diff --git a/evaluation/openrag_eval/.env.example b/evaluation/openrag_eval/.env.example new file mode 100644 index 00000000..e6a5fd6f --- /dev/null +++ b/evaluation/openrag_eval/.env.example @@ -0,0 +1,17 @@ +# OpenRAG Evaluation Environment Variables + +# OpenRAG API Key (required) +# This is your API key for authenticating with the OpenRAG instance. +# Format: orag_your_api_key_here +# +# To obtain an API key: +# 1. Navigate to your OpenRAG instance (e.g., http://localhost:3000) +# 2. Go to Settings or API Keys section +# 3. Generate a new API key +# 4. Copy the key and paste it below +OPENRAG_API_KEY= + +# OpenRAG URL (optional) +# Base URL of your OpenRAG instance +# Default: http://localhost:3000 +# OPENRAG_URL=http://localhost:3000 \ No newline at end of file diff --git a/evaluation/openrag_eval/.gitignore b/evaluation/openrag_eval/.gitignore new file mode 100644 index 00000000..29a074ac --- /dev/null +++ b/evaluation/openrag_eval/.gitignore @@ -0,0 +1,203 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# UV +# Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +#uv.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/latest/usage/project/#working-with-version-control +.pdm.toml +.pdm-python +.pdm-build/ + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +.idea/ + +# Ruff stuff: +.ruff_cache/ + +# PyPI configuration file +.pypirc + +# IDEs +.vscode/ +*.swp +*.swo +*~ + +# OS +.DS_Store +Thumbs.db + +# Specific to project +mlflow.db +*milvus.db* +output*/ + +# From unitxt +inference_engine_cache + +# Cache directories +cache/ + +# Task logs +logs/ + +src/openrag_eval/data_loaders/ait_qa_pdf/documents/ + +# RagWorkbench data directory +ragworkbench_data/ \ No newline at end of file diff --git a/evaluation/openrag_eval/README.md b/evaluation/openrag_eval/README.md new file mode 100644 index 00000000..feffb715 --- /dev/null +++ b/evaluation/openrag_eval/README.md @@ -0,0 +1,79 @@ +# openrag-eval + +OpenRAG Evaluation Tool - an evaluation framework for OpenRAG. + +## Installation + +### Prerequisites + +Install [uv](https://docs.astral.sh/uv/): +```bash +curl -LsSf https://astral.sh/uv/install.sh | sh +``` + +### Installation + +```bash +cd openrag/evaluation/openrag_eval + +# Create virtual environment +uv venv + +# Install with dependencies +uv sync +``` + +## Usage + +Run the main evaluation script: + +```bash +uv run python -m openrag_eval.evaluate +``` + + +## Development + +For development, install with development dependencies: + +```bash +# Install with dev dependencies +uv sync --extra dev + +# Run tests with uv +uv run pytest + +# Format code with uv +uv run black src/ tests/ + +# Lint code with uv +uv run ruff check src/ tests/ + +# Type check with uv +uv run mypy src/ +``` + +## Structure + +The project is organized as follows: + +``` +openrag_eval/ +├── src/openrag_eval/ # Main package +│ ├── pipelines/ # Ingest and inference pipelines +│ ├── boards/ # Evaluation board configurations +│ └── evaluate.py # Main evaluation script +└── tests/ # Tests +``` + +The main components are: + +### Pipelines (`src/openrag_eval/pipelines/`) +Contains implementations of RAG pipelines: +- **`ingest.py`**: A RagWorkbench ingestion pipeline implemented with the OpenRAG SDK +- **`inference.py`**: A RagWorkbench inference pipeline implemented with the OpenRAG SDK + +### Boards (`src/openrag_eval/boards/`) +Contains board configurations for evaluation experiments: +- **`table_rich/`**: A definition for RAG experiments over table-rich documents. + diff --git a/evaluation/openrag_eval/pyproject.toml b/evaluation/openrag_eval/pyproject.toml new file mode 100644 index 00000000..9adefc44 --- /dev/null +++ b/evaluation/openrag_eval/pyproject.toml @@ -0,0 +1,69 @@ +[build-system] +requires = ["setuptools>=61.0", "wheel"] +build-backend = "setuptools.build_meta" + +[project] +name = "openrag-eval" +version = "0.1.0" +description = "OpenRAG Evaluation Tool" +readme = "README.md" +requires-python = ">=3.13" +license = {text = "Apache-2.0"} + +classifiers = [ + "Development Status :: 3 - Alpha", + "Intended Audience :: Developers", + "License :: OSI Approved :: Apache Software License", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", +] + +dependencies = [ + "openrag-sdk", + "ragworkbench @ git+ssh://git@github.com/IBM/RagWorkbench.git", + "pydantic>=2.0.0", + "python-dotenv>=1.0.0", + "openai>=2.0.0", +] + +[project.optional-dependencies] +dev = [ + "pytest>=7.0.0", + "pytest-cov>=4.0.0", + "black>=26.1.0", + "ruff>=0.14.14", + "mypy>=1.14.0", +] + +[tool.setuptools] +package-dir = {"" = "src"} + +[tool.setuptools.packages.find] +where = ["src"] +include = ["openrag_eval*"] + +[tool.black] +line-length = 88 +target-version = ['py311'] + +[tool.ruff] +line-length = 88 +target-version = "py311" +src = ["src"] + +[tool.ruff.lint] +select = ["E", "W", "F", "I", "B", "C4", "UP"] +ignore = ["E501", "B008"] + +[tool.ruff.lint.per-file-ignores] +"__init__.py" = ["F401"] + +[tool.pytest.ini_options] +testpaths = ["tests"] +pythonpath = ["src"] +python_files = ["test_*.py"] +python_classes = ["Test*"] +python_functions = ["test_*"] +addopts = "-v -s --strict-markers" \ No newline at end of file diff --git a/evaluation/openrag_eval/src/openrag_eval/__init__.py b/evaluation/openrag_eval/src/openrag_eval/__init__.py new file mode 100644 index 00000000..21ee5302 --- /dev/null +++ b/evaluation/openrag_eval/src/openrag_eval/__init__.py @@ -0,0 +1,6 @@ +"""OpenRAG Evaluation Tool.""" + +from openrag_eval import boards + +__version__ = "0.1.0" +__all__ = ["boards"] diff --git a/evaluation/openrag_eval/src/openrag_eval/boards/__init__.py b/evaluation/openrag_eval/src/openrag_eval/boards/__init__.py new file mode 100644 index 00000000..f88bdff3 --- /dev/null +++ b/evaluation/openrag_eval/src/openrag_eval/boards/__init__.py @@ -0,0 +1 @@ +"""Boards package for OpenRAG evaluation.""" diff --git a/evaluation/openrag_eval/src/openrag_eval/boards/table_rich/board.yaml b/evaluation/openrag_eval/src/openrag_eval/boards/table_rich/board.yaml new file mode 100644 index 00000000..1b924f81 --- /dev/null +++ b/evaluation/openrag_eval/src/openrag_eval/boards/table_rich/board.yaml @@ -0,0 +1,73 @@ +name: OpenRAG Benchmarking Results + +description: An example of an OpenRAG benchamrking results with the AIT-QA dataset. + +datasets: + + - name: ait_qa + sampling: + question_limit: 100 + +configurations: + - name: openrag_baseline_emb-large + description: OpenRAG with text-embedding-3-large + ingest: + name: openrag + params: + embedding_model: + model_id: text-embedding-3-large + chunking: + chunk_size: 512 + inference: + name: openrag + params: + generative_model: + provider_id: openai + model_id: gpt-5.2 + + - name: openrag_baseline_emb-small + description: OpenRAG with text-embedding-3-small + ingest: + name: openrag + params: + embedding_model: + model_id: text-embedding-3-small + chunking: + chunk_size: 512 + inference: + name: openrag + params: + generative_model: + provider_id: openai + model_id: gpt-5.2 + +metrics: + - unitxt.context_correctness.retrieval_at_k + - unitxt.context_correctness.mrr + - unitxt.context_correctness.map + - unitxt.answer_correctness.llmaaj_llama_rits + +report: + screens: + - title: Answer-Correctness + scores: + metrics.rag.external_rag.answer_correctness.llama_3_3_70b_instruct_rits_judge_mean: AC + chart: + type: bar + title: Answer-correctness + - title: Match@K + scores: + metrics.rag.context_correctness.match_at_1_mean: M@1 + metrics.rag.context_correctness.match_at_3_mean: M@3 + metrics.rag.context_correctness.match_at_5_mean: M@5 + metrics.rag.context_correctness.match_at_10_mean: M@10 + chart: + type: line + title: Match@K + - title: MAP & MRR + scores: + metrics.rag.context_correctness.mrr_mean: MRR + metrics.rag.context_correctness.map_mean: MAP + chart: + type: scatter + title: MAP & MRR diff --git a/evaluation/openrag_eval/src/openrag_eval/evaluate.py b/evaluation/openrag_eval/src/openrag_eval/evaluate.py new file mode 100644 index 00000000..26a5e21d --- /dev/null +++ b/evaluation/openrag_eval/src/openrag_eval/evaluate.py @@ -0,0 +1,32 @@ +import logging +from importlib import resources as impresources +from pathlib import Path + +from ragworkbench.boards.board_generator import BoardGenerator +from ragworkbench.datasets_loader import ait_qa_data_loader + +from openrag_eval import boards as boards_package +from openrag_eval.logging_config import init_logger +from openrag_eval.pipelines import inference as openrag_inference_module +from openrag_eval.pipelines import ingest as openrag_ingest_module +from openrag_eval.utils import load_env_file + +logger = logging.getLogger(__name__) + + +def main() -> None: + init_logger() + + # Load environment variables from .env file + load_env_file() + + boards_directory = impresources.files(boards_package) + + board = BoardGenerator(board_path=Path(str(boards_directory / "table_rich"))) + board.process() + + logger.info(f"Output written to '{board.output_path}'") + + +if __name__ == "__main__": + main() diff --git a/evaluation/openrag_eval/src/openrag_eval/logging_config.py b/evaluation/openrag_eval/src/openrag_eval/logging_config.py new file mode 100644 index 00000000..68a2bcb7 --- /dev/null +++ b/evaluation/openrag_eval/src/openrag_eval/logging_config.py @@ -0,0 +1,27 @@ +"""Shared logging configuration for openrag_eval.""" + +import logging + + +class ShortNameFormatter(logging.Formatter): + """Custom formatter that shows only the last part of the logger name.""" + + def format(self, record: logging.LogRecord) -> str: + # Extract just the module name (last part after the last dot) + if "." in record.name: + record.name = record.name.split(".")[-1] + return super().format(record) + + +def init_logger() -> None: + """Initialize logging configuration with custom format.""" + handler = logging.StreamHandler() + handler.setFormatter( + ShortNameFormatter("%(asctime)s [%(levelname)s] %(name)s: %(message)s") + ) + + logging.basicConfig( + level=logging.INFO, + handlers=[handler], + ) + diff --git a/evaluation/openrag_eval/src/openrag_eval/pipelines/__init__.py b/evaluation/openrag_eval/src/openrag_eval/pipelines/__init__.py new file mode 100644 index 00000000..572d8934 --- /dev/null +++ b/evaluation/openrag_eval/src/openrag_eval/pipelines/__init__.py @@ -0,0 +1,27 @@ +"""OpenRAG evaluation pipelines.""" + +from openrag_eval.pipelines.inference import ( + GenerativeModelParams, + OpenRAGInference, + OpenRAGInferenceParams, +) +from openrag_eval.pipelines.ingest import ( + ChunkingParams, + EmbeddingModelParams, + OpenRAGIngest, + OpenRAGIngestArtifact, + OpenRAGIngestParams, +) + +__all__ = [ + # Inference + "OpenRAGInference", + "OpenRAGInferenceParams", + "GenerativeModelParams", + # Ingest + "OpenRAGIngest", + "OpenRAGIngestParams", + "OpenRAGIngestArtifact", + "ChunkingParams", + "EmbeddingModelParams", +] diff --git a/evaluation/openrag_eval/src/openrag_eval/pipelines/inference.py b/evaluation/openrag_eval/src/openrag_eval/pipelines/inference.py new file mode 100644 index 00000000..087cb966 --- /dev/null +++ b/evaluation/openrag_eval/src/openrag_eval/pipelines/inference.py @@ -0,0 +1,254 @@ +"""OpenRAG inference pipeline implementation for ragbench.""" + +import asyncio +import logging +import traceback +from typing import Any + +from openrag_sdk import ContentEvent, DoneEvent, OpenRAGClient, SourcesEvent +from openrag_sdk.models import SettingsUpdateOptions +from pydantic import BaseModel, Field +from ragworkbench.api.inference import InferenceParams, InferencePipeline +from ragworkbench.api.inference_result import InferenceResult, Trajectory +from ragworkbench.api.ingest_artifact import IngestArtifact +from ragworkbench.boards.board_registry import inference_pipeline +from ragworkbench.datasets_loader.data_models import RagBenchmarkEntry + +from openrag_eval.pipelines.ingest import OpenRAGIngestArtifact + +logger = logging.getLogger(__name__) + + +class GenerativeModelParams(BaseModel): + """Parameters for generative model configuration.""" + + provider_id: str = Field( + description="LLM provider identifier (e.g., 'anthropic', 'openai')" + ) + model_id: str = Field( + description="Model identifier (e.g., 'claude-3-5-sonnet-20241022')" + ) + + +class OpenRAGInferenceParams(InferenceParams): + """Parameters for OpenRAG inference pipeline.""" + + generative_model: GenerativeModelParams = Field( + description="Generative model configuration" + ) + timeout: float = Field( + default=300.0, + description="HTTP request timeout in seconds (default: 5 minutes)", + ) + + +@inference_pipeline(name="openrag", params_class=OpenRAGInferenceParams) +class OpenRAGInference(InferencePipeline): + """Inference pipeline for OpenRAG backend.""" + + def __init__( + self, + params: OpenRAGInferenceParams, + cache_dir: str | None = None, + ) -> None: + """ + Initialize OpenRAG inference pipeline. + + Args: + params: OpenRAG inference parameters + cache_dir: Optional directory for caching generation results + """ + super().__init__(params, cache_dir=cache_dir) + self.params: OpenRAGInferenceParams = params + self._ingest_artifact: OpenRAGIngestArtifact | None = None + + def set_ingest_artifacts(self, ingest_artifacts: list[IngestArtifact]) -> None: + """ + Set the ingest artifacts from the ingestion pipeline. + + Args: + ingest_artifacts: List of ingest artifacts (expects single OpenRAGIngestArtifact) + """ + if len(ingest_artifacts) != 1: + raise ValueError( + f"Expected exactly 1 ingest artifact, got {len(ingest_artifacts)}" + ) + + artifact = ingest_artifacts[0] + if not isinstance(artifact, OpenRAGIngestArtifact): + raise TypeError( + f"Expected OpenRAGIngestArtifact, got {type(artifact).__name__}" + ) + + self._ingest_artifact = artifact + logger.info( + f"Set ingest artifact with index: {artifact.index_name}" + ) + + # Configure generative model settings using async SDK client + asyncio.run(self._configure_generative_model_async(artifact)) + + def _get_additional_cache_params(self) -> dict[str, Any] | None: + """ + Include index_name in the cache key to differentiate between different indices. + + Returns: + Dictionary with index_name if artifact is set, None otherwise + """ + if self._ingest_artifact is not None: + return {"index_name": self._ingest_artifact.index_name} + return None + + async def _configure_generative_model_async( + self, artifact: OpenRAGIngestArtifact + ) -> None: + """Configure the generative model settings using SDK client. + + Args: + artifact: The ingest artifact containing configuration + """ + logger.info("Configuring generative model settings") + + # Use SDK client for LLM configuration and index_name (URL from environment) + async with OpenRAGClient(timeout=self.params.timeout) as sdk_client: + settings_options = SettingsUpdateOptions( + llm_provider=self.params.generative_model.provider_id, + llm_model=self.params.generative_model.model_id, + index_name=artifact.index_name, + ) + await sdk_client.settings.update(settings_options) + + # Get updated settings to verify + settings_response = await sdk_client.settings.get() + logger.info(f"Configured settings via SDK: {settings_response}") + + def process_no_cache(self, benchmark_entry: RagBenchmarkEntry) -> InferenceResult: + """ + Process a single benchmark entry without using cache. + + Args: + benchmark_entry: The benchmark entry to process + + Returns: + InferenceResult with answer, retrieved context, and trajectory + """ + if self._ingest_artifact is None: + raise RuntimeError( + "Ingest artifacts not set. Call set_ingest_artifacts() first." + ) + + question = benchmark_entry.question + logger.debug(f"Processing question: {question}") + + # Perform inference using async SDK client + answer, context_ids, trajectory = asyncio.run( + self._infer_single_async(question) + ) + + # Build inference result + return InferenceResult( + answer=answer, + context_ids=context_ids, + trajectory=trajectory, + **benchmark_entry.model_dump(), + ) + + @staticmethod + async def _stream_chat_response( + sdk_client: OpenRAGClient, question: str + ) -> tuple[str, list[dict[str, Any]]]: + """ + Stream chat response from SDK client and parse events. + + Args: + sdk_client: The OpenRAG SDK client + question: The question to ask + + Returns: + Tuple of (answer, trajectory) + """ + # Stream the chat response + answer_parts = [] + trajectory: list[dict[str, Any]] = [] + + # Use async context manager to properly initialize the stream + async with sdk_client.chat.stream(message=question) as stream: + async for event in stream: + # Log each event received + logger.debug(f"Received event: {type(event).