commit f80792d7397f761f6d24c2a6b9bbab8b2a96e395 Author: DotNaos Date: Sat May 24 12:15:48 2025 +0200 Initialize Monorepo diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..e692c4e --- /dev/null +++ b/.gitignore @@ -0,0 +1,364 @@ +# Created by https://www.toptal.com/developers/gitignore/api/macos,windows,linux,python,web,pycharm+all +# Edit at https://www.toptal.com/developers/gitignore?templates=macos,windows,linux,python,web,pycharm+all + +### Linux ### +*~ + +# temporary files which can be created if a process still has a handle open of a deleted file +.fuse_hidden* + +# KDE directory preferences +.directory + +# Linux trash folder which might appear on any partition or disk +.Trash-* + +# .nfs files are created when an open file is removed but is still being accessed +.nfs* + +### macOS ### +# General +.DS_Store +.AppleDouble +.LSOverride + +# Icon must end with two \r +Icon + + +# Thumbnails +._* + +# Files that might appear in the root of a volume +.DocumentRevisions-V100 +.fseventsd +.Spotlight-V100 +.TemporaryItems +.Trashes +.VolumeIcon.icns +.com.apple.timemachine.donotpresent + +# Directories potentially created on remote AFP share +.AppleDB +.AppleDesktop +Network Trash Folder +Temporary Items +.apdisk + +### macOS Patch ### +# iCloud generated files +*.icloud + +### PyCharm+all ### +# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio, WebStorm and Rider +# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839 + +# User-specific stuff +.idea/**/workspace.xml +.idea/**/tasks.xml +.idea/**/usage.statistics.xml +.idea/**/dictionaries +.idea/**/shelf + +# AWS User-specific +.idea/**/aws.xml + +# Generated files +.idea/**/contentModel.xml + +# Sensitive or high-churn files +.idea/**/dataSources/ +.idea/**/dataSources.ids +.idea/**/dataSources.local.xml +.idea/**/sqlDataSources.xml +.idea/**/dynamic.xml +.idea/**/uiDesigner.xml +.idea/**/dbnavigator.xml + +# Gradle +.idea/**/gradle.xml +.idea/**/libraries + +# Gradle and Maven with auto-import +# When using Gradle or Maven with auto-import, you should exclude module files, +# since they will be recreated, and may cause churn. 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For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ + +### Python Patch ### +# Poetry local configuration file - https://python-poetry.org/docs/configuration/#local-configuration +poetry.toml + +# ruff +.ruff_cache/ + +# LSP config files +pyrightconfig.json + +### Web ### +*.asp +*.cer +*.csr +*.css +*.htm +*.html +*.js +*.jsp +*.php +*.rss +*.wasm +*.wat +*.xhtml + +### Windows ### +# Windows thumbnail cache files +Thumbs.db +Thumbs.db:encryptable +ehthumbs.db +ehthumbs_vista.db + +# Dump file +*.stackdump + +# Folder config file +[Dd]esktop.ini + +# Recycle Bin used on file shares +$RECYCLE.BIN/ + +# Windows Installer files +*.cab +*.msi +*.msix +*.msm +*.msp + +# Windows shortcuts +*.lnk + +# End of https://www.toptal.com/developers/gitignore/api/macos,windows,linux,python,web,pycharm+all +# local env files +**/.env*.local +**/.env +!**/.env.example + +# vercel +.vercel + +# typescript +*.tsbuildinfo +next-env.d.ts diff --git a/librarian/librarian-core/README.md b/librarian/librarian-core/README.md new file mode 100644 index 0000000..ab9de3d --- /dev/null +++ b/librarian/librarian-core/README.md @@ -0,0 +1,13 @@ +# Usage + +In your `pyproject.toml` add the following code: + +```toml +dependencies = [ + "librarian-core", + "...other dependencies" +] + +[tool.uv.sources] +librarian-core = { git = "https://github.com/DotNaos/librarian-core", rev = "dev" } +``` diff --git a/librarian/librarian-core/pyproject.toml b/librarian/librarian-core/pyproject.toml new file mode 100644 index 0000000..108dc78 --- /dev/null +++ b/librarian/librarian-core/pyproject.toml @@ -0,0 +1,38 @@ +[project] +name = "librarian-core" +version = "0.1.6" +readme = "README.md" +description = "Shared datamodel & utils for the Librarian project" +requires-python = ">=3.10" +authors = [ + { name = "DotNaos", email = "schuetzoliver00@gmail.com" } +] +dependencies = [ + "pandas>=2.2.3", + "platformdirs>=4.3.7", + "pydantic-settings>=2.9.1", + "supabase", + "tabulate>=0.9.0", +] + +[project.optional-dependencies] +dev = [ + "pytest>=8.0", # Testing framework + "pytest-cov", # Coverage reporting +] + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +# src/ layout +[tool.hatch.build.targets.wheel] +packages = ["src/librarian_core"] + +[tool.pytest.ini_options] +pythonpath = ["src"] +testpaths = ["tests"] +addopts = "--cov=librarian_core --cov-report=term-missing" + +[tool.coverage.run] +source = ["librarian_core"] diff --git a/librarian/librarian-core/src/librarian_core/__init__.py b/librarian/librarian-core/src/librarian_core/__init__.py new file mode 100644 index 0000000..064660a --- /dev/null +++ b/librarian/librarian-core/src/librarian_core/__init__.py @@ -0,0 +1,20 @@ +import pkgutil +import importlib + +__all__ = [] + +# Iterate over all modules in this package +for finder, module_name, is_pkg in pkgutil.iter_modules(__path__): + # import the sub-module + module = importlib.import_module(f"{__name__}.{module_name}") + + # decide which names to re-export: + # use module.__all__ if it exists, otherwise every non-private attribute + public_names = getattr( + module, "__all__", [n for n in dir(module) if not n.startswith("_")] + ) + + # bring each name into the package namespace + for name in public_names: + globals()[name] = getattr(module, name) + __all__.append(name) # type: ignore diff --git a/librarian/librarian-core/src/librarian_core/py.typed b/librarian/librarian-core/src/librarian_core/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/librarian/librarian-core/src/librarian_core/storage/__init__.py b/librarian/librarian-core/src/librarian_core/storage/__init__.py new file mode 100644 index 0000000..e8b4872 --- /dev/null +++ b/librarian/librarian-core/src/librarian_core/storage/__init__.py @@ -0,0 +1,5 @@ +from librarian_core.storage.worker_store import WorkerStore + +__all__ = [ + "WorkerStore", +] diff --git a/librarian/librarian-core/src/librarian_core/storage/worker_store.py b/librarian/librarian-core/src/librarian_core/storage/worker_store.py new file mode 100644 index 0000000..cf78544 --- /dev/null +++ b/librarian/librarian-core/src/librarian_core/storage/worker_store.py @@ -0,0 +1,243 @@ +""" +librarian_core.storage.worker_store +=================================== + +Persistent directory layout +--------------------------- +/flows/// + meta.json # worker_name, state, timestamps … + result.json # pydantic-serialised return model + data/ # files staged by the worker +""" + +from __future__ import annotations + +import json +import shutil +import tempfile +from datetime import datetime, timezone +from pathlib import Path +from typing import Any, Optional, Type + +from pydantic import BaseModel, TypeAdapter + +from librarian_core.utils import path_utils + + +class WorkerStore: + """Never exposed to worker code – all access is via helper methods.""" + + # ------------------------------------------------------------------ # + # constructors # + # ------------------------------------------------------------------ # + @classmethod + def new(cls, *, worker_name: str, flow_id: str) -> "WorkerStore": + run_dir = path_utils.get_run_dir(worker_name, flow_id, create=True) + store = cls(run_dir, worker_name, flow_id) + store._write_meta(state="RUNNING") + return store + + @classmethod + def open(cls, run_id: str) -> "WorkerStore": + """ + Locate `//` regardless of worker name. + """ + flows_dir = path_utils.get_flows_dir() + for worker_dir in flows_dir.iterdir(): + candidate = worker_dir / run_id + if candidate.exists(): + meta_path = candidate / "meta.json" + if not meta_path.is_file(): + continue + meta = json.loads(meta_path.read_text()) + return cls(candidate, meta["worker_name"], run_id) + raise FileNotFoundError(run_id) + + # ------------------------------------------------------------------ # + # life-cycle # + # ------------------------------------------------------------------ # + def __init__(self, run_dir: Path, worker_name: str, flow_id: str): + self._run_dir = run_dir + self._worker_name = worker_name + self._flow_id = flow_id + + cache_root = path_utils.get_cache_root() + self._work_dir = Path( + tempfile.mkdtemp(prefix=f"{self._flow_id}-", dir=cache_root) + ) + + self._entry_dir = self._work_dir / "entry" + self._exit_dir = self._work_dir / "exit" + self._entry_dir.mkdir(parents=True, exist_ok=True) + self._exit_dir.mkdir(parents=True, exist_ok=True) + + # ------------------------------------------------------------------ # + # entry / exit handling # + # ------------------------------------------------------------------ # + @property + def entry_dir(self) -> Path: + return self._entry_dir + + def prime_with_input(self, src: Optional[Path]) -> None: + if src and src.exists(): + shutil.copytree(src, self._entry_dir, dirs_exist_ok=True) + + def stage( + self, + src: Path | str, + *, + new_name: str | None = None, + sanitize: bool = True, + move: bool = False, + ) -> Path: + src_path = Path(src).expanduser().resolve() + if not src_path.exists(): + raise FileNotFoundError(src_path) + + name = new_name or src_path.name + if sanitize: + name = path_utils._sanitize(name) + + dst = self._exit_dir / name + if dst.exists(): + if dst.is_file(): + dst.unlink() + else: + shutil.rmtree(dst) + + if move: + src_path.rename(dst) + else: + if src_path.is_dir(): + shutil.copytree(src_path, dst) + else: + dst.parent.mkdir(parents=True, exist_ok=True) + shutil.copy2(src_path, dst) + return dst + + # ------------------------------------------------------------------ # + # result persistence # + # ------------------------------------------------------------------ # + def save_model( + self, + model: BaseModel, + *, + filename: str = "result.json", + **json_kwargs: Any, + ) -> Path: + json_kwargs.setdefault("indent", 2) + target = self._run_dir / filename + target.write_text(model.model_dump_json(**json_kwargs)) + return target + + def persist_exit(self) -> Path: + """ + Move the *exit* directory to the persistent *data* slot and mark + the run completed. + """ + data_dir = self.data_dir + if data_dir.exists(): + shutil.rmtree(data_dir) + self._exit_dir.rename(data_dir) + self._write_meta(state="COMPLETED") + return data_dir + + def cleanup(self) -> None: + shutil.rmtree(self._work_dir, ignore_errors=True) + + # ------------------------------------------------------------------ # + # public helpers (API needs these) # + # ------------------------------------------------------------------ # + @property + def data_dir(self) -> Path: + return self._run_dir / "data" + + @property + def meta_path(self) -> Path: + return self._run_dir / "meta.json" + + @property + def metadata(self) -> dict[str, Any]: + return json.loads(self.meta_path.read_text()) + + def load_model(self, *, as_dict: bool = False) -> dict | BaseModel | None: + res_file = self._run_dir / "result.json" + if not res_file.is_file(): + return None + data = json.loads(res_file.read_text()) + if as_dict: + return data + # try to reconstruct a Pydantic model if possible + try: + OutputModel: Type[BaseModel] | None = self._guess_output_model() + if OutputModel: + return TypeAdapter(OutputModel).validate_python(data) + except Exception: + pass + return data + + @staticmethod + # TODO: Should return a Flowartifact, but circular import is messing + def load_latest(worker_name: str) -> dict[str, Any] | None: + flows_dir = path_utils.get_flows_dir() + worker_dir = flows_dir / worker_name + if not worker_dir.exists(): + return None + + runs: list[tuple[datetime, Path]] = [] + for run_id in worker_dir.iterdir(): + if not run_id.is_dir(): + continue + meta_path = run_id / "meta.json" + if not meta_path.is_file(): + continue + meta = json.loads(meta_path.read_text()) + if meta["state"] == "COMPLETED": + runs.append((datetime.fromisoformat(meta["timestamp"]), run_id)) + + if not runs: + return None + sorted_runs = sorted(runs, key=lambda x: x[0]) + + latest_run_dir = sorted_runs[-1][1] + + # Load the model + return { # That is a FlowArtifact + "run_id": latest_run_dir.name, + "dir": latest_run_dir / "data", + "data": WorkerStore.open(latest_run_dir.name).load_model(as_dict=True), # type: ignore + } + + # ------------------------------------------------------------------ # + # internals # + # ------------------------------------------------------------------ # + def _write_meta(self, *, state: str) -> None: + meta = { + "worker_name": self._worker_name, + "run_id": self._flow_id, + "state": state, + "timestamp": datetime.now(timezone.utc).isoformat(), + } + self.meta_path.write_text(json.dumps(meta, indent=2)) + + def _guess_output_model(self) -> Optional[Type[BaseModel]]: + """ + Best-effort import of `.output_model`. + """ + try: + from importlib import import_module + + # workers are registered with dotted names in plugin_loader + mod = import_module(self._worker_name) + return getattr(mod, "output_model", None) + except Exception: + return None + + # ------------------------------------------------------------------ # + # clean-up # + # ------------------------------------------------------------------ # + def __del__(self) -> None: + try: + shutil.rmtree(self._work_dir, ignore_errors=True) + except Exception: + pass diff --git a/librarian/librarian-core/src/librarian_core/supabase/__init__.py b/librarian/librarian-core/src/librarian_core/supabase/__init__.py new file mode 100644 index 0000000..6c5ff50 --- /dev/null +++ b/librarian/librarian-core/src/librarian_core/supabase/__init__.py @@ -0,0 +1,3 @@ +from .client import get_client, SupabaseGateway + +__all__ = ["get_client", "SupabaseGateway"] diff --git a/librarian/librarian-core/src/librarian_core/supabase/client.py b/librarian/librarian-core/src/librarian_core/supabase/client.py new file mode 100644 index 0000000..24c7202 --- /dev/null +++ b/librarian/librarian-core/src/librarian_core/supabase/client.py @@ -0,0 +1,60 @@ +from __future__ import annotations +from typing import Any, Dict +from pydantic import BaseModel +from supabase import create_client, Client +import os, logging + +log = logging.getLogger(__name__) + + +class _Cfg(BaseModel): + url: str + key: str + db_schema: str = "library" + + +def _load_cfg() -> _Cfg: + return _Cfg( + url=os.getenv("SUPABASE_URL", ""), + key=os.getenv("SUPABASE_API_KEY", ""), + ) + + +_client: Client | None = None +_cfg: _Cfg | None = None + + +def get_client() -> Client: + global _client, _cfg + if _client: + return _client + _cfg = _load_cfg() + if not _cfg.url or not _cfg.key: + raise RuntimeError("SUPABASE_URL or SUPABASE_API_KEY missing") + _client = create_client(_cfg.url, _cfg.key) + return _client + + +class SupabaseGateway: + """ + Thin wrapper around Client with `schema()` pre-selected + and a helper `_rpc()` that raises RuntimeError on error. + """ + + def __init__(self) -> None: + self.client = get_client() + self.schema = _cfg.db_schema if _cfg else "library" + + # ---------- internal ---------- + def _rpc(self, fn: str, payload: Dict[str, Any] | None = None): + resp = ( + self.client.schema(self.schema) + .rpc(fn, payload or {}) + .execute() + .model_dump() + ) + if resp.get("error"): + log.error("%s error: %s", fn, resp["error"]) + raise RuntimeError(resp["error"]) + log.debug("%s OK", fn) + return resp.get("data") diff --git a/librarian/librarian-core/src/librarian_core/supabase/py.typed b/librarian/librarian-core/src/librarian_core/supabase/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/librarian/librarian-core/src/librarian_core/supabase/rpc.py b/librarian/librarian-core/src/librarian_core/supabase/rpc.py new file mode 100644 index 0000000..8c6e97d --- /dev/null +++ b/librarian/librarian-core/src/librarian_core/supabase/rpc.py @@ -0,0 +1,61 @@ +from __future__ import annotations + +from typing import List + +from librarian_scraper.models import CrawlCourse, CrawlTerm, MoodleIndex + +from librarian_core.supabase.client import SupabaseGateway + +gw = SupabaseGateway() # singleton gateway + + +# -------- public API -------- +def upload_index(index: MoodleIndex) -> None: + dp = index.degree_program + _upsert_degree_program(dp.id, dp.name) + for term in dp.terms: + _upsert_term(term) + _upsert_courses(term.courses, term_id=term.id, prog_id=dp.id) + + +def upload_modules(modules_index) -> None: + # TODO – same pattern + ... + + +# -------- helpers -------- +def _upsert_degree_program(dp_id: str, name: str): + gw._rpc( + "upsert_degree_program", + { + "p_program_id": dp_id, + "p_program_name": name, + }, + ) + + +def _upsert_term(term: CrawlTerm): + # TODO: Change to term, when supabase is updated + gw._rpc( + "upsert_semester", + { + "p_semester_id": term.id, + "p_semester_name": term.name, + }, + ) + + +def _upsert_courses(courses: List[CrawlCourse], *, term_id: str, prog_id: str): + # TODO: Change to term, when supabase is updated + for c in courses: + gw._rpc( + "upsert_course", + { + "p_course_id": c.id, + "p_course_name": c.name, + "p_semester_id": term_id, + "p_program_id": prog_id, + "p_hero_image": c.hero_image, + "p_content_ressource_id": c.content_ressource_id, + }, + ) diff --git a/librarian/librarian-core/src/librarian_core/temp_payloads/__init__.py b/librarian/librarian-core/src/librarian_core/temp_payloads/__init__.py new file mode 100644 index 0000000..620b30a --- /dev/null +++ b/librarian/librarian-core/src/librarian_core/temp_payloads/__init__.py @@ -0,0 +1,11 @@ +from .chunk_data import ( + ChunkCourse, + ChunkFile, + ChunkData, +) + +__all__ = [ + "ChunkData", + "ChunkCourse", + "ChunkFile", +] diff --git a/librarian/librarian-core/src/librarian_core/temp_payloads/chunk_data.py b/librarian/librarian-core/src/librarian_core/temp_payloads/chunk_data.py new file mode 100644 index 0000000..9839194 --- /dev/null +++ b/librarian/librarian-core/src/librarian_core/temp_payloads/chunk_data.py @@ -0,0 +1,19 @@ +from typing import List + +from pydantic import BaseModel, Field + +# TODO: Move to librarian-chunker + +class ChunkFile(BaseModel): + name: str = Field(..., description="Name of the file") + id: str = Field(..., description="ID of the file") + + +class ChunkCourse(BaseModel): + id: str = Field(..., description="ID of the course") + name: str = Field(..., description="Name of the course") + files: List[ChunkFile] = Field(..., description="List of files in the course") + + +class ChunkData(BaseModel): + courses: List[ChunkCourse] = Field(..., description="List of courses") diff --git a/librarian/librarian-core/src/librarian_core/temp_payloads/py.typed b/librarian/librarian-core/src/librarian_core/temp_payloads/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/librarian/librarian-core/src/librarian_core/utils/__init__.py b/librarian/librarian-core/src/librarian_core/utils/__init__.py new file mode 100644 index 0000000..369c2db --- /dev/null +++ b/librarian/librarian-core/src/librarian_core/utils/__init__.py @@ -0,0 +1,25 @@ +from librarian_core.utils.path_utils import ( + copy_to_temp_dir, + get_cache_root, + get_config_root, + get_data_root, + get_flow_name_from_id, + get_flows_dir, + get_run_dir, + get_temp_path, + get_workers_dir, +) +from librarian_core.utils.secrets_loader import load_env + +__all__ = [ + "load_env", + "get_temp_path", + "get_run_dir", + "get_flow_name_from_id", + "copy_to_temp_dir", + "get_cache_root", + "get_data_root", + "get_config_root", + "get_flows_dir", + "get_workers_dir", +] diff --git a/librarian/librarian-core/src/librarian_core/utils/path_utils.py b/librarian/librarian-core/src/librarian_core/utils/path_utils.py new file mode 100644 index 0000000..fddb59d --- /dev/null +++ b/librarian/librarian-core/src/librarian_core/utils/path_utils.py @@ -0,0 +1,196 @@ +""" +librarian_core/utils/path_utils.py +================================== + +Unified helpers for every path the Atlas-Librarian project uses. + +Key features +------------ +* XDG- and ENV-aware roots for **data**, **config**, and **cache**. +* Dedicated sub-trees for *flows* (per-worker run directories) and + *workers* (registrations, static assets, …). +* Convenience helpers: + - `get_run_dir(worker, run_id)` + - `get_flow_name_from_id(run_id)` ← Prefect lookup (lazy import) + - `get_temp_path()` / `copy_to_temp_dir()` +* **Single source of truth** – change the root once, everything follows. +""" + +from __future__ import annotations + +import os +import shutil +import tempfile +import uuid +from datetime import datetime +from pathlib import Path +from typing import Optional + +from platformdirs import ( + user_cache_dir, + user_config_dir, + user_data_dir, +) + +# --------------------------------------------------------------------------- # +# Root directories (honours $LIBRARIAN_*_DIR, falls back to XDG) # +# --------------------------------------------------------------------------- # + +_APP_NAME = "atlas-librarian" + +_DATA_ROOT = Path( + os.getenv("LIBRARIAN_DATA_DIR", user_data_dir(_APP_NAME)) +).expanduser() +_CONFIG_ROOT = Path( + os.getenv("LIBRARIAN_CONFIG_DIR", user_config_dir(_APP_NAME)) +).expanduser() +_CACHE_ROOT = Path( + os.getenv("LIBRARIAN_CACHE_DIR", user_cache_dir(_APP_NAME)) +).expanduser() + +# Project-specific sub-trees +_FLOWS_DIR = _DATA_ROOT / "flows" # /flows/// +_WORKERS_DIR = _DATA_ROOT / "workers" # static registration cache, etc. + +# Ensure that the basic tree always exists +for p in (_DATA_ROOT, _CONFIG_ROOT, _CACHE_ROOT, _FLOWS_DIR, _WORKERS_DIR): + p.mkdir(parents=True, exist_ok=True) + +# --------------------------------------------------------------------------- # +# Public helpers # +# --------------------------------------------------------------------------- # + +# -- roots -- + + +def get_data_root() -> Path: + return _DATA_ROOT + + +def get_config_root() -> Path: + return _CONFIG_ROOT + + +def get_cache_root() -> Path: + return _CACHE_ROOT + + +def get_flows_dir() -> Path: + return _FLOWS_DIR + + +def get_workers_dir() -> Path: + return _WORKERS_DIR + + +# -- flow-run directories ---------------------------------------------------- # + + +def get_run_dir(worker_name: str, run_id: str, *, create: bool = True) -> Path: + """ + Absolute path for one specific Prefect flow-run. + + Example + ------- + >>> get_run_dir("downloader", "1234abcd") + ~/.local/share/atlas-librarian/flows/downloader/1234abcd + """ + safe_worker = _sanitize(worker_name) + path = _FLOWS_DIR / safe_worker / run_id + if create: + path.mkdir(parents=True, exist_ok=True) + return path + + +def get_flow_name_from_id(run_id: str) -> Optional[str]: + """ + Resolve a Prefect *run-id* → *flow name*. + + Returns + ------- + The flow (worker) name or *None* if the ID cannot be found. + """ + try: + from prefect.client.orchestration import get_client + except ImportError: # Prefect not installed in caller env + return None + + try: + import anyio + + async def _lookup() -> Optional[str]: + async with get_client() as client: + fr = await client.read_flow_run(uuid.UUID(run_id)) + return fr.flow_name # type: ignore[attr-defined] + + return anyio.run(_lookup) + except Exception: + return None + + +# -- temporary workspace helpers -------------------------------------------- # + + +def get_temp_path(prefix: str = "atlas") -> Path: + """ + Create a *unique* temporary directory inside the user cache. + + The directory is **not** deleted automatically – callers decide. + """ + ts = datetime.now().strftime("%Y-%m-%d_%H-%M-%S") + rand = uuid.uuid4().hex[:8] + tmp_root = _CACHE_ROOT / "tmp" + tmp_root.mkdir(parents=True, exist_ok=True) + + path = Path( + tempfile.mkdtemp( + dir=tmp_root, + prefix=f"{prefix}-{ts}-{rand}-", + ) + ) + return path + + +def copy_to_temp_dir(src: Path | str, *, prefix: str = "atlas") -> Path: + """ + Recursively copy *src* into a fresh temporary directory. + + Returns the destination path. + """ + src_path = Path(src).expanduser().resolve() + if not src_path.exists(): + raise FileNotFoundError(src_path) + + dst = get_temp_path(prefix=prefix) + shutil.copytree(src_path, dst, dirs_exist_ok=True) + return dst + + +# --------------------------------------------------------------------------- # +# internal helpers # +# --------------------------------------------------------------------------- # + + +def _sanitize(name: str) -> str: + """Replace path-hostile characters – keeps things safe across OSes.""" + return "".join(c if c.isalnum() or c in "-._" else "_" for c in name) + + +# --------------------------------------------------------------------------- # +# exports # +# --------------------------------------------------------------------------- # + +__all__ = [ + # roots + "get_data_root", + "get_config_root", + "get_cache_root", + "get_flows_dir", + "get_workers_dir", + # flow-run helpers + "get_run_dir", + "get_flow_name_from_id", + # temporary space + "get_temp_path", + "copy_to_temp_dir", +] diff --git a/librarian/librarian-core/src/librarian_core/utils/secrets_loader.py b/librarian/librarian-core/src/librarian_core/utils/secrets_loader.py new file mode 100644 index 0000000..500fcdb --- /dev/null +++ b/librarian/librarian-core/src/librarian_core/utils/secrets_loader.py @@ -0,0 +1,25 @@ +""" +Secrets live in a classic .env **outside** the JSON settings file. +Load order: + +1. ENV LIBRARIAN_CREDENTIALS_PATH (override) +2. ~/.config/atlas-librarian/credentials.env (XDG path) +""" + +from pathlib import Path +import os +import logging +import dotenv +from librarian_core.utils.path_utils import get_config_root + +log = logging.getLogger(__name__) + + +def load_env() -> None: + path = Path(os.getenv("LIBRARIAN_CREDENTIALS_PATH", get_config_root() / "credentials.env")) + + if path.exists(): + dotenv.load_dotenv(path) + log.debug("Secrets loaded from %s", path) + else: + log.debug("No credentials.env found (looked in %s)", path) diff --git a/librarian/librarian-core/src/librarian_core/workers/__init__.py b/librarian/librarian-core/src/librarian_core/workers/__init__.py new file mode 100644 index 0000000..315bfa6 --- /dev/null +++ b/librarian/librarian-core/src/librarian_core/workers/__init__.py @@ -0,0 +1,3 @@ +from librarian_core.workers.base import Worker + +__all__ = ["Worker"] diff --git a/librarian/librarian-core/src/librarian_core/workers/base.py b/librarian/librarian-core/src/librarian_core/workers/base.py new file mode 100644 index 0000000..74d22e6 --- /dev/null +++ b/librarian/librarian-core/src/librarian_core/workers/base.py @@ -0,0 +1,192 @@ +from __future__ import annotations + +import inspect +import uuid +from collections.abc import Awaitable, Callable +from pathlib import Path +from typing import ClassVar, Generic, TypeVar + +import pandas as pd +import anyio +from prefect import flow, get_run_logger +from prefect.runtime import flow_run +from pydantic import BaseModel, Field, create_model +from prefect.artifacts import acreate_markdown_artifact + +from librarian_core.storage.worker_store import WorkerStore + +# --------------------------------------------------------------------------- # +# type parameters # +# --------------------------------------------------------------------------- # +InT = TypeVar("InT", bound=BaseModel) +OutT = TypeVar("OutT", bound=BaseModel) + + +# --------------------------------------------------------------------------- # +# envelope returned by every worker flow # +# --------------------------------------------------------------------------- # +class FlowArtifact(BaseModel, Generic[OutT]): + run_id: str | None = None + dir: Path | None = None + data: OutT | None = None + + @classmethod + def new(cls, run_id: str | None = None, dir: Path | None = None, data: OutT | None = None) -> FlowArtifact: + if not data: + raise ValueError("data is required") + # Intermediate Worker + if run_id and dir: + return FlowArtifact(run_id=run_id, dir=dir, data=data) + + # Initial Worker + else: + return FlowArtifact(data=data) + +# --------------------------------------------------------------------------- # +# metaclass: adds a Prefect flow + envelope to each Worker # +# --------------------------------------------------------------------------- # +class _WorkerMeta(type): + def __new__(mcls, name, bases, ns, **kw): + cls = super().__new__(mcls, name, bases, dict(ns)) + + if name == "Worker" and cls.__module__ == __name__: + return cls # abstract base + + if not (hasattr(cls, "input_model") and hasattr(cls, "output_model")): + raise TypeError(f"{name}: declare 'input_model' / 'output_model'.") + if "__run__" not in cls.__dict__: + raise TypeError(f"{name}: implement async '__run__(payload)'.") + + cls.worker_name = name # type: ignore + cls._create_input_artifact() + cls._prefect_flow = mcls._build_prefect_flow(cls) # type: ignore + return cls + + # --------------------------------------------------------------------- # + @staticmethod + def _build_prefect_flow(cls_ref): + """Create the Prefect flow and return it.""" + InArt = cls_ref.input_artifact # noqa: F841 + OutModel: type[BaseModel] = cls_ref.output_model # noqa: F841 + worker_name: str = cls_ref.worker_name + + async def _core(in_art: FlowArtifact[InT]): # type: ignore[name-defined] + logger = get_run_logger() + run_id = flow_run.get_id() or uuid.uuid4().hex + logger.info("%s started (run_id=%s)", worker_name, run_id) + + store = WorkerStore.new(worker_name=worker_name, flow_id=run_id) + + if in_art.dir and in_art.dir.exists() and in_art.dir != Path("."): + store.prime_with_input(in_art.dir) + + inst = cls_ref() + inst._inject_store(store) + # run worker ------------------------------------------------ + run_res = inst.__run__(in_art.data) + # allow sync or async implementations + if inspect.iscoroutine(run_res): + result = await run_res + else: + result = run_res + + store.save_model(result) + store.persist_exit() + store.cleanup() + logger.info("%s finished", worker_name) + + artifact = FlowArtifact(run_id=run_id, dir=store.data_dir, data=result) + + md_table = await inst._to_markdown(result) + await acreate_markdown_artifact( + key=f"{worker_name.lower()}-artifact", + markdown=md_table, + description=f"{worker_name} output" + ) + + # save the markdown artifact in the flow directory + md_file = store._run_dir / "artifact.md" + md_file.write_text(md_table) + + return artifact + + return flow(name=worker_name, log_prints=True)(_core) + + # --------------------------------------------------------------------- # + def _create_input_artifact(cls): + """Create & attach a pydantic model ‹InputArtifact› = {dir?, data}.""" + DirField = (Path | None, None) + DataField = (cls.input_model, ...) # type: ignore # required + art_name = f"{cls.__name__}InputArtifact" + + artifact = create_model(art_name, dir=DirField, data=DataField) # type: ignore[arg-type] + artifact.__doc__ = f"Artifact for {cls.