/test/pyproject.toml diff --git a/services/ai-service/.gitignore b/services/ai-service/.gitignore new file mode 100644 index 0000000..d3ead36 --- /dev/null +++ b/services/ai-service/.gitignore @@ -0,0 +1,36 @@ +# Python +__pycache__/ +*.py[cod] +*$py.class +*.so +.Python +env/ +venv/ +ENV/ +.venv + +# Data directories (user uploads and generated content) +data/ +config/vector_db/ +config/frameworks/ + +# Environment variables +.env +.env.local + +# IDE +.vscode/ +.idea/ +*.swp +*.swo + +# OS +.DS_Store +Thumbs.db + +# Logs +*.log + +# Model cache (if downloading models) +.cache/ +models/ diff --git a/services/test/.python-version b/services/ai-service/.python-version similarity index 100% rename from services/test/.python-version rename to services/ai-service/.python-version diff --git a/services/ai-service/README.md b/services/ai-service/README.md new file mode 100644 index 0000000..9362842 --- /dev/null +++ b/services/ai-service/README.md @@ -0,0 +1,111 @@ +# Compliance Framework Evaluation System + +AI service for extracting compliance controls from framework documents and evaluating applicant documents against those frameworks. + +## Directory Structure + +### Input Directories (Place your PDFs here) + +``` +data/ +├── inputs/ +│ ├── frameworks/ # Place framework PDF files here +│ └── applicants/ # Place applicant PDF files here +``` + +**Usage:** +- **Framework PDFs**: Place compliance framework PDFs in `data/inputs/frameworks/` +- **Applicant PDFs**: Place applicant documents to evaluate in `data/inputs/applicants/` + +### Output Directories (Generated automatically) + +``` +config/ +├── frameworks/ # Framework outputs (controls.json, evaluation_prompt.txt) +│ └── {framework_name}/ +│ ├── controls.json +│ └── evaluation_prompt.txt +└── vector_db/ # Qdrant vector database storage + +data/ +└── outputs/ + └── evaluations/ # Evaluation reports (JSON files) +``` + +**What gets saved where:** +- **Framework Data**: `config/frameworks/{framework_name}/` + - `controls.json` - Extracted compliance controls + - `evaluation_prompt.txt` - Generated evaluation prompt + +- **Evaluation Reports**: `data/outputs/evaluations/` + - Format: `{framework_name}_{applicant_name}_{timestamp}.json` + +- **Vector Database**: `config/vector_db/` (Qdrant local storage) + +## Quick Start + +### 1. Setup a Framework + +```python +from main import setup_framework + +# Place your framework PDF in data/inputs/frameworks/ +setup_framework( + 'data/inputs/frameworks/my_framework.pdf', + 'my_framework' +) +``` + +This will: +- Extract controls from the PDF +- Generate an evaluation prompt +- Save everything to `config/frameworks/my_framework/` + +### 2. Index Framework Documents (Optional) + +```python +from main import index_framework_documents + +# Index framework for vector search +index_framework_documents( + 'data/inputs/frameworks/my_framework.pdf', + 'my_framework' +) +``` + +### 3. Evaluate Applicant Documents + +```python +from main import evaluate_applicant_documents + +# Place applicant PDFs in data/inputs/applicants/ +evaluate_applicant_documents( + ['data/inputs/applicants/applicant1.pdf'], + 'my_framework', + applicant_name='applicant1' +) +``` + +The evaluation report will be saved to `data/outputs/evaluations/` + +## Future API Integration + +When you build the API later, you can: + +1. **Upload endpoints**: + - `POST /api/frameworks/upload` → Save to `data/inputs/frameworks/` + - `POST /api/applicants/upload` → Save to `data/inputs/applicants/` + +2. **Processing endpoints**: + - `POST /api/frameworks/process` → Run `setup_framework()` + - `POST /api/evaluations/create` → Run `evaluate_applicant_documents()` + +3. **Retrieval endpoints**: + - `GET /api/frameworks` → List from `config/frameworks/` + - `GET /api/evaluations/{id}` → Load from `data/outputs/evaluations/` + +## Notes + +- All data directories are in `.gitignore` (user uploads and generated content) +- The directory structure is designed to be API-friendly +- Paths are relative to the project root (`services/ai-service/`) diff --git a/services/ai-service/main.py b/services/ai-service/main.py new file mode 100644 index 0000000..f5d0244 --- /dev/null +++ b/services/ai-service/main.py @@ -0,0 +1,309 @@ +""" +Main entry point for the Compliance Framework Evaluation System. + +This demonstrates the complete pipeline: +1. Framework Setup: Extract controls from framework PDF +2. Document Processing: Process and index framework documents +3. Evaluation: Evaluate applicant documents against framework +""" + +from pathlib import Path +import glob +from src.core import ( + extract_controls_from_framework, + evaluate_applicant, + organize_pdfs_by_section, + validate_framework_sections, + extract_controls_from_sections, + create_master_prompt +) +from src.processing import extract_text_from_pdf, chunk_text +from src.prompts import generate_evaluation_prompt +from src.utils import ( + save_framework_data, + load_framework_data, + list_saved_frameworks, + save_evaluation_report, + get_input_paths +) +# from src.embeddings import ( +# GemmaEmbedder, initialize_qdrant, add_documents, search_similar +#) + + +def setup_framework(pdf_paths: str | list[str], framework_name: str): + """ + Setup a new compliance framework from PDF(s). + + Supports single file, list of files, or directory path. + For multiple PDFs, organizes them into sections, validates, extracts controls, + and creates a master evaluation prompt. + + Pipeline: + 1. Normalize input to list of PDF paths + 2. Organize PDFs into sections (using LLM content analysis) + 3. Validate framework sections + 4. Extract controls from each section + 5. Create master evaluation prompt + 6. Save framework data with metadata + + Args: + pdf_paths: Single file path (str), list of file paths, or directory path + framework_name: Name of the framework + + Returns: + Tuple of (consolidated_controls_json, master_evaluation_prompt, metadata) + """ + # Normalize input to list of PDF paths + if isinstance(pdf_paths, list): + pdf_paths_list = pdf_paths + else: + pdf_path = Path(pdf_paths) + if pdf_path.is_dir(): + pdf_paths_list = sorted(glob.glob(f"{pdf_paths}/*.pdf")) + else: + pdf_paths_list = [pdf_paths] + + if not pdf_paths_list: + raise ValueError(f"No PDF files found: {pdf_paths}") + + # For single PDF, use original workflow for backward compatibility + if len(pdf_paths_list) == 1: + pdf_text = extract_text_from_pdf(pdf_paths_list[0]) + controls_json = extract_controls_from_framework(pdf_text, framework_name) + evaluation_prompt = generate_evaluation_prompt(controls_json, framework_name) + save_framework_data(framework_name, controls_json, evaluation_prompt) + return controls_json, evaluation_prompt + + # Multi-PDF workflow + # 1. Organize PDFs into sections + organized_sections = organize_pdfs_by_section(pdf_paths_list, framework_name) + + # 2. Validate framework sections + validation_result = validate_framework_sections(organized_sections, framework_name) + + # 3. Extract controls from each section + section_controls = extract_controls_from_sections(organized_sections, framework_name) + + # 4. Create master evaluation prompt + master_prompt = create_master_prompt(section_controls, organized_sections, framework_name) + + # 5. Consolidate controls JSON (combine all sections) + consolidated_controls = { + "framework_name": framework_name, + "sections": list(organized_sections.keys()), + "controls": [], + "metadata": { + "total_sections": len(organized_sections), + "extraction_method": "multi_pdf_consolidated" + }, + "filters": {} + } + + # Combine controls from all sections + all_categories = set() + for section_name, controls in section_controls.items(): + if not controls.get('incomplete'): + if 'controls' in controls: + for control in controls['controls']: + control['section'] = section_name + consolidated_controls['controls'].append(control) + if 'category' in control: + all_categories.add(control['category']) + + consolidated_controls['filters']['categories'] = list(all_categories) + consolidated_controls['metadata']['total_controls'] = len(consolidated_controls['controls']) + + # 6. Prepare metadata + from datetime import datetime + metadata = { + "sections": list(organized_sections.keys()), + "total_size": validation_result.get('total_size', 0), + "extracted_at": datetime.now().isoformat(), + "keywords_found": validation_result.get('keywords_found', {}), + "validation_status": validation_result.get('status', 'pass'), + "validation_checks": validation_result.get('checks', {}), + "section_paths": organized_sections + } + + # 7. Save framework data + save_framework_data(framework_name, consolidated_controls, master_prompt, metadata) + + return consolidated_controls, master_prompt, metadata + + +def index_framework_documents( + pdf_paths: str | list[str], + framework_name: str +): + """ + Index framework documents in vector database. + + Supports single file, list of files, or directory path. + Each PDF is processed individually: extract text, chunk, then all chunks are embedded together. + + Pipeline: + 1. For each PDF: + - Extract text from PDF + - Chunk text + 2. Generate embeddings for all chunks + 3. Store in Qdrant + + Args: + pdf_paths: Single file path (str), list of file paths, or directory path + framework_name: Name of the framework + + Returns: + Tuple of (qdrant_client, collection_name, embedder) + """ + # Lazy import - only load heavy ML libraries when this function is called + from src.embeddings import ( + GemmaEmbedder, initialize_qdrant, add_documents + ) + + # Normalize input to list of PDF paths + if isinstance(pdf_paths, list): + pdf_paths_list = pdf_paths + else: + pdf_path = Path(pdf_paths) + if pdf_path.is_dir(): + pdf_paths_list = sorted(glob.glob(f"{pdf_paths}/*.pdf")) + else: + pdf_paths_list = [pdf_paths] + + # Process each PDF individually: extract text, then chunk + all_chunks = [] + for pdf_path in pdf_paths_list: + # Extract text from this PDF + pdf_text = extract_text_from_pdf(pdf_path) + + # Chunk this PDF's text + pdf_chunks = chunk_text(pdf_text, framework_name=framework_name) + + # Add source PDF metadata to each chunk + pdf_name = Path(pdf_path).name + for chunk in pdf_chunks: + chunk["metadata"]["source_pdf"] = pdf_name + chunk["metadata"]["source_path"] = str(pdf_path) + + all_chunks.extend(pdf_chunks) + + if not all_chunks: + raise ValueError("No chunks could be created from the provided PDFs") + + # Initialize embedder and generate embeddings for all chunks + embedder = GemmaEmbedder() + embedding_dim = embedder.get_embedding_dim() + + chunk_texts = [chunk["text"] for chunk in all_chunks] + embeddings = embedder.embed_batch(chunk_texts) + + collection_name = f"{framework_name}_chunks" + qdrant_client = initialize_qdrant(collection_name, embedding_dim) + add_documents(qdrant_client, collection_name, all_chunks, embeddings) + + return qdrant_client, collection_name, embedder + + +def evaluate_applicant_documents( + applicant_pdf_paths: list[str], + framework_name: str, + applicant_name: str = None, + save_report: bool = True +): + """ + Evaluate applicant documents against a framework. + + Pipeline: + 1. Load framework data + 2. Extract text from applicant PDFs + 3. Evaluate using LLM + 4. Save evaluation report (optional) + + Args: + applicant_pdf_paths: List of paths to applicant PDF files + framework_name: Name of the framework to evaluate against + applicant_name: Optional name/identifier for the applicant + save_report: Whether to save the evaluation report to disk + """ + controls_json, evaluation_prompt = load_framework_data(framework_name) + + applicant_docs = [] + for pdf_path in applicant_pdf_paths: + doc_text = extract_text_from_pdf(pdf_path) + applicant_docs.append(doc_text) + + evaluation_report = evaluate_applicant( + applicant_docs, evaluation_prompt, controls_json + ) + + if save_report: + save_evaluation_report( + evaluation_report, + framework_name, + applicant_name + ) + + return evaluation_report + + +def main(): + """Main entry point demonstrating the pipeline.""" + print("\n" + "="*60) + print("Compliance Framework Evaluation System") + print("="*60) + + # Show directory structure + paths = get_input_paths() + print("\n📁 Directory Structure:") + print(f" Input PDFs (Frameworks): {paths['frameworks']}") + print(f" Input PDFs (Applicants): {paths['applicants']}") + print(f" Input PDFs (Vector DB): {paths['vector_db']}") + print(f" Framework Outputs: config/frameworks/") + print(f" Evaluation Reports: data/outputs/evaluations/") + print(f" Vector Database: config/vector_db/") + + # Example usage (commented out - user should provide actual paths) + print("\n📝 Example pipeline usage:") + print("\n1. Setup a framework:") + print(" Place framework PDF in: data/inputs/frameworks/") + print(" setup_framework('data/inputs/frameworks/my_framework.pdf', 'my_framework')") + + print("\n2. Index framework documents (supports single file, list, or directory):") + print(" # Single file:") + print(" index_framework_documents('data/inputs/frameworks/my_framework.pdf', 'my_framework')") + print(" # Directory (all PDFs in directory):") + print(" index_framework_documents('data/inputs/vector_db/my_framework/', 'my_framework')") + print(" # List of files:") + print(" index_framework_documents(['file1.pdf', 'file2.pdf'], 'my_framework')") + + print("\n3. Evaluate applicant documents:") + print(" Place applicant PDFs in: data/inputs/applicants/") + print(" evaluate_applicant_documents(") + print(" ['data/inputs/applicants/applicant1.pdf'],") + print(" 'my_framework',") + print(" applicant_name='applicant1'") + print(" )") + + print("\n" + "="*60) + print("Pipeline ready! Place your PDFs in the directories above and run the functions.") + print("="*60 + "\n") + + + +def test_paths(): + paths = get_input_paths() + + print(f" Input PDFs (Frameworks): {paths['frameworks']}") + print(f" Input PDFs (Applicants): {paths['applicants']}") + +if __name__ == "__main__": + #main() + test_paths() + vector_db_path = "data/inputs/vector_db" + framework_name = "NDI" + #index_framework_documents(vector_db_path, framework_name) + framework_path = "data/inputs/frameworks/" + setup_framework(framework_path, framework_name) + + diff --git a/services/ai-service/pyproject.toml b/services/ai-service/pyproject.toml new file mode 100644 index 0000000..0508901 --- /dev/null +++ b/services/ai-service/pyproject.toml @@ -0,0 +1,20 @@ +[project] +name = "ai-service" +version = "0.1.0" +description = "Add your description here" +readme = "README.md" +requires-python = ">=3.13" +dependencies = [ + "openai>=1.0.0", + "pdfplumber>=0.10.0", + "python-dotenv>=1.0.0", + "pydantic>=2.0.0", + "pypdf>=3.0.0", + "ipykernel>=7.1.0", + "qdrant-client>=1.8.0", + "haystack-ai>=2.0.0", + "transformers>=4.35.0", + "torch>=2.0.0", + "sentence-transformers>=2.2.0", + "accelerate>=0.24.0", +] \ No newline at end of file diff --git a/services/ai-service/src/ARCHITECTURE.md b/services/ai-service/src/ARCHITECTURE.md new file mode 100644 index 0000000..5a3a95f --- /dev/null +++ b/services/ai-service/src/ARCHITECTURE.md @@ -0,0 +1,544 @@ +# Code Architecture Documentation + +This document explains the codebase structure and where to find specific functionality when making adjustments. + +## Overview + +The Compliance Framework Evaluation System is organized into logical modules that handle different aspects of the pipeline: + +1. **Core** - Business logic for framework extraction and evaluation +2. **Processing** - Document parsing and text chunking +3. **Embeddings** - Vector embeddings and database operations +4. **Prompts** - LLM prompt generation +5. **Utils** - File I/O and data management + +## Directory Structure + +``` +src/ +├── core/ # Core business logic +│ ├── evaluator.py # Document evaluation against frameworks +│ └── framework_extractor.py # Extract controls from framework PDFs +│ +├── processing/ # Document processing pipeline +│ ├── pdf_parser.py # PDF text extraction +│ └── text_chunker.py # Text chunking for embeddings +│ +├── embeddings/ # Vector operations and storage +│ ├── gemma_embedder.py # Embedding model wrapper +│ ├── qdrant_manager.py # Vector database operations +│ └── haystack_retriever.py # Haystack integration +│ +├── prompts/ # Prompt management +│ └── prompt_generator.py # Generate evaluation prompts +│ +└── utils/ # Utilities and helpers + └── framework_utils.py # Framework data save/load +``` + +--- + +## Module Details + +### Core (`src/core/`) + +**Purpose**: Contains the main business logic for framework extraction and document evaluation. + +#### `framework_extractor.py` +**What it does**: Extracts compliance controls from framework PDF text using LLM. + +**Key Functions**: +- `extract_controls_from_framework(pdf_text, framework_name)` - Main extraction function + +**When to modify**: +- To change how controls are extracted +- To adjust the extraction prompt structure +- To modify the output JSON schema +- To change the LLM model or parameters + +**Dependencies**: OpenAI API, requires `OPENAI_API_KEY` environment variable + +#### `framework_organizer.py` +**What it does**: Organizes multiple PDFs into sections using LLM content analysis and validates framework completeness. + +**Key Functions**: +- `organize_pdfs_by_section(pdf_paths_list, framework_name)` - Analyzes PDF content to determine section names (part1, part2, rubric, etc.) +- `validate_framework_sections(organized_sections, framework_name)` - Validates sections for completeness, quality, keywords, and control patterns + +**When to modify**: +- To change section identification logic +- To adjust validation criteria +- To modify section naming conventions +- To add custom validation checks + +**Dependencies**: OpenAI API, requires `OPENAI_API_KEY` environment variable + +#### `framework_consolidator.py` +**What it does**: Extracts controls from each section and creates a master evaluation prompt consolidating all sections. + +**Key Functions**: +- `extract_controls_from_sections(organized_sections, framework_name)` - Extracts controls from each section individually +- `create_master_prompt(section_controls, organized_sections, framework_name)` - Consolidates all sections into unified master prompt + +**When to modify**: +- To change master prompt structure +- To adjust consolidation logic +- To modify how controls are combined +- To change prompt formatting + +**Dependencies**: OpenAI API, requires `OPENAI_API_KEY` environment variable + +#### `framework_organizer.py` +**What it does**: Organizes multiple PDFs into sections using LLM content analysis and validates framework completeness. + +**Key Functions**: +- `organize_pdfs_by_section(pdf_paths_list, framework_name)` - Analyzes PDF content to determine section names (part1, part2, rubric, etc.) +- `validate_framework_sections(organized_sections, framework_name)` - Validates sections for completeness, quality, keywords, and control patterns + +**When to modify**: +- To change section identification logic +- To adjust validation criteria +- To modify section naming conventions +- To add custom validation checks + +**Dependencies**: OpenAI API, requires `OPENAI_API_KEY` environment variable + +#### `framework_consolidator.py` +**What it does**: Extracts controls from each section and creates a master evaluation prompt consolidating all sections. + +**Key Functions**: +- `extract_controls_from_sections(organized_sections, framework_name)` - Extracts controls from each section individually +- `create_master_prompt(section_controls, organized_sections, framework_name)` - Consolidates all sections into unified master prompt + +**When to modify**: +- To change master prompt structure +- To adjust consolidation logic +- To modify how controls are combined +- To change prompt formatting + +**Dependencies**: OpenAI API, requires `OPENAI_API_KEY` environment variable + +#### `evaluator.py` +**What it does**: Evaluates applicant documents against saved frameworks using LLM. + +**Key Functions**: +- `evaluate_applicant(applicant_docs, evaluation_prompt, controls_json)` - Main evaluation function + +**When to modify**: +- To change evaluation logic +- To adjust evaluation prompt format +- To modify the evaluation report structure +- To change the LLM model or temperature settings + +**Dependencies**: OpenAI API, requires `OPENAI_API_KEY` environment variable + +--- + +### Processing (`src/processing/`) + +**Purpose**: Handles document parsing and text preparation for further processing. + +#### `pdf_parser.py` +**What it does**: Extracts text from PDF files with multilingual support (Arabic/English). + +**Key Functions**: +- `extract_text_from_pdf(pdf_path)` - Extracts all text from a PDF file + - Used individually for each PDF when processing multiple files + +**When to modify**: +- To change PDF extraction library (currently uses `pdfplumber`) +- To add support for other file formats (Word, images, etc.) +- To improve multilingual text extraction +- To add OCR capabilities + +**Dependencies**: `pdfplumber` + +#### `text_chunker.py` +**What it does**: Splits text into chunks with overlap for vector database storage. + +**Key Functions**: +- `chunk_text(text, chunk_size, overlap, framework_name)` - Main chunking function +- `chunk_text_by_sentences(text, sentences_per_chunk)` - Alternative sentence-based chunking +- `_is_valid_chunk(text)` - Validates chunks (filters formatting artifacts) + +**When to modify**: +- To adjust chunk size or overlap parameters +- To change chunking strategy (by paragraphs, sections, etc.) +- To improve semantic boundary detection +- To modify chunk validation logic +- To add custom chunking algorithms + +**Dependencies**: None (pure Python) + +--- + +### Embeddings (`src/embeddings/`) + +**Purpose**: Handles embedding generation and vector database operations. + +#### `gemma_embedder.py` +**What it does**: Wraps Google EmbeddingGemma 300M model for generating embeddings. + +**Key Classes**: +- `GemmaEmbedder` - Main embedder class + +**Key Methods**: +- `embed_text(text)` - Generate embedding for single text +- `embed_batch(texts, batch_size)` - Generate embeddings for multiple texts +- `get_embedding_dim()` - Get embedding dimension + +**When to modify**: +- To change the embedding model (e.g., switch to different HuggingFace model) +- To adjust batch processing parameters +- To add GPU/CPU optimization +- To change authentication method +- To add caching for embeddings + +**Dependencies**: `sentence-transformers`, `torch`, `huggingface_hub` + +#### `qdrant_manager.py` +**What it does**: Manages Qdrant vector database operations (local or remote). + +**Key Functions**: +- `initialize_qdrant(collection_name, vector_size, path, url)` - Initialize client and create collection +- `add_documents(client, collection_name, documents, embeddings)` - Store documents with embeddings +- `search_similar(client, collection_name, query_embedding, top_k)` - Search for similar documents +- `create_collection(client, collection_name, vector_size)` - Create new collection +- `delete_collection(client, collection_name)` - Delete collection + +**When to modify**: +- To change vector database (e.g., switch to Pinecone, Weaviate) +- To adjust batch size for document insertion +- To modify search parameters (distance metric, filters) +- To add collection management features +- To change storage location or configuration + +**Dependencies**: `qdrant-client` + +#### `haystack_retriever.py` +**What it does**: Integrates Haystack AI framework with Qdrant for document retrieval. + +**Key Classes**: +- `HaystackQdrantRetriever` - Retriever class combining Haystack and Qdrant + +**When to modify**: +- To change retrieval strategy +- To integrate different Haystack components +- To modify document loading from Qdrant +- To add filtering or ranking logic + +**Dependencies**: `haystack-ai`, `qdrant-client` + +--- + +### Prompts (`src/prompts/`) + +**Purpose**: Manages prompt generation for LLM interactions. + +#### `prompt_generator.py` +**What it does**: Generates evaluation prompts from extracted controls JSON. + +**Key Functions**: +- `generate_evaluation_prompt(controls_json, framework_name)` - Generate evaluation prompt + +**When to modify**: +- To change prompt structure or format +- To adjust prompt generation instructions +- To modify LLM model or parameters +- To add prompt templates or variations +- To customize prompts for different framework types + +**Dependencies**: OpenAI API, requires `OPENAI_API_KEY` environment variable + +--- + +### Utils (`src/utils/`) + +**Purpose**: Provides utility functions for data persistence and path management. + +#### `framework_utils.py` +**What it does**: Handles saving/loading framework data and evaluation reports. + +**Key Functions**: +- `save_framework_data(framework_name, controls_json, evaluation_prompt)` - Save framework to disk +- `load_framework_data(framework_name)` - Load framework from disk +- `list_saved_frameworks()` - List all saved frameworks +- `save_evaluation_report(evaluation_report, framework_name, applicant_name)` - Save evaluation report +- `get_input_paths()` - Get standard input directory paths + +**When to modify**: +- To change storage location or format +- To add database integration (instead of file system) +- To modify file naming conventions +- To add metadata management +- To change data serialization format + +**Storage Locations**: +- Frameworks: `config/frameworks/{framework_name}/` +- Evaluation reports: `data/outputs/evaluations/` +- Input PDFs: `data/inputs/frameworks/` and `data/inputs/applicants/` +- Vector DB input PDFs: `data/inputs/vector_db/` + +**Dependencies**: None (standard library only) + +--- + +## Data Flow + +### Framework Setup Pipeline + +**Function**: `setup_framework(pdf_paths: str | list[str], framework_name: str)` + +Extracts compliance controls from framework PDF(s) and generates evaluation prompts. Supports single PDF (backward compatible) or multiple PDFs with section organization and consolidation. + +```mermaid +flowchart TD + Start([setup_framework
pdf_paths, framework_name]) --> Normalize[Normalize Input
file/list/directory] + Normalize -->|pdf_paths_list| Check{Number of PDFs?} + + Check -->|Single PDF| SingleFlow[Single PDF Workflow
Backward Compatible] + SingleFlow --> Extract1[extract_text_from_pdf] + Extract1 --> LLM1[extract_controls_from_framework] + LLM1 --> Gen1[generate_evaluation_prompt] + Gen1 --> Save1[save_framework_data] + Save1 --> End1([Return controls_json, prompt]) + + Check -->|Multiple PDFs| MultiFlow[Multi-PDF Workflow] + MultiFlow --> Organize[organize_pdfs_by_section
framework_organizer.py] + Organize -->|LLM: Content Analysis| Sections[Organized Sections
part1, part2, rubric, etc.] + + Sections --> Validate[validate_framework_sections
framework_organizer.py] + Validate -->|LLM: Validation Checks| Valid{Validation
Pass?} + Valid -->|Fail| Error[Raise Exception
with details] + Valid -->|Pass| Extract2[extract_controls_from_sections
framework_consolidator.py] + + Extract2 -->|For each section| SectionControls[Section Controls JSON
per section] + SectionControls --> Consolidate[create_master_prompt
framework_consolidator.py] + Consolidate -->|LLM: Consolidate All| MasterPrompt[Master Evaluation Prompt
All sections combined] + + MasterPrompt --> ConsolidateControls[Consolidate Controls JSON
Combine all sections] + ConsolidateControls --> Metadata[Prepare Metadata
sections, validation, keywords] + Metadata --> Save2[save_framework_data
with metadata] + Save2 -->|Save Files| Files[config/frameworks/framework_name/
- controls.json
- evaluation_prompt.txt
- framework_metadata.json] + Files --> End2([Return consolidated_controls,
master_prompt, metadata]) + + style Start fill:#e1f5ff + style End1 fill:#e1f5ff + style End2 fill:#e1f5ff + style Organize fill:#fff4e6 + style Validate fill:#fff4e6 + style Extract2 fill:#fff4e6 + style Consolidate fill:#fff4e6 + style Error fill:#ffccbc + style Files fill:#e8f5e9 +``` + +**Detailed Steps (Multi-PDF Workflow)**: +1. **Input Normalization**: Normalize to list of PDF paths (handles single file/list/directory) +2. **Section Organization**: `organize_pdfs_by_section()` → LLM analyzes each PDF content to determine section names (part1, part2, rubric, etc.) +3. **Validation**: `validate_framework_sections()` → LLM validates: + - Completeness (all required sections present) + - Text quality (readable, not corrupted) + - Keywords (compliance keywords detected) + - Control patterns (compliance patterns identified) + - Structure (proper document structure) +4. **Control Extraction**: `extract_controls_from_sections()` → Extract controls from each section individually +5. **Master Prompt Creation**: `create_master_prompt()` → LLM consolidates all sections into unified master prompt with: + - Full text from each section + - Combined controls + - Unified evaluation rules + - Rubric/scoring criteria +6. **Consolidation**: Combine all section controls into single controls JSON +7. **Save**: `save_framework_data()` → saves controls, master prompt, and metadata to `config/frameworks/{framework_name}/` + +**Note**: Single PDF workflow maintains backward compatibility. The same PDF file(s) can be indexed into the vector database using `index_framework_documents()`. + +--- + +### Document Indexing Pipeline + +**Function**: `index_framework_documents(pdf_paths: str | list[str], framework_name: str)` + +Processes PDF documents and stores them in a vector database. **The same PDF files from Framework Setup can be indexed here.** + +```mermaid +flowchart TD + A[PDF Paths
file/list/dir] -->|Normalize| B[PDF List] + B -->|For each PDF| C[Extract Text] + C -->|chunk_text| D[Chunks with Metadata] + D -->|Collect All| E[All Chunks] + E -->|embed_batch| F[Embeddings] + F -->|add_documents| G[Qdrant Vector DB] +``` + +**Steps**: +1. Normalize input (single file/list/directory) → `pdf_paths_list` +2. For each PDF: extract text → chunk → add source metadata +3. Generate embeddings for all chunks (batch) +4. Store in Qdrant at `config/vector_db/collection/{framework_name}_chunks/` + +**Returns**: `(qdrant_client, collection_name, embedder)` + +--- + +### Evaluation Pipeline + +**Function**: `evaluate_applicant_documents(applicant_pdf_paths: list[str], framework_name: str, applicant_name: str = None, save_report: bool = True)` + +Evaluates applicant documents against a previously set up framework. + +```mermaid +flowchart TD + Start([evaluate_applicant_documents
applicant_pdf_paths, framework_name]) --> Load[load_framework_data
framework_utils.py] + + Load -->|Load from disk| Framework[config/frameworks/framework_name/
- controls.json
- evaluation_prompt.txt] + Framework -->|controls_json: dict
evaluation_prompt: str| Loaded[Framework Data Loaded] + + Start --> ExtractLoop[For each PDF in applicant_pdf_paths] + ExtractLoop -->|extract_text_from_pdf
pdf_parser.py| Extract[Extract Text from PDF] + Extract -->|doc_text: str| Collect[Collect All Texts] + Collect -->|applicant_docs: list| Docs[All Applicant Documents] + + Loaded --> Eval[evaluate_applicant
evaluator.py] + Docs --> Eval + + Eval -->|LLM Call: OpenAI GPT-4o| LLM[Evaluate Documents
Against Framework] + LLM -->|Uses evaluation_prompt
and controls_json| Analyze[Compare Applicant Docs
with Framework Controls] + Analyze -->|evaluation_report: dict| Report[Evaluation Report
- Compliance Status
- Control Assessments
- Recommendations] + + Report --> Check{save_report
== True?} + Check -->|Yes| Save[save_evaluation_report
framework_utils.py] + Check -->|No| End1([Return Report]) + Save -->|Save to file| Output[data/outputs/evaluations/