__name__} - {event}") + + # Handle different event types from the stream + if isinstance(event, ContentEvent): + # Content event contains the answer text (delta field) + answer_parts.append(event.delta) + elif isinstance(event, SourcesEvent): + # Sources event contains retrieved documents + # Each SourcesEvent represents a query's results + current_sources = [ + {"filename": source.filename, "text": source.text} + for source in event.sources + ] + # Add this query's results to the list + # Use the query field from the SourcesEvent + trajectory.append( + { + "query": event.query, + "results": current_sources, + } + ) + elif isinstance(event, DoneEvent): + # Done event signals completion + logger.debug("Stream completed") + + answer = "".join(answer_parts) + return answer, trajectory + + async def _infer_single_async( + self, question: str + ) -> tuple[str, list[str], Trajectory | None]: + """ + Perform inference for a single question using SDK client. + + Args: + question: The question to answer + + Returns: + Tuple of (answer, context_ids, trajectory) + """ + if self._ingest_artifact is None: + raise RuntimeError( + "Ingest artifact not set. Call set_ingest_artifacts() first." + ) + + max_retries = 3 + for attempt in range(1, max_retries + 1): + try: + logger.debug( + f"Inference attempt {attempt}/{max_retries} for question: {question}" + ) + + # Use SDK client for chat/inference (URL from environment) + async with OpenRAGClient(timeout=self.params.timeout) as sdk_client: + answer, trajectory = await self._stream_chat_response( + sdk_client, question + ) + + context_ids = [ + result["filename"] + for query_result in trajectory + for result in query_result["results"] + ] + + return answer, context_ids, trajectory + + except Exception as e: + logger.error( + f"Inference attempt {attempt}/{max_retries} failed: {e}\n{traceback.format_exc()}" + ) + if attempt < max_retries: + # Wait before retry (exponential backoff) + await asyncio.sleep(2**attempt) + + error_msg = f"Cannot run inference on question '{question}' after {max_retries} attempts. See log for details." + logger.error(error_msg) + raise RuntimeError(error_msg) diff --git a/evaluation/openrag_eval/src/openrag_eval/pipelines/ingest.py b/evaluation/openrag_eval/src/openrag_eval/pipelines/ingest.py new file mode 100644 index 00000000..bdd83643 --- /dev/null +++ b/evaluation/openrag_eval/src/openrag_eval/pipelines/ingest.py @@ -0,0 +1,265 @@ +import asyncio +import logging +import os +from typing import Any, cast + +from openrag_sdk import OpenRAGClient +from openrag_sdk.models import IngestResponse, IngestTaskStatus, SettingsUpdateOptions +from pydantic import BaseModel, Field +from ragworkbench import RagDataLoader +from ragworkbench.api.ingest import IngestParams, IngestPipeline +from ragworkbench.api.ingest_artifact import IngestArtifact +from ragworkbench.boards.board_registry import ingest_pipeline +from ragworkbench.datasets_loader.data_models.rag_corpus import RagCorpus + +logger = logging.getLogger(__name__) + + +class ChunkingParams(BaseModel): + """Parameters for document chunking.""" + + chunk_size: int = Field(default=512, description="Size of each chunk") + chunk_overlap: int = Field(default=50, description="Overlap between chunks") + + +class EmbeddingModelParams(BaseModel): + """Parameters for embedding model.""" + + model_id: str = Field(description="Embedding model identifier") + + +class OpenRAGIngestParams(IngestParams): + """Parameters for OpenRAG ingestion pipeline.""" + + embedding_model: EmbeddingModelParams = Field( + description="Embedding model configuration" + ) + chunking: ChunkingParams | None = Field( + default=None, description="Chunking configuration" + ) + timeout: float = Field( + default=300.0, + description="HTTP request timeout in seconds (default: 5 minutes)", + ) + + +class OpenRAGIngestArtifact(IngestArtifact): + """Artifact returned after OpenRAG ingestion.""" + + index_name: str = Field(description="Name of the created index") + + +@ingest_pipeline(name="openrag", params_class=OpenRAGIngestParams) +class OpenRAGIngest(IngestPipeline): + """Ingestion pipeline for OpenRAG backend.""" + + def __init__(self, params: OpenRAGIngestParams) -> None: + super().