diff --git a/backend/app.py b/backend/app.py index 3b7bb91..c27732e 100644 --- a/backend/app.py +++ b/backend/app.py @@ -16,6 +16,7 @@ from backend.routes.admin import router as admin_router from backend.routes.auth import router as auth_router from backend.routes.chat import router as chat_router from backend.routes.health import router as health_router +from backend.routes.rag import router as rag_router from backend.routes.users import router as users_router logger = logging.getLogger("agent_alpha") @@ -86,6 +87,7 @@ class AppBuilder: app.include_router(health_router) app.include_router(chat_router) app.include_router(users_router) + app.include_router(rag_router) return app diff --git a/backend/core/config.py b/backend/core/config.py index 0c1b468..102c17e 100644 --- a/backend/core/config.py +++ b/backend/core/config.py @@ -2,6 +2,9 @@ from __future__ import annotations +from pathlib import Path + +from pydantic import Field from pydantic_settings import BaseSettings, SettingsConfigDict @@ -30,6 +33,58 @@ class Settings(BaseSettings): database_url: str = "postgresql+asyncpg://agent_alpha:agent_alpha@localhost:5432/agent_alpha" valkey_url: str = "redis://localhost:6379/0" + # ── Milvus (Vector Database) ─────────────────────────────────────────── + + milvus_uri: str = "http://localhost:19530" + milvus_token: str = "" + + # ── Redis pub/sub (for RAG status SSE) ───────────────────────────────── + + redis_host: str = "localhost" + redis_port: int = 6379 + redis_db: int = 0 + + # ── Media & File Storage ─────────────────────────────────────────────── + + media_dir: str = "media" + max_upload_size_mb: int = 50 + + # ── RAG Parsing ──────────────────────────────────────────────────────── + + pdf_parser: str = "pymupdf" + + # ── Cross-Encoder Reranker ───────────────────────────────────────────── + + cross_encoder_model: str = "cross-encoder/ms-marco-MiniLM-L6-v2" + hf_token: str = "" + models_cache_dir: Path = Path.home() / ".cache" / "agent-alpha" / "models" + + # ── AI Configuration ─────────────────────────────────────────────────── + + ai_model: str = "gpt-4o" + rag_image_description_model: str = "" + + # ── RAG Settings (derived) ──────────────────────────────────────────── + + @property + def rag(self) -> object: + """Return a ``RAGSettings`` instance configured from application settings. + + Lazy import to avoid circular dependencies at module level. + """ + from backend.services.rag.config import RAGSettings + + return RAGSettings( + collection_name="documents", + chunk_size=512, + chunk_overlap=50, + chunking_strategy="recursive", + enable_hybrid_search=False, + enable_ocr=False, + enable_image_description=True, + image_description_model=self.rag_image_description_model, + ) + # ── Factories ────────────────────────────────────────────────────────── @classmethod diff --git a/backend/core/database.py b/backend/core/database.py index b097127..7a36916 100644 --- a/backend/core/database.py +++ b/backend/core/database.py @@ -45,6 +45,8 @@ from sqlalchemy.ext.asyncio import ( from backend.core.config import settings from backend.core.models import Base, User +# Import RAG models so they are registered with Base.metadata for create_all +from backend.db.models import RAGDocument, SyncLog, SyncSource, ChatFile # noqa: F401 # ── PostgreSQL (SQLAlchemy async) ────────────────────────────────────────── diff --git a/backend/core/exceptions.py b/backend/core/exceptions.py new file mode 100644 index 0000000..3081aa8 --- /dev/null +++ b/backend/core/exceptions.py @@ -0,0 +1,36 @@ +"""Custom exceptions for Agent Alpha.""" + +from __future__ import annotations + + +class AgentAlphaException(Exception): + """Base exception for application-specific errors.""" + + def __init__( + self, + message: str = "An error occurred", + details: dict | None = None, + ) -> None: + self.message = message + self.details = details or {} + super().