__name__} input" + cls.input_artifact = artifact # type: ignore[attr-defined] + + +# --------------------------------------------------------------------------- # +# public Worker base # +# --------------------------------------------------------------------------- # +class Worker(Generic[InT, OutT], metaclass=_WorkerMeta): + """ + Derive from this class, set *input_model* / *output_model*, and implement + an **async** ``__run__(payload: input_model)``. + """ + + input_model: ClassVar[type[BaseModel]] + output_model: ClassVar[type[BaseModel]] + input_artifact: ClassVar[type[BaseModel]] # injected by metaclass + worker_name: ClassVar[str] + _prefect_flow: ClassVar[Callable[[FlowArtifact[InT]], Awaitable[FlowArtifact[OutT]]]] + + # injected at runtime + entry: Path + _store: WorkerStore + + # ------------------------------------------------------------------ # + # internal wiring # + # ------------------------------------------------------------------ # + def _inject_store(self, store: WorkerStore) -> None: + self._store = store + self.entry = store.entry_dir + + # ------------------------------------------------------------------ # + # developer helper # + # ------------------------------------------------------------------ # + def stage( + self, + src: Path | str, + *, + new_name: str | None = None, + sanitize: bool = True, + move: bool = False, + ) -> Path: + return self._store.stage(src, new_name=new_name, sanitize=sanitize, move=move) + + # ------------------------------------------------------------------ # + # convenience wrappers # + # ------------------------------------------------------------------ # + @classmethod + def flow(cls): + """Return the auto-generated Prefect flow.""" + return cls._prefect_flow + + # submit variants --------------------------------------------------- # + @classmethod + def submit(cls, payload: FlowArtifact[InT]) -> FlowArtifact[OutT]: + async def _runner(): + art = await cls._prefect_flow(payload) # type: ignore[arg-type] + return art + + return anyio.run(_runner) + + # ------------------------------------------------------------------ # + # abstract # + # ------------------------------------------------------------------ # + async def __run__(self, payload: InT) -> OutT: ... + + + # Should be overridden by the worker + async def _to_markdown(self, data: OutT) -> str: + md_table = pd.DataFrame([data.dict()]).to_markdown(index=False) + 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b/librarian/plugins/librarian-chunker/pyproject.toml new file mode 100644 index 0000000..a81367e --- /dev/null +++ b/librarian/plugins/librarian-chunker/pyproject.toml @@ -0,0 +1,40 @@ +[project] +name = "librarian-chunker" +version = "0.1.0" +description = "Chunker for Librarian" +readme = "README.md" +requires-python = ">=3.10" +dependencies = [ + "pdfplumber", + "pymupdf", + "tiktoken", + "spacy", + "sentence-transformers", + "pydantic", + "prefect", + "librarian-core", + "python-pptx", + "python-docx", +] + +[build-system] +requires = ["hatchling>=1.21"] +build-backend = "hatchling.build" + + +[tool.hatch.build.targets.wheel] +packages = ["src/librarian_chunker"] + +[tool.hatch.metadata] +allow-direct-references = true + +[tool.uv.sources] +#librarian-core = { git = "https://github.com/DotNaos/librarian-core", rev = "dev" } + +[project.entry-points."librarian.workers"] +chunker = "librarian_chunker.chunker:Chunker" + + +# ───────── optional: dev / test extras ───────── +[project.optional-dependencies] +dev = ["ruff", "pytest", "mypy"] diff --git a/librarian/plugins/librarian-chunker/src/librarian_chunker/__init__.py b/librarian/plugins/librarian-chunker/src/librarian_chunker/__init__.py new file mode 100644 index 0000000..ed18089 --- /dev/null +++ b/librarian/plugins/librarian-chunker/src/librarian_chunker/__init__.py @@ -0,0 +1,3 @@ +from .chunker import Chunker + +__all__ = ["Chunker"] diff --git a/librarian/plugins/librarian-chunker/src/librarian_chunker/chunker.py b/librarian/plugins/librarian-chunker/src/librarian_chunker/chunker.py new file mode 100644 index 0000000..e4ccd29 --- /dev/null +++ b/librarian/plugins/librarian-chunker/src/librarian_chunker/chunker.py @@ -0,0 +1,217 @@ +import os +import shutil +from pathlib import Path + +import pdfplumber +import pymupdf +import spacy +import tiktoken +from librarian_core.utils.path_utils import get_temp_path +from librarian_core.workers.base import Worker +from librarian_extractor.models.extract_data import ExtractData, ExtractedFile +from prefect import get_run_logger, task +from prefect.cache_policies import NO_CACHE +from prefect.futures import wait + +from librarian_chunker.models.chunk_data import ( + Chunk, + ChunkData, + ChunkedCourse, + ChunkedTerm, +) + +MAX_TOKENS = 800 +OVERLAP_TOKENS = 100 + + +class Chunker(Worker[ExtractData, ChunkData]): + input_model = ExtractData + output_model = ChunkData + + async def __run__(self, payload: ExtractData) -> ChunkData: # noqa: D401 + lg = get_run_logger() + lg.info("Chunker started") + + working_dir = get_temp_path() + + # load NLP and tokenizer + Chunker.nlp = spacy.load("xx_ent_wiki_sm") + Chunker.nlp.add_pipe("sentencizer") + Chunker.enc = tiktoken.get_encoding("cl100k_base") + + # chunk parameters + Chunker.max_tokens = MAX_TOKENS + Chunker.overlap_tokens = OVERLAP_TOKENS + + result = ChunkData(terms=[]) + + # Loading files + for term in payload.terms: + chunked_term = ChunkedTerm(id=term.id, name=term.name) + in_term_dir = self.entry / term.name + + out_term_dir = working_dir / term.name + out_term_dir.mkdir(parents=True, exist_ok=True) + + for course in term.courses: + chunked_course = ChunkedCourse( + id=course.id, name=course.name, chunks=[] + ) + in_course_dir = in_term_dir / course.name + + out_course_dir = out_term_dir / course.name + out_course_dir.mkdir(parents=True, exist_ok=True) + + futs = [] + # All chunks are just in the course dir, so no new dir + for chap in course.chapters: + chapter_path = in_course_dir / chap.name + + for f in chap.content_files: + futs.append( + self._chunk_file.submit(f, chapter_path, out_course_dir) + ) + wait(futs) + for fut in futs: + chunks, images = fut.result() + chunked_course.chunks.extend(chunks) + chunked_course.images.extend(images) + + chunked_term.courses.append(chunked_course) + + # Add the chunked term to the result + result.terms.append(chunked_term) + self.stage(out_term_dir) + + return result + @staticmethod + @task(log_prints=True) + def _chunk_file( + f: ExtractedFile, chapter_path: Path, out_course_dir: Path + ) -> tuple[list[Chunk], list[str]]: + lg = get_run_logger() + lg.info(f"Chunking file {f.name}") + lg.info(f"Chapter path: {chapter_path}") + lg.info(f"Out course dir: {out_course_dir}") + + # Extract the Text + file_text = Chunker._extract_text(chapter_path / f.name) + + # Chunk the Text + chunks = Chunker._chunk_text(file_text, f.name, out_course_dir) + + images_dir = out_course_dir / "images" + images_dir.mkdir(parents=True, exist_ok=True) + + # Extract the Images + images = Chunker._extract_images(chapter_path / f.name, images_dir) + + return chunks, images + + + @staticmethod + def _extract_text(file_path: Path) -> str: + if not file_path.suffix == ".pdf": + return "" + + extracted_text = "" + + with pdfplumber.open(file_path) as pdf: + for i in range(len(pdf.pages)): + current_page = pdf.pages[i] + text = current_page.extract_text() or "" + extracted_text += text + + return extracted_text + + @staticmethod + def _chunk_text(text: str, f_name: str, out_course_dir: Path) -> list[Chunk]: + lg = get_run_logger() + lg.info(f"Chunking text for file {f_name}") + # split text into sentences and get tokens + nlp_doc = Chunker.nlp(text) + sentences = [sent.text.strip() for sent in nlp_doc.sents] + sentence_token_counts = [len(Chunker.enc.encode(s)) for s in sentences] + lg.info(f"Extracted {len(sentences)} sentences with token counts: {sentence_token_counts}") + + # Buffers + chunks: list[Chunk] = [] + current_chunk = [] + current_token_total = 0 + + chunk_id = 0 + + for s, tc in zip(sentences, sentence_token_counts): # Pair sentences and tokens + if tc + current_token_total <= MAX_TOKENS: # Check Token limit + # Add sentences to chunk + current_chunk.append(s) + current_token_total += tc + else: + # Flush Chunk + chunk_text = "\n\n".join(current_chunk) + + chunk_name = f"{f_name}_{chunk_id}" + with open( + out_course_dir / f"{chunk_name}.md", "w", encoding="utf-8" + ) as f: + f.write(chunk_text) + chunk_id += 1 + + chunks.append( + Chunk( + id=f"{f_name}_{chunk_id}", + name=f"{f_name}_{chunk_id}.md", + tokens=len(Chunker.enc.encode(chunk_text)), + ) + ) + + # Get Overlap from Chunk + token_ids = Chunker.enc.encode(chunk_text) + overlap_ids = token_ids[-OVERLAP_TOKENS :] + overlap_text = Chunker.enc.decode(overlap_ids) + overlap_doc = Chunker.nlp(overlap_text) + overlap_sents = [sent.text for sent in overlap_doc.sents] + + # Start new Chunk + current_chunk = overlap_sents + [s] + current_token_total = sum( + len(Chunker.enc.encode(s)) for s in current_chunk + ) + + if current_chunk: + chunk_text = "\n\n".join(current_chunk) + chunk_name = f"{f_name}_{chunk_id}" + with open(out_course_dir / f"{chunk_name}.md", "w", encoding="utf-8") as f: + f.write(chunk_text) + chunks.append( + Chunk( + id=f"{f_name}_{chunk_id}", + name=f"{f_name}_{chunk_id}", + tokens=len(Chunker.enc.encode(chunk_text)), + ) + ) + lg.info(f"Created {len(chunks)} chunks for file {f_name}") + return chunks + + @staticmethod + def _extract_images(file: Path, img_folder: Path) -> list[str]: + images_list = [] + if not file.suffix == ".pdf": + return [] + + with pymupdf.open(file) as doc: + for i in range(len(doc)): + images = doc.get_page_images(i) + + for img in images: + img_xref = img[0] + image = doc.extract_image(img_xref) + img_content = image["image"] + img_ext = image["ext"] + img_name = f"img_page{i + 1}_{img_xref}.{img_ext}" + img_file_path = img_folder / img_name + + with open(img_file_path, "wb") as img_file: + img_file.write(img_content) + images_list.append(img_name) + return images_list diff --git a/librarian/plugins/librarian-chunker/src/librarian_chunker/models/__init__.py b/librarian/plugins/librarian-chunker/src/librarian_chunker/models/__init__.py new file mode 100644 index 0000000..7d21a53 --- /dev/null +++ b/librarian/plugins/librarian-chunker/src/librarian_chunker/models/__init__.py @@ -0,0 +1,3 @@ +from .chunk_data import Chunk, ChunkedCourse, ChunkedTerm, ChunkData + +__all__ = ["Chunk", "ChunkedCourse", "ChunkedTerm", "ChunkData"] diff --git a/librarian/plugins/librarian-chunker/src/librarian_chunker/models/chunk_data.py b/librarian/plugins/librarian-chunker/src/librarian_chunker/models/chunk_data.py new file mode 100644 index 0000000..8006faa --- /dev/null +++ b/librarian/plugins/librarian-chunker/src/librarian_chunker/models/chunk_data.py @@ -0,0 +1,29 @@ +from typing import List + +from pydantic import BaseModel, Field + +# --------------------------------------------------------------------------- # +# Output models # +# --------------------------------------------------------------------------- # + + +class Chunk(BaseModel): + id: str + name: str + tokens: int + +class ChunkedCourse(BaseModel): + id: str + name: str + chunks: List[Chunk] = Field(default_factory=list) + images: List[str] = Field(default_factory=list) + + +class ChunkedTerm(BaseModel): + id: str + name: str + courses: List[ChunkedCourse] = Field(default_factory=list) + + +class ChunkData(BaseModel): + terms: List[ChunkedTerm] diff --git a/librarian/plugins/librarian-chunker/src/librarian_chunker/models/py.typed b/librarian/plugins/librarian-chunker/src/librarian_chunker/models/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/librarian/plugins/librarian-chunker/src/librarian_chunker/py.typed b/librarian/plugins/librarian-chunker/src/librarian_chunker/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/librarian/plugins/librarian-chunker/uv.lock b/librarian/plugins/librarian-chunker/uv.lock new file mode 100644 index 0000000..0ab975f --- /dev/null +++ b/librarian/plugins/librarian-chunker/uv.lock @@ -0,0 +1,3544 @@ +version = 1 +revision = 2 +requires-python = ">=3.10" +resolution-markers = [ + "python_full_version >= '3.13'", + "python_full_version == '3.12.*'", + "python_full_version < '3.12'", +] + +[[package]] +name = "aiosqlite" +version = "0.21.0" +source = { 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"DotNaos", email = "schuetzoliver00@gmail.com" }, +] +requires-python = ">=3.10" +dependencies = [ + "librarian-core", + "importlib_metadata; python_version<'3.10'", + "ollama>=0.4.8", + "parsel>=1.10.0", + "prefect>=3.4.1", + "openai>=1.78.1", +] + +#[tool.uv.sources] +#librarian-core = { git = "https://github.com/DotNaos/librarian-core", rev = "main" } + +[build-system] +requires = ["hatchling>=1.21"] +build-backend = "hatchling.build" + + +[tool.hatch.build.targets.wheel] +packages = ["src/librarian_extractor/"] + +[tool.hatch.metadata] +allow-direct-references = true + + +# ───────── optional: dev / test extras ───────── +[project.optional-dependencies] +dev = ["ruff", "pytest", "mypy"] + +[project.entry-points."librarian.workers"] +extractor = "librarian_extractor.extractor:Extractor" +ai_sanitizer = "librarian_extractor.ai_sanitizer:AISanitizer" diff --git a/librarian/plugins/librarian-extractor/src/librarian_extractor/__init__.py b/librarian/plugins/librarian-extractor/src/librarian_extractor/__init__.py new file mode 100644 index 0000000..b62d376 --- /dev/null +++ b/librarian/plugins/librarian-extractor/src/librarian_extractor/__init__.py @@ -0,0 +1,4 @@ +from librarian_extractor.ai_sanitizer.ai_sanitizer import AISanitizer +from librarian_extractor.extractor.extractor import Extractor + +__all__ = ["Extractor", "AISanitizer"] diff --git a/librarian/plugins/librarian-extractor/src/librarian_extractor/ai_sanitizer/__init__.py b/librarian/plugins/librarian-extractor/src/librarian_extractor/ai_sanitizer/__init__.py new file mode 100644 index 0000000..344ecb9 --- /dev/null +++ b/librarian/plugins/librarian-extractor/src/librarian_extractor/ai_sanitizer/__init__.py @@ -0,0 +1,3 @@ +from librarian_extractor.ai_sanitizer.ai_sanitizer import AISanitizer + +__all__ = ["AISanitizer"] diff --git a/librarian/plugins/librarian-extractor/src/librarian_extractor/ai_sanitizer/ai_sanitizer.py b/librarian/plugins/librarian-extractor/src/librarian_extractor/ai_sanitizer/ai_sanitizer.py new file mode 100644 index 0000000..89acf6d --- /dev/null +++ b/librarian/plugins/librarian-extractor/src/librarian_extractor/ai_sanitizer/ai_sanitizer.py @@ -0,0 +1,215 @@ +""" +AI-powered sanitizer +==================== +• in : ExtractData (tree from Extractor) +• out : ExtractData (same graph but with prettier names) + +Changes vs. previous revision +----------------------------- +✓ Media files resolved at course-level `media/` folder +✓ Missing sources are warned, not raised +""" + +from __future__ import annotations + +import json +import os +from pathlib import Path +from typing import List + +import openai +from prefect import get_run_logger, task +from prefect.futures import PrefectFuture, wait +from pydantic import ValidationError + +from librarian_core.workers.base import Worker +from librarian_extractor.prompts import PROMPT_COURSE +from librarian_extractor.models.extract_data import ( + ExtractData, + ExtractedCourse, + ExtractedFile, + ExtractedTerm, +) + + +# --------------------------------------------------------------------------- # +# helpers # +# --------------------------------------------------------------------------- # +def _clean_json(txt: str) -> str: + txt = txt.strip() + if txt.startswith("```"): + txt = txt.lstrip("`") + if "\n" in txt: + txt = txt.split("\n", 1)[1] + if txt.rstrip().endswith("```"): + txt = txt.rstrip()[:-3] + return txt.strip() + + +def _safe_json_load(txt: str) -> dict: + return json.loads(_clean_json(txt)) + + +def _merge_with_original(src: ExtractedCourse, patch: dict, lg) -> ExtractedCourse: + """Return *patch* merged with *src* so every id is preserved.""" + try: + tgt = ExtractedCourse.model_validate(patch) + except ValidationError as err: + lg.warning("LLM payload invalid – keeping original (%s)", err) + return src + + if not tgt.id: + tgt.id = src.id + + for ch_src, ch_tgt in zip(src.chapters, tgt.chapters): + if not ch_tgt.name: + ch_tgt.name = ch_src.name + for f_src, f_tgt in zip(ch_src.content_files, ch_tgt.content_files): + if not f_tgt.id: + f_tgt.id = f_src.id + for f_src, f_tgt in zip(ch_src.media_files, ch_tgt.media_files): + if not f_tgt.id: + f_tgt.id = f_src.id + return tgt + + +# --------------------------------------------------------------------------- # +# OpenAI call – Prefect task # +# --------------------------------------------------------------------------- # +@task( + name="sanitize_course_json", + retries=2, + retry_delay_seconds=5, + log_prints=True, +) +def sanitize_course_json(course_json: str, model: str, temperature: float) -> dict: + rsp = openai.chat.completions.create( + model=model, + temperature=temperature, + messages=[ + {"role": "system", "content": PROMPT_COURSE}, + {"role": "user", "content": course_json}, + ], + ) + usage = rsp.usage + get_run_logger().info( + "LLM tokens – prompt: %s, completion: %s", + usage.prompt_tokens, + usage.completion_tokens, + ) + return _safe_json_load(rsp.choices[0].message.content or "{}") + + +# --------------------------------------------------------------------------- # +# Worker # +# --------------------------------------------------------------------------- # +class AISanitizer(Worker[ExtractData, ExtractData]): + input_model = ExtractData + output_model = ExtractData + + def __init__(self, model_name: str | None = None, temperature: float = 0.0): + super().__init__() + self.model_name = model_name or os.getenv("OPENAI_MODEL", "gpt-4o-mini") + self.temperature = temperature + + # ------------------------------------------------------------------ # + def __run__(self, data: ExtractData) -> ExtractData: + lg = get_run_logger() + + futures: List[PrefectFuture] = [] + originals: List[ExtractedCourse] = [] + + # 1) submit all courses to the LLM + for term in data.terms: + for course in term.courses: + futures.append( + sanitize_course_json.submit( + json.dumps(course.model_dump(), ensure_ascii=False), + self.model_name, + self.temperature, + ) + ) + originals.append(course) + + wait(futures) + + # 2) build new graph with merged results + terms_out: List[ExtractedTerm] = [] + idx = 0 + for term in data.terms: + new_courses: List[ExtractedCourse] = [] + for _ in term.courses: + clean_dict = futures[idx].result() + merged = _merge_with_original(originals[idx], clean_dict, lg) + new_courses.append(merged) + idx += 1 + terms_out.append( + ExtractedTerm(id=term.id, name=term.name, courses=new_courses) + ) + + renamed = ExtractData(terms=terms_out) + + # 3) stage files with their new names + self._export_with_new_names(data, renamed, lg) + + return renamed + + # ------------------------------------------------------------------ # + # staging helpers # + # ------------------------------------------------------------------ # + def _stage_or_warn(self, src: Path, dst: Path, lg): + """Copy *src* → *dst* (via self.stage). Warn if src missing.""" + if not src.exists(): + lg.warning("Source missing – skipped %s", src) + return + self.stage(src, new_name=str(dst), sanitize=False) + lg.debug("Stage %s → %s", src.name, dst) + + def _export_with_new_names( + self, + original: ExtractData, + renamed: ExtractData, + lg, + ): + entry = Path(self.entry) + + for term_old, term_new in zip(original.terms, renamed.terms): + for course_old, course_new in zip(term_old.courses, term_new.courses): + # ---------- content files (per chapter) ----------------- + for chap_old, chap_new in zip(course_old.chapters, course_new.chapters): + n = min(len(chap_old.content_files), len(chap_new.content_files)) + for i in range(n): + fo = chap_old.content_files[i] + fn = chap_new.content_files[i] + src = ( + entry + / term_old.name + / course_old.name + / chap_old.name + / fo.name + ) + dst = ( + Path(term_new.name) + / course_new.name + / chap_new.name + / fn.name + ) + self._stage_or_warn(src, dst, lg) + + # ---------- media files (course-level “media”) ---------- + src_media_dir = ( + entry / term_old.name / course_old.name / "media" + ) # <─ fixed! + dst_media_dir = Path(term_new.name) / course_new.name / "media" + if not src_media_dir.is_dir(): + continue + + # build a flat list of (old, new) media filenames + media_pairs: List[tuple[ExtractedFile, ExtractedFile]] = [] + for ch_o, ch_n in zip(course_old.chapters, course_new.chapters): + media_pairs.extend(zip(ch_o.media_files, ch_n.media_files)) + + for fo, fn in media_pairs: + src = src_media_dir / fo.name + dst = dst_media_dir / fn.name + self._stage_or_warn(src, dst, lg) diff --git a/librarian/plugins/librarian-extractor/src/librarian_extractor/ai_sanitizer/py.typed b/librarian/plugins/librarian-extractor/src/librarian_extractor/ai_sanitizer/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/librarian/plugins/librarian-extractor/src/librarian_extractor/constants.py b/librarian/plugins/librarian-extractor/src/librarian_extractor/constants.py new file mode 100644 index 0000000..d44edf4 --- /dev/null +++ b/librarian/plugins/librarian-extractor/src/librarian_extractor/constants.py @@ -0,0 +1,66 @@ +""" +Shared lists and prompts +""" + +# -------------------------------------------------------------------- # +# file selection – keep only real documents we can show / convert # +# -------------------------------------------------------------------- # +CONTENT_FILE_EXTENSIONS = [ + "*.pdf", + "*.doc", + "*.docx", + "*.ppt", + "*.pptx", + "*.txt", + "*.rtf", +] + +MEDIA_FILE_EXTENSIONS = [ + "*.jpg", + "*.jpeg", + "*.png", + "*.gif", + "*.svg", + "*.mp4", + "*.mov", + "*.mp3", +] + +# -------------------------------------------------------------------- # +# naming rules # +# -------------------------------------------------------------------- # +SANITIZE_REGEX = { + "base": [r"\s*\(\d+\)$"], + "course": [ + r"^\d+\.\s*", + r"\s*\([^)]*\)", + r"\s*(?:FS|HS)\d{2}$", + ], + "chapter": [ + r"^\d+\.?\s*", + r"\s*SW_\d+\s*(?:___)?\s*KW_\d+\s*", + r"\bKapitel[_\s]*\d+\b", + ], + "file": [ + r",", # ← new : drop commas + r",?\s*inkl\.?\s*", + r"\(File\)", + r"```json", + ], +} + +BLACKLIST_REGEX = { + "chapter": [r"^allgemeine informationen$"], + "ressource_types": [ + "(Forum)", + "(URL)", + "(External tool)", + "(Text and media area)", + ], +} + +RESSOURCE_TYPES = BLACKLIST_REGEX["ressource_types"] +BASE_BLACKLIST_REGEX = SANITIZE_REGEX["base"] + +MAX_FILENAME_LENGTH = 100 + diff --git a/librarian/plugins/librarian-extractor/src/librarian_extractor/extractor/__init__.py b/librarian/plugins/librarian-extractor/src/librarian_extractor/extractor/__init__.py new file mode 100644 index 0000000..c97adff --- /dev/null +++ b/librarian/plugins/librarian-extractor/src/librarian_extractor/extractor/__init__.py @@ -0,0 +1,3 @@ +from librarian_extractor.extractor.extractor import Extractor + +__all__ = ["Extractor"] diff --git a/librarian/plugins/librarian-extractor/src/librarian_extractor/extractor/extractor.py b/librarian/plugins/librarian-extractor/src/librarian_extractor/extractor/extractor.py new file mode 100644 index 0000000..0b504a8 --- /dev/null +++ b/librarian/plugins/librarian-extractor/src/librarian_extractor/extractor/extractor.py @@ -0,0 +1,301 @@ +""" +Extractor Worker – resilient version +------------------------------------ +* Finds the real payload even when the link goes to + File_…/index.html first. +* No `iterdir` on non-directories. +* Keeps all earlier features: id parsing, allowed-suffix filter, + media-folder, sanitising. +""" + +from __future__ import annotations + +import hashlib +import re +import shutil +import zipfile +from pathlib import Path +from typing import Tuple + +import lxml.html +import parsel +from librarian_core.utils.path_utils import get_temp_path +from librarian_core.workers.base import Worker +from librarian_scraper.models.download_data import DownloadData +from prefect import get_run_logger, task +from prefect.futures import wait + +from librarian_extractor.constants import ( + CONTENT_FILE_EXTENSIONS, + MEDIA_FILE_EXTENSIONS, +) +from librarian_extractor.models.extract_data import ( + ExtractData, + ExtractedChapter, + ExtractedCourse, + ExtractedFile, + ExtractedTerm, +) +from librarian_extractor.sanitizers import ( + annotate_chapter_name, + is_chapter_allowed, + sanitize_chapter_name, + sanitize_course_name, + sanitize_file_name, +) + +CONTENT_EXTS = {Path(p).suffix.lower() for p in CONTENT_FILE_EXTENSIONS} +MEDIA_EXTS = {Path(p).suffix.lower() for p in MEDIA_FILE_EXTENSIONS} +ALL_EXTS = CONTENT_EXTS | MEDIA_EXTS + +_id_rx = re.compile(r"\.(\d{4,})[./]") # 1172180 from “..._.1172180/index.html” + + +# --------------------------------------------------------------------------- # +# helpers # +# --------------------------------------------------------------------------- # +def _hash_id(fname: str) -> str: + return hashlib.sha1(fname.encode()).hexdigest()[:10] + + +def _html_stub_target(html_file: Path) -> Path | None: + """Parse a Moodle *index.html* stub and return the first file link.""" + try: + tree = lxml.html.parse(html_file) # type: ignore[arg-type] + hrefs = tree.xpath("//ul/li/a/@href") + for h in hrefs: + h = h.split("?")[0].split("#")[0] + p = html_file.parent / h + if p.exists(): + return p + except Exception: + pass + return None + + +def _best_payload(node: Path) -> Path | None: # noqa: C901 + """ + Return the real document given *node* which may be: + • the actual file → return it + • File_xxx/dir → search inside /content or dir itself + • File_xxx/index.html stub → parse to find linked file + """ + # 1) immediate hit + if node.is_file() and node.suffix.lower() in ALL_EXTS: + return node + + # 2) if html stub try to parse inner link + if node.is_file() and node.suffix.lower() in {".html", ".htm"}: + hinted = _html_stub_target(node) + if hinted: + return _best_payload(hinted) # recurse + + # 3) directories to search + roots: list[Path] = [] + if node.is_dir(): + roots.append(node) + elif node.is_file(): + roots.append(node.parent) + + for r in list(roots): + if r.is_dir() and (r / "content").is_dir(): + roots.insert(0, r / "content") # prefer content folder + + for r in roots: + if not r.is_dir(): + continue + files = [p for p in r.iterdir() if p.is_file() and p.suffix.lower() in ALL_EXTS] + if len(files) == 1: + return files[0] + return None + + +def _file_id_from_href(href: str) -> str: + m = _id_rx.search(href) + return m.group(1) if m else "" + + +def task_(**kw): + kw.setdefault("log_prints", True) + return task(**kw) + + +# --------------------------------------------------------------------------- # +# Worker # +# --------------------------------------------------------------------------- # +class Extractor(Worker[DownloadData, ExtractData]): + input_model = DownloadData + output_model = ExtractData + + async def __run__(self, downloads: DownloadData) -> ExtractData: + lg = get_run_logger() + work_root = Path(get_temp_path()) / "extract" + work_root.mkdir(parents=True, exist_ok=True) + self.out_dir = work_root + + result = ExtractData() + futs = [] + entry_dir = self.entry + + for t in downloads.terms: + (work_root / t.name).mkdir(exist_ok=True) + result.terms.append(ExtractedTerm(id=t.id, name=t.name)) + for c in t.courses: + futs.append( + self._extract_course.submit(t.name, c.id, work_root, entry_dir) + ) + + done, _ = wait(futs) + for fut in done: + term, meta = fut.result() + if meta: + next(t for t in result.terms if t.name == term).courses.append(meta) + + for term in result.terms: + self.stage( + work_root / term.name, new_name=term.name, sanitize=False, move=True + ) + lg.info("Extractor finished – %d terms", len(result.terms)) + return result + + # ------------------------------------------------------------------ # + @staticmethod + @task_() + def _extract_course( # noqa: C901 + term: str, cid: str, out_root: Path, entry_dir: Path + ) -> Tuple[str, ExtractedCourse | None]: + lg = get_run_logger() + z = entry_dir / term / f"{cid}.zip" + if not z.is_file(): + lg.warning("ZIP missing %s", z) + return term, None + + tmp = Path(get_temp_path()) / f"u{cid}" + tmp.mkdir(exist_ok=True) + try: + with zipfile.ZipFile(z) as zf: + zf.extractall(tmp) + + html, root = Extractor._index_html(tmp) + if not html: + lg.warning("index.html missing for %s", cid) + return term, None + + cname = Extractor._course_name(html) or cid + c_meta = ExtractedCourse(id=cid, name=cname) + media_dir = out_root / term / cname / "media" + + structure = Extractor._outline(html) + if not structure: + Extractor._copy_all( + root, out_root / term / cname, c_meta, media_dir, lg # type: ignore + ) + return term, c_meta + + chap_no = 0 + for title, links in structure: + if not is_chapter_allowed(title): + continue + chap_no += 1 + chap_name = annotate_chapter_name(sanitize_chapter_name(title), chap_no) + chap_dir = out_root / term / cname / chap_name + chap_dir.mkdir(parents=True, exist_ok=True) + chap_meta = ExtractedChapter(name=chap_name) + + for text, href in links: + target = _best_payload(root / href.lstrip("./")) + if not target: + lg.debug("payload not found %s", href) + continue + + base = sanitize_file_name(text) + if not Path(base).suffix: + base += target.suffix # ensure extension + + dst = ( + media_dir / base + if target.suffix.lower() in MEDIA_EXTS + else chap_dir / base + ) + dst.parent.mkdir(parents=True, exist_ok=True) + shutil.copy2(target, dst) + + fid = _file_id_from_href(href) or _hash_id(dst.name) + meta_obj = ExtractedFile(id=fid, name=dst.name) + ( + chap_meta.media_files + if dst.is_relative_to(media_dir) + else chap_meta.content_files + ).append(meta_obj) + + if chap_meta.content_files or chap_meta.media_files: + c_meta.chapters.append(chap_meta) + + if c_meta.chapters: + lg.info("Extracted %s (%d chap.)", cname, len(c_meta.chapters)) + return term, c_meta + return term, None + finally: + shutil.rmtree(tmp, ignore_errors=True) + + # ------------------------------------------------------------------ # + # internal helpers # + # ------------------------------------------------------------------ # + @staticmethod + def _copy_all( + root: Path, dst_root: Path, c_meta: ExtractedCourse, media_dir: Path, lg + ): + chap = ExtractedChapter(name="Everything") + dst_root.mkdir(parents=True, exist_ok=True) + for fp in root.rglob("*"): + if fp.is_file() and fp.suffix.lower() in ALL_EXTS: + dst = ( + media_dir if fp.suffix.lower() in MEDIA_EXTS else dst_root + ) / fp.name + dst.parent.mkdir(parents=True, exist_ok=True) + shutil.copy2(fp, dst) + chap.content_files.append( + ExtractedFile(id=_hash_id(fp.name), name=dst.name) + ) + if chap.content_files: + c_meta.chapters.append(chap) + lg.info("Fallback copy %d files", len(chap.content_files)) + + @staticmethod + def _index_html(root: Path) -> Tuple[str, Path | None]: + for idx in root.rglob("index.html"): + try: + return idx.read_text("utf-8", errors="ignore"), idx.parent + except Exception: + continue + return "", None + + @staticmethod + def _course_name(html: str) -> str: + sel = parsel.Selector(text=html) + return sanitize_course_name(sel.css("h1 a::text").get(default="").strip()) + + @staticmethod + def _outline(html: str): + t = lxml.html.fromstring(html) + res = [] + for h3 in t.xpath("//h3"): + title = h3.text_content().strip() + ul = next((s for s in h3.itersiblings() if s.tag == "ul"), None) + if ul is None: + continue + links = [] + for a in ul.findall(".//a"): + if "(File)" in (a.text_content() or ""): + sel = parsel.Selector( + text=lxml.html.tostring(a, encoding="unicode") # type: ignore + ) + links.append( + ( + sel.css("::text").get().strip(), # type: ignore + sel.css("::attr(href)").get().strip(), # type: ignore + ) + ) + if links: + res.append((title, links)) + return res diff --git a/librarian/plugins/librarian-extractor/src/librarian_extractor/extractor/py.typed b/librarian/plugins/librarian-extractor/src/librarian_extractor/extractor/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/librarian/plugins/librarian-extractor/src/librarian_extractor/models/extract_data.py b/librarian/plugins/librarian-extractor/src/librarian_extractor/models/extract_data.py new file mode 100644 index 0000000..e67d510 --- /dev/null +++ b/librarian/plugins/librarian-extractor/src/librarian_extractor/models/extract_data.py @@ -0,0 +1,30 @@ +from typing import List + +from pydantic import BaseModel, Field + + +class ExtractedFile(BaseModel): + id: str + name: str # Name of the file, relative to ExtractedChapter.name + + +class ExtractedChapter(BaseModel): + name: str # Name of the chapter directory, relative to ExtractedCourse.name + content_files: List[ExtractedFile] = Field(default_factory=list) + media_files: List[ExtractedFile] = Field(default_factory=list) + + +class ExtractedCourse(BaseModel): + id: str + name: str # Name of the course directory, relative to ExtractedTerm.name + chapters: List[ExtractedChapter] = Field(default_factory=list) + + +class ExtractedTerm(BaseModel): + id: str + name: str # Name of the term directory, relative to ExtractMeta.dir + courses: List[ExtractedCourse] = Field(default_factory=list) + + +class ExtractData(BaseModel): + terms: List[ExtractedTerm] = Field(default_factory=list) diff --git a/librarian/plugins/librarian-extractor/src/librarian_extractor/prompts.py b/librarian/plugins/librarian-extractor/src/librarian_extractor/prompts.py new file mode 100644 index 0000000..60c8e39 --- /dev/null +++ b/librarian/plugins/librarian-extractor/src/librarian_extractor/prompts.py @@ -0,0 +1,29 @@ +# -------------------------------------------------------------------- # +# LLM prompts # +# -------------------------------------------------------------------- # + +PROMPT_COURSE = """ +General naming rules +==================== +* Use underscores instead of spaces. +* Keep meaningful numbers / IDs. +* Remove Date information, except it is absolutely necessary. +* -> Normalize dates / months ("Februar" → "02"). +* Remove redundant semester / university codes (e.g. FS24, HS, FHGR, CDS). +* Remove redundancy in general. ( DRY - Don't Repeat Yourself ) +* Trim superfluous parts like duplicate week information ("1_SW_01_KW_08" → "SW_01"). +* Only keep have one enumarator at a time, so "1_SW_01" → "SW_01". +* Preserve file extensions! +* Avoid repeated dots and illegal filesystem characters (colon, slash, …). + +The most important rule is to keep everything as consistent as possible. + +Important – DO NOT: +* change the JSON structure, +* change or reorder any `id`, +* add any keys. + +Return **only** the modified JSON for the course you receive. + +Everything should be in english after the sanitization. +""".strip() diff --git a/librarian/plugins/librarian-extractor/src/librarian_extractor/py.typed b/librarian/plugins/librarian-extractor/src/librarian_extractor/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/librarian/plugins/librarian-extractor/src/librarian_extractor/sanitizers.py b/librarian/plugins/librarian-extractor/src/librarian_extractor/sanitizers.py new file mode 100644 index 0000000..0910f11 --- /dev/null +++ b/librarian/plugins/librarian-extractor/src/librarian_extractor/sanitizers.py @@ -0,0 +1,71 @@ +""" +Name-sanitising helpers +""" + +from __future__ import annotations + +import re +from typing import Optional + +from librarian_extractor.constants import ( + BASE_BLACKLIST_REGEX, + BLACKLIST_REGEX, + MAX_FILENAME_LENGTH, + RESSOURCE_TYPES, + SANITIZE_REGEX, +) + +_INVALID_FS_CHARS = re.compile(r'[\\/:*?"<>|]') +_WS = re.compile(r"\s+") +_DUP_DOTS = re.compile(r"\.\.+") +_TRAILING_NUM = re.compile(r"_\(\d+\)$") + + +def _sanitize_name(name: str, extra_patterns: list[str]) -> str: + original = name + for rt in RESSOURCE_TYPES: + name = name.replace(rt, "") + for rx in BASE_BLACKLIST_REGEX + extra_patterns: + name = re.sub(rx, "", name, flags=re.IGNORECASE) + name = _INVALID_FS_CHARS.sub("_", name) + name = _DUP_DOTS.sub(".", name) + name = _WS.sub(" ", name).replace(" ", "_") + name = re.sub(r"_+", "_", name).strip("_") + base, dot, ext = name.rpartition(".") + if dot: + base = _TRAILING_NUM.sub("", base) + dup = re.compile(rf"(?i)[._]{re.escape(ext)}$") + base = dup.sub("", base) + name = f"{base}.{ext}" if base else f".{ext}" + else: + name = _TRAILING_NUM.sub("", name) + name = name.strip("_.") + if len(name) > MAX_FILENAME_LENGTH: + if dot and len(ext) < 10: + avail = MAX_FILENAME_LENGTH - len(ext) - 1 + name = f"{base[:avail]}.