__init__(params) + self.params: OpenRAGIngestParams = params + + def process( + self, + data_loader: RagDataLoader, + ) -> list[IngestArtifact]: + """ + Process documents through OpenRAG ingestion pipeline. + + Args: + data_loader: Data loader providing the corpus to ingest + + Returns: + List containing a single OpenRAGIngestArtifact with ingestion details + """ + logger.info("Starting OpenRAG ingestion process") + + # Get corpus from data loader + rag_corpus = data_loader.get_corpus() + + # Generate index name based on corpus and configuration + index_name = self._get_index_name(rag_corpus) + + # Run async operations + asyncio.run(self._async_process(rag_corpus, index_name)) + + logger.info(f"OpenRAG ingestion completed. Index name: {index_name}") + + return [ + OpenRAGIngestArtifact( + index_name=index_name, + ) + ] + + async def _async_process(self, rag_corpus: RagCorpus, index_name: str) -> None: + """Async processing of ingestion pipeline.""" + # Initialize SDK client with configured timeout (URL from environment) + async with OpenRAGClient(timeout=self.params.timeout) as sdk_client: + # Update settings with index name and chunking configuration (using SDK) + await self._update_settings(sdk_client, index_name) + + # Onboard with embedding model (using SDK) + await self._onboard(sdk_client) + + # Ingest the corpus (using SDK) + await self._ingest_corpus_with_retries(sdk_client, rag_corpus) + + async def _update_settings( + self, sdk_client: OpenRAGClient, index_name: str + ) -> None: + """Update OpenRAG settings with chunking and index configuration using SDK.""" + logger.info(f"Updating settings for index: '{index_name}'") + + # Build settings update options + settings_dict: dict[str, Any] = { + "embedding_model": self.params.embedding_model.model_id, + "index_name": index_name, + } + + if self.params.chunking: + settings_dict["chunk_size"] = self.params.chunking.chunk_size + settings_dict["chunk_overlap"] = self.params.chunking.chunk_overlap + + # Use SDK to update settings + options = SettingsUpdateOptions(**settings_dict) + logger.info(f"Updating settings to {options}..") + await sdk_client.settings.update(options) + logger.info("Settings update completed.") + + async def _onboard(self, sdk_client: OpenRAGClient) -> None: + """Onboard with embedding model configuration using SDK. + + Args: + sdk_client: The OpenRAG SDK client + """ + logger.info("Onboarding with embedding model") + + await sdk_client.onboarding.onboarding( + embedding_model=self.params.embedding_model.model_id, + ) + + async def _ingest_corpus_with_retries( + self, sdk_client: OpenRAGClient, rag_corpus: RagCorpus + ) -> None: + """ + Ingest corpus documents into OpenRAG with retry mechanism. + + Raises: + RuntimeError: If ingestion fails after max_attempts retry attempts + """ + max_attempts = 3 + attempt = 1 + while attempt <= max_attempts: + logger.info(f"Ingestion attempt {attempt}/{max_attempts}") + num_failures = await self._ingest_corpus(sdk_client, rag_corpus) + + if num_failures == 0: + logger.info("Ingest corpus completed successfully.") + return + + # If there were failures but not the last attempt, retry + if attempt < max_attempts: + logger.warning( + f"Ingestion attempt {attempt} had {num_failures} batch failures. Retrying..." + ) + attempt += 1 + else: + # Last attempt failed + raise RuntimeError( + f"Unable to ingest corpus after {max_attempts} attempts, num_failures = {num_failures}." + ) + + async def _ingest_corpus( + self, sdk_client: OpenRAGClient, rag_corpus: RagCorpus + ) -> int: + """ + Implementation of corpus ingestion logic using SDK. + + Returns: + Number of batch failures + """ + documents = rag_corpus.documents + logger.info(f"Checking which of {len(documents)} documents need ingestion") + + # Filter out documents already in index + documents_not_in_index = [ + document + for document in documents + if not await sdk_client.documents.filename_exists(document.name) + ] + + num_documents_to_index = len(documents_not_in_index) + if num_documents_to_index == 0: + logger.info( + f"All {len(documents)} documents already in index, skipping ingestion" + ) + return 0 + + logger.info(f"Ingesting {num_documents_to_index}/{len(documents)} documents") + + # Ingest documents one by one using SDK + num_failures = 0 + for i, document in enumerate(documents_not_in_index, start=1): + logger.info( + f"Ingesting document {i}/{num_documents_to_index}: {document.name}" + ) + try: + # Reset stream position before ingestion + document.stream.seek(0) + + # Use SDK's ingest method with wait=True + # Returns IngestTaskStatus when wait=True + result: IngestResponse | IngestTaskStatus = ( + await sdk_client.documents.ingest( + file=document.stream, + filename=document.name, + wait=True, + poll_interval=30.0, + timeout=60.0 * 20, # 20 minutes + ) + ) + + # Cast to IngestTaskStatus since wait=True + task_status = cast(IngestTaskStatus, result) + + # Check if ingestion was successful + if task_status.status == "failed" or task_status.failed_files > 0: + logger.error( + f"Failed to ingest {document.name}: " + f"status={task_status.status}, " + f"failed_files={task_status.failed_files}/{task_status.total_files}" + ) + num_failures += 1 + else: + logger.info( + f"Successfully ingested {document.name}: " + f"processed={task_status.processed_files}/{task_status.total_files}" + ) + except Exception as e: + logger.error(f"Error ingesting {document.name}: {e}") + num_failures += 1 + + return num_failures + + def _get_index_name(self, rag_corpus: RagCorpus) -> str: + """Generate a unique index name based on corpus and configuration.""" + import hashlib + import json + + file_names = [document.name for document in rag_corpus.documents] + file_names_sorted = sorted(file_names) + + chunking_dump = "" + if self.params.chunking: + chunking_dump = self.params.chunking.model_dump_json() + + index_config = { + "chunking": chunking_dump, + "embedding_model": self.params.embedding_model.model_dump_json(), + "files_names": file_names_sorted, + } + + config_str = json.dumps(index_config, sort_keys=True) + index_hash = hashlib.sha256(config_str.encode()).hexdigest()[:16] + + # Prefix must be documents to be consistent with OpenRAG index permissions + index_name = f"documents_{index_hash}" + return index_name diff --git a/evaluation/openrag_eval/src/openrag_eval/utils.py b/evaluation/openrag_eval/src/openrag_eval/utils.py new file mode 100644 index 00000000..da003474 --- /dev/null +++ b/evaluation/openrag_eval/src/openrag_eval/utils.py @@ -0,0 +1,30 @@ +"""Utility functions for openrag_eval.""" + +import logging +from pathlib import Path + +from dotenv import load_dotenv + +logger = logging.getLogger(__name__) + + +def load_env_file() -> None: + """ + Load environment variables from .env file in openrag_evaluation directory. + + This function locates the .env file relative to the package structure and + loads it using python-dotenv. It can be used by both application code and tests. + """ + + # Navigate up from utils.py location to find .env + # utils.py is in openrag/evaluation/openrag_eval/src/openrag_eval/utils.py + # .env is in openrag/evaluation/openrag_eval/.env + env_path = Path(__file__).parent.parent.parent / ".env" + + if env_path.exists(): + logger.info(f"Loading environment variables from: {env_path}") + load_dotenv(dotenv_path=env_path) + logger.info("Environment variables loaded successfully") + else: + logger.warning(f".env file not found at: {env_path}") + diff --git a/evaluation/openrag_eval/tests/__init__.py b/evaluation/openrag_eval/tests/__init__.py new file mode 100644 index 00000000..7ba41836 --- /dev/null +++ b/evaluation/openrag_eval/tests/__init__.py @@ -0,0 +1 @@ +"""Tests for openrag_eval.""" diff --git a/evaluation/openrag_eval/tests/conftest.py b/evaluation/openrag_eval/tests/conftest.py new file mode 100644 index 00000000..c6c45c53 --- /dev/null +++ b/evaluation/openrag_eval/tests/conftest.py @@ -0,0 +1,39 @@ +"""Shared pytest fixtures for openrag_eval tests. + +This file contains fixtures that can be used across all test modules. +Fixtures defined here are automatically discovered by pytest. +""" + +import logging + +import pytest + +from openrag_eval.logging_config import ShortNameFormatter +from openrag_eval.utils import load_env_file + + +@pytest.fixture(scope="session", autouse=True) +def load_environment(): + """Load environment variables from .env file for all tests.""" + load_env_file() + yield + + +@pytest.fixture(scope="session", autouse=True) +def configure_logging(): + """Configure logging to match init_logger() format for all tests.""" + handler = logging.StreamHandler() + handler.setFormatter( + ShortNameFormatter("%(asctime)s [%(levelname)s] %(name)s: %(message)s") + ) + + # Get root logger and configure it + root_logger = logging.getLogger() + root_logger.handlers.clear() # Remove any existing handlers + root_logger.addHandler(handler) + root_logger.setLevel(logging.INFO) + + yield + + # Cleanup after tests + root_logger.handlers.clear() diff --git a/evaluation/openrag_eval/tests/pipelines/__init__.py b/evaluation/openrag_eval/tests/pipelines/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/evaluation/openrag_eval/tests/pipelines/test_inference.py b/evaluation/openrag_eval/tests/pipelines/test_inference.py new file mode 100644 index 00000000..859d7375 --- /dev/null +++ b/evaluation/openrag_eval/tests/pipelines/test_inference.py @@ -0,0 +1,232 @@ +"""Integration tests for OpenRAGInference pipeline. + +These tests require a running OpenRAG backend at localhost:8000 with documents already ingested. +The tests assume an index named 'documents' exists with test documents. +""" + +import logging + +import pytest +from ragworkbench.datasets_loader.data_models import ( + GroundTruthContextId, + RagBenchmarkEntry, +) + +from openrag_eval.pipelines import ( + GenerativeModelParams, + OpenRAGInference, + OpenRAGInferenceParams, + OpenRAGIngestArtifact, +) + +logger = logging.getLogger(__name__) + + +@pytest.fixture +def test_ingest_artifact(): + """ + Hardcoded ingest artifact for testing. + Assumes the backend has an index with this name already set up. + """ + return OpenRAGIngestArtifact( + index_name="documents", + ) + + +@pytest.fixture +def inference_params(): + """Create test inference parameters.""" + return OpenRAGInferenceParams( + generative_model=GenerativeModelParams( + provider_id="openai", + model_id="gpt-5.2", + # provider_id="ollama", + # model_id="gpt-oss:20b", + ), + ) + + +@pytest.fixture +def test_benchmark_entry(): + """Create a test benchmark entry with a question about the document.""" + return RagBenchmarkEntry( + question_id="test_q1", + question="What is Docling?", + ground_truth_answers=["Docling is a document processing library"], + ground_truths_context_ids=[ + GroundTruthContextId(document_id="docling.pdf", page=1) + ], + is_answerable=True, + ) + + +class TestOpenRAGInference: + """ + Integration tests for OpenRAGInference. + + Prerequisites: + - Running OpenRAG backend at localhost:8000 + - Index named 'documents' with test documents ingested + - ANTHROPIC_API_KEY environment variable set + """ + + def test_inference_single_entry( + self, + inference_params, + test_ingest_artifact, + test_benchmark_entry, + ): + """ + Test inference on a single benchmark entry. + + This test: + 1. Creates an