framework_name_applicant_name_timestamp.json] + Output --> End2([Return Report]) + + style Start fill:#e1f5ff + style End1 fill:#e1f5ff + style End2 fill:#e1f5ff + style LLM fill:#fff4e6 + style Analyze fill:#fff4e6 + style Framework fill:#e8f5e9 + style Output fill:#e8f5e9 +``` + +**Detailed Steps**: +1. **Load Framework**: `load_framework_data(framework_name)` → loads `controls_json` and `evaluation_prompt` from saved files +2. **Extract Applicant Text**: For each PDF in `applicant_pdf_paths`: + - `extract_text_from_pdf(pdf_path)` → extracts text + - Collects all texts into `applicant_docs` list +3. **Evaluate**: `evaluate_applicant(applicant_docs, evaluation_prompt, controls_json)` → LLM: + - Uses the evaluation prompt as instructions + - Compares applicant documents against framework controls + - Generates compliance assessment for each control +4. **Save Report** (optional): `save_evaluation_report()` → saves JSON report to `data/outputs/evaluations/` + +--- + +## Common Modification Scenarios + +### Change LLM Model + +**Files to modify**: +- `core/framework_extractor.py` - Line 92: `model="gpt-4o"` +- `core/evaluator.py` - Line 60: `model="gpt-4o"` +- `prompts/prompt_generator.py` - Line 61: `model="gpt-4o"` + +### Adjust Chunking Strategy + +**Files to modify**: +- `processing/text_chunker.py` - Modify `chunk_text()` function +- Default parameters: `chunk_size=1500`, `overlap=200` + +### Change Embedding Model + +**Files to modify**: +- `embeddings/gemma_embedder.py` - Line 22: `model_name="google/embeddinggemma-300m"` +- Update `get_embedding_dim()` return value if dimension changes + +### Modify Storage Location + +**Files to modify**: +- `utils/framework_utils.py` - Update path construction in all functions +- `embeddings/qdrant_manager.py` - Line 35: Update default path + +### Add New File Format Support + +**Files to modify**: +- `processing/pdf_parser.py` - Add new extraction function +- Update `main.py` to use new function + +### Change Evaluation Criteria + +**Files to modify**: +- `prompts/prompt_generator.py` - Modify prompt generation instructions +- `core/evaluator.py` - Modify evaluation request format + +--- + +## Environment Variables + +Required environment variables: + +- `OPENAI_API_KEY` - Used by: + - `core/framework_extractor.py` + - `core/evaluator.py` + - `prompts/prompt_generator.py` + +- `HF_TOKEN` or `HUGGINGFACE_TOKEN` - Used by: + - `embeddings/gemma_embedder.py` (for gated models) + +--- + +## Dependencies Overview + +| Module | Key Dependencies | +|--------|------------------| +| `core/` | `openai`, `python-dotenv` | +| `processing/` | `pdfplumber` | +| `embeddings/` | `sentence-transformers`, `torch`, `qdrant-client`, `haystack-ai` | +| `prompts/` | `openai`, `python-dotenv` | +| `utils/` | None (standard library) | + +--- + +## Testing and Debugging Tips + +1. **Test PDF parsing**: Use `processing/pdf_parser.py` directly with a test PDF +2. **Test chunking**: Pass sample text to `processing/text_chunker.py` functions +3. **Test embeddings**: Create `GemmaEmbedder()` instance and test `embed_text()` +4. **Test Qdrant**: Use `embeddings/qdrant_manager.py` functions with test data +5. **Test LLM calls**: Run individual functions from `core/` and `prompts/` modules + +--- + +## Entry Point + +The main entry point is `main.py` in the project root, which demonstrates the complete pipeline: + +- `setup_framework()` - Setup a new framework +- `index_framework_documents(pdf_paths, framework_name)` - Index documents for vector search + - Supports single file path, list of file paths, or directory path + - Each PDF is processed individually (extract → chunk) + - All chunks are embedded together and stored in Qdrant + - Each chunk includes source PDF metadata +- `evaluate_applicant_documents()` - Evaluate applicant documents + +All functions can be imported and used independently: + +```python +from src.core import extract_controls_from_framework, evaluate_applicant +from src.processing import extract_text_from_pdf, chunk_text +from src.embeddings import GemmaEmbedder, initialize_qdrant +``` + +--- + +## Notes + +- All paths are relative to project root (`services/ai-service/`) +- Configuration and data directories are created automatically +- Error handling is minimal - exceptions propagate to caller +- No logging framework - add if needed for production use + + + diff --git a/services/ai-service/src/__init__.py b/services/ai-service/src/__init__.py new file mode 100644 index 0000000..152ff5e --- /dev/null +++ b/services/ai-service/src/__init__.py @@ -0,0 +1,53 @@ +""" +Compliance Framework Evaluation System +""" + +__version__ = "0.1.0" + +# Core exports +from .core import evaluate_applicant, extract_controls_from_framework + +# Embedding exports - LAZY LOADED to avoid slow startup +# These imports heavy ML libraries (sentence-transformers, sklearn, etc.) +# Import them directly from src.embeddings when needed instead +# from .embeddings import ( +# GemmaEmbedder, load_gemma_embedder, +# initialize_qdrant, add_documents, search_similar, +# HaystackQdrantRetriever, create_retrieval_pipeline +# ) + +# Processing exports +from .processing import ( + extract_text_from_pdf, + chunk_text, + chunk_text_by_sentences +) + +# Prompt exports +from .prompts import generate_evaluation_prompt + +# Utils exports +from .utils import ( + save_framework_data, + load_framework_data, + list_saved_frameworks, + save_evaluation_report, + get_input_paths +) + +__all__ = [ + # Core + "evaluate_applicant", "extract_controls_from_framework", + # Embeddings - Note: Import directly from src.embeddings when needed + # "GemmaEmbedder", "load_gemma_embedder", + # "initialize_qdrant", "add_documents", "search_similar", + # "HaystackQdrantRetriever", "create_retrieval_pipeline", + # Processing + "extract_text_from_pdf", + "chunk_text", "chunk_text_by_sentences", + # Prompts + "generate_evaluation_prompt", + # Utils + "save_framework_data", "load_framework_data", "list_saved_frameworks", + "save_evaluation_report", "get_input_paths" +] diff --git a/services/ai-service/src/core/__init__.py b/services/ai-service/src/core/__init__.py new file mode 100644 index 0000000..ccbfc36 --- /dev/null +++ b/services/ai-service/src/core/__init__.py @@ -0,0 +1,13 @@ +from .evaluator import evaluate_applicant +from .framework_extractor import extract_controls_from_framework +from .framework_organizer import organize_pdfs_by_section, validate_framework_sections +from .framework_consolidator import extract_controls_from_sections, create_master_prompt + +__all__ = [ + "evaluate_applicant", + "extract_controls_from_framework", + "organize_pdfs_by_section", + "validate_framework_sections", + "extract_controls_from_sections", + "create_master_prompt" +] diff --git a/services/ai-service/src/core/evaluator.py b/services/ai-service/src/core/evaluator.py new file mode 100644 index 0000000..775989f --- /dev/null +++ b/services/ai-service/src/core/evaluator.py @@ -0,0 +1,95 @@ +""" +Evaluator Module +Evaluates applicant documents against saved evaluation prompts and controls +""" + +import json +from openai import OpenAI +from typing import Dict, Any, List +import os +from dotenv import load_dotenv + +load_dotenv() + + +def evaluate_applicant( + applicant_docs: List[str], + evaluation_prompt: str, + controls_json: Dict[str, Any] +) -> Dict[str, Any]: + """ + Evaluate applicant documents using the saved evaluation prompt and controls. + + Args: + applicant_docs: List of text content from applicant documents + evaluation_prompt: Saved evaluation prompt from setup phase + controls_json: Saved controls JSON from setup phase + + Returns: + Dictionary containing evaluation report with scores/compliance status + """ + api_key = os.getenv("OPENROUTER_API_KEY") + if not api_key: + raise ValueError("OPENROUTER_API_KEY not found in environment variables") + + client = OpenAI( + api_key=api_key, + base_url="https://openrouter.ai/api/v1" + ) + + # Combine all applicant documents + combined_docs = "\n\n--- Document Separator ---\n\n".join(applicant_docs) + + # Truncate if too long + max_chars = 100000 + if len(combined_docs) > max_chars: + combined_docs = combined_docs[:max_chars] + "\n\n[Documents truncated due to length...]" + + # Create the evaluation request + evaluation_request = f"""{evaluation_prompt} + +--- +APPLICANT DOCUMENTS TO EVALUATE: +{combined_docs} + +--- +CONTROLS REFERENCE (for context): +{json.dumps(controls_json, indent=2, ensure_ascii=False)} + +Now evaluate the applicant documents against the framework and provide a comprehensive evaluation report.""" + + response = client.chat.completions.create( + model="deepseek/deepseek-r1", + messages=[ + { + "role": "system", + "content": "You are a compliance evaluation expert. Evaluate documents against compliance frameworks accurately and provide detailed reports." + }, + { + "role": "user", + "content": evaluation_request + } + ], + temperature=0.2, + response_format={"type": "json_object"} + ) + + result_text = response.choices[0].message.content + + # Parse JSON response, handle markdown code blocks if present + try: + evaluation_report = json.loads(result_text) + return evaluation_report + except json.JSONDecodeError as e: + if "```json" in result_text: + json_start = result_text.find("```json") + 7 + json_end = result_text.find("```", json_start) + result_text = result_text[json_start:json_end].strip() + evaluation_report = json.loads(result_text) + return evaluation_report + else: + return { + "report_type": "text", + "content": result_text, + "error": f"Could not parse as JSON: {e}" + } diff --git a/services/ai-service/src/core/framework_consolidator.py b/services/ai-service/src/core/framework_consolidator.py new file mode 100644 index 0000000..fcf2876 --- /dev/null +++ b/services/ai-service/src/core/framework_consolidator.py @@ -0,0 +1,199 @@ +""" +Framework Consolidator Module +Extracts controls from sections and creates a master evaluation prompt +""" + +import json +from pathlib import Path +from typing import Dict, Any +from openai import OpenAI +import os +from dotenv import load_dotenv +from src.processing import extract_text_from_pdf +from src.core.framework_extractor import extract_controls_from_framework + +load_dotenv() + + +def extract_controls_from_sections(organized_sections: Dict[str, str], framework_name: str) -> Dict[str, Dict[str, Any]]: + """ + Extract controls from each section by calling extract_controls_from_framework for each. + + Args: + organized_sections: Dictionary mapping section names to PDF paths + framework_name: Name of the framework + + Returns: + Dictionary mapping section names to their controls JSON: + {'section_name': controls_json, ...} + """ + section_controls = {} + + for section_name, pdf_path in organized_sections.items(): + try: + # Extract text from PDF + pdf_text = extract_text_from_pdf(pdf_path) + + # Extract controls for this section + controls_json = extract_controls_from_framework(pdf_text, f"{framework_name}_{section_name}") + + # Add section context to controls + controls_json['section_name'] = section_name + controls_json['section_path'] = pdf_path + + section_controls[section_name] = controls_json + + except Exception as e: + print(f"Warning: Failed to extract controls from section '{section_name}' ({pdf_path}): {e}") + # Mark section as incomplete + section_controls[section_name] = { + 'section_name': section_name, + 'section_path': pdf_path, + 'error': str(e), + 'incomplete': True + } + + return section_controls + + +def create_master_prompt( + section_controls: Dict[str, Dict[str, Any]], + organized_sections: Dict[str, str], + framework_name: str +) -> str: + """ + Create a master evaluation prompt by consolidating all section controls and texts. + + Uses LLM to consolidate all section controls into a unified master prompt with: + - Full text from each section (organized by section) + - Combined extracted controls + - Unified evaluation rules + - Rubric/scoring criteria from all sections + + Args: + section_controls: Dictionary mapping section names to their controls JSON + organized_sections: Dictionary mapping section names to PDF paths + framework_name: Name of the framework + + Returns: + Master evaluation prompt string + """ + api_key = os.getenv("OPENROUTER_API_KEY") + if not api_key: + raise ValueError("OPENROUTER_API_KEY not found in environment variables") + + client = OpenAI( + api_key=api_key, + base_url="https://openrouter.ai/api/v1" + ) + + # Extract text from each section (with aggressive truncation to stay under token limits) + section_texts = {} + for section_name, pdf_path in organized_sections.items(): + try: + text = extract_text_from_pdf(pdf_path) + # Reduce to 10000 chars per section to stay under token limits + if len(text) > 10000: + text = text[:10000] + "\n\n[Text truncated - see full content in original PDFs]" + section_texts[section_name] = text + except Exception as e: + print(f"Warning: Could not extract text from {pdf_path}: {e}") + section_texts[section_name] = "" + + # Prepare summaries instead of full data to reduce token usage + section_summaries = [] + total_controls = 0 + + for section_name in organized_sections.keys(): + controls = section_controls.get(section_name, {}) + controls_list = controls.get('controls', []) if not controls.get('incomplete') else [] + total_controls += len(controls_list) + + # Create summary instead of full JSON + section_summary = { + 'section_name': section_name, + 'text_sample': section_texts.get(section_name, '')[:5000], # Only 5k chars + 'controls_count': len(controls_list), + 'controls_summary': [ + { + 'id': c.get('id', ''), + 'title': c.get('title', '')[:100], # Truncate titles + 'category': c.get('category', '') + } + for c in controls_list[:20] # Only first 20 controls per section + ] + } + section_summaries.append(section_summary) + + consolidation_prompt = f"""You are creating a master evaluation prompt for a compliance framework. + +Framework Name: {framework_name} +Total Sections: {len(organized_sections)} +Total Controls: {total_controls} + +Create a comprehensive master evaluation prompt that: +1. Provides clear evaluation instructions +2. References all sections and their key controls +3. Includes unified evaluation rules +4. Provides rubric/scoring guidance + +Section Summaries: +{json.dumps(section_summaries, indent=2, ensure_ascii=False)} + +Create the master prompt. Structure it as: + +# FRAMEWORK: {framework_name} + +## EVALUATION INSTRUCTIONS +[Clear instructions for evaluating applicant documents] + +## FRAMEWORK SECTIONS OVERVIEW +[Brief overview of each section and its purpose] + +## EVALUATION RULES +[Unified rules combining all sections] + +## RUBRIC & SCORING +[Evaluation criteria and scoring guidance] + +## CONTROLS REFERENCE +[Summary of key controls to evaluate] + +NOTE: The full framework text is available in the original PDFs. This prompt focuses on evaluation methodology and key controls. + +Create the master evaluation prompt now.""" + + response = client.chat.completions.create( + model="deepseek/deepseek-r1", + messages=[ + { + "role": "system", + "content": "You are an expert in creating comprehensive evaluation prompts for compliance frameworks. Consolidate multiple sections into a unified, well-structured master prompt. The prompt should be clear, complete, and ready to use." + }, + { + "role": "user", + "content": consolidation_prompt + } + ], + temperature=0.3 + ) + + master_prompt = response.choices[0].message.content.strip() + + # If the LLM response is too short, build prompt manually + if len(master_prompt) < 2000: + master_prompt = f"# FRAMEWORK: {framework_name}\n\n" + master_prompt += "## EVALUATION INSTRUCTIONS\n\n" + master_prompt += "Evaluate applicant documents against all sections of this framework.\n\n" + + for section_name, text in section_texts.items(): + master_prompt += f"## {section_name.upper()}\n\n{text}\n\n" + + master_prompt += "\n## CONTROLS SUMMARY\n\n" + for section_name, controls in section_controls.items(): + if not controls.get('incomplete'): + master_prompt += f"### {section_name.upper()}\n\n" + for control in controls.get('controls', [])[:10]: + master_prompt += f"- {control.get('title', 'Control')}\n" + + return master_prompt diff --git a/services/ai-service/src/core/framework_extractor.py b/services/ai-service/src/core/framework_extractor.py new file mode 100644 index 0000000..88b8291 --- /dev/null +++ b/services/ai-service/src/core/framework_extractor.py @@ -0,0 +1,123 @@ +""" +Framework Extractor Module +Uses LLM to extract compliance controls/rules/criteria from framework PDF text +""" + +import json +from openai import OpenAI +from typing import Dict, Any +import os +from dotenv import load_dotenv + +load_dotenv() + + +def extract_controls_from_framework(pdf_text: str, framework_name: str) -> Dict[str, Any]: + """ + Extract compliance controls, rules, and criteria from framework PDF text using LLM. + + Handles multilingual content (Arabic/English) and identifies controls regardless + of terminology used (controls, rules, criteria, etc.). + + Args: + pdf_text: Extracted text from framework PDF + framework_name: Name of the framework being processed + + Returns: + Dictionary with structure: + { + "framework_name": str, + "controls": [...], + "metadata": {...}, + "filters": {...} + } + """ + api_key = os.getenv("OPENROUTER_API_KEY") + if not api_key: + raise ValueError("OPENROUTER_API_KEY not found in environment variables") + + client = OpenAI( + api_key=api_key, + base_url="https://openrouter.ai/api/v1" + ) + + # Truncate text if too long (model has context limits) + # Keep first ~100k characters to ensure we capture the framework structure + max_chars = 100000 + if len(pdf_text) > max_chars: + pdf_text = pdf_text[:max_chars] + "\n\n[Text truncated due to length...]" + + extraction_prompt = f"""You are an expert in compliance frameworks and regulatory standards. +Your task is to extract compliance controls, rules, criteria, or requirements from the following framework document. + +Framework Name: {framework_name} + +The document may be in Arabic, English, or both. The terminology may vary: +- Controls (ضوابط) +- Rules (قواعد) +- Criteria (معايير) +- Requirements (متطلبات) +- Standards (معايير) + +Extract ALL compliance-related items regardless of what they're called in the document. + +Return a JSON object with the following structure: +{{ + "framework_name": "{framework_name}", + "controls": [ + {{ + "id": "unique identifier or number", + "title": "control title in original language", + "description": "detailed description of the control/rule/criteria", + "category": "category or domain", + "language": "ar" or "en" or "both" + }} + ], + "metadata": {{ + "total_controls": number, + "extraction_date": "ISO format date", + "languages_detected": ["ar", "en"], + "framework_type": "type of framework" + }}, + "filters": {{ + "categories": ["list of unique categories"], + "domains": ["list of unique domains if available"] + }} +}} + +Document text: +{pdf_text} + +Extract the compliance controls/rules/criteria and return ONLY valid JSON, no additional text.""" + + response = client.chat.completions.create( + model="deepseek/deepseek-r1", + messages=[ + { + "role": "system", + "content": "You are a compliance framework expert. Extract structured compliance controls from documents. Always return valid JSON only." + }, + { + "role": "user", + "content": extraction_prompt + } + ], + temperature=0.3, + response_format={"type": "json_object"} + ) + + result_text = response.choices[0].message.content + + # Parse JSON response, handle markdown code blocks if present + try: + controls_json = json.loads(result_text) + return controls_json + except json.JSONDecodeError as e: + if "```json" in result_text: + json_start = result_text.find("```json") + 7 + json_end = result_text.find("```", json_start) + result_text = result_text[json_start:json_end].strip() + controls_json = json.loads(result_text) + return controls_json + else: + raise ValueError(f"Failed to parse JSON response: {e}") diff --git a/services/ai-service/src/core/framework_organizer.py b/services/ai-service/src/core/framework_organizer.py new file mode 100644 index 0000000..3b0b265 --- /dev/null +++ b/services/ai-service/src/core/framework_organizer.py @@ -0,0 +1,329 @@ +""" +Framework Organizer Module +Organizes multiple PDFs into sections and validates framework completeness +""" + +import json +from pathlib import Path +from typing import Dict, Any, List +from openai import OpenAI +import os +from dotenv import load_dotenv +from src.processing import extract_text_from_pdf + +load_dotenv() + + +def organize_pdfs_by_section(pdf_paths_list: List[str], framework_name: str) -> Dict[str, str]: + """ + Organize PDFs into sections by analyzing their content using LLM. + + Uses LLM to determine section names (part1, part2, rubric, etc.) based on content analysis. + + Args: + pdf_paths_list: List of PDF file paths + framework_name: Name of the framework + + Returns: + Dictionary mapping section names to PDF paths: {'section_name': 'pdf_path', ...} + """ + api_key = os.getenv("OPENROUTER_API_KEY") + if not api_key: + raise ValueError("OPENROUTER_API_KEY not found in environment variables") + + client = OpenAI( + api_key=api_key, + base_url="https://openrouter.ai/api/v1" + ) + + # Extract text from each PDF (first 5000 chars for analysis) + pdf_samples = {} + for pdf_path in pdf_paths_list: + try: + full_text = extract_text_from_pdf(pdf_path) + # Use first 5000 chars for section identification + sample_text = full_text[:5000] if len(full_text) > 5000 else full_text + pdf_samples[pdf_path] = { + 'filename': Path(pdf_path).name, + 'sample': sample_text, + 'full_length': len(full_text) + } + except Exception as e: + print(f"Warning: Could not extract text from {pdf_path}: {e}") + # Use filename as fallback section name + pdf_samples[pdf_path] = { + 'filename': Path(pdf_path).name, + 'sample': '', + 'full_length': 0 + } + + # Prepare analysis prompt + pdf_info = [] + for pdf_path, info in pdf_samples.items(): + pdf_info.append({ + 'path': pdf_path, + 'filename': info['filename'], + 'sample': info['sample'], + 'length': info['full_length'] + }) + + analysis_prompt = f"""You are analyzing multiple PDF documents that form parts of a compliance framework. + +Framework Name: {framework_name} + +Analyze each PDF and determine its section name and role in the framework. Common section types include: +- part1, part2, part3, etc. (main framework parts) +- rubric (evaluation rubric/scoring criteria) +- appendix (appendices) +- guidelines (guidelines or instructions) +- controls (specific controls list) + +For each PDF, return: +1. A section name (use lowercase, no spaces, e.g., "part1", "rubric", "appendix") +2. A brief description of its role + +PDF Documents to analyze: +{json.dumps(pdf_info, indent=2, ensure_ascii=False)} + +Return a JSON object with this structure: +{{ + "sections": [ + {{ + "pdf_path": "path/to/file.pdf", + "section_name": "part1", + "description": "Main framework part 1", + "confidence": "high|medium|low" + }} + ] +}} + +Analyze each PDF and return the JSON.""" + + response = client.chat.completions.create( + model="deepseek/deepseek-r1", + messages=[ + { + "role": "system", + "content": "You are an expert in analyzing compliance framework documents. Identify section types and roles based on content analysis. Always return valid JSON only." + }, + { + "role": "user", + "content": analysis_prompt + } + ], + temperature=0.3, + response_format={"type": "json_object"} + ) + + result_text = response.choices[0].message.content + + # Parse JSON response + try: + analysis_result = json.loads(result_text) + except json.JSONDecodeError as e: + if "```json" in result_text: + json_start = result_text.find("```json") + 7 + json_end = result_text.find("```", json_start) + result_text = result_text[json_start:json_end].strip() + analysis_result = json.loads(result_text) + else: + raise ValueError(f"Failed to parse JSON response: {e}") + + # Build organized sections dictionary + organized_sections = {} + for section_info in analysis_result.get("sections", []): + pdf_path = section_info.get("pdf_path") + section_name = section_info.get("section_name", "unknown") + + # If section name couldn't be determined, use filename (sanitized) + if section_name == "unknown" or not section_name: + section_name = Path(pdf_path).stem.lower().replace(" ", "_") + + organized_sections[section_name] = pdf_path + + # Fallback: if LLM didn't return all PDFs, add missing ones + for pdf_path in pdf_paths_list: + if pdf_path not in organized_sections.values(): + # Use filename as section name + section_name = Path(pdf_path).stem.lower().replace(" ", "_") + organized_sections[section_name] = pdf_path + + return organized_sections + + +def validate_framework_sections(organized_sections: Dict[str, str], framework_name: str) -> Dict[str, Any]: + """ + Validate framework sections using LLM-based checks. + + Validates: + - All required sections exist + - Text quality is sufficient + - Critical keywords are present + - Control patterns are detected + - Proper document structure maintained + + Args: + organized_sections: Dictionary mapping section names to PDF paths + framework_name: Name of the framework + + Returns: + Dictionary with validation results: + { + "status": "pass" | "fail", + "checks": {...}, + "errors": [...], + "warnings": [...] + } + """ + api_key = os.getenv("OPENROUTER_API_KEY") + if not api_key: + raise ValueError("OPENROUTER_API_KEY not found in environment variables") + + client = OpenAI( + api_key=api_key, + base_url="https://openrouter.ai/api/v1" + ) + + # Extract text from all sections for validation + section_texts = {} + total_size = 0 + for section_name, pdf_path in organized_sections.items(): + try: + text = extract_text_from_pdf(pdf_path) + section_texts[section_name] = { + 'path': pdf_path, + 'text': text[:10000] if len(text) > 10000 else text, # Sample for validation + 'full_length': len(text) + } + total_size += len(text) + except Exception as e: + section_texts[section_name] = { + 'path': pdf_path, + 'text': '', + 'full_length': 0, + 'error': str(e) + } + + validation_prompt = f"""You are validating a compliance framework that has been organized into sections. + +Framework Name: {framework_name} +Sections Found: {list(organized_sections.keys())} + +IMPORTANT: Do NOT expect specific section names or structures. Different frameworks have different organizations. +Focus on whether the content is useful for compliance evaluation, not whether specific sections exist. + +Validate the following aspects: + +1. **Content Suitability**: Can compliance controls, requirements, or evaluation criteria be extracted from the text? + - Look for any compliance-related content (controls, rules, criteria, requirements, standards, guidelines) + - Check if there's enough content to perform meaningful evaluation + - DO NOT require specific section names - frameworks vary in structure + +2. **Text Quality**: Is the text readable and not corrupted? Are there formatting issues that would prevent extraction? + +3. **Evaluation-Relevant Content**: Are there elements that would be useful for evaluating applicant documents? + - Compliance requirements + - Control statements + - Evaluation criteria + - Scoring rubrics (if present, but not required) + - Standards or benchmarks + +4. **Control Patterns**: Are compliance control patterns detected in the text? (e.g., "must", "shall", "required", numbered controls) + +5. **Structure**: Is the document structure logical and coherent? Can sections be understood in context? + +Section Texts (samples): +{json.dumps({k: {'length': v['full_length'], 'sample': v['text'][:2000]} for k, v in section_texts.items()}, indent=2, ensure_ascii=False)} + +Return a JSON object with this structure: +{{ + "status": "pass" | "fail", + "checks": {{ + "content_suitability": {{"status": "pass|fail|warning", "details": "..."}}, + "text_quality": {{"status": "pass|fail|warning", "details": "..."}}, + "evaluation_content": {{"status": "pass|fail|warning", "details": "..."}}, + "control_patterns": {{"status": "pass|fail|warning", "details": "..."}}, + "structure": {{"status": "pass|fail|warning", "details": "..."}} + }}, + "errors": ["error1", "error2", ...], + "warnings": ["warning1", "warning2", ...], + "keywords_found": {{"section_name": ["keyword1", ...], ...}} +}} + +CRITICAL: Only mark status as "fail" if there are CRITICAL issues: +- Text is completely corrupted or unreadable +- No compliance-related content can be found at all +- Text is too short or empty + +Missing optional sections, different naming conventions, or non-standard structures should be warnings, NOT errors. +The framework structure should be flexible - focus on whether controls and evaluation info can be extracted. + +Validate the framework sections and return the JSON.""" + + response = client.chat.completions.create( + model="deepseek/deepseek-r1", + messages=[ + { + "role": "system", + "content": "You are an expert in validating compliance framework documents. Focus on whether the content contains extractable compliance controls and evaluation criteria. Be flexible about document structure - different frameworks organize content differently. Only fail validation for critical issues like corrupted text or complete absence of compliance content. Always return valid JSON only." + }, + { + "role": "user", + "content": validation_prompt + } + ], + temperature=0.3, + response_format={"type": "json_object"} + ) + + result_text = response.choices[0].message.content + + # Parse JSON response + try: + validation_result = json.loads(result_text) + except json.JSONDecodeError as e: + if "```json" in result_text: + json_start = result_text.find("```json") + 7 + json_end = result_text.find("```", json_start) + result_text = result_text[json_start:json_end].strip() + validation_result = json.loads(result_text) + else: + raise ValueError(f"Failed to parse JSON response: {e}") + + # Add total size to validation result + validation_result['total_size'] = total_size + + # Only fail on critical errors (corrupted text, no content, unreadable) + # Missing optional sections should be warnings, not errors + errors = validation_result.get("errors", []) + warnings = validation_result.get("warnings", []) + + # Filter for critical errors only + critical_keywords = ["corrupted", "unreadable", "no content", "empty", "too short", "cannot extract"] + critical_errors = [ + e for e in errors + if any(keyword in e.lower() for keyword in critical_keywords) + ] + + # Only raise exception for critical errors + if validation_result.get("status") == "fail" and critical_errors: + error_msg = f"Framework validation failed for '{framework_name}': " + "; ".join(critical_errors) + raise ValueError(error_msg) + elif validation_result.get("status") == "fail" and not critical_errors: + # If status is fail but no critical errors, downgrade to warning and continue + print(f"⚠️ Validation warnings for '{framework_name}' (continuing anyway):") + if warnings: + for warning in warnings: + print(f" - {warning}") + if errors: + for error in errors: + print(f" - {error}") + # Change status to pass since we're continuing + validation_result["status"] = "pass" + elif warnings: + # Log warnings but don't fail + print(f"ℹ️ Validation warnings for '{framework_name}':") + for warning in warnings: + print(f" - {warning}") + + return validation_result diff --git a/services/ai-service/src/embeddings/__init__.py b/services/ai-service/src/embeddings/__init__.py new file mode 100644 index 0000000..9d4413a --- /dev/null +++ b/services/ai-service/src/embeddings/__init__.py @@ -0,0 +1,20 @@ +from .gemma_embedder import GemmaEmbedder, load_gemma_embedder +from .qdrant_manager import ( + initialize_qdrant, + add_documents, + search_similar, + create_collection, + delete_collection +) +from .haystack_retriever import ( + HaystackQdrantRetriever, + create_retrieval_pipeline, + retrieve_documents +) + +__all__ = [ + "GemmaEmbedder", "load_gemma_embedder", + "initialize_qdrant", "add_documents", "search_similar", + "create_collection", "delete_collection", + "HaystackQdrantRetriever", "create_retrieval_pipeline", "retrieve_documents" +] diff --git a/services/ai-service/src/embeddings/gemma_embedder.py b/services/ai-service/src/embeddings/gemma_embedder.py new file mode 100644 index 0000000..651e04e --- /dev/null +++ b/services/ai-service/src/embeddings/gemma_embedder.py @@ -0,0 +1,149 @@ +""" +Gemma Embedder Module +Uses Google EmbeddingGemma 300M model for generating embeddings +This is specifically designed for embeddings (not text generation) +Supports HuggingFace token authentication for gated models + +Model: https://huggingface.co/google/embeddinggemma-300m +""" + +from sentence_transformers import SentenceTransformer +from typing import List, Optional +import os +import torch +from huggingface_hub import login + + +class GemmaEmbedder: + """Wrapper for Google EmbeddingGemma 300M embedding model.""" + + def __init__( + self, + model_name: str = "google/embeddinggemma-300m", + device: Optional[str] = None, + token: Optional[str] = None + ): + """ + Initialize EmbeddingGemma embedder. + + Args: + model_name: HuggingFace model name (default: google/embeddinggemma-300m) + This is the 300M parameter embedding model designed for embeddings. + Model page: https://huggingface.co/google/embeddinggemma-300m + device: Device to use ('cpu', 'cuda', or None for auto-detection) + token: HuggingFace token for gated models (or set HF_TOKEN env var) + """ + self.model_name = model_name + self.device = device or ("cuda" if torch.cuda.is_available() else "cpu") + self.model = None + self.token = token or os.getenv("HF_TOKEN") or os.getenv("HUGGINGFACE_TOKEN") + self._load_model() + + def _load_model(self): + """Load the EmbeddingGemma model using sentence-transformers.""" + if self.token: + login(token=self.token) + + model_kwargs = {} + if self.token: + model_kwargs["token"] = self.token + + try: + self.model = SentenceTransformer( + self.model_name, + device=self.device, + **model_kwargs + ) + except Exception as e: + error_msg = str(e) + if "gated" in error_msg.lower() or "401" in error_msg or "403" in error_msg: + token_status = "Token found" if self.token else "No token found" + raise RuntimeError( + f"Failed to load model: {error_msg}\n\n" + f"Token status: {token_status}\n" + "This model requires HuggingFace authentication.