{ext}" + else: + name = name[:MAX_FILENAME_LENGTH].rstrip("_") + if not name or name == ".": + name = re.sub(_INVALID_FS_CHARS, "_", original)[:MAX_FILENAME_LENGTH] or "file" + return name + + +def sanitize_course_name(name: str) -> str: + return _sanitize_name(name, SANITIZE_REGEX["course"]) + + +def sanitize_chapter_name(name: str) -> str: + return _sanitize_name(name, SANITIZE_REGEX["chapter"]) + + +def sanitize_file_name(name: str) -> str: + return _sanitize_name(name, SANITIZE_REGEX["file"]) + + +def annotate_chapter_name(name: str, idx: Optional[int] = None) -> str: + return 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a/librarian/plugins/librarian-scraper/README.md b/librarian/plugins/librarian-scraper/README.md new file mode 100644 index 0000000..ed0dddf --- /dev/null +++ b/librarian/plugins/librarian-scraper/README.md @@ -0,0 +1 @@ +# Librarian Scraper diff --git a/librarian/plugins/librarian-scraper/pyproject.toml b/librarian/plugins/librarian-scraper/pyproject.toml new file mode 100644 index 0000000..dcc0abc --- /dev/null +++ b/librarian/plugins/librarian-scraper/pyproject.toml @@ -0,0 +1,41 @@ +[project] +name = "librarian-scraper" +version = "0.2.1" +description = "FastAPI gateway and runtime pipeline for Librarian" +readme = "README.md" +authors = [{ name = "DotNaos", email = "schuetzoliver00@gmail.com" }] +requires-python = ">=3.10" +dependencies = [ + "importlib_metadata; python_version<'3.10'", + "playwright>=1.51.0", + "dotenv>=0.9.9", + "parsel>=1.10.0", + "librarian-core", + "httpx>=0.28.1", +] + +[build-system] +requires = ["hatchling>=1.21"] +build-backend = "hatchling.build" + + +[tool.hatch.build.targets.wheel] +packages = ["src/librarian_scraper"] + +[tool.hatch.metadata] +allow-direct-references = true + +[tool.uv.sources] +#librarian-core = { git = "https://github.com/DotNaos/librarian-core", rev = "dev" } + +[project.entry-points."librarian.workers"] +crawler = "librarian_scraper.crawler:Crawler" +downloader = "librarian_scraper.downloader:Downloader" + + +# ───────── optional: dev / test extras ───────── +[project.optional-dependencies] +dev = ["ruff", "pytest", "mypy"] + +[project.scripts] +example = "examples.app:app" diff --git a/librarian/plugins/librarian-scraper/src/librarian_scraper/__init__.py b/librarian/plugins/librarian-scraper/src/librarian_scraper/__init__.py new file mode 100644 index 0000000..74de708 --- /dev/null +++ b/librarian/plugins/librarian-scraper/src/librarian_scraper/__init__.py @@ -0,0 +1,12 @@ +from .crawler import ( + Crawler, +) +from .downloader import ( + Downloader, +) + +__all__ = [ + "Crawler", + "Downloader", + "Extractor", +] diff --git a/librarian/plugins/librarian-scraper/src/librarian_scraper/constants.py b/librarian/plugins/librarian-scraper/src/librarian_scraper/constants.py new file mode 100644 index 0000000..e78543e --- /dev/null +++ b/librarian/plugins/librarian-scraper/src/librarian_scraper/constants.py @@ -0,0 +1,29 @@ +""" +URLs used by the scraper. +Functions marked as PUBLIC can be accessed without authentication. +Functions marked as PRIVATE require authentication. +""" + +BASE_URL = "https://moodle.fhgr.ch" + +CRAWLER = { + "DELAY_SLOW": 2.0, + "DELAY_FAST": 0.5, + "BATCH_SLOW": 2, + "BATCH_FAST": 8, +} + +class PUBLIC_URLS: + base_url = BASE_URL + login = f"{BASE_URL}/login/index.php" + index = f"{BASE_URL}/course/index.php" + degree_program = lambda degree_program_id: f"{BASE_URL}/course/index.php?categoryid={degree_program_id}" + category = lambda category_id: f"{BASE_URL}/course/index.php?categoryid={category_id}" + term = lambda term_id: f"{BASE_URL}/course/index.php?categoryid={term_id}" + +class PRIVATE_URLS: + user_courses = f"{BASE_URL}/my/courses.php" + dashboard = f"{BASE_URL}/my/" + course = lambda course_id: f"{BASE_URL}/course/view.php?id={course_id}" + files = lambda context_id: f"{BASE_URL}/course/downloadcontent.php?contextid={context_id}" + file = lambda file_id: f"{BASE_URL}/mod/resource/view.php?id={file_id}" diff --git a/librarian/plugins/librarian-scraper/src/librarian_scraper/crawler/__init__.py b/librarian/plugins/librarian-scraper/src/librarian_scraper/crawler/__init__.py new file mode 100644 index 0000000..786ad10 --- /dev/null +++ b/librarian/plugins/librarian-scraper/src/librarian_scraper/crawler/__init__.py @@ -0,0 +1,7 @@ +from librarian_scraper.crawler.cookie_crawler import CookieCrawler +from librarian_scraper.crawler.crawler import Crawler + +__all__ = [ + "CookieCrawler", + "Crawler", +] diff --git a/librarian/plugins/librarian-scraper/src/librarian_scraper/crawler/cookie_crawler.py b/librarian/plugins/librarian-scraper/src/librarian_scraper/crawler/cookie_crawler.py new file mode 100644 index 0000000..8d3b7de --- /dev/null +++ b/librarian/plugins/librarian-scraper/src/librarian_scraper/crawler/cookie_crawler.py @@ -0,0 +1,138 @@ +from __future__ import annotations + +import asyncio +import logging +import os +from typing import List, Optional + +from httpx import Cookies +from playwright.async_api import Browser, Cookie, Page, async_playwright + +from librarian_scraper.constants import PRIVATE_URLS, PUBLIC_URLS + + +class CookieCrawler: + """ + Retrieve Moodle session cookies + sesskey via Playwright. + + Usage + ----- + >>> crawler = CookieCrawler() + >>> cookies, sesskey = await crawler.crawl() # inside async code + # or + >>> cookies, sesskey = CookieCrawler.crawl_sync() # plain scripts + """ + + # ------------------------------------------------------------------ # + # construction # + # ------------------------------------------------------------------ # + def __init__(self, *, headless: bool = True) -> None: + self.headless = headless + self.cookies: Optional[List[Cookie]] = None + self.sesskey: str = "" + + self.username: str = os.getenv("MOODLE_USERNAME", "") + self.password: str = os.getenv("MOODLE_PASSWORD", "") + if not self.username or not self.password: + raise ValueError( + "Set MOODLE_USERNAME and MOODLE_PASSWORD as environment variables." + ) + + # ------------------------------------------------------------------ # + # public API # + # ------------------------------------------------------------------ # + async def crawl(self) -> tuple[Cookies, str]: + """ + Async entry-point – await this inside FastAPI / Prefect etc. + """ + async with async_playwright() as p: + browser: Browser = await p.chromium.launch(headless=self.headless) + page = await browser.new_page() + await page.goto(PUBLIC_URLS.login) + logging.info("Login page loaded: %s", page.url) + + await self._login(page) + await browser.close() + + if not self.cookies: + raise RuntimeError("Login failed – no cookies retrieved.") + + return self._to_cookiejar(self.cookies), self.sesskey + + @classmethod + def crawl_sync(cls, **kwargs) -> tuple[Cookies, str]: + """ + Synchronous helper for CLI / notebooks. + + Detects whether an event loop is already running. If so, it + schedules the coroutine and waits; otherwise it starts a fresh loop. + """ + self = cls(**kwargs) + + try: + loop = asyncio.get_running_loop() + except RuntimeError: # no loop running → safe to create one + return asyncio.run(self.crawl()) + + # An event loop exists – schedule coroutine + return loop.run_until_complete(self.crawl()) + + # ------------------------------------------------------------------ # + # internal helpers # + # ------------------------------------------------------------------ # + async def _login(self, page: Page) -> None: + """Fill the SSO form and extract cookies + sesskey.""" + + # Select organisation / IdP + await page.click("#wayf_submit_button") + + # Wait for the credential form + await page.wait_for_selector("form[method='post']", state="visible") + + # Credentials + await page.fill("input[id='username']", self.username) + await page.fill("input[id='password']", self.password) + await page.click("button[class='aai_login_button']") + + # Wait for redirect to /my/ page (dashboard), this means the login is complete + await page.wait_for_url(PRIVATE_URLS.dashboard) + await page.wait_for_selector("body", state="attached") + + # Navigate to personal course overview + await page.goto(PRIVATE_URLS.user_courses) + await page.wait_for_selector("body", state="attached") + + # Collect session cookies + self.cookies = await page.context.cookies() + + # Extract sesskey from injected Moodle config + try: + self.sesskey = await page.evaluate( + "() => window.M && M.cfg && M.cfg.sesskey" + ) + except Exception as exc: + raise RuntimeError("sesskey not found via JS evaluation") from exc + + if not self.sesskey: + raise RuntimeError("sesskey is empty after evaluation.") + + logging.debug("sesskey: %s", self.sesskey) + logging.debug("cookies: %s", self.cookies) + + # Dev convenience + if not self.headless: + await page.wait_for_timeout(5000) + + # ------------------------------------------------------------------ # + # cookie conversion # + # ------------------------------------------------------------------ # + def _to_cookiejar(self, raw: List[Cookie]) -> Cookies: + jar = Cookies() + for c in raw: + jar.set( + name=c.get("name", ""), + value=c.get("value", ""), + domain=c.get("domain", "").lstrip("."), + path=c.get("path", "/"), + ) + return jar diff --git a/librarian/plugins/librarian-scraper/src/librarian_scraper/crawler/crawler.py b/librarian/plugins/librarian-scraper/src/librarian_scraper/crawler/crawler.py new file mode 100644 index 0000000..8fe68c4 --- /dev/null +++ b/librarian/plugins/librarian-scraper/src/librarian_scraper/crawler/crawler.py @@ -0,0 +1,264 @@ +""" +librarian_scraper.crawler.crawler +--------------------------------- +Scrapes Moodle degree programmes into CrawlData. +• Hero images +• Polite throttling / batching +• Term-filter: only the latest two terms (dev) +• USER_SPECIFIC flag to keep / drop inaccessible courses +""" + +from __future__ import annotations + +import json +import os +import re +import time +from datetime import timedelta +from typing import List, Tuple +import sys +import asyncio + +if sys.platform == "win32": + # Switch from Selector to Proactor so asyncio.subprocess works + asyncio.set_event_loop_policy(asyncio.WindowsProactorEventLoopPolicy()) + +import httpx +import parsel +from librarian_core.utils.path_utils import get_cache_root +from librarian_core.workers.base import Worker +from prefect import get_run_logger, task +from prefect.futures import wait + +from librarian_scraper.constants import CRAWLER, PRIVATE_URLS, PUBLIC_URLS +from librarian_scraper.crawler.cookie_crawler import CookieCrawler +from librarian_scraper.models.crawl_data import ( + CrawlCourse, + CrawlData, + CrawlProgram, + CrawlTerm, +) + +# --------------------------------------------------------------------------- # +# module-level shared items for static task # +# --------------------------------------------------------------------------- # +_COOKIE_JAR: httpx.Cookies | None = None +_DELAY: float = 0.0 + +CACHE_FILE = get_cache_root() / "librarian_no_access_cache.json" + + +# --------------------------------------------------------------------------- # +# utility # +# --------------------------------------------------------------------------- # +def looks_like_enrol(resp: httpx.Response) -> bool: + txt = resp.text.lower() + return ( + "login" in str(resp.url).lower() + or "#page-enrol" in txt + or "you need to enrol" in txt + ) + + +# --------------------------------------------------------------------------- # +# main worker # +# --------------------------------------------------------------------------- # +class Crawler(Worker[CrawlProgram, CrawlData]): + input_model = CrawlProgram + output_model = CrawlData + + # toggles (env overrides) + RELAXED: bool + USER_SPECIFIC: bool + CLEAR_CACHE: bool + + # ------------------------------------------------------------------ # + # flow entry-point # + # ------------------------------------------------------------------ # + async def __run__(self, program: CrawlProgram) -> CrawlData: + global _COOKIE_JAR, _DELAY + lg = get_run_logger() + + self.RELAXED = os.getenv("SCRAPER_RELAXED", "true").lower() == "true" + self.USER_SPECIFIC = os.getenv("SCRAPER_USER_SPECIFIC", "true").lower() == "true" + self.CLEAR_CACHE = os.getenv("SCRAPER_CLEAR_CACHE", "false").lower() == "true" + + _DELAY = CRAWLER["DELAY_SLOW"] if self.RELAXED else CRAWLER["DELAY_FAST"] + batch = CRAWLER["BATCH_SLOW"] if self.RELAXED else CRAWLER["BATCH_FAST"] + lg.info( + "Mode=%s user_specific=%s delay=%.1fs batch=%s", + "RELAXED" if self.RELAXED else "FAST", + self.USER_SPECIFIC, + _DELAY, + batch, + ) + + # --------------------------- login + cookies, _ = await CookieCrawler().crawl() + _COOKIE_JAR = cookies + self._client = httpx.Client(cookies=cookies, follow_redirects=True) + + if not self._logged_in(): + lg.error("Guest session detected – aborting crawl.") + raise RuntimeError("Login failed") + + # --------------------------- cache + no_access: set[str] = set() if self.CLEAR_CACHE else self._load_cache() + + # --------------------------- scrape terms (first two for dev) + terms = self._crawl_terms(program.id)[:2] + lg.info("Terms discovered: %d", len(terms)) + + # --------------------------- scrape courses + for term in terms: + courses = self._crawl_courses(term.id) + lg.info("[%s] raw courses: %d", term.name, len(courses)) + + for i in range(0, len(courses), batch): + futs = [ + self._crawl_course_task.submit(course.id) + for course in courses[i : i + batch] + ] + done, _ = wait(futs) + + for fut in done: + cid, res_id = fut.result() + if res_id: + next( + c for c in courses if c.id == cid + ).content_ressource_id = res_id + else: + no_access.add(cid) + + term.courses = ( + [c for c in courses if c.content_ressource_id] + if self.USER_SPECIFIC + else courses + ) + lg.info("[%s] kept: %d", term.name, len(term.courses)) + + # --------------------------- persist cache + self._save_cache(no_access) + + return CrawlData( + degree_program=CrawlProgram( + id=program.id, + name=program.name, + terms=[t for t in terms if t.courses], + ) + ) + + # ------------------------------------------------------------------ # + # static task inside class # + # ------------------------------------------------------------------ # + @staticmethod + @task( + name="crawl_course", + retries=2, + retry_delay_seconds=5, + log_prints=True, + cache_expiration=timedelta(days=1), + ) + def _crawl_course_task(course_id: str) -> Tuple[str, str]: + """ + Returns (course_id, content_resource_id or ""). + Never raises; logs reasons instead. + """ + lg = get_run_logger() + assert _COOKIE_JAR is not None + + url = PRIVATE_URLS.course(course_id) + for attempt in (1, 2): + try: + r = httpx.get( + url, cookies=_COOKIE_JAR, follow_redirects=True, timeout=30 + ) + r.raise_for_status() + time.sleep(_DELAY) + break + except Exception as exc: + lg.warning("GET %s failed (%s) attempt %d/2", url, exc, attempt) + time.sleep(_DELAY) + else: + lg.warning("Course %s unreachable.", course_id) + return course_id, "" + + if looks_like_enrol(r): + lg.info("No access to course %s (enrol / login page).", course_id) + return course_id, "" + + href = ( + parsel.Selector(r.text) + .css('a[data-downloadcourse="1"]::attr(href)') + .get("") + ) + if not href: + lg.info("Course %s has no downloadable content.", course_id) + return course_id, "" + + return course_id, href.split("=")[-1] + + # ------------------------------------------------------------------ # + # helpers # + # ------------------------------------------------------------------ # + def _logged_in(self) -> bool: + html = self._get_html(PUBLIC_URLS.index) + return not parsel.Selector(text=html).css("div.usermenu span.login a") + + def _crawl_terms(self, dp_id: str) -> List[CrawlTerm]: + html = self._get_html(PUBLIC_URLS.degree_program(dp_id)) + sel = parsel.Selector(text=html) + out = [] + for a in sel.css("div.category h3.categoryname a"): + name = a.xpath("text()").get("").strip() + if re.match(r"^(FS|HS)\d{2}$", name): + out.append( + CrawlTerm(name=name, id=a.xpath("@href").get("").split("=")[-1]) + ) + order = {"FS": 0, "HS": 1} + return sorted( + out, key=lambda t: (2000 + int(t.name[2:]), order[t.name[:2]]), reverse=True + ) + + def _crawl_courses(self, term_id: str) -> List[CrawlCourse]: + html = self._get_html(PUBLIC_URLS.term(term_id)) + sel = parsel.Selector(text=html) + courses = [] + for box in sel.css("div.coursebox"): + anchor = box.css("h3.coursename a") + if not anchor: + continue + cid = anchor.attrib.get("href", "").split("=")[-1] + raw = anchor.xpath("text()").get("").strip() + name = re.sub(r"\s*(FS|HS)\d{2}\s*", "", raw) + name = re.sub(r"\s*\(.*?\)\s*", "", name).strip() + hero = box.css("div.courseimage img::attr(src)").get("") or "" + courses.append(CrawlCourse(id=cid, name=name, hero_image=hero)) + return courses + + def _get_html(self, url: str) -> str: + try: + r = self._client.get(url, timeout=30) + r.raise_for_status() + time.sleep(_DELAY) + return r.text + except Exception as exc: + get_run_logger().warning("GET %s failed (%s)", url, exc) + return "" + + # ------------------------------------------------------------------ # + # cache helpers # + # ------------------------------------------------------------------ # + @staticmethod + def _load_cache() -> set[str]: + try: + return set(json.loads(CACHE_FILE.read_text())) + except Exception: + return set() + + @staticmethod + def _save_cache(cache: set[str]) -> None: + try: + CACHE_FILE.write_text(json.dumps(sorted(cache), indent=2)) + except Exception as exc: + get_run_logger().warning("Could not save cache: %s", exc) diff --git a/librarian/plugins/librarian-scraper/src/librarian_scraper/crawler/index_crawler.py b/librarian/plugins/librarian-scraper/src/librarian_scraper/crawler/index_crawler.py new file mode 100644 index 0000000..d2f1ffb --- /dev/null +++ b/librarian/plugins/librarian-scraper/src/librarian_scraper/crawler/index_crawler.py @@ -0,0 +1,357 @@ +import concurrent.futures +import json +import logging +import re +import tempfile +import time +from pathlib import Path + +import httpx +import parsel +from librarian_core.model import Course, DegreeProgram, FileEntry, MoodleIndex, Semester + +from . import URLs + +CACHE_FILENAME = "librarian_no_access_cache.json" +NO_ACCESS_CACHE_FILE = Path(tempfile.gettempdir()) / CACHE_FILENAME + + +class IndexCrawler: + def __init__(self, degree_program: DegreeProgram, cookies: httpx.Cookies, debug: bool = False, *, max_workers: int = 8) -> None: + self.degree_program = degree_program + self.debug = debug + self.client = httpx.Client(cookies=cookies, follow_redirects=True) + self.max_workers = max_workers + + # When True the cached “no-access” set is ignored for this run + self._ignore_cache: bool = False + + # Load persisted cache of course-IDs the user cannot access + if NO_ACCESS_CACHE_FILE.exists(): + try: + self._no_access_cache: set[str] = set(json.loads(NO_ACCESS_CACHE_FILE.read_text())) + except Exception: + logging.warning("Failed to read no-access cache, starting fresh.") + self._no_access_cache = set() + else: + self._no_access_cache = set() + + def __enter__(self): + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + self.client.close() + + def __del__(self): + # Fallback in case the context manager isn’t used + if not self.client.is_closed: + self.client.close() + + """ + Crawl a single instance of MoodleIndex. + This returns a MoodleIndex object populated with data. + """ + + def crawl_index(self, userSpecific: bool = True, *, use_cache: bool = True) -> MoodleIndex: + """ + Build and return a `MoodleIndex`. + + Parameters + ---------- + userSpecific : bool + When True, include only courses that expose a downloadable content resource. + use_cache : bool, default True + If False, bypass the persisted “no-access” cache so every course is probed + afresh. Newly discovered “no-access” courses are still written back to the + cache at the end of the crawl. + """ + # Set runtime flag for has_user_access() + self._ignore_cache = not use_cache + + semesters = [] + # Get all courses for each semester and the courseid and name for each course. + semesters = self.crawl_semesters() + # Crawl only the latest two semesters to reduce load (remove once caching is implemented) + for semester in semesters[:2]: + courses = self.crawl_courses(semester) + + # Crawl courses in parallel to speed things up + with concurrent.futures.ThreadPoolExecutor(max_workers=self.max_workers) as pool: + list(pool.map(self.crawl_course, courses)) + + # Filter courses once all have been processed + for course in courses: + if userSpecific: + if course.content_ressource_id: + semester.courses.append(course) + else: + semester.courses.append(course) + + # Only add semesters that have at least one course + # Filter out semesters that ended up with no courses after crawling + semesters: list[Semester] = [ + semester for semester in semesters if semester.courses + ] + + created_index = MoodleIndex( + degree_program=DegreeProgram( + name=self.degree_program.name, + id=self.degree_program.id, + semesters=semesters, + ), + ) + # Persist any newly discovered no-access courses + self._save_no_access_cache() + + # Restore default behaviour for subsequent calls + self._ignore_cache = False + + return created_index + + # --------------------------------------------------------------------- # + # High-level crawling helpers + # --------------------------------------------------------------------- # + def crawl_semesters(self) -> list[Semester]: + """ + Crawl the semesters from the Moodle index page. + """ + url = URLs.get_degree_program_url(self.degree_program.id) + res = self.get_with_retries(url) + + if res.status_code == 200: + semesters = self.extract_semesters(res.text) + logging.debug(f"Found semesters: {semesters}") + return semesters + + return [] + + def crawl_courses(self, semester: Semester) -> list[Course]: + """ + Crawl the courses from the Moodle index page. + """ + url = URLs.get_semester_url(semester_id=semester.id) + res = self.get_with_retries(url) + + if res.status_code == 200: + courses = self.extract_courses(res.text) + logging.debug(f"Found courses: {courses}") + return courses + + return [] + + def crawl_course(self, course: Course) -> None: + """ + Crawl a single Moodle course page. + """ + + hasAccess = self.has_user_access(course) + + if not hasAccess: + return + + # TODO: Cache which courses the user has no access to, to avoid repeated requests + + course.content_ressource_id = self.crawl_content_ressource_id(course) + course.files = self.crawl_course_files(course) + + # --------------------------------------------------------------------- # + # Networking utilities + # --------------------------------------------------------------------- # + def get_with_retries(self, url: str, retries: int = 3, delay: int = 1) -> httpx.Response: + """ + Simple GET with retries and exponential back-off. + """ + for attempt in range(1, retries + 1): + try: + response = self.client.get(url) + + response.raise_for_status() + + return response + except Exception as e: + logging.warning(f"Request to {url} failed ({e}), attempt {attempt}/{retries}") + if attempt < retries: + time.sleep(delay * (2 ** (attempt - 1))) + raise Exception(f"Failed to GET {url} after {retries} attempts") + + def save_html(self, url: str, response: httpx.Response) -> None: + """ + Persist raw HTML locally for debugging. + """ + filename = url.split("/")[-1] + ".html" + with open(filename, "w", encoding="utf-8") as f: + f.write(response.text) + logging.info(f"Saved HTML to {filename}") + + # --------------------------------------------------------------------- # + # Extractors + # --------------------------------------------------------------------- # + def extract_semesters(self, html: str) -> list[Semester]: + selector = parsel.Selector(text=html) + + logging.info("Extracting semesters from the HTML content.") + + semesters: list[Semester] = [] + + # Each semester sits in a collapsed container + semester_containers = selector.css("div.category.notloaded.with_children.collapsed") + + for container in semester_containers: + anchor = container.css("h3.categoryname.aabtn a") + if not anchor: + continue + + anchor = anchor[0] + semester_name = ( + anchor.xpath("text()").get("").replace("\n", "").replace("\t", "").strip() + ) + semester_id = anchor.attrib.get("href", "").split("=")[-1] + + # Only keep semesters labeled FS or HS + if "FS" not in semester_name and "HS" not in semester_name: + continue + + semesters.append(Semester(name=semester_name, id=semester_id)) + + semester_order = { + "FS": 0, # Frühjahrs‐/Spring Semester + "HS": 1, # Herbst‐/Fall Semester + } + # Sort by year and then by FS before HS + sorted_semesters = sorted( + semesters, + key=lambda s: ( + 2000 + int(s.name[2:]), # parse "25" → int 25, add 2000 → 2025 + semester_order[s.name[:2]] # map "FS" → 0, "HS" → 1 + ), + reverse=True, + ) + return sorted_semesters + + def extract_courses(self, html: str) -> list[Course]: + """ + Parse courses and capture optional “hero_image” (overview image) if present. + """ + selector = parsel.Selector(text=html) + + logging.info("Extracting courses from the HTML content.") + + courses: list[Course] = [] + + for header in selector.css("h3.coursename"): + anchor = header.css("a") + if not anchor: + logging.warning("No course anchor found in the course header.") + continue + + anchor = anchor[0] + course_name = ( + anchor.xpath("text()").get("").replace("\n", "").replace("\t", "").strip() + ) + course_id = anchor.attrib.get("href", "").split("=")[-1] + + # Remove trailing semester tag and code patterns + course_name = re.sub(r"\s*(FS|HS)\d{2}\s*", "", course_name) + course_name = re.sub(r"\s*\(.*?\)\s*", "", course_name).strip() + + # Try to locate a hero/overview image that belongs to this course box + # Traverse up to the containing course box, then look for
+ course_container = header.xpath('./ancestor::*[contains(@class,"coursebox")][1]') + hero_src = ( + course_container.css("div.courseimage img::attr(src)").get("") + if course_container else "" + ) + + courses.append( + Course( + id=course_id, + name=course_name, + activity_type="", # TODO: Make optional + hero_image=hero_src or "" + ) + ) + + logging.info(f"{len(courses)} courses extracted.") + return courses + + def has_user_access(self, course: Course) -> bool: + """ + Return True only if the authenticated user can access the course (result cached). + (i.e. the response is HTTP 200 **and** is not a redirected login/enrol page). + """ + if not self._ignore_cache and course.id in self._no_access_cache: + return False + + url = URLs.get_course_url(course.id) + res = self.get_with_retries(url) + + if res.status_code != 200: + self._no_access_cache.add(course.id) + return False + + # Detect Moodle redirection to a login or enrolment page + final_url = str(res.url).lower() + if "login" in final_url or "enrol" in final_url: + self._no_access_cache.add(course.id) + return False + + # Some enrolment pages still return 200; look for HTML markers + if "#page-enrol" in res.text or "you need to enrol" in res.text.lower(): + self._no_access_cache.add(course.id) + return False + + # If we got here the user has access; otherwise cache the deny + return True + + def crawl_content_ressource_id(self, course: Course) -> str: + course_id = course.id + url = URLs.get_course_url(course_id) + res = self.get_with_retries(url) + psl = parsel.Selector(res.text) + + try: + logging.info("Searching for 'Download course content' link.") + # Use parsel CSS selector to find the anchor tag with the specific data attribute + download_link_selector = psl.css('a[data-downloadcourse="1"]') + if not download_link_selector: + raise ValueError("Download link not found.") + + # Extract the href attribute from the first matching element + href = download_link_selector[0].attrib.get("href") + if not href: + raise ValueError("Href attribute not found on the download link.") + + context_id = href.split("=")[-1] + course.content_ressource_id = context_id + + return context_id + except Exception as e: + logging.error( + f"Error extracting content resource ID for course '{course.name}': {e}", + exc_info=False, + ) + logging.debug("Debugging info: Error accessing course content.", exc_info=True) + return '' + + def crawl_course_files(self, course: Course) -> list[FileEntry]: + """ + Crawl the course files from the Moodle course page. + """ + url = URLs.get_course_url(course.id) + res = self.get_with_retries(url) + + if res.status_code == 200: + files = [] # TODO: either implement this or remove, because files are extracted from the .zip file + logging.debug(f"Found files: {files}") + return files + + return [] + + # ----------------------------------------------------------------- # + # Cache persistence helpers + # ----------------------------------------------------------------- # + def _save_no_access_cache(self) -> None: + try: + NO_ACCESS_CACHE_FILE.write_text(json.dumps(sorted(self._no_access_cache))) + except Exception as exc: + logging.warning(f"Could not persist no-access cache: {exc}") diff --git a/librarian/plugins/librarian-scraper/src/librarian_scraper/crawler/py.typed b/librarian/plugins/librarian-scraper/src/librarian_scraper/crawler/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/librarian/plugins/librarian-scraper/src/librarian_scraper/crawler/urls.py b/librarian/plugins/librarian-scraper/src/librarian_scraper/crawler/urls.py new file mode 100644 index 0000000..bc7b4ba --- /dev/null +++ b/librarian/plugins/librarian-scraper/src/librarian_scraper/crawler/urls.py @@ -0,0 +1,59 @@ +# TODO: Move to librarian-core +""" +All URLs used in the crawler. +Functions marked as PUBLIC can be accessed without authentication. +Functions marked as PRIVATE require authentication. +""" +class URLs: + base_url = "https://moodle.fhgr.ch" + + @classmethod + def get_base_url(cls): + """PUBLIC""" + return cls.base_url + + # ------------------------- Moodle URLs ------------------------- + @classmethod + def get_login_url(cls): + """PUBLIC""" + return f"{cls.base_url}/login/index.php" + + @classmethod + def get_index_url(cls): + """PUBLIC""" + return f"{cls.base_url}/course/index.php" + + @classmethod + def get_degree_program_url(cls, degree_program_id): + """PUBLIC""" + return f"{cls.base_url}/course/index.php?categoryid={degree_program_id}" + + @classmethod + def get_category_url(cls, category_id): + """PUBLIC""" + return f"{cls.base_url}/course/index.php?categoryid={category_id}" + + @classmethod + def get_semester_url(cls, semester_id): + """PUBLIC""" + return f"{cls.base_url}/course/index.php?categoryid={semester_id}" + + @classmethod + def get_user_courses_url(cls): + """PRIVATE""" + return f"{cls.base_url}/my/courses.php" + + @classmethod + def get_course_url(cls, course_id): + """PRIVATE""" + return f"{cls.base_url}/course/view.php?id={course_id}" + + @classmethod + def get_files_url(cls, context_id): + """PRIVATE""" + return f"{cls.base_url}/course/downloadcontent.php?contextid={context_id}" + + @classmethod + def get_file_url(cls, file_id): + """PRIVATE""" + return f"{cls.base_url}/mod/resource/view.php?id={file_id}" diff --git a/librarian/plugins/librarian-scraper/src/librarian_scraper/downloader/__init__.py b/librarian/plugins/librarian-scraper/src/librarian_scraper/downloader/__init__.py new file mode 100644 index 0000000..26316b2 --- /dev/null +++ b/librarian/plugins/librarian-scraper/src/librarian_scraper/downloader/__init__.py @@ -0,0 +1,5 @@ +from .downloader import * + +__all__ = [ + "Downloader", +] diff --git a/librarian/plugins/librarian-scraper/src/librarian_scraper/downloader/downloader.py b/librarian/plugins/librarian-scraper/src/librarian_scraper/downloader/downloader.py new file mode 100644 index 0000000..79146a8 --- /dev/null +++ b/librarian/plugins/librarian-scraper/src/librarian_scraper/downloader/downloader.py @@ -0,0 +1,151 @@ +""" +Downloader Worker +================= +Input : CrawlData (from the crawler) +Output : DownloadData (metadata only; files staged) + +Folder tree after run +--------------------- +export_dir/ +└─ {TERM_NAME}/ + ├─ {course_id}.zip + └─ … +""" + +from __future__ import annotations + +import asyncio +import time +from pathlib import Path +from typing import List, Tuple + +import httpx +from librarian_core.utils.path_utils import get_temp_path +from librarian_core.workers.base import Worker +from prefect import get_run_logger, task +from prefect.futures import wait + +from librarian_scraper.constants import CRAWLER +from librarian_scraper.crawler.cookie_crawler import CookieCrawler +from librarian_scraper.models.crawl_data import CrawlData +from librarian_scraper.models.download_data import ( + DownloadCourse, + DownloadData, + DownloadTerm, +) + + +# --------------------------------------------------------------------------- # +# helper decorator # +# --------------------------------------------------------------------------- # +def task_(**kw): + kw.setdefault("log_prints", True) + kw.setdefault("retries", 2) + kw.setdefault("retry_delay_seconds", 5) + return task(**kw) + + +# --------------------------------------------------------------------------- # +# shared state for static task # +# --------------------------------------------------------------------------- # +_COOKIE_JAR: httpx.Cookies | None = None +_SESSKEY: str = "" +_LIMIT: int = 2 +_DELAY: float = 0.0 + + +class Downloader(Worker[CrawlData, DownloadData]): + DOWNLOAD_URL = "https://moodle.fhgr.ch/course/downloadcontent.php" + + # tuning + CONCURRENCY = 8 + RELAXED = True # False → faster + + input_model = CrawlData + output_model = DownloadData + + # ------------------------------------------------------------------ # + async def __run__(self, crawl: CrawlData) -> DownloadData: + global _COOKIE_JAR, _SESSKEY, _LIMIT, _DELAY + lg = get_run_logger() + + # ------------ login + cookies, sesskey = await CookieCrawler().crawl() + _COOKIE_JAR, _SESSKEY = cookies, sesskey + + # ------------ tuning + _LIMIT = 1 if self.RELAXED else max(1, min(self.CONCURRENCY, 8)) + _DELAY = CRAWLER["DELAY_SLOW"] if self.RELAXED else CRAWLER["DELAY_FAST"] + + # ------------ working dir + work_root = Path(get_temp_path()) / f"dl_{int(time.time())}" + work_root.mkdir(parents=True, exist_ok=True) + + result = DownloadData() + futures = [] + term_dirs: List[Tuple[str, Path]] = [] + + # schedule downloads + for term in crawl.degree_program.terms: + term_dir = work_root / term.name + term_dir.mkdir(parents=True, exist_ok=True) + term_dirs.append((term.name, term_dir)) + + dl_term = DownloadTerm(id=term.id, name=term.name) + result.terms.append(dl_term) + + for course in term.courses: + dest = term_dir / f"{course.id}.zip" + dl_term.courses.append(DownloadCourse(id=course.id, name=course.name)) + futures.append( + self._download_task.submit(course.content_ressource_id, dest) + ) + + wait(futures) # block for all downloads + + # stage term directories + for name, dir_path in term_dirs: + self.stage(dir_path, new_name=name, sanitize=False, move=True) + + lg.info("Downloader finished – staged %d term folders", len(term_dirs)) + return result + + # ------------------------------------------------------------------ # + # static task # + # ------------------------------------------------------------------ # + @staticmethod + @task_() + def _download_task(context_id: str, dest: Path) -> None: + lg = get_run_logger() + if not context_id: + lg.info("Skip (no context id) → %s", dest.name) + return + + async def fetch() -> bool: + sem = asyncio.Semaphore(_LIMIT) + + async with sem: + data = {"sesskey": _SESSKEY, "download": 1, "contextid": context_id} + async with httpx.AsyncClient(cookies=_COOKIE_JAR) as cli: + try: + async with cli.stream( + "POST", Downloader.DOWNLOAD_URL, data=data, timeout=60 + ) as r: + r.raise_for_status() + with dest.open("wb") as fh: + async for chunk in r.aiter_bytes(): + fh.write(chunk) + lg.info("Downloaded %s", dest) + return True + except httpx.HTTPStatusError as exc: + lg.warning( + "HTTP %s for %s", exc.response.status_code, dest.name + ) + except Exception as exc: + lg.warning("Error downloading %s (%s)", dest.name, exc) + return False + + ok = asyncio.run(fetch()) + if not ok and dest.exists(): + dest.unlink(missing_ok=True) + time.sleep(_DELAY) diff --git a/librarian/plugins/librarian-scraper/src/librarian_scraper/downloader/py.typed b/librarian/plugins/librarian-scraper/src/librarian_scraper/downloader/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/librarian/plugins/librarian-scraper/src/librarian_scraper/models/__init__.py b/librarian/plugins/librarian-scraper/src/librarian_scraper/models/__init__.py new file mode 100644 index 0000000..60c0f07 --- /dev/null +++ b/librarian/plugins/librarian-scraper/src/librarian_scraper/models/__init__.py @@ -0,0 +1,24 @@ +from librarian_scraper.models.crawl_data import ( + CrawlCourse, + CrawlData, + CrawlFile, + CrawlProgram, + CrawlTerm, +) + +from librarian_scraper.models.download_data import ( + DownloadCourse, + DownloadData, + DownloadTerm, +) + +__all__ = [ + "CrawlData", + "CrawlCourse", + "CrawlFile", + "CrawlProgram", + "CrawlTerm", + "DownloadData", + "DownloadCourse", + "DownloadTerm", +] diff --git a/librarian/plugins/librarian-scraper/src/librarian_scraper/models/crawl_data.py b/librarian/plugins/librarian-scraper/src/librarian_scraper/models/crawl_data.py new file mode 100644 index 0000000..4d51430 --- /dev/null +++ b/librarian/plugins/librarian-scraper/src/librarian_scraper/models/crawl_data.py @@ -0,0 +1,188 @@ +from __future__ import annotations + +import re +from datetime import datetime, timezone + +from pydantic import BaseModel, Field + +""" +Example of a MoodleIndex (JSON): +MoodleIndex: { + degree_program: { + id: '1157', + name: 'Computational and Data Science', + terms: [ + { + id: '1745', + name: 'FS25', + courses: [ + { + id: '18863', + name: 'Programmierung und Prompt Engineering II', + activity_type: '', + hero_image: + 'https://moodle.fhgr.ch/pluginfile.php/1159522/course/overviewfiles/PythonBooks.PNG', + content_ressource_id: '1159522', + files: [], + }, + { + id: '18240', + name: 'Effiziente Algorithmen', + activity_type: '', + hero_image: '', + content_ressource_id: '1125554', + files: [], + }, + { + id: '18237', + name: 'Mathematik II', + activity_type: '', + hero_image: + 'https://moodle.fhgr.ch/pluginfile.php/1125458/course/overviewfiles/Integration_Differential_b.png', + content_ressource_id: '1125458', + files: [], + }, + { + id: '18236', + name: '2025 FS FHGR CDS Numerische Methoden', + activity_type: '', + hero_image: '', + content_ressource_id: '1125426', + files: [], + }, + { + id: '18228', + name: 'Datenbanken und Datenverarbeitung', + activity_type: '', + hero_image: '', + content_ressource_id: '1125170', + files: [], + }, + ], + }, + { + id: '1746', + name: 'HS24', + courses: [ + { + id: '18030', + name: 'Bootcamp Wissenschaftliches Arbeiten', + activity_type: '', + hero_image: '', + content_ressource_id: '1090544', + files: [], + }, + { + id: '17527', + name: 'Einführung in Data Science', + activity_type: '', + hero_image: + 'https://moodle.fhgr.ch/pluginfile.php/1059194/course/overviewfiles/cds1010.jpg', + content_ressource_id: '1059194', + files: [], + }, + { + id: '17526', + name: 'Einführung in Computational Science', + activity_type: '', + hero_image: + 'https://moodle.fhgr.ch/pluginfile.php/1059162/course/overviewfiles/cds_intro_sim.jpg', + content_ressource_id: '1059162', + files: [], + }, + { + id: '17525', + name: 'Mathematik I', + activity_type: '', + hero_image: + 'https://moodle.fhgr.ch/pluginfile.php/1059130/course/overviewfiles/AdobeStock_452512134.png', + content_ressource_id: '1059130', + files: [], + }, + { + id: '17507', + name: 'Programmierung und Prompt Engineering', + activity_type: '', + hero_image: + 'https://moodle.fhgr.ch/pluginfile.php/1058554/course/overviewfiles/10714013_33861.jpg', + content_ressource_id: '1058554', + files: [], + }, + { + id: '17505', + name: 'Algorithmen und Datenstrukturen', + activity_type: '', + hero_image: + 'https://moodle.fhgr.ch/pluginfile.php/1058490/course/overviewfiles/Bild1.png', + content_ressource_id: '1058490', + files: [], + }, + { + id: '17503', + name: 'Computer Science', + activity_type: '', + hero_image: + 'https://moodle.fhgr.ch/pluginfile.php/1058426/course/overviewfiles/Titelbild.jpg', + content_ressource_id: '1058426', + files: [], + }, + ], + }, + ], + }, + timestamp: '2025-04-27T14:20:11.354825+00:00', +}; +""" + + +# --------------------------------------------------------------------------- +# Base Model +# --------------------------------------------------------------------------- +class CrawlData(BaseModel): + degree_program: CrawlProgram = Field( + default_factory=lambda: CrawlProgram(id="", name="") + ) + timestamp: str = Field( + default_factory=lambda: datetime.now(timezone.utc).isoformat() + ) + + +# --------------------------------------------------------------------------- +# Degree Program +# --------------------------------------------------------------------------- +class CrawlProgram(BaseModel): + id: str = Field("1157", description="Unique identifier for the degree program.") + name: str = Field("Computational and Data Science", description="Name of the degree program.") + terms: list[CrawlTerm] = Field(default_factory=list) + + +# --------------------------------------------------------------------------- +# Term +# --------------------------------------------------------------------------- +_TERM_RE = re.compile(r"^(HS|FS)\d{2}$") # HS24 / FS25 … + + +class CrawlTerm(BaseModel): + id: str + name: str = Field(..., pattern=_TERM_RE.pattern) # e.g. “HS24” + courses: list[CrawlCourse] = Field(default_factory=list) + + +# --------------------------------------------------------------------------- +# Course +# --------------------------------------------------------------------------- +class CrawlCourse(BaseModel): + id: str + name: str + hero_image: str = "" + content_ressource_id: str = "" + files: list[CrawlFile] = Field(default_factory=list) + + +# --------------------------------------------------------------------------- +# Files +# --------------------------------------------------------------------------- +class CrawlFile(BaseModel): + id: str + res_id: str + name: str diff --git a/librarian/plugins/librarian-scraper/src/librarian_scraper/models/download_data.py b/librarian/plugins/librarian-scraper/src/librarian_scraper/models/download_data.py new file mode 100644 index 0000000..be41bfc --- /dev/null +++ b/librarian/plugins/librarian-scraper/src/librarian_scraper/models/download_data.py @@ -0,0 +1,18 @@ +from typing import List + +from pydantic import BaseModel, Field + + +class DownloadCourse(BaseModel): + id: str + name: str # Stores the name of the zip file inside the term directory + + +class DownloadTerm(BaseModel): + id: str + name: str # Stores the name of the term directory inside DownloadMeta.dir + courses: List[DownloadCourse] = Field(default_factory=list) + + +class DownloadData(BaseModel): + terms: List[DownloadTerm] = Field(default_factory=list) diff --git a/librarian/plugins/librarian-scraper/src/librarian_scraper/py.typed b/librarian/plugins/librarian-scraper/src/librarian_scraper/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/librarian/plugins/librarian-scraper/uv.lock b/librarian/plugins/librarian-scraper/uv.lock new file mode 100644 index 0000000..a5a6266 --- /dev/null +++ b/librarian/plugins/librarian-scraper/uv.lock @@ -0,0 +1,1426 @@ +version = 1 +revision = 1 +requires-python = ">=3.10" + +[[package]] +name = "aiohappyeyeballs" +version = "2.6.1" +source = { registry = 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Args: + input_file (str): Path to the input file + output_dir (str, optional): Directory to save chunk files. Defaults to current directory. + start_num (int, optional): Starting number for the chunk files. Defaults to 1. + padding (int, optional): Number of digits to pad the incremental numbers. Defaults to 2. + """ + if output_dir and not os.path.exists(output_dir): + os.makedirs(output_dir) + + with open(input_file) as f: + content = f.read() + chunks = content.split("---") + + chunk_count = start_num + for chunk in chunks: + chunk = chunk.replace('---', '').strip() + if not chunk: # Skip empty chunks + continue + + # Define output path with padded incremental number + file_name = f'chunk_{chunk_count:0{padding}d}.md' + if output_dir: + outfile_path = os.path.join(output_dir, file_name) + else: + outfile_path = file_name + + with open(outfile_path, 'w') as outfile: + outfile.write(chunk) + + chunk_count += 1 + + return chunk_count - start_num # Return the number of chunks written + +# Example usage +if __name__ == "__main__": + #input_file = "/home/gra/PycharmProjects/librarian_vspace/examples/chunks/knowledge_chunks_detailed.md" + input_file = "/home/gra/PycharmProjects/librarian_vspace/examples/chunks/knowledge_chunks_1500.md" + # You can specify an output directory or omit it to use the current directory + output_dir = "/examples/chunks/chunk_md_x" + chunk_file(input_file, output_dir) + diff --git a/librarian/plugins/librarian-vspace/examples/demo_run_cluster_export.py b/librarian/plugins/librarian-vspace/examples/demo_run_cluster_export.py new file mode 100644 index 0000000..3d8c304 --- /dev/null +++ b/librarian/plugins/librarian-vspace/examples/demo_run_cluster_export.py @@ -0,0 +1,43 @@ + +#!/usr/bin/env python3 +"""examples/demo_run_cluster_export.py + +Launch ClusterExportWorker via FlowArtifact wrapper, mirroring the embedder demo. +""" + +from __future__ import annotations +import asyncio +import logging +from pathlib import Path +import os + +from librarian_vspace.vquery.cluster_export_worker import ClusterExportWorker, ClusterExportInput +from librarian_core.workers.base import FlowArtifact + +COURSE_ID = 15512 # example id + +logger = logging.getLogger(__name__) + +def _load_env(path: Path) -> None: + if not path.is_file(): + return + for line in path.read_text().splitlines(): + if line.strip() and not line.startswith("#") and "=" in line: + k, v = [p.strip() for p in line.split("=", 1)] + os.environ.setdefault(k, v) + +async def _main() -> None: + payload = ClusterExportInput(course_id=COURSE_ID) + + worker = ClusterExportWorker() + art = FlowArtifact.new(run_id="", dir=Path.cwd(), data=payload) + result_artifact = await worker.flow()(art) # FlowArtifact + + output = result_artifact.data # ClusterExportOutput + logger.info("✅ Worker finished – output directory: %s", output.output_dir) + +if __name__ == "__main__": + logging.basicConfig(level=logging.INFO, format="[%(levelname)s] %(message)s") + APP_DIR = Path(__file__).resolve().parent + _load_env(APP_DIR / ".env") + asyncio.run(_main()) diff --git a/librarian/plugins/librarian-vspace/examples/demo_run_embedder.py b/librarian/plugins/librarian-vspace/examples/demo_run_embedder.py new file mode 100644 index 0000000..54d720b --- /dev/null +++ b/librarian/plugins/librarian-vspace/examples/demo_run_embedder.py @@ -0,0 +1,86 @@ +#!/usr/bin/env python3 + +from __future__ import annotations + +import asyncio +import logging +import os +import random +from pathlib import Path +from typing import Any, List, Dict +import json + +from librarian_vspace.vecembed.embedder_worker import EmbedderWorker, EmbedderInput +from librarian_core.workers.base import FlowArtifact +from librarian_core.temp_payloads.chunk_data import ChunkData + +# ------------------------------------------------------------------ # +# Configuration +# ------------------------------------------------------------------ # +# Folder with the small sample dataset (3 × .md files) +DEMO_PATH: Path = Path("/home/gra/PycharmProjects/librarian_vspace/examples/chunks/moodle_chunks/51cd7cf6-e782-4f17-af00-30852cdcd5fc/51cd7cf6-e782-4f17-af00-30852cdcd5fc/data/FS25/Effiziente_Algorithmen").expanduser() +#DEMO_PATH: Path = Path("/home/gra/PycharmProjects/librarian_vspace/examples/chunks/chunk_md").expanduser() + +# Where to write the concatenated text file +# (one level above the dataset folder keeps things tidy) +COURSE_ID_POOL = [16301, 16091, 17505, 18239, 17503, 15512] + +logger = logging.getLogger(__name__) +INPUT_MODEL=Path("/home/gra/PycharmProjects/librarian_vspace/examples/chunks/moodle_chunks/51cd7cf6-e782-4f17-af00-30852cdcd5fc/51cd7cf6-e782-4f17-af00-30852cdcd5fc/result.json") + +# ------------------------------------------------------------------ # +def _load_env(path: Path) -> None: + """Load KEY=VALUE pairs from a .env file if present.""" + if not path.is_file(): + return + for line in path.read_text().splitlines(): + if line.strip() and not line.startswith("#") and "=" in line: + k, v = [p.strip() for p in line.split("=", 1)] + os.environ.setdefault(k, v) + + +def discover_chunks(root: Path) -> List[Path]: + """Return all markdown files in the dataset folder.""" + return sorted(root.glob("*.md")) + + +def build_course(root: Path) -> Dict[str, Any]: + """Minimal dict that satisfies EmbedderWorker's `chunk_course`.""" + files = [ + {"file_name": p.name, "file_id": str(random.getrandbits(24))} + for p in discover_chunks(root) + ] + if not files: + raise FileNotFoundError(f"No .md files found in {root}") + return { + "path": str(root), + "files": files, + #"course_id": str(random.choice(COURSE_ID_POOL)), + "course_id": "18240" + } + + +# ------------------------------------------------------------------ # +async def _main() -> None: + course = build_course(DEMO_PATH) + concat_path = DEMO_PATH + + with open(INPUT_MODEL, 'r') as file: + json_data = json.load(file) + + #payload = EmbedderInput(chunk_course=course, concat_path=concat_path) + payload = ChunkData.model_validate_json(json_data) + worker = EmbedderWorker() + logger.info("🔨 Launching EmbedderWorker …") + art = FlowArtifact.new(run_id="", dir=concat_path, data=payload) + result = await worker.flow()(art) # type: ignore[arg-type] + + logger.info("✅ Worker finished: %s", result) + +# ------------------------------------------------------------------ # +if __name__ == "__main__": + APP_DIR = Path(__file__).resolve().parent + _load_env(APP_DIR / ".env") + + logging.basicConfig(level=logging.INFO, format="[%(levelname)s] %(message)s") + asyncio.run(_main()) diff --git a/librarian/plugins/librarian-vspace/examples/demo_run_query.py b/librarian/plugins/librarian-vspace/examples/demo_run_query.py new file mode 100644 index 0000000..c9972e9 --- /dev/null +++ b/librarian/plugins/librarian-vspace/examples/demo_run_query.py @@ -0,0 +1,66 @@ + +#!/usr/bin/env python3 +"""examples/demo_run_query.py + +Runs QueryWorker via FlowArtifact wrapper (mirrors cluster export demo). +""" + +from __future__ import annotations +import asyncio +import logging +import os +from pathlib import Path + +from librarian_vspace.vquery.query_worker import QueryWorker, QueryInput +from librarian_vspace.models.query_model import VectorSearchRequest +from librarian_core.workers.base import FlowArtifact + +# ------------------------------------------------------------------ # +# Config +# ------------------------------------------------------------------ # +SEARCH_STRING = "integration" +COURSE_FILTER_GT = 900 # adjust if needed + +logger = logging.getLogger(__name__) + +def _load_env(path: Path) -> None: + if not path.is_file(): + return + for line in path.read_text().splitlines(): + if line.strip() and not line.startswith("#") and "=" in line: + k, v = [p.strip() for p in line.split("=", 1)] + os.environ.setdefault(k, v) + +# ------------------------------------------------------------------ # +async def _main() -> None: + # Vector search request + vs_req = VectorSearchRequest( + interface_name=os.getenv("EMBED_INTERFACE", "ollama"), + model_name=os.getenv("EMBED_MODEL", "snowflake-arctic-embed2"), + search_string=SEARCH_STRING, + filters={"file_id": ("gt", COURSE_FILTER_GT)}, + top_k=10, + ) + + payload = QueryInput( + request=vs_req, + db_schema=os.getenv("VECTOR_SCHEMA", "librarian"), + rpc_function=os.getenv("VECTOR_FUNCTION", "pdf_chunking"), + embed_model=os.getenv("EMBED_MODEL", "snowflake-arctic-embed2"), + ) + + worker = QueryWorker() + art = FlowArtifact.new(run_id="", dir=Path.cwd(), data=payload) + result_artifact = await worker.flow()(art) # FlowArtifact + + response = result_artifact.data # VectorSearchResponse + logger.info("✅ Worker finished – received %s results", response.total) + for idx, ck in enumerate(response.results, 1): + logger.info("• %s: %s", idx, ck.chunk[:80] + ("…" if len(ck.chunk or '') > 80 else "")) + +# ------------------------------------------------------------------ # +if __name__ == "__main__": + logging.basicConfig(level=logging.INFO, format="[%(levelname)s] %(message)s") + APP_DIR = Path(__file__).resolve().parent + _load_env(APP_DIR / ".env") + asyncio.run(_main()) diff --git a/librarian/plugins/librarian-vspace/examples/demo_run_tsne_export.py b/librarian/plugins/librarian-vspace/examples/demo_run_tsne_export.py new file mode 100644 index 0000000..959f16b --- /dev/null +++ b/librarian/plugins/librarian-vspace/examples/demo_run_tsne_export.py @@ -0,0 +1,43 @@ + +#!/usr/bin/env python3 +"""examples/demo_run_tsne_export.py + +Launch TsneExportWorker via FlowArtifact wrapper. +""" + +from __future__ import annotations +import asyncio +import logging +from pathlib import Path +import os + +from librarian_vspace.vecview.tsne_export_worker import TsneExportWorker, TsneExportInput +from librarian_core.workers.base import FlowArtifact + +COURSE_ID = 18240 # choose a course with embeddings + +logger = logging.getLogger(__name__) + +def _load_env(path: Path) -> None: + if not path.is_file(): + return + for line in path.read_text().splitlines(): + if line.strip() and not line.startswith("#") and "=" in line: + k, v = [p.strip() for p in line.split("=", 1)] + os.environ.setdefault(k, v) + +async def _main() -> None: + payload = TsneExportInput(course_id=COURSE_ID) + + worker = TsneExportWorker() + art = FlowArtifact.new(run_id="", dir=Path.cwd(), data=payload) + result_artifact = await worker.flow()(art) # FlowArtifact + + output = result_artifact.data # TsneExportOutput + logger.info("✅ Worker finished – JSON file: %s", output.json_path) + +if __name__ == "__main__": + logging.basicConfig(level=logging.INFO, format="[%(levelname)s] %(message)s") + APP_DIR = Path(__file__).resolve().parent + _load_env(APP_DIR / ".env") + asyncio.run(_main()) diff --git a/librarian/plugins/librarian-vspace/examples/parallelism_test.py b/librarian/plugins/librarian-vspace/examples/parallelism_test.py new file mode 100644 index 0000000..ccc53a2 --- /dev/null +++ b/librarian/plugins/librarian-vspace/examples/parallelism_test.py @@ -0,0 +1,4 @@ +from librarian_vspace.vutils.parallelism_advisor import recommended_workers +print(recommended_workers(kind="cpu")) +print(recommended_workers(kind="io")) +print(recommended_workers(kind="gpu")) diff --git a/librarian/plugins/librarian-vspace/examples/run_visualizer.py b/librarian/plugins/librarian-vspace/examples/run_visualizer.py new file mode 100644 index 0000000..14fbd84 --- /dev/null +++ b/librarian/plugins/librarian-vspace/examples/run_visualizer.py @@ -0,0 +1,141 @@ +#!/usr/bin/env python3 +""" +Loads vector data using vecmap.loader, reduces dimensions via t-SNE, +and launches an interactive 3D visualization using vecmap.visualizer (Dash/Plotly). + +Configuration is primarily driven by environment variables. +""" + +from __future__ import annotations + +import logging +import os +import pathlib +import sys +import pandas as pd + +# Define application directory relative to this script file +APP_DIR = pathlib.Path(__file__).resolve().parent +# Define the source directory containing vecmap, vutils, etc. +SRC_DIR = APP_DIR.parent / "src" +# Define path to .env file relative to APP_DIR +DOTENV_PATH = APP_DIR / ".env" + +# --- Explicitly Manage sys.path --- +app_dir_str = str(APP_DIR) +src_dir_str = str(SRC_DIR) +if app_dir_str in sys.path: + try: sys.path.remove(app_dir_str) + except ValueError: pass +if src_dir_str not in sys.path: + sys.path.insert(0, src_dir_str) +elif sys.path[0] != src_dir_str: + try: sys.path.remove(src_dir_str) + except ValueError: pass + sys.path.insert(0, src_dir_str) +print(f"[DEBUG] sys.path start: {sys.path[:3]}") + +# --- .env Loader --- +def _load_env_file(path: pathlib.Path) -> None: + print(f"Attempting to load .env file from: {path}") + if not path.is_file(): print(f".env file not found at {path}, skipping."); return + loaded, skipped = 0, 0 + try: + with open(path, 'r', encoding='utf-8') as f: + for line in f: + line = line.strip(); + if not line or line.startswith("#") or "=" not in line: continue + key, val = line.split("=", 1); key, val = key.strip(), val.strip() + if key not in os.environ: os.environ[key] = val; loaded += 1 + else: skipped += 1 + print(f"Loaded {loaded} new vars, skipped {skipped} existing vars from .env") + except Exception as e: print(f"Error reading .env file at {path}: {e}") +_load_env_file(DOTENV_PATH) + +# --- Logging Setup --- +log_level_str = os.getenv("VECMAP_DEBUG", "false").lower() +log_level = logging.DEBUG if log_level_str in ("true", "1") else logging.INFO +logging.basicConfig(level=log_level, format='[%(asctime)s] [%(levelname)s] [%(name)s:%(lineno)d] %(message)s', datefmt='%Y-%m-%d %H:%M:%S') +if log_level > logging.DEBUG: + for logger_name in ["urllib3", "httpx", "supabase"]: logging.getLogger(logger_name).setLevel(logging.WARNING) +logger = logging.getLogger(__name__) + +# --- Imports --- +try: + from librarian_vspace.vecmap.loader import VectorLoader, VectorLoaderError + from librarian_vspace.vecmap.visualizer import VectorVisualizer # Removed DEFAULT_N_CLUSTERS import + import librarian_vspace.vutils + import librarian_vspace.vecembed + logger.debug("Successfully imported components.") +except ImportError as e: + logger.error(f"Failed to import necessary modules: {e}", exc_info=True) + sys.exit(1) + +# --- Main Logic --- +def main() -> None: + logger.info("--- Starting VecMap Visualizer ---") + + # --- Configuration --- + db_schema = os.getenv("VECTOR_SCHEMA", "librarian") + db_function = os.getenv("VECTOR_FUNCTION", "pdf_chunking") + model_name = os.getenv("EMBED_MODEL", "snowflake-arctic-embed2") + interface_name = os.getenv("EMBED_INTERFACE", "ollama") + embedding_column = os.getenv("EMBEDDING_COLUMN", "embedding") + try: limit_str = os.getenv("VECMAP_LIMIT"); data_limit = int(limit_str) if limit_str else None + except ValueError: logger.warning(f"Invalid VECMAP_LIMIT. Ignoring."); data_limit = None + try: perplexity_str = os.getenv("VECMAP_PERPLEXITY", "30.0"); tsne_perplexity = float(perplexity_str) + except ValueError: logger.warning(f"Invalid VECMAP_PERPLEXITY. Using 30.0."); tsne_perplexity = 30.0 + + # n_clusters configuration removed + + dash_host = os.getenv("VECMAP_HOST", "127.0.0.1") + try: port_str = os.getenv("VECMAP_PORT", "8050"); dash_port = int(port_str) + except ValueError: logger.warning(f"Invalid VECMAP_PORT. Using 8050."); dash_port = 8050 + dash_debug = log_level == logging.DEBUG + + logger.info("Effective Configuration:") + logger.info(f" Database: schema={db_schema}, function={db_function}") + logger.info(f" Model/Interface: model={model_name}, interface={interface_name}") + logger.info(f" Data Params: column={embedding_column}, limit={data_limit}") + logger.info(f" Processing: perplexity={tsne_perplexity} (n_clusters is now dynamic)") # Updated log + logger.info(f" Server: host={dash_host}, port={dash_port}, debug={dash_debug}") + + # --- 1. Initial Load and Reduce --- + initial_df_reduced = pd.DataFrame() + try: + logger.info("Performing initial data load and processing...") + loader = VectorLoader(schema=db_schema, function=db_function, model=model_name, embedding_column=embedding_column) + tsne_params = {"perplexity": tsne_perplexity} + initial_df_reduced = loader.load_and_reduce(limit=data_limit, tsne_params=tsne_params) + if initial_df_reduced.empty: logger.warning("Initial data load resulted in an empty dataset.") + else: logger.info(f"Successfully loaded and reduced {len(initial_df_reduced)} vectors initially.") + except VectorLoaderError as e: logger.error(f"Initial data load failed: {e}", exc_info=dash_debug) + except Exception as e: logger.error(f"Unexpected error during initial data load: {e}", exc_info=dash_debug) + + # --- 2. Initialize and Start Visualization --- + try: + logger.info("Initializing VectorVisualizer...") + visualizer = VectorVisualizer( + initial_data=initial_df_reduced, + db_schema=db_schema, + db_function=db_function, + interface_name=interface_name, + model_name=model_name, + embedding_column=embedding_column, + initial_limit=data_limit, + initial_perplexity=tsne_perplexity + # n_clusters argument removed + ) + logger.info("Launching visualizer...") + visualizer.run(host=dash_host, port=dash_port, debug=dash_debug) + except TypeError as te: + logger.error(f"TypeError during VectorVisualizer initialization: {te}", exc_info=True) + sys.exit(1) + except Exception as e: + logger.error(f"Failed to initialize or run visualizer: {e}", exc_info=dash_debug) + sys.exit(1) + + logger.info("--- VecMap Visualizer finished ---") + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/librarian/plugins/librarian-vspace/pyproject.toml b/librarian/plugins/librarian-vspace/pyproject.toml new file mode 100644 index 0000000..c905480 --- /dev/null +++ b/librarian/plugins/librarian-vspace/pyproject.toml @@ -0,0 +1,52 @@ +[project] +name = "librarian-vspace" +version = "0.1.0" +description = "Add your description here" +readme = "README.md" +authors = [ + { name = "TheOriginalGraLargeShrimpakaReaper", email = "graber-michael@hotmail.com" } +] +requires-python = ">=3.10" +dependencies = [ + "librarian-core", + "importlib_metadata; python_version<'3.10'", + "dotenv>=0.9.9", + "psycopg2-binary>=2.9.10", + "python-dotenv>=1.1.0", + "requests>=2.32.3", + "supabase>=2.15.0", + "numpy>=2.2.5", + "dash>=3.0.4", + "scikit-learn>=1.6.1", + "plotly>=6.0.1", + "pandas>=2.2.3", + "pathlib>=1.0.1", + "prefect>=3.4.1", +] + +[tool.uv.sources] +librarian-core = { git = "https://github.com/DotNaos/librarian-core", rev = "dev" } + +[build-system] +requires = ["hatchling>=1.21"] +build-backend = "hatchling.build" + + +[tool.hatch.build.targets.wheel] +packages = ["src/librarian_vspace"] + + +[tool.hatch.metadata] +allow-direct-references = true + + +# ───────── optional: dev / test extras ───────── +[project.optional-dependencies] +dev = ["ruff", "pytest", "mypy"] + +[project.entry-points."librarian.workers"] +embedder = "librarian_vspace.vecembed:EmbedderWorker" +clusterexporter = "librarian_vspace.vquery:ClusterExportWorker" +tnseexport = "librarian_vspace.vecview:TsneExportWorker" +vectorquerying = "librarian_vspace.vquery:QueryWorker" + diff --git a/librarian/plugins/librarian-vspace/src/librarian_vspace/__init__.py b/librarian/plugins/librarian-vspace/src/librarian_vspace/__init__.py new file mode 100644 index 0000000..8609640 --- /dev/null +++ b/librarian/plugins/librarian-vspace/src/librarian_vspace/__init__.py @@ -0,0 +1,22 @@ + +"""Embedding‑related helpers.""" +import pkgutil +import importlib + +__all__ = [] + +# Iterate over all modules in this package +for finder, module_name, is_pkg in pkgutil.iter_modules(__path__): + # import the sub-module + module = importlib.import_module(f"{__name__}.{module_name}") + + # decide which names to re-export: + # use module.