__init__(self.message) + + @property + def status_code(self) -> int: + return 500 + + +class NotFoundError(AgentAlphaException): + """Raised when a requested resource does not exist.""" + + @property + def status_code(self) -> int: + return 404 + + +class BadRequestError(AgentAlphaException): + """Raised when the request is invalid.""" + + @property + def status_code(self) -> int: + return 400 diff --git a/backend/db/__init__.py b/backend/db/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/db/models/__init__.py b/backend/db/models/__init__.py new file mode 100644 index 0000000..343bcfc --- /dev/null +++ b/backend/db/models/__init__.py @@ -0,0 +1,8 @@ +"""SQLAlchemy ORM models for Agent Alpha.""" + +from backend.db.models.rag_document import RAGDocument +from backend.db.models.sync_log import SyncLog +from backend.db.models.sync_source import SyncSource +from backend.db.models.chat_file import ChatFile + +__all__ = ["RAGDocument", "SyncLog", "SyncSource", "ChatFile"] diff --git a/backend/db/models/chat_file.py b/backend/db/models/chat_file.py new file mode 100644 index 0000000..ee8843f --- /dev/null +++ b/backend/db/models/chat_file.py @@ -0,0 +1,45 @@ +"""Chat file ORM model.""" + +from __future__ import annotations + +import uuid +from datetime import datetime, timezone + +from sqlalchemy import DateTime, Integer, String, Text +from sqlalchemy.dialects.postgresql import UUID +from sqlalchemy.orm import Mapped, mapped_column + +from backend.core.models import Base + + +class ChatFile(Base): + """Tracks a file uploaded by a user in a chat session.""" + + __tablename__ = "chat_files" + + id: Mapped[uuid.UUID] = mapped_column( + UUID(as_uuid=True), primary_key=True, default=uuid.uuid4 + ) + user_id: Mapped[uuid.UUID] = mapped_column( + UUID(as_uuid=True), nullable=False + ) + filename: Mapped[str] = mapped_column(String(512), nullable=False) + mime_type: Mapped[str] = mapped_column(String(255), nullable=False, default="") + size: Mapped[int] = mapped_column(Integer, nullable=False, default=0) + storage_path: Mapped[str] = mapped_column( + String(1024), nullable=False, default="" + ) + file_type: Mapped[str] = mapped_column( + String(20), nullable=False, default="text" + ) + parsed_content: Mapped[str | None] = mapped_column( + Text, nullable=True, default=None + ) + created_at: Mapped[datetime] = mapped_column( + DateTime(timezone=True), + nullable=False, + default=lambda: datetime.now(timezone.utc), + ) + + def __repr__(self) -> str: + return f"" diff --git a/backend/db/models/rag_document.py b/backend/db/models/rag_document.py new file mode 100644 index 0000000..a3ca5df --- /dev/null +++ b/backend/db/models/rag_document.py @@ -0,0 +1,50 @@ +"""RAG document ORM model.""" + +from __future__ import annotations + +import uuid +from datetime import datetime, timezone + +from sqlalchemy import Boolean, DateTime, Integer, String, Text +from sqlalchemy.dialects.postgresql import UUID +from sqlalchemy.orm import Mapped, mapped_column + +from backend.core.models import Base + + +class RAGDocument(Base): + """Tracks a document ingested into the RAG pipeline.""" + + __tablename__ = "rag_documents" + + id: Mapped[uuid.UUID] = mapped_column( + UUID(as_uuid=True), primary_key=True, default=uuid.uuid4 + ) + collection_name: Mapped[str] = mapped_column( + String(255), nullable=False, default="documents" + ) + filename: Mapped[str] = mapped_column(String(512), nullable=False) + filesize: Mapped[int] = mapped_column(Integer, nullable=False, default=0) + filetype: Mapped[str] = mapped_column(String(50), nullable=False, default="") + status: Mapped[str] = mapped_column( + String(20), nullable=False, default="processing" + ) + error_message: Mapped[str | None] = mapped_column(Text, nullable=True, default=None) + vector_document_id: Mapped[str | None] = mapped_column( + String(255), nullable=True, default=None + ) + chunk_count: Mapped[int] = mapped_column(Integer, nullable=False, default=0) + storage_path: Mapped[str] = mapped_column( + String(1024), nullable=False, default="" + ) + created_at: Mapped[datetime] = mapped_column( + DateTime(timezone=True), + nullable=False, + default=lambda: datetime.now(timezone.utc), + ) + completed_at: Mapped[datetime | None] = mapped_column( + DateTime(timezone=True), nullable=True, default=None + ) + + def __repr__(self) -> str: + return f"" diff --git a/backend/db/models/sync_log.py b/backend/db/models/sync_log.py new file mode 100644 index 0000000..3db846b --- /dev/null +++ b/backend/db/models/sync_log.py @@ -0,0 +1,50 @@ +"""Sync log ORM model.""" + +from __future__ import annotations + +import uuid +from datetime import datetime, timezone + +from sqlalchemy import DateTime, Integer, String, Text +from sqlalchemy.dialects.postgresql import UUID +from sqlalchemy.orm import Mapped, mapped_column + +from backend.core.models import Base + + +class SyncLog(Base): + """Logs a RAG sync operation.""" + + __tablename__ = "sync_logs" + + id: Mapped[uuid.UUID] = mapped_column( + UUID(as_uuid=True), primary_key=True, default=uuid.uuid4 + ) + source: Mapped[str] = mapped_column(String(100), nullable=False, default="") + collection_name: Mapped[str] = mapped_column( + String(255), nullable=False, default="documents" + ) + status: Mapped[str] = mapped_column( + String(20), nullable=False, default="running" + ) + mode: Mapped[str] = mapped_column( + String(20), nullable=False, default="full" + ) + total_files: Mapped[int] = mapped_column(Integer, nullable=False, default=0) + ingested: Mapped[int] = mapped_column(Integer, nullable=False, default=0) + updated: Mapped[int] = mapped_column(Integer, nullable=False, default=0) + skipped: Mapped[int] = mapped_column(Integer, nullable=False, default=0) + failed: Mapped[int] = mapped_column(Integer, nullable=False, default=0) + error_message: Mapped[str | None] = mapped_column(Text, nullable=True, default=None) + sync_source_id: Mapped[uuid.UUID | None] = mapped_column( + UUID(as_uuid=True), nullable=True, default=None + ) + started_at: Mapped[datetime | None] = mapped_column( + DateTime(timezone=True), nullable=True, default=None + ) + completed_at: Mapped[datetime | None] = mapped_column( + DateTime(timezone=True), nullable=True, default=None + ) + + def __repr__(self) -> str: + return f"" diff --git a/backend/db/models/sync_source.py b/backend/db/models/sync_source.py new file mode 100644 index 0000000..4bb8284 --- /dev/null +++ b/backend/db/models/sync_source.py @@ -0,0 +1,56 @@ +"""Sync source ORM model.""" + +from __future__ import annotations + +import uuid +from datetime import datetime, timezone + +from sqlalchemy import Boolean, DateTime, Integer, String, Text +from sqlalchemy.dialects.postgresql import JSON, UUID +from sqlalchemy.orm import Mapped, mapped_column + +from backend.core.models import Base + + +class SyncSource(Base): + """Configuration for a RAG sync source (local folder, GDrive, S3, etc.).""" + + __tablename__ = "sync_sources" + + id: Mapped[uuid.UUID] = mapped_column( + UUID(as_uuid=True), primary_key=True, default=uuid.uuid4 + ) + name: Mapped[str] = mapped_column(String(255), nullable=False) + connector_type: Mapped[str] = mapped_column( + String(50), nullable=False + ) + collection_name: Mapped[str] = mapped_column( + String(255), nullable=False, default="documents" + ) + config: Mapped[dict | None] = mapped_column(JSON, nullable=True, default=None) + sync_mode: Mapped[str] = mapped_column( + String(20), nullable=False, default="full" + ) + schedule_minutes: Mapped[int | None] = mapped_column( + Integer, nullable=True, default=None + ) + is_active: Mapped[bool] = mapped_column( + Boolean, nullable=False, default=True + ) + last_sync_at: Mapped[datetime | None] = mapped_column( + DateTime(timezone=True), nullable=True, default=None + ) + last_sync_status: Mapped[str | None] = mapped_column( + String(20), nullable=True, default=None + ) + last_error: Mapped[str | None] = mapped_column( + Text, nullable=True, default=None + ) + created_at: Mapped[datetime] = mapped_column( + DateTime(timezone=True), + nullable=False, + default=lambda: datetime.now(timezone.utc), + ) + + def __repr__(self) -> str: + return f"" diff --git a/backend/rag/connectors.py b/backend/rag/connectors.py new file mode 100644 index 0000000..184c2e1 --- /dev/null +++ b/backend/rag/connectors.py @@ -0,0 +1,38 @@ +"""Connector registry for RAG sync sources. + +Connectors provide a pluggable way to ingest documents from various sources +(local folders, GDrive, S3, OneDrive, etc.). The registry is populated at +import time by each connector module. +""" + +from __future__ import annotations + +from abc import ABC, abstractmethod +from typing import Any + + +class BaseConnector(ABC): + """Abstract base class for sync source connectors.""" + + CONNECTOR_TYPE: str = "" + DISPLAY_NAME: str = "" + CONFIG_SCHEMA: dict[str, dict[str, Any]] = {} + + @abstractmethod + async def validate_config(self, config: dict[str, Any]) -> tuple[bool, str | None]: + """Validate connector configuration. Returns (is_valid, error_message).""" + ... + + @abstractmethod + async def list_files(self, config: dict[str, Any]) -> list[dict[str, Any]]: + """List files available from the source. Returns list of file info dicts.""" + ... + + @abstractmethod + async def download_file(self, config: dict[str, Any], file_path: str) -> bytes: + """Download a file from the source as bytes.""" + ... + + +# Registry populated by connector modules at import time +CONNECTOR_REGISTRY: dict[str, type[BaseConnector]] = {} diff --git a/backend/repositories/__init__.py b/backend/repositories/__init__.py new file mode 100644 index 0000000..898a4d2 --- /dev/null +++ b/backend/repositories/__init__.py @@ -0,0 +1,15 @@ +"""Repository module — async CRUD helpers for DB models.""" + +from backend.repositories import ( + rag_document_repo, + sync_log_repo, + sync_source_repo, + chat_file_repo, +) + +__all__ = [ + "rag_document_repo", + "sync_log_repo", + "sync_source_repo", + "chat_file_repo", +] diff --git a/backend/repositories/chat_file_repo.py b/backend/repositories/chat_file_repo.py new file mode 100644 index 0000000..a117e89 --- /dev/null +++ b/backend/repositories/chat_file_repo.py @@ -0,0 +1,41 @@ +"""Repository for ChatFile CRUD operations.""" + +from __future__ import annotations + +from uuid import UUID + +from sqlalchemy import select +from sqlalchemy.ext.asyncio import AsyncSession + +from backend.db.models.chat_file import ChatFile + + +async def get_by_id(session: AsyncSession, file_id: UUID) -> ChatFile | None: + """Get a chat file by ID.""" + return await session.get(ChatFile, file_id) + + +async def create( + session: AsyncSession, + *, + user_id: UUID, + filename: str, + mime_type: str, + size: int, + storage_path: str, + file_type: str, + parsed_content: str | None = None, +) -> ChatFile: + """Create a new chat file record.""" + chat_file = ChatFile( + user_id=user_id, + filename=filename, + mime_type=mime_type, + size=size, + storage_path=storage_path, + file_type=file_type, + parsed_content=parsed_content, + ) + session.add(chat_file) + await session.flush() + return chat_file diff --git a/backend/repositories/rag_document_repo.py b/backend/repositories/rag_document_repo.py new file mode 100644 index 0000000..cd2181e --- /dev/null +++ b/backend/repositories/rag_document_repo.py @@ -0,0 +1,86 @@ +"""Repository for RAGDocument CRUD operations.""" + +from __future__ import annotations + +from datetime import datetime +from uuid import UUID + +from sqlalchemy import delete, func, select, update +from sqlalchemy.ext.asyncio import AsyncSession + +from backend.db.models.rag_document import RAGDocument + + +async def get_all( + session: AsyncSession, + collection_name: str | None = None, +) -> list[RAGDocument]: + """List all RAG documents, optionally filtered by collection.""" + stmt = select(RAGDocument).order_by(RAGDocument.created_at.desc()) + if collection_name: + stmt = stmt.where(RAGDocument.collection_name == collection_name) + result = await session.execute(stmt) + return list(result.scalars().all()) + + +async def get_by_id(session: AsyncSession, doc_id: UUID) -> RAGDocument | None: + """Get a RAG document by ID.""" + return await session.get(RAGDocument, doc_id) + + +async def create( + session: AsyncSession, + *, + collection_name: str, + filename: str, + filesize: int, + filetype: str, + storage_path: str = "", +) -> RAGDocument: + """Create a new RAG document record.""" + doc = RAGDocument( + collection_name=collection_name, + filename=filename, + filesize=filesize, + filetype=filetype, + storage_path=storage_path, + ) + session.add(doc) + await session.flush() + return doc + + +async def update_status( + session: AsyncSession, + doc_id: UUID, + **kwargs, +) -> RAGDocument | None: + """Update document status and optional fields.""" + stmt = ( + update(RAGDocument) + .where(RAGDocument.id == doc_id) + .values(**kwargs) + .returning(RAGDocument) + ) + result = await session.execute(stmt) + await session.flush() + return result.scalar_one_or_none() + + +async def delete(session: AsyncSession, doc_id: UUID) -> None: + """Delete a RAG document record.""" + stmt = delete(RAGDocument).where(RAGDocument.id == doc_id) + await session.execute(stmt) + await session.flush() + + +async def delete_by_collection(session: AsyncSession, collection_name: str) -> int: + """Delete all RAG documents for a collection. Returns count.""" + stmt = ( + delete(RAGDocument) + .where(RAGDocument.collection_name == collection_name) + .returning(RAGDocument.id) + ) + result = await session.execute(stmt) + await session.flush() + return len(result.fetchall()) diff --git a/backend/repositories/sync_log_repo.py b/backend/repositories/sync_log_repo.py new file mode 100644 index 0000000..ea4ae95 --- /dev/null +++ b/backend/repositories/sync_log_repo.py @@ -0,0 +1,71 @@ +"""Repository for SyncLog CRUD operations.""" + +from __future__ import annotations + +from datetime import datetime +from uuid import UUID + +from sqlalchemy import select, update +from sqlalchemy.ext.asyncio import AsyncSession + +from backend.db.models.sync_log import SyncLog + + +async def get_all( + session: AsyncSession, + collection_name: str | None = None, + limit: int = 20, +) -> list[SyncLog]: + """List sync logs, optionally filtered by collection.""" + stmt = ( + select(SyncLog) + .order_by(SyncLog.started_at.desc().nulls_last()) + .limit(limit) + ) + if collection_name: + stmt = stmt.where(SyncLog.collection_name == collection_name) + result = await session.execute(stmt) + return list(result.scalars().all()) + + +async def get_by_id(session: AsyncSession, log_id: UUID) -> SyncLog | None: + """Get a sync log by ID.""" + return await session.get(SyncLog, log_id) + + +async def create( + session: AsyncSession, + *, + source: str, + collection_name: str, + mode: str, + sync_source_id: UUID | None = None, +) -> SyncLog: + """Create a new sync log entry.""" + log = SyncLog( + source=source, + collection_name=collection_name, + mode=mode, + sync_source_id=sync_source_id, + status="running", + ) + session.add(log) + await session.flush() + return log + + +async def update_status( + session: AsyncSession, + log_id: UUID, + **kwargs, +) -> SyncLog | None: + """Update sync log status and counters.""" + stmt = ( + update(SyncLog) + .where(SyncLog.id == log_id) + .values(**kwargs) + .returning(SyncLog) + ) + result = await session.execute(stmt) + await session.flush() + return result.scalar_one_or_none() diff --git a/backend/repositories/sync_source_repo.py b/backend/repositories/sync_source_repo.py new file mode 100644 index 0000000..102de50 --- /dev/null +++ b/backend/repositories/sync_source_repo.py @@ -0,0 +1,100 @@ +"""Repository for SyncSource CRUD operations.""" + +from __future__ import annotations + +from datetime import datetime +from uuid import UUID + +from sqlalchemy import delete, select, update +from sqlalchemy.ext.asyncio import AsyncSession + +from backend.db.models.sync_source import SyncSource + + +async def get_all( + session: AsyncSession, + is_active: bool | None = None, +) -> list[SyncSource]: + """List all sync sources, optionally filtered by active status.""" + stmt = select(SyncSource).order_by(SyncSource.created_at.desc()) + if is_active is not None: + stmt = stmt.where(SyncSource.is_active == is_active) + result = await session.execute(stmt) + return list(result.scalars().all()) + + +async def get_by_id(session: AsyncSession, source_id: UUID) -> SyncSource | None: + """Get a sync source by ID.""" + return await session.get(SyncSource, source_id) + + +async def create( + session: AsyncSession, + *, + name: str, + connector_type: str, + collection_name: str, + config: dict | None = None, + sync_mode: str = "full", + schedule_minutes: int | None = None, +) -> SyncSource: + """Create a new sync source.""" + source = SyncSource( + name=name, + connector_type=connector_type, + collection_name=collection_name, + config=config or {}, + sync_mode=sync_mode, + schedule_minutes=schedule_minutes, + ) + session.add(source) + await session.flush() + return source + + +async def update( + session: AsyncSession, + source_id: UUID, + **kwargs, +) -> SyncSource | None: + """Update a sync source.""" + stmt = ( + update(SyncSource) + .where(SyncSource.id == source_id) + .values(**kwargs) + .returning(SyncSource) + ) + result = await session.execute(stmt) + await session.flush() + return result.scalar_one_or_none() + + +async def delete(session: AsyncSession, source_id: UUID) -> None: + """Delete a sync source.""" + stmt = delete(SyncSource).where(SyncSource.id == source_id) + await session.execute(stmt) + await session.flush() + + +async def update_sync_status( + session: AsyncSession, + source_id: UUID, + *, + last_sync_at: datetime | None = None, + last_sync_status: str | None = None, + last_error: str | None = None, +) -> SyncSource | None: + """Update sync source status after a sync operation.""" + stmt = ( + update(SyncSource) + .where(SyncSource.id == source_id) + .values( + last_sync_at=last_sync_at, + last_sync_status=last_sync_status, + last_error=last_error, + ) + .returning(SyncSource) + ) + result = await session.execute(stmt) + await session.flush() + return result.scalar_one_or_none() diff --git a/backend/routes/rag.py b/backend/routes/rag.py new file mode 100644 index 0000000..30cce83 --- /dev/null +++ b/backend/routes/rag.py @@ -0,0 +1,487 @@ +"""RAG API routes — collections, search, documents, upload, sync, SSE status. + +All endpoints are prefixed with ``/api/rag``. +Authentication is optional for search; admin/owner checks apply on mutations. +""" + +from __future__ import annotations + +import uuid +from pathlib import Path + +from fastapi import APIRouter, Depends, Header, HTTPException, Query, UploadFile +from fastapi.responses import FileResponse, StreamingResponse +from sqlalchemy.ext.asyncio import AsyncSession + +from backend.core.config import settings +from backend.core.dependencies import get_db_session +from backend.schemas.rag import ( + RAGCollectionInfo, + RAGCollectionList, + RAGDocumentList, + RAGIngestResponse, + RAGMessageResponse, + RAGRetryResponse, + RAGSearchRequest, + RAGSearchResponse, + RAGSyncLogList, + RAGSyncRequest, + RAGSyncResponse, + RAGTrackedDocumentList, +) +from backend.schemas.sync_source import ( + ConnectorList, + SyncSourceCreate, + SyncSourceList, + SyncSourceRead, + SyncSourceUpdate, +) +from backend.services.rag.embeddings import EmbeddingService +from backend.services.rag.vectorstore import MilvusVectorStore as VectorStore +from backend.services.rag.retrieval