\n\n" + "REQUIRED STEPS:\n" + "1. Get a HuggingFace token:\n" + " - Go to: https://huggingface.co/settings/tokens\n" + " - Click 'New token'\n" + " - Name it (e.g., 'gemma-access')\n" + " - Select 'Read' access\n" + " - Click 'Generate token'\n" + " - Copy the token (starts with 'hf_')\n\n" + "2. Accept the model license:\n" + " - Go to: https://huggingface.co/google/embeddinggemma-300m\n" + " - Make sure you're logged in\n" + " - Click 'Agree and access repository'\n" + " - Wait for approval (usually instant)\n\n" + "3. Set the token:\n" + " - Set environment variable: export HF_TOKEN=your_token" + ) + raise RuntimeError(f"Failed to load model: {e}") + + def embed_text(self, text: str) -> List[float]: + """ + Generate embedding for a single text. + + Args: + text: Text to embed + + Returns: + Embedding vector as list of floats + """ + return self.embed_batch([text])[0] + + def embed_batch(self, texts: List[str], batch_size: int = 32) -> List[List[float]]: + """ + Generate embeddings for a batch of texts. + + Args: + texts: List of texts to embed + batch_size: Batch size for processing + + Returns: + List of embedding vectors + """ + if not texts: + return [] + + # Use sentence-transformers encode method + embeddings = self.model.encode( + texts, + batch_size=batch_size, + show_progress_bar=len(texts) > 10, + convert_to_numpy=True + ) + + # Convert to list of lists + return embeddings.tolist() + + def get_embedding_dim(self) -> int: + """ + Get the dimension of embeddings produced by this model. + + Returns: + Embedding dimension (768 for EmbeddingGemma-300M) + """ + if self.model is None: + raise RuntimeError("Model not loaded") + return self.model.get_sentence_embedding_dimension() + + +# Convenience function for quick usage +def load_gemma_embedder( + model_name: str = "google/embeddinggemma-300m", + device: Optional[str] = None, + token: Optional[str] = None +) -> GemmaEmbedder: + """ + Load and return a Gemma embedder instance. + + Args: + model_name: HuggingFace model name (default: google/embeddinggemma-300m) + device: Device to use ('cpu', 'cuda', or None for auto-detection) + token: HuggingFace token for gated models (or set HF_TOKEN env var) + + Returns: + GemmaEmbedder instance + """ + return GemmaEmbedder(model_name=model_name, device=device, token=token) diff --git a/services/ai-service/src/embeddings/haystack_retriever.py b/services/ai-service/src/embeddings/haystack_retriever.py new file mode 100644 index 0000000..dc7c1b0 --- /dev/null +++ b/services/ai-service/src/embeddings/haystack_retriever.py @@ -0,0 +1,142 @@ +""" +Haystack Retriever Module +Integrates Haystack AI with Qdrant for document retrieval +""" + +from haystack.dataclasses import Document +from qdrant_client import QdrantClient +from typing import List, Dict, Optional +from .gemma_embedder import GemmaEmbedder + + +class HaystackQdrantRetriever: + """Haystack retriever integrated with Qdrant and custom embedder.""" + + def __init__( + self, + qdrant_client: QdrantClient, + collection_name: str, + embedder: GemmaEmbedder, + top_k: int = 5 + ): + """ + Initialize Haystack retriever with Qdrant. + + Args: + qdrant_client: QdrantClient instance + collection_name: Name of Qdrant collection + embedder: GemmaEmbedder instance + top_k: Number of documents to retrieve + """ + self.qdrant_client = qdrant_client + self.collection_name = collection_name + self.embedder = embedder + self.top_k = top_k + + # Store documents in memory for Haystack (we'll sync from Qdrant) + self.documents = [] + self._load_documents_from_qdrant() + + def _load_documents_from_qdrant(self): + """Load all documents from Qdrant into Haystack format.""" + try: + scroll_result = self.qdrant_client.scroll( + collection_name=self.collection_name, + limit=10000, + with_payload=True, + with_vectors=False + ) + + self.documents = [] + for point in scroll_result[0]: + doc = Document( + content=point.payload.get("text", ""), + meta={ + "chunk_id": point.payload.get("chunk_id"), + "framework_name": point.payload.get("framework_name", "unknown"), + **{k: v for k, v in point.payload.items() + if k not in ["text", "chunk_id", "framework_name"]} + } + ) + self.documents.append(doc) + except Exception: + self.documents = [] + + def retrieve_documents(self, query: str, top_k: Optional[int] = None) -> List[Dict]: + """ + Retrieve documents using Haystack with custom Gemma embedder. + + Args: + query: Query text + top_k: Number of documents to retrieve (overrides default) + + Returns: + List of dictionaries with retrieved documents: + { + "text": str, + "score": float, + "metadata": dict + } + """ + if top_k is None: + top_k = self.top_k + + # Generate query embedding using Gemma + query_embedding = self.embedder.embed_text(query) + + # Search in Qdrant directly (more efficient than Haystack for this use case) + from .qdrant_manager import search_similar + + results = search_similar( + client=self.qdrant_client, + collection_name=self.collection_name, + query_embedding=query_embedding, + top_k=top_k + ) + + return results + + +def create_retrieval_pipeline( + qdrant_client: QdrantClient, + collection_name: str, + embedder: GemmaEmbedder, + top_k: int = 5 +) -> HaystackQdrantRetriever: + """ + Create a retrieval pipeline with Haystack and Qdrant. + + Args: + qdrant_client: QdrantClient instance + collection_name: Name of Qdrant collection + embedder: GemmaEmbedder instance + top_k: Number of documents to retrieve + + Returns: + HaystackQdrantRetriever instance + """ + return HaystackQdrantRetriever( + qdrant_client=qdrant_client, + collection_name=collection_name, + embedder=embedder, + top_k=top_k + ) + + +def retrieve_documents( + retriever: HaystackQdrantRetriever, + query: str, + top_k: Optional[int] = None +) -> List[Dict]: + """ + Retrieve documents for a query. + + Args: + retriever: HaystackQdrantRetriever instance + query: Query text + top_k: Number of documents to retrieve + + Returns: + List of retrieved document dictionaries + """ + return retriever.retrieve_documents(query, top_k=top_k) diff --git a/services/ai-service/src/embeddings/qdrant_manager.py b/services/ai-service/src/embeddings/qdrant_manager.py new file mode 100644 index 0000000..e0b6073 --- /dev/null +++ b/services/ai-service/src/embeddings/qdrant_manager.py @@ -0,0 +1,254 @@ +""" +Qdrant Manager Module +Handles Qdrant vector database operations for storing and retrieving document chunks +""" + +from qdrant_client import QdrantClient +from qdrant_client.models import Distance, VectorParams, PointStruct, ScoredPoint +from typing import List, Dict, Optional +from pathlib import Path +import uuid + + +def initialize_qdrant( + collection_name: str, + vector_size: int, + path: Optional[str] = None, + url: Optional[str] = None +) -> QdrantClient: + """ + Initialize Qdrant client and create collection if it doesn't exist. + + Args: + collection_name: Name of the collection + vector_size: Size of the embedding vectors + path: Local path for Qdrant (default: config/vector_db) + url: Qdrant server URL (for remote/cloud) + + Returns: + QdrantClient instance + """ + # Default to local path if neither path nor url provided + if path is None and url is None: + # Get project root (services/ai-service/) + project_root = Path(__file__).parent.parent.parent + path = str(project_root / "config" / "vector_db") + Path(path).mkdir(parents=True, exist_ok=True) + + # Initialize client + # For local mode, use path (this creates a QdrantLocal client) + # For remote mode, use url (this creates a QdrantRemote client) + if url: + # Remote/server mode + client = QdrantClient(url=url, prefer_grpc=False) # Disable gRPC for compatibility + else: + # Local mode - use path, this is the correct way for local Qdrant + client = QdrantClient(path=path) + + # Create collection if it doesn't exist + try: + client.get_collection(collection_name) + except Exception: + client.create_collection( + collection_name=collection_name, + vectors_config=VectorParams( + size=vector_size, + distance=Distance.COSINE + ) + ) + + return client + + +def create_collection( + client: QdrantClient, + collection_name: str, + vector_size: int +) -> None: + """ + Create a new collection in Qdrant. + + Args: + client: QdrantClient instance + collection_name: Name of the collection + vector_size: Size of the embedding vectors + """ + client.create_collection( + collection_name=collection_name, + vectors_config=VectorParams( + size=vector_size, + distance=Distance.COSINE + ) + ) + + +def add_documents( + client: QdrantClient, + collection_name: str, + documents: List[Dict], + embeddings: List[List[float]] +) -> None: + """ + Add documents with embeddings to Qdrant collection. + + Args: + client: QdrantClient instance + collection_name: Name of the collection + documents: List of document dictionaries (from chunker) + embeddings: List of embedding vectors + """ + if len(documents) != len(embeddings): + raise ValueError(f"Number of documents ({len(documents)}) must match number of embeddings ({len(embeddings)})") + + points = [] + for doc, embedding in zip(documents, embeddings): + point_id = doc.get("chunk_id", str(uuid.uuid4())) + + # Prepare payload with metadata + payload = { + "text": doc.get("text", ""), + "chunk_id": doc.get("chunk_id", point_id), + "framework_name": doc.get("framework_name", "unknown"), + **doc.get("metadata", {}) + } + + point = PointStruct( + id=point_id, + vector=embedding, + payload=payload + ) + points.append(point) + + # Upsert points in batches + batch_size = 100 + for i in range(0, len(points), batch_size): + batch = points[i:i + batch_size] + client.upsert( + collection_name=collection_name, + points=batch + ) + + +def search_similar( + client: QdrantClient, + collection_name: str, + query_embedding: List[float], + top_k: int = 5, + score_threshold: Optional[float] = None +) -> List[Dict]: + """ + Search for similar documents in Qdrant collection. + + For local Qdrant, uses query_points with Query object. + This is the correct method for local mode. + + Args: + client: QdrantClient instance + collection_name: Name of the collection + query_embedding: Query embedding vector + top_k: Number of results to return + score_threshold: Minimum similarity score threshold + + Returns: + List of dictionaries with search results: + { + "text": str, + "score": float, + "metadata": dict + } + """ + try: + # Use query_points - can accept vector directly as list[float] + # According to the API signature, query can be list[float] for vector similarity search + # This is the simplest and most direct approach for local Qdrant + query_response = client.query_points( + collection_name=collection_name, + query=query_embedding, # Pass vector directly + limit=top_k, + score_threshold=score_threshold, + with_payload=True, + with_vectors=False + ) + + # Extract points from query response + # The response structure: query_response.points is a list of ScoredPoint objects + if hasattr(query_response, 'points'): + search_results = query_response.points + elif isinstance(query_response, (list, tuple)): + # If response is directly a list + search_results = query_response + else: + # Fallback: try to get result attribute + search_results = getattr(query_response, 'result', []) + + if not search_results: + return [] + + except AttributeError as e: + error_msg = str(e) + raise RuntimeError( + f"Qdrant client method error: {error_msg}. " + f"Client type: {type(client).__name__}. " + f"Local Qdrant requires query_points method. " + f"Please ensure qdrant-client >= 1.8.0 is installed. " + f"Run: uv add 'qdrant-client>=1.8.0'" + ) + except Exception as e: + error_msg = str(e) + if "gRPC" in error_msg or "grpc" in error_msg.lower(): + raise RuntimeError( + f"Qdrant gRPC error (local mode doesn't support gRPC). " + f"Error: {error_msg}. " + f"Make sure you're initializing with: QdrantClient(path='...') for local mode." + ) + raise RuntimeError( + f"Failed to query Qdrant collection '{collection_name}'. " + f"Error: {error_msg}. " + f"Make sure the collection exists and documents were added successfully." + ) + + # Process search results + results = [] + for result in search_results: + # Result should be a ScoredPoint object + if isinstance(result, ScoredPoint): + results.append({ + "text": result.payload.get("text", ""), + "score": result.score, + "metadata": { + "chunk_id": result.payload.get("chunk_id"), + "framework_name": result.payload.get("framework_name"), + **{k: v for k, v in result.payload.items() + if k not in ["text", "chunk_id", "framework_name"]} + } + }) + else: + # Fallback for different result formats + payload = getattr(result, 'payload', {}) or {} + score = getattr(result, 'score', 0.0) + results.append({ + "text": payload.get("text", ""), + "score": score, + "metadata": { + "chunk_id": payload.get("chunk_id"), + "framework_name": payload.get("framework_name"), + **{k: v for k, v in payload.items() + if k not in ["text", "chunk_id", "framework_name"]} + } + }) + + return results + + +def delete_collection( + client: QdrantClient, + collection_name: str +) -> None: + """ + Delete a collection from Qdrant. + + Args: + client: QdrantClient instance + collection_name: Name of the collection to delete + """ + client.delete_collection(collection_name=collection_name) diff --git a/services/ai-service/src/processing/__init__.py b/services/ai-service/src/processing/__init__.py new file mode 100644 index 0000000..e21c40d --- /dev/null +++ b/services/ai-service/src/processing/__init__.py @@ -0,0 +1,8 @@ +from .pdf_parser import extract_text_from_pdf +from .text_chunker import chunk_text, chunk_text_by_sentences + +__all__ = [ + "extract_text_from_pdf", + "chunk_text", + "chunk_text_by_sentences" +] diff --git a/services/ai-service/src/processing/pdf_parser.py b/services/ai-service/src/processing/pdf_parser.py new file mode 100644 index 0000000..8109931 --- /dev/null +++ b/services/ai-service/src/processing/pdf_parser.py @@ -0,0 +1,43 @@ +""" +PDF Parser Module +Handles extraction of text from PDF files with multilingual support (Arabic/English) +""" + +import pdfplumber +from pathlib import Path + + +def extract_text_from_pdf(pdf_path: str) -> str: + """ + Extract text from a PDF file with support for multilingual content. + + Args: + pdf_path: Path to the PDF file + + Returns: + Extracted text as a string + + Raises: + FileNotFoundError: If PDF file doesn't exist + ValueError: If PDF is corrupted or cannot be read + """ + pdf_path_obj = Path(pdf_path) + + if not pdf_path_obj.exists(): + raise FileNotFoundError(f"PDF file not found: {pdf_path}") + + text_content = [] + + with pdfplumber.open(pdf_path) as pdf: + for page in pdf.pages: + try: + page_text = page.extract_text() + if page_text: + text_content.append(page_text) + except Exception: + continue + + if not text_content: + raise ValueError(f"No text could be extracted from PDF: {pdf_path}") + + return "\n\n".join(text_content) diff --git a/services/ai-service/src/processing/text_chunker.py b/services/ai-service/src/processing/text_chunker.py new file mode 100644 index 0000000..3baef04 --- /dev/null +++ b/services/ai-service/src/processing/text_chunker.py @@ -0,0 +1,235 @@ +""" +Text Chunker Module +Chunks PDF text into manageable segments with overlap for vector database storage +""" + +from typing import List, Dict, Optional +import re + + +def chunk_text( + text: str, + chunk_size: int = 1500, + overlap: int = 200, + framework_name: Optional[str] = None, + preserve_sentences: bool = True +) -> List[Dict]: + """ + Split text into fixed-size chunks with overlap, preserving semantic boundaries. + + Improved version that: + - Uses larger default chunk sizes (1500 chars) for better context + - Preserves paragraph boundaries when possible + - Filters out chunks that are mostly formatting artifacts + - Better sentence boundary detection with larger lookback window + + Args: + text: Text to chunk + chunk_size: Size of each chunk in characters (default: 1500) + overlap: Number of characters to overlap between chunks (default: 200) + framework_name: Optional framework name for metadata + preserve_sentences: If True, try to break at sentence boundaries + + Returns: + List of dictionaries, each containing: + { + "text": str, + "chunk_id": int, + "chunk_index": int, + "framework_name": str, + "metadata": dict + } + """ + if not text or len(text.strip()) == 0: + return [] + + chunks = [] + chunk_id = 0 + start = 0 + text_length = len(text) + + # Lookback window for finding sentence/paragraph boundaries (20% of chunk_size) + lookback_window = max(200, int(chunk_size * 0.2)) + + while start < text_length: + # Calculate end position + end = min(start + chunk_size, text_length) + + # Extract chunk text + chunk_text_segment = text[start:end] + + # If preserving sentences and not at the end, try to break at semantic boundary + if preserve_sentences and end < text_length: + # Look for paragraph breaks first (double newlines) + lookback_start = max(start, end - lookback_window) + lookback_text = text[lookback_start:end] + + # Try paragraph breaks first (double newline or newline followed by section header) + para_pattern = r'\n\s*\n+' + para_breaks = list(re.finditer(para_pattern, lookback_text)) + + if para_breaks: + # Use the last paragraph break + last_match = para_breaks[-1] + end = lookback_start + last_match.end() + chunk_text_segment = text[start:end] + else: + # Fall back to sentence endings + # Improved sentence pattern: handles multiple punctuation and various newlines + sentence_pattern = r'[.!?]+[\s\n]+|[.!?]+\n+' + sentence_endings = list(re.finditer(sentence_pattern, lookback_text)) + + if sentence_endings: + # Use the last sentence ending, but prefer those followed by uppercase + # (likely start of new sentence) + best_match = sentence_endings[-1] + + # Check if there's a better break (sentence followed by uppercase letter) + for match in reversed(sentence_endings): + match_end = lookback_start + match.end() + if match_end < text_length: + next_char = text[match_end:match_end+1].strip() + if next_char and next_char.isupper(): + best_match = match + break + + end = lookback_start + best_match.end() + chunk_text_segment = text[start:end] + + # Clean up chunk text + chunk_text_segment = chunk_text_segment.strip() + + # Filter out chunks that are mostly formatting artifacts + if chunk_text_segment and _is_valid_chunk(chunk_text_segment): + chunk_dict = { + "text": chunk_text_segment, + "chunk_id": chunk_id, + "chunk_index": chunk_id, + "framework_name": framework_name or "unknown", + "metadata": { + "start_char": start, + "end_char": end, + "length": len(chunk_text_segment), + "chunk_size": chunk_size, + "overlap": overlap + } + } + chunks.append(chunk_dict) + chunk_id += 1 + + # Move start position with overlap + # Make sure we don't go backwards + new_start = end - overlap if end < text_length else end + start = max(start + 1, new_start) # Ensure progress + + # Prevent infinite loop + if start >= text_length: + break + if start == end - overlap and overlap == 0: + start = end # Force progress if no overlap + + return chunks + + +def _is_valid_chunk(text: str, min_meaningful_chars: int = 100) -> bool: + """ + Check if a chunk is valid (not mostly formatting artifacts). + + Args: + text: Chunk text to validate + min_meaningful_chars: Minimum number of meaningful characters required + + Returns: + True if chunk is valid, False if it's mostly formatting + """ + if not text or len(text) < 20: + return False + + # Remove common formatting patterns for analysis + # Count non-whitespace, non-punctuation-only lines + lines = text.split('\n') + meaningful_lines = 0 + total_chars = 0 + meaningful_chars = 0 + + for line in lines: + stripped = line.strip() + if not stripped: + continue + + total_chars += len(line) + + # Check if line is mostly dots/dashes (table of contents formatting) + if re.match(r'^[.\-_\s]+$', stripped): + continue + + # Check if line is mostly numbers/spaces (page numbers) + if re.match(r'^\s*\d+\s*$', stripped) and len(stripped) < 10: + continue + + # Count meaningful characters (letters, numbers, meaningful punctuation) + meaningful = re.sub(r'[^\w\s]', '', stripped) + if len(meaningful.strip()) > 5: + meaningful_lines += 1 + meaningful_chars += len(meaningful) + + # Reject if too many formatting-only lines + if meaningful_lines == 0: + return False + + # Reject if meaningful content is too low + meaningful_ratio = meaningful_chars / max(total_chars, 1) + if meaningful_ratio < 0.3: # Less than 30% meaningful content + return False + + # Reject if chunk is too short after filtering + if meaningful_chars < min_meaningful_chars: + return False + + return True + + +def chunk_text_by_sentences( + text: str, + sentences_per_chunk: int = 5, + framework_name: Optional[str] = None +) -> List[Dict]: + """ + Chunk text by sentences instead of fixed character size. + + Args: + text: Text to chunk + sentences_per_chunk: Number of sentences per chunk + framework_name: Optional framework name for metadata + + Returns: + List of chunk dictionaries + """ + # Split into sentences + sentence_pattern = r'[.!?]+\s+|[\n]{2,}' + sentences = re.split(sentence_pattern, text) + sentences = [s.strip() for s in sentences if s.strip()] + + chunks = [] + chunk_id = 0 + + for i in range(0, len(sentences), sentences_per_chunk): + chunk_sentences = sentences[i:i + sentences_per_chunk] + chunk_text_segment = ' '.join(chunk_sentences) + + chunk_dict = { + "text": chunk_text_segment, + "chunk_id": chunk_id, + "chunk_index": chunk_id, + "framework_name": framework_name or "unknown", + "metadata": { + "sentence_start": i, + "sentence_end": min(i + sentences_per_chunk, len(sentences)), + "sentences_per_chunk": sentences_per_chunk, + "length": len(chunk_text_segment) + } + } + chunks.append(chunk_dict) + chunk_id += 1 + + return chunks diff --git a/services/ai-service/src/prompts/__init__.py b/services/ai-service/src/prompts/__init__.py new file mode 100644 index 0000000..a3b6743 --- /dev/null +++ b/services/ai-service/src/prompts/__init__.py @@ -0,0 +1,3 @@ +from .prompt_generator import generate_evaluation_prompt + +__all__ = ["generate_evaluation_prompt"] diff --git a/services/ai-service/src/prompts/prompt_generator.py b/services/ai-service/src/prompts/prompt_generator.py new file mode 100644 index 0000000..793671d --- /dev/null +++ b/services/ai-service/src/prompts/prompt_generator.py @@ -0,0 +1,77 @@ +""" +Prompt Generator Module +Generates evaluation prompts from extracted controls JSON +""" + +import json +from openai import OpenAI +from typing import Dict, Any +import os +from dotenv import load_dotenv + +load_dotenv() + + +def generate_evaluation_prompt(controls_json: Dict[str, Any], framework_name: str) -> str: + """ + Generate an evaluation prompt from extracted controls JSON. + + The prompt will contain framework logic and clear evaluation criteria + that can be used directly for applicant evaluation. + + Args: + controls_json: Dictionary containing extracted controls, metadata, and filters + framework_name: Name of the framework + + Returns: + Evaluation prompt string ready to use for applicant evaluation + """ + api_key = os.getenv("OPENROUTER_API_KEY") + if not api_key: + raise ValueError("OPENROUTER_API_KEY not found in environment variables") + + client = OpenAI( + api_key=api_key, + base_url="https://openrouter.ai/api/v1" + ) + + # Convert controls JSON to string for the prompt + controls_str = json.dumps(controls_json, indent=2, ensure_ascii=False) + + generation_prompt = f"""You are an expert in compliance evaluation. Based on the extracted compliance framework controls below, +create a comprehensive evaluation prompt that will be used to evaluate applicant documents against this framework. + +Framework Name: {framework_name} + +The evaluation prompt should: +1. Clearly explain the framework and its purpose +2. List all controls/rules/criteria that need to be evaluated +3. Provide clear evaluation criteria for each control +4. Specify how to score or assess compliance (e.g., compliant/non-compliant, or scoring scale) +5. Include instructions for handling multilingual documents (Arabic/English) +6. Be structured so it can be used directly with an LLM to evaluate applicant documents +7. Include instructions on how to format the evaluation report + +The prompt should be self-contained and can be used independently to evaluate any applicant's documents. + +Extracted Controls JSON: +{controls_str} + +Generate the evaluation prompt now. The prompt should be clear, comprehensive, and ready to use.""" + + response = client.chat.completions.create( + model="deepseek/deepseek-r1", + messages=[ + { + "role": "system", + "content": "You are an expert in creating evaluation prompts for compliance frameworks. Create clear, comprehensive prompts that can be used to evaluate documents against compliance standards." + }, + { + "role": "user", + "content": generation_prompt + } + ], + temperature=0.3 + ) + + return response.choices[0].message.content.strip() diff --git a/services/ai-service/src/utils/__init__.py b/services/ai-service/src/utils/__init__.py new file mode 100644 index 0000000..06b590e --- /dev/null +++ b/services/ai-service/src/utils/__init__.py @@ -0,0 +1,15 @@ +from .framework_utils import ( + save_framework_data, + load_framework_data, + list_saved_frameworks, + save_evaluation_report, + get_input_paths +) + +__all__ = [ + "save_framework_data", + "load_framework_data", + "list_saved_frameworks", + "save_evaluation_report", + "get_input_paths" +] diff --git a/services/ai-service/src/utils/framework_utils.py b/services/ai-service/src/utils/framework_utils.py new file mode 100644 index 0000000..837d2c3 --- /dev/null +++ b/services/ai-service/src/utils/framework_utils.py @@ -0,0 +1,179 @@ +""" +Utilities Module +Handles saving and loading of framework data (JSON controls and evaluation prompts) +""" + +import json +from pathlib import Path +from typing import Dict, Any, Tuple, List, Optional +from datetime import datetime + + +def save_framework_data( + framework_name: str, + controls_json: Dict[str, Any], + evaluation_prompt: str, + metadata: Dict[str, Any] = None +) -> None: + """ + Save framework controls JSON and evaluation prompt to config/frameworks/{framework_name}/ + + Creates directory structure if it doesn't exist. + Saves: + - controls.json: The extracted controls JSON + - evaluation_prompt.txt: The generated evaluation prompt + - framework_metadata.json: Metadata about sections, validation, etc. (if provided) + + Args: + framework_name: Name of the framework (used for directory name) + controls_json: Dictionary containing controls, metadata, and filters + evaluation_prompt: Generated evaluation prompt string + metadata: Optional dictionary containing framework metadata: + - sections: list of section names + - total_size: total character count + - extracted_at: timestamp + - keywords_found: dictionary of keywords per section + - validation_status: validation results + """ + # Get project root (services/ai-service/) + project_root = Path(__file__).parent.parent.parent + base_dir = project_root / "config" / "frameworks" + framework_dir = base_dir / framework_name + framework_dir.mkdir(parents=True, exist_ok=True) + + # Save controls JSON + controls_path = framework_dir / "controls.json" + with open(controls_path, "w", encoding="utf-8") as f: + json.dump(controls_json, f, indent=2, ensure_ascii=False) + + # Save evaluation prompt + prompt_path = framework_dir / "evaluation_prompt.txt" + with open(prompt_path, "w", encoding="utf-8") as f: + f.write(evaluation_prompt) + + # Save metadata if provided + if metadata: + from datetime import datetime + # Add timestamp if not present + if 'extracted_at' not in metadata: + metadata['extracted_at'] = datetime.now().isoformat() + + metadata_path = framework_dir / "framework_metadata.json" + with open(metadata_path, "w", encoding="utf-8") as f: + json.dump(metadata, f, indent=2, ensure_ascii=False) + + +def load_framework_data(framework_name: str) -> Tuple[Dict[str, Any], str]: + """ + Load saved framework controls JSON and evaluation prompt. + + Args: + framework_name: Name of the framework to load + + Returns: + Tuple of (controls_json, evaluation_prompt) + + Raises: + FileNotFoundError: If framework data doesn't exist + """ + # Get project root (services/ai-service/) + project_root = Path(__file__).parent.parent.parent + framework_dir = project_root / "config" / "frameworks" / framework_name + + # Load controls JSON + controls_path = framework_dir / "controls.json" + if not controls_path.exists(): + raise FileNotFoundError(f"Controls JSON not found: {controls_path}") + + with open(controls_path, "r", encoding="utf-8") as f: + controls_json = json.load(f) + + # Load evaluation prompt + prompt_path = framework_dir / "evaluation_prompt.txt" + if not prompt_path.exists(): + raise FileNotFoundError(f"Evaluation prompt not found: {prompt_path}") + + with open(prompt_path, "r", encoding="utf-8") as f: + evaluation_prompt = f.read() + + return controls_json, evaluation_prompt + + +def list_saved_frameworks() -> List[str]: + """ + List all saved frameworks in config/frameworks/ + + Returns: + List of framework names + """ + # Get project root (services/ai-service/) + project_root = Path(__file__).parent.parent.parent + frameworks_dir = project_root / "config" / "frameworks" + + if not frameworks_dir.exists(): + return [] + + frameworks = [ + d.name for d in frameworks_dir.iterdir() + if d.is_dir() and (d / "controls.json").exists() and (d / "evaluation_prompt.txt").exists() + ] + return sorted(frameworks) + + +def save_evaluation_report( + evaluation_report: Dict[str, Any], + framework_name: str, + applicant_name: Optional[str] = None +) -> Path: + """ + Save evaluation report to data/outputs/evaluations/ + + Args: + evaluation_report: Dictionary containing evaluation results + framework_name: Name of the framework used for evaluation + applicant_name: Optional name/identifier for the applicant + + Returns: + Path to the saved evaluation report file + """ + # Get project root (services/ai-service/) + project_root = Path(__file__).parent.parent.parent + outputs_dir = project_root / "data" / "outputs" / "evaluations" + outputs_dir.mkdir(parents=True, exist_ok=True) + + # Generate filename with timestamp + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + + if applicant_name: + # Sanitize applicant name for filename + safe_name = "".join(c for c in applicant_name if c.isalnum() or c in (' ', '-', '_')).strip() + safe_name = safe_name.replace(' ', '_') + filename = f"{framework_name}_{safe_name}_{timestamp}.json" + else: + filename = f"{framework_name}_evaluation_{timestamp}.json" + + report_path = outputs_dir / filename + + # Save evaluation report + with open(report_path, "w", encoding="utf-8") as f: + json.dump(evaluation_report, f, indent=2, ensure_ascii=False) + + return report_path + + +def get_input_paths() -> Dict[str, Path]: + """ + Get standard input directory paths. + + Returns: + Dictionary with paths: + - frameworks: Path to framework PDFs directory + - applicants: Path to applicant PDFs directory + - vector_db: Path to vector DB input PDFs directory + """ + project_root = Path(__file__).parent.parent.parent + return { + "frameworks": project_root / "data" / "inputs" / "frameworks", + "applicants": project_root / "data" / "inputs" / "applicants", + "vector_db": project_root / "data" / "inputs" / "vector_db" + } diff --git a/services/ai-service/uv.lock b/services/ai-service/uv.lock new file mode 100644 index 0000000..2bb9e1c --- /dev/null +++ b/services/ai-service/uv.lock @@ -0,0 +1,2167 @@ +version = 1 +revision = 3 +requires-python = ">=3.13" +resolution-markers = [ + "python_full_version 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from test!") - - -if __name__ == "__main__": - main() diff --git a/services/test/pyproject.toml b/services/test/pyproject.toml deleted file mode 100644 index a8ec473..0000000 --- a/services/test/pyproject.toml +++ /dev/null @@ -1,7 +0,0 @@ -[project] -name = "test" -version = "0.1.0" -description = "Add your description here" -readme = "README.md" -requires-python = ">=3.13" -dependencies = [] From c3f93260c4f67e69a4b0214e00b45f2918803cbd Mon Sep 17 00:00:00 2001 From: Mohammed-Balkhair-hub Date: Sat, 24 Jan 2026 07:04:13 +0300 Subject: [PATCH 2/4] update controls extractor refine controls extractor --- services/ai-service/main.py | 274 ++------------- services/ai-service/src/core/__init__.py | 8 +- services/ai-service/src/core/evaluator.py | 17 +- .../src/core/framework_consolidator.py | 227 +++++------- .../src/core/framework_extractor.py | 158 +++++---- .../src/core/framework_organizer.py | 329 ------------------ .../src/prompts/prompt_generator.py | 60 ++-- services/ai-service/uv.lock | 105 +++--- 8 files changed, 308 insertions(+), 870 deletions(-) delete mode 100644 services/ai-service/src/core/framework_organizer.py diff --git a/services/ai-service/main.py b/services/ai-service/main.py index f5d0244..0eb6922 100644 --- a/services/ai-service/main.py +++ b/services/ai-service/main.py @@ -1,58 +1,28 @@ """ Main entry point for the Compliance Framework Evaluation System. - -This demonstrates the complete pipeline: -1. Framework Setup: Extract controls from framework PDF -2. Document Processing: Process and index framework documents -3. Evaluation: Evaluate applicant documents against framework +Simple flow: Extract controls → Compose → Generate prompt """ from pathlib import Path import glob -from src.core import ( - extract_controls_from_framework, - evaluate_applicant, - organize_pdfs_by_section, - validate_framework_sections, - extract_controls_from_sections, - create_master_prompt -) -from src.processing import extract_text_from_pdf, chunk_text +from src.core.framework_extractor import extract_controls_from_framework +from src.core.framework_consolidator import extract_controls_from_pdfs, compose_master_framework +from src.processing import extract_text_from_pdf from src.prompts import generate_evaluation_prompt -from src.utils import ( - save_framework_data, - load_framework_data, - list_saved_frameworks, - save_evaluation_report, - get_input_paths -) -# from src.embeddings import ( -# GemmaEmbedder, initialize_qdrant, add_documents, search_similar -#) +from src.utils import save_framework_data, get_input_paths def setup_framework(pdf_paths: str | list[str], framework_name: str): """ - Setup a new compliance framework from PDF(s). - - Supports single file, list of files, or directory path. - For multiple PDFs, organizes them into sections, validates, extracts controls, - and creates a master evaluation prompt. - - Pipeline: - 1. Normalize input to list of PDF paths - 2. Organize PDFs into sections (using LLM content analysis) - 3. Validate framework sections - 4. Extract controls from each section - 5. Create master evaluation prompt - 6. Save framework data with metadata + Setup a compliance framework from PDF(s). + Simple flow: Extract controls → Compose → Generate prompt Args: - pdf_paths: Single file path (str), list of file paths, or directory path + pdf_paths: Single file path, list of files, or directory path framework_name: Name of the framework Returns: - Tuple of (consolidated_controls_json, master_evaluation_prompt, metadata) + Tuple of (controls_json, evaluation_prompt) """ # Normalize input to list of PDF paths if isinstance(pdf_paths, list): @@ -67,7 +37,7 @@ def setup_framework(pdf_paths: str | list[str], framework_name: str): if not pdf_paths_list: raise ValueError(f"No PDF files found: {pdf_paths}") - # For single PDF, use original workflow for backward compatibility + # Single PDF - simple flow if len(pdf_paths_list) == 1: pdf_text = extract_text_from_pdf(pdf_paths_list[0]) controls_json = extract_controls_from_framework(pdf_text, framework_name) @@ -75,235 +45,45 @@ def setup_framework(pdf_paths: str | list[str], framework_name: str): save_framework_data(framework_name, controls_json, evaluation_prompt) return controls_json, evaluation_prompt - # Multi-PDF workflow - # 1. Organize PDFs into sections - organized_sections = organize_pdfs_by_section(pdf_paths_list, framework_name) - - # 2. Validate framework sections - validation_result = validate_framework_sections(organized_sections, framework_name) - - # 3. Extract controls from each section - section_controls = extract_controls_from_sections(organized_sections, framework_name) - - # 4. Create master evaluation prompt - master_prompt = create_master_prompt(section_controls, organized_sections, framework_name) - - # 5. Consolidate controls JSON (combine all sections) - consolidated_controls = { - "framework_name": framework_name, - "sections": list(organized_sections.keys()), - "controls": [], - "metadata": { - "total_sections": len(organized_sections), - "extraction_method": "multi_pdf_consolidated" - }, - "filters": {} - } - - # Combine controls from all sections - all_categories = set() - for section_name, controls in section_controls.items(): - if not controls.get('incomplete'): - if 'controls' in controls: - for control in controls['controls']: - control['section'] = section_name - consolidated_controls['controls'].append(control) - if 'category' in control: - all_categories.add(control['category']) - - consolidated_controls['filters']['categories'] = list(all_categories) - consolidated_controls['metadata']['total_controls'] = len(consolidated_controls['controls']) - - # 6. Prepare metadata - from datetime import datetime - metadata = { - "sections": list(organized_sections.keys()), - "total_size": validation_result.get('total_size', 0), - "extracted_at": datetime.now().isoformat(), - "keywords_found": validation_result.get('keywords_found', {}), - "validation_status": validation_result.get('status', 'pass'), - "validation_checks": validation_result.get('checks', {}), - "section_paths": organized_sections - } - - # 7. Save framework data - save_framework_data(framework_name, consolidated_controls, master_prompt, metadata) - - return consolidated_controls, master_prompt, metadata - - -def index_framework_documents( - pdf_paths: str | list[str], - framework_name: str -): - """ - Index framework documents in vector database. - - Supports single file, list of files, or directory path. - Each PDF is processed individually: extract text, chunk, then all chunks are embedded together. + # Multiple PDFs - extract, compose, generate + controls_arrays = extract_controls_from_pdfs(pdf_paths_list, framework_name) - Pipeline: - 1. For each PDF: - - Extract text from PDF - - Chunk text - 2. Generate embeddings for all chunks - 3. Store in Qdrant + # Filter out empty arrays + json_list = [arr for arr in controls_arrays if arr] - Args: - pdf_paths: Single file path (str), list of file paths, or directory path - framework_name: Name of the framework - - Returns: - Tuple of (qdrant_client, collection_name, embedder) - """ - # Lazy import - only load heavy ML libraries when this function is called - from src.embeddings import ( - GemmaEmbedder, initialize_qdrant, add_documents - ) - - # Normalize input to list of PDF paths - if isinstance(pdf_paths, list): - pdf_paths_list = pdf_paths + # Compose into master framework + if json_list: + master_framework = compose_master_framework(json_list, framework_name) else: - pdf_path = Path(pdf_paths) - if pdf_path.is_dir(): - pdf_paths_list = sorted(glob.glob(f"{pdf_paths}/*.pdf")) - else: - pdf_paths_list = [pdf_paths] + master_framework = {"framework_name": framework_name, "controls": []} - # Process