__all__ if it exists, otherwise every non-private attribute + public_names = getattr( + module, "__all__", [n for n in dir(module) if not n.startswith("_")] + ) + + # bring each name into the package namespace + for name in public_names: + globals()[name] = getattr(module, name) + __all__.append(name) # type: ignore \ No newline at end of file diff --git a/librarian/plugins/librarian-vspace/src/librarian_vspace/models/__init__.py b/librarian/plugins/librarian-vspace/src/librarian_vspace/models/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/librarian/plugins/librarian-vspace/src/librarian_vspace/models/query_model.py b/librarian/plugins/librarian-vspace/src/librarian_vspace/models/query_model.py new file mode 100644 index 0000000..e610557 --- /dev/null +++ b/librarian/plugins/librarian-vspace/src/librarian_vspace/models/query_model.py @@ -0,0 +1,38 @@ + +"""Pydantic models for vector search requests and responses.""" + +from __future__ import annotations + +from typing import Any, Dict, List, Optional +from pydantic import BaseModel, Field + + +class VectorSearchRequest(BaseModel): + """Input payload for a vector search.""" + + interface_name: str = Field(..., description="Name of the embedding interface") + model_name: str = Field(..., description="Name of the embedding model") + search_string: str = Field(..., description="The natural language query to embed and search for") + filters: Optional[Dict[str, Any]] = Field( + default=None, + description="Optional key/value filters applied server‑side", + ) + top_k: int = Field(10, ge=1, le=100, description="Number of matches to return") + embedding_column: str = Field( + "embedding", + description="Name of the embedding column in the database table", + ) + + +class Chunklet(BaseModel): + """Single result row returned by the database RPC.""" + + chunk: Optional[str] = None + file_id: Optional[str | int] = None + + +class VectorSearchResponse(BaseModel): + """Output payload wrapping vector‑search results.""" + + total: int + results: List[Chunklet] diff --git a/librarian/plugins/librarian-vspace/src/librarian_vspace/models/tsne_model.py b/librarian/plugins/librarian-vspace/src/librarian_vspace/models/tsne_model.py new file mode 100644 index 0000000..3a5fedf --- /dev/null +++ b/librarian/plugins/librarian-vspace/src/librarian_vspace/models/tsne_model.py @@ -0,0 +1,31 @@ + +"""Data models for t‑SNE exports. + +These models are used by *vecview* and any endpoint that needs to return or +validate t‑SNE projection data. +""" + +from __future__ import annotations + +from typing import List, Optional +from pydantic import BaseModel + + +class TSNEPoint(BaseModel): + """A single point in a 3‑D t‑SNE projection.""" + + x: float + y: float + z: float + file_id: str + chunk: str + cluster: Optional[str] = None + hover_text: Optional[str] = None + + +class TSNEData(BaseModel): + """Container returned to callers requesting a t‑SNE view.""" + + course_id: Optional[int] = None + total: int + points: List[TSNEPoint] diff --git a/librarian/plugins/librarian-vspace/src/librarian_vspace/py.typed b/librarian/plugins/librarian-vspace/src/librarian_vspace/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/librarian/plugins/librarian-vspace/src/librarian_vspace/vecembed/__init__.py b/librarian/plugins/librarian-vspace/src/librarian_vspace/vecembed/__init__.py new file mode 100644 index 0000000..6453485 --- /dev/null +++ b/librarian/plugins/librarian-vspace/src/librarian_vspace/vecembed/__init__.py @@ -0,0 +1,9 @@ + +"""Embedding‑related helpers.""" +from __future__ import annotations + +from .vector_inserter import VectorInserter +from .embedding_generator import EmbeddingGenerator +from .embedding_workflow import EmbeddingWorkflow + +__all__ = ["VectorInserter", "EmbeddingGenerator", "EmbeddingWorkflow"] diff --git a/librarian/plugins/librarian-vspace/src/librarian_vspace/vecembed/embedder.py b/librarian/plugins/librarian-vspace/src/librarian_vspace/vecembed/embedder.py new file mode 100644 index 0000000..e22a685 --- /dev/null +++ b/librarian/plugins/librarian-vspace/src/librarian_vspace/vecembed/embedder.py @@ -0,0 +1,155 @@ + +"""Parallel‑aware embedding helpers. + +* **embed_single_file()** – embed one file (sync). +* **run_embedder()** – embed all files in a course (async, kept for back‑compat). +* **_create_hnsw_index()** – helper to (re)build PGVector HNSW index. + +This file contains no Prefect code; it’s pure embedding logic. +""" + +from __future__ import annotations + +import asyncio +import logging +from functools import lru_cache +from pathlib import Path +from types import SimpleNamespace +from typing import Any, List, Union + +from postgrest import APIResponse + +from librarian_core.temp_payloads.chunk_data import ChunkCourse, ChunkFile +from librarian_vspace.vecembed.embedding_generator import EmbeddingGenerator +from librarian_vspace.vecembed.vector_inserter import VectorInserter +from librarian_vspace.vecembed.embedding_workflow import EmbeddingWorkflow +from librarian_vspace.vutils.supabase_singleton import MySupabase + +logger = logging.getLogger(__name__) + +@lru_cache(maxsize=1) +def _autodiscover_pg_conn(): + supa = MySupabase.get_client() # type: ignore + if supa is None: + raise RuntimeError("MySupabase.get_client() returned None – no DB connection.") + return supa + + +def _create_hnsw_index( + supa, + table_fqn: str, + *, + column_name: str = "embedding", + query_operator: str = "<=>", + m: int = 16, + ef: int = 64, +) -> None: + if "." not in table_fqn: + raise ValueError("table_fqn must be schema.table") + schema, table = table_fqn.split(".", 1) + try: + supa.schema(schema).rpc( + "create_or_reindex_hnsw", + dict( + p_schema=schema, + p_table=table, + p_column=column_name, + p_operator=query_operator, + p_m=m, + p_ef=ef, + ), + ).execute() + except Exception: + logger.exception("Failed to run create_or_reindex_hnsw") + +# --------------------------------------------------------------------------- # +# single file # +# --------------------------------------------------------------------------- # +def embed_single_file( + *, + course_id: str, + file_entry: dict | ChunkFile | SimpleNamespace, + concat_path: Union[str, Path], + db_schema: str = "librarian", + db_function: str = "pdf_chunking", + interface_name: str = "ollama", + model_name: str = "snowflake-arctic-embed2", + file_type: str = "md", +) -> Path | None: + + if isinstance(file_entry, (dict, SimpleNamespace)): + file_name = file_entry["file_name"] if isinstance(file_entry, dict) else file_entry.file_name + file_id = file_entry["file_id"] if isinstance(file_entry, dict) else file_entry.file_id + else: + file_name, file_id = file_entry.file_name, file_entry.file_id + + chunk_path = Path(concat_path) / file_name + if not chunk_path.exists(): + logger.warning("Missing chunk file %s – skipping", chunk_path) + return None + + generator = EmbeddingGenerator() + inserter = VectorInserter(schema=db_schema, function=db_function, model=model_name) + + wf = EmbeddingWorkflow( + chunk_path=chunk_path, + course_id=course_id, + file_id=file_id, + file_type=file_type, + interface_name=interface_name, + model_name=model_name, + generator=generator, + inserter=inserter, + ) + wf.process() + return chunk_path + +# --------------------------------------------------------------------------- # +async def run_embedder( + course: ChunkCourse, + concat_path: Union[str, Path], + *, + db_schema: str = "librarian", + db_function: str = "pdf_chunking", + interface_name: str = "ollama", + model_name: str = "snowflake-arctic-embed2", + file_type: str = "md", + vector_column: str = "embedding", + query_operator: str = "<=>", + hnsw_m: int = 16, + hnsw_ef: int = 64, + max_parallel_files: int | None = None, +) -> Path: + + supa_client = _autodiscover_pg_conn() + root = Path(concat_path) + sem = asyncio.Semaphore(max_parallel_files or len(course.files) or 1) + + async def _wrapper(cf): + async with sem: + return await asyncio.to_thread( + embed_single_file, + course_id=course.course_id, + file_entry=cf, + concat_path=root, + db_schema=db_schema, + db_function=db_function, + interface_name=interface_name, + model_name=model_name, + file_type=file_type, + ) + + await asyncio.gather(*[asyncio.create_task(_wrapper(cf)) for cf in course.files]) + + inserter = VectorInserter(schema=db_schema, function=db_function, model=model_name) + _create_hnsw_index( + supa_client, + inserter.table_fqn(), + column_name=vector_column, + query_operator=query_operator, + m=hnsw_m, + ef=hnsw_ef, + ) + return root + +__all__ = ["embed_single_file", "run_embedder", "_create_hnsw_index", "_autodiscover_pg_conn"] diff --git a/librarian/plugins/librarian-vspace/src/librarian_vspace/vecembed/embedder_worker.py b/librarian/plugins/librarian-vspace/src/librarian_vspace/vecembed/embedder_worker.py new file mode 100644 index 0000000..c58c03a --- /dev/null +++ b/librarian/plugins/librarian-vspace/src/librarian_vspace/vecembed/embedder_worker.py @@ -0,0 +1,67 @@ + +"""EmbedderWorker – Prefect‑mapped per file.""" + +from __future__ import annotations + +from pathlib import Path +from types import SimpleNamespace +from typing import Any, List + +from prefect import get_run_logger, task, unmapped +from pydantic import BaseModel, Field +from librarian_core.workers.base import Worker + +@task(name="embed_file", retries=2, retry_delay_seconds=5, log_prints=True, tags=["embed_file"]) +def embed_file_task(course_dict: dict | SimpleNamespace, file_entry: dict, concat_path: Path) -> Path | None: + from librarian_vspace.vecembed.embedder import embed_single_file + cid = course_dict["course_id"] if isinstance(course_dict, dict) else course_dict.course_id + return embed_single_file(course_id=cid, file_entry=file_entry, concat_path=concat_path) + +class EmbedderInput(BaseModel): + chunk_courses: List[Any] = Field(default_factory=list, alias="chunk_courses") + concat_path: Path + chunk_course: Any | None = None + def model_post_init(self, _): + if not self.chunk_courses and self.chunk_course is not None: + self.chunk_courses = [self.chunk_course] + model_config = dict(populate_by_name=True, extra="allow") + +class EmbedderOutput(BaseModel): + result_paths: List[Path] + +class EmbedderWorker(Worker[EmbedderInput, EmbedderOutput]): + input_model = EmbedderInput + output_model = EmbedderOutput + + async def __run__(self, payload: EmbedderInput) -> EmbedderOutput: + log = get_run_logger() + total_files = sum(len(c["files"]) if isinstance(c, dict) else len(c.files) for c in payload.chunk_courses) + log.info("Embedding %d files", total_files) + + result_paths: List[Path] = [] + + # constants – could be parameterised later + schema = "librarian" + func = "pdf_chunking" + model_name = "snowflake-arctic-embed2" + + for course in payload.chunk_courses: + files = course["files"] if isinstance(course, dict) else course.files + futures = embed_file_task.map(unmapped(course), files, unmapped(payload.concat_path)) + for fut in futures: + path = fut.result() + if path: + result_paths.append(path) + + # rebuild index once per course + from librarian_vspace.vecembed.embedder import _create_hnsw_index, _autodiscover_pg_conn + from librarian_vspace.vecembed.vector_inserter import VectorInserter + + supa = _autodiscover_pg_conn() + inserter = VectorInserter(schema=schema, function=func, model=model_name) + _create_hnsw_index(supa, inserter.table_fqn()) + + for p in result_paths: + self.stage(p, new_name=p.name) + + return EmbedderOutput(result_paths=result_paths) diff --git a/librarian/plugins/librarian-vspace/src/librarian_vspace/vecembed/embedding_generator.py b/librarian/plugins/librarian-vspace/src/librarian_vspace/vecembed/embedding_generator.py new file mode 100644 index 0000000..ee4c324 --- /dev/null +++ b/librarian/plugins/librarian-vspace/src/librarian_vspace/vecembed/embedding_generator.py @@ -0,0 +1,21 @@ + +"""Factory for embedding back‑ends.""" +import logging +from typing import Any, List, Optional, Tuple, Dict, Type + +from librarian_vspace.vecembed.embedding_interface import EmbeddingInterface +from librarian_vspace.vecembed.ollama_embedder import OllamaEmbedder + +logger = logging.getLogger(__name__) + +class EmbeddingGenerator: + _registry: Dict[str, Type[EmbeddingInterface]] = { + "ollama": OllamaEmbedder, + } + + def generate_embedding(self, interface_name: str, model_name: str, text_to_embed: str, identifier: Any) -> Tuple[str, Optional[List[float]], Any]: + cls = self._registry.get(interface_name.lower()) + if not cls: + raise ValueError(f"Unsupported embedding interface: {interface_name}") + embedder = cls(model_name=model_name) + return embedder.embed(text_to_embed, identifier) diff --git a/librarian/plugins/librarian-vspace/src/librarian_vspace/vecembed/embedding_interface.py b/librarian/plugins/librarian-vspace/src/librarian_vspace/vecembed/embedding_interface.py new file mode 100644 index 0000000..037ff92 --- /dev/null +++ b/librarian/plugins/librarian-vspace/src/librarian_vspace/vecembed/embedding_interface.py @@ -0,0 +1,14 @@ +from __future__ import annotations +from abc import ABC, abstractmethod +from typing import Any, List, Optional, Tuple + +class EmbeddingInterface(ABC): + """Contract for any embedding service implementation.""" + + def __init__(self, model_name: str, **kwargs: Any) -> None: + self.model_name = model_name + + @abstractmethod + def embed(self, text_or_chunk: str, identifier: Any) -> Tuple[str, Optional[List[float]], Any]: + """Return (original_text, embedding, identifier) — embedding may be None on failure.""" + pass diff --git a/librarian/plugins/librarian-vspace/src/librarian_vspace/vecembed/embedding_workflow.py b/librarian/plugins/librarian-vspace/src/librarian_vspace/vecembed/embedding_workflow.py new file mode 100644 index 0000000..e082f66 --- /dev/null +++ b/librarian/plugins/librarian-vspace/src/librarian_vspace/vecembed/embedding_workflow.py @@ -0,0 +1,92 @@ +"""Orchestrates loading, embedding, and storing a text chunk.""" +from __future__ import annotations +import logging +from pathlib import Path +from typing import Any, Optional, Dict, Union + +# Import the worker classes for type hinting +from librarian_vspace.vecembed.embedding_generator import EmbeddingGenerator +from librarian_vspace.vecembed.vector_inserter import VectorInserter + +logger = logging.getLogger(__name__) + +class EmbeddingWorkflow: + # Accept generator and inserter instances in __init__ + def __init__(self, + chunk_path: Union[str, Path], + course_id: Any, + file_id: Any, + file_type: str, + interface_name: str, # Still needed for generate_embedding method + model_name: str, # Still needed for generate_embedding method + generator: EmbeddingGenerator, # Accept pre-instantiated generator + inserter: VectorInserter, # Accept pre-instantiated inserter + # db_schema and db_function are now implicit via the inserter + # db_schema: str = "librarian", + # db_function: str = "pdf_chunking", + ): + self.chunk_path = Path(chunk_path) + self.course_id = course_id + self.file_id = file_id + self.file_type = file_type + # Keep interface_name and model_name as they are passed to the generator's method + self.interface_name = interface_name + self.model_name = model_name + + # Assign the passed instances instead of creating new ones + self.generator = generator + self.inserter = inserter + + # No need to store db_schema/db_function here if inserter handles it + + # ---------------- helpers ---------------- + def _load_chunk(self) -> Optional[str]: + try: + text = self.chunk_path.read_text(encoding="utf-8").strip() + if not text: + logger.warning("Chunk %s is empty", self.chunk_path) + return None + return text + except Exception as exc: + logger.error("Failed to read %s: %s", self.chunk_path, exc) + return None + + def process(self) -> bool: + chunk_text = self._load_chunk() + if chunk_text is None: + return False + + # Use the shared generator instance + original_text, vector, _ = self.generator.generate_embedding( + interface_name=self.interface_name, # Pass parameters to the method + model_name=self.model_name, # Pass parameters to the method + text_to_embed=chunk_text, + identifier=self.file_id, + ) + + if vector is None: + # Log failure within generator if not already done, or here + logger.error(f"Failed to generate embedding for {self.chunk_path}") + return False + + payload: Dict[str, Any] = { + "chunk": original_text, + "course_id": self.course_id, + "file_id": self.file_id, + "file_type": self.file_type, + "embedding": vector, + } + + # Use the shared inserter instance + insert_result = self.inserter.insert_vector(payload) + + if insert_result is None: + logger.error(f"Failed to insert vector for {self.chunk_path}") + return False + + logger.debug(f"Successfully processed and inserted {self.chunk_path}") + return True # Indicate success + + +# Keep __all__ if needed +# __all__ = ["EmbeddingWorkflow"] \ No newline at end of file diff --git a/librarian/plugins/librarian-vspace/src/librarian_vspace/vecembed/ollama_embedder.py b/librarian/plugins/librarian-vspace/src/librarian_vspace/vecembed/ollama_embedder.py new file mode 100644 index 0000000..a4eb7ff --- /dev/null +++ b/librarian/plugins/librarian-vspace/src/librarian_vspace/vecembed/ollama_embedder.py @@ -0,0 +1,44 @@ + +"""Ollama-based embedding implementation (env handled at application layer).""" +from __future__ import annotations + +import logging +import os +from typing import Any, List, Optional, Tuple + +import requests + +from librarian_vspace.vecembed.embedding_interface import EmbeddingInterface + +logger = logging.getLogger(__name__) + + +class OllamaEmbedder(EmbeddingInterface): + def __init__(self, model_name: str, **kwargs: Any) -> None: + super().__init__(model_name=model_name) + self.base_url = os.getenv("OLLAMA_BASE_URL") + if not self.base_url: + raise ValueError("OLLAMA_BASE_URL not configured – ensure env is set in the examples layer") + self.api_endpoint = f"{self.base_url.rstrip('/')}/api/embeddings" + + + def embed(self, text_or_chunk: str, identifier: Any) -> Tuple[str, Optional[List[float]], Any]: + payload = {"model": self.model_name, "prompt": text_or_chunk} + vector: Optional[List[float]] = None + try: + logger.debug("Requesting embedding for id=%s", identifier) + resp = requests.post(self.api_endpoint, json=payload, timeout=3600, headers={"Content-Type": "application/json"}) + resp.raise_for_status() + data = resp.json() + if isinstance(data.get("embedding"), list): + vector = data["embedding"] + logger.debug("Received embedding dim=%d for id=%s", len(vector), identifier) + else: + logger.error("Invalid response from Ollama: %s", data) + except requests.exceptions.Timeout: + logger.error("Timeout contacting Ollama at %s", self.api_endpoint) + except requests.exceptions.RequestException as exc: + logger.error("HTTP error contacting Ollama: %s", exc) + except Exception: + logger.exception("Unexpected error during embed for id=%s", identifier) + return text_or_chunk, vector, identifier diff --git a/librarian/plugins/librarian-vspace/src/librarian_vspace/vecembed/py.typed b/librarian/plugins/librarian-vspace/src/librarian_vspace/vecembed/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/librarian/plugins/librarian-vspace/src/librarian_vspace/vecembed/vector_inserter.py b/librarian/plugins/librarian-vspace/src/librarian_vspace/vecembed/vector_inserter.py new file mode 100644 index 0000000..9b37389 --- /dev/null +++ b/librarian/plugins/librarian-vspace/src/librarian_vspace/vecembed/vector_inserter.py @@ -0,0 +1,23 @@ + +from __future__ import annotations +import logging +from typing import Any, Dict, List, Optional +from librarian_vspace.vutils.vector_class import BaseVectorOperator + +logger = logging.getLogger(__name__) + +class VectorInserter(BaseVectorOperator): + """High-level write helper for embeddings.""" + + def insert_vector(self, data: Dict[str, Any]) -> Optional[List[Dict[str, Any]]]: + if not self.table: + logger.error("Table resolution failed earlier") + return None + preview = {k: (f"" if k == "embedding" else v) for k, v in data.items()} + logger.debug("Insert → %s.%s :: %s", self.schema, self.table, preview) + try: + resp = self.spc.schema(self.schema).table(self.table).insert(data).execute() + return resp.data if isinstance(resp.data, list) else [] + except Exception: + logger.exception("Insert failed for %s", self.table_fqn()) + return None diff --git a/librarian/plugins/librarian-vspace/src/librarian_vspace/vecmap/__init__.py b/librarian/plugins/librarian-vspace/src/librarian_vspace/vecmap/__init__.py new file mode 100644 index 0000000..e00b59d --- /dev/null +++ b/librarian/plugins/librarian-vspace/src/librarian_vspace/vecmap/__init__.py @@ -0,0 +1,2 @@ +def hello() -> str: + return "Hello from librarian_vspace!" diff --git a/librarian/plugins/librarian-vspace/src/librarian_vspace/vecmap/loader.py b/librarian/plugins/librarian-vspace/src/librarian_vspace/vecmap/loader.py new file mode 100644 index 0000000..1bbd950 --- /dev/null +++ b/librarian/plugins/librarian-vspace/src/librarian_vspace/vecmap/loader.py @@ -0,0 +1,264 @@ +"""Loads vectors from Supabase, reduces dimensions using t-SNE.""" +from __future__ import annotations + +import logging +import json # Import json for parsing +from typing import Any, Dict, List, Optional, Tuple + +import numpy as np +import pandas as pd +from sklearn.manifold import TSNE + +# Assuming vutils is installed or in the python path +try: + from librarian_vspace.vutils.vector_class import BaseVectorOperator +except ImportError as e: + logging.error(f"Failed to import vutils: {e}. Ensure vutils package is installed.") + raise + +logger = logging.getLogger(__name__) + +class VectorLoaderError(Exception): + """Custom exception for loader errors.""" + pass + + +class VectorLoader: + """Fetches vectors and applies t-SNE.""" + + DEFAULT_TSNE_PARAMS = { + "n_components": 3, + "perplexity": 30.0, # Adjust based on dataset size (5-50 typically) + "n_iter": 1000, # Minimum recommended iterations + "learning_rate": "auto", # Usually a good default + "init": "pca", # PCA initialization is often faster and more stable + "random_state": 42, # For reproducibility + "n_jobs": -1, # Use all available CPU cores + "verbose": 1, # Log progress (controls scikit-learn's verbosity) + } + + def __init__(self, schema: str, function: str, model: str, embedding_column: str = "embedding"): + """ + Initializes the loader. + (Constructor remains the same) + """ + logger.info(f"Initializing VectorLoader for {schema=}, {function=}, {model=}") + try: + self.operator = BaseVectorOperator(schema=schema, function=function, model=model) + self.embedding_column = embedding_column + if not self.operator.table: + raise VectorLoaderError("BaseVectorOperator failed to resolve table.") + logger.info(f"Target table resolved to: {self.operator.table_fqn()}") + except (ImportError, ValueError, RuntimeError) as e: + logger.exception("Failed to initialize BaseVectorOperator.") + raise VectorLoaderError(f"Failed to initialize BaseVectorOperator: {e}") from e + + + def _parse_vector_string(self, vector_str: Any) -> Optional[List[float]]: + """Safely parses the string representation of a vector into a list of floats.""" + if not isinstance(vector_str, str): + # If it's already a list (less likely now, but safe check), return it if valid + if isinstance(vector_str, list) and all(isinstance(n, (int, float)) for n in vector_str): + return vector_str # Assume it's already correctly parsed + logger.debug(f"Unexpected type for vector parsing: {type(vector_str)}. Skipping.") + return None + try: + # Use json.loads which correctly handles [...] syntax + parsed_list = json.loads(vector_str) + if isinstance(parsed_list, list) and all(isinstance(n, (int, float)) for n in parsed_list): + return [float(n) for n in parsed_list] # Ensure elements are floats + else: + logger.warning(f"Parsed vector string '{vector_str[:50]}...' but result is not a list of numbers.") + return None + except json.JSONDecodeError: + logger.warning(f"Failed to JSON decode vector string: '{vector_str[:50]}...'") + return None + except Exception as e: + logger.error(f"Unexpected error parsing vector string '{vector_str[:50]}...': {e}", exc_info=True) + return None + + + def fetch_all_vectors(self, limit: Optional[int] = None) -> pd.DataFrame: + """ + Fetches all vectors and metadata from the resolved table. + Parses string representations of vectors into lists. + + Args: + limit: Optional limit on the number of rows to fetch (for large tables). + + Returns: + A pandas DataFrame with columns like 'file_id', 'chunk', 'embedding' (as list). + + Raises: + VectorLoaderError: If fetching fails or no data is found. + """ + if not self.operator.table or not self.operator.schema: + raise VectorLoaderError("Operator not initialized, table name or schema is unknown.") + + table_name = self.operator.table + schema_name = self.operator.schema + select_columns = f"file_id, chunk, {self.embedding_column}" + + logger.info(f"Fetching data from {schema_name}.{table_name} (columns: {select_columns})...") + try: + query = self.operator.spc.schema(schema_name).table(table_name).select(select_columns) + if limit: + logger.info(f"Applying limit: {limit}") + query = query.limit(limit) + response = query.execute() + + if not response.data: + logger.warning(f"No data found in table {self.operator.table_fqn()}.") + return pd.DataFrame(columns=['file_id', 'chunk', self.embedding_column]) + + logger.info(f"Fetched {len(response.data)} rows.") + df = pd.DataFrame(response.data) + + # --- FIX: Parse the embedding string into a list --- + logger.info(f"Parsing string representation in '{self.embedding_column}' column...") + parsed_embeddings = df[self.embedding_column].apply(self._parse_vector_string) + # Overwrite the original string column with the parsed list (or None if parsing failed) + df[self.embedding_column] = parsed_embeddings + logger.debug(f"Sample '{self.embedding_column}' data after parsing (first 5 rows):\n{df[[self.embedding_column]].head()}") + # --- END FIX --- + + + # === Enhanced Debugging for Embedding Column (Now checks the parsed list) === + logger.info(f"Checking validity of parsed '{self.embedding_column}' column...") + if self.embedding_column not in df.columns: + raise VectorLoaderError(f"Required embedding column '{self.embedding_column}' missing after processing.") + + # 1. Check for NULLs (includes rows where parsing failed and returned None) + initial_count = len(df) + null_mask = df[self.embedding_column].isnull() + null_count = null_mask.sum() + if null_count > 0: + logger.warning(f"Found {null_count} rows with NULL or unparsable vectors in '{self.embedding_column}'.") + + df_no_nulls = df.dropna(subset=[self.embedding_column]) + count_after_null_drop = len(df_no_nulls) + logger.debug(f"{count_after_null_drop} rows remaining after dropping NULLs/unparsable.") + + # 2. Check for non-empty list type (This check might be slightly redundant now if parsing worked, but keep for safety) + if not df_no_nulls.empty: + def is_valid_list(x): + # Check should pass if parsing was successful + return isinstance(x, list) and len(x) > 0 + + valid_list_mask = df_no_nulls[self.embedding_column].apply(is_valid_list) + invalid_list_count = len(df_no_nulls) - valid_list_mask.sum() + + if invalid_list_count > 0: + # This indicates an issue with the parsing logic or unexpected data format + logger.error(f"Found {invalid_list_count} rows where '{self.embedding_column}' is not a non-empty list *after parsing*. This should not happen.") + invalid_entries = df_no_nulls[~valid_list_mask][self.embedding_column] + for i, entry in enumerate(invalid_entries.head(5)): + logger.debug(f" Problematic entry example {i+1}: Type={type(entry)}, Value='{str(entry)[:100]}...'") + + df_filtered = df_no_nulls[valid_list_mask].copy() + else: + df_filtered = df_no_nulls + + final_count = len(df_filtered) + # === End Enhanced Debugging === + + if final_count < initial_count: + logger.warning(f"Filtered out {initial_count - final_count} rows total due to missing/invalid '{self.embedding_column}'.") + + if df_filtered.empty: + logger.warning(f"No valid embedding data found after filtering. Check data in table {self.operator.table_fqn()} and parsing logic.") + return pd.DataFrame(columns=['file_id', 'chunk', self.embedding_column]) + + logger.info(f"Proceeding with {final_count} valid rows.") + + # Validate and potentially add placeholder metadata columns AFTER filtering + if 'file_id' not in df_filtered.columns: + logger.warning("'file_id' column missing, using index instead.") + df_filtered['file_id'] = df_filtered.index + if 'chunk' not in df_filtered.columns: + logger.warning("'chunk' column missing, hover text will be limited.") + df_filtered['chunk'] = "N/A" + + return df_filtered + + except Exception as e: + logger.exception(f"Failed to fetch data from {self.operator.table_fqn()}.") + if 'relation' in str(e) and 'does not exist' in str(e): + raise VectorLoaderError(f"Table/Relation not found error: {e}. Check schema/table name and permissions.") from e + else: + raise VectorLoaderError(f"Database query failed: {e}") from e + + # reduce_dimensions and load_and_reduce methods remain the same as the previous version + # (they expect df with a valid list in the embedding column) + + def reduce_dimensions(self, df: pd.DataFrame, tsne_params: Optional[Dict[str, Any]] = None) -> pd.DataFrame: + """ + Applies t-SNE to reduce embedding dimensions to 3D. + (Code remains the same as previous correct version) + """ + if df.empty: + logger.warning("Input DataFrame for reduce_dimensions is empty. Returning empty DataFrame.") + empty_df_with_cols = df.copy() + for col in ['x', 'y', 'z']: + if col not in empty_df_with_cols: + empty_df_with_cols[col] = pd.Series(dtype=float) + return empty_df_with_cols + + if self.embedding_column not in df.columns: + raise VectorLoaderError(f"Embedding column '{self.embedding_column}' missing in DataFrame passed to reduce_dimensions.") + + try: + embeddings = np.array(df[self.embedding_column].tolist(), dtype=float) + except ValueError as ve: + logger.error(f"Failed to convert embedding list to numeric numpy array: {ve}") + raise VectorLoaderError(f"Data in '{self.embedding_column}' could not be converted to numeric vectors.") from ve + + if embeddings.ndim != 2: + raise VectorLoaderError(f"Embedding data has unexpected dimensions: {embeddings.ndim} (expected 2). Shape: {embeddings.shape}") + + n_samples = embeddings.shape[0] + + if n_samples < 2: + logger.warning(f"Found only {n_samples} valid vector(s). t-SNE requires at least 2. Assigning default 3D coordinates.") + default_coords = [[0.0, 0.0, 0.0]] * n_samples + df[['x', 'y', 'z']] = default_coords + return df + + logger.info(f"Applying t-SNE to {n_samples} vectors of dimension {embeddings.shape[1]}...") + + current_tsne_params = self.DEFAULT_TSNE_PARAMS.copy() + if tsne_params: + current_tsne_params.update(tsne_params) + logger.info(f"Using custom t-SNE params: {tsne_params}") + + if n_samples <= current_tsne_params['perplexity']: + new_perplexity = max(5.0, float(n_samples - 1)) + logger.warning(f"Adjusting t-SNE perplexity from {current_tsne_params['perplexity']:.1f} " + f"to {new_perplexity:.1f} due to low sample count ({n_samples}).") + current_tsne_params['perplexity'] = new_perplexity + + if n_samples * embeddings.shape[1] > 100000 and current_tsne_params['n_iter'] < 1000: + logger.warning(f"Dataset size seems large, increasing t-SNE n_iter from {current_tsne_params['n_iter']} to 1000 for better convergence.") + current_tsne_params['n_iter'] = 1000 + + try: + logger.debug(f"Final t-SNE parameters: {current_tsne_params}") + tsne = TSNE(**current_tsne_params) + reduced_embeddings = tsne.fit_transform(embeddings) + + df[['x', 'y', 'z']] = reduced_embeddings + logger.info("t-SNE reduction complete.") + return df + + except Exception as e: + logger.exception("t-SNE dimensionality reduction failed.") + raise VectorLoaderError(f"t-SNE failed: {e}") from e + + + def load_and_reduce(self, limit: Optional[int] = None, tsne_params: Optional[Dict[str, Any]] = None) -> pd.DataFrame: + """Orchestrates fetching vectors and reducing dimensions.""" + logger.info("Starting vector load and reduction process...") + df_raw_filtered = self.fetch_all_vectors(limit=limit) + df_reduced = self.reduce_dimensions(df_raw_filtered, tsne_params=tsne_params) + logger.info("Vector load and reduction process finished.") + return df_reduced \ No newline at end of file diff --git a/librarian/plugins/librarian-vspace/src/librarian_vspace/vecmap/py.typed b/librarian/plugins/librarian-vspace/src/librarian_vspace/vecmap/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/librarian/plugins/librarian-vspace/src/librarian_vspace/vecmap/visualizer.py b/librarian/plugins/librarian-vspace/src/librarian_vspace/vecmap/visualizer.py new file mode 100644 index 0000000..a2ec5e5 --- /dev/null +++ b/librarian/plugins/librarian-vspace/src/librarian_vspace/vecmap/visualizer.py @@ -0,0 +1,776 @@ +# --- START OF FILE visualizer.py --- + +"""Dash/Plotly based 3D visualizer for vector embeddings with tabs, clustering, filtering, and centroid click interaction.""" +from __future__ import annotations + +import logging +import os +from io import StringIO +from typing import Any, Dict, List, Optional, Tuple + +import dash +import numpy as np +import pandas as pd +import plotly.graph_objects as go +import plotly.express as px +from dash import dcc, html, ctx # Import ctx +from dash.dependencies import Input, Output, State +from dash.exceptions import PreventUpdate +from sklearn.cluster import KMeans + +# --- Imports --- +try: + from librarian_vspace.vecembed.embedding_generator import EmbeddingGenerator +except ImportError as e: + logging.error(f"Import vecembed failed: {e}. Using Dummy.") + + + # Define dummy class correctly indented + class EmbeddingGenerator: + """Dummy class if vecembed import fails.""" + + def generate_embedding(*args, **kwargs) -> Tuple[ + str, None, Any]: # Match expected output type Optional[List[float]] + logging.error("Dummy EmbeddingGenerator called.") + text_to_embed = kwargs.get("text_to_embed", args[3] if len(args) > 3 else "unknown") + identifier = kwargs.get("identifier", args[4] if len(args) > 4 else "unknown") + logger.debug(f"Dummy generate_embedding called for text='{text_to_embed}', id='{identifier}'") + # Return None for the vector part to match expected type + return text_to_embed, None, identifier + +try: + from librarian_vspace.vutils.vector_query_loader import VectorQueryLoader as VectorLoader, VectorQueryLoaderError as VectorLoaderError +except ImportError as e: + logging.error(f"Import loader failed: {e}. Using Dummy.") + + + # Define dummy classes correctly indented + class VectorLoader: + """Dummy class if loader import fails.""" + + def __init__(self, *args, **kwargs): + logging.error("Dummy VectorLoader initialized.") + pass + + def load_and_reduce(self, *args, **kwargs) -> pd.DataFrame: + logging.error("Dummy VectorLoader load_and_reduce called.") + return pd.DataFrame() # Return empty DataFrame + + + class VectorLoaderError(Exception): + """Dummy exception if loader import fails.""" + pass +# --- End Imports --- + +logger = logging.getLogger(__name__) +DEFAULT_N_CLUSTERS = 8 + +# Opacity constants +OPACITY_DEFAULT = 0.8 +OPACITY_SEARCH_DIMMED = 0.1 # Reduced from 0.6 to 0.3 for better visual distinction +OPACITY_SELECTED_CLUSTER = 0.9 + + +class VectorVisualizer: + def __init__(self, initial_data: pd.DataFrame, + db_schema: str, db_function: str, + interface_name: str, model_name: str, + embedding_column: str = "embedding", + initial_limit: Optional[int] = None, + initial_perplexity: float = 30.0, + n_clusters: int = DEFAULT_N_CLUSTERS + ): + required_cols = ['x', 'y', 'z', 'file_id', 'chunk', embedding_column] + processed_data_json: Optional[str] = None + processed_color_map: Dict = {} + processed_original_embeddings: np.ndarray = np.array([]) + processed_cluster_centroids: Dict[str, List[float]] = {} + + self.embedding_column = embedding_column + self.n_clusters = n_clusters + self.db_schema = db_schema + self.db_function = db_function + self.model_name = model_name + self.limit = initial_limit + self.perplexity = initial_perplexity + self.interface_name = interface_name + # Use the correctly defined EmbeddingGenerator (either real or dummy) + self.app = dash.Dash(__name__, suppress_callback_exceptions=True) + self.embedding_generator = EmbeddingGenerator() # Instantiated here + + if initial_data.empty or not all(col in initial_data.columns for col in required_cols): + logger.warning("Initial DataFrame empty/invalid.") + base_cols = required_cols + ['cluster', 'hover_text'] + initial_df_processed = pd.DataFrame(columns=base_cols) + else: + try: + logger.info("Processing initial data...") + df_copy = initial_data.copy() + df_after_kmeans, kmeans_color_map = self._run_kmeans(df_copy, self.n_clusters) + if not isinstance(df_after_kmeans, pd.DataFrame): raise TypeError("KMeans failed.") + processed_color_map = kmeans_color_map + df_after_prepare = self._prepare_plot_data(df_after_kmeans) + if not isinstance(df_after_prepare, pd.DataFrame): raise TypeError("Prep data failed.") + initial_df_processed = df_after_prepare + if not initial_df_processed.empty and all( + c in initial_df_processed for c in ['x', 'y', 'z', 'cluster']): + processed_cluster_centroids = self._calculate_centroids(initial_df_processed) + else: + logger.warning("Could not calculate initial centroids.") + if not initial_df_processed.empty: + processed_data_json = initial_df_processed.to_json(date_format='iso', orient='split') + else: + logger.warning("DataFrame empty after processing.") + if not initial_df_processed.empty and self.embedding_column in initial_df_processed.columns: + try: + emb = initial_df_processed[self.embedding_column].iloc[0] + if isinstance(emb, np.ndarray): + processed_original_embeddings = np.stack(initial_df_processed[self.embedding_column].values) + elif isinstance(emb, list): + processed_original_embeddings = np.array( + initial_df_processed[self.embedding_column].tolist(), dtype=float) + else: + raise TypeError("Unsupported embedding type.") + except Exception as emb_err: + logger.error(f"Embed processing error: {emb_err}"); processed_original_embeddings = np.array([]) + else: + logger.warning("Could not extract original embeddings.") + except Exception as e: + logger.error(f"Initial processing error: {e}", exc_info=True) + processed_data_json, processed_color_map, processed_original_embeddings, processed_cluster_centroids = None, {}, np.array( + []), {} + initial_df_processed = pd.DataFrame() + + self.initial_data_json = processed_data_json + self.initial_cluster_color_map = processed_color_map + self.initial_cluster_centroids = processed_cluster_centroids + self.original_embeddings = processed_original_embeddings + # Determine slider limits and elbow‑based default + self.max_clusters = max(1, len(initial_data)) + try: + self.optimal_clusters = self._estimate_optimal_clusters(processed_original_embeddings, + max_k=min(10, self.max_clusters)) + except Exception: + self.optimal_clusters = self.n_clusters + # Use elbow result as the current cluster count + self.n_clusters = self.optimal_clusters + self._build_layout(); + self._register_callbacks() + + def _run_kmeans(self, df: pd.DataFrame, n_clusters: int) -> Tuple[pd.DataFrame, Dict[str, str]]: + """Runs K-Means, assigns string cluster labels.""" + default_map = {"-1": "grey"} + if df.empty or self.embedding_column not in df.columns: df['cluster'] = "-1"; return df, default_map + try: + emb_col = df[self.embedding_column] + if isinstance(emb_col.iloc[0], np.ndarray): + embeddings = np.stack(emb_col.values) + elif isinstance(emb_col.iloc[0], list): + embeddings = np.array(emb_col.tolist(), dtype=float) + else: + raise TypeError("Unsupported embedding type.") + if embeddings.ndim != 2: raise ValueError("Embeddings must be 2D.") + + eff_clusters = min(n_clusters, embeddings.shape[0]) + if embeddings.shape[0] < 2 or eff_clusters < 1: + lbl = "0" if embeddings.shape[0] > 0 else "-1"; + df['cluster'] = lbl + colors = px.colors.qualitative.Plotly; + return df, {lbl: colors[0 % len(colors)]} if lbl == "0" else default_map + if eff_clusters == 1: df['cluster'] = "0"; colors = px.colors.qualitative.Plotly; return df, { + "0": colors[0 % len(colors)]} + + kmeans = KMeans(n_clusters=eff_clusters, random_state=42, n_init='auto') + df['cluster'] = kmeans.fit_predict(embeddings).astype(str) + unique_labels = sorted(df['cluster'].unique()) + colors = px.colors.qualitative.Plotly + color_map = {lbl: colors[i % len(colors)] for i, lbl in enumerate(unique_labels)} + return df, color_map + except (TypeError, ValueError) as e: + logger.error(f"KMeans input error: {e}"); df['cluster'] = "-1"; return df, default_map + except Exception as e: + logger.exception("KMeans failed."); df['cluster'] = "-1"; return df, default_map + + def _prepare_plot_data(self, df: pd.DataFrame) -> pd.DataFrame: + """Prepares hover text.""" + if df.empty: return df + if 'cluster' not in df.columns: df['cluster'] = 'N/A' + df_copy = df.copy() + + def gen_hover(row): + try: + return f"ID: {row.get('file_id', 'N/A')}
