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..6aee078 --- /dev/null +++ b/services/ai-service/README.md @@ -0,0 +1,100 @@ +# 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 (one JSON per section) +│ └── {framework_name}/ +│ └── {section_name}.json +└── vector_db/ # Qdrant vector database storage + +data/ +└── outputs/ + └── evaluations/ # Evaluation reports (JSON files) +``` + +**What gets saved where:** +- **Framework Data**: `config/frameworks/{framework_name}/` + - 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 + +### CLI: Setup a Framework + +```python +from main import setup_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 extracts controls from each PDF and saves one JSON per PDF under `config/frameworks/{framework_name}/` (filename = PDF stem). + +### API: Setup a Framework + +Run the API server (from `services/ai-service/`): + +```bash +python src/api/app.py +# or +python run.py +``` + +- **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`. + +### Other (from `src`) + +- **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). + +## Dependencies + +Use **uv** from the project root (`services/ai-service/`): + +```bash +# Install all dependencies (after cloning or when pyproject.toml changes) +uv sync + +# 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 +``` + +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 new file mode 100644 index 0000000..c11249c --- /dev/null +++ b/services/ai-service/main.py @@ -0,0 +1,71 @@ +""" +Main entry point for the Compliance Framework Extraction System. +CLI: extract controls from each PDF and save one JSON per PDF (no compose, no prompt). +Uses the same service layer as the API. +""" + +import glob +from pathlib import Path + +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 stem (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: + List of paths to the saved JSON files + """ + if isinstance(pdf_paths, list): + pdf_paths_list = pdf_paths + else: + 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}") + + 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(): + """Main entry point.""" + print("\n" + "=" * 60) + print("Compliance Framework Extraction System") + print("=" * 60) + + paths = get_input_paths() + print("\nDirectory structure:") + print(f" Input PDFs (Frameworks): {paths['frameworks']}") + print(f" Input PDFs (Applicants): {paths['applicants']}") + 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/NDI" + framework_name = "NDI" + 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..23fbfde --- /dev/null +++ b/services/ai-service/pyproject.toml @@ -0,0 +1,29 @@ +[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", + "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 new file mode 100644 index 0000000..1cb1f86 --- /dev/null +++ b/services/ai-service/src/ARCHITECTURE.md @@ -0,0 +1,422 @@ +# Code Architecture Documentation + +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 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 | +| **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. + +--- + +## Directory Structure + +``` +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_extraction.py # extract_controls_from_framework + extract_controls_from_pdfs +│ +├── processing/ +│ ├── pdf_parser.py # extract_text_from_pdf +│ └── text_chunker.py # chunk_text, chunk_text_by_sentences +│ +├── 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) +│ +├── 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 +│ +├── services/ +│ └── framework_service.py # FrameworkService.setup_framework (orchestration) +│ +├── utils/ +│ └── 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 points**: `main.py` (CLI) — `setup_framework()`, `get_input_paths()`. `python src/api/app.py` or `python run.py` — API server. + +--- + +## Module Details + +### Core (`src/core/`) + +#### `framework_extraction.py` + +**Purpose**: Extract compliance controls from PDF text (LLM) and from multiple PDFs in parallel. + +**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 extraction prompt or JSON schema; switch LLM (currently `openai/gpt-4.1`); adjust temperature or retry logic. + +**Dependencies**: `openai`, `python-dotenv`, `OPENROUTER_API_KEY`; `processing.extract_text_from_pdf`. + +--- + +#### `evaluator.py` + +**Purpose**: Evaluate applicant documents against a framework using LLM. + +**Key function**: `evaluate_applicant(applicant_docs, evaluation_prompt, controls_json) → dict` + +- Uses **externally provided** `evaluation_prompt` and `controls_json`. The extraction pipeline does **not** generate evaluation prompts. + +**When to modify**: +- Change evaluation logic or report structure. +- Switch LLM or parameters. + +**Dependencies**: OpenAI API, `OPENAI_API_KEY`. + +--- + +### Processing (`src/processing/`) + +#### `pdf_parser.py` + +**Key function**: `extract_text_from_pdf(pdf_path) → str` + +**When to modify**: Change PDF library (`pdfplumber`), add formats, OCR. + +--- + +#### `text_chunker.py` + +**Key functions**: +- `chunk_text(text, chunk_size, overlap, framework_name, ...) → list[dict]` +- `chunk_text_by_sentences(text, sentences_per_chunk, ...) → list[dict]` + +Chunks include `text`, `metadata` (e.g. `length`). Used by RAG ingestion for PDF chunking. + +**When to modify**: Chunk size, overlap, or chunking strategy. + +--- + +### Embeddings (`src/embeddings/`) + +#### `gemma_embedder.py` + +**Purpose**: Embedding model wrapper (Google EmbeddingGemma 300M via `sentence-transformers`). + +**Important**: At module load, **before** any HuggingFace imports: + +```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**: +- 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`. + +--- + +#### `qdrant_manager.py` + +**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**: +- 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`. + +--- + +#### `haystack_retriever.py` + +**Purpose**: Optional Haystack + Qdrant integration. + +**When to modify**: Haystack-specific retrieval or pipeline changes. + +--- + +### RAG (`src/rag/`) + +#### `_shared.py` + +**Purpose**: Single embedder instance per process to avoid repeated model loads. + +**Key**: `get_shared_embedder() → GemmaEmbedder`. Caches on first use. Used by `index_framework` and `retrieve_control_details`. + +**When to modify**: Only if you change embedder lifecycle (e.g. multi-process). + +--- + +#### `ingestion.py` + +**Purpose**: Index a framework for RAG — JSON control cards + PDF chunks. + +**Key function**: `index_framework(framework_name, pdf_paths=None) → None` + +- **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**: +- Change JSON vs PDF sourcing or payload schema. +- Change collection naming or embedder usage. + +--- + +#### `retrieval.py` + +**Purpose**: Retrieve control details by ID — JSON cards + relevant PDF chunks. **Agent-tool friendly.** + +**Key function**: `retrieve_control_details(control_id, framework_name, *, top_k_pdf=5) → dict` + +- **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}"`. + +**Returns**: `{"json_cards": [...], "pdf_chunks": [...]}`. Each item has `text` and `metadata`; chunks also have `score`. + +**Tool schema**: `RETRIEVE_CONTROL_DETAILS_TOOL_SCHEMA` — use for OpenAI tools, LangChain, etc. + +**When to modify**: +- Change filter logic, query construction, or `top_k_pdf`. +- Update tool schema if API changes. + +--- + +### Utils (`src/utils/`) + +#### `framework_utils.py` + +**Key functions**: +- `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`. + +**When to modify**: Storage paths, file naming, or vector_db resolution. + +--- + +### 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`) + +**Function**: `setup_framework(pdf_paths: str | list[str], framework_name: str) → list[Path]` + +```mermaid +flowchart TD + 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 (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`. + +**No** composition, evaluation prompt generation, or framework metadata files. + +--- + +### 2. RAG indexing (`index_framework`) + +**Function**: `index_framework(framework_name, pdf_paths=None)` + +```mermaid +flowchart TD + 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] +``` + +**Storage**: +- **JSON cards**: `config/frameworks/{framework_name}/*.json`. +- **PDFs**: `data/inputs/vector_db/{framework_name}/` or `vector_db/`. + +--- + +### 3. RAG retrieval (`retrieve_control_details`) + +**Function**: `retrieve_control_details(control_id, framework_name, top_k_pdf=5)` + +```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 +``` + +Use `RETRIEVE_CONTROL_DETAILS_TOOL_SCHEMA` when registering as an agent tool. + +--- + +## Common Modification Scenarios + +| Goal | Files to change | +|------|------------------| +| **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 | +| **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 + +| Variable | Used by | Purpose | +|----------|---------|---------| +| `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 | + +--- + +## Dependencies Overview + +| Module | Key dependencies | +|--------|-------------------| +| `core/` | `openai`, `python-dotenv` | +| `processing/` | `pdfplumber` | +| `embeddings/` | `sentence-transformers`, `torch`, `qdrant-client`, `huggingface_hub` | +| `rag/` | (uses `embeddings`, `utils`, `processing`) | +| `utils/` | stdlib only | + +--- + +## Entry Points and Imports + +**`main.py`**: +- `setup_framework(pdf_paths, framework_name)` — extract + save per-PDF JSON. +- `get_input_paths()` — frameworks, applicants, vector_db dirs. + +**Typical imports**: + +```python +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, +) +``` + +--- + +## 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_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. +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/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/__init__.py b/services/ai-service/src/__init__.py new file mode 100644 index 0000000..707f01b --- /dev/null +++ b/services/ai-service/src/__init__.py @@ -0,0 +1,69 @@ +""" +Compliance Framework Extraction System +""" + +__version__ = "0.1.0" + +# Core exports +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.) +# 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, +) + +# Utils exports +from .utils import ( + 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", + "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", + # Utils + "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/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 new file mode 100644 index 0000000..0beb09e --- /dev/null +++ b/services/ai-service/src/core/__init__.py @@ -0,0 +1,11 @@ +from .evaluator import evaluate_applicant +from .framework_extraction import ( + extract_controls_from_framework, + extract_controls_from_pdfs, +) + +__all__ = [ + "evaluate_applicant", + "extract_controls_from_framework", + "extract_controls_from_pdfs", +] diff --git a/services/ai-service/src/core/evaluator.py b/services/ai-service/src/core/evaluator.py new file mode 100644 index 0000000..f770b63 --- /dev/null +++ b/services/ai-service/src/core/evaluator.py @@ -0,0 +1,110 @@ +""" +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="openai/gpt-4.1", + 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"} + ) + + # 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) + 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_extraction.py b/services/ai-service/src/core/framework_extraction.py new file mode 100644 index 0000000..60cdbad --- /dev/null +++ b/services/ai-service/src/core/framework_extraction.py @@ -0,0 +1,228 @@ +""" +Framework Extraction Module +Extracts compliance controls from PDF text (LLM) and from multiple PDFs in parallel. +""" + +import json +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() + + +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" + ) + + 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. + +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 = 0.15 + 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 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 = 0.15 + + # Low temperature + fixed seed for reproducible, consistent extraction + response = client.chat.completions.create( + model="openai/gpt-4.1", + messages=[ + {"role": "system", "content": system_content}, + {"role": "user", "content": extraction_prompt}, + ], + temperature=temperature, + seed=42, + response_format={"type": "json_object"} + ) + + result_text = response.choices[0].message.content + 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: + 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 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: List[Dict[str, Any]] = [] + 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 + } + + +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/embeddings/__init__.py b/services/ai-service/src/embeddings/__init__.py new file mode 100644 index 0000000..e65a290 --- /dev/null +++ b/services/ai-service/src/embeddings/__init__.py @@ -0,0 +1,30 @@ +from .gemma_embedder import GemmaEmbedder, load_gemma_embedder +from .qdrant_manager import ( + initialize_qdrant, + add_documents, + search_similar, + search_similar_filtered, + fetch_by_filter, + 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", + "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 new file mode 100644 index 0000000..8d7b06b --- /dev/null +++ b/services/ai-service/src/embeddings/gemma_embedder.py @@ -0,0 +1,158 @@ +""" +Gemma Embedder Module +Uses Google EmbeddingGemma 300M model for generating embeddings. +Supports HuggingFace token authentication for gated models. +Model: https://huggingface.co/google/embeddinggemma-300m +""" + +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.""" + + 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.""" + 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) + _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, + 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..14435b7 --- /dev/null +++ b/services/ai-service/src/embeddings/qdrant_manager.py @@ -0,0 +1,344 @@ +""" +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, + Filter, + FieldCondition, + MatchValue, +) +from typing import List, Dict, Optional, Any +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)})") + + namespace = uuid.UUID("a0e8520b-12b4-5f3d-9c7e-8a1b2c3d4e5f") + points = [] + for doc, embedding in zip(documents, embeddings): + 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": chunk_id, + "framework_name": doc.get("framework_name", "unknown"), + **doc.get("metadata", {}) + } + + point = PointStruct( + id=point_uuid, + 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 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 + """ + client.delete_collection(collection_name=collection_name) 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/__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..d23ac15 --- /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 + +# not used anymore +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/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/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/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/__init__.py b/services/ai-service/src/utils/__init__.py new file mode 100644 index 0000000..d560f4e --- /dev/null +++ b/services/ai-service/src/utils/__init__.py @@ -0,0 +1,13 @@ +from .framework_utils import ( + save_extraction_json, + get_input_paths, + list_framework_jsons, + get_vector_db_pdf_paths, +) + +__all__ = [ + "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 new file mode 100644 index 0000000..ba87114 --- /dev/null +++ b/services/ai-service/src/utils/framework_utils.py @@ -0,0 +1,101 @@ +""" +Utilities Module +Handles per-PDF extraction saves, input paths, and RAG helpers. +""" + +import json +from pathlib import Path +from typing import Any, Dict, List, Optional, Tuple + + +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], + custom_name: Optional[str] = None, +) -> Path: + """ + Save extracted controls for a single PDF as JSON under config/frameworks/{framework_name}/. + + 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 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 + """ + project_root = _project_root() + base_dir = project_root / "config" / "frameworks" + framework_dir = base_dir / framework_name + framework_dir.mkdir(parents=True, exist_ok=True) + + 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) + 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 = _project_root() + return { + "frameworks": project_root / "data" / "inputs" / "frameworks", + "applicants": project_root / "data" / "inputs" / "applicants", + "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..b54ce0e --- /dev/null +++ b/services/ai-service/test.ipynb @@ -0,0 +1,94 @@ +{ + "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" + ] + } + ], + "source": [ + "import os\n", + "os.environ[\"HF_HUB_OFFLINE\"] = \"1\" \n", + "from src.rag import retrieve_control_details, RETRIEVE_CONTROL_DETAILS_TOOL_SCHEMA\n", + "\n", + "\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": 2, + "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": 2, + "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": 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