each PDF individually: extract text, then chunk - all_chunks = [] - for pdf_path in pdf_paths_list: - # Extract text from this PDF - pdf_text = extract_text_from_pdf(pdf_path) - - # Chunk this PDF's text - pdf_chunks = chunk_text(pdf_text, framework_name=framework_name) - - # Add source PDF metadata to each chunk - pdf_name = Path(pdf_path).name - for chunk in pdf_chunks: - chunk["metadata"]["source_pdf"] = pdf_name - chunk["metadata"]["source_path"] = str(pdf_path) - - all_chunks.extend(pdf_chunks) - - if not all_chunks: - raise ValueError("No chunks could be created from the provided PDFs") - - # Initialize embedder and generate embeddings for all chunks - embedder = GemmaEmbedder() - embedding_dim = embedder.get_embedding_dim() - - chunk_texts = [chunk["text"] for chunk in all_chunks] - embeddings = embedder.embed_batch(chunk_texts) - - collection_name = f"{framework_name}_chunks" - qdrant_client = initialize_qdrant(collection_name, embedding_dim) - add_documents(qdrant_client, collection_name, all_chunks, embeddings) - - return qdrant_client, collection_name, embedder - - -def evaluate_applicant_documents( - applicant_pdf_paths: list[str], - framework_name: str, - applicant_name: str = None, - save_report: bool = True -): - """ - Evaluate applicant documents against a framework. - - Pipeline: - 1. Load framework data - 2. Extract text from applicant PDFs - 3. Evaluate using LLM - 4. Save evaluation report (optional) - - Args: - applicant_pdf_paths: List of paths to applicant PDF files - framework_name: Name of the framework to evaluate against - applicant_name: Optional name/identifier for the applicant - save_report: Whether to save the evaluation report to disk - """ - controls_json, evaluation_prompt = load_framework_data(framework_name) - - applicant_docs = [] - for pdf_path in applicant_pdf_paths: - doc_text = extract_text_from_pdf(pdf_path) - applicant_docs.append(doc_text) + # Generate prompt + evaluation_prompt = generate_evaluation_prompt(master_framework, framework_name) - evaluation_report = evaluate_applicant( - applicant_docs, evaluation_prompt, controls_json - ) + # Save + save_framework_data(framework_name, master_framework, evaluation_prompt) - if save_report: - save_evaluation_report( - evaluation_report, - framework_name, - applicant_name - ) - - return evaluation_report + return master_framework, evaluation_prompt def main(): - """Main entry point demonstrating the pipeline.""" + """Main entry point.""" print("\n" + "="*60) print("Compliance Framework Evaluation System") print("="*60) - # Show directory structure paths = get_input_paths() print("\n📁 Directory Structure:") print(f" Input PDFs (Frameworks): {paths['frameworks']}") print(f" Input PDFs (Applicants): {paths['applicants']}") - print(f" Input PDFs (Vector DB): {paths['vector_db']}") print(f" Framework Outputs: config/frameworks/") - print(f" Evaluation Reports: data/outputs/evaluations/") - print(f" Vector Database: config/vector_db/") - - # Example usage (commented out - user should provide actual paths) - print("\n📝 Example pipeline usage:") - print("\n1. Setup a framework:") - print(" Place framework PDF in: data/inputs/frameworks/") - print(" setup_framework('data/inputs/frameworks/my_framework.pdf', 'my_framework')") - - print("\n2. Index framework documents (supports single file, list, or directory):") - print(" # Single file:") - print(" index_framework_documents('data/inputs/frameworks/my_framework.pdf', 'my_framework')") - print(" # Directory (all PDFs in directory):") - print(" index_framework_documents('data/inputs/vector_db/my_framework/', 'my_framework')") - print(" # List of files:") - print(" index_framework_documents(['file1.pdf', 'file2.pdf'], 'my_framework')") - - print("\n3. Evaluate applicant documents:") - print(" Place applicant PDFs in: data/inputs/applicants/") - print(" evaluate_applicant_documents(") - print(" ['data/inputs/applicants/applicant1.pdf'],") - print(" 'my_framework',") - print(" applicant_name='applicant1'") - print(" )") - print("\n" + "="*60) - print("Pipeline ready! Place your PDFs in the directories above and run the functions.") + print("\n📝 Usage:") + print(" setup_framework('data/inputs/frameworks/', 'framework_name')") print("="*60 + "\n") - -def test_paths(): - paths = get_input_paths() - - print(f" Input PDFs (Frameworks): {paths['frameworks']}") - print(f" Input PDFs (Applicants): {paths['applicants']}") - if __name__ == "__main__": - #main() - test_paths() - vector_db_path = "data/inputs/vector_db" - framework_name = "NDI" - #index_framework_documents(vector_db_path, framework_name) framework_path = "data/inputs/frameworks/" + framework_name = "NDI" setup_framework(framework_path, framework_name) - - diff --git a/services/ai-service/src/core/__init__.py b/services/ai-service/src/core/__init__.py index ccbfc36..5c616a0 100644 --- a/services/ai-service/src/core/__init__.py +++ b/services/ai-service/src/core/__init__.py @@ -1,13 +1,9 @@ from .evaluator import evaluate_applicant from .framework_extractor import extract_controls_from_framework -from .framework_organizer import organize_pdfs_by_section, validate_framework_sections -from .framework_consolidator import extract_controls_from_sections, create_master_prompt +from .framework_consolidator import extract_controls_from_pdfs __all__ = [ "evaluate_applicant", "extract_controls_from_framework", - "organize_pdfs_by_section", - "validate_framework_sections", - "extract_controls_from_sections", - "create_master_prompt" + "extract_controls_from_pdfs" ] diff --git a/services/ai-service/src/core/evaluator.py b/services/ai-service/src/core/evaluator.py index 775989f..f770b63 100644 --- a/services/ai-service/src/core/evaluator.py +++ b/services/ai-service/src/core/evaluator.py @@ -59,7 +59,7 @@ def evaluate_applicant( Now evaluate the applicant documents against the framework and provide a comprehensive evaluation report.""" response = client.chat.completions.create( - model="deepseek/deepseek-r1", + model="openai/gpt-4.1", messages=[ { "role": "system", @@ -74,8 +74,23 @@ def evaluate_applicant( response_format={"type": "json_object"} ) + # Check if response has content + if not response.choices or not response.choices[0].message: + raise ValueError( + f"API returned empty response. Response object: {response}, " + f"Choices: {getattr(response, 'choices', None)}" + ) + result_text = response.choices[0].message.content + # Check if result_text is None or empty + if not result_text or not result_text.strip(): + raise ValueError( + f"API returned empty content. Response: {response}, " + f"Content: {repr(result_text)}. " + f"This may indicate an API error, rate limit, or model issue." + ) + # Parse JSON response, handle markdown code blocks if present try: evaluation_report = json.loads(result_text) diff --git a/services/ai-service/src/core/framework_consolidator.py b/services/ai-service/src/core/framework_consolidator.py index fcf2876..5706392 100644 --- a/services/ai-service/src/core/framework_consolidator.py +++ b/services/ai-service/src/core/framework_consolidator.py @@ -1,199 +1,146 @@ """ Framework Consolidator Module -Extracts controls from sections and creates a master evaluation prompt +Merges controls from multiple sections into a master framework """ import json -from pathlib import Path -from typing import Dict, Any +from typing import Dict, Any, List from openai import OpenAI import os from dotenv import load_dotenv from src.processing import extract_text_from_pdf from src.core.framework_extractor import extract_controls_from_framework +from concurrent.futures import ThreadPoolExecutor load_dotenv() -def extract_controls_from_sections(organized_sections: Dict[str, str], framework_name: str) -> Dict[str, Dict[str, Any]]: +def extract_controls_from_pdfs(pdf_paths_list: List[str], framework_name: str) -> List[List[Dict[str, Any]]]: """ - Extract controls from each section by calling extract_controls_from_framework for each. + Extract controls from multiple PDFs in parallel. + Each PDF gets one LLM call. Args: - organized_sections: Dictionary mapping section names to PDF paths + pdf_paths_list: List of PDF file paths framework_name: Name of the framework Returns: - Dictionary mapping section names to their controls JSON: - {'section_name': controls_json, ...} + List of controls arrays (one per PDF) """ - section_controls = {} - - for section_name, pdf_path in organized_sections.items(): + def extract_pdf(pdf_path: str): + """Extract controls from a single PDF.""" try: - # Extract text from PDF pdf_text = extract_text_from_pdf(pdf_path) - - # Extract controls for this section - controls_json = extract_controls_from_framework(pdf_text, f"{framework_name}_{section_name}") - - # Add section context to controls - controls_json['section_name'] = section_name - controls_json['section_path'] = pdf_path - - section_controls[section_name] = controls_json - + controls_json = extract_controls_from_framework(pdf_text, framework_name) + return controls_json.get("controls", []) except Exception as e: - print(f"Warning: Failed to extract controls from section '{section_name}' ({pdf_path}): {e}") - # Mark section as incomplete - section_controls[section_name] = { - 'section_name': section_name, - 'section_path': pdf_path, - 'error': str(e), - 'incomplete': True - } + print(f"Error extracting from {pdf_path}: {e}") + return [] + + # Process all PDFs in parallel + with ThreadPoolExecutor(max_workers=len(pdf_paths_list)) as executor: + controls_arrays = list(executor.map(extract_pdf, pdf_paths_list)) - return section_controls + return controls_arrays -def create_master_prompt( - section_controls: Dict[str, Dict[str, Any]], - organized_sections: Dict[str, str], - framework_name: str -) -> str: +def compose_master_framework(json_list: List[Dict[str, Any]], framework_name: str) -> Dict[str, Any]: """ - Create a master evaluation prompt by consolidating all section controls and texts. - - Uses LLM to consolidate all section controls into a unified master prompt with: - - Full text from each section (organized by section) - - Combined extracted controls - - Unified evaluation rules - - Rubric/scoring criteria from all sections + Merge multiple JSON arrays into a single Master Framework. Args: - section_controls: Dictionary mapping section names to their controls JSON - organized_sections: Dictionary mapping section names to PDF paths + json_list: List of controls arrays from different sections framework_name: Name of the framework Returns: - Master evaluation prompt string + Single master framework JSON object """ api_key = os.getenv("OPENROUTER_API_KEY") if not api_key: - raise ValueError("OPENROUTER_API_KEY not found in environment variables") + raise ValueError("OPENROUTER_API_KEY not found") client = OpenAI( api_key=api_key, base_url="https://openrouter.ai/api/v1" ) - # Extract text from each section (with aggressive truncation to stay under token limits) - section_texts = {} - for section_name, pdf_path in organized_sections.items(): - try: - text = extract_text_from_pdf(pdf_path) - # Reduce to 10000 chars per section to stay under token limits - if len(text) > 10000: - text = text[:10000] + "\n\n[Text truncated - see full content in original PDFs]" - section_texts[section_name] = text - except Exception as e: - print(f"Warning: Could not extract text from {pdf_path}: {e}") - section_texts[section_name] = "" - - # Prepare summaries instead of full data to reduce token usage - section_summaries = [] - total_controls = 0 + # Prepare JSON for prompt + json_list_str = json.dumps(json_list, indent=2, ensure_ascii=False) + if len(json_list_str) > 50000: + json_list_str = json_list_str[:50000] + "\n\n[Data truncated...]" - for section_name in organized_sections.keys(): - controls = section_controls.get(section_name, {}) - controls_list = controls.get('controls', []) if not controls.get('incomplete') else [] - total_controls += len(controls_list) - - # Create summary instead of full JSON - section_summary = { - 'section_name': section_name, - 'text_sample': section_texts.get(section_name, '')[:5000], # Only 5k chars - 'controls_count': len(controls_list), - 'controls_summary': [ - { - 'id': c.get('id', ''), - 'title': c.get('title', '')[:100], # Truncate titles - 'category': c.get('category', '') - } - for c in controls_list[:20] # Only first 20 controls per section - ] - } - section_summaries.append(section_summary) - - consolidation_prompt = f"""You are creating a master evaluation prompt for a compliance framework. - -Framework Name: {framework_name} -Total Sections: {len(organized_sections)} -Total Controls: {total_controls} - -Create a comprehensive master evaluation prompt that: -1. Provides clear evaluation instructions -2. References all sections and their key controls -3. Includes unified evaluation rules -4. Provides rubric/scoring guidance - -Section Summaries: -{json.dumps(section_summaries, indent=2, ensure_ascii=False)} - -Create the master prompt. Structure it as: - -# FRAMEWORK: {framework_name} - -## EVALUATION INSTRUCTIONS -[Clear instructions for evaluating applicant documents] - -## FRAMEWORK SECTIONS OVERVIEW -[Brief overview of each section and its purpose] - -## EVALUATION RULES -[Unified rules combining all sections] - -## RUBRIC & SCORING -[Evaluation criteria and scoring guidance] - -## CONTROLS REFERENCE -[Summary of key controls to evaluate] - -NOTE: The full framework text is available in the original PDFs. This prompt focuses on evaluation methodology and key controls. - -Create the master evaluation prompt now.""" + composition_prompt = f"""### ROLE: JSON Integrator & Data Architect +### TASK: Merge these {len(json_list)} JSON arrays into a single Master Framework. +### INSTRUCTIONS: +1. Take all controls from the input arrays and combine them into a single flat array. +2. De-duplicate IDs: If a Control exists in multiple files, merge the descriptions (keep the most complete version). +3. Return a JSON object with this structure: + {{ + "framework_name": "{framework_name}", + "controls": [array of all merged controls] + }} +4. DO NOT create Domain structures or nested hierarchies - just a flat array of controls. +### INPUT DATA: +{json_list_str} + +### OUTPUT +Return ONLY a JSON object with "framework_name" and "controls" keys. The "controls" must be a flat array of all controls from the input.""" response = client.chat.completions.create( - model="deepseek/deepseek-r1", + model="openai/gpt-4.1", messages=[ { "role": "system", - "content": "You are an expert in creating comprehensive evaluation prompts for compliance frameworks. Consolidate multiple sections into a unified, well-structured master prompt. The prompt should be clear, complete, and ready to use." + "content": "You are a JSON Integrator. Merge multiple JSON arrays into a single master framework. Return valid JSON only." }, { "role": "user", - "content": consolidation_prompt + "content": composition_prompt } ], - temperature=0.3 + temperature=0.3, + response_format={"type": "json_object"} ) - master_prompt = response.choices[0].message.content.strip() + result_text = response.choices[0].message.content + print("\n" + "="*60) + print("COMPOSITION RESPONSE:") + print("="*60) + print(result_text) + print("="*60 + "\n") - # If the LLM response is too short, build prompt manually - if len(master_prompt) < 2000: - master_prompt = f"# FRAMEWORK: {framework_name}\n\n" - master_prompt += "## EVALUATION INSTRUCTIONS\n\n" - master_prompt += "Evaluate applicant documents against all sections of this framework.\n\n" + master_framework = json.loads(result_text) + + # Find controls array from any key + controls_array = [] + if isinstance(master_framework, dict): + # Check common keys first + for key in ['controls', 'compliance_controls', 'items', 'data']: + if key in master_framework and isinstance(master_framework[key], list): + controls_array = master_framework[key] + break - for section_name, text in section_texts.items(): - master_prompt += f"## {section_name.upper()}\n\n{text}\n\n" + # If Domain structure, extract controls from it + if not controls_array and 'Domain' in master_framework: + domain_list = master_framework['Domain'] + if isinstance(domain_list, list): + # Flatten controls from all domains + for domain in domain_list: + if isinstance(domain, dict) and 'Controls' in domain: + if isinstance(domain['Controls'], list): + controls_array.extend(domain['Controls']) - master_prompt += "\n## CONTROLS SUMMARY\n\n" - for section_name, controls in section_controls.items(): - if not controls.get('incomplete'): - master_prompt += f"### {section_name.upper()}\n\n" - for control in controls.get('controls', [])[:10]: - master_prompt += f"- {control.get('title', 'Control')}\n" + # If still not found, find any array + if not controls_array: + for value in master_framework.values(): + if isinstance(value, list) and len(value) > 0: + # Check if it's a list of control objects (have 'id' field) + if all(isinstance(item, dict) and 'id' in item for item in value): + controls_array = value + break + + # Normalize to controls key + master_framework["framework_name"] = framework_name + master_framework["controls"] = controls_array - return master_prompt + return master_framework diff --git a/services/ai-service/src/core/framework_extractor.py b/services/ai-service/src/core/framework_extractor.py index 88b8291..6049a4a 100644 --- a/services/ai-service/src/core/framework_extractor.py +++ b/services/ai-service/src/core/framework_extractor.py @@ -1,6 +1,6 @@ """ Framework Extractor Module -Uses LLM to extract compliance controls/rules/criteria from framework PDF text +Extracts compliance controls from PDF text using LLM """ import json @@ -14,88 +14,65 @@ def extract_controls_from_framework(pdf_text: str, framework_name: str) -> Dict[str, Any]: """ - Extract compliance controls, rules, and criteria from framework PDF text using LLM. - - Handles multilingual content (Arabic/English) and identifies controls regardless - of terminology used (controls, rules, criteria, etc.). + Extract compliance controls from PDF text using LLM. Args: pdf_text: Extracted text from framework PDF - framework_name: Name of the framework being processed + framework_name: Name of the framework Returns: - Dictionary with structure: - { - "framework_name": str, - "controls": [...], - "metadata": {...}, - "filters": {...} - } + Dictionary with controls array """ api_key = os.getenv("OPENROUTER_API_KEY") if not api_key: - raise ValueError("OPENROUTER_API_KEY not found in environment variables") + raise ValueError("OPENROUTER_API_KEY not found") client = OpenAI( api_key=api_key, base_url="https://openrouter.ai/api/v1" ) - # Truncate text if too long (model has context limits) - # Keep first ~100k characters to ensure we capture the framework structure - max_chars = 100000 - if len(pdf_text) > max_chars: - pdf_text = pdf_text[:max_chars] + "\n\n[Text truncated due to length...]" - - extraction_prompt = f"""You are an expert in compliance frameworks and regulatory standards. -Your task is to extract compliance controls, rules, criteria, or requirements from the following framework document. - -Framework Name: {framework_name} - -The document may be in Arabic, English, or both. The terminology may vary: -- Controls (ضوابط) -- Rules (قواعد) -- Criteria (معايير) -- Requirements (متطلبات) -- Standards (معايير) + extraction_prompt = f""" +### ROLE +Senior Regulatory Data Architect specializing in GRC (Governance, Risk, and Compliance) systems. -Extract ALL compliance-related items regardless of what they're called in the document. +### TASK +Perform a high-fidelity extraction of compliance controls, specifications, and performance metrics from the provided document. -Return a JSON object with the following structure: -{{ - "framework_name": "{framework_name}", - "controls": [ - {{ - "id": "unique identifier or number", - "title": "control title in original language", - "description": "detailed description of the control/rule/criteria", - "category": "category or domain", - "language": "ar" or "en" or "both" - }} - ], - "metadata": {{ - "total_controls": number, - "extraction_date": "ISO format date", - "languages_detected": ["ar", "en"], - "framework_type": "type of framework" - }}, - "filters": {{ - "categories": ["list of unique categories"], - "domains": ["list of unique domains if available"] - }} +### SCHEMA CONSTRAINTS +For each identified item, you must populate the following JSON structure: +- "id": Unique alphanumeric identifier (e.g., DSI.OE.01, DG.1). +- "title": Original title from the text. +- "type": Classify as 'Administrative Policy', 'Technical Control', or 'Performance Metric'. +- "semantic_intent": The underlying goal or risk this item addresses (often found in "This aims to..." sections). +- "description": A clear, concise description of this measurement or control—what it measures, how it applies, and what it covers. +- "requirements": A list of specific, actionable conditions that must be met. +- "evaluation_logic": {{ + "calculation": "Formula or logic for measurement (if applicable)", + "threshold": "The minimum passing score or condition (e.g., 70%, 10 days)", + "scale": "Scoring intervals (e.g., 0-5 scale criteria)" }} +- "evidence_suggested": Examples of artifacts needed to prove compliance (e.g., API logs, DMO charter). -Document text: +### EXTRACTION RULES +1. **No Summarization:** Extract the full technical detail. Do not paraphrase. +2. **Multilingual Mapping:** If a control is in Arabic, maintain the 'title' in Arabic but provide a technical English summary in 'semantic_intent'. +3. **Description:** For every control, write a "description" that explains what the measurement covers, how it applies, and what it measures. Keep it concise but complete. +4. **Hierarchy:** If a control has sub-specifications, nest them within a 'sub_controls' array. + +### DOCUMENT CONTENT: {pdf_text} -Extract the compliance controls/rules/criteria and return ONLY valid JSON, no additional text.""" +### OUTPUT +Return ONLY a valid JSON object. Ensure every ID in the document is represented. +""" response = client.chat.completions.create( - model="deepseek/deepseek-r1", + model="openai/gpt-4.1", messages=[ { "role": "system", - "content": "You are a compliance framework expert. Extract structured compliance controls from documents. Always return valid JSON only." + "content": "You are a Senior Regulatory Data Architect. Extract compliance controls and return valid JSON only." }, { "role": "user", @@ -107,17 +84,62 @@ def extract_controls_from_framework(pdf_text: str, framework_name: str) -> Dict[ ) result_text = response.choices[0].message.content + print("\n" + "="*60) + print("EXTRACTION RESPONSE:") + print("="*60) + print(result_text) + print("="*60 + "\n") + + # Try to extract JSON from markdown code blocks first + if "```json" in result_text: + json_start = result_text.find("```json") + 7 + json_end = result_text.find("```", json_start) + if json_end > json_start: + result_text = result_text[json_start:json_end].strip() - # Parse JSON response, handle markdown code blocks if present + # Parse JSON with error handling try: - controls_json = json.loads(result_text) - return controls_json + result_json = json.loads(result_text) except json.JSONDecodeError as e: - if "```json" in result_text: - json_start = result_text.find("```json") + 7 - json_end = result_text.find("```", json_start) - result_text = result_text[json_start:json_end].strip() - controls_json = json.loads(result_text) - return controls_json + print(f"Warning: JSON parsing error: {e}") + # Try to fix truncated JSON by finding last complete structure + last_brace = result_text.rfind('}') + last_bracket = result_text.rfind(']') + end_pos = max(last_brace, last_bracket) + + if end_pos > 0: + try: + result_json = json.loads(result_text[:end_pos + 1]) + print("Successfully parsed truncated JSON") + except: + print("Could not parse JSON, returning empty controls") + return {"framework_name": framework_name, "controls": []} else: - raise ValueError(f"Failed to parse JSON response: {e}") + return {"framework_name": framework_name, "controls": []} + + # Find controls array - check common keys first + controls_array = [] + if isinstance(result_json, list): + controls_array = result_json + elif isinstance(result_json, dict): + # Try common keys (including compliance_controls) + for key in ['controls', 'compliance_controls', 'items', 'data', 'Domain']: + if key in result_json and isinstance(result_json[key], list): + controls_array = result_json[key] + break + + # If not found, find any array + if not controls_array: + for value in result_json.values(): + if isinstance(value, list) and len(value) > 0: + controls_array = value + break + + # If still not found and it's a single control object, wrap it in array + if not controls_array and 'id' in result_json: + controls_array = [result_json] + + return { + "framework_name": framework_name, + "controls": controls_array + } diff --git a/services/ai-service/src/core/framework_organizer.py b/services/ai-service/src/core/framework_organizer.py deleted file mode 100644 index 3b0b265..0000000 --- a/services/ai-service/src/core/framework_organizer.py +++ /dev/null @@ -1,329 +0,0 @@ -""" -Framework Organizer Module -Organizes multiple PDFs into sections and validates framework completeness -""" - -import json -from pathlib import Path -from typing import Dict, Any, List -from openai import OpenAI -import os -from dotenv import load_dotenv -from src.processing import extract_text_from_pdf - -load_dotenv() - - -def organize_pdfs_by_section(pdf_paths_list: List[str], framework_name: str) -> Dict[str, str]: - """ - Organize PDFs into sections by analyzing their content using LLM. - - Uses LLM to determine section names (part1, part2, rubric, etc.) based on content analysis. - - Args: - pdf_paths_list: List of PDF file paths - framework_name: Name of the framework - - Returns: - Dictionary mapping section names to PDF paths: {'section_name': 'pdf_path', ...} - """ - api_key = os.getenv("OPENROUTER_API_KEY") - if not api_key: - raise ValueError("OPENROUTER_API_KEY not found in environment variables") - - client = OpenAI( - api_key=api_key, - base_url="https://openrouter.ai/api/v1" - ) - - # Extract text from each PDF (first 5000 chars for analysis) - pdf_samples = {} - for pdf_path in pdf_paths_list: - try: - full_text = extract_text_from_pdf(pdf_path) - # Use first 5000 chars for section identification - sample_text = full_text[:5000] if len(full_text) > 5000 else full_text - pdf_samples[pdf_path] = { - 'filename': Path(pdf_path).name, - 'sample': sample_text, - 'full_length': len(full_text) - } - except Exception as e: - print(f"Warning: Could not extract text from {pdf_path}: {e}") - # Use filename as fallback section name - pdf_samples[pdf_path] = { - 'filename': Path(pdf_path).name, - 'sample': '', - 'full_length': 0 - } - - # Prepare analysis prompt - pdf_info = [] - for pdf_path, info in pdf_samples.items(): - pdf_info.append({ - 'path': pdf_path, - 'filename': info['filename'], - 'sample': info['sample'], - 'length': info['full_length'] - }) - - analysis_prompt = f"""You are analyzing multiple PDF documents that form parts of a compliance framework. - -Framework Name: {framework_name} - -Analyze each PDF and determine its section name and role in the framework. Common section types include: -- part1, part2, part3, etc. (main framework parts) -- rubric (evaluation rubric/scoring criteria) -- appendix (appendices) -- guidelines (guidelines or instructions) -- controls (specific controls list) - -For each PDF, return: -1. A section name (use lowercase, no spaces, e.g., "part1", "rubric", "appendix") -2. A brief description of its role - -PDF Documents to analyze: -{json.dumps(pdf_info, indent=2, ensure_ascii=False)} - -Return a JSON object with this structure: -{{ - "sections": [ - {{ - "pdf_path": "path/to/file.pdf", - "section_name": "part1", - "description": "Main framework part 1", - "confidence": "high|medium|low" - }} - ] -}} - -Analyze each PDF and return the JSON.""" - - response = client.chat.completions.create( - model="deepseek/deepseek-r1", - messages=[ - { - "role": "system", - "content": "You are an expert in analyzing compliance framework documents. Identify section types and roles based on content analysis. Always return valid JSON only." - }, - { - "role": "user", - "content": analysis_prompt - } - ], - temperature=0.3, - response_format={"type": "json_object"} - ) - - result_text = response.choices[0].message.content - - # Parse JSON response - try: - analysis_result = json.loads(result_text) - except json.JSONDecodeError as e: - if "```json" in result_text: - json_start = result_text.find("```json") + 7 - json_end = result_text.find("```", json_start) - result_text = result_text[json_start:json_end].strip() - analysis_result = json.loads(result_text) - else: - raise ValueError(f"Failed to parse JSON response: {e}") - - # Build organized sections dictionary - organized_sections = {} - for section_info in analysis_result.get("sections", []): - pdf_path = section_info.get("pdf_path") - section_name = section_info.get("section_name", "unknown") - - # If section name couldn't be determined, use filename (sanitized) - if section_name == "unknown" or not section_name: - section_name = Path(pdf_path).stem.lower().replace(" ", "_") - - organized_sections[section_name] = pdf_path - - # Fallback: if LLM didn't return all PDFs, add missing ones - for pdf_path in pdf_paths_list: - if pdf_path not in organized_sections.values(): - # Use filename as section name - section_name = Path(pdf_path).stem.lower().replace(" ", "_") - organized_sections[section_name] = pdf_path - - return organized_sections - - -def validate_framework_sections(organized_sections: Dict[str, str], framework_name: str) -> Dict[str, Any]: - """ - Validate framework sections using LLM-based checks. - - Validates: - - All required sections exist - - Text quality is sufficient - - Critical keywords are present - - Control patterns are detected - - Proper document structure maintained - - Args: - organized_sections: Dictionary mapping section names to PDF paths - framework_name: Name of the framework - - Returns: - Dictionary with validation results: - { - "status": "pass" | "fail", - "checks": {...}, - "errors": [...], - "warnings": [...] - } - """ - api_key = os.getenv("OPENROUTER_API_KEY") - if not api_key: - raise ValueError("OPENROUTER_API_KEY not found in environment variables") - - client = OpenAI( - api_key=api_key, - base_url="https://openrouter.ai/api/v1" - ) - - # Extract text from all sections for validation - section_texts = {} - total_size = 0 - for section_name, pdf_path in organized_sections.items(): - try: - text = extract_text_from_pdf(pdf_path) - section_texts[section_name] = { - 'path': pdf_path, - 'text': text[:10000] if len(text) > 10000 else text, # Sample for validation - 'full_length': len(text) - } - total_size += len(text) - except Exception as e: - section_texts[section_name] = { - 'path': pdf_path, - 'text': '', - 'full_length': 0, - 'error': str(e) - } - - validation_prompt = f"""You are validating a compliance framework that has been organized into sections. - -Framework Name: {framework_name} -Sections Found: {list(organized_sections.keys())} - -IMPORTANT: Do NOT expect specific section names or structures. Different frameworks have different organizations. -Focus on whether the content is useful for compliance evaluation, not whether specific sections exist. - -Validate the following aspects: - -1. **Content Suitability**: Can compliance controls, requirements, or evaluation criteria be extracted from the text? - - Look for any compliance-related content (controls, rules, criteria, requirements, standards, guidelines) - - Check if there's enough content to perform meaningful evaluation - - DO NOT require specific section names - frameworks vary in structure - -2. **Text Quality**: Is the text readable and not corrupted? Are there formatting issues that would prevent extraction? - -3. **Evaluation-Relevant Content**: Are there elements that would be useful for evaluating applicant documents? - - Compliance requirements - - Control statements - - Evaluation criteria - - Scoring rubrics (if present, but not required) - - Standards or benchmarks - -4. **Control Patterns**: Are compliance control patterns detected in the text? (e.g., "must", "shall", "required", numbered controls) - -5. **Structure**: Is the document structure logical and coherent? Can sections be understood in context? - -Section Texts (samples): -{json.dumps({k: {'length': v['full_length'], 'sample': v['text'][:2000]} for k, v in section_texts.items()}, indent=2, ensure_ascii=False)} - -Return a JSON object with this structure: -{{ - "status": "pass" | "fail", - "checks": {{ - "content_suitability": {{"status": "pass|fail|warning", "details": "..."}}, - "text_quality": {{"status": "pass|fail|warning", "details": "..."}}, - "evaluation_content": {{"status": "pass|fail|warning", "details": "..."}}, - "control_patterns": {{"status": "pass|fail|warning", "details": "..."}}, - "structure": {{"status": "pass|fail|warning", "details": "..."}} - }}, - "errors": ["error1", "error2", ...], - "warnings": ["warning1", "warning2", ...], - "keywords_found": {{"section_name": ["keyword1", ...], ...