Cluster: {str(row.get('cluster', 'N/A'))}
Chunk: {str(row.get('chunk', ''))[:200]}{'...' if len(str(row.get('chunk', ''))) > 200 else ''}" + except Exception: + return "Hover gen error" + + try: + df_copy['hover_text'] = df_copy.apply(gen_hover, axis=1); return df_copy + except Exception as e: + logger.error(f"Hover gen failed: {e}"); return df + + def _calculate_centroids(self, df: pd.DataFrame) -> Dict[str, List[float]]: + """Calculates 3D centroids.""" + centroids = {} + required = ['x', 'y', 'z', 'cluster']; + numeric_cols = ['x', 'y', 'z'] + if df.empty or not all(col in df.columns for col in required): return centroids + df_copy = df.copy(); + df_copy['cluster'] = df['cluster'].astype(str) + for col in numeric_cols: + if not pd.api.types.is_numeric_dtype(df_copy[col]): + try: + df_copy[col] = pd.to_numeric(df_copy[col], errors='coerce') + except Exception: + logger.error(f"Centroid calc: conv error '{col}'"); return {} + if df_copy[col].isnull().any(): logger.warning(f"Centroid calc: NaNs in '{col}'") + try: + # Calculate mean, drop rows where ALL numeric_cols are NaN, then drop rows where the resulting mean is NaN + centroid_data = df_copy.dropna(subset=numeric_cols, how='all').groupby('cluster')[ + numeric_cols].mean().dropna() + return {str(idx): row.tolist() for idx, row in centroid_data.iterrows()} + except Exception as e: + logger.exception("Centroid calc failed."); return {} + + def _create_base_figure(self) -> go.Figure: + """Creates base Plotly figure.""" + fig = go.Figure() + fig.update_layout(title='3D t-SNE', margin=dict(l=0, r=0, b=0, t=40), + scene_camera_eye=dict(x=1.5, y=1.5, z=0.5), + scene=dict(xaxis_title='TSNE-1', yaxis_title='TSNE-2', zaxis_title='TSNE-3', + aspectmode='data'), + legend=dict(yanchor="top", y=0.99, xanchor="left", x=0.01, bgcolor='rgba(255,255,255,0.7)'), + hovermode='closest') + return fig + + def _build_layout(self) -> None: + """Builds the Dash layout.""" + self.app.layout = html.Div([ + dcc.Store(id='stored-data', data=self.initial_data_json), + dcc.Store(id='cluster-color-map-store', data=self.initial_cluster_color_map), + dcc.Store(id='cluster-centroids-store', data=self.initial_cluster_centroids), + dcc.Store(id='search-results-store', data=None), + dcc.Store(id='selected-cluster-store', data=None), # Store for click state + html.H1("Vector Embedding Visualizer"), + dcc.Tabs(id="main-tabs", value='tab-vis', children=[ + dcc.Tab(label='Visualization', value='tab-vis', children=[ + html.Div([ # Controls + html.Div( + [html.Button('Reload Data', id='reload-button', n_clicks=0, style={'marginRight': '10px'}), + dcc.Input(id='search-input', type='text', placeholder='Search term...', debounce=True, + style={'width': '40%', 'marginRight': '5px'}), + html.Button('Search', id='search-button', n_clicks=0)], + style={'padding': '10px', 'display': 'flex'}), + html.Div([html.Label("Similarity:", style={'marginRight': '10px'}), + dcc.Slider(id='similarity-slider', min=0, max=1, step=0.01, value=0.0, + marks={i / 10: f'{i / 10:.1f}' for i in range(11)}, + tooltip={"placement": "bottom", "always_visible": True}, disabled=True)], + id='slider-container', style={'display': 'none', 'padding': '10px 20px'}), + html.Div([ + html.Label("Clusters:", style={'marginRight': '10px'}), + dcc.Slider( + id='cluster-slider', + min=1, + max=self.max_clusters, + step=1, + value=self.optimal_clusters, + marks=self._cluster_marks(), + tooltip={'placement': 'bottom', 'always_visible': True} + ) + ], style={'padding': '10px 20px'}), + html.Div(id='status-output', style={'padding': '10px', 'color': 'blue', 'minHeight': '20px'}), + dcc.Loading(id="loading-graph", type="circle", + children=dcc.Graph(id='vector-graph', style={'height': '70vh'})) + ]) + ]), + dcc.Tab(label='Settings', value='tab-settings', children=[ + html.Div([html.H3("Settings"), html.Div([html.Label("Marker Size:", style={'marginRight': '10px'}), + dcc.Slider(id='size-slider', min=1, max=15, step=1, + value=4, + marks={i: str(i) for i in range(1, 16)}, + tooltip={'placement': 'bottom', + 'always_visible': True})], + style={'padding': '10px 20px'})], style={'padding': '20px'}) + ]), + ]), + ]) + + def _cosine_similarity(self, vec1: np.ndarray, vec2: np.ndarray) -> np.ndarray | float: + """Calculates cosine similarity.""" + if not isinstance(vec1, np.ndarray): vec1 = np.array(vec1, dtype=float) + if not isinstance(vec2, np.ndarray): vec2 = np.array(vec2, dtype=float) + if vec1.ndim == 1: vec1 = vec1.reshape(1, -1) + if vec2.ndim == 1: vec2 = vec2.reshape(1, -1) + if vec1.shape[1] != vec2.shape[1]: raise ValueError("Vector dimension mismatch") + norm1 = np.linalg.norm(vec1, axis=1, keepdims=True); + norm2 = np.linalg.norm(vec2, axis=1, keepdims=True) + z1 = (norm1 == 0).flatten(); + z2 = (norm2 == 0).flatten() + # Handle potential division by zero for zero vectors + norm1[z1] = 1.0; + norm2[z2] = 1.0 + sim = np.dot(vec1 / norm1, (vec2 / norm2).T) + # Ensure zero vectors result in zero similarity + if np.any(z1): sim[:, :] = 0.0; + sim[:, z2] = 0.0 + sim = np.clip(sim, -1.0, 1.0) + return sim.item() if sim.size == 1 else sim.flatten() + + def _find_neighbors(self, search_vector: List[float], k: int = 10) -> Optional[Tuple[np.ndarray, np.ndarray]]: + """Finds k nearest neighbors.""" + if not search_vector or not isinstance(search_vector, list): return None + if self.original_embeddings is None or self.original_embeddings.size == 0: return None + try: + vec = np.array(search_vector, dtype=float) + if vec.ndim != 1: raise ValueError("Search vector != 1D.") + if self.original_embeddings.ndim != 2: raise ValueError("Embeddings != 2D.") + if self.original_embeddings.shape[1] != vec.shape[0]: raise ValueError("Dimension mismatch.") + sims = self._cosine_similarity(vec, self.original_embeddings) + if not isinstance(sims, np.ndarray) or sims.ndim != 1 or sims.shape[0] != self.original_embeddings.shape[ + 0]: raise TypeError("Similarity calc failed.") + k_actual = min(k, len(sims)); + if k_actual <= 0: return None + idx = np.argpartition(sims, -k_actual)[-k_actual:] # Get indices of top k + sorted_idx = idx[np.argsort(sims[idx])][::-1] # Sort top k indices by similarity + return sorted_idx, sims[sorted_idx] + except (ValueError, TypeError) as e: + logger.error(f"Neighbor input error: {e}"); return None + except Exception as e: + logger.exception(f"Neighbor search error: {e}"); return None + + # --- Callbacks --- + def _register_callbacks(self) -> None: + """Sets up Dash callbacks.""" + + # --- Callback 1: Reload Button --- + @self.app.callback( + Output('stored-data', 'data', allow_duplicate=True), Output('cluster-color-map-store', 'data'), + Output('cluster-centroids-store', 'data'), + Output('status-output', 'children'), Output('search-results-store', 'data', allow_duplicate=True), + Output('selected-cluster-store', 'data', allow_duplicate=True), + Input('reload-button', 'n_clicks'), prevent_initial_call=True) + def handle_reload(n_clicks: int) -> Tuple[Optional[str], Dict, Dict, str, None, None]: + if n_clicks == 0: raise PreventUpdate + logger.info("Reload triggered...") + status = "Reloading..."; + color_map, centroids, data_json = {}, {}, None; + self.original_embeddings = np.array([]) + try: + # Ensure VectorLoader is properly imported or defined (dummy used if import fails) + loader = VectorLoader(self.db_schema, self.db_function, self.model_name, self.embedding_column) + reduced_data = loader.load_and_reduce(limit=self.limit, tsne_params={"perplexity": self.perplexity}) + if not isinstance(reduced_data, pd.DataFrame) or reduced_data.empty: raise VectorLoaderError("No data.") + + df_clustered, color_map = self._run_kmeans(reduced_data.copy(), self.n_clusters) + if not isinstance(df_clustered, pd.DataFrame): raise TypeError( + "KMeans failed post-reload.") # Add check + df_final = self._prepare_plot_data(df_clustered) + if not isinstance(df_final, pd.DataFrame): raise TypeError( + "Prepare plot failed post-reload.") # Add check + + if not df_final.empty and all(c in df_final for c in ['x', 'y', 'z', 'cluster']): + centroids = self._calculate_centroids(df_final) + else: + logger.warning("Could not calculate centroids after reload (missing cols or empty).") + + if not reduced_data.empty and self.embedding_column in reduced_data.columns: + try: + emb_col = reduced_data[self.embedding_column] + # Check type of first element before processing + if not emb_col.empty: + first_emb = emb_col.iloc[0] + if isinstance(first_emb, np.ndarray): + self.original_embeddings = np.stack(emb_col.values) + elif isinstance(first_emb, list): + self.original_embeddings = np.array(emb_col.tolist(), dtype=float) + else: + raise TypeError(f"Unsupported reloaded embed type: {type(first_emb)}") + logger.info(f"Stored reloaded embeddings (shape: {self.original_embeddings.shape}).") + else: + logger.warning("Embedding column empty during reload storage.") + except Exception as e: + logger.error(f"Store embed fail: {e}"); self.original_embeddings = np.array([]) + else: + logger.warning("Embedding column missing or df empty during reload storage.") + + if not df_final.empty: + data_json = df_final.to_json(date_format='iso', + orient='split'); status = f"Reloaded ({len(df_final)} pts)." + else: + status = "Warning: Reload empty post-process." + except (VectorLoaderError, TypeError, Exception) as e: + logger.exception( + f"Reload error: {e}"); status = f"Error: {e}"; data_json, color_map, centroids = None, {}, {}; self.original_embeddings = np.array( + []) + return data_json, color_map, centroids, status, None, None + + # --- Callback 1 b: Cluster‑count Slider --- + @self.app.callback( + Output('stored-data', 'data', allow_duplicate=True), + Output('cluster-color-map-store', 'data', allow_duplicate=True), + Output('cluster-centroids-store', 'data', allow_duplicate=True), + Output('status-output', 'children', allow_duplicate=True), + Input('cluster-slider', 'value'), + State('stored-data', 'data'), + prevent_initial_call=True + ) + def update_n_clusters(k: int, stored_json: str): + if not stored_json: + raise PreventUpdate + + # Update the visualizer state + self.n_clusters = k + + try: + df = pd.read_json(StringIO(stored_json), orient='split') + df, color_map = self._run_kmeans(df, k) + df = self._prepare_plot_data(df) + centroids = self._calculate_centroids(df) + + status = f"Cluster count set to {k}." + return (df.to_json(date_format='iso', orient='split'), + color_map, + centroids, + status) + except Exception as err: + logger.error(f"Clustering update failed: {err}") + raise PreventUpdate + + # --- Callback 2: Search Button --- + @self.app.callback( + Output('search-results-store', 'data', allow_duplicate=True), + Output('status-output', 'children', allow_duplicate=True), + Input('search-button', 'n_clicks'), State('search-input', 'value'), prevent_initial_call=True) + def handle_search(n_clicks: int, term: str) -> Tuple[Optional[Dict], str]: + if n_clicks == 0 or not term: return None, "Enter search term." + logger.info(f"Search: '{term}'"); + status = f"Embedding '{term}'..." + try: + if self.original_embeddings is None or self.original_embeddings.size == 0: return None, "Error: No data." + _, vec, _ = self.embedding_generator.generate_embedding(self.interface_name, self.model_name, term, + "search") + if vec is None: return None, f"Error: Embed failed." + status = f"Finding neighbors..."; + neighbors = self._find_neighbors(vec, k=20) + if neighbors is None: return None, f"No neighbors found." + idx, sims = neighbors; + results = {"indices": idx.tolist(), "similarities": sims.tolist(), "term": term} + status = f"Found {len(idx)} neighbors."; + return results, status + except Exception as e: + logger.exception("Search error."); return None, f"Error: {e}" + + # --- Callback 3: Slider Visibility --- + @self.app.callback( + Output('slider-container', 'style'), Output('similarity-slider', 'disabled'), + Output('similarity-slider', 'value'), + Input('search-results-store', 'data'), prevent_initial_call=True) + def update_slider_visibility(res: Optional[Dict]) -> Tuple[Dict, bool, float]: + show = res and isinstance(res, dict) and "indices" in res + style = {'display': 'block' if show else 'none', 'padding': '10px 20px'} + return style, not show, 0.0 + + # --- Callback 4: Graph Update (Main Logic with clickData fix and logging) --- + @self.app.callback( + Output('vector-graph', 'figure'), Output('selected-cluster-store', 'data'), + Output('status-output', 'children', allow_duplicate=True), + Input('stored-data', 'data'), Input('cluster-color-map-store', 'data'), + Input('cluster-centroids-store', 'data'), + Input('search-results-store', 'data'), Input('similarity-slider', 'value'), Input('size-slider', 'value'), + Input('vector-graph', 'clickData'), # Input for clicks + State('selected-cluster-store', 'data'), # Get current selection + prevent_initial_call='initial_duplicate' # Allow initial run + ) + def update_graph(stored_data_json: Optional[str], cluster_color_map: Optional[Dict], + cluster_centroids: Optional[Dict[str, List[float]]], + search_results: Optional[Dict], similarity_threshold: float, size_value: int, + click_data: Optional[Dict], + current_selected_cluster: Optional[str]) -> Tuple[go.Figure, Optional[str], str]: + + fig = self._create_base_figure(); + status_msg = ""; + new_selected_cluster = current_selected_cluster + trigger = ctx.triggered_id if ctx.triggered else "Initial" + logger.debug(f"--- Graph Update | Trigger: {trigger} | CurrentSel: {current_selected_cluster} ---") + + # --- Data Load & Validation --- + if not stored_data_json: return fig, None, "No data." + try: + df = pd.read_json(StringIO(stored_data_json), orient='split') + if df.empty: return fig, None, "Empty data." + required = ['x', 'y', 'z', 'cluster', 'hover_text']; + assert all(col in df.columns for col in required) + df['cluster'] = df['cluster'].astype(str) + color_map = cluster_color_map if isinstance(cluster_color_map, dict) else {} + centroids = cluster_centroids if isinstance(cluster_centroids, dict) else {} + if not color_map: + logger.warning("Missing color map, generating default.") + unique_clusters = df['cluster'].unique(); + colors = px.colors.qualitative.Plotly + color_map = {str(c): colors[i % len(colors)] for i, c in enumerate(unique_clusters)} or { + '0': 'grey'} + + # Calculate overall data range (handle potential NaNs/Infs in full data) + df_finite = df[['x', 'y', 'z']].replace([np.inf, -np.inf], np.nan).dropna() + if not df_finite.empty: + overall_x_min, overall_x_max = df_finite['x'].min(), df_finite['x'].max() + overall_y_min, overall_y_max = df_finite['y'].min(), df_finite['y'].max() + overall_z_min, overall_z_max = df_finite['z'].min(), df_finite['z'].max() + logger.debug( + f"Overall Finite Range: X=[{overall_x_min:.2f}, {overall_x_max:.2f}], Y=[{overall_y_min:.2f}, {overall_y_max:.2f}], Z=[{overall_z_min:.2f}, {overall_z_max:.2f}]") + else: + logger.warning("No finite data points found in the dataset to calculate overall range.") + overall_x_min, overall_x_max = -10, 10 # Default ranges if no finite data + overall_y_min, overall_y_max = -10, 10 + overall_z_min, overall_z_max = -10, 10 + + except Exception as e: + logger.exception("Graph data error."); return fig, current_selected_cluster, f"Error: {e}" + + # --- Click Processing --- + if trigger == 'vector-graph': + logger.debug(f"Click Data Received: {click_data}") + if click_data and 'points' in click_data and click_data['points']: + point_data = click_data['points'][0] + clicked_customdata = point_data.get('customdata'); + clicked_text = point_data.get('text', '') + logger.debug(f"Clicked Point Customdata: {clicked_customdata}"); + logger.debug(f"Clicked Point Text: '{clicked_text}'") + is_centroid_click = False; + clicked_cluster_id = None + if isinstance(clicked_customdata, list) and len(clicked_customdata) > 0: clicked_customdata = \ + clicked_customdata[0] + if isinstance(clicked_customdata, (str, int)): # Accept string or int cluster IDs + is_centroid_click = True; + clicked_cluster_id = str(clicked_customdata); + logger.info(f"Centroid Click Parsed via customdata: Cluster '{clicked_cluster_id}'") + elif isinstance(clicked_text, str) and clicked_text.startswith("Centroid: Cluster "): + try: + clicked_cluster_id = clicked_text.split("Centroid: Cluster ")[ + 1]; is_centroid_click = True; logger.info( + f"Centroid Click Parsed via text: Cluster '{clicked_cluster_id}'") + except Exception as parse_err: + logger.warning(f"Failed text parse: {parse_err}") + if is_centroid_click and clicked_cluster_id is not None: + if current_selected_cluster == clicked_cluster_id: + new_selected_cluster = None; status_msg = "Cluster view reset."; logger.info("Deselecting.") + else: + new_selected_cluster = clicked_cluster_id; status_msg = f"Showing Cluster {new_selected_cluster}."; logger.info( + f"Selecting {new_selected_cluster}.") + elif not is_centroid_click and current_selected_cluster is not None: + new_selected_cluster = None; status_msg = "Cluster view reset."; logger.info("Deselecting.") + else: # Click background + if current_selected_cluster is not None: new_selected_cluster = None; status_msg = "Cluster view reset."; logger.info( + "Deselecting.") + logger.debug(f"Click Result: new_selected_cluster = {new_selected_cluster}") + else: + logger.debug("No click trigger.") + + # --- Data Filtering --- + active_selection_id = new_selected_cluster + df_to_plot = df.copy(); + centroids_to_plot = centroids.copy() + logger.debug(f"Filtering based on active_selection_id: {active_selection_id}") + if active_selection_id is not None: + df_to_plot = df_to_plot[df_to_plot['cluster'] == active_selection_id] + centroids_to_plot = {cid: coords for cid, coords in centroids_to_plot.items() if + cid == active_selection_id} + logger.debug(f"Filtered DF rows: {len(df_to_plot)}") + if not df_to_plot.empty: + logger.debug(f"Coordinates of filtered points:\n{df_to_plot[['x', 'y', 'z']]}") + else: + logger.warning("Filtered DataFrame is empty.") + + # --- Search Highlighting --- + search_highlight_mask = np.zeros(len(df_to_plot), dtype=bool) + search_term = None; + is_search_active = False; + highlight_sims = {} + if search_results and isinstance(search_results, dict) and "indices" in search_results: + is_search_active = True; + search_term = search_results.get("term", "N/A") + orig_indices = search_results.get("indices", []); + orig_sims = search_results.get("similarities", []) + if not df_to_plot.empty: + orig_to_current_map = {orig_idx: current_idx for current_idx, orig_idx in + enumerate(df_to_plot.index)} + current_indices_hl = [orig_to_current_map[oi] for i, oi in enumerate(orig_indices) if + i < len(orig_sims) and orig_sims[ + i] >= similarity_threshold and oi in orig_to_current_map] + if current_indices_hl: + search_highlight_mask[current_indices_hl] = True + for i, orig_idx in enumerate(orig_indices): + if i < len(orig_sims) and orig_sims[ + i] >= similarity_threshold and orig_idx in orig_to_current_map: + highlight_sims[orig_to_current_map[orig_idx]] = orig_sims[i] + else: + logger.warning("Cannot apply search highlighting - filtered df empty.") + + # --- Plotting --- + df_search_hl = df_to_plot[search_highlight_mask]; + df_normal = df_to_plot[~search_highlight_mask] + base_size = size_value; + normal_op = OPACITY_SELECTED_CLUSTER if active_selection_id else ( + OPACITY_SEARCH_DIMMED if is_search_active else OPACITY_DEFAULT) + + # --- Add Dummy Points if needed --- + num_points_to_plot = len(df_normal) + len(df_search_hl) + if active_selection_id is not None and num_points_to_plot <= 2: + logger.info( + f"Adding dummy invisible points to aid auto-ranging for cluster {active_selection_id} (points={num_points_to_plot}).") + # Use overall range calculated earlier + dummy_x = [overall_x_min, overall_x_max] + dummy_y = [overall_y_min, overall_y_max] + dummy_z = [overall_z_min, overall_z_max] + # Ensure dummy points are valid numbers (in case overall range calc failed) + if np.isfinite(dummy_x + dummy_y + dummy_z).all(): + fig.add_trace(go.Scatter3d( + x=dummy_x, y=dummy_y, z=dummy_z, + mode='markers', marker=dict(size=1, opacity=0), # Invisible + hoverinfo='skip', showlegend=False, name='_dummy_' + )) + else: + logger.warning("Could not add dummy points because overall range contained non-finite values.") + + # Plot Normal Points + if not df_normal.empty: + finite_mask_normal = np.isfinite(df_normal[['x', 'y', 'z']]).all(axis=1) + df_normal_finite = df_normal[finite_mask_normal] + if not df_normal_finite.empty: + logger.debug(f"Plotting df_normal (len={len(df_normal_finite)}).") + colors = df_normal_finite['cluster'].map(color_map).fillna('darkgrey') + name = 'Embeddings' if active_selection_id is None else f'Cluster {active_selection_id}' + fig.add_trace( + go.Scatter3d(x=df_normal_finite['x'], y=df_normal_finite['y'], z=df_normal_finite['z'], + mode='markers', + marker=dict(color=colors, size=base_size, opacity=normal_op, line=dict(width=0.5)), + text=df_normal_finite['hover_text'], hoverinfo='text', name=name)) + else: + logger.warning("No finite normal points to plot.") + + # Plot Search Highlighted Points + if not df_search_hl.empty: + finite_mask_search = np.isfinite(df_search_hl[['x', 'y', 'z']]).all(axis=1) + df_search_hl_finite = df_search_hl[finite_mask_search] + if not df_search_hl_finite.empty: + hl_size = max(base_size * 1.5, base_size + 2); + hl_texts = [] + # Need mapping from df_search_hl_finite index back to df_to_plot positional index for sims + positions_in_df_to_plot = df_to_plot.index.get_indexer_for(df_search_hl_finite.index) + + for i, (global_index, row) in enumerate(df_search_hl_finite.iterrows()): + pos = positions_in_df_to_plot[i] # Get original position in df_to_plot + sim = highlight_sims.get(pos, float('nan')) + sim_txt = f"{sim:.4f}" if not np.isnan(sim) else "N/A" + hl_texts.append(f"{row['hover_text']}
Sim: {sim_txt}") + + fig.add_trace( + go.Scatter3d(x=df_search_hl_finite['x'], y=df_search_hl_finite['y'], z=df_search_hl_finite['z'], + mode='markers', + marker=dict(color='red', size=hl_size, opacity=1.0, symbol='diamond', + line=dict(color='black', width=1)), text=hl_texts, hoverinfo='text', + name=f'Search Neighbors')) + if not df_search_hl_finite[['x', 'y', 'z']].isnull().values.any(): # Search Centroid + try: + sc = df_search_hl_finite[['x', 'y', 'z']].mean().values; fig.add_trace( + go.Scatter3d(x=[sc[0]], y=[sc[1]], z=[sc[2]], mode='markers', + marker=dict(color='magenta', size=max(hl_size, 10), symbol='cross', + line=dict(width=1)), text=f"Search: '{search_term}' Centroid", + hoverinfo='text', name='Search Centroid')) + except Exception as e: + logger.warning(f"Search centroid plot fail: {e}") + else: + logger.warning("No finite search highlighted points to plot.") + + # Plot Centroids (filtered) + if centroids_to_plot: + cent_size = base_size + 1; + logger.debug(f"Plotting centroids: {list(centroids_to_plot.keys())}") + for cid, coords in centroids_to_plot.items(): + if isinstance(coords, list) and len(coords) == 3: + logger.debug(f"Plotting Centroid {cid} at coords: {coords}") + if np.isnan(coords).any() or np.isinf(coords).any(): logger.error( + f"!!! Centroid {cid} NaN/Inf coords !!!"); continue + color = color_map.get(str(cid), 'grey'); + name = f"Centroid {cid}"; + hover_txt = f"Centroid: Cluster {cid}" + fig.add_trace(go.Scatter3d( + x=[coords[0]], y=[coords[1]], z=[coords[2]], mode='markers', + marker=dict(color=color, size=cent_size, symbol='circle', opacity=0.9, + line=dict(color='black', width=1.5)), + customdata=[str(cid)], text=hover_txt, hoverinfo='text', name=name, + legendgroup="centroids", showlegend=True + )) + else: + logger.warning(f"Invalid centroid data for {cid}") + + # --- Final Layout & Status --- + title = f"3D t-SNE ({len(df)} points)" + if active_selection_id is not None: + title = f"Cluster {active_selection_id} ({len(df_to_plot)} points)" + elif is_search_active: + title = f"3D t-SNE - Search: '{search_term}'" + if active_selection_id and is_search_active: title += f" - Search: '{search_term}'" + base_layout = self._create_base_figure().layout + fig.update_layout( + title=title, legend_title_text='Legend', legend=base_layout.legend, + scene=base_layout.scene # Use base scene settings (includes aspectmode='data') + # Rely on auto-ranging (potentially helped by dummy points if added) + ) + + final_status = status_msg + if not final_status: # Default status + base = f"{len(df_to_plot)} points shown." + if active_selection_id: base = f"Cluster {active_selection_id}: {len(df_to_plot)} points." + final_status = base; + if is_search_active: final_status += f" (Search: '{search_term}')" + + return fig, new_selected_cluster, final_status + + def run(self, host: str = "127.0.0.1", port: int = 8050, debug: bool = False) -> None: + """Starts the Dash server.""" + logger.info(f"Starting Dash server on http://{host}:{port}") + try: + self.app.run(host=host, port=port, debug=debug) + except OSError as e: + logger.error(f"Server start failed: {e}. Port {port} busy?") + except Exception as e: + logger.exception(f"Server error: {e}") + + # ────────────────────────────────────────────────────────────── + # >>> Helpers for automatic cluster‐count selection <<< + # ────────────────────────────────────────────────────────────── + def _estimate_optimal_clusters(self, embeddings: np.ndarray, max_k: int = 10) -> int: + """ + Estimate an optimal number of clusters using a quick elbow heuristic. + Computes K‑means inertia for k = 1…max_k and picks the k that is farthest + from the straight line connecting (1, inertia₁) and (max_k, inertiaₘₐₓ). + """ + if embeddings is None or embeddings.size == 0: + return 1 + n_samples = embeddings.shape[0] + if n_samples < 3: + return 1 + + max_k = min(max_k, n_samples) + inertias = [] + for k in range(1, max_k + 1): + km = KMeans(n_clusters=k, random_state=42, n_init="auto").fit(embeddings) + inertias.append(km.inertia_) + + # distance from each point to the line between first and last + x = np.arange(1, max_k + 1) + x1, y1 = 1, inertias[0] + x2, y2 = max_k, inertias[-1] + numerator = np.abs((y2 - y1) * x - (x2 - x1) * np.array(inertias) + x2 * y1 - y2 * x1) + denominator = np.sqrt((y2 - y1) ** 2 + (x2 - x1) ** 2) + elbow_idx = int(np.argmax(numerator / denominator)) + return elbow_idx + 1 # since k starts at 1 + + def _cluster_marks(self) -> Dict[int, str]: + """Generate tick marks for the cluster-count slider.""" + if self.max_clusters <= 15: + return {i: str(i) for i in range(1, self.max_clusters + 1)} + # Show first, optimal, and max for large data sets + return {1: "1", self.optimal_clusters: str(self.optimal_clusters), self.max_clusters: str(self.max_clusters)} + + # --- END OF FILE visualizer.py --- diff --git a/librarian/plugins/librarian-vspace/src/librarian_vspace/vecview/__init__.py b/librarian/plugins/librarian-vspace/src/librarian_vspace/vecview/__init__.py new file mode 100644 index 0000000..e00b59d --- /dev/null +++ b/librarian/plugins/librarian-vspace/src/librarian_vspace/vecview/__init__.py @@ -0,0 +1,2 @@ +def hello() -> str: + return "Hello from librarian_vspace!" diff --git a/librarian/plugins/librarian-vspace/src/librarian_vspace/vecview/py.typed b/librarian/plugins/librarian-vspace/src/librarian_vspace/vecview/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/librarian/plugins/librarian-vspace/src/librarian_vspace/vecview/tsne_export_worker.py b/librarian/plugins/librarian-vspace/src/librarian_vspace/vecview/tsne_export_worker.py new file mode 100644 index 0000000..6d42903 --- /dev/null +++ b/librarian/plugins/librarian-vspace/src/librarian_vspace/vecview/tsne_export_worker.py @@ -0,0 +1,93 @@ + +"""TsneExportWorker – Prefect worker that generates a t‑SNE JSON export. + +It wraps Vspace.get_tnse → vecview.get_tsne_json, writes the JSON to a file, +stages it, and returns the file path. + +Minimal Pydantic payload models are defined locally to avoid extra deps. +""" + +from __future__ import annotations + +import asyncio +import json +import logging +import tempfile +from pathlib import Path +from typing import Optional + +from prefect import get_run_logger +from pydantic import BaseModel +from librarian_core.workers.base import Worker + +from librarian_vspace.vecview.vecview import get_tsne_json + +# ------------------------------------------------------------------ # +def _safe_get_logger(name: str): + try: + return get_run_logger() + except Exception: + return logging.getLogger(name) + + +# ------------------------------------------------------------------ # +# Pydantic payloads +# ------------------------------------------------------------------ # +class TsneExportInput(BaseModel): + course_id: int + limit: Optional[int] = None + perplexity: float = 30.0 + db_schema: str = "librarian" + rpc_function: str = "pdf_chunking" + embed_model: str = "snowflake-arctic-embed2" + embedding_column: str = "embedding" + base_output_dir: Optional[Path] = None # where to place JSON file + + +class TsneExportOutput(BaseModel): + json_path: Path + + +# ------------------------------------------------------------------ # +class TsneExportWorker(Worker[TsneExportInput, TsneExportOutput]): + """Runs the t‑SNE export inside a Prefect worker.""" # noqa: D401 + + input_model = TsneExportInput + output_model = TsneExportOutput + + async def __run__(self, payload: TsneExportInput) -> TsneExportOutput: + logger = _safe_get_logger(self.worker_name) + logger.info("🔨 %s startet (payload=%r)", self.worker_name, payload) + + # Run get_tsne_json in a thread + data_json = await asyncio.to_thread( + get_tsne_json, + db_schema=payload.db_schema, + db_function=payload.rpc_function, + model_name=payload.embed_model, + limit=payload.limit, + course_id=payload.course_id, + perplexity=payload.perplexity, + embedding_column=payload.embedding_column, + ) + + # Determine output file + if payload.base_output_dir: + out_dir = Path(payload.base_output_dir).expanduser() + out_dir.mkdir(parents=True, exist_ok=True) + json_path = out_dir / f"{payload.course_id}_tsne.json" + else: + tf = tempfile.NamedTemporaryFile( + mode="w+", suffix="_tsne.json", prefix="vspace_", delete=False + ) + json_path = Path(tf.name) + + # Write JSON to file + json_path.write_text(data_json, encoding="utf-8") + + # Stage file for Prefect + self.stage(json_path, new_name=json_path.name) + + result = TsneExportOutput(json_path=json_path) + logger.info("✅ %s fertig: %r", self.worker_name, result) + return result diff --git a/librarian/plugins/librarian-vspace/src/librarian_vspace/vecview/vecview.py b/librarian/plugins/librarian-vspace/src/librarian_vspace/vecview/vecview.py new file mode 100644 index 0000000..4bdba48 --- /dev/null +++ b/librarian/plugins/librarian-vspace/src/librarian_vspace/vecview/vecview.py @@ -0,0 +1,104 @@ + +"""Utility functions to fetch vectors from Supabase, apply t‑SNE, add simple K‑means +clustering and hover text – prepared exactly like the `VectorVisualizer` expects. +""" + +from __future__ import annotations + +import logging +from typing import Optional, List + +import numpy as np +import pandas as pd +from sklearn.cluster import KMeans + +from librarian_vspace.vutils.vector_query_loader import VectorQueryLoader, VectorQueryLoaderError +from librarian_vspace.models.tsne_model import TSNEPoint, TSNEData + +logger = logging.getLogger(__name__) + +DEFAULT_N_CLUSTERS = 8 + + +# --------------------------------------------------------------------- # +# Internal helpers (kept minimal – no extra bells & whistles) +# --------------------------------------------------------------------- # +def _run_kmeans(df: pd.DataFrame, *, embedding_column: str, k: int = DEFAULT_N_CLUSTERS) -> pd.DataFrame: + """Adds a 'cluster' column using K‑means (string labels).""" + if df.empty or embedding_column not in df.columns: + df['cluster'] = "-1" + return df + + embeddings = np.array(df[embedding_column].tolist(), dtype=float) + n_samples = embeddings.shape[0] + k = max(1, min(k, n_samples)) # ensure 1 ≤ k ≤ n_samples + if n_samples < 2: + df['cluster'] = "0" + return df + + km = KMeans(n_clusters=k, random_state=42, n_init='auto') + df['cluster'] = km.fit_predict(embeddings).astype(str) + return df + + +def _add_hover(df: pd.DataFrame) -> pd.DataFrame: + if df.empty: + return df + + df = df.copy() + + def _hover(row): + preview = str(row.get('chunk', ''))[:200] + if len(str(row.get('chunk', ''))) > 200: + preview += "..." + return ( + f"ID: {row.get('file_id', 'N/A')}