OpenRAGInference pipeline with real client + 2. Sets the hardcoded ingest artifact + 3. Processes a single benchmark entry + 4. Verifies the returned InferenceResult contains an answer + + Requires: Running OpenRAG backend with 'documents' index already set up + """ + logger.info("Starting test_inference_single_entry") + logger.info(f"Question: {test_benchmark_entry.question}") + logger.info(f"Using index: {test_ingest_artifact.index_name}") + + # Create inference pipeline + inference = OpenRAGInference( + params=inference_params, + cache_dir=None, # No caching for this test + ) + + # Set ingest artifacts + logger.info(f"Setting ingest artifact: {test_ingest_artifact.index_name}") + inference.set_ingest_artifacts([test_ingest_artifact]) + + # Process the benchmark entry + logger.info("Processing benchmark entry...") + result = inference.process(test_benchmark_entry) + + # Verify results + assert result is not None, "Result should not be None" + assert result.answer is not None, "Answer should not be None" + assert len(result.answer) > 0, "Answer should not be empty" + assert result.question == test_benchmark_entry.question, "Question should match" + assert ( + result.question_id == test_benchmark_entry.question_id + ), "Question ID should match" + + logger.info("✓ Successfully generated answer") + logger.info(f" Question: {result.question}") + logger.info(f" Answer: {result.answer[:200]}...") # First 200 chars + + # Verify context retrieval + if result.context_ids: + logger.info(f" Retrieved {len(result.context_ids)} context(s)") + for ctx_id in result.context_ids: + logger.info(f" - {ctx_id}") + else: + logger.warning(" No contexts retrieved") + + def test_inference_with_caching( + self, + inference_params, + test_ingest_artifact, + test_benchmark_entry, + tmp_path, + ): + """ + Test inference with caching enabled. + + This test verifies that: + 1. First call generates and caches the result + 2. Second call retrieves from cache (should be faster) + """ + logger.info("Starting test_inference_with_caching") + + cache_dir = tmp_path / "inference_cache" + + # Create inference pipeline with caching + inference = OpenRAGInference( + params=inference_params, + cache_dir=str(cache_dir), + ) + inference.set_ingest_artifacts([test_ingest_artifact]) + + # Access the generation cache + assert inference.generation_cache is not None, "Generation cache should be initialized" + cache = inference.generation_cache + + # Verify initial cache state + initial_cache_hit = cache.cache_hit + initial_cache_miss = cache.cache_miss + logger.info(f"Initial cache state - hits: {initial_cache_hit}, misses: {initial_cache_miss}") + + # First call - should generate and cache (cache miss) + logger.info("First call (should generate)...") + result1 = inference.process(test_benchmark_entry) + assert result1.answer is not None + logger.info(f"✓ First call completed: {result1.answer[:100]}...") + + # Verify cache miss occurred + assert cache.cache_miss == initial_cache_miss + 1, "First call should result in cache miss" + assert cache.cache_hit == initial_cache_hit, "First call should not result in cache hit" + logger.info(f"✓ Cache miss recorded: {cache.cache_miss}") + + # Second call - should retrieve from cache (cache hit) + logger.info("Second call (should use cache)...") + result2 = inference.process(test_benchmark_entry) + assert result2.answer is not None + logger.info(f"✓ Second call completed: {result2.answer[:100]}...") + + # Verify cache hit occurred + assert cache.cache_hit == initial_cache_hit + 1, "Second call should result in cache hit" + assert cache.cache_miss == initial_cache_miss + 1, "Second call should not result in additional cache miss" + logger.info(f"✓ Cache hit recorded: {cache.cache_hit}") + + # Results should be identical + assert result1.answer == result2.answer, "Cached result should match original" + assert result1.context_ids == result2.context_ids, "Context IDs should match" + logger.info("✓ Cache working correctly - results match") + + def test_set_ingest_artifacts_validation(self, inference_params): + """ + Test that set_ingest_artifacts validates input correctly. + """ + logger.info("Starting test_set_ingest_artifacts_validation") + + inference = OpenRAGInference(params=inference_params, cache_dir=None) + + # Test with empty list + with pytest.raises(ValueError, match="Expected exactly 1 ingest artifact"): + inference.set_ingest_artifacts([]) + + # Test with multiple artifacts + artifact1 = OpenRAGIngestArtifact( + index_name="test1", + ) + artifact2 = OpenRAGIngestArtifact( + index_name="test2", + ) + + with pytest.raises(ValueError, match="Expected exactly 1 ingest artifact"): + inference.set_ingest_artifacts([artifact1, artifact2]) + + logger.info("✓ Validation working correctly") + + def test_process_without_artifacts_raises_error( + self, + inference_params, + test_benchmark_entry, + ): + """ + Test that processing without setting artifacts raises an error. + """ + logger.info("Starting test_process_without_artifacts_raises_error") + + inference = OpenRAGInference(params=inference_params, cache_dir=None) + + # Try to process without setting artifacts + with pytest.raises(RuntimeError, match="Ingest artifacts not set"): + inference.process(test_benchmark_entry) + + logger.info("✓ Error raised correctly when artifacts not set") + diff --git