}} -}} - -CRITICAL: Only mark status as "fail" if there are CRITICAL issues: -- Text is completely corrupted or unreadable -- No compliance-related content can be found at all -- Text is too short or empty - -Missing optional sections, different naming conventions, or non-standard structures should be warnings, NOT errors. -The framework structure should be flexible - focus on whether controls and evaluation info can be extracted. - -Validate the framework sections and return the JSON.""" - - response = client.chat.completions.create( - model="deepseek/deepseek-r1", - messages=[ - { - "role": "system", - "content": "You are an expert in validating compliance framework documents. Focus on whether the content contains extractable compliance controls and evaluation criteria. Be flexible about document structure - different frameworks organize content differently. Only fail validation for critical issues like corrupted text or complete absence of compliance content. Always return valid JSON only." - }, - { - "role": "user", - "content": validation_prompt - } - ], - temperature=0.3, - response_format={"type": "json_object"} - ) - - result_text = response.choices[0].message.content - - # Parse JSON response - try: - validation_result = json.loads(result_text) - except json.JSONDecodeError as e: - if "```json" in result_text: - json_start = result_text.find("```json") + 7 - json_end = result_text.find("```", json_start) - result_text = result_text[json_start:json_end].strip() - validation_result = json.loads(result_text) - else: - raise ValueError(f"Failed to parse JSON response: {e}") - - # Add total size to validation result - validation_result['total_size'] = total_size - - # Only fail on critical errors (corrupted text, no content, unreadable) - # Missing optional sections should be warnings, not errors - errors = validation_result.get("errors", []) - warnings = validation_result.get("warnings", []) - - # Filter for critical errors only - critical_keywords = ["corrupted", "unreadable", "no content", "empty", "too short", "cannot extract"] - critical_errors = [ - e for e in errors - if any(keyword in e.lower() for keyword in critical_keywords) - ] - - # Only raise exception for critical errors - if validation_result.get("status") == "fail" and critical_errors: - error_msg = f"Framework validation failed for '{framework_name}': " + "; ".join(critical_errors) - raise ValueError(error_msg) - elif validation_result.get("status") == "fail" and not critical_errors: - # If status is fail but no critical errors, downgrade to warning and continue - print(f"⚠️ Validation warnings for '{framework_name}' (continuing anyway):") - if warnings: - for warning in warnings: - print(f" - {warning}") - if errors: - for error in errors: - print(f" - {error}") - # Change status to pass since we're continuing - validation_result["status"] = "pass" - elif warnings: - # Log warnings but don't fail - print(f"ℹ️ Validation warnings for '{framework_name}':") - for warning in warnings: - print(f" - {warning}") - - return validation_result diff --git a/services/ai-service/src/prompts/prompt_generator.py b/services/ai-service/src/prompts/prompt_generator.py index 793671d..8f2c967 100644 --- a/services/ai-service/src/prompts/prompt_generator.py +++ b/services/ai-service/src/prompts/prompt_generator.py @@ -16,55 +16,58 @@ def generate_evaluation_prompt(controls_json: Dict[str, Any], framework_name: st """ Generate an evaluation prompt from extracted controls JSON. - The prompt will contain framework logic and clear evaluation criteria - that can be used directly for applicant evaluation. - Args: - controls_json: Dictionary containing extracted controls, metadata, and filters + controls_json: Dictionary containing extracted controls framework_name: Name of the framework Returns: - Evaluation prompt string ready to use for applicant evaluation + Evaluation prompt string """ api_key = os.getenv("OPENROUTER_API_KEY") if not api_key: - raise ValueError("OPENROUTER_API_KEY not found in environment variables") + raise ValueError("OPENROUTER_API_KEY not found") client = OpenAI( api_key=api_key, base_url="https://openrouter.ai/api/v1" ) - # Convert controls JSON to string for the prompt + # Convert controls JSON to string controls_str = json.dumps(controls_json, indent=2, ensure_ascii=False) + if len(controls_str) > 50000: + controls_str = controls_str[:50000] + "\n\n[Data truncated...]" - generation_prompt = f"""You are an expert in compliance evaluation. Based on the extracted compliance framework controls below, -create a comprehensive evaluation prompt that will be used to evaluate applicant documents against this framework. - -Framework Name: {framework_name} + generation_prompt = f""" +### ROLE +Prompt Engineer & Compliance Auditor. -The evaluation prompt should: -1. Clearly explain the framework and its purpose -2. List all controls/rules/criteria that need to be evaluated -3. Provide clear evaluation criteria for each control -4. Specify how to score or assess compliance (e.g., compliant/non-compliant, or scoring scale) -5. Include instructions for handling multilingual documents (Arabic/English) -6. Be structured so it can be used directly with an LLM to evaluate applicant documents -7. Include instructions on how to format the evaluation report +### TASK +Using the provided Master JSON containing multiple compliance frameworks, generate a sophisticated **System Prompt** for an "AI Compliance Auditor." -The prompt should be self-contained and can be used independently to evaluate any applicant's documents. +### SYSTEM PROMPT REQUIREMENTS +The generated prompt must instruct the AI Auditor to: +1. **Role Adoption:** Act as a lead auditor for Saudi National Data Governance (NDMO) and Operational Excellence (SDAIA). +2. **Cross-Framework Mapping:** When evaluating a document, identify which controls from WHICH framework apply (e.g., mapping user evidence to both a Policy and an OE Metric). +3. **Evidence Analysis Logic:** + - Step A: Extract claims from the applicant's document. + - Step B: Compare claims against the 'Requirements' and 'Thresholds' in the Master JSON. + - Step C: Check for specific 'Evidence Suggested' artifacts. +4. **Scoring Protocol:** Apply the strict 0-5 scale for metrics and binary (Compliant/Non-Compliant) for policies as defined in the source data. +5. **Gap Analysis:** For every non-compliant item, specify exactly what is missing based on the 'Semantic Intent'. -Extracted Controls JSON: +### SOURCE FRAMEWORKS (JSON): {controls_str} -Generate the evaluation prompt now. The prompt should be clear, comprehensive, and ready to use.""" +### FINAL OUTPUT +Generate the full System Prompt text. The prompt should be optimized for a model with a large context window and include instructions on generating a 'Compliance Gap Report' table at the end of every evaluation. +""" response = client.chat.completions.create( - model="deepseek/deepseek-r1", + model="openai/gpt-4.1", messages=[ { "role": "system", - "content": "You are an expert in creating evaluation prompts for compliance frameworks. Create clear, comprehensive prompts that can be used to evaluate documents against compliance standards." + "content": "You are a Prompt Engineer & Compliance Auditor. Generate sophisticated system prompts for AI Compliance Auditors." }, { "role": "user", @@ -74,4 +77,11 @@ def generate_evaluation_prompt(controls_json: Dict[str, Any], framework_name: st temperature=0.3 ) - return response.choices[0].message.content.strip() + result_text = response.choices[0].message.content.strip() + print("\n" + "="*60) + print("PROMPT GENERATION RESPONSE:") + print("="*60) + print(result_text) + print("="*60 + "\n") + + return result_text diff --git a/services/ai-service/uv.lock b/services/ai-service/uv.lock index 2bb9e1c..ffa3908 100644 --- a/services/ai-service/uv.lock +++ b/services/ai-service/uv.lock @@ -1179,60 +1179,57 @@ wheels = [ [[package]] name = "pillow" -version = "12.1.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/d0/02/d52c733a2452ef1ffcc123b68e6606d07276b0e358db70eabad7e40042b7/pillow-12.1.0.tar.gz", hash = "sha256:5c5ae0a06e9ea030ab786b0251b32c7e4ce10e58d983c0d5c56029455180b5b9", 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+++++++----------- services/ai-service/src/__init__.py | 54 +- .../src/core/framework_consolidator.py | 143 +--- .../src/core/framework_extractor.py | 111 +-- .../ai-service/src/embeddings/__init__.py | 20 +- .../src/embeddings/gemma_embedder.py | 31 +- .../src/embeddings/qdrant_manager.py | 108 ++- services/ai-service/src/prompts/__init__.py | 3 - .../src/prompts/prompt_generator.py | 87 --- services/ai-service/src/rag/__init__.py | 14 + services/ai-service/src/rag/_shared.py | 18 + services/ai-service/src/rag/ingestion.py | 90 +++ services/ai-service/src/rag/retrieval.py | 109 +++ services/ai-service/src/utils/__init__.py | 18 +- .../ai-service/src/utils/framework_utils.py | 214 ++---- services/ai-service/test.ipynb | 96 +++ 17 files changed, 947 insertions(+), 888 deletions(-) delete mode 100644 services/ai-service/src/prompts/__init__.py delete mode 100644 services/ai-service/src/prompts/prompt_generator.py create mode 100644 services/ai-service/src/rag/__init__.py create mode 100644 services/ai-service/src/rag/_shared.py create mode 100644 services/ai-service/src/rag/ingestion.py create mode 100644 services/ai-service/src/rag/retrieval.py create mode 100644 services/ai-service/test.ipynb diff --git a/services/ai-service/main.py b/services/ai-service/main.py index 0eb6922..f16f71b 100644 --- a/services/ai-service/main.py +++ b/services/ai-service/main.py @@ -1,89 +1,68 @@ """ -Main entry point for the Compliance Framework Evaluation System. -Simple flow: Extract controls → Compose → Generate prompt +Main entry point for the Compliance Framework Extraction System. +Extract controls from each PDF and save one JSON per PDF (no compose, no prompt). """ from pathlib import Path import glob -from src.core.framework_extractor import extract_controls_from_framework -from src.core.framework_consolidator import extract_controls_from_pdfs, compose_master_framework -from src.processing import extract_text_from_pdf -from src.prompts import generate_evaluation_prompt -from src.utils import save_framework_data, get_input_paths +from src.core.framework_consolidator import extract_controls_from_pdfs +from src.utils import save_extraction_json, get_input_paths -def setup_framework(pdf_paths: str | list[str], framework_name: str): + +def setup_framework(pdf_paths: str | list[str], framework_name: str) -> list[Path]: """ - Setup a compliance framework from PDF(s). - Simple flow: Extract controls → Compose → Generate prompt - + Extract controls from each PDF and save one JSON per PDF under config/frameworks/{framework_name}/. + + Each file is named after the source PDF (e.g. section-a.pdf -> section-a.json). + No composition or evaluation prompt generation. + Args: pdf_paths: Single file path, list of files, or directory path framework_name: Name of the framework - + Returns: - Tuple of (controls_json, evaluation_prompt) + List of paths to the saved JSON files """ - # Normalize input to list of PDF paths if isinstance(pdf_paths, list): pdf_paths_list = pdf_paths else: - pdf_path = Path(pdf_paths) - if pdf_path.is_dir(): + p = Path(pdf_paths) + if p.is_dir(): pdf_paths_list = sorted(glob.glob(f"{pdf_paths}/*.pdf")) else: pdf_paths_list = [pdf_paths] - + if not pdf_paths_list: raise ValueError(f"No PDF files found: {pdf_paths}") - - # Single PDF - simple flow - if len(pdf_paths_list) == 1: - pdf_text = extract_text_from_pdf(pdf_paths_list[0]) - controls_json = extract_controls_from_framework(pdf_text, framework_name) - evaluation_prompt = generate_evaluation_prompt(controls_json, framework_name) - save_framework_data(framework_name, controls_json, evaluation_prompt) - return controls_json, evaluation_prompt - - # Multiple PDFs - extract, compose, generate + controls_arrays = extract_controls_from_pdfs(pdf_paths_list, framework_name) - - # Filter out empty arrays - json_list = [arr for arr in controls_arrays if arr] - - # Compose into master framework - if json_list: - master_framework = compose_master_framework(json_list, framework_name) - else: - master_framework = {"framework_name": framework_name, "controls": []} - - # Generate prompt - evaluation_prompt = generate_evaluation_prompt(master_framework, framework_name) - - # Save - save_framework_data(framework_name, master_framework, evaluation_prompt) - - return master_framework, evaluation_prompt + saved: list[Path] = [] + for pdf_path, controls_array in zip(pdf_paths_list, controls_arrays): + obj = {"framework_name": framework_name, "controls": controls_array} + path = save_extraction_json(framework_name, pdf_path, obj) + saved.append(path) + return saved def main(): """Main entry point.""" - print("\n" + "="*60) - print("Compliance Framework Evaluation System") - print("="*60) - + print("\n" + "=" * 60) + print("Compliance Framework Extraction System") + print("=" * 60) + paths = get_input_paths() - print("\n📁 Directory Structure:") + print("\nDirectory structure:") print(f" Input PDFs (Frameworks): {paths['frameworks']}") print(f" Input PDFs (Applicants): {paths['applicants']}") - print(f" Framework Outputs: config/frameworks/") - - print("\n📝 Usage:") - print(" setup_framework('data/inputs/frameworks/', 'framework_name')") - print("="*60 + "\n") + print(f" Framework outputs: config/frameworks/") + + print("\nUsage:") + print(" setup_framework('data/inputs/frameworks/', 'framework_name')") + print("=" * 60 + "\n") if __name__ == "__main__": - framework_path = "data/inputs/frameworks/" + framework_path = "data/inputs/frameworks/NDI" framework_name = "NDI" setup_framework(framework_path, framework_name) diff --git a/services/ai-service/src/ARCHITECTURE.md b/services/ai-service/src/ARCHITECTURE.md index 5a3a95f..2ffb7d1 100644 --- a/services/ai-service/src/ARCHITECTURE.md +++ b/services/ai-service/src/ARCHITECTURE.md @@ -1,544 +1,408 @@ # Code Architecture Documentation -This document explains the codebase structure and where to find specific functionality when making adjustments. +This document explains the codebase structure and serves as the **canonical guide** for future edits. Use it to locate functionality and understand data flows before modifying code. + +--- ## Overview -The Compliance Framework Evaluation System is organized into logical modules that handle different aspects of the pipeline: +The **Compliance Framework Extraction & RAG System** has two main pipelines: + +1. **Extraction** – Extract controls from framework PDFs via LLM, save one JSON per PDF. No composition or evaluation prompt generation. +2. **RAG** – Hybrid indexing (JSON control cards + PDF chunks) into Qdrant, and retrieval by control ID (agent-tool friendly). + +**Modules**: + +| Module | Purpose | +|--------|---------| +| **Core** | Extraction (LLM), retries, and evaluation | +| **Processing** | PDF parsing, text chunking | +| **Embeddings** | Gemma embedder, Qdrant (local/remote) | +| **RAG** | Index framework (JSON + PDF), retrieve by control ID | +| **Utils** | Per-PDF JSON save/load, input paths, vector_db PDF resolution | -1. **Core** - Business logic for framework extraction and evaluation -2. **Processing** - Document parsing and text chunking -3. **Embeddings** - Vector embeddings and database operations -4. **Prompts** - LLM prompt generation -5. **Utils** - File I/O and data management +**Removed / obsolete**: `prompts/` directory, `compose_master_framework`, `save_framework_data`, `load_framework_data`, `list_saved_frameworks`, `save_evaluation_report`, `generate_evaluation_prompt`. Section organization and multi-PDF validation flows are no longer used. + +--- ## Directory Structure ``` src/ -├── core/ # Core business logic -│ ├── evaluator.py # Document evaluation against frameworks -│ └── framework_extractor.py # Extract controls from framework PDFs +├── core/ +│ ├── evaluator.py # Evaluate applicant docs vs framework (uses external prompt) +│ ├── framework_extractor.py # LLM extraction: PDF text → controls JSON +│ └── framework_consolidator.py # Parallel PDF extraction + retry on empty +│ +├── processing/ +│ ├── pdf_parser.py # extract_text_from_pdf +│ └── text_chunker.py # chunk_text, chunk_text_by_sentences │ -├── processing/ # Document processing pipeline -│ ├── pdf_parser.py # PDF text extraction -│ └── text_chunker.py # Text chunking for embeddings +├── embeddings/ +│ ├── gemma_embedder.py # GemmaEmbedder, load_gemma_embedder (HF_HUB_OFFLINE) +│ ├── qdrant_manager.py # Qdrant CRUD, fetch_by_filter, search_similar_filtered +│ └── haystack_retriever.py # Haystack + Qdrant (optional) │ -├── embeddings/ # Vector operations and storage -│ ├── gemma_embedder.py # Embedding model wrapper -│ ├── qdrant_manager.py # Vector database operations -│ └── haystack_retriever.py # Haystack integration +├── rag/ +│ ├── _shared.py # get_shared_embedder() — singleton per process +│ ├── ingestion.py # index_framework (JSON + PDF → Qdrant) +│ └── retrieval.py # retrieve_control_details, RETRIEVE_CONTROL_DETAILS_TOOL_SCHEMA │ -├── prompts/ # Prompt management -│ └── prompt_generator.py # Generate evaluation prompts +├── utils/ +│ └── framework_utils.py # save_extraction_json, get_input_paths, list_framework_jsons, +│ # get_vector_db_pdf_paths │ -└── utils/ # Utilities and helpers - └── framework_utils.py # Framework data save/load +├── __init__.py # Package exports +└── ARCHITECTURE.md # This file ``` +**Entry point**: `main.py` (project root) — `setup_framework()`, `get_input_paths()`. + --- ## Module Details ### Core (`src/core/`) -**Purpose**: Contains the main business logic for framework extraction and document evaluation. - #### `framework_extractor.py` -**What it does**: Extracts compliance controls from framework PDF text using LLM. -**Key Functions**: -- `extract_controls_from_framework(pdf_text, framework_name)` - Main extraction function +**Purpose**: Extract compliance controls from raw PDF text using LLM (OpenRouter). -**When to modify**: -- To change how controls are extracted -- To adjust the extraction prompt structure -- To modify the output JSON schema -- To change the LLM model or parameters - -**Dependencies**: OpenAI API, requires `OPENAI_API_KEY` environment variable - -#### `framework_organizer.py` -**What it does**: Organizes multiple PDFs into sections using LLM content analysis and validates framework completeness. +**Key function**: `extract_controls_from_framework(pdf_text, framework_name, use_fallback_prompt=False) → dict` -**Key Functions**: -- `organize_pdfs_by_section(pdf_paths_list, framework_name)` - Analyzes PDF content to determine section names (part1, part2, rubric, etc.) -- `validate_framework_sections(organized_sections, framework_name)` - Validates sections for completeness, quality, keywords, and control patterns +- Returns `{"framework_name": str, "controls": [ {...}, ... ]}`. +- Each control has **exactly**: `id`, `description`, `calculation`, `threshold`, `scale` (strict JSON schema). +- `use_fallback_prompt=True`: stricter prompt that forbids empty controls (used for retries). **When to modify**: -- To change section identification logic -- To adjust validation criteria -- To modify section naming conventions -- To add custom validation checks +- Change extraction prompt or JSON schema. +- Switch LLM model (currently `openai/gpt-4.1` via OpenRouter). +- Adjust temperature (0.3 normal, 0.5 fallback) or response handling. -**Dependencies**: OpenAI API, requires `OPENAI_API_KEY` environment variable - -#### `framework_consolidator.py` -**What it does**: Extracts controls from each section and creates a master evaluation prompt consolidating all sections. +**Dependencies**: `openai`, `python-dotenv`. Requires `OPENROUTER_API_KEY`. -**Key Functions**: -- `extract_controls_from_sections(organized_sections, framework_name)` - Extracts controls from each section individually -- `create_master_prompt(section_controls, organized_sections, framework_name)` - Consolidates all sections into unified master prompt +--- -**When to modify**: -- To change master prompt structure -- To adjust consolidation logic -- To modify how controls are combined -- To change prompt formatting +#### `framework_consolidator.py` -**Dependencies**: OpenAI API, requires `OPENAI_API_KEY` environment variable +**Purpose**: Run extraction over multiple PDFs in parallel; retry with fallback prompt if a PDF yields no controls. -#### `framework_organizer.py` -**What it does**: Organizes multiple PDFs into sections using LLM content analysis and validates framework completeness. +**Key function**: `extract_controls_from_pdfs(pdf_paths_list, framework_name) → list[list[dict]]` -**Key Functions**: -- `organize_pdfs_by_section(pdf_paths_list, framework_name)` - Analyzes PDF content to determine section names (part1, part2, rubric, etc.) -- `validate_framework_sections(organized_sections, framework_name)` - Validates sections for completeness, quality, keywords, and control patterns +- One LLM call per PDF. Uses `ThreadPoolExecutor`. +- If a PDF returns empty controls, retries up to `MAX_RETRIES` (2) with `use_fallback_prompt=True`. **When to modify**: -- To change section identification logic -- To adjust validation criteria -- To modify section naming conventions -- To add custom validation checks +- Change parallelism (e.g. `max_workers`). +- Adjust retry count or retry logic. -**Dependencies**: OpenAI API, requires `OPENAI_API_KEY` environment variable - -#### `framework_consolidator.py` -**What it does**: Extracts controls from each section and creates a master evaluation prompt consolidating all sections. +**Dependencies**: `framework_extractor`, `processing.extract_text_from_pdf`. -**Key Functions**: -- `extract_controls_from_sections(organized_sections, framework_name)` - Extracts controls from each section individually -- `create_master_prompt(section_controls, organized_sections, framework_name)` - Consolidates all sections into unified master prompt +--- -**When to modify**: -- To change master prompt structure -- To adjust consolidation logic -- To modify how controls are combined -- To change prompt formatting +#### `evaluator.py` -**Dependencies**: OpenAI API, requires `OPENAI_API_KEY` environment variable +**Purpose**: Evaluate applicant documents against a framework using LLM. -#### `evaluator.py` -**What it does**: Evaluates applicant documents against saved frameworks using LLM. +**Key function**: `evaluate_applicant(applicant_docs, evaluation_prompt, controls_json) → dict` -**Key Functions**: -- `evaluate_applicant(applicant_docs, evaluation_prompt, controls_json)` - Main evaluation function +- Uses **externally provided** `evaluation_prompt` and `controls_json`. The extraction pipeline does **not** generate evaluation prompts. **When to modify**: -- To change evaluation logic -- To adjust evaluation prompt format -- To modify the evaluation report structure -- To change the LLM model or temperature settings +- Change evaluation logic or report structure. +- Switch LLM or parameters. -**Dependencies**: OpenAI API, requires `OPENAI_API_KEY` environment variable +**Dependencies**: OpenAI API, `OPENAI_API_KEY`. --- ### Processing (`src/processing/`) -**Purpose**: Handles document parsing and text preparation for further processing. - #### `pdf_parser.py` -**What it does**: Extracts text from PDF files with multilingual support (Arabic/English). -**Key Functions**: -- `extract_text_from_pdf(pdf_path)` - Extracts all text from a PDF file - - Used individually for each PDF when processing multiple files +**Key function**: `extract_text_from_pdf(pdf_path) → str` -**When to modify**: -- To change PDF extraction library (currently uses `pdfplumber`) -- To add support for other file formats (Word, images, etc.) -- To improve multilingual text extraction -- To add OCR capabilities +**When to modify**: Change PDF library (`pdfplumber`), add formats, OCR. -**Dependencies**: `pdfplumber` +--- #### `text_chunker.py` -**What it does**: Splits text into chunks with overlap for vector database storage. -**Key Functions**: -- `chunk_text(text, chunk_size, overlap, framework_name)` - Main chunking function -- `chunk_text_by_sentences(text, sentences_per_chunk)` - Alternative sentence-based chunking -- `_is_valid_chunk(text)` - Validates chunks (filters formatting artifacts) +**Key functions**: +- `chunk_text(text, chunk_size, overlap, framework_name, ...) → list[dict]` +- `chunk_text_by_sentences(text, sentences_per_chunk, ...) → list[dict]` -**When to modify**: -- To adjust chunk size or overlap parameters -- To change chunking strategy (by paragraphs, sections, etc.) -- To improve semantic boundary detection -- To modify chunk validation logic -- To add custom chunking algorithms +Chunks include `text`, `metadata` (e.g. `length`). Used by RAG ingestion for PDF chunking. -**Dependencies**: None (pure Python) +**When to modify**: Chunk size, overlap, or chunking strategy. --- ### Embeddings (`src/embeddings/`) -**Purpose**: Handles embedding generation and vector database operations. - #### `gemma_embedder.py` -**What it does**: Wraps Google EmbeddingGemma 300M model for generating embeddings. -**Key Classes**: -- `GemmaEmbedder` - Main embedder class +**Purpose**: Embedding model wrapper (Google EmbeddingGemma 300M via `sentence-transformers`). + +**Important**: At module load, **before** any HuggingFace imports: -**Key Methods**: -- `embed_text(text)` - Generate embedding for single text -- `embed_batch(texts, batch_size)` - Generate embeddings for multiple texts -- `get_embedding_dim()` - Get embedding dimension +```python +import os +os.environ["HF_HUB_OFFLINE"] = "1" # Set to "0" for first-time model download. +``` + +- **`"1"`** (default): Load from cache only — fast restarts, no hub calls. +- **`"0"`**: Use hub (e.g. first-time download). Change this line in the script when needed, then set back to `"1"`. + +**Key**: `GemmaEmbedder`, `load_gemma_embedder(model_name, device, token)`. Methods: `embed_text`, `embed_batch`, `get_embedding_dim`. **When to modify**: -- To change the embedding model (e.g., switch to different HuggingFace model) -- To adjust batch processing parameters -- To add GPU/CPU optimization -- To change authentication method -- To add caching for embeddings +- Change default model or device. +- Adjust batch size or auth (HF token). +- **Keep** `HF_HUB_OFFLINE` logic; update comment if behaviour changes. + +**Dependencies**: `sentence-transformers`, `torch`, `huggingface_hub`. -**Dependencies**: `sentence-transformers`, `torch`, `huggingface_hub` +--- #### `qdrant_manager.py` -**What it does**: Manages Qdrant vector database operations (local or remote). -**Key Functions**: -- `initialize_qdrant(collection_name, vector_size, path, url)` - Initialize client and create collection -- `add_documents(client, collection_name, documents, embeddings)` - Store documents with embeddings -- `search_similar(client, collection_name, query_embedding, top_k)` - Search for similar documents -- `create_collection(client, collection_name, vector_size)` - Create new collection -- `delete_collection(client, collection_name)` - Delete collection +**Purpose**: Qdrant vector DB — local (default) or remote. + +**Key functions**: +- `initialize_qdrant(collection_name, vector_size, path, url)` — create client and collection. +- `add_documents(client, collection_name, documents, embeddings)` — upsert. **Point IDs**: deterministic UUID from `chunk_id` via `uuid.uuid5` (required by Qdrant). `chunk_id` kept in payload. +- `search_similar(client, collection_name, query_embedding, top_k, score_threshold)` — vector search, no filter. +- `fetch_by_filter(client, collection_name, query_filter, limit)` — **filter-only** lookup (e.g. `control_id` + `source=json`). Uses `scroll`. +- `search_similar_filtered(client, collection_name, query_embedding, top_k, query_filter, score_threshold)` — vector search **with** payload filter. Uses `query_points`. **When to modify**: -- To change vector database (e.g., switch to Pinecone, Weaviate) -- To adjust batch size for document insertion -- To modify search parameters (distance metric, filters) -- To add collection management features -- To change storage location or configuration +- Change DB (e.g. Pinecone, Weaviate). +- Adjust batch size, distance metric, or filter behaviour. +- **Do not** use arbitrary strings as point IDs; keep UUID derivation from `chunk_id`. -**Dependencies**: `qdrant-client` +**Dependencies**: `qdrant-client`. -#### `haystack_retriever.py` -**What it does**: Integrates Haystack AI framework with Qdrant for document retrieval. +--- -**Key Classes**: -- `HaystackQdrantRetriever` - Retriever class combining Haystack and Qdrant +#### `haystack_retriever.py` -**When to modify**: -- To change retrieval strategy -- To integrate different Haystack components -- To modify document loading from Qdrant -- To add filtering or ranking logic +**Purpose**: Optional Haystack + Qdrant integration. -**Dependencies**: `haystack-ai`, `qdrant-client` +**When to modify**: Haystack-specific retrieval or pipeline changes. --- -### Prompts (`src/prompts/`) - -**Purpose**: Manages prompt generation for LLM interactions. +### RAG (`src/rag/`) -#### `prompt_generator.py` -**What it does**: Generates evaluation prompts from extracted controls JSON. +#### `_shared.py` -**Key Functions**: -- `generate_evaluation_prompt(controls_json, framework_name)` - Generate evaluation prompt +**Purpose**: Single embedder instance per process to avoid repeated model loads. -**When to modify**: -- To change prompt structure or format -- To adjust prompt generation instructions -- To modify LLM model or parameters -- To add prompt templates or variations -- To customize prompts for different framework types +**Key**: `get_shared_embedder() → GemmaEmbedder`. Caches on first use. Used by `index_framework` and `retrieve_control_details`. -**Dependencies**: OpenAI API, requires `OPENAI_API_KEY` environment variable +**When to modify**: Only if you change embedder lifecycle (e.g. multi-process). --- -### Utils (`src/utils/`) +#### `ingestion.py` -**Purpose**: Provides utility functions for data persistence and path management. +**Purpose**: Index a framework for RAG — JSON control cards + PDF chunks. -#### `framework_utils.py` -**What it does**: Handles saving/loading framework data and evaluation reports. +**Key function**: `index_framework(framework_name, pdf_paths=None) → None` -**Key Functions**: -- `save_framework_data(framework_name, controls_json, evaluation_prompt)` - Save framework to disk -- `load_framework_data(framework_name)` - Load framework from disk -- `list_saved_frameworks()` - List all saved frameworks -- `save_evaluation_report(evaluation_report, framework_name, applicant_name)` - Save evaluation report -- `get_input_paths()` - Get standard input directory paths +- **JSON cards**: From `config/frameworks/{framework_name}/*.json`. Each control → one document with `source=json`, `control_id`, `source_pdf` (stem). +- **PDF chunks**: From `data/inputs/vector_db/{framework_name}/*.pdf`, or `vector_db/*.pdf` if no subdir. Override with `pdf_paths` if provided. +- **Collection**: `{framework_name}_rag`. +- Uses `get_shared_embedder()`, `list_framework_jsons`, `get_vector_db_pdf_paths`, `extract_text_from_pdf`, `chunk_text`, `add_documents`. **When to modify**: -- To change storage location or format -- To add database integration (instead of file system) -- To modify file naming conventions -- To add metadata management -- To change data serialization format +- Change JSON vs PDF sourcing or payload schema. +- Change collection naming or embedder usage. -**Storage Locations**: -- Frameworks: `config/frameworks/{framework_name}/` -- Evaluation reports: `data/outputs/evaluations/` -- Input PDFs: `data/inputs/frameworks/` and `data/inputs/applicants/` -- Vector DB input PDFs: `data/inputs/vector_db/` +--- -**Dependencies**: None (standard library only) +#### `retrieval.py` ---- +**Purpose**: Retrieve control details by ID — JSON cards + relevant PDF chunks. **Agent-tool friendly.** -## Data Flow +**Key function**: `retrieve_control_details(control_id, framework_name, *, top_k_pdf=5) → dict` -### Framework Setup Pipeline +- **JSON**: `fetch_by_filter` with `control_id` + `source=json`. +- **PDF**: Semantic search over `source=pdf` + `framework_name` using `search_similar_filtered`. Query = `"Control {id}. {description_snippet}"`. -**Function**: `setup_framework(pdf_paths: str | list[str], framework_name: str)` +**Returns**: `{"json_cards": [...], "pdf_chunks": [...]}`. Each item has `text` and `metadata`; chunks also have `score`. -Extracts compliance controls from framework PDF(s) and generates evaluation prompts. Supports single PDF (backward compatible) or multiple PDFs with section organization and consolidation. +**Tool schema**: `RETRIEVE_CONTROL_DETAILS_TOOL_SCHEMA` — use for OpenAI tools, LangChain, etc. -```mermaid -flowchart TD - Start([setup_framework
pdf_paths, framework_name]) --> Normalize[Normalize Input
file/list/directory] - Normalize -->|pdf_paths_list| Check{Number of PDFs?} - - Check -->|Single PDF| SingleFlow[Single PDF Workflow
Backward Compatible] - SingleFlow --> Extract1[extract_text_from_pdf] - Extract1 --> LLM1[extract_controls_from_framework] - LLM1 --> Gen1[generate_evaluation_prompt] - Gen1 --> Save1[save_framework_data] - Save1 --> End1([Return controls_json, prompt]) - - Check -->|Multiple PDFs| MultiFlow[Multi-PDF Workflow] - MultiFlow --> Organize[organize_pdfs_by_section
framework_organizer.py] - Organize -->|LLM: Content Analysis| Sections[Organized Sections
part1, part2, rubric, etc.] - - Sections --> Validate[validate_framework_sections
framework_organizer.py] - Validate -->|LLM: Validation Checks| Valid{Validation
Pass?} - Valid -->|Fail| Error[Raise Exception
with details] - Valid -->|Pass| Extract2[extract_controls_from_sections
framework_consolidator.py] - - Extract2 -->|For each section| SectionControls[Section Controls JSON
per section] - SectionControls --> Consolidate[create_master_prompt
framework_consolidator.py] - Consolidate -->|LLM: Consolidate All| MasterPrompt[Master Evaluation Prompt
All sections combined] - - MasterPrompt --> ConsolidateControls[Consolidate Controls JSON
Combine all sections] - ConsolidateControls --> Metadata[Prepare Metadata
sections, validation, keywords] - Metadata --> Save2[save_framework_data
with metadata] - Save2 -->|Save Files| Files[config/frameworks/framework_name/
- controls.json
- evaluation_prompt.txt
- framework_metadata.json] - Files --> End2([Return consolidated_controls,
master_prompt, metadata]) - - style Start fill:#e1f5ff - style End1 fill:#e1f5ff - style End2 fill:#e1f5ff - style Organize fill:#fff4e6 - style Validate fill:#fff4e6 - style Extract2 fill:#fff4e6 - style Consolidate fill:#fff4e6 - style Error fill:#ffccbc - style Files fill:#e8f5e9 -``` +**When to modify**: +- Change filter logic, query construction, or `top_k_pdf`. +- Update tool schema if API changes. + +--- + +### Utils (`src/utils/`) -**Detailed Steps (Multi-PDF Workflow)**: -1. **Input Normalization**: Normalize to list of PDF paths (handles single file/list/directory) -2. **Section Organization**: `organize_pdfs_by_section()` → LLM analyzes each PDF content to determine section names (part1, part2, rubric, etc.) -3. **Validation**: `validate_framework_sections()` → LLM validates: - - Completeness (all required sections present) - - Text quality (readable, not corrupted) - - Keywords (compliance keywords detected) - - Control patterns (compliance patterns identified) - - Structure (proper document structure) -4. **Control Extraction**: `extract_controls_from_sections()` → Extract controls from each section individually -5. **Master Prompt Creation**: `create_master_prompt()` → LLM consolidates all sections into unified master prompt with: - - Full text from each section - - Combined controls - - Unified evaluation rules - - Rubric/scoring criteria -6. **Consolidation**: Combine all section controls into single controls JSON -7. **Save**: `save_framework_data()` → saves controls, master prompt, and metadata to `config/frameworks/{framework_name}/` - -**Note**: Single PDF workflow maintains backward compatibility. The same PDF file(s) can be indexed into the vector database using `index_framework_documents()`. +#### `framework_utils.py` + +**Key functions**: +- `save_extraction_json(framework_name, pdf_path, controls_json) → Path` — save under `config/frameworks/{framework_name}/{stem}.json`. +- `get_input_paths() → dict` — `frameworks`, `applicants`, `vector_db` under `data/inputs/`. +- `list_framework_jsons(framework_name) → list[(stem, dict)]` — load all `*.json` for a framework. +- `get_vector_db_pdf_paths(framework_name) → list[Path]` — PDFs in `data/inputs/vector_db/`; prefers `vector_db/{framework_name}/` then flat `vector_db/*.pdf`. + +**When to modify**: Storage paths, file naming, or vector_db resolution. --- -### Document Indexing Pipeline +## Data Flows -**Function**: `index_framework_documents(pdf_paths: str | list[str], framework_name: str)` +### 1. Extraction pipeline (`setup_framework`) -Processes PDF documents and stores them in a vector database. **The same PDF files from Framework Setup can be indexed here.** +**Function**: `setup_framework(pdf_paths: str | list[str], framework_name: str) → list[Path]` ```mermaid flowchart TD - A[PDF Paths