" + f"Cluster: {row.get('cluster', 'N/A')}
" + f"Chunk: {preview}" + ) + + df['hover_text'] = df.apply(_hover, axis=1) + return df + + +# --------------------------------------------------------------------- # +# Public helpers +# --------------------------------------------------------------------- # +def get_tsne_dataframe( + db_schema: str, + db_function: str, + model_name: str, + *, + limit: Optional[int] = None, + course_id: Optional[int] = None, + perplexity: float = 30.0, + embedding_column: str = "embedding", + n_clusters: int = DEFAULT_N_CLUSTERS, +) -> pd.DataFrame: + """Returns a pandas DataFrame with tsne (x,y,z) & metadata ready for plotting.""" + loader = VectorQueryLoader(db_schema, db_function, model_name, embedding_column) + df = loader.load_and_reduce( + limit=limit, + course_id=course_id, + tsne_params={"perplexity": perplexity}, + ) + + if df.empty: + return df + + df = _run_kmeans(df, embedding_column=embedding_column, k=n_clusters) + df = _add_hover(df) + return df + + +def get_tsne_json(**kwargs) -> str: + """Convenience wrapper returning DataFrame as JSON (orient='split').""" + df = get_tsne_dataframe(**kwargs) + return df.to_json(date_format='iso', orient='split') + + +def get_tsne_response(**kwargs) -> TSNEData: + """Returns a validated `TSNEResponse` Pydantic model.""" + df = get_tsne_dataframe(**kwargs) + points: List[TSNEPoint] = [TSNEPoint(**row.dropna().to_dict()) for _, row in df.iterrows()] + return TSNEData(course_id=kwargs.get('course_id'), total=len(points), points=points) diff --git a/librarian/plugins/librarian-vspace/src/librarian_vspace/vquery/__init__.py b/librarian/plugins/librarian-vspace/src/librarian_vspace/vquery/__init__.py new file mode 100644 index 0000000..1b31f3c --- /dev/null +++ b/librarian/plugins/librarian-vspace/src/librarian_vspace/vquery/__init__.py @@ -0,0 +1,10 @@ +""" +vquery package for high-level read operations against vector tables. +""" +from .query import VectorQuery + +__all__ = ["VectorQuery"] # Defines the public interface of the package + +# Optional: Add package-level logging setup if desired, but often handled by the application +# import logging +# logging.getLogger(__name__).addHandler(logging.NullHandler()) \ No newline at end of file diff --git a/librarian/plugins/librarian-vspace/src/librarian_vspace/vquery/cluster_export.py b/librarian/plugins/librarian-vspace/src/librarian_vspace/vquery/cluster_export.py new file mode 100644 index 0000000..545f9df --- /dev/null +++ b/librarian/plugins/librarian-vspace/src/librarian_vspace/vquery/cluster_export.py @@ -0,0 +1,438 @@ +# --- START OF FILE cluster_export.py (Refactored & Workaround - Import Updated) --- + +""" +cluster_export.py – Generate IVFFlat‑equivalent clusters from Supabase/Vectorbase +pgvector data and export each cluster’s chunks to Markdown. + +This version fetches vectors filtered by course ID at the database level using +VectorQueryLoader, performs k-means clustering, and exports to Markdown. + +Includes automatic k-downsizing. + +Environment variables (used by the script entry point) +--------------------- +* **Vectorbase credentials** (auto‑mapped to Supabase): + * `VECTORBASE_URL` → `SUPABASE_URL` + * `VECTORBASE_API_KEY` → `SUPABASE_KEY` + * `VECTORBASE_USER_UUID` → `SUPABASE_USER_UUID` (optional) +* **Embedding/table config** + * `VECTOR_SCHEMA` – Postgres schema (default `librarian`) + * `VECTOR_FUNCTION` – RPC / Postgres function name (optional) + * `EMBED_MODEL` – embedding model label (default `snowflake-arctic-embed2`) +* **Clustering hyper‑parameters** + * `K` – requested number of clusters / IVFFlat *nlist* (default 128) + * `TRAIN_SAMPLE` – how many rows to feed into k‑means (default 20 000, but + capped at the table size) +* **Export** + * `OUTPUT_DIR` – directory for the generated Markdown files (default + `./cluster_md`) + * `CLUSTER_COURSE_ID` - Optional course ID to filter vectors (used by script) + + +Usage +~~~~~ + # Via script entry point + export VECTORBASE_URL="https://xyz.vectorbase.co" + export VECTORBASE_API_KEY="service_role_key" + export VECTOR_SCHEMA=librarian + export EMBED_MODEL=snowflake-arctic-embed2 + export CLUSTER_COURSE_ID=123 # Optional filtering + export K=64 + python -m librarian_vspace.vquery.cluster_export + + # As a callable function + from librarian_vspace.vquery.cluster_export import run_cluster_export_job + output_path = run_cluster_export_job(course_id=456, output_dir="/tmp/clusters_456", ...) +""" +from __future__ import annotations + +import json +import logging +import os +import sys +from pathlib import Path +from typing import List, Optional, Dict, Any, Union # Added Union + +import numpy as np +import pandas as pd +from sklearn.cluster import KMeans +from sklearn.metrics import pairwise_distances_argmin_min + +# --------------------------------------------------------------------------- +# Map Vectorbase credential names → Supabase names expected by loader code +# --------------------------------------------------------------------------- +_ALIAS_ENV_MAP = { + "VECTORBASE_URL": "SUPABASE_URL", + "VECTORBASE_API_KEY": "SUPABASE_KEY", + "VECTORBASE_USER_UUID": "SUPABASE_USER_UUID", # optional +} +for src, dest in _ALIAS_ENV_MAP.items(): + if dest not in os.environ and src in os.environ: + os.environ[dest] = os.environ[src] + +# Import the NEW data‑loading helper with filtering capabilities +try: + # --- FIX: Import VectorQueryLoader from vutils --- + from librarian_vspace.vutils.vector_query_loader import VectorQueryLoader, VectorQueryLoaderError + # VectorLoaderError is now VectorQueryLoaderError +# --- END FIX --- +except ImportError as e: + # Keep the original script's error handling for standalone use + sys.stderr.write( + "\n[ERROR] Could not import VectorQueryLoader – check PYTHONPATH. " + f"Original error: {e}\n" + ) + # For callable use, we should raise an ImportError or custom exception + raise ImportError(f"Could not import VectorQueryLoader: {e}") from e + + +# --------------------------------------------------------------------------- +# Logging setup (used by both script and callable function) +# --------------------------------------------------------------------------- +# This basicConfig runs when the module is imported. +# Callers might want to configure logging before importing. +# If logging is already configured, basicConfig does nothing. +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s %(levelname)s: %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", +) +logger = logging.getLogger(__name__) # Use __name__ for module-specific logger + +# --------------------------------------------------------------------------- +# Helper – JSON dump for centroid in YAML front‑matter +# --------------------------------------------------------------------------- +def centroid_to_json(vec: np.ndarray) -> str: + """Converts a numpy vector to a JSON string suitable for YAML frontmatter.""" + return json.dumps([float(x) for x in vec], ensure_ascii=False) + + +# --------------------------------------------------------------------------- +# Main clustering and export logic as a callable function +# --------------------------------------------------------------------------- +def run_cluster_export_job( + course_id: Optional[int] = None, # Added course_id parameter + output_dir: Union[str, Path] = "./cluster_md", # Output directory parameter + schema: str = "librarian", + rpc_function: str = "pdf_chunking", # Default to actual function name + model: str = "snowflake-arctic-embed2", + k_clusters: int = 128, # Requested number of clusters (k) + train_sample_size: int = 20000, # Sample size for K-means training + embedding_column: str = "embedding" # Added embedding column parameter +) -> Path: + """ + Fetches vectors, performs K-means clustering, and exports clustered chunks to Markdown. + + Args: + course_id: Optional ID to filter vectors belonging to a specific course. + output_dir: Directory path where the cluster Markdown files will be saved. + schema: Postgres schema containing the vector table. + rpc_function: Optional RPC function name used by VectorQueryLoader (needed for table lookup). + model: Embedding model label used by VectorQueryLoader (needed for table lookup). + k_clusters: The requested number of clusters (k). Will be downsized if fewer + vectors are available. + train_sample_size: The maximum number of vectors to use for K-means training. + Capped by the total number of vectors fetched. + embedding_column: The name of the column containing the vector embeddings. + + Returns: + The absolute path to the output directory. + + Raises: + VectorQueryLoaderError: If vector loading fails. + RuntimeError: If no embeddings are retrieved or training sample is empty after filtering. + Exception: For other errors during clustering or export. + """ + output_path = Path(output_dir).expanduser().resolve() # Resolve path early + output_path.mkdir(parents=True, exist_ok=True) + logger.info("Writing Markdown files to %s", output_path) + + # --------------------------------------------------------------------------- + # Fetch embeddings - Now using VectorQueryLoader with filtering + # --------------------------------------------------------------------------- + try: + # Use parameters for loader config + # --- FIX: Instantiate VectorQueryLoader --- + loader = VectorQueryLoader(schema=schema, function=rpc_function, model=model, embedding_column=embedding_column) + # --- END FIX --- + + # --- FIX: Call fetch_vectors WITH the course_id argument --- + # VectorQueryLoader.fetch_vectors handles the DB-level filtering + df = loader.fetch_vectors(limit=None, course_id=course_id) + # --- END FIX --- + + # --- REMOVE: In-memory filtering logic is no longer needed --- + # initial_rows = len(df) + # if course_id is not None and not df.empty: + # ... (removed filtering code) ... + # elif course_id is not None and df.empty: + # ... (removed warning) ... + # --- END REMOVE --- + + # --- FIX: Catch VectorQueryLoaderError --- + except VectorQueryLoaderError as e: + logger.error("Vector loading failed: %s", e) + raise e # Re-raise the specific exception for the caller + # --- END FIX --- + except Exception as e: + # Catch other unexpected errors during loading + logger.exception("An unexpected error occurred during vector loading.") + raise RuntimeError(f"An unexpected error occurred during vector loading: {e}") from e + + + # --- Check if DataFrame is empty *after* fetching (which includes DB filtering) --- + if df.empty: + logger.error("No embeddings retrieved or found for course_id %s – aborting.", course_id) + # Raise a RuntimeError as no clustering can be done + raise RuntimeError(f"No embeddings retrieved or found for course_id {course_id} – nothing to cluster.") + # ---------------------------------------------------------------------------------- + + + # Use the actual embedding column name from the loader instance + # This check is crucial *after* fetching + if not hasattr(loader, 'embedding_column') or loader.embedding_column not in df.columns: + # This should ideally be caught by VectorQueryLoader's internal checks, but double-check + logger.error("Embedding column '%s' not found in fetched data.", embedding_column) # Use the input param name for error msg + raise RuntimeError(f"Embedding column '{embedding_column}' not found in fetched data.") + + + # --- Ensure embeddings are numeric lists before stacking --- + # The VectorQueryLoader.fetch_vectors method now handles parsing and dropping invalid rows. + # We just need to safely stack the potentially filtered/cleaned data. + try: + # Ensure data is list of floats before stacking + # This check might be redundant if VectorQueryLoader guarantees cleaned data, + # but it adds safety. + if not all(isinstance(x, list) and all(isinstance(n, float) for n in x) for x in df[embedding_column]): + logger.error(f"Data in '{embedding_column}' is not strictly list[float] format after fetching. Attempting conversion.") + # This might catch issues the loader missed or unexpected data structures + try: + # Attempt robust conversion similar to the loader's parse method + embeddings_list = [] + for item in df[embedding_column]: + parsed_item = None + if isinstance(item, str): + try: parsed_item = json.loads(item) + except json.JSONDecodeError: pass + elif isinstance(item, (list, tuple, np.ndarray)): + parsed_item = item + elif isinstance(item, dict) and 'vector' in item and isinstance(item['vector'], (list, tuple, np.ndarray)): + parsed_item = item['vector'] + + if isinstance(parsed_item, (list, tuple, np.ndarray)) and all(isinstance(val, (int, float, np.number)) for val in parsed_item): + embeddings_list.append([float(n) for n in parsed_item]) + else: + logger.debug(f"Skipping problematic embedding during secondary clean: {str(item)[:100]}...") + + + if not embeddings_list: + logger.error("No valid embeddings remained after secondary cleaning.") + raise ValueError("No valid embeddings for stacking.") + embeddings = np.array(embeddings_list, dtype=float) + logger.warning("Successfully converted problematic embedding data for stacking.") + except Exception as e: + logger.exception(f"Failed secondary attempt to convert embeddings for stacking: {e}") + raise RuntimeError(f"Failed to process embedding data for stacking: {e}") from e + else: + # Data is in the expected list of float format, proceed directly + embeddings = np.stack(df[embedding_column].to_list()).astype(float) + + logger.info("Prepared %d embeddings for clustering.", embeddings.shape[0]) + + + except ValueError as ve: + logger.exception(f"Failed to stack embeddings into a numpy array: {ve}. Ensure '{embedding_column}' contains valid vector data.") + raise RuntimeError(f"Failed to process embedding data: {ve}") from ve + except Exception as e: + logger.exception(f"An unexpected error occurred while processing '{embedding_column}' column for stacking.") + raise RuntimeError(f"An unexpected error occurred while processing embedding data for stacking: {e}") from e + # ------------------------------------------------------------- + + + # --------------------------------------------------------------------------- + # Prepare training sample and determine effective k + # --------------------------------------------------------------------------- + # Use the parameter train_sample_size + train_vecs = embeddings[:train_sample_size] + + if train_vecs.shape[0] == 0: + # If course_id filtering resulted in 0 vectors, this check prevents the crash + # but the df.empty check earlier should already handle this. + # Keep this check for robustness in case train_sample_size is 0 or negative. + logger.error("Training sample is empty – nothing to cluster.") + raise RuntimeError("Training sample is empty – nothing to cluster.") + + # Use the parameter k_clusters + K = min(k_clusters, train_vecs.shape[0]) + if K < k_clusters: + logger.warning( + "Requested k=%d but only %d training vectors available; " + "using k=%d.", + k_clusters, + train_vecs.shape[0], + K, + ) + # Ensure K is at least 1 if there's any data + if K == 0 and train_vecs.shape[0] > 0: + K = 1 + logger.warning("Adjusted k to 1 as requested k resulted in 0 but data exists.") + + if K == 0: + # If after adjustments K is still 0 (meaning train_vecs.shape[0] was 0) + logger.error("Effective k is 0. Cannot train k-means.") + raise RuntimeError("Effective k is 0. Cannot train k-means.") + + + logger.info("Training k‑means (k=%d) on %d vectors", K, train_vecs.shape[0]) + + try: + kmeans = KMeans( + n_clusters=K, + init="k-means++", + n_init="auto", # Use 'auto' for better handling of small k/n_samples + algorithm="lloyd", # 'lloyd' is the standard, 'elkan' can be faster but has limitations + max_iter=300, + random_state=0, + ) + kmeans.fit(train_vecs) + centroids: np.ndarray = kmeans.cluster_centers_ + logger.info("K‑means converged in %d iterations", kmeans.n_iter_) + except Exception as e: + logger.exception("K-means clustering failed.") + raise RuntimeError(f"K-means clustering failed: {e}") from e + + + # --------------------------------------------------------------------------- + # Assign every vector to its nearest centroid (full table) + # --------------------------------------------------------------------------- + logger.info("Assigning vectors to centroids...") + try: + # Use the determined embedding column for assignment as well + labels_full, _ = pairwise_distances_argmin_min(embeddings, centroids, metric="euclidean") + df["cluster_id"] = labels_full + logger.info("Assigned cluster labels to all embeddings.") + except Exception as e: + logger.exception("Failed to assign vectors to centroids.") + raise RuntimeError(f"Failed to assign vectors to centroids: {e}") from e + + # --------------------------------------------------------------------------- + # Write one Markdown file per cluster + # --------------------------------------------------------------------------- + files_written_count = 0 + + logger.info("Writing cluster Markdown files to %s", output_path) + try: + # Only iterate up to the number of actual clusters found by KMeans + # KMeans might return fewer clusters than K if there are issues or identical points + num_actual_clusters = len(centroids) + if num_actual_clusters < K: + logger.warning(f"KMeans returned only {num_actual_clusters} centroids, expected {K}. Iterating over actual centroids.") + + + for cid in range(num_actual_clusters): # Iterate over actual cluster IDs + # Find all data points assigned to this cluster ID + subset = df[df.cluster_id == cid] + + # Ensure centroid_vec corresponds to the centroid of the *current* cluster ID (cid) + # This check is more robust now iterating up to num_actual_clusters + if cid < len(centroids): + centroid_vec = centroids[cid] + else: + # This case should theoretically not be reached with the loop range + logger.error(f"Centroid for cluster ID {cid} missing! Using zero vector.") + centroid_vec = np.zeros(embeddings.shape[1]) + + + # Use .get() and .fillna("") defensively in case 'chunk' column is missing + # Ensure chunk column exists - it should if SELECT * worked + if 'chunk' not in subset.columns: + logger.warning("'chunk' column missing in subset data for cluster %d. Using empty strings.", cid) + chunks = [""] * len(subset) + else: + chunks = subset['chunk'].fillna("").tolist() + + + md_lines = [ + #"---", + #f"cluster_id: {cid}", + #f"centroid: {centroid_to_json(centroid_vec)}", + #"---\n", # Separator between frontmatter and content + ] + # Add chunks, ensuring each chunk is on a new line or separated by blank lines + md_lines.extend(chunks) + + outfile = output_path / f"cluster_{cid:03d}.md" + # Use a different separator for chunks within the file if needed, + # currently just joins with newline, but chunks might contain newlines. + # Joining with "\n\n" provides separation *between* chunks. + try: + outfile.write_text("\n\n".join(md_lines), encoding="utf-8") + files_written_count += 1 + logger.debug("Wrote %s (%d chunks)", outfile.name, len(chunks)) # Use debug for per-file + except Exception as write_exc: + logger.error(f"Failed to write cluster file {outfile}: {write_exc}", exc_info=True) + # Decide whether to continue or raise here. Continuing allows other clusters to be saved. + # For robustness in script, maybe continue. For library function, maybe raise. + # For now, we'll just log and continue. + + + except Exception as e: + logger.exception("Failed during Markdown file writing loop.") + raise RuntimeError(f"Failed during Markdown file writing: {e}") from e + + + logger.info("Done. %d Markdown files created in %s", files_written_count, output_path) + return output_path + + +# --------------------------------------------------------------------------- +# Script entry point +# --------------------------------------------------------------------------- +if __name__ == "__main__": + # Configuration via environment for script + script_output_dir = Path(os.environ.get("OUTPUT_DIR", "./cluster_md")).expanduser() + script_schema = os.environ.get("VECTOR_SCHEMA", "librarian") + script_rpc_function = os.environ.get("VECTOR_FUNCTION", "pdf_chunking") # Default to actual function name + script_model = os.environ.get("EMBED_MODEL", "snowflake-arctic-embed2") + script_k_req = int(os.environ.get("K", "128")) + script_train_sample = int(os.environ.get("TRAIN_SAMPLE", "20000")) + # Added course ID specific to script entry point + script_course_id_str = os.environ.get("CLUSTER_COURSE_ID") + script_course_id = int(script_course_id_str) if script_course_id_str and script_course_id_str.isdigit() else None # Added isdigit check + + + # Configure basic logging for the script entry point + # (The module-level config above might not run if imported in specific ways) + logging.basicConfig( + level=logging.INFO, + format="%(asctime)s %(levelname)s: %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + ) + # Re-get the logger after basicConfig to ensure it's configured + logger = logging.getLogger(__name__) + + + try: + logger.info("Starting cluster export script...") + final_output_path = run_cluster_export_job( + course_id=script_course_id, + output_dir=script_output_dir, + schema=script_schema, + rpc_function=script_rpc_function, + model=script_model, + k_clusters=script_k_req, + train_sample_size=script_train_sample, + # embedding_column defaults to 'embedding' in the function + ) + logger.info("Script finished successfully. Output in %s", final_output_path) + sys.exit(0) # Explicit success exit + # --- FIX: Catch VectorQueryLoaderError --- + except (VectorQueryLoaderError, RuntimeError) as e: # Catch the new error type + # --- END FIX --- + # Specific errors we raised + logger.error("Script failed: %s", e) + sys.exit(1) # Indicate failure + except Exception as e: + # Catch any other unexpected errors + logger.exception("An unhandled error occurred during script execution.") + sys.exit(1) # Indicate failure \ No newline at end of file diff --git a/librarian/plugins/librarian-vspace/src/librarian_vspace/vquery/cluster_export_worker.py b/librarian/plugins/librarian-vspace/src/librarian_vspace/vquery/cluster_export_worker.py new file mode 100644 index 0000000..7d50b22 --- /dev/null +++ b/librarian/plugins/librarian-vspace/src/librarian_vspace/vquery/cluster_export_worker.py @@ -0,0 +1,73 @@ + +"""ClusterExportWorker – Prefect worker that wraps run_cluster_export_job.""" + +from __future__ import annotations +import asyncio +import logging +import tempfile +from pathlib import Path +from typing import Optional + +from prefect import get_run_logger +from pydantic import BaseModel +from librarian_core.workers.base import Worker + +from librarian_vspace.vquery.cluster_export import run_cluster_export_job + +def _safe_get_logger(name: str): + try: + return get_run_logger() + except Exception: + return logging.getLogger(name) + +class ClusterExportInput(BaseModel): + course_id: int + k_clusters: int = 128 + train_sample_size: int = 20_000 + db_schema: str = "librarian" + rpc_function: str = "pdf_chunking" + model: str = "snowflake-arctic-embed2" + embedding_column: str = "embedding" + base_output_dir: Optional[Path] = None + +class ClusterExportOutput(BaseModel): + output_dir: Path + +class ClusterExportWorker(Worker[ClusterExportInput, ClusterExportOutput]): + input_model = ClusterExportInput + output_model = ClusterExportOutput + + async def __run__(self, payload: ClusterExportInput) -> ClusterExportOutput: + logger = _safe_get_logger(self.worker_name) + logger.info("🔨 %s startet (payload=%r)", self.worker_name, payload) + + # Prepare output directory + if payload.base_output_dir: + base_dir = Path(payload.base_output_dir).expanduser() + base_dir.mkdir(parents=True, exist_ok=True) + tmp_base = tempfile.mkdtemp(dir=base_dir) + else: + tmp_base = tempfile.mkdtemp() + + output_dir = Path(tmp_base) / str(payload.course_id) + output_dir.mkdir(parents=True, exist_ok=True) + + logger.debug("Output directory: %s", output_dir) + + final_dir = await asyncio.to_thread( + run_cluster_export_job, + course_id=payload.course_id, + output_dir=output_dir, + schema=payload.db_schema, + rpc_function=payload.rpc_function, + model=payload.model, + k_clusters=payload.k_clusters, + train_sample_size=payload.train_sample_size, + embedding_column=payload.embedding_column, + ) + + self.stage(final_dir, new_name=final_dir.name) + + result = ClusterExportOutput(output_dir=final_dir) + logger.info("✅ %s fertig: %r", self.worker_name, result) + return result diff --git a/librarian/plugins/librarian-vspace/src/librarian_vspace/vquery/py.typed b/librarian/plugins/librarian-vspace/src/librarian_vspace/vquery/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/librarian/plugins/librarian-vspace/src/librarian_vspace/vquery/query.py b/librarian/plugins/librarian-vspace/src/librarian_vspace/vquery/query.py new file mode 100644 index 0000000..c0f4b4e --- /dev/null +++ b/librarian/plugins/librarian-vspace/src/librarian_vspace/vquery/query.py @@ -0,0 +1,127 @@ + +"""VectorQuery – helper for vector searches against chunklet tables. + +This module provides: +* A Pydantic‑powered request / response API (see ``librarian_vspace.models.query_model``). +* A single public method :py:meth:`VectorQuery.search` that returns a + :class:`~librarian_vspace.models.query_model.VectorSearchResponse`. +* A thin legacy wrapper ``get_chucklets_by_vector`` that produces the + historical ``List[Dict[str, Any]]`` format, built on top of ``search``. +""" + +from __future__ import annotations + +import logging +from typing import Any, Dict, List, Optional + +try: + from librarian_vspace.vutils.vector_class import BaseVectorOperator + from librarian_vspace.vecembed.embedding_generator import EmbeddingGenerator +except ImportError as exc: # pragma: no cover + logging.error( + "Failed to import vutils or vecembed sub‑packages: %s. " "Ensure they are on PYTHONPATH.", exc + ) + + class BaseVectorOperator: # type: ignore + """Minimal stub if real class is unavailable (runtime error later).""" + + class EmbeddingGenerator: # type: ignore + """Minimal stub; will raise at runtime if used.""" + +from librarian_vspace.models.query_model import ( + VectorSearchRequest, + VectorSearchResponse, + Chunklet, +) + +logger = logging.getLogger(__name__) + + +# --------------------------------------------------------------------- # +# Main helper +# --------------------------------------------------------------------- # +class VectorQuery(BaseVectorOperator): + """High‑level helper for vector searches via Supabase RPC.""" + + # ----------------------------------------------------------------- + # Public – modern API + # ----------------------------------------------------------------- + def search(self, request: VectorSearchRequest) -> VectorSearchResponse: + """Perform a similarity search and return structured results.""" + + if not getattr(self, "table", None): + logger.error("VectorQuery: target table not determined (self.table is None).") + return VectorSearchResponse(total=0, results=[]) + + # 1) Generate query embedding + try: + _tts, query_vec, _ = EmbeddingGenerator().generate_embedding( + interface_name=request.interface_name, + model_name=request.model_name, + text_to_embed=request.search_string, + identifier="query", + ) + if query_vec is None: + logger.error("Embedding generation returned None.") + return VectorSearchResponse(total=0, results=[]) + except Exception as exc: # pragma: no cover + logger.exception("Embedding generation failed: %s", exc) + return VectorSearchResponse(total=0, results=[]) + + # 2) Build RPC parameters + rpc_params = { + "p_query_embedding": query_vec, + "p_target_table": self.table, + "p_embedding_column": request.embedding_column, + "p_match_count": request.top_k, + "p_filters": request.filters or {}, + } + + # 3) Execute RPC + try: + if not getattr(self, "spc", None): + logger.error("Supabase client (self.spc) not available.") + return VectorSearchResponse(total=0, results=[]) + + resp = ( + self.spc + .schema(self.schema) + .rpc("vector_search", rpc_params) + .execute() + ) + data = resp.data or [] + results = [ + Chunklet(chunk=row.get("chunk"), file_id=row.get("file_id")) if isinstance(row.get("file_id"), str) else Chunklet(chunk=row.get("chunk"), file_id=str(row.get("file_id"))) + for row in data + ] + return VectorSearchResponse(total=len(results), results=results) + + except Exception as exc: # pragma: no cover + logger.exception("RPC 'vector_search' failed: %s", exc) + return VectorSearchResponse(total=0, results=[]) + + # ----------------------------------------------------------------- + # Public – legacy compatibility + # ----------------------------------------------------------------- + def get_chucklets_by_vector( + self, + *, + interface_name: str, + model_name: str, + search_string: str, + filters: Optional[Dict[str, Any]] = None, + top_k: int = 10, + embedding_column: str = "embedding", + ) -> List[Dict[str, Any]]: + """Backward‑compatible wrapper returning ``{'chunk', 'file_id'}`` dicts.""" + + req = VectorSearchRequest( + interface_name=interface_name, + model_name=model_name, + search_string=search_string, + filters=filters, + top_k=top_k, + embedding_column=embedding_column, + ) + resp = self.search(req) + return [ck.dict() for ck in resp.results] diff --git a/librarian/plugins/librarian-vspace/src/librarian_vspace/vquery/query_worker.py b/librarian/plugins/librarian-vspace/src/librarian_vspace/vquery/query_worker.py new file mode 100644 index 0000000..8154fa2 --- /dev/null +++ b/librarian/plugins/librarian-vspace/src/librarian_vspace/vquery/query_worker.py @@ -0,0 +1,62 @@ + +"""QueryWorker – Prefect worker that performs a vector search. + +It instantiates VectorQuery directly (no vspace dependency) and returns the +VectorSearchResponse. +""" + +from __future__ import annotations + +import asyncio +import logging +from pathlib import Path + +from prefect import get_run_logger +from pydantic import BaseModel +from librarian_core.workers.base import Worker + +from librarian_vspace.vquery.query import VectorQuery +from librarian_vspace.models.query_model import VectorSearchRequest, VectorSearchResponse + +def _safe_get_logger(name: str): + try: + return get_run_logger() + except Exception: + return logging.getLogger(name) + + +class QueryInput(BaseModel): + request: VectorSearchRequest + db_schema: str = "librarian" + rpc_function: str = "pdf_chunking" + embed_model: str = "snowflake-arctic-embed2" + embedding_column: str = "embedding" + + +class QueryWorker(Worker[QueryInput, VectorSearchResponse]): + """Runs a Supabase vector search via VectorQuery.""" + + input_model = QueryInput + output_model = VectorSearchResponse + + async def __run__(self, payload: QueryInput) -> VectorSearchResponse: + logger = _safe_get_logger(self.worker_name) + logger.info("🔨 %s startet (payload=%r)", self.worker_name, payload) + + def _do_search() -> VectorSearchResponse: + try: + vq = VectorQuery( + schema=payload.db_schema, + function=payload.rpc_function, + model=payload.embed_model, + embedding_column=payload.embedding_column, + ) + except TypeError: + # fallback to positional signature + vq = VectorQuery(payload.db_schema, payload.rpc_function, payload.embed_model) + return vq.search(payload.request) + + response = await asyncio.to_thread(_do_search) + + logger.info("✅ %s fertig: %s results", self.worker_name, response.total) + return response diff --git a/librarian/plugins/librarian-vspace/src/librarian_vspace/vutils/__init__.py b/librarian/plugins/librarian-vspace/src/librarian_vspace/vutils/__init__.py new file mode 100644 index 0000000..e00b59d --- /dev/null +++ b/librarian/plugins/librarian-vspace/src/librarian_vspace/vutils/__init__.py @@ -0,0 +1,2 @@ +def hello() -> str: + return "Hello from librarian_vspace!" diff --git a/librarian/plugins/librarian-vspace/src/librarian_vspace/vutils/parallelism_advisor.py b/librarian/plugins/librarian-vspace/src/librarian_vspace/vutils/parallelism_advisor.py new file mode 100644 index 0000000..9829348 --- /dev/null +++ b/librarian/plugins/librarian-vspace/src/librarian_vspace/vutils/parallelism_advisor.py @@ -0,0 +1,248 @@ + +#!