a/evaluation/openrag_eval/tests/pipelines/test_ingest.py b/evaluation/openrag_eval/tests/pipelines/test_ingest.py new file mode 100644 index 00000000..87db94ca --- /dev/null +++ b/evaluation/openrag_eval/tests/pipelines/test_ingest.py @@ -0,0 +1,158 @@ +import asyncio +import io +import logging +from pathlib import Path + +import pytest +from openrag_sdk import OpenRAGClient +from ragworkbench.datasets_loader.data_models import DocumentObject, RagCorpus + +from openrag_eval.pipelines.ingest import ( + ChunkingParams, + EmbeddingModelParams, + OpenRAGIngest, + OpenRAGIngestArtifact, + OpenRAGIngestParams, +) + +logger = logging.getLogger(__name__) + + +@pytest.fixture +def test_document(): + """Create a test document from real file.""" + # Path to the real test document + file_name = "docling.pdf" + + doc_path = ( + Path(__file__).parent.parent.parent.parent.parent + / "openrag-documents" + / file_name + ) + # Read the file content + content = doc_path.read_bytes() + stream = io.BytesIO(content) + + return DocumentObject( + name=file_name, + stream=stream, + mime_type="image/pdf", + ) + + +@pytest.fixture +def test_rag_corpus(test_document): + """Create a RagCorpus with a single test document.""" + return RagCorpus(documents=[test_document]) + + +@pytest.fixture +def mock_data_loader(test_rag_corpus): + """Create a mock data loader that returns the test corpus.""" + + class MockDataLoader: + def __init__(self, corpus): + self.corpus = corpus + self.dataset_name = "test_ingest_pipeline" + + def get_corpus(self): + return self.corpus + + return MockDataLoader(test_rag_corpus) + + +@pytest.fixture +def ingest_params(): + """Create test ingestion parameters.""" + return OpenRAGIngestParams( + embedding_model=EmbeddingModelParams(model_id="text-embedding-3-small"), + chunking=ChunkingParams( + chunk_size=256, + chunk_overlap=25, + ), + ) + + +class TestOpenRAGIngestPipeline: + """ + Integration tests for OpenRAGIngest. + These tests require a running OpenRAG backend at localhost:8000. + """ + + def test_ingest_single_document( + self, + ingest_params: OpenRAGIngestParams, + mock_data_loader, + test_document: DocumentObject, + ): + """ + Test ingesting a single document through the complete pipeline. + + This test: + 1. Creates an OpenRAGIngest with real SDK client + 2. Processes a single test document + 3. Waits for task completion using SDK's built-in wait functionality + 4. Verifies the returned artifact contains correct information + + Requires: Running OpenRAG backend at localhost:8000 + """ + logger.info("Starting test_ingest_single_document") + + # Create pipeline with real client + pipeline = OpenRAGIngest(ingest_params) + + # Process the document + logger.info(f"Processing document: {test_document.name}") + artifacts = pipeline.process(data_loader=mock_data_loader) + + # Verify results + assert len(artifacts) == 1, "Should return exactly one artifact" + assert isinstance( + artifacts[0], OpenRAGIngestArtifact + ), "Should return OpenRAGIngestArtifact" + + artifact = artifacts[0] + assert artifact.index_name.startswith( + "documents_" + ), "Index name should have correct prefix" + assert len(artifact.index_name) > len( + "documents_" + ), "Index name should include hash" + + logger.info("✓ Successfully ingested document") + logger.info(f" Index name: {artifact.index_name}") + + # Verify document exists in index using SDK client (URL from environment) + async def check_document_exists(): + async with OpenRAGClient() as sdk_client: + return await sdk_client.documents.filename_exists(test_document.name) + + exists = asyncio.run(check_document_exists()) + assert ( + exists + ), f"Document {test_document.name} should exist in index after ingestion" + logger.info("✓ Verified document exists in index") + + def test_index_name_consistency( + self, ingest_params: OpenRAGIngestParams, test_rag_corpus: RagCorpus + ): + """Test that index names are generated consistently for the same corpus.""" + logger.info("Starting test_index_name_consistency") + + pipeline = OpenRAGIngest(ingest_params) + + # Generate index name multiple times + index_name_1 = pipeline._get_index_name(test_rag_corpus) # type: ignore[attr-defined] + index_name_2 = pipeline._get_index_name(test_rag_corpus) # type: ignore[attr-defined] + index_name_3 = pipeline._get_index_name(test_rag_corpus) # type: ignore[attr-defined] + + # All should be identical + assert ( + index_name_1 == index_name_2 == index_name_3 + ), "Index names should be consistent" + assert index_name_1.startswith( + "documents_" + ), "Index name should have correct prefix" + + logger.info(f"✓ Index name generated consistently: {index_name_1}") + diff --git a/evaluation/openrag_eval/uv.lock b/evaluation/openrag_eval/uv.lock new file mode 100644 index 00000000..f49bd707 --- /dev/null +++ b/evaluation/openrag_eval/uv.lock @@ -0,0 +1,3121 @@ +version = 1 +revision = 3 +requires-python = ">=3.13" +resolution-markers = [ + "python_full_version >= '3.14' and sys_platform == 'win32'", + "python_full_version >= '3.14' and sys_platform == 'emscripten'", + "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version < '3.14' and sys_platform == 'win32'", + "python_full_version < '3.14' and sys_platform == 'emscripten'", + "python_full_version < '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", +] + +[[package]] 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