file/list/dir] -->|Normalize| B[PDF List] - B -->|For each PDF| C[Extract Text] - C -->|chunk_text| D[Chunks with Metadata] - D -->|Collect All| E[All Chunks] - E -->|embed_batch| F[Embeddings] - F -->|add_documents| G[Qdrant Vector DB] + A[pdf_paths] --> B[Normalize to list] + B --> C[extract_controls_from_pdfs] + C --> D[For each PDF: extract_text → LLM extract] + D --> E[Retry with fallback if empty] + E --> F[save_extraction_json per PDF] + F --> G[config/frameworks/name/stem.json] ``` **Steps**: -1. Normalize input (single file/list/directory) → `pdf_paths_list` -2. For each PDF: extract text → chunk → add source metadata -3. Generate embeddings for all chunks (batch) -4. Store in Qdrant at `config/vector_db/collection/{framework_name}_chunks/` +1. Normalize input (file, list, or directory) → list of PDF paths. +2. `extract_controls_from_pdfs` → parallel extraction, retry on empty. +3. For each PDF: `{"framework_name", "controls"}` → `save_extraction_json` → `config/frameworks/{framework_name}/{stem}.json`. -**Returns**: `(qdrant_client, collection_name, embedder)` +**No** composition, evaluation prompt generation, or framework metadata files. --- -### Evaluation Pipeline +### 2. RAG indexing (`index_framework`) -**Function**: `evaluate_applicant_documents(applicant_pdf_paths: list[str], framework_name: str, applicant_name: str = None, save_report: bool = True)` - -Evaluates applicant documents against a previously set up framework. +**Function**: `index_framework(framework_name, pdf_paths=None)` ```mermaid flowchart TD - Start([evaluate_applicant_documents
applicant_pdf_paths, framework_name]) --> Load[load_framework_data
framework_utils.py] - - Load -->|Load from disk| Framework[config/frameworks/framework_name/
- controls.json
- evaluation_prompt.txt] - Framework -->|controls_json: dict
evaluation_prompt: str| Loaded[Framework Data Loaded] - - Start --> ExtractLoop[For each PDF in applicant_pdf_paths] - ExtractLoop -->|extract_text_from_pdf
pdf_parser.py| Extract[Extract Text from PDF] - Extract -->|doc_text: str| Collect[Collect All Texts] - Collect -->|applicant_docs: list| Docs[All Applicant Documents] - - Loaded --> Eval[evaluate_applicant
evaluator.py] - Docs --> Eval - - Eval -->|LLM Call: OpenAI GPT-4o| LLM[Evaluate Documents
Against Framework] - LLM -->|Uses evaluation_prompt
and controls_json| Analyze[Compare Applicant Docs
with Framework Controls] - Analyze -->|evaluation_report: dict| Report[Evaluation Report
- Compliance Status
- Control Assessments
- Recommendations] - - Report --> Check{save_report
== True?} - Check -->|Yes| Save[save_evaluation_report
framework_utils.py] - Check -->|No| End1([Return Report]) - Save -->|Save to file| Output[data/outputs/evaluations/
framework_name_applicant_name_timestamp.json] - Output --> End2([Return Report]) - - style Start fill:#e1f5ff - style End1 fill:#e1f5ff - style End2 fill:#e1f5ff - style LLM fill:#fff4e6 - style Analyze fill:#fff4e6 - style Framework fill:#e8f5e9 - style Output fill:#e8f5e9 + A[framework_name] --> B[get_shared_embedder] + B --> C[JSON: list_framework_jsons] + C --> D[PDF: get_vector_db_pdf_paths or pdf_paths] + D --> E[extract_text_from_pdf + chunk_text] + E --> F[Build docs: json cards + pdf chunks] + F --> G[embed_batch] + G --> H[add_documents → Qdrant] + H --> I[Collection: name_rag] ``` -**Detailed Steps**: -1. **Load Framework**: `load_framework_data(framework_name)` → loads `controls_json` and `evaluation_prompt` from saved files -2. **Extract Applicant Text**: For each PDF in `applicant_pdf_paths`: - - `extract_text_from_pdf(pdf_path)` → extracts text - - Collects all texts into `applicant_docs` list -3. **Evaluate**: `evaluate_applicant(applicant_docs, evaluation_prompt, controls_json)` → LLM: - - Uses the evaluation prompt as instructions - - Compares applicant documents against framework controls - - Generates compliance assessment for each control -4. **Save Report** (optional): `save_evaluation_report()` → saves JSON report to `data/outputs/evaluations/` +**Storage**: +- **JSON cards**: `config/frameworks/{framework_name}/*.json`. +- **PDFs**: `data/inputs/vector_db/{framework_name}/` or `vector_db/`. --- -## Common Modification Scenarios - -### Change LLM Model - -**Files to modify**: -- `core/framework_extractor.py` - Line 92: `model="gpt-4o"` -- `core/evaluator.py` - Line 60: `model="gpt-4o"` -- `prompts/prompt_generator.py` - Line 61: `model="gpt-4o"` - -### Adjust Chunking Strategy - -**Files to modify**: -- `processing/text_chunker.py` - Modify `chunk_text()` function -- Default parameters: `chunk_size=1500`, `overlap=200` - -### Change Embedding Model +### 3. RAG retrieval (`retrieve_control_details`) -**Files to modify**: -- `embeddings/gemma_embedder.py` - Line 22: `model_name="google/embeddinggemma-300m"` -- Update `get_embedding_dim()` return value if dimension changes +**Function**: `retrieve_control_details(control_id, framework_name, top_k_pdf=5)` -### Modify Storage Location - -**Files to modify**: -- `utils/framework_utils.py` - Update path construction in all functions -- `embeddings/qdrant_manager.py` - Line 35: Update default path +```mermaid +flowchart TD + A[control_id, framework_name] --> B[get_shared_embedder] + B --> C[fetch_by_filter: control_id + source=json] + C --> D[json_cards] + B --> E[Query: Control id + description snippet] + E --> F[embed_text] + F --> G[search_similar_filtered: source=pdf, framework_name] + G --> H[pdf_chunks] + D --> I[Return json_cards + pdf_chunks] + H --> I +``` -### Add New File Format Support +Use `RETRIEVE_CONTROL_DETAILS_TOOL_SCHEMA` when registering as an agent tool. -**Files to modify**: -- `processing/pdf_parser.py` - Add new extraction function -- Update `main.py` to use new function +--- -### Change Evaluation Criteria +## Common Modification Scenarios -**Files to modify**: -- `prompts/prompt_generator.py` - Modify prompt generation instructions -- `core/evaluator.py` - Modify evaluation request format +| Goal | Files to change | +|------|------------------| +| **LLM model (extraction)** | `core/framework_extractor.py` — model, base_url | +| **LLM model (evaluation)** | `core/evaluator.py` | +| **Embedding model** | `embeddings/gemma_embedder.py` — `model_name`, `get_embedding_dim` | +| **Chunking** | `processing/text_chunker.py` — `chunk_text` params | +| **HF offline default** | `embeddings/gemma_embedder.py` — `HF_HUB_OFFLINE` line + comment | +| **Storage paths** | `utils/framework_utils.py` | +| **Qdrant path** | `embeddings/qdrant_manager.py` — `initialize_qdrant` default path | +| **RAG JSON/PDF sources** | `rag/ingestion.py`, `utils/framework_utils.py` | +| **Retrieval filters / top_k** | `rag/retrieval.py` | +| **Tool schema for agents** | `rag/retrieval.py` — `RETRIEVE_CONTROL_DETAILS_TOOL_SCHEMA` | --- ## Environment Variables -Required environment variables: - -- `OPENAI_API_KEY` - Used by: - - `core/framework_extractor.py` - - `core/evaluator.py` - - `prompts/prompt_generator.py` - -- `HF_TOKEN` or `HUGGINGFACE_TOKEN` - Used by: - - `embeddings/gemma_embedder.py` (for gated models) +| Variable | Used by | Purpose | +|----------|---------|---------| +| `OPENROUTER_API_KEY` | `framework_extractor.py` | LLM extraction (OpenRouter) | +| `OPENAI_API_KEY` | `evaluator.py` | Applicant evaluation | +| `HF_TOKEN` or `HUGGINGFACE_TOKEN` | `gemma_embedder.py` | Gated models (when not offline) | +| `HF_HUB_OFFLINE` | Set in `gemma_embedder.py` | `"1"` cache-only, `"0"` hub access | --- ## Dependencies Overview -| Module | Key Dependencies | -|--------|------------------| +| Module | Key dependencies | +|--------|-------------------| | `core/` | `openai`, `python-dotenv` | | `processing/` | `pdfplumber` | -| `embeddings/` | `sentence-transformers`, `torch`, `qdrant-client`, `haystack-ai` | -| `prompts/` | `openai`, `python-dotenv` | -| `utils/` | None (standard library) | - ---- - -## Testing and Debugging Tips - -1. **Test PDF parsing**: Use `processing/pdf_parser.py` directly with a test PDF -2. **Test chunking**: Pass sample text to `processing/text_chunker.py` functions -3. **Test embeddings**: Create `GemmaEmbedder()` instance and test `embed_text()` -4. **Test Qdrant**: Use `embeddings/qdrant_manager.py` functions with test data -5. **Test LLM calls**: Run individual functions from `core/` and `prompts/` modules +| `embeddings/` | `sentence-transformers`, `torch`, `qdrant-client`, `huggingface_hub` | +| `rag/` | (uses `embeddings`, `utils`, `processing`) | +| `utils/` | stdlib only | --- -## Entry Point - -The main entry point is `main.py` in the project root, which demonstrates the complete pipeline: +## Entry Points and Imports -- `setup_framework()` - Setup a new framework -- `index_framework_documents(pdf_paths, framework_name)` - Index documents for vector search - - Supports single file path, list of file paths, or directory path - - Each PDF is processed individually (extract → chunk) - - All chunks are embedded together and stored in Qdrant - - Each chunk includes source PDF metadata -- `evaluate_applicant_documents()` - Evaluate applicant documents +**`main.py`**: +- `setup_framework(pdf_paths, framework_name)` — extract + save per-PDF JSON. +- `get_input_paths()` — frameworks, applicants, vector_db dirs. -All functions can be imported and used independently: +**Typical imports**: ```python -from src.core import extract_controls_from_framework, evaluate_applicant -from src.processing import extract_text_from_pdf, chunk_text -from src.embeddings import GemmaEmbedder, initialize_qdrant +from src import ( + setup_framework, + extract_controls_from_framework, + extract_controls_from_pdfs, + index_framework, + retrieve_control_details, + RETRIEVE_CONTROL_DETAILS_TOOL_SCHEMA, + get_shared_embedder, + save_extraction_json, + get_input_paths, + list_framework_jsons, + get_vector_db_pdf_paths, + extract_text_from_pdf, + chunk_text, +) ``` --- -## Notes - -- All paths are relative to project root (`services/ai-service/`) -- Configuration and data directories are created automatically -- Error handling is minimal - exceptions propagate to caller -- No logging framework - add if needed for production use +## Implementation Notes (memorise for edits) +1. **Extraction is extract-only**: One JSON per PDF. No master composition, no evaluation prompt generation. +2. **Control schema**: Exactly `id`, `description`, `calculation`, `threshold`, `scale`. Enforced in `framework_extractor` prompts. +3. **Retries**: Empty extraction → up to 2 retries with `use_fallback_prompt=True` in `framework_consolidator`. +4. **Embedder**: `HF_HUB_OFFLINE=1` in script by default. Set to `"0"` only for first-time download; then revert. +5. **Shared embedder**: `get_shared_embedder()` used by both `index_framework` and `retrieve_control_details`. Single load per process. +6. **Qdrant point IDs**: Must be UUIDs. Use `uuid.uuid5(namespace, chunk_id)`. Store `chunk_id` in payload. +7. **RAG collection**: `{framework_name}_rag`. Payload includes `source` (`json` | `pdf`), `control_id`, `framework_name`, `source_pdf`. +8. **Vector DB PDF input**: `data/inputs/vector_db/`. Prefer `vector_db/{framework_name}/*.pdf`, else `vector_db/*.pdf`. +9. **Framework JSON output**: `config/frameworks/{framework_name}/{pdf_stem}.json`. +--- +*Paths are relative to project root `services/ai-service/`. Configuration and data directories are created as needed. Use this document as the primary reference when editing the codebase.* diff --git a/services/ai-service/src/__init__.py b/services/ai-service/src/__init__.py index 152ff5e..707f01b 100644 --- a/services/ai-service/src/__init__.py +++ b/services/ai-service/src/__init__.py @@ -1,11 +1,15 @@ """ -Compliance Framework Evaluation System +Compliance Framework Extraction System """ __version__ = "0.1.0" # Core exports -from .core import evaluate_applicant, extract_controls_from_framework +from .core import ( + evaluate_applicant, + extract_controls_from_framework, + extract_controls_from_pdfs, +) # Embedding exports - LAZY LOADED to avoid slow startup # These imports heavy ML libraries (sentence-transformers, sklearn, etc.) @@ -18,36 +22,48 @@ # Processing exports from .processing import ( - extract_text_from_pdf, - chunk_text, - chunk_text_by_sentences + extract_text_from_pdf, + chunk_text, + chunk_text_by_sentences, ) -# Prompt exports -from .prompts import generate_evaluation_prompt - # Utils exports from .utils import ( - save_framework_data, - load_framework_data, - list_saved_frameworks, - save_evaluation_report, - get_input_paths + save_extraction_json, + get_input_paths, + list_framework_jsons, + get_vector_db_pdf_paths, +) + +# RAG exports (index JSON + PDF, retrieve by control ID; agent-tool friendly) +from .rag import ( + index_framework, + retrieve_control_details, + RETRIEVE_CONTROL_DETAILS_TOOL_SCHEMA, + get_shared_embedder, ) __all__ = [ # Core - "evaluate_applicant", "extract_controls_from_framework", + "evaluate_applicant", + "extract_controls_from_framework", + "extract_controls_from_pdfs", # Embeddings - Note: Import directly from src.embeddings when needed # "GemmaEmbedder", "load_gemma_embedder", # "initialize_qdrant", "add_documents", "search_similar", # "HaystackQdrantRetriever", "create_retrieval_pipeline", # Processing "extract_text_from_pdf", - "chunk_text", "chunk_text_by_sentences", - # Prompts - "generate_evaluation_prompt", + "chunk_text", + "chunk_text_by_sentences", # Utils - "save_framework_data", "load_framework_data", "list_saved_frameworks", - "save_evaluation_report", "get_input_paths" + "save_extraction_json", + "get_input_paths", + "list_framework_jsons", + "get_vector_db_pdf_paths", + # RAG + "index_framework", + "retrieve_control_details", + "RETRIEVE_CONTROL_DETAILS_TOOL_SCHEMA", + "get_shared_embedder", ] diff --git a/services/ai-service/src/core/framework_consolidator.py b/services/ai-service/src/core/framework_consolidator.py index 5706392..ca24cb5 100644 --- a/services/ai-service/src/core/framework_consolidator.py +++ b/services/ai-service/src/core/framework_consolidator.py @@ -1,146 +1,53 @@ """ Framework Consolidator Module -Merges controls from multiple sections into a master framework +Extracts controls from multiple PDFs in parallel. """ -import json from typing import Dict, Any, List -from openai import OpenAI -import os -from dotenv import load_dotenv -from src.processing import extract_text_from_pdf -from src.core.framework_extractor import extract_controls_from_framework from concurrent.futures import ThreadPoolExecutor -load_dotenv() +from src.processing import extract_text_from_pdf +from src.core.framework_extractor import extract_controls_from_framework -def extract_controls_from_pdfs(pdf_paths_list: List[str], framework_name: str) -> List[List[Dict[str, Any]]]: +def extract_controls_from_pdfs( + pdf_paths_list: List[str], framework_name: str +) -> List[List[Dict[str, Any]]]: """ Extract controls from multiple PDFs in parallel. Each PDF gets one LLM call. - + Args: pdf_paths_list: List of PDF file paths framework_name: Name of the framework - + Returns: List of controls arrays (one per PDF) """ + + MAX_RETRIES = 2 + def extract_pdf(pdf_path: str): - """Extract controls from a single PDF.""" + """Extract controls from a single PDF. Retries with fallback prompt if empty.""" try: pdf_text = extract_text_from_pdf(pdf_path) controls_json = extract_controls_from_framework(pdf_text, framework_name) - return controls_json.get("controls", []) + controls = controls_json.get("controls", []) + retries = 0 + while len(controls) == 0 and retries < MAX_RETRIES: + retries += 1 + print(f"Empty controls for {pdf_path}, retry {retries}/{MAX_RETRIES} with fallback prompt") + controls_json = extract_controls_from_framework( + pdf_text, framework_name, use_fallback_prompt=True + ) + controls = controls_json.get("controls", []) + print(f"Controls extracted from {pdf_path}") + return controls except Exception as e: print(f"Error extracting from {pdf_path}: {e}") return [] - - # Process all PDFs in parallel + with ThreadPoolExecutor(max_workers=len(pdf_paths_list)) as executor: controls_arrays = list(executor.map(extract_pdf, pdf_paths_list)) - - return controls_arrays - -def compose_master_framework(json_list: List[Dict[str, Any]], framework_name: str) -> Dict[str, Any]: - """ - Merge multiple JSON arrays into a single Master Framework. - - Args: - json_list: List of controls arrays from different sections - framework_name: Name of the framework - - Returns: - Single master framework JSON object - """ - api_key = os.getenv("OPENROUTER_API_KEY") - if not api_key: - raise ValueError("OPENROUTER_API_KEY not found") - - client = OpenAI( - api_key=api_key, - base_url="https://openrouter.ai/api/v1" - ) - - # Prepare JSON for prompt - json_list_str = json.dumps(json_list, indent=2, ensure_ascii=False) - if len(json_list_str) > 50000: - json_list_str = json_list_str[:50000] + "\n\n[Data truncated...]" - - composition_prompt = f"""### ROLE: JSON Integrator & Data Architect -### TASK: Merge these {len(json_list)} JSON arrays into a single Master Framework. -### INSTRUCTIONS: -1. Take all controls from the input arrays and combine them into a single flat array. -2. De-duplicate IDs: If a Control exists in multiple files, merge the descriptions (keep the most complete version). -3. Return a JSON object with this structure: - {{ - "framework_name": "{framework_name}", - "controls": [array of all merged controls] - }} -4. DO NOT create Domain structures or nested hierarchies - just a flat array of controls. -### INPUT DATA: -{json_list_str} - -### OUTPUT -Return ONLY a JSON object with "framework_name" and "controls" keys. The "controls" must be a flat array of all controls from the input.""" - - response = client.chat.completions.create( - model="openai/gpt-4.1", - messages=[ - { - "role": "system", - "content": "You are a JSON Integrator. Merge multiple JSON arrays into a single master framework. Return valid JSON only." - }, - { - "role": "user", - "content": composition_prompt - } - ], - temperature=0.3, - response_format={"type": "json_object"} - ) - - result_text = response.choices[0].message.content - print("\n" + "="*60) - print("COMPOSITION RESPONSE:") - print("="*60) - print(result_text) - print("="*60 + "\n") - - master_framework = json.loads(result_text) - - # Find controls array from any key - controls_array = [] - if isinstance(master_framework, dict): - # Check common keys first - for key in ['controls', 'compliance_controls', 'items', 'data']: - if key in master_framework and isinstance(master_framework[key], list): - controls_array = master_framework[key] - break - - # If Domain structure, extract controls from it - if not controls_array and 'Domain' in master_framework: - domain_list = master_framework['Domain'] - if isinstance(domain_list, list): - # Flatten controls from all domains - for domain in domain_list: - if isinstance(domain, dict) and 'Controls' in domain: - if isinstance(domain['Controls'], list): - controls_array.extend(domain['Controls']) - - # If still not found, find any array - if not controls_array: - for value in master_framework.values(): - if isinstance(value, list) and len(value) > 0: - # Check if it's a list of control objects (have 'id' field) - if all(isinstance(item, dict) and 'id' in item for item in value): - controls_array = value - break - - # Normalize to controls key - master_framework["framework_name"] = framework_name - master_framework["controls"] = controls_array - - return master_framework + return controls_arrays diff --git a/services/ai-service/src/core/framework_extractor.py b/services/ai-service/src/core/framework_extractor.py index 6049a4a..fb51528 100644 --- a/services/ai-service/src/core/framework_extractor.py +++ b/services/ai-service/src/core/framework_extractor.py @@ -12,74 +12,105 @@ load_dotenv() -def extract_controls_from_framework(pdf_text: str, framework_name: str) -> Dict[str, Any]: +def extract_controls_from_framework( + pdf_text: str, framework_name: str, use_fallback_prompt: bool = False +) -> Dict[str, Any]: """ Extract compliance controls from PDF text using LLM. - + Args: pdf_text: Extracted text from framework PDF framework_name: Name of the framework - + use_fallback_prompt: If True, use a stricter prompt that forbids empty controls (for retries). + Returns: Dictionary with controls array """ api_key = os.getenv("OPENROUTER_API_KEY") if not api_key: raise ValueError("OPENROUTER_API_KEY not found") - + client = OpenAI( api_key=api_key, base_url="https://openrouter.ai/api/v1" ) - - extraction_prompt = f""" -### ROLE -Senior Regulatory Data Architect specializing in GRC (Governance, Risk, and Compliance) systems. -### TASK -Perform a high-fidelity extraction of compliance controls, specifications, and performance metrics from the provided document. - -### SCHEMA CONSTRAINTS -For each identified item, you must populate the following JSON structure: -- "id": Unique alphanumeric identifier (e.g., DSI.OE.01, DG.1). -- "title": Original title from the text. -- "type": Classify as 'Administrative Policy', 'Technical Control', or 'Performance Metric'. -- "semantic_intent": The underlying goal or risk this item addresses (often found in "This aims to..." sections). -- "description": A clear, concise description of this measurement or control—what it measures, how it applies, and what it covers. -- "requirements": A list of specific, actionable conditions that must be met. -- "evaluation_logic": {{ - "calculation": "Formula or logic for measurement (if applicable)", - "threshold": "The minimum passing score or condition (e.g., 70%, 10 days)", - "scale": "Scoring intervals (e.g., 0-5 scale criteria)" -}} -- "evidence_suggested": Examples of artifacts needed to prove compliance (e.g., API logs, DMO charter). + schema_and_doc = f""" +### STRICT JSON SCHEMA +Return a JSON object with a single key "controls" whose value is an array. Each control object must have exactly these 5 fields: +- "id": string — Unique alphanumeric identifier (e.g., DSI.OE.01, DG.1). +- "description": string — Clear description of what the control measures, how it applies, and what it covers. +- "calculation": string — Formula or logic for measurement (use empty string "" if not applicable). +- "threshold": string — The minimum passing score or condition (e.g., "70%", "10 days"). Use empty string "" if not applicable. +- "scale": string — Scoring intervals or criteria (e.g., "0-5 scale"). Use empty string "" if not applicable. -### EXTRACTION RULES -1. **No Summarization:** Extract the full technical detail. Do not paraphrase. -2. **Multilingual Mapping:** If a control is in Arabic, maintain the 'title' in Arabic but provide a technical English summary in 'semantic_intent'. -3. **Description:** For every control, write a "description" that explains what the measurement covers, how it applies, and what it measures. Keep it concise but complete. -4. **Hierarchy:** If a control has sub-specifications, nest them within a 'sub_controls' array. +Example: +{{ + "controls": [ + {{ + "id": "DSI.OE.01", + "description": "Measures X; applies to Y.", + "calculation": "count(events) / total * 100", + "threshold": "70%", + "scale": "0-5" + }} + ] +}} ### DOCUMENT CONTENT: {pdf_text} +""" + + if use_fallback_prompt: + extraction_prompt = f""" +### ROLE +Senior Regulatory Data Architect specializing in GRC (Governance, Risk, and Compliance) systems. + +### CRITICAL — RETRY AFTER EMPTY EXTRACTION +A previous extraction attempt for this document returned no controls. You MUST extract at least one control. + +- Treat every policy clause, requirement, checklist item, or distinct section as a control. +- Use section headings, numbering, or paragraph labels as control IDs (e.g. "Section 3.1", "Requirement A", "Policy-1"). +- If the document is a form or list, each item is a control. +- Return an empty "controls" array ONLY if the document is completely blank or non-text (e.g. images only). +{schema_and_doc} + +### OUTPUT +Return ONLY a valid JSON object matching the schema above. No other keys or fields. +""" + system_content = ( + "You are a Senior Regulatory Data Architect. Extract compliance controls. " + "Never return an empty 'controls' array unless the document has no extractable content. " + "Return ONLY valid JSON with a 'controls' array. Each control must have exactly: id, description, calculation, threshold, scale." + ) + temperature = 0.5 + else: + extraction_prompt = f""" +### ROLE +Senior Regulatory Data Architect specializing in GRC (Governance, Risk, and Compliance) systems. + +### TASK +Extract compliance controls, specifications, and performance metrics from the provided document. Output strictly adheres to the JSON schema below. +{schema_and_doc} + +### RULES +1. Extract the full technical detail. Do not paraphrase. +2. Every control in the document must appear in the "controls" array with exactly the 5 fields above. +3. Use empty string "" for calculation, threshold, or scale when the document does not specify them. ### OUTPUT -Return ONLY a valid JSON object. Ensure every ID in the document is represented. +Return ONLY a valid JSON object matching the schema above. No other keys or fields. """ + system_content = "You are a Senior Regulatory Data Architect. Extract compliance controls. Return ONLY valid JSON with a 'controls' array. Each control must have exactly: id, description, calculation, threshold, scale." + temperature = 0.3 response = client.chat.completions.create( model="openai/gpt-4.1", messages=[ - { - "role": "system", - "content": "You are a Senior Regulatory Data Architect. Extract compliance controls and return valid JSON only." - }, - { - "role": "user", - "content": extraction_prompt - } + {"role": "system", "content": system_content}, + {"role": "user", "content": extraction_prompt}, ], - temperature=0.3, + temperature=temperature, response_format={"type": "json_object"} ) diff --git a/services/ai-service/src/embeddings/__init__.py b/services/ai-service/src/embeddings/__init__.py index 9d4413a..e65a290 100644 --- a/services/ai-service/src/embeddings/__init__.py +++ b/services/ai-service/src/embeddings/__init__.py @@ -3,8 +3,10 @@ initialize_qdrant, add_documents, search_similar, + search_similar_filtered, + fetch_by_filter, create_collection, - delete_collection + delete_collection, ) from .haystack_retriever import ( HaystackQdrantRetriever, @@ -13,8 +15,16 @@ ) __all__ = [ - "GemmaEmbedder", "load_gemma_embedder", - "initialize_qdrant", "add_documents", "search_similar", - "create_collection", "delete_collection", - "HaystackQdrantRetriever", "create_retrieval_pipeline", "retrieve_documents" + "GemmaEmbedder", + "load_gemma_embedder", + "initialize_qdrant", + "add_documents", + "search_similar", + "search_similar_filtered", + "fetch_by_filter", + "create_collection", + "delete_collection", + "HaystackQdrantRetriever", + "create_retrieval_pipeline", + "retrieve_documents", ] diff --git a/services/ai-service/src/embeddings/gemma_embedder.py b/services/ai-service/src/embeddings/gemma_embedder.py index 651e04e..8d7b06b 100644 --- a/services/ai-service/src/embeddings/gemma_embedder.py +++ b/services/ai-service/src/embeddings/gemma_embedder.py @@ -1,18 +1,22 @@ """ Gemma Embedder Module -Uses Google EmbeddingGemma 300M model for generating embeddings -This is specifically designed for embeddings (not text generation) -Supports HuggingFace token authentication for gated models - +Uses Google EmbeddingGemma 300M model for generating embeddings. +Supports HuggingFace token authentication for gated models. Model: https://huggingface.co/google/embeddinggemma-300m """ -from sentence_transformers import SentenceTransformer -from typing import List, Optional import os + +os.environ["HF_HUB_OFFLINE"] = "1" # Set to "0" for first-time model download. + import torch +from sentence_transformers import SentenceTransformer +from typing import List, Optional + from huggingface_hub import login +_LOGIN_DONE = False + class GemmaEmbedder: """Wrapper for Google EmbeddingGemma 300M embedding model.""" @@ -41,13 +45,18 @@ def __init__( def _load_model(self): """Load the EmbeddingGemma model using sentence-transformers.""" - if self.token: + global _LOGIN_DONE + offline = os.getenv("HF_HUB_OFFLINE", "").strip().lower() == "1" + local_files_only = offline + + if not offline and self.token and not _LOGIN_DONE: login(token=self.token) - - model_kwargs = {} - if self.token: + _LOGIN_DONE = True + + model_kwargs: dict = {"local_files_only": local_files_only} + if self.token and not offline: model_kwargs["token"] = self.token - + try: self.model = SentenceTransformer( self.model_name, diff --git a/services/ai-service/src/embeddings/qdrant_manager.py b/services/ai-service/src/embeddings/qdrant_manager.py index e0b6073..14435b7 100644 --- a/services/ai-service/src/embeddings/qdrant_manager.py +++ b/services/ai-service/src/embeddings/qdrant_manager.py @@ -4,8 +4,16 @@ """ from qdrant_client import QdrantClient -from qdrant_client.models import Distance, VectorParams, PointStruct, ScoredPoint -from typing import List, Dict, Optional +from qdrant_client.models import ( + Distance, + VectorParams, + PointStruct, + ScoredPoint, + Filter, + FieldCondition, + MatchValue, +) +from typing import List, Dict, Optional, Any from pathlib import Path import uuid @@ -100,20 +108,21 @@ def add_documents( if len(documents) != len(embeddings): raise ValueError(f"Number of documents ({len(documents)}) must match number of embeddings ({len(embeddings)})") + namespace = uuid.UUID("a0e8520b-12b4-5f3d-9c7e-8a1b2c3d4e5f") points = [] for doc, embedding in zip(documents, embeddings): - point_id = doc.get("chunk_id", str(uuid.uuid4())) - - # Prepare payload with metadata + chunk_id = doc.get("chunk_id") or str(uuid.uuid4()) + point_uuid = uuid.uuid5(namespace, str(chunk_id)) + payload = { "text": doc.get("text", ""), - "chunk_id": doc.get("chunk_id", point_id), + "chunk_id": chunk_id, "framework_name": doc.get("framework_name", "unknown"), **doc.get("metadata", {}) } - + point = PointStruct( - id=point_id, + id=point_uuid, vector=embedding, payload=payload ) @@ -240,13 +249,94 @@ def search_similar( return results +def fetch_by_filter( + client: QdrantClient, + collection_name: str, + query_filter: Filter, + limit: int = 1000, +) -> List[Dict[str, Any]]: + """ + Fetch points matching a filter (no vector search). Used for exact lookups + e.g. control_id + source=json. + + Returns: + List of {"text": str, "metadata": dict} for each matching point. + """ + results, _ = client.scroll( + collection_name=collection_name, + scroll_filter=query_filter, + limit=limit, + with_payload=True, + with_vectors=False, + ) + out: List[Dict[str, Any]] = [] + for point in results: + payload = getattr(point, "payload", {}) or {} + out.append({ + "text": payload.get("text", ""), + "metadata": { + "chunk_id": payload.get("chunk_id"), + "framework_name": payload.get("framework_name", "unknown"), + **{k: v for k, v in payload.items() + if k not in ("text", "chunk_id", "framework_name")}, + }, + }) + return out + + +def search_similar_filtered( + client: QdrantClient, + collection_name: str, + query_embedding: List[float], + top_k: int = 5, + query_filter: Optional[Filter] = None, + score_threshold: Optional[float] = None, +) -> List[Dict[str, Any]]: + """ + Vector similarity search with optional payload filter. + + Returns: + List of {"text": str, "score": float, "metadata": dict}. + """ + kwargs: Dict[str, Any] = { + "collection_name": collection_name, + "query": query_embedding, + "limit": top_k, + "with_payload": True, + "with_vectors": False, + } + if query_filter is not None: + kwargs["query_filter"] = query_filter + if score_threshold is not None: + kwargs["score_threshold"] = score_threshold + + resp = client.query_points(**kwargs) + points = getattr(resp, "points", None) or [] + + out: List[Dict[str, Any]] = [] + for p in points: + payload = getattr(p, "payload", {}) or {} + score = getattr(p, "score", 0.0) + out.append({ + "text": payload.get("text", ""), + "score": float(score), + "metadata": { + "chunk_id": payload.get("chunk_id"), + "framework_name": payload.get("framework_name", "unknown"), + **{k: v for k, v in payload.items() + if k not in ("text", "chunk_id", "framework_name")}, + }, + }) + return out + + def delete_collection( client: QdrantClient, collection_name: str ) -> None: """ Delete a collection from Qdrant. - + Args: client: QdrantClient instance collection_name: Name of the collection to delete diff --git a/services/ai-service/src/prompts/__init__.py b/services/ai-service/src/prompts/__init__.py deleted file mode 100644 index a3b6743..0000000 --- a/services/ai-service/src/prompts/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .prompt_generator import generate_evaluation_prompt - -__all__ = ["generate_evaluation_prompt"] diff --git a/services/ai-service/src/prompts/prompt_generator.py b/services/ai-service/src/prompts/prompt_generator.py deleted file mode 100644 index 8f2c967..0000000 --- a/services/ai-service/src/prompts/prompt_generator.py +++ /dev/null @@ -1,87 +0,0 @@ -""" -Prompt Generator Module -Generates evaluation prompts from extracted controls JSON -""" - -import json -from openai import OpenAI -from typing import Dict, Any -import os -from dotenv import load_dotenv - -load_dotenv() - - -def generate_evaluation_prompt(controls_json: Dict[str, Any], framework_name: str) -> str: - """ - Generate an evaluation prompt from extracted controls JSON. - - Args: - controls_json: Dictionary containing extracted controls - framework_name: Name of the framework - - Returns: - Evaluation prompt string - """ - api_key = os.getenv("OPENROUTER_API_KEY") - if not api_key: - raise ValueError("OPENROUTER_API_KEY not found") - - client = OpenAI( - api_key=api_key, - base_url="https://openrouter.ai/api/v1" - ) - - # Convert controls JSON to string - controls_str = json.dumps(controls_json, indent=2, ensure_ascii=False) - if len(controls_str) > 50000: - controls_str = controls_str[:50000] + "\n\n[Data truncated...]" - - generation_prompt = f""" -### ROLE -Prompt Engineer & Compliance Auditor. - -### TASK -Using the provided Master JSON containing multiple compliance frameworks, generate a sophisticated **System Prompt** for an "AI Compliance Auditor." - -### SYSTEM PROMPT REQUIREMENTS -The generated prompt must instruct the AI Auditor to: -1. **Role Adoption:** Act as a lead auditor for Saudi National Data Governance (NDMO) and Operational Excellence (SDAIA). -2. **Cross-Framework Mapping:** When evaluating a document, identify which controls from WHICH framework apply (e.g., mapping user evidence to both a Policy and an OE Metric). -3. **Evidence Analysis