/usr/bin/env python3 +"""parallelism_advisor.py +===================================== +Cross‑platform helper to inspect the hardware parallelism available on the +current machine *and* to give rule‑of‑thumb recommendations for the number of +workers you should spin up for CPU‑bound, IO‑bound, or GPU workloads. + +Highlights +---------- +* Works on Linux, macOS (Intel **and** Apple Silicon) and Windows. +* Respects Docker / Kubernetes CPU quotas (cgroup v1 & v2) automatically. +* Falls back gracefully when optional dependencies (``psutil``, ``torch``) + are missing. +* Caches expensive hardware queries. +* Tiny CLI: ``python -m parallelism_advisor --json`` + +MIT‑licensed. Drop the file somewhere on ``$PYTHONPATH`` or +``pip install .`` if you convert it into a proper package. + +Usage +----- +>>> from librarian_vspace.vutils.parallelism_advisor import recommended_workers +>>> recommended_workers(kind="cpu") +8 +>>> recommended_workers(kind="io") +64 +""" + +from __future__ import annotations + +import argparse +import functools +import json +import os +import platform +import sys +from pathlib import Path +from typing import Any, Dict, Literal, Optional + +# --------------------------------------------------------------------------- # +# Optional dependencies # +# --------------------------------------------------------------------------- # +try: + import psutil # type: ignore +except ModuleNotFoundError: # pragma: no cover + psutil = None # type: ignore + +try: + import torch # type: ignore +except ModuleNotFoundError: # pragma: no cover + torch = None # type: ignore + +# --------------------------------------------------------------------------- # +# Hardware discovery helpers # +# --------------------------------------------------------------------------- # + + +@functools.lru_cache(maxsize=None) +def logical_cores() -> int: + """Return the number of *logical* CPUs visible to the process. + + Falls back to 1 if Python or the OS cannot determine the value. + """ + return os.cpu_count() or 1 + + +@functools.lru_cache(maxsize=None) +def physical_cores() -> int: + """Return the number of *physical* CPU cores (no SMT). + + If ``psutil`` is not installed or fails to determine the value, the function + falls back to ``logical_cores()``. + """ + if psutil is not None: + pc = psutil.cpu_count(logical=False) + if pc: # 0 or None means "could not determine" + return pc + # Heuristic fallback + return logical_cores() + + +def _read_first_line(path: Path) -> Optional[str]: + try: + return path.read_text().strip() + except FileNotFoundError: + return None + except Exception: # pragma: no cover + return None + + +@functools.lru_cache(maxsize=None) +def cgroup_cpu_limit() -> Optional[int]: + """Return Docker/Kubernetes CPU quota if present (else ``None``). + + Supports both cgroup v1 and v2. *Unit tests mock this function.* + """ + # --- cgroup v2 --------------------------------------------------------- + max_path = Path("/sys/fs/cgroup/cpu.max") + line = _read_first_line(max_path) + if line and " " in line: + quota, period = line.split()[:2] + try: + quota_i = int(quota) + period_i = int(period) + if quota_i > 0 and period_i > 0: + return max(1, quota_i // period_i) + except ValueError: # pragma: no cover + pass + + # --- cgroup v1 --------------------------------------------------------- + quota_path = Path("/sys/fs/cgroup/cpu/cpu.cfs_quota_us") + period_path = Path("/sys/fs/cgroup/cpu/cpu.cfs_period_us") + quota_str = _read_first_line(quota_path) + period_str = _read_first_line(period_path) + if quota_str and period_str: + try: + quota_i = int(quota_str) + period_i = int(period_str) + if quota_i > 0 and period_i > 0: + return max(1, quota_i // period_i) + except ValueError: # pragma: no cover + pass + return None + + +# --------------------------------------------------------------------------- # +# GPU helpers (CUDA only for now) # +# --------------------------------------------------------------------------- # + + +@functools.lru_cache(maxsize=None) +def gpu_info() -> Optional[Dict[str, Any]]: + """Return basic info for the first CUDA device via *torch* (or ``None``).""" + if torch is None or not torch.cuda.is_available(): + return None + try: + device = torch.cuda.get_device_properties(0) + except Exception: # pragma: no cover + return None + + return { + "device_name": device.name, + "sm_count": device.multi_processor_count, + "max_threads_per_sm": device.max_threads_per_multi_processor, + "total_memory_mb": device.total_memory // (1024 ** 2), + } + + +# --------------------------------------------------------------------------- # +# Recommendation logic # +# --------------------------------------------------------------------------- # + + +def recommended_workers( + *, + kind: Literal["cpu", "io", "gpu"] = "cpu", + io_factor: int = 4, + io_max: Optional[int] = 512, +) -> int: + """Return a *rule‑of‑thumb* worker count for the given workload *kind*. + + Parameters + ---------- + kind: + "cpu": CPU‑bound pure‑Python or C‑extension code (GIL matters) + "io" : Network / disk I/O where threads mostly block waiting + "gpu": GPU kernels (CUDA only, requires *torch*) + io_factor: + Multiplier for IO‑bound worker calculation (defaults to ``4``). + io_max: + Hard cap for IO‑bound workers (``None`` = no cap). Defaults to 512. + + The function always respects container CPU quotas when present. + """ + if kind == "cpu": + base = physical_cores() + elif kind == "io": + base = logical_cores() * io_factor + if io_max is not None: + base = min(base, io_max) + elif kind == "gpu": + info = gpu_info() + if info is None: + return 0 + # A very rough heuristic: one worker per SM + base = info["sm_count"] + else: # pragma: no cover + raise ValueError("kind must be 'cpu', 'io', or 'gpu'") from None + + quota = cgroup_cpu_limit() + if quota is not None and kind != "gpu": + return max(1, min(base, quota)) + return max(1, base) + + +# --------------------------------------------------------------------------- # +# Convenience snapshot of system info # +# --------------------------------------------------------------------------- # + + +def system_snapshot() -> Dict[str, Any]: + """Return a JSON‑serialisable snapshot of parallelism‑related facts.""" + return { + "platform_system": platform.system(), + "platform_machine": platform.machine(), + "logical_cores": logical_cores(), + "physical_cores": physical_cores(), + "docker_cpu_quota": cgroup_cpu_limit(), + "gpu": gpu_info(), + } + + +# --------------------------------------------------------------------------- # +# CLI # +# --------------------------------------------------------------------------- # + + +def _cli() -> None: # pragma: no cover + parser = argparse.ArgumentParser( + prog="parallelism_advisor", description="Rule‑of‑thumb worker estimator" + ) + parser.add_argument( + "--json", + action="store_true", + help="Print full hardware snapshot as JSON (default: pretty table)", + ) + parser.add_argument( + "--kind", + choices=["cpu", "io", "gpu"], + default="cpu", + help="Workload kind to estimate workers for (default: cpu)", + ) + args = parser.parse_args() + + if args.json: + print(json.dumps(system_snapshot(), indent=2)) + else: + snap = system_snapshot() + workers = recommended_workers(kind=args.kind) + print("✦ System snapshot:") + for k, v in snap.items(): + print(f" {k:20}: {v}") + print(f"\nRecommended {args.kind} workers: {workers}") + + +if __name__ == "__main__": # pragma: no cover + _cli() diff --git a/librarian/plugins/librarian-vspace/src/librarian_vspace/vutils/py.typed b/librarian/plugins/librarian-vspace/src/librarian_vspace/vutils/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/librarian/plugins/librarian-vspace/src/librarian_vspace/vutils/supabase_singleton.py b/librarian/plugins/librarian-vspace/src/librarian_vspace/vutils/supabase_singleton.py new file mode 100644 index 0000000..3dce379 --- /dev/null +++ b/librarian/plugins/librarian-vspace/src/librarian_vspace/vutils/supabase_singleton.py @@ -0,0 +1,50 @@ + +"""Singleton wrapper around the Supabase Python client. + +Lives in **vutils** because it is pure infrastructure that other +packages depend on (vecembed, vecmap, vquery, …). Nothing outside +vutils should create its own Supabase client – always import +:pyclass:`MySupabase` and call :pymeth:`MySupabase.get_client`. +""" +from __future__ import annotations + +import logging +import os +from typing import Optional + +from supabase import Client, create_client + +try: + from .config_utils import load_dotenv_from_root # type: ignore + load_dotenv_from_root() +except ModuleNotFoundError: + pass + +logger = logging.getLogger(__name__) + + +class MySupabase: + """A tiny singleton around :class:`supabase.Client`.""" + + _instance: Optional[Client] = None + + @classmethod + def _create_client(cls) -> Client: + url = os.getenv("VECTORBASE_URL") + key = os.getenv("VECTORBASE_API_KEY") or os.getenv("SUPABASE_SERVICE_ROLE_KEY") + if not url or not key: + logger.error("VECTORBASE_URL / VECTORBASE_API_KEY missing – check your env") + raise ValueError("Supabase credentials not configured") + logger.info("Creating Supabase client …") + return create_client(url, key) + + @classmethod + def get_client(cls) -> Client: + """Return a singleton client instance.""" + if cls._instance is None: + cls._instance = cls._create_client() + return cls._instance + + # convenience for ``MySupabase()`` + def __new__(cls) -> Client: # pragma: no cover + return cls.get_client() diff --git a/librarian/plugins/librarian-vspace/src/librarian_vspace/vutils/vector_class.py b/librarian/plugins/librarian-vspace/src/librarian_vspace/vutils/vector_class.py new file mode 100644 index 0000000..b1f31a5 --- /dev/null +++ b/librarian/plugins/librarian-vspace/src/librarian_vspace/vutils/vector_class.py @@ -0,0 +1,65 @@ + +"""Core utilities for resolving (schema, function, model) → table mapping.""" +from __future__ import annotations + +import logging +from typing import Optional + +from postgrest import APIResponse # type: ignore + +from librarian_vspace.vutils.supabase_singleton import MySupabase + +logger = logging.getLogger(__name__) + + +class BaseVectorOperator: + """Resolve mapping and expose *table_fqn()* for children.""" + + def __init__(self, schema: str, function: str, model: str): + self.spc = MySupabase.get_client() + self.schema = schema + self.function = function + self.model = model + self.model_id: Optional[int] = None + self.table: Optional[str] = None + self._resolve_ids() + + # ---------------- public helpers ---------------- + def table_fqn(self) -> str: + if not self.table: + raise RuntimeError("VectorOperator not initialised – no table") + return f"{self.schema}.{self.table}" + + # ---------------- internals ---------------- + def _resolve_ids(self) -> None: + self.model_id = self._rpc_get_model_id(self.model) + if self.model_id is None: + raise ValueError(f"Unknown model: {self.model}") + self.table = self._rpc_get_table(self.function, self.model_id) + if not self.table: + raise ValueError(f"Could not map (function={self.function}, model_id={self.model_id}) → table") + logger.info("VectorOperator ready → %s", self.table_fqn()) + + def _rpc_get_model_id(self, model: str) -> Optional[int]: + try: + resp: APIResponse = self.spc.schema(self.schema).rpc( + "get_model_id_by_model", {"p_model": model} + ).execute() + logger.debug("get_model_id_by_model → %s", resp.data) + return int(resp.data) if resp.data not in (None, "") else None + except Exception: # noqa: BLE001 + logger.exception("RPC get_model_id_by_model failed for %s", model) + return None + + def _rpc_get_table(self, function: str, model_id: int) -> Optional[str]: + try: + resp: APIResponse = self.spc.schema(self.schema).rpc( + "get_table_by_model", {"p_function": function, "p_model_library_id": model_id} + ).execute() + logger.debug("get_table_by_model → %s", resp.data) + if isinstance(resp.data, str): + return resp.data.strip() or None + return None + except Exception: # noqa: BLE001 + logger.exception("RPC get_table_by_model failed") + return None diff --git a/librarian/plugins/librarian-vspace/src/librarian_vspace/vutils/vector_query_loader.py b/librarian/plugins/librarian-vspace/src/librarian_vspace/vutils/vector_query_loader.py new file mode 100644 index 0000000..7c7a225 --- /dev/null +++ b/librarian/plugins/librarian-vspace/src/librarian_vspace/vutils/vector_query_loader.py @@ -0,0 +1,413 @@ +# --- START OF FILE librarian_vspace/vutils/vector_query_loader.py (Fixed Again) --- + +"""Loads vectors from Supabase, with filtering capabilities, and handles parsing. +Intended for querying/clustering tasks where filtering is needed. +""" +from __future__ import annotations + +import logging +import json +from typing import Any, Dict, List, Optional, Tuple, Union # Added Union + +import numpy as np +import pandas as pd +# You might not need TSNE here if this class is only for fetching, +# but keeping it for generality as requested. +from sklearn.manifold import TSNE + +# Assuming vutils is installed or in the python path +try: + from librarian_vspace.vutils.vector_class import BaseVectorOperator +except ImportError as e: + logging.error(f"Failed to import vutils: {e}. Ensure vutils package is installed.") + raise + +logger = logging.getLogger(__name__) + +# Define a specific error for this loader +class VectorQueryLoaderError(Exception): + """Custom exception for VectorQueryLoader errors.""" + pass + + +class VectorQueryLoader: + """Fetches vectors with filtering capabilities.""" + + # --- Define the constant for the course ID column name --- + COURSE_ID_COLUMN_NAME = "course_id" + # ---------------------------------------------------------- + + # Keep TSNE defaults for generality, though perhaps not used by clustering logic + DEFAULT_TSNE_PARAMS = { + "n_components": 3, + "perplexity": 30.0, # Adjust based on dataset size (5-50 typically) + "n_iter": 1000, # Minimum recommended iterations + "learning_rate": "auto", # Usually a good default + "init": "pca", # PCA initialization is often faster and more stable + "random_state": 42, # For reproducibility + "n_jobs": -1, # Use all available CPU cores + "verbose": 1, # Log progress (controls scikit-learn's verbosity) + } + + + def __init__(self, schema: str, function: str, model: str, embedding_column: str = "embedding"): + """ + Initializes the loader. + (Constructor remains the same) + """ + logger.info(f"Initializing VectorQueryLoader for {schema=}, {function=}, {model=}") + try: + self.operator = BaseVectorOperator(schema=schema, function=function, model=model) + self.embedding_column = embedding_column + if not self.operator.table: + raise VectorQueryLoaderError("BaseVectorOperator failed to resolve table.") + logger.info(f"Target table resolved to: {self.operator.table_fqn()}") + except (ImportError, ValueError, RuntimeError) as e: + logger.exception("Failed to initialize BaseVectorOperator.") + # Raise the specific error for this loader + raise VectorQueryLoaderError(f"Failed to initialize BaseVectorOperator: {e}") from e + + + def _parse_vector_string(self, vector_str: Any) -> Optional[List[float]]: + """Safely parses the string representation of a vector into a list of floats.""" + if not isinstance(vector_str, str): + # If it's already a list, tuple, or numpy array, assume it's valid vector data + if isinstance(vector_str, (list, tuple, np.ndarray)) and all(isinstance(n, (int, float, np.number)) for n in vector_str): + return [float(n) for n in vector_str] # Ensure elements are floats + # Handle potential dict representation of vector (e.g., {"vector": [...]}) + if isinstance(vector_str, dict) and 'vector' in vector_str and isinstance(vector_str['vector'], (list, tuple, np.ndarray)): + if all(isinstance(n, (int, float, np.number)) for n in vector_str['vector']): + return [float(n) for n in vector_str['vector']] + logger.debug(f"Unexpected type for vector parsing: {type(vector_str)}. Value starts with: {str(vector_str)[:50]}. Skipping.") + return None + + try: + # Use json.loads which correctly handles [...] syntax from pgvector/Supabase + parsed_list = json.loads(vector_str) + if isinstance(parsed_list, list) and all(isinstance(n, (int, float)) for n in parsed_list): + return [float(n) for n in parsed_list] # Ensure elements are floats + else: + logger.warning(f"Parsed vector string '{vector_str[:50]}...' but result is not a list of numbers. Result type: {type(parsed_list)}") + return None + except json.JSONDecodeError: + logger.warning(f"Failed to JSON decode vector string: '{vector_str[:50]}...'") + return None + except Exception as e: + logger.error(f"Unexpected error parsing vector string '{vector_str[:50]}...': {e}", exc_info=True) + return None + + + def fetch_vectors(self, limit: Optional[int] = None, course_id: Optional[int] = None) -> pd.DataFrame: + """ + Fetches vectors and metadata from the resolved table, optionally filtered by course_id. + Parses string representations of vectors into lists. + + Args: + limit: Optional limit on the number of rows to fetch (for large tables). + course_id: Optional ID to filter vectors by the COURSE_ID_COLUMN_NAME. + + Returns: + A pandas DataFrame with columns like 'file_id', 'chunk', 'course_id', + and the embedding_column (as list). Includes all columns found if not specified. + + Raises: + VectorQueryLoaderError: If fetching fails or no data is found after filtering. + """ + if not self.operator.table or not self.operator.schema: + raise VectorQueryLoaderError("Operator not initialized, table name or schema is unknown.") + + table_name = self.operator.table + schema_name = self.operator.schema + # Select all columns (*) to ensure we get 'course_id' and 'chunk' if they exist + select_columns = "*" # Changed from fixed columns + + logger.info(f"Fetching data from {schema_name}.{table_name} (selecting: {select_columns})...") + try: + query = self.operator.spc.schema(schema_name).table(table_name).select(select_columns) + + # --- Add filtering logic using the constant --- + if course_id is not None: + logger.info(f"Filtering by {self.COURSE_ID_COLUMN_NAME}: {course_id}") + # This will raise a Supabase API error if the column doesn't exist, which is caught below + query = query.eq(self.COURSE_ID_COLUMN_NAME, course_id) + # ----------------------------------------------------------------- + + if limit: + logger.info(f"Applying limit: {limit}") + query = query.limit(limit) + + # Execute the query + # --- REMOVE THIS LINE: query.url does not exist --- + # logger.debug(f"Executing query: {query.url}") # Log the actual URL for debugging + # --- END REMOVE --- + response = query.execute() + + if not response.data: + logger.warning(f"No data found in table {self.operator.table_fqn()} matching criteria (course_id={course_id}).") + # Return empty DataFrame with expected key columns for consistency + # Use the columns from the initial DataFrame if available, otherwise assume minimal + cols_if_empty = ['file_id', 'chunk', self.COURSE_ID_COLUMN_NAME, self.embedding_column] + # Try to infer column dtypes from potential previous successful fetches or assume basic types + return pd.DataFrame(columns=cols_if_empty) # Simple approach for empty case + + logger.info(f"Fetched {len(response.data)} rows.") + df = pd.DataFrame(response.data) + + # --- Parse the embedding string into a list --- + logger.info(f"Parsing string representation in '{self.embedding_column}' column...") + if self.embedding_column not in df.columns: + logger.warning(f"Embedding column '{self.embedding_column}' not found in fetched data. Cannot parse vectors.") + # Return the DataFrame as is, but log the issue + # You might want to drop rows missing the embedding column here if they shouldn't exist + else: + # Apply parsing only if the column exists + parsed_embeddings = df[self.embedding_column].apply(self._parse_vector_string) + # Overwrite the original string column with the parsed list (or None if parsing failed) + df[self.embedding_column] = parsed_embeddings + logger.debug(f"Sample '{self.embedding_column}' data after parsing (first 5 rows):\n{df[[self.embedding_column]].head()}") + + # === Enhanced Debugging and Filtering for Embedding Column === + logger.info(f"Checking validity of parsed '{self.embedding_column}' column...") + if self.embedding_column not in df.columns: + # If column wasn't found and parsing wasn't attempted + raise VectorQueryLoaderError(f"Required embedding column '{self.embedding_column}' missing in fetched data.") + + # 1. Check for NULLs (includes rows where parsing failed and returned None) + initial_count = len(df) # Count *before* filtering + null_mask = df[self.embedding_column].isnull() + null_count = null_mask.sum() + if null_count > 0: + logger.warning(f"Found {null_count} rows with NULL or unparsable vectors in '{self.embedding_column}'. These will be dropped.") + + df_no_nulls = df.dropna(subset=[self.embedding_column]).copy() # Use copy after dropna + count_after_null_drop = len(df_no_nulls) + if count_after_null_drop < initial_count: + logger.debug(f"{count_after_null_drop} rows remaining after dropping NULLs/unparsable.") + + # 2. Check for non-empty list type (Ensures parsed data is correct) + if not df_no_nulls.empty: + def is_valid_list_of_floats(x): + return isinstance(x, list) and len(x) > 0 and all(isinstance(n, float) for n in x) + + valid_list_mask = df_no_nulls[self.embedding_column].apply(is_valid_list_of_floats) + invalid_list_count = len(df_no_nulls) - valid_list_mask.sum() + + if invalid_list_count > 0: + # This indicates an issue with the parsing logic or unexpected data format + logger.error(f"Found {invalid_list_count} rows where '{self.embedding_column}' is not a non-empty list of floats *after parsing*. These will be dropped.") + # Optional: log examples of problematic entries here if needed + # invalid_entries = df_no_nulls[~valid_list_mask][self.embedding_column] + # for i, entry in enumerate(invalid_entries.head(5)): + # logger.debug(f" Problematic entry example {i+1}: Type={type(entry)}, Value='{str(entry)[:100]}...'") + + + df_filtered_embeddings = df_no_nulls[valid_list_mask].copy() # Use copy after filtering + else: + df_filtered_embeddings = df_no_nulls # Still empty + + final_count = len(df_filtered_embeddings) + # === End Enhanced Debugging === + + if final_count == 0 and initial_count > 0: + # This happens if all rows were filtered out due to invalid embeddings + logger.error(f"All {initial_count} rows were filtered out due to missing/invalid '{self.embedding_column}'. Check raw data and parsing.") + # Return empty DF, but potentially raise a more critical error if this is unexpected + # raise VectorQueryLoaderError(f"No valid embedding data found after processing. All {initial_count} rows were invalid.") + # Ensure empty DF has expected columns + # Try to preserve the original columns found before dropping + cols_to_keep = [col for col in df.columns if col != self.embedding_column] + [self.embedding_column] + return pd.DataFrame(columns=cols_to_keep) # Return empty DataFrame with original columns + + + if final_count < initial_count: + logger.warning(f"Filtered out {initial_count - final_count} rows total due to missing/invalid '{self.embedding_column}'.") + + logger.info(f"Proceeding with {final_count} valid rows.") + # Use the DataFrame *after* filtering out invalid embeddings + df_result = df_filtered_embeddings + + # Validate and potentially add placeholder metadata columns AFTER filtering + # Use .get() for robustness if columns aren't always present + required_metadata_cols = ['file_id', 'chunk', self.COURSE_ID_COLUMN_NAME] + for col in required_metadata_cols: + if col not in df_result.columns: + # If filtering by course_id was requested and successful, + # the COURSE_ID_COLUMN_NAME must exist. If not, it's an error. + if col == self.COURSE_ID_COLUMN_NAME and course_id is not None: + logger.error(f"Required column '{col}' missing in fetched data despite successful filtering. This is unexpected.") + # Raise a critical error as filtering implies this column should exist + raise VectorQueryLoaderError(f"Required column '{col}' missing in fetched data.") + else: + logger.warning(f"Required metadata column '{col}' missing in fetched data.") + # Add a placeholder column of appropriate type if needed downstream + if col == 'file_id': + df_result['file_id'] = df_result.index.astype(str) # Use index as string ID + elif col == 'chunk': + df_result['chunk'] = "" # Empty string placeholder + elif col == self.COURSE_ID_COLUMN_NAME and course_id is None: + # Only add None if we weren't filtering and the column is missing + df_result[self.COURSE_ID_COLUMN_NAME] = pd.Series([None] * len(df_result), index=df_result.index, dtype=int) # Or object + + # Reorder columns for consistency if desired, e.g., [file_id, chunk, course_id, embedding, other_cols] + # Example reordering (optional): + # cols = required_metadata_cols + [col for col in df_result.columns if col not in required_metadata_cols] + # # Ensure embedding column is towards the end if needed + # if self.embedding_column in cols: + # cols.remove(self.embedding_column) + # cols.append(self.embedding_column) + # # Handle duplicate column names if SELECT * returned duplicates (unlikely but possible) + # df_result = df_result[list(dict.fromkeys(cols))] # Use dict to preserve order and get unique columns + + + return df_result # Return the cleaned and validated DataFrame + + except Exception as e: + logger.exception(f"Failed to fetch data from {self.operator.table_fqn()}.") + if 'relation' in str(e) and 'does not exist' in str(e): + raise VectorQueryLoaderError(f"Table/Relation not found error: {e}. Check schema/table name and permissions.") from e + # Catch the specific Supabase/PostgREST error if the column for filtering doesn't exist + elif 'column' in str(e).lower() and self.COURSE_ID_COLUMN_NAME.lower() in str(e).lower() and 'does not exist' in str(e).lower(): + raise VectorQueryLoaderError(f"Filtering column '{self.COURSE_ID_COLUMN_NAME}' does not exist in table {self.operator.table_fqn()}. Query failed: {e}") from e + else: + raise VectorQueryLoaderError(f"Database query failed: {e}") from e + + + # You can keep or remove the TSNE methods depending on whether this + # loader should also handle visualization prep. If not, remove these. + # Keeping for "generalized" request, but they use fetch_vectors now. + + def reduce_dimensions(self, df: pd.DataFrame, tsne_params: Optional[Dict[str, Any]] = None) -> pd.DataFrame: + """ + Applies t-SNE to reduce embedding dimensions to 3D. + (Code remains the same as previous correct version, assuming it works) + """ + logger.info("Starting t-SNE dimensionality reduction...") + if df.empty: + logger.warning("Input DataFrame for reduce_dimensions is empty. Returning empty DataFrame.") + empty_df_with_cols = df.copy() + for col in ['x', 'y', 'z']: + if col not in empty_df_with_cols: + empty_df_with_cols[col] = pd.Series(dtype=float) + return empty_df_with_cols + + # Use the embedding_column name from the instance + embedding_col = self.embedding_column # Use local variable for clarity + if embedding_col not in df.columns: + # This should be caught by fetch_vectors, but defensive check + raise VectorQueryLoaderError(f"Embedding column '{embedding_col}' missing in DataFrame passed to reduce_dimensions.") + + # Ensure embeddings are lists of floats + try: + # Check if all entries in the embedding column are lists of floats + # (This should be true if fetch_vectors's parsing/filtering worked) + # If not strictly list[float], try robust conversion again + if not all(isinstance(x, list) and all(isinstance(n, float) for n in x) for x in df[embedding_col]): + logger.error(f"Data in '{embedding_col}' is not strictly list[float] format after fetching. Attempting conversion before stacking.") + # Attempt robust conversion similar to the loader's parse method + embeddings_list = [] + for item in df[embedding_col]: + parsed_item = None + if isinstance(item, str): + try: parsed_item = json.loads(item) + except json.JSONDecodeError: pass + elif isinstance(item, (list, tuple, np.ndarray)): + parsed_item = item + elif isinstance(item, dict) and 'vector' in item and isinstance(item['vector'], (list, tuple, np.ndarray)): + parsed_item = item['vector'] + + if isinstance(parsed_item, (list, tuple, np.ndarray)) and all(isinstance(val, (int, float, np.number)) for val in parsed_item): + embeddings_list.append([float(n) for n in parsed_item]) + else: + logger.debug(f"Skipping problematic embedding during secondary clean for t-SNE: {str(item)[:100]}...") + + + if not embeddings_list: + logger.error("No valid embeddings remained after secondary cleaning for t-SNE.") + # No embeddings to process, return empty DF with t-SNE cols + empty_df_with_cols = df.copy() + for col in ['x', 'y', 'z']: + if col not in empty_df_with_cols: + empty_df_with_cols[col] = pd.Series(dtype=float) + return empty_df_with_cols + + + embeddings = np.array(embeddings_list, dtype=float) + logger.warning("Successfully converted problematic embedding data for t-SNE stacking.") + else: + # Data is in the expected list of float format, proceed directly + embeddings = np.array(df[embedding_col].to_list(), dtype=float) + + except ValueError as ve: + logger.exception(f"Failed to convert embedding list to numeric numpy array for t-SNE: {ve}. Ensure '{embedding_col}' contains valid vector data.") + raise VectorQueryLoaderError(f"Data in '{embedding_col}' could not be converted to numeric vectors for t-SNE.") from ve + except Exception as e: + logger.exception(f"An unexpected error occurred while processing '{embedding_col}' column for t-SNE.") + raise VectorQueryLoaderError(f"An unexpected error occurred while processing embedding data for t-SNE: {e}") from e + + + if embeddings.ndim != 2: + raise VectorQueryLoaderError(f"Embedding data has unexpected dimensions for t-SNE: {embeddings.ndim} (expected 2). Shape: {embeddings.shape}") + + n_samples = embeddings.shape[0] + + if n_samples < 2: + logger.warning(f"Found only {n_samples} valid vector(s). t-SNE requires at least 2. Assigning default 3D coordinates.") + default_coords = [[0.0, 0.0, 0.0]] * n_samples + df_copy = df.copy() # Work on a copy + df_copy[['x', 'y', 'z']] = default_coords + return df_copy + + + logger.info(f"Applying t-SNE to {n_samples} vectors of dimension {embeddings.shape[1]}...") + + current_tsne_params = self.DEFAULT_TSNE_PARAMS.copy() + if tsne_params: + current_tsne_params.update(tsne_params) + logger.info(f"Using custom t-SNE params: {tsne_params}") + + # Adjust perplexity if sample size is too small + if n_samples <= current_tsne_params['perplexity']: + new_perplexity = max(5.0, float(n_samples - 1)) # Perplexity must be less than num samples + if new_perplexity < 5.0: # Ensure minimum perplexity if possible + if n_samples > 5: new_perplexity = 5.0 # if samples > 5, use 5 + else: new_perplexity = max(1.0, float(n_samples - 1)) # Otherwise just samples - 1 (min 1) + if current_tsne_params['perplexity'] != new_perplexity: # Only log if changed + logger.warning(f"Adjusting t-SNE perplexity from {current_tsne_params['perplexity']:.1f} " + f"to {new_perplexity:.1f} due to low sample count ({n_samples}).") + current_tsne_params['perplexity'] = new_perplexity + + # Adjust n_iter for potential large datasets if not already high + # This check is copied from your original loader, keep it consistent + # if n_samples * embeddings.shape[1] > 100000 and current_tsne_params['n_iter'] < 1000: + # logger.warning(f"Dataset size seems large, increasing t-SNE n_iter from {current_tsne_params['n_iter']} to 1000 for better convergence.") + # current_tsne_params['n_iter'] = 1000 + + + try: + logger.debug(f"Final t-SNE parameters: {current_tsne_params}") + tsne = TSNE(**current_tsne_params) + reduced_embeddings = tsne.fit_transform(embeddings) + + # Ensure the DataFrame index aligns with the embeddings if rows were dropped + df_copy = df.copy() # Work on a copy to avoid modifying the original index alignment issue if any + df_copy[['x', 'y', 'z']] = reduced_embeddings + logger.info("t-SNE reduction complete.") + return df_copy + + except Exception as e: + logger.exception("t-SNE dimensionality reduction failed.") + raise VectorQueryLoaderError(f"t-SNE failed: {e}") from e + + + # Keep load_and_reduce for generality, though clustering might not use it + def load_and_reduce(self, limit: Optional[int] = None, course_id: Optional[int] = None, tsne_params: Optional[Dict[str, Any]] = None) -> pd.DataFrame: + """Orchestrates fetching vectors (optionally filtered) and reducing dimensions.""" + logger.info("Starting vector load (filtered) and reduction process...") + # Use the fetch_vectors method which handles filtering + df_raw_filtered = self.fetch_vectors(limit=limit, course_id=course_id) + df_reduced = self.reduce_dimensions(df_raw_filtered, tsne_params=tsne_params) + logger.info("Vector load and reduction process finished.") + return df_reduced + + +# --- END OF FILE librarian_vspace/vutils/vector_query_loader.py --- \ No newline at end of file diff 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