Logic:** - - Step A: Extract claims from the applicant's document. - - Step B: Compare claims against the 'Requirements' and 'Thresholds' in the Master JSON. - - Step C: Check for specific 'Evidence Suggested' artifacts. -4. **Scoring Protocol:** Apply the strict 0-5 scale for metrics and binary (Compliant/Non-Compliant) for policies as defined in the source data. -5. **Gap Analysis:** For every non-compliant item, specify exactly what is missing based on the 'Semantic Intent'. - -### SOURCE FRAMEWORKS (JSON): -{controls_str} - -### FINAL OUTPUT -Generate the full System Prompt text. The prompt should be optimized for a model with a large context window and include instructions on generating a 'Compliance Gap Report' table at the end of every evaluation. -""" - - response = client.chat.completions.create( - model="openai/gpt-4.1", - messages=[ - { - "role": "system", - "content": "You are a Prompt Engineer & Compliance Auditor. Generate sophisticated system prompts for AI Compliance Auditors." - }, - { - "role": "user", - "content": generation_prompt - } - ], - temperature=0.3 - ) - - result_text = response.choices[0].message.content.strip() - print("\n" + "="*60) - print("PROMPT GENERATION RESPONSE:") - print("="*60) - print(result_text) - print("="*60 + "\n") - - return result_text diff --git a/services/ai-service/src/rag/__init__.py b/services/ai-service/src/rag/__init__.py new file mode 100644 index 0000000..48b9f2b --- /dev/null +++ b/services/ai-service/src/rag/__init__.py @@ -0,0 +1,14 @@ +""" +RAG module: hybrid indexing (JSON + PDF) and retrieval by control ID. +""" + +from .ingestion import index_framework +from .retrieval import retrieve_control_details, RETRIEVE_CONTROL_DETAILS_TOOL_SCHEMA +from ._shared import get_shared_embedder + +__all__ = [ + "index_framework", + "retrieve_control_details", + "RETRIEVE_CONTROL_DETAILS_TOOL_SCHEMA", + "get_shared_embedder", +] diff --git a/services/ai-service/src/rag/_shared.py b/services/ai-service/src/rag/_shared.py new file mode 100644 index 0000000..3f3ee09 --- /dev/null +++ b/services/ai-service/src/rag/_shared.py @@ -0,0 +1,18 @@ +""" +Shared RAG state: single embedder instance per process. +Reuse across index_framework and retrieve_control_details to avoid repeated model loads. +""" + +from typing import Any, Optional + +from src.embeddings import load_gemma_embedder + +_cached_embedder: Optional[Any] = None + + +def get_shared_embedder(): + """Return a single embedder instance, creating and caching on first use.""" + global _cached_embedder + if _cached_embedder is None: + _cached_embedder = load_gemma_embedder() + return _cached_embedder diff --git a/services/ai-service/src/rag/ingestion.py b/services/ai-service/src/rag/ingestion.py new file mode 100644 index 0000000..9a69fde --- /dev/null +++ b/services/ai-service/src/rag/ingestion.py @@ -0,0 +1,90 @@ +""" +RAG ingestion: index JSON control cards + PDF chunks into Qdrant. +""" + +from pathlib import Path +from typing import List, Optional + +from src.processing import extract_text_from_pdf, chunk_text +from src.utils import list_framework_jsons, get_vector_db_pdf_paths +from src.embeddings import initialize_qdrant, add_documents + +from src.rag._shared import get_shared_embedder + + +def index_framework( + framework_name: str, + pdf_paths: Optional[List[str]] = None, +) -> None: + """ + Index a framework for RAG: JSON control cards + PDF chunks from vector_db input. + + - JSON cards: one per control from config/frameworks/{framework_name}/*.json. + - PDF chunks: from data/inputs/vector_db/{framework_name}/*.pdf, or vector_db/*.pdf + if no subdir. Use pdf_paths when provided instead. + + Collection name: {framework_name}_rag. + """ + embedder = get_shared_embedder() + dim = embedder.get_embedding_dim() + collection = f"{framework_name}_rag" + client = initialize_qdrant(collection_name=collection, vector_size=dim) + + all_docs: List[dict] = [] + + # --- JSON control cards --- + for stem, data in list_framework_jsons(framework_name): + fw_name = data.get("framework_name", framework_name) + for c in data.get("controls", []): + cid = c.get("id", "") + desc = c.get("description", "") + calc = c.get("calculation", "") + thresh = c.get("threshold", "") + scale = c.get("scale", "") + text = ( + f"Control {cid}. {desc} " + f"Calculation: {calc}. Threshold: {thresh}. Scale: {scale}." + ) + safe_id = (cid or "unknown").replace(".", "_") + chunk_id = f"json_{framework_name}_{safe_id}_{stem}" + all_docs.append({ + "text": text, + "chunk_id": chunk_id, + "framework_name": fw_name, + "metadata": { + "source": "json", + "control_id": cid, + "source_pdf": stem, + }, + }) + + # --- PDF chunks --- + if pdf_paths is None: + pdf_paths = [str(p) for p in get_vector_db_pdf_paths(framework_name)] + for pdf_path in pdf_paths: + try: + raw = extract_text_from_pdf(pdf_path) + except Exception: + continue + stem = Path(pdf_path).stem + chunks = chunk_text(raw, framework_name=framework_name) + for i, ch in enumerate(chunks): + chunk_id = f"pdf_{stem}_{i}" + doc = { + "text": ch["text"], + "chunk_id": chunk_id, + "framework_name": framework_name, + "metadata": { + **ch.get("metadata", {}), + "source": "pdf", + "source_pdf": stem, + }, + } + all_docs.append(doc) + + if not all_docs: + return + + texts = [d["text"] for d in all_docs] + embeddings = embedder.embed_batch(texts) + add_documents(client, collection, all_docs, embeddings) diff --git a/services/ai-service/src/rag/retrieval.py b/services/ai-service/src/rag/retrieval.py new file mode 100644 index 0000000..4a51e23 --- /dev/null +++ b/services/ai-service/src/rag/retrieval.py @@ -0,0 +1,109 @@ +""" +RAG retrieval: fetch control details by ID (JSON cards + PDF chunks). +Designed for use by agents as a callable tool. +""" + +from typing import Dict, Any, Optional + +from qdrant_client.models import Filter, FieldCondition, MatchValue + +from src.embeddings import ( + initialize_qdrant, + fetch_by_filter, + search_similar_filtered, +) +from src.rag._shared import get_shared_embedder + + +# Tool schema for agent/tool registries (OpenAI tools, LangChain, etc.) +RETRIEVE_CONTROL_DETAILS_TOOL_SCHEMA = { + "type": "function", + "function": { + "name": "retrieve_control_details", + "description": ( + "Retrieve full details for a compliance control by ID. Returns structured " + "JSON control cards (id, description, calculation, threshold, scale) plus " + "relevant PDF passages from the framework. Use this when you need to look up " + "what a specific control requires or when evaluating evidence against a control." + ), + "parameters": { + "type": "object", + "properties": { + "control_id": { + "type": "string", + "description": "The control identifier (e.g. DSI.OE.01, DG.1, DG.1.1).", + }, + "framework_name": { + "type": "string", + "description": "The framework name (e.g. NDI) used during indexing.", + }, + "top_k_pdf": { + "type": "integer", + "description": "Max number of PDF chunks to return. Default 5.", + "default": 5, + }, + }, + "required": ["control_id", "framework_name"], + }, + }, +} + + +def retrieve_control_details( + control_id: str, + framework_name: str, + *, + top_k_pdf: int = 5, +) -> Dict[str, Any]: + """ + Retrieve all details for a control: JSON cards (filtered by control_id) + top-k + PDF chunks (semantic search). Safe to use as an agent tool. + + Args: + control_id: Control identifier (e.g. DSI.OE.01, DG.1). + framework_name: Framework name (e.g. NDI) matching the indexed collection. + top_k_pdf: Max PDF chunks to return. Default 5. + + Returns: + { + "json_cards": [{"text": str, "metadata": dict}, ...], + "pdf_chunks": [{"text": str, "score": float, "metadata": dict}, ...], + } + """ + embedder = get_shared_embedder() + collection = f"{framework_name}_rag" + dim = embedder.get_embedding_dim() + client = initialize_qdrant(collection_name=collection, vector_size=dim) + + json_filter = Filter( + must=[ + FieldCondition(key="control_id", match=MatchValue(value=control_id)), + FieldCondition(key="source", match=MatchValue(value="json")), + ] + ) + json_cards = fetch_by_filter(client, collection, json_filter) + + description_snippet = "" + if json_cards and json_cards[0].get("text"): + description_snippet = json_cards[0]["text"][:500] + query = f"Control {control_id}. {description_snippet}" + + pdf_filter = Filter( + must=[ + FieldCondition(key="source", match=MatchValue(value="pdf")), + FieldCondition(key="framework_name", match=MatchValue(value=framework_name)), + ] + ) + query_embedding = embedder.embed_text(query) + pdf_chunks = search_similar_filtered( + client, + collection, + query_embedding, + top_k=top_k_pdf, + query_filter=pdf_filter, + ) + + return { + "json_cards": json_cards, + "pdf_chunks": pdf_chunks, + } diff --git a/services/ai-service/src/utils/__init__.py b/services/ai-service/src/utils/__init__.py index 06b590e..d560f4e 100644 --- a/services/ai-service/src/utils/__init__.py +++ b/services/ai-service/src/utils/__init__.py @@ -1,15 +1,13 @@ from .framework_utils import ( - save_framework_data, - load_framework_data, - list_saved_frameworks, - save_evaluation_report, - get_input_paths + save_extraction_json, + get_input_paths, + list_framework_jsons, + get_vector_db_pdf_paths, ) __all__ = [ - "save_framework_data", - "load_framework_data", - "list_saved_frameworks", - "save_evaluation_report", - "get_input_paths" + "save_extraction_json", + "get_input_paths", + "list_framework_jsons", + "get_vector_db_pdf_paths", ] diff --git a/services/ai-service/src/utils/framework_utils.py b/services/ai-service/src/utils/framework_utils.py index 837d2c3..0bd286b 100644 --- a/services/ai-service/src/utils/framework_utils.py +++ b/services/ai-service/src/utils/framework_utils.py @@ -1,179 +1,97 @@ """ Utilities Module -Handles saving and loading of framework data (JSON controls and evaluation prompts) +Handles per-PDF extraction saves, input paths, and RAG helpers. """ import json from pathlib import Path -from typing import Dict, Any, Tuple, List, Optional -from datetime import datetime +from typing import Dict, Any, List, Tuple -def save_framework_data( - framework_name: str, - controls_json: Dict[str, Any], - evaluation_prompt: str, - metadata: Dict[str, Any] = None -) -> None: +def _project_root() -> Path: + return Path(__file__).parent.parent.parent + + +def save_extraction_json( + framework_name: str, pdf_path: str, controls_json: Dict[str, Any] +) -> Path: """ - Save framework controls JSON and evaluation prompt to config/frameworks/{framework_name}/ - - Creates directory structure if it doesn't exist. - Saves: - - controls.json: The extracted controls JSON - - evaluation_prompt.txt: The generated evaluation prompt - - framework_metadata.json: Metadata about sections, validation, etc. (if provided) - + Save extracted controls for a single PDF as JSON under config/frameworks/{framework_name}/. + + Filename is the PDF stem + .json (e.g. section-a.pdf -> section-a.json). + Args: framework_name: Name of the framework (used for directory name) - controls_json: Dictionary containing controls, metadata, and filters - evaluation_prompt: Generated evaluation prompt string - metadata: Optional dictionary containing framework metadata: - - sections: list of section names - - total_size: total character count - - extracted_at: timestamp - - keywords_found: dictionary of keywords per section - - validation_status: validation results + pdf_path: Path to the source PDF (used for filename) + controls_json: Dictionary with framework_name and controls + + Returns: + Path to the written JSON file """ - # Get project root (services/ai-service/) - project_root = Path(__file__).parent.parent.parent + project_root = _project_root() base_dir = project_root / "config" / "frameworks" framework_dir = base_dir / framework_name framework_dir.mkdir(parents=True, exist_ok=True) - - # Save controls JSON - controls_path = framework_dir / "controls.json" - with open(controls_path, "w", encoding="utf-8") as f: + + stem = Path(pdf_path).stem + out_path = framework_dir / f"{stem}.json" + with open(out_path, "w", encoding="utf-8") as f: json.dump(controls_json, f, indent=2, ensure_ascii=False) - - # Save evaluation prompt - prompt_path = framework_dir / "evaluation_prompt.txt" - with open(prompt_path, "w", encoding="utf-8") as f: - f.write(evaluation_prompt) - - # Save metadata if provided - if metadata: - from datetime import datetime - # Add timestamp if not present - if 'extracted_at' not in metadata: - metadata['extracted_at'] = datetime.now().isoformat() - - metadata_path = framework_dir / "framework_metadata.json" - with open(metadata_path, "w", encoding="utf-8") as f: - json.dump(metadata, f, indent=2, ensure_ascii=False) - - -def load_framework_data(framework_name: str) -> Tuple[Dict[str, Any], str]: - """ - Load saved framework controls JSON and evaluation prompt. - - Args: - framework_name: Name of the framework to load - - Returns: - Tuple of (controls_json, evaluation_prompt) - - Raises: - FileNotFoundError: If framework data doesn't exist - """ - # Get project root (services/ai-service/) - project_root = Path(__file__).parent.parent.parent - framework_dir = project_root / "config" / "frameworks" / framework_name - - # Load controls JSON - controls_path = framework_dir / "controls.json" - if not controls_path.exists(): - raise FileNotFoundError(f"Controls JSON not found: {controls_path}") - - with open(controls_path, "r", encoding="utf-8") as f: - controls_json = json.load(f) - - # Load evaluation prompt - prompt_path = framework_dir / "evaluation_prompt.txt" - if not prompt_path.exists(): - raise FileNotFoundError(f"Evaluation prompt not found: {prompt_path}") - - with open(prompt_path, "r", encoding="utf-8") as f: - evaluation_prompt = f.read() - - return controls_json, evaluation_prompt - - -def list_saved_frameworks() -> List[str]: - """ - List all saved frameworks in config/frameworks/ - - Returns: - List of framework names - """ - # Get project root (services/ai-service/) - project_root = Path(__file__).parent.parent.parent - frameworks_dir = project_root / "config" / "frameworks" - - if not frameworks_dir.exists(): - return [] - - frameworks = [ - d.name for d in frameworks_dir.iterdir() - if d.is_dir() and (d / "controls.json").exists() and (d / "evaluation_prompt.txt").exists() - ] - return sorted(frameworks) - - -def save_evaluation_report( - evaluation_report: Dict[str, Any], - framework_name: str, - applicant_name: Optional[str] = None -) -> Path: - """ - Save evaluation report to data/outputs/evaluations/ - - Args: - evaluation_report: Dictionary containing evaluation results - framework_name: Name of the framework used for evaluation - applicant_name: Optional name/identifier for the applicant - - Returns: - Path to the saved evaluation report file - """ - # Get project root (services/ai-service/) - project_root = Path(__file__).parent.parent.parent - outputs_dir = project_root / "data" / "outputs" / "evaluations" - outputs_dir.mkdir(parents=True, exist_ok=True) - - # Generate filename with timestamp - timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - - if applicant_name: - # Sanitize applicant name for filename - safe_name = "".join(c for c in applicant_name if c.isalnum() or c in (' ', '-', '_')).strip() - safe_name = safe_name.replace(' ', '_') - filename = f"{framework_name}_{safe_name}_{timestamp}.json" - else: - filename = f"{framework_name}_evaluation_{timestamp}.json" - - report_path = outputs_dir / filename - - # Save evaluation report - with open(report_path, "w", encoding="utf-8") as f: - json.dump(evaluation_report, f, indent=2, ensure_ascii=False) - - return report_path + return out_path def get_input_paths() -> Dict[str, Path]: """ Get standard input directory paths. - + Returns: Dictionary with paths: - frameworks: Path to framework PDFs directory - applicants: Path to applicant PDFs directory - vector_db: Path to vector DB input PDFs directory """ - project_root = Path(__file__).parent.parent.parent + project_root = _project_root() return { "frameworks": project_root / "data" / "inputs" / "frameworks", "applicants": project_root / "data" / "inputs" / "applicants", - "vector_db": project_root / "data" / "inputs" / "vector_db" + "vector_db": project_root / "data" / "inputs" / "vector_db", } + + +def list_framework_jsons(framework_name: str) -> List[Tuple[str, Dict[str, Any]]]: + """ + Load all JSON files for a framework from config/frameworks/{framework_name}/. + + Returns: + List of (stem, parsed_json) tuples. Stem = filename without .json (e.g. PoliciesEn-1). + """ + base = _project_root() / "config" / "frameworks" / framework_name + if not base.is_dir(): + return [] + out: List[Tuple[str, Dict[str, Any]]] = [] + for p in sorted(base.glob("*.json")): + try: + with open(p, "r", encoding="utf-8") as f: + data = json.load(f) + out.append((p.stem, data)) + except Exception: + continue + return out + + +def get_vector_db_pdf_paths(framework_name: str) -> List[Path]: + """ + PDF paths used as input for the vector DB. Looks in data/inputs/vector_db/. + + Prefers vector_db/{framework_name}/*.pdf. If that dir has no PDFs, falls back to + vector_db/*.pdf (flat). + """ + paths = get_input_paths() + vdb = paths["vector_db"] + sub = vdb / framework_name + if sub.is_dir(): + pdfs = sorted(sub.glob("*.pdf")) + sorted(sub.glob("*.PDF")) + if pdfs: + return pdfs + pdfs = sorted(vdb.glob("*.pdf")) + sorted(vdb.glob("*.PDF")) + return pdfs diff --git a/services/ai-service/test.ipynb b/services/ai-service/test.ipynb new file mode 100644 index 0000000..d8e6224 --- /dev/null +++ b/services/ai-service/test.ipynb @@ -0,0 +1,96 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "06e781f7", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mohammedbalkhair/Documents/Governance-Agent/services/ai-service/.venv/lib/python3.13/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n", + "Batches: 100%|██████████| 14/14 [00:32<00:00, 2.34s/it]\n" + ] + } + ], + "source": [ + "import os\n", + "os.environ[\"HF_HUB_OFFLINE\"] = \"1\" \n", + "from src.rag import index_framework, retrieve_control_details, RETRIEVE_CONTROL_DETAILS_TOOL_SCHEMA\n", + "\n", + "# 1. Index (PDFs from data/inputs/vector_db/ or vector_db/{framework_name}/)\n", + "index_framework(\"NDI\")\n", + "\n", + "# 2. Retrieve (e.g. from an agent)\n", + "out = retrieve_control_details(\"DG.1\", \"NDI\", top_k_pdf=5)\n", + "# out[\"json_cards\"] → structured control summaries\n", + "# out[\"pdf_chunks\"] → relevant PDF passages\n", + "\n", + "# 3. Register as tool\n", + "# Use RETRIEVE_CONTROL_DETAILS_TOOL_SCHEMA in your agent’s tool list." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a9e7ac20", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'json_cards': [{'text': 'Control DSI.OE.02. This metric measures the percentage of systems shared by the entity with the National Data Lake (NDL) against the total number of the entity’s systems requested by the NDL team. Note that a system will be considered integrated only if all required dimensions of its data are fully sourced to NDL. Calculation: Number of systems integrated with NDL by the entity / Total number of the entity’s systems requested by NDL * 100. Threshold: 70%. Scale: Unacceptable: ≤ 70%; Low: (70%, 75%]; Fair: (75%, 80%]; Good: (80%, 85%]; Excellent: (85%, 90%]; Leader: > 90%.',\n", + " 'metadata': {'chunk_id': 'json_NDI_DSI_OE_02_OperationalExcellence-OE',\n", + " 'framework_name': 'NDI',\n", + " 'source': 'json',\n", + " 'control_id': 'DSI.OE.02',\n", + " 'source_pdf': 'OperationalExcellence-OE'}}],\n", + " 'pdf_chunks': [{'text': 'NDL) against the total number of the entity’s systems requested\\nby the NDL team. Note that a system will be considered integrated only if all required\\ndimensions of its data are fully sourced to NDL.\\nThis metric aims to accelerate the efforts to enrich NDL with high-value and wide-\\nspectrum data assets generated by various government entities. It also helps in\\n10\\nمﺎﻋ\\n\\n18/11/2025\\nElement Name Element Details\\nachieving the goal of making NDL the unified single source of truth for analytical\\ndata assets.\\nDomain Name Data Sharing and Interoperability (DSI)\\nData Platforms National Data Lake (NDL)\\nDefinitions • Number of systems integrated with NDL by the entity\\n• Total number of the entity’s systems requested by NDL\\nCalculation = Number of systems integrated with NDL by the entity / Total number of the\\nentity’s systems requested by NDL * 100\\nMeasurement Unit Percentage\\nAcceptable Threshold 70%\\nScale Intervals Unacceptable: ≤ 70%\\nLow: (70%, 75%]\\nFair: (75%, 80%]\\nGood: (80%, 85%]\\nExcellent: (85%, 90%]\\nLeader: > 90%\\nVersion History\\nDependencies\\nElement Name Element Details\\nMetric ID DSI.OE.03\\nMetric Name Data sharing agreement processing\\nMetric Description This metric measures the amount of time taken by the data producer to process the\\ndata sharing requests raised by the consumer entities. This metric considers the time\\ntaken for either approving or rejecting the requests as part of the processing time.',\n", + " 'score': 0.84288440527831,\n", + " 'metadata': {'chunk_id': 'pdf_OperationalExcellence-OE_11',\n", + " 'framework_name': 'NDI',\n", + " 'start_char': 14175,\n", + " 'end_char': 15595,\n", + " 'length': 1419,\n", + " 'chunk_size': 1500,\n", + " 'overlap': 200,\n", + " 'source': 'pdf',\n", + " 'source_pdf': 'OperationalExcellence-OE'}}]}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "out = retrieve_control_details(\"DSI.OE.02\", \"NDI\", top_k_pdf=1)\n", + "out" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 30f24597ce0124e8fd990ed9c36f0926201ddd2c Mon Sep 17 00:00:00 2001 From: Mohammed-Balkhair-hub Date: Fri, 30 Jan 2026 06:43:13 +0300 Subject: [PATCH 4/4] define function as endpoints --- services/ai-service/README.md | 89 ++- services/ai-service/main.py | 27 +- services/ai-service/pyproject.toml | 11 +- services/ai-service/run.py | 3 + services/ai-service/src/ARCHITECTURE.md | 90 ++-- services/ai-service/src/README.md | 14 + services/ai-service/src/api/README.md | 14 + services/ai-service/src/api/__init__.py | 1 + services/ai-service/src/api/app.py | 56 ++ services/ai-service/src/api/routers/README.md | 4 + .../ai-service/src/api/routers/__init__.py | 1 + .../ai-service/src/api/routers/frameworks.py | 63 +++ services/ai-service/src/api/routers/health.py | 11 + services/ai-service/src/core/README.md | 13 + services/ai-service/src/core/__init__.py | 10 +- .../src/core/framework_consolidator.py | 53 -- ...k_extractor.py => framework_extraction.py} | 96 +++- services/ai-service/src/embeddings/README.md | 10 + services/ai-service/src/processing/README.md | 9 + .../ai-service/src/processing/text_chunker.py | 2 +- services/ai-service/src/rag/README.md | 10 + services/ai-service/src/services/README.md | 7 + services/ai-service/src/services/__init__.py | 5 + .../src/services/framework_service.py | 101 ++++ services/ai-service/src/utils/README.md | 11 + .../ai-service/src/utils/framework_utils.py | 14 +- services/ai-service/test.ipynb | 12 +- services/ai-service/uv.lock | 507 ++++++++++++++++++ 28 files changed, 1051 insertions(+), 193 deletions(-) create mode 100644 services/ai-service/run.py create mode 100644 services/ai-service/src/README.md create mode 100644 services/ai-service/src/api/README.md create mode 100644 services/ai-service/src/api/__init__.py create mode 100644 services/ai-service/src/api/app.py create mode 100644 services/ai-service/src/api/routers/README.md create mode 100644 services/ai-service/src/api/routers/__init__.py create mode 100644 services/ai-service/src/api/routers/frameworks.py create mode 100644 services/ai-service/src/api/routers/health.py create mode 100644 services/ai-service/src/core/README.md delete mode 100644 services/ai-service/src/core/framework_consolidator.py rename services/ai-service/src/core/{framework_extractor.py => framework_extraction.py} (70%) create mode 100644 services/ai-service/src/embeddings/README.md create mode 100644 services/ai-service/src/processing/README.md create mode 100644 services/ai-service/src/rag/README.md create mode 100644 services/ai-service/src/services/README.md create mode 100644 services/ai-service/src/services/__init__.py create mode 100644 services/ai-service/src/services/framework_service.py create mode 100644 services/ai-service/src/utils/README.md diff --git a/services/ai-service/README.md b/services/ai-service/README.md index 9362842..6aee078 100644 --- a/services/ai-service/README.md +++ b/services/ai-service/README.md @@ -21,10 +21,9 @@ data/ ``` config/ -├── frameworks/ # Framework outputs (controls.json, evaluation_prompt.txt) +├── frameworks/ # Framework outputs (one JSON per section) │ └── {framework_name}/ -│ ├── controls.json -│ └── evaluation_prompt.txt +│ └── {section_name}.json └── vector_db/ # Qdrant vector database storage data/ @@ -34,78 +33,68 @@ data/ **What gets saved where:** - **Framework Data**: `config/frameworks/{framework_name}/` - - `controls.json` - Extracted compliance controls - - `evaluation_prompt.txt` - Generated evaluation prompt - -- **Evaluation Reports**: `data/outputs/evaluations/` - - Format: `{framework_name}_{applicant_name}_{timestamp}.json` - + - One JSON per section (e.g. `section_name.json`) — extracted compliance controls - **Vector Database**: `config/vector_db/` (Qdrant local storage) +- **Evaluation Reports**: `data/outputs/evaluations/` (when using evaluator) ## Quick Start -### 1. Setup a Framework +### CLI: Setup a Framework ```python from main import setup_framework -# Place your framework PDF in data/inputs/frameworks/ -setup_framework( - 'data/inputs/frameworks/my_framework.pdf', - 'my_framework' -) +# Single file, list of files, or directory path +setup_framework('data/inputs/frameworks/my_framework.pdf', 'my_framework') +# or +setup_framework('data/inputs/frameworks/my_framework/', 'my_framework') ``` -This will: -- Extract controls from the PDF -- Generate an evaluation prompt -- Save everything to `config/frameworks/my_framework/` +This extracts controls from each PDF and saves one JSON per PDF under `config/frameworks/{framework_name}/` (filename = PDF stem). -### 2. Index Framework Documents (Optional) +### API: Setup a Framework -```python -from main import index_framework_documents +Run the API server (from `services/ai-service/`): -# Index framework for vector search -index_framework_documents( - 'data/inputs/frameworks/my_framework.pdf', - 'my_framework' -) +```bash +python src/api/app.py +# or +python run.py ``` -### 3. Evaluate Applicant Documents +- **Docs**: [http://localhost:8000/api/docs](http://localhost:8000/api/docs) +- **Health**: `GET /health` +- **Setup framework**: `POST /api/v1/frameworks/setup` + - Form fields: `framework_name` (string), `section_names` (list of strings), `files` (list of PDFs). Same order for section_names and files. Each PDF is saved as `config/frameworks/{framework_name}/{section_name}.json`. -```python -from main import evaluate_applicant_documents - -# Place applicant PDFs in data/inputs/applicants/ -evaluate_applicant_documents( - ['data/inputs/applicants/applicant1.pdf'], - 'my_framework', - applicant_name='applicant1' -) -``` +### Other (from `src`) -The evaluation report will be saved to `data/outputs/evaluations/` +- **RAG indexing**: `from src.rag import index_framework` — index framework JSON + PDFs into Qdrant. +- **Evaluation**: `from src.core import evaluate_applicant` — evaluate applicant docs (requires external evaluation prompt and controls JSON). -## Future API Integration +## Dependencies -When you build the API later, you can: +Use **uv** from the project root (`services/ai-service/`): -1. **Upload endpoints**: - - `POST /api/frameworks/upload` → Save to `data/inputs/frameworks/` - - `POST /api/applicants/upload` → Save to `data/inputs/applicants/` +```bash +# Install all dependencies (after cloning or when pyproject.toml changes) +uv sync -2. **Processing endpoints**: - - `POST /api/frameworks/process` → Run `setup_framework()` - - `POST /api/evaluations/create` → Run `evaluate_applicant_documents()` +# Add a new runtime dependency +uv add + +# Add FastAPI and uvicorn +uv add fastapi uvicorn[standard] + +# Dev dependencies are separate: use a dependency group +uv add --group dev pytest +``` -3. **Retrieval endpoints**: - - `GET /api/frameworks` → List from `config/frameworks/` - - `GET /api/evaluations/{id}` → Load from `data/outputs/evaluations/` +Run these in your terminal; dev dependencies stay in a separate group (e.g. `[project.optional-dependencies.dev]` or `[tool.uv]` dev-dependencies) so production installs stay lean. ## Notes - All data directories are in `.gitignore` (user uploads and generated content) - The directory structure is designed to be API-friendly - Paths are relative to the project root (`services/ai-service/`) +- Each folder under `src/` has a `README.md` for quick context (e.g. for code agents) diff --git a/services/ai-service/main.py b/services/ai-service/main.py index f16f71b..c11249c 100644 --- a/services/ai-service/main.py +++ b/services/ai-service/main.py @@ -1,20 +1,21 @@ """ Main entry point for the Compliance Framework Extraction System. -Extract controls from each PDF and save one JSON per PDF (no compose, no prompt). +CLI: extract controls from each PDF and save one JSON per PDF (no compose, no prompt). +Uses the same service layer as the API. """ -from pathlib import Path import glob +from pathlib import Path -from src.core.framework_consolidator import extract_controls_from_pdfs -from src.utils import save_extraction_json, get_input_paths +from src.services import FrameworkService +from src.utils import get_input_paths def setup_framework(pdf_paths: str | list[str], framework_name: str) -> list[Path]: """ Extract controls from each PDF and save one JSON per PDF under config/frameworks/{framework_name}/. - Each file is named after the source PDF (e.g. section-a.pdf -> section-a.json). + Each file is named after the source PDF stem (e.g. section-a.pdf -> section-a.json). No composition or evaluation prompt generation. Args: @@ -36,13 +37,15 @@ def setup_framework(pdf_paths: str | list[str], framework_name: str) -> list[Pat if not pdf_paths_list: raise ValueError(f"No PDF files found: {pdf_paths}") - controls_arrays = extract_controls_from_pdfs(pdf_paths_list, framework_name) - saved: list[Path] = [] - for pdf_path, controls_array in zip(pdf_paths_list, controls_arrays): - obj = {"framework_name": framework_name, "controls": controls_array} - path = save_extraction_json(framework_name, pdf_path, obj) - saved.append(path) - return saved + pdf_sections = [ + (Path(p).stem, Path(p).read_bytes()) + for p in pdf_paths_list + ] + service = FrameworkService() + result = service.setup_framework(framework_name=framework_name, pdf_sections=pdf_sections) + + project_root = Path(__file__).resolve().parent + return [project_root / s["json_path"] for s in result["sections"]] def main(): diff --git a/services/ai-service/pyproject.toml b/services/ai-service/pyproject.toml index 0508901..23fbfde 100644 --- a/services/ai-service/pyproject.toml +++ b/services/ai-service/pyproject.toml @@ -17,4 +17,13 @@ dependencies = [ "torch>=2.0.0", "sentence-transformers>=2.2.0", "accelerate>=0.24.0", -] \ No newline at end of file + "python-multipart>=0.0.9", + "aiofiles>=24.0.0", + "fastapi>=0.128.0", +] + +[dependency-groups] +dev = [ + "fastapi[standard]>=0.128.0", +] + diff --git a/services/ai-service/run.py b/services/ai-service/run.py new file mode 100644 index 0000000..40726ed --- /dev/null +++ b/services/ai-service/run.py @@ -0,0 +1,3 @@ +"""Run the API: python run.py (from ai-service directory). Use --reload for auto-restart on code changes.""" +import uvicorn +uvicorn.run("src.api.app:app", host="0.0.0.0", port=8000) diff --git a/services/ai-service/src/ARCHITECTURE.md b/services/ai-service/src/ARCHITECTURE.md index 2ffb7d1..1cb1f86 100644 --- a/services/ai-service/src/ARCHITECTURE.md +++ b/services/ai-service/src/ARCHITECTURE.md @@ -20,6 +20,8 @@ The **Compliance Framework Extraction & RAG System** has two main pipelines: | **Embeddings** | Gemma embedder, Qdrant (local/remote) | | **RAG** | Index framework (JSON + PDF), retrieve by control ID | | **Utils** | Per-PDF JSON save/load, input paths, vector_db PDF resolution | +| **API** | FastAPI app, routers (frameworks, health), Pydantic models | +| **Services** | Orchestration (FrameworkService for setup_framework) | **Removed / obsolete**: `prompts/` directory, `compose_master_framework`, `save_framework_data`, `load_framework_data`, `list_saved_frameworks`, `save_evaluation_report`, `generate_evaluation_prompt`. Section organization and multi-PDF validation flows are no longer used. @@ -29,10 +31,18 @@ The **Compliance Framework Extraction & RAG System** has two main pipelines: ``` src/ +├── api/ # FastAPI application layer +│ ├── app.py # FastAPI app, CORS, exception handlers +│ ├── routers/ +│ │ ├── frameworks.py # POST /api/v1/frameworks/setup +│ │ └── health.py # GET /health +│ └── models/ +│ ├── requests.py # (form/file validated in routers) +│ └── responses.py # ControlSummary, SetupFrameworkResponse, ErrorResponse, ExtractionError +│ ├── core/ │ ├── evaluator.py # Evaluate applicant docs vs framework (uses external prompt) -│ ├── framework_extractor.py # LLM extraction: PDF text → controls JSON -│ └── framework_consolidator.py # Parallel PDF extraction + retry on empty +│ └── framework_extraction.py # extract_controls_from_framework + extract_controls_from_pdfs │ ├── processing/ │ ├── pdf_parser.py # extract_text_from_pdf @@ -48,15 +58,18 @@ src/ │ ├── ingestion.py # index_framework (JSON + PDF → Qdrant) │ └── retrieval.py # retrieve_control_details, RETRIEVE_CONTROL_DETAILS_TOOL_SCHEMA │ +├── services/ +│ └── framework_service.py # FrameworkService.setup_framework (orchestration) +│ ├── utils/ -│ └── framework_utils.py # save_extraction_json, get_input_paths, list_framework_jsons, -│ # get_vector_db_pdf_paths +│ └── framework_utils.py # save_extraction_json (custom_name), get_input_paths, +│ # list_framework_jsons, get_vector_db_pdf_paths │ ├── __init__.py # Package exports └── ARCHITECTURE.md # This file ``` -**Entry point**: `main.py` (project root) — `setup_framework()`, `get_input_paths()`. +**Entry points**: `main.py` (CLI) — `setup_framework()`, `get_input_paths()`. `python src/api/app.py` or `python run.py` — API server. --- @@ -64,39 +77,18 @@ src/ ### Core (`src/core/`) -#### `framework_extractor.py` - -**Purpose**: Extract compliance controls from raw PDF text using LLM (OpenRouter). - -**Key function**: `extract_controls_from_framework(pdf_text, framework_name, use_fallback_prompt=False) → dict` - -- Returns `{"framework_name": str, "controls": [ {...}, ... ]}`. -- Each control has **exactly**: `id`, `description`, `calculation`, `threshold`, `scale` (strict JSON schema). -- `use_fallback_prompt=True`: stricter prompt that forbids empty controls (used for retries). - -**When to modify**: -- Change extraction prompt or JSON schema. -- Switch LLM model (currently `openai/gpt-4.1` via OpenRouter). -- Adjust temperature (0.3 normal, 0.5 fallback) or response handling. - -**Dependencies**: `openai`, `python-dotenv`. Requires `OPENROUTER_API_KEY`. - ---- - -#### `framework_consolidator.py` - -**Purpose**: Run extraction over multiple PDFs in parallel; retry with fallback prompt if a PDF yields no controls. +#### `framework_extraction.py` -**Key function**: `extract_controls_from_pdfs(pdf_paths_list, framework_name) → list[list[dict]]` +**Purpose**: Extract compliance controls from PDF text (LLM) and from multiple PDFs in parallel. -- One LLM call per PDF. Uses `ThreadPoolExecutor`. -- If a PDF returns empty controls, retries up to `MAX_RETRIES` (2) with `use_fallback_prompt=True`. +**Key functions**: +- `extract_controls_from_framework(pdf_text, framework_name, use_fallback_prompt=False) → dict` — LLM extraction (OpenRouter). Returns `{"framework_name": str, "controls": [ {...}, ... ]}`. Each control has **exactly**: `id`, `description`, `calculation`, `threshold`, `scale`. `use_fallback_prompt=True` for retries (stricter prompt). +- `extract_controls_from_pdfs(pdf_paths_list, framework_name) → list[list[dict]]` — Parallel extraction; one LLM call per PDF; retries with fallback prompt if empty (up to 2 retries). Uses `ThreadPoolExecutor`. **When to modify**: -- Change parallelism (e.g. `max_workers`). -- Adjust retry count or retry logic. +- Change extraction prompt or JSON schema; switch LLM (currently `openai/gpt-4.1`); adjust temperature or retry logic. -**Dependencies**: `framework_extractor`, `processing.extract_text_from_pdf`. +**Dependencies**: `openai`, `python-dotenv`, `OPENROUTER_API_KEY`; `processing.extract_text_from_pdf`. --- @@ -246,7 +238,7 @@ os.environ["HF_HUB_OFFLINE"] = "1" # Set to "0" for first-time model download. #### `framework_utils.py` **Key functions**: -- `save_extraction_json(framework_name, pdf_path, controls_json) → Path` — save under `config/frameworks/{framework_name}/{stem}.json`. +- `save_extraction_json(framework_name, pdf_path, controls_json, custom_name=None) → Path` — save under `config/frameworks/{framework_name}/{stem}.json`. Use `custom_name` for section name when provided; otherwise PDF stem. - `get_input_paths() → dict` — `frameworks`, `applicants`, `vector_db` under `data/inputs/`. - `list_framework_jsons(framework_name) → list[(stem, dict)]` — load all `*.json` for a framework. - `get_vector_db_pdf_paths(framework_name) → list[Path]` — PDFs in `data/inputs/vector_db/`; prefers `vector_db/{framework_name}/` then flat `vector_db/*.pdf`. @@ -255,6 +247,28 @@ os.environ["HF_HUB_OFFLINE"] = "1" # Set to "0" for first-time model download. --- +### API (`src/api/`) + +**Purpose**: FastAPI application layer — CORS, routers, Pydantic models, exception handlers. + +**Key**: +- `app.py` — FastAPI app; includes health and frameworks routers; handlers for `ExtractionError` (500), `ValueError` (400). +- `routers/frameworks.py` — `POST /api/v1/frameworks/setup`: form `framework_name`, `section_names[]`, `files[]` (PDFs). Validates, calls `FrameworkService.setup_framework`, returns `SetupFrameworkResponse`. +- `routers/health.py` — `GET /health` → `{"status": "ok"}`. +- `models/responses.py` — `ControlSummary`, `SetupFrameworkResponse`, `ErrorResponse`, `ExtractionError`. + +**Run**: From `ai-service/`: `python src/api/app.py` or `python run.py`. Docs: `/api/docs`, `/api/redoc`. + +--- + +### Services (`src/services/`) + +**Purpose**: Orchestration for API and CLI. + +**Key**: `framework_service.py` — `FrameworkService.setup_framework(framework_name, pdf_sections: list[tuple[str, bytes]])`. Saves PDFs to temp dir, calls `extract_controls_from_pdfs`, saves JSON per section via `save_extraction_json(..., custom_name=section_name)`, returns summary; cleans up temp dir. Used by `main.py` (CLI) and `POST /api/v1/frameworks/setup`. + +--- + ## Data Flows ### 1. Extraction pipeline (`setup_framework`) @@ -327,7 +341,7 @@ Use `RETRIEVE_CONTROL_DETAILS_TOOL_SCHEMA` when registering as an agent tool. | Goal | Files to change | |------|------------------| -| **LLM model (extraction)** | `core/framework_extractor.py` — model, base_url | +| **LLM model (extraction)** | `core/framework_extraction.py` — model, base_url | | **LLM model (evaluation)** | `core/evaluator.py` | | **Embedding model** | `embeddings/gemma_embedder.py` — `model_name`, `get_embedding_dim` | | **Chunking** | `processing/text_chunker.py` — `chunk_text` params | @@ -344,7 +358,7 @@ Use `RETRIEVE_CONTROL_DETAILS_TOOL_SCHEMA` when registering as an agent tool. | Variable | Used by | Purpose | |----------|---------|---------| -| `OPENROUTER_API_KEY` | `framework_extractor.py` | LLM extraction (OpenRouter) | +| `OPENROUTER_API_KEY` | `framework_extraction.py` | LLM extraction (OpenRouter) | | `OPENAI_API_KEY` | `evaluator.py` | Applicant evaluation | | `HF_TOKEN` or `HUGGINGFACE_TOKEN` | `gemma_embedder.py` | Gated models (when not offline) | | `HF_HUB_OFFLINE` | Set in `gemma_embedder.py` | `"1"` cache-only, `"0"` hub access | @@ -394,8 +408,8 @@ from src import ( ## Implementation Notes (memorise for edits) 1. **Extraction is extract-only**: One JSON per PDF. No master composition, no evaluation prompt generation. -2. **Control schema**: Exactly `id`, `description`, `calculation`, `threshold`, `scale`. Enforced in `framework_extractor` prompts. -3. **Retries**: Empty extraction → up to 2 retries with `use_fallback_prompt=True` in `framework_consolidator`. +2. **Control schema**: Exactly `id`, `description`, `calculation`, `threshold`, `scale`. Enforced in `framework_extraction` prompts. +3. **Retries**: Empty extraction → up to 2 retries with `use_fallback_prompt=True` in `framework_extraction.extract_controls_from_pdfs`. 4. **Embedder**: `HF_HUB_OFFLINE=1` in script by default. Set to `"0"` only for first-time download; then revert. 5. **Shared embedder**: `get_shared_embedder()` used by both `index_framework` and `retrieve_control_details`. Single load per process. 6. **Qdrant point IDs**: Must be UUIDs. Use `uuid.uuid5(namespace, chunk_id)`. Store `chunk_id` in payload. diff --git a/services/ai-service/src/README.md b/services/ai-service/src/README.md new file mode 100644 index 0000000..5c81ee6 --- /dev/null +++ b/services/ai-service/src/README.md @@ -0,0 +1,14 @@ +# src — AI Service Source + +Root package for the Compliance Framework Extraction & RAG system. + +**Subpackages:** +- **core/** — Framework extraction (LLM) and applicant evaluation +- **processing/** — PDF parsing and text chunking +- **embeddings/** — Gemma embedder and Qdrant vector DB +- **rag/** — Index framework (JSON + PDF) and retrieve by control ID +- **utils/** — Paths, JSON save/load, vector_db helpers +- **api/** — FastAPI app, routers, request/response models (when present) +- **services/** — Service-layer orchestration (when present) + +**Entry:** Package exports in `__init__.py`. See `ARCHITECTURE.md` for full data flows. diff --git a/services/ai-service/src/api/README.md b/services/ai-service/src/api/README.md new file mode 100644 index 0000000..befed81 --- /dev/null +++ b/services/ai-service/src/api/README.md @@ -0,0 +1,14 @@ +# api — FastAPI Application Layer + +HTTP API for the Compliance Framework Extraction & Evaluation system. + +**Contents:** +- **app.py** — FastAPI app instance, CORS, exception handlers. Include routers here. +- **routers/** — Route handlers: frameworks (setup), health. +- **models/** — Pydantic request/response schemas (responses.py). ErrorResponse, ExtractionError, SetupFrameworkResponse, ControlSummary. + +**Endpoints:** +- `GET /health` — Health check. +- `POST /api/v1/frameworks/setup` — Setup framework: form fields `framework_name`, `section_names` (list), `files` (list of PDFs). Same order for section_names and files. + +**Run:** From `ai-service/`: `python src/api/app.py` or `python run.py`. Docs: `/api/docs`, `/api/redoc`. diff --git a/services/ai-service/src/api/__init__.py b/services/ai-service/src/api/__init__.py new file mode 100644 index 0000000..0c3c4ef --- /dev/null +++ b/services/ai-service/src/api/__init__.py @@ -0,0 +1 @@ +"""FastAPI application layer.""" diff --git a/services/ai-service/src/api/app.py b/services/ai-service/src/api/app.py new file mode 100644 index 0000000..df6d238 --- /dev/null +++ b/services/ai-service/src/api/app.py @@ -0,0 +1,56 @@ +"""FastAPI application: CORS, routers, exception handlers.""" + +from fastapi import FastAPI +from fastapi.middleware.cors import CORSMiddleware +from fastapi.responses import JSONResponse + +from src.api.models import ExtractionError +from src.api.routers import frameworks, health + +app = FastAPI( + title="Governance Agent API", + description="Compliance Framework Extraction & Evaluation", + version="0.1.0", + docs_url="/api/docs", + redoc_url="/api/redoc", +) + +app.add_middleware( + CORSMiddleware, + allow_origins=["http://localhost:3000", "http://127.0.0.1:3000"], + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + +app.include_router(health.router) +app.include_router(frameworks.router) + + +@app.exception_handler(ExtractionError) +async def extraction_error_handler(request, exc: ExtractionError): + return JSONResponse( + status_code=500, + content={ + "error": "extraction_failed", + "message": exc.message, + "detail": exc.framework_name, + }, + ) + + +@app.exception_handler(ValueError) +async def value_error_handler(request, exc: ValueError): + return JSONResponse( + status_code=400, + content={ + "error": "bad_request", + "message": str(exc), + "detail": None, + }, + ) + + +if __name__ == "__main__": + import uvicorn + uvicorn.run(app, host="0.0.0.0", port=8000) diff --git a/services/ai-service/src/api/routers/README.md b/services/ai-service/src/api/routers/README.md new file mode 100644 index 0000000..925f9f0 --- /dev/null +++ b/services/ai-service/src/api/routers/README.md @@ -0,0 +1,4 @@ +# api/routers — API Route Handlers + +- **frameworks.py** — `POST /api/v1/frameworks/setup`: multipart form with `framework_name`, `section_names[]`, `files[]`. Validates PDFs, calls FrameworkService, returns SetupFrameworkResponse. +- **health.py** — `GET /health`: returns `{"status": "ok"}`. diff --git a/services/ai-service/src/api/routers/__init__.py b/services/ai-service/src/api/routers/__init__.py new file mode 100644 index 0000000..f7ec5ce --- /dev/null +++ b/services/ai-service/src/api/routers/__init__.py @@ -0,0 +1 @@ +"""API routers.""" diff --git a/services/ai-service/src/api/routers/frameworks.py b/services/ai-service/src/api/routers/frameworks.py new file mode 100644 index 0000000..9e64447 --- /dev/null +++ b/services/ai-service/src/api/routers/frameworks.py @@ -0,0 +1,63 @@ +"""Framework endpoints: setup, list (future).""" + +from fastapi import APIRouter, File, Form, HTTPException, UploadFile + +from src.api.models import ControlSummary, SetupFrameworkResponse +from src.services import FrameworkService + +router = APIRouter(prefix="/api/v1/frameworks", tags=["frameworks"]) + + +@router.post("/setup", response_model=SetupFrameworkResponse) +async def setup_framework( + framework_name: str = Form(..., min_length=1, description="Framework identifier"), + section_names: str = Form( + ..., + description="Comma-separated section names (one per PDF, same order as files). Example: policies,procedures,controls", + ), + files: list[UploadFile] = File(..., description="PDF files (same order as section_names)"), +) -> SetupFrameworkResponse: + """ + Setup a new framework by extracting controls from multiple PDFs. + Each PDF has a section name; controls are saved as config/frameworks/{framework_name}/{section_name}.json. + Send section_names as one string: comma-separated names, one per file, in the same order as files. + """ + section_names_list = [s.strip() for s in section_names.split(",") if s.strip()] + if len(section_names_list) != len(files): + raise HTTPException( + status_code=400, + detail=f"section_names ({len(section_names_list)} after splitting by comma) and files ({len(files)}) must have the same length. Use comma-separated names, e.g. name1,name2,name3", + ) + if not files: + raise HTTPException(status_code=400, detail="At least one PDF file is required") + + # Validate PDFs and read bytes + pdf_sections: list[tuple[str, bytes]] = [] + for name, upload in zip(section_names_list, files): + if not name or not name.strip(): + raise HTTPException( + status_code=400, + detail="Section names cannot be empty", + ) + if not (upload.filename and upload.filename.lower().endswith(".pdf")): + raise HTTPException( + status_code=400, + detail=f"File for section '{name}' must be a PDF (filename ending in .pdf)", + ) + body = await upload.read() + if not body: + raise HTTPException( + status_code=400, + detail=f"File for section '{name}' is empty", + ) + pdf_sections.append((name.strip(), body)) + + service = FrameworkService() + result = service.setup_framework(framework_name=framework_name, pdf_sections=pdf_sections) + + return SetupFrameworkResponse( + framework_name=result["framework_name"], + total_controls=result["total_controls"], + sections=[ControlSummary(**s) for s in result["sections"]], + created_at=result["created_at"], + ) diff --git a/services/ai-service/src/api/routers/health.py b/services/ai-service/src/api/routers/health.py new file mode 100644 index 0000000..d1b4da6 --- /dev/null +++ b/services/ai-service/src/api/routers/health.py @@ -0,0 +1,11 @@ +"""Health check endpoint.""" + +from fastapi import APIRouter + +router = APIRouter(tags=["health"]) + + +@router.get("/health") +def health() -> dict[str, str]: + """Basic health check.""" + return {"status": "ok"} diff --git a/services/ai-service/src/core/README.md b/services/ai-service/src/core/README.md new file mode 100644 index 0000000..0d74ead --- /dev/null +++ b/services/ai-service/src/core/README.md @@ -0,0 +1,13 @@ +# core — Business Logic + +Framework extraction and applicant evaluation. + +**Modules:** +- **framework_extraction.py** — Extract compliance controls from framework PDFs (merged module): + - `extract_controls_from_framework(pdf_text, framework_name, use_fallback_prompt)` — LLM extraction (OpenRouter), returns `{framework_name, controls}`. + - `extract_controls_from_pdfs(pdf_paths_list, framework_name)` — Parallel extraction over multiple PDFs with retries on empty. +- **evaluator.py** — `evaluate_applicant(applicant_docs, evaluation_prompt, controls_json)` — Evaluate applicant documents against a framework (externally provided prompt and controls). + +**Control schema:** Each control has `id`, `description`, `calculation`, `threshold`, `scale`. + +**Dependencies:** `openai`, `python-dotenv`; `OPENROUTER_API_KEY`. Evaluator uses same API. Processing used for PDF text. diff --git a/services/ai-service/src/core/__init__.py b/services/ai-service/src/core/__init__.py index 5c616a0..0beb09e 100644 --- a/services/ai-service/src/core/__init__.py +++ b/services/ai-service/src/core/__init__.py @@ -1,9 +1,11 @@ from .evaluator import evaluate_applicant -from .framework_extractor import extract_controls_from_framework -from .framework_consolidator import extract_controls_from_pdfs +from .framework_extraction import ( + extract_controls_from_framework, + extract_controls_from_pdfs, +) __all__ = [ - "evaluate_applicant", + "evaluate_applicant", "extract_controls_from_framework", - "extract_controls_from_pdfs" + "extract_controls_from_pdfs", ] diff --git a/services/ai-service/src/core/framework_consolidator.py b/services/ai-service/src/core/framework_consolidator.py deleted file mode 100644 index ca24cb5..0000000 --- a/services/ai-service/src/core/framework_consolidator.py +++ /dev/null @@ -1,53 +0,0 @@ -""" -Framework Consolidator Module -Extracts controls from multiple PDFs in parallel. -""" - -from typing import Dict, Any, List -from concurrent.futures import ThreadPoolExecutor - -from src.processing import extract_text_from_pdf -from src.core.framework_extractor import extract_controls_from_framework - - -def extract_controls_from_pdfs( - pdf_paths_list: List[str], framework_name: str -) -> List[List[Dict[str, Any]]]: - """ - Extract controls from multiple PDFs in parallel. - Each PDF gets one LLM call. - - Args: - pdf_paths_list: List of PDF file paths - framework_name: Name of the framework - - Returns: - List of controls arrays (one per PDF) - """ - - MAX_RETRIES = 2 - - def extract_pdf(pdf_path: str): - """Extract controls from a single PDF. Retries with fallback prompt if empty.""" - try: - pdf_text = extract_text_from_pdf(pdf_path) - controls_json = extract_controls_from_framework(pdf_text, framework_name) - controls = controls_json.get("controls", []) - retries = 0 - while len(controls) == 0 and retries < MAX_RETRIES: - retries += 1 - print(f"Empty controls for {pdf_path}, retry {retries}/{MAX_RETRIES} with fallback prompt") - controls_json = extract_controls_from_framework( - pdf_text, framework_name, use_fallback_prompt=True - ) - controls = controls_json.get("controls", []) - print(f"Controls extracted from {pdf_path}") - return controls - except Exception as e: - print(f"Error extracting from {pdf_path}: {e}") - return [] - - with ThreadPoolExecutor(max_workers=len(pdf_paths_list)) as executor: - controls_arrays = list(executor.map(extract_pdf, pdf_paths_list)) - - return controls_arrays diff --git a/services/ai-service/src/core/framework_extractor.py b/services/ai-service/src/core/framework_extraction.py similarity index 70% rename from services/ai-service/src/core/framework_extractor.py rename to services/ai-service/src/core/framework_extraction.py index fb51528..60cdbad 100644 --- a/services/ai-service/src/core/framework_extractor.py +++ b/services/ai-service/src/core/framework_extraction.py @@ -1,13 +1,17 @@ """ -Framework Extractor Module -Extracts compliance controls from PDF text using LLM +Framework Extraction Module +Extracts compliance controls from PDF text (LLM) and from multiple PDFs in parallel. """ import json -from openai import OpenAI -from typing import Dict, Any import os +from concurrent.futures import ThreadPoolExecutor +from typing import Any, Dict, List + from dotenv import load_dotenv +from openai import OpenAI + +from src.processing import extract_text_from_pdf load_dotenv() @@ -83,7 +87,7 @@ def extract_controls_from_framework( "Never return an empty 'controls' array unless the document has no extractable content. " "Return ONLY valid JSON with a 'controls' array. Each control must have exactly: id, description, calculation, threshold, scale." ) - temperature = 0.5 + temperature = 0.15 else: extraction_prompt = f""" ### ROLE @@ -102,8 +106,9 @@ def extract_controls_from_framework( Return ONLY a valid JSON object matching the schema above. No other keys or fields. """ system_content = "You are a Senior Regulatory Data Architect. Extract compliance controls. Return ONLY valid JSON with a 'controls' array. Each control must have exactly: id, description, calculation, threshold, scale." - temperature = 0.3 + temperature = 0.15 + # Low temperature + fixed seed for reproducible, consistent extraction response = client.chat.completions.create( model="openai/gpt-4.1", messages=[ @@ -111,45 +116,47 @@ def extract_controls_from_framework( {"role": "user", "content": extraction_prompt}, ], temperature=temperature, + seed=42, response_format={"type": "json_object"} ) - + result_text = response.choices[0].message.content - print("\n" + "="*60) - print("EXTRACTION RESPONSE:") - print("="*60) - print(result_text) - print("="*60 + "\n") - + if not result_text or not str(result_text).strip(): + print("API returned empty response (possible rate limit or error)") + return {"framework_name": framework_name, "controls": []} + + result_text = str(result_text).strip() + # Try to extract JSON from markdown code blocks first if "```json" in result_text: json_start = result_text.find("```json") + 7 json_end = result_text.find("```", json_start) if json_end > json_start: result_text = result_text[json_start:json_end].strip() - + # Parse JSON with error handling try: result_json = json.loads(result_text) except json.JSONDecodeError as e: - print(f"Warning: JSON parsing error: {e}") + preview = (result_text or "")[:200] + print(f"Warning: JSON parsing error: {e}. Raw response (len={len(result_text or '')}): {preview!r}") # Try to fix truncated JSON by finding last complete structure last_brace = result_text.rfind('}') last_bracket = result_text.rfind(']') end_pos = max(last_brace, last_bracket) - + if end_pos > 0: try: result_json = json.loads(result_text[:end_pos + 1]) print("Successfully parsed truncated JSON") - except: + except Exception: print("Could not parse JSON, returning empty controls") return {"framework_name": framework_name, "controls": []} else: return {"framework_name": framework_name, "controls": []} - + # Find controls array - check common keys first - controls_array = [] + controls_array: List[Dict[str, Any]] = [] if isinstance(result_json, list): controls_array = result_json elif isinstance(result_json, dict): @@ -158,19 +165,64 @@ def extract_controls_from_framework( if key in result_json and isinstance(result_json[key], list): controls_array = result_json[key] break - + # If not found, find any array if not controls_array: for value in result_json.values(): if isinstance(value, list) and len(value) > 0: controls_array = value break - + # If still not found and it's a single control object, wrap it in array if not controls_array and 'id' in result_json: controls_array = [result_json] - + return { "framework_name": framework_name, "controls": controls_array } + + +def extract_controls_from_pdfs( + pdf_paths_list: List[str], framework_name: str +) -> List[List[Dict[str, Any]]]: + """ + Extract controls from multiple PDFs in parallel. + Each PDF gets one LLM call. + + Args: + pdf_paths_list: List of PDF file paths + framework_name: Name of the framework + + Returns: + List of controls arrays (one per PDF) + """ + MAX_RETRIES = 3 + + def extract_pdf(pdf_path: str) -> List[Dict[str, Any]]: + """Extract controls from a single PDF. Retries with fallback prompt if empty.""" + try: + pdf_text = extract_text_from_pdf(pdf_path) + if not (pdf_text and pdf_text.strip()): + print(f"Skipping {pdf_path}: no text extracted") + return [] + controls_json = extract_controls_from_framework(pdf_text, framework_name) + controls = controls_json.get("controls", []) + retries = 0 + while len(controls) == 0 and retries < MAX_RETRIES: + retries += 1 + print(f"Empty controls for {pdf_path}, retry {retries}/{MAX_RETRIES} with fallback prompt") + controls_json = extract_controls_from_framework( + pdf_text, framework_name, use_fallback_prompt=True + ) + controls = controls_json.get("controls", []) + print(f"Controls extracted from {pdf_path}") + return controls + except Exception as e: + print(f"Error extracting from {pdf_path}: {e}") + return [] + + with ThreadPoolExecutor(max_workers=len(pdf_paths_list)) as executor: + controls_arrays = list(executor.map(extract_pdf, pdf_paths_list)) + + return controls_arrays diff --git a/services/ai-service/src/embeddings/README.md b/services/ai-service/src/embeddings/README.md new file mode 100644 index 0000000..8892114 --- /dev/null +++ b/services/ai-service/src/embeddings/README.md @@ -0,0 +1,10 @@ +# embeddings — Vector Embeddings and Qdrant + +Embedding model and vector store for RAG. + +**Modules:** +- **gemma_embedder.py** — `GemmaEmbedder`, `load_gemma_embedder()`. Google EmbeddingGemma 300M via `sentence-transformers`. Set `HF_HUB_OFFLINE=1` in script for cache-only; `0` for first-time download. +- **qdrant_manager.py** — Qdrant CRUD: `initialize_qdrant`, `add_documents`, `search_similar`, `fetch_by_filter`, `search_similar_filtered`. Point IDs must be UUIDs (e.g. `uuid5` from `chunk_id`). +- **haystack_retriever.py** — Optional Haystack + Qdrant integration. + +**Used by:** RAG (ingestion, retrieval). Heavy deps; prefer importing only when needed. diff --git a/services/ai-service/src/processing/README.md b/services/ai-service/src/processing/README.md new file mode 100644 index 0000000..f6a47a1 --- /dev/null +++ b/services/ai-service/src/processing/README.md @@ -0,0 +1,9 @@ +# processing — PDF and Text Processing + +Low-level PDF parsing and text chunking used by extraction and RAG. + +**Modules:** +- **pdf_parser.py** — `extract_text_from_pdf(pdf_path) -> str`. Uses `pdfplumber`; supports multilingual (e.g. Arabic/English). +- **text_chunker.py** — `chunk_text(text, chunk_size, overlap, ...)` and `chunk_text_by_sentences(text, sentences_per_chunk, ...)`. Returns list of dicts with `text` and `metadata`. + +**Used by:** Core (extraction), RAG (ingestion). No LLM or DB here. diff --git a/services/ai-service/src/processing/text_chunker.py b/services/ai-service/src/processing/text_chunker.py index 3baef04..d23ac15 100644 --- a/services/ai-service/src/processing/text_chunker.py +++ b/services/ai-service/src/processing/text_chunker.py @@ -188,7 +188,7 @@ def _is_valid_chunk(text: str, min_meaningful_chars: int = 100) -> bool: return True - +# not used anymore def chunk_text_by_sentences( text: str, sentences_per_chunk: int = 5, diff --git a/services/ai-service/src/rag/README.md b/services/ai-service/src/rag/README.md new file mode 100644 index 0000000..4ac4130 --- /dev/null +++ b/services/ai-service/src/rag/README.md @@ -0,0 +1,10 @@ +# rag — RAG Indexing and Retrieval + +Index framework (JSON control cards + PDF chunks) and retrieve by control ID. + +**Modules:** +- **_shared.py** — `get_shared_embedder()` — singleton embedder per process. +- **ingestion.py** — `index_framework(framework_name, pdf_paths=None)`. Loads JSON from `config/frameworks/{name}/`, PDFs from `data/inputs/vector_db/`; chunks and embeds; writes to Qdrant collection `{framework_name}_rag`. +- **retrieval.py** — `retrieve_control_details(control_id, framework_name, top_k_pdf=5)` — JSON cards + similar PDF chunks. `RETRIEVE_CONTROL_DETAILS_TOOL_SCHEMA` for agent tools. + +**Payload:** `source` (json|pdf), `control_id`, `framework_name`, `source_pdf`. diff --git a/services/ai-service/src/services/README.md b/services/ai-service/src/services/README.md new file mode 100644 index 0000000..9b38a9a --- /dev/null +++ b/services/ai-service/src/services/README.md @@ -0,0 +1,7 @@ +# services — Service Layer (Orchestration) + +Orchestrates core modules for API and CLI. + +- **framework_service.py** — `FrameworkService.setup_framework(framework_name, pdf_sections: list[tuple[str, bytes]])`. Saves PDFs to temp dir, calls `extract_controls_from_pdfs`, saves JSON per section via `save_extraction_json(..., custom_name=section_name)`, returns summary dict. Cleans up temp dir. + +Used by: `src.api.routers.frameworks`, `main.py` (CLI). diff --git a/services/ai-service/src/services/__init__.py b/services/ai-service/src/services/__init__.py new file mode 100644 index 0000000..ccb76bc --- /dev/null +++ b/services/ai-service/src/services/__init__.py @@ -0,0 +1,5 @@ +"""Service layer for orchestration.""" + +from .framework_service import FrameworkService + +__all__ = ["FrameworkService"] diff --git a/services/ai-service/src/services/framework_service.py b/services/ai-service/src/services/framework_service.py new file mode 100644 index 0000000..5a00395 --- /dev/null +++ b/services/ai-service/src/services/framework_service.py @@ -0,0 +1,101 @@ +""" +Framework service: orchestrates framework setup (extraction + save). +Used by API and CLI. +""" + +import shutil +import tempfile +from datetime import datetime +from pathlib import Path +from typing import Any + +from src.core import extract_controls_from_pdfs +from src.rag import index_framework +from src.utils import get_input_paths, save_extraction_json + + +def _project_root() -> Path: + return Path(__file__).resolve().parent.parent.parent + + +class FrameworkService: + """Orchestrates framework setup operations.""" + + def setup_framework( + self, + framework_name: str, + pdf_sections: list[tuple[str, bytes]], + ) -> dict[str, Any]: + """ + Process multiple PDFs with section names: extract controls, save JSON per section, and index into vector DB. + + Steps: + 1. Save uploaded PDFs to a temp directory with section names as filenames. + 2. Extract controls from each PDF in parallel (existing core logic). + 3. Save one JSON per section under config/frameworks/{framework_name}/{section_name}.json. + 4. Persist PDFs to data/inputs/vector_db/{framework_name}/ and call index_framework to populate vector DB. + 5. Return summary (framework_name, total_controls, sections, created_at). + + Args: + framework_name: Framework identifier. + pdf_sections: List of (section_name, pdf_bytes). Section name is used as the JSON filename. + + Returns: + Dict with keys: framework_name, total_controls, sections (list of {section_name, controls_count, json_path}), created_at. + """ + if not pdf_sections: + raise ValueError("At least one PDF section is required") + + temp_dir = Path(tempfile.mkdtemp()) + try: + pdf_paths_list: list[str] = [] + section_names_order: list[str] = [] + for section_name, content in pdf_sections: + safe_name = Path(section_name).name or "section" + path = temp_dir / f"{safe_name}.pdf" + path.write_bytes(content) + pdf_paths_list.append(str(path)) + section_names_order.append(safe_name) + + controls_arrays = extract_controls_from_pdfs(pdf_paths_list, framework_name) + + project_root = _project_root() + sections_out: list[dict[str, Any]] = [] + total_controls = 0 + + for section_name, controls_array in zip(section_names_order, controls_arrays): + obj: dict[str, Any] = {"framework_name": framework_name, "controls": controls_array} + pdf_path = str(temp_dir / f"{section_name}.pdf") + saved_path = save_extraction_json( + framework_name, pdf_path, obj, custom_name=section_name + ) + try: + json_path_rel = saved_path.relative_to(project_root) + except ValueError: + json_path_rel = saved_path + sections_out.append({ + "section_name": section_name, + "controls_count": len(controls_array), + "json_path": str(json_path_rel), + }) + total_controls += len(controls_array) + + # Persist PDFs for RAG and index into vector DB + paths = get_input_paths() + vector_db_dir = paths["vector_db"] / framework_name + vector_db_dir.mkdir(parents=True, exist_ok=True) + pdf_paths_for_rag: list[str] = [] + for (section_name, content), safe_name in zip(pdf_sections, section_names_order): + out_pdf = vector_db_dir / f"{safe_name}.pdf" + out_pdf.write_bytes(content) + pdf_paths_for_rag.append(str(out_pdf)) + index_framework(framework_name, pdf_paths=pdf_paths_for_rag) + + return { + "framework_name": framework_name, + "total_controls": total_controls, + "sections": sections_out, + "created_at": datetime.utcnow(), + } + finally: + shutil.rmtree(temp_dir, ignore_errors=True) diff --git a/services/ai-service/src/utils/README.md b/services/ai-service/src/utils/README.md new file mode 100644 index 0000000..57195ac --- /dev/null +++ b/services/ai-service/src/utils/README.md @@ -0,0 +1,11 @@ +# utils — Paths and Framework Helpers + +Project paths and per-PDF JSON save/load for extraction and RAG. + +**Module:** **framework_utils.py** +- `save_extraction_json(framework_name, pdf_path, controls_json, custom_name=None)` — Save to `config/frameworks/{framework_name}/{stem}.json`. Use `custom_name` for section name when provided. +- `get_input_paths()` — Returns `frameworks`, `applicants`, `vector_db` under `data/inputs/`. +- `list_framework_jsons(framework_name)` — Load all `*.json` for a framework; returns `list[(stem, dict)]`. +- `get_vector_db_pdf_paths(framework_name)` — PDFs in `data/inputs/vector_db/`; prefers `vector_db/{framework_name}/` then flat `vector_db/*.pdf`. + +**Paths relative to:** `services/ai-service/` (project root). diff --git a/services/ai-service/src/utils/framework_utils.py b/services/ai-service/src/utils/framework_utils.py index 0bd286b..ba87114 100644 --- a/services/ai-service/src/utils/framework_utils.py +++ b/services/ai-service/src/utils/framework_utils.py @@ -5,7 +5,7 @@ import json from pathlib import Path -from typing import Dict, Any, List, Tuple +from typing import Any, Dict, List, Optional, Tuple def _project_root() -> Path: @@ -13,17 +13,21 @@ def _project_root() -> Path: def save_extraction_json( - framework_name: str, pdf_path: str, controls_json: Dict[str, Any] + framework_name: str, + pdf_path: str, + controls_json: Dict[str, Any], + custom_name: Optional[str] = None, ) -> Path: """ Save extracted controls for a single PDF as JSON under config/frameworks/{framework_name}/. - Filename is the PDF stem + .json (e.g. section-a.pdf -> section-a.json). + Filename is custom_name (if provided) or the PDF stem + .json (e.g. section-a.pdf -> section-a.json). Args: framework_name: Name of the framework (used for directory name) - pdf_path: Path to the source PDF (used for filename) + pdf_path: Path to the source PDF (used for filename when custom_name is not set) controls_json: Dictionary with framework_name and controls + custom_name: Optional name for the JSON file (e.g. section name). If set, used instead of PDF stem. Returns: Path to the written JSON file @@ -33,7 +37,7 @@ def save_extraction_json( framework_dir = base_dir / framework_name framework_dir.mkdir(parents=True, exist_ok=True) - stem = Path(pdf_path).stem + stem = Path(custom_name).name if custom_name else Path(pdf_path).stem out_path = framework_dir / f"{stem}.json" with open(out_path, "w", encoding="utf-8") as f: json.dump(controls_json, f, indent=2, ensure_ascii=False) diff --git a/services/ai-service/test.ipynb b/services/ai-service/test.ipynb index d8e6224..b54ce0e 100644 --- a/services/ai-service/test.ipynb +++ b/services/ai-service/test.ipynb @@ -11,18 +11,16 @@ "output_type": "stream", "text": [ "/Users/mohammedbalkhair/Documents/Governance-Agent/services/ai-service/.venv/lib/python3.13/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n", - "Batches: 100%|██████████| 14/14 [00:32<00:00, 2.34s/it]\n" + " from .autonotebook import tqdm as notebook_tqdm\n" ] } ], "source": [ "import os\n", "os.environ[\"HF_HUB_OFFLINE\"] = \"1\" \n", - "from src.rag import index_framework, retrieve_control_details, RETRIEVE_CONTROL_DETAILS_TOOL_SCHEMA\n", + "from src.rag import retrieve_control_details, RETRIEVE_CONTROL_DETAILS_TOOL_SCHEMA\n", + "\n", "\n", - "# 1. Index (PDFs from data/inputs/vector_db/ or vector_db/{framework_name}/)\n", - "index_framework(\"NDI\")\n", "\n", "# 2. Retrieve (e.g. from an agent)\n", "out = retrieve_control_details(\"DG.1\", \"NDI\", top_k_pdf=5)\n", @@ -35,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "a9e7ac20", "metadata": {}, "outputs": [ @@ -61,7 +59,7 @@ " 'source_pdf': 'OperationalExcellence-OE'}}]}" ] }, - "execution_count": 3, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } diff --git a/services/ai-service/uv.lock b/services/ai-service/uv.lock index ffa3908..3cf9aa4 100644 --- a/services/ai-service/uv.lock +++ b/services/ai-service/uv.lock @@ -30,6 +30,8 @@ version = "0.1.0" source = { virtual = "." } dependencies = [ { name = "accelerate" }, + { name = "aiofiles" }, + { name = "fastapi" }, { name = "haystack-ai" }, { name = "ipykernel" }, { name = "openai" }, @@ -37,15 +39,23 @@ dependencies = [ { name = "pydantic" }, { name = "pypdf" }, { name = "python-dotenv" }, + { name = "python-multipart" }, { name = "qdrant-client" }, { name = 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