import RetrievalService +from backend.services.rag.ingestion import IngestionService +from backend.services.rag.documents import DocumentProcessor +from backend.services.rag_document import RAGDocumentService +from backend.services.rag_sync import RAGSyncService +from backend.services.rag_status import RAGStatusService +from backend.services.sync_source import SyncSourceService +from backend.rag.connectors import CONNECTOR_REGISTRY + +router = APIRouter(prefix="/api/rag", tags=["rag"]) + +# ── Vector store helpers (lazily initialised) ───────────────────────────── + + +def _get_vector_store() -> VectorStore: + """Return a shared VectorStore instance for route handlers.""" + rag_settings = settings.rag + embed_service = EmbeddingService(settings=rag_settings) + return VectorStore(settings=rag_settings, embedding_service=embed_service) + + +def _get_ingestion_service() -> IngestionService: + """Return a shared IngestionService instance.""" + rag_settings = settings.rag + embed_service = EmbeddingService(settings=rag_settings) + vector_store = VectorStore(settings=rag_settings, embedding_service=embed_service) + processor = DocumentProcessor(settings=rag_settings) + return IngestionService(processor=processor, vector_store=vector_store) + + +# ── Auth helper ─────────────────────────────────────────────────────────── + + +async def _resolve_user_id( + authorization: str | None = Header(None), +) -> str | None: + """Resolve user from Bearer token (optional for RAG endpoints).""" + if authorization is None: + return None + scheme, _, token = authorization.partition(" ") + if scheme.lower() == "bearer" and token: + from backend.core.database import resolve_auth_token + + return await resolve_auth_token(token) + return None + + +# ═══════════════════════════════════════════════════════════════════════════ +# Collection Management +# ═══════════════════════════════════════════════════════════════════════════ + + +@router.get("/collections", response_model=RAGCollectionList) +async def list_collections() -> RAGCollectionList: + """List all available vector store collections.""" + store = _get_vector_store() + names = await store.list_collections() + return RAGCollectionList(items=names) + + +@router.get("/collections/{name}", response_model=RAGCollectionInfo) +async def get_collection_info(name: str) -> RAGCollectionInfo: + """Get metadata and stats about a specific collection.""" + store = _get_vector_store() + try: + info = await store.get_collection_info(name) + return RAGCollectionInfo( + name=info.name, + total_vectors=info.total_vectors, + dim=info.dim, + indexing_status=info.indexing_status, + ) + except Exception as exc: + raise HTTPException(status_code=404, detail=f"Collection not found: {exc}") from exc + + +@router.post("/collections", response_model=RAGMessageResponse) +async def create_collection(name: str = Query(..., description="Collection name")) -> RAGMessageResponse: + """Create a new empty collection.""" + store = _get_vector_store() + try: + await store.create_collection(name) + return RAGMessageResponse(message=f"Collection '{name}' created") + except ValueError as exc: + raise HTTPException(status_code=400, detail=str(exc)) from exc + + +@router.delete("/collections/{name}", response_model=RAGMessageResponse) +async def delete_collection(name: str) -> RAGMessageResponse: + """Delete a collection and all its data.""" + store = _get_vector_store() + await store.delete_collection(name) + return RAGMessageResponse(message=f"Collection '{name}' deleted") + + +# ═══════════════════════════════════════════════════════════════════════════ +# Vector Search +# ═══════════════════════════════════════════════════════════════════════════ + + +@router.post("/search", response_model=RAGSearchResponse) +async def search_rag( + body: RAGSearchRequest, +) -> RAGSearchResponse: + """Search across one or more collections.""" + rag_settings = settings.rag + embed_service = EmbeddingService(settings=rag_settings) + store = VectorStore(settings=rag_settings, embedding_service=embed_service) + retrieval = RetrievalService(vector_store=store, settings=rag_settings) + + collection_names = body.collection_names or [body.collection_name] + + if len(collection_names) == 1: + results = await retrieval.retrieve( + query=body.query, + collection_name=collection_names[0], + limit=body.limit, + min_score=body.min_score, + filter=body.filter or "", + ) + else: + results = await retrieval.retrieve_multi( + query=body.query, + collection_names=collection_names, + limit=body.limit, + min_score=body.min_score, + ) + + return RAGSearchResponse( + results=[ + { + "content": r.content, + "score": r.score, + "metadata": r.metadata, + "parent_doc_id": r.parent_doc_id or "", + } + for r in results + ] + ) + + +# ═══════════════════════════════════════════════════════════════════════════ +# Document Tracking (SQL-backed) +# ═══════════════════════════════════════════════════════════════════════════ + + +@router.get("/documents", response_model=RAGTrackedDocumentList) +async def list_documents( + collection_name: str | None = Query(None), + session: AsyncSession = Depends(get_db_session), +) -> RAGTrackedDocumentList: + """List tracked RAG documents, optionally filtered by collection.""" + service = RAGDocumentService(db=session) + return await service.list_documents(collection_name=collection_name) + + +@router.get("/documents/{doc_id}", response_model=RAGTrackedDocumentList) +async def get_document( + doc_id: str, + session: AsyncSession = Depends(get_db_session), +) -> RAGTrackedDocumentList: + """Get details for a specific tracked document (wrapped in list for consistency).""" + service = RAGDocumentService(db=session) + doc = await service.get_document(doc_id) + from backend.schemas.rag import RAGTrackedDocumentItem, RAGTrackedDocumentList + + return RAGTrackedDocumentList( + items=[ + RAGTrackedDocumentItem( + id=str(doc.id), + collection_name=doc.collection_name, + filename=doc.filename, + filesize=doc.filesize, + filetype=doc.filetype, + status=doc.status, + error_message=doc.error_message, + vector_document_id=doc.vector_document_id, + chunk_count=doc.chunk_count, + has_file=bool(doc.storage_path), + created_at=doc.created_at.isoformat() if doc.created_at else None, + completed_at=doc.completed_at.isoformat() if doc.completed_at else None, + ) + ], + total=1, + ) + + +@router.delete("/documents/{doc_id}", response_model=RAGMessageResponse) +async def delete_document( + doc_id: str, + session: AsyncSession = Depends(get_db_session), +) -> RAGMessageResponse: + """Delete a document from tracking and cascade to vector store + file storage.""" + service = RAGDocumentService(db=session) + ingestion = _get_ingestion_service() + await service.delete_document(doc_id, ingestion_service=ingestion) + return RAGMessageResponse(message=f"Document '{doc_id}' deleted") + + +@router.post("/documents/{doc_id}/retry", response_model=RAGRetryResponse) +async def retry_document( + doc_id: str, + session: AsyncSession = Depends(get_db_session), +) -> RAGRetryResponse: + """Retry ingestion for a failed document.""" + service = RAGDocumentService(db=session) + try: + doc = await service.retry_ingestion(doc_id) + return RAGRetryResponse( + id=str(doc.id), + status=doc.status, + message="Document queued for retry", + ) + except ValueError as exc: + raise HTTPException(status_code=400, detail=str(exc)) from exc + + +@router.get("/documents/{doc_id}/download") +async def download_document( + doc_id: str, + session: AsyncSession = Depends(get_db_session), +): + """Download the original file for a tracked document.""" + service = RAGDocumentService(db=session) + try: + file_path, filename, mime_type = await service.get_download_info(doc_id) + return FileResponse( + path=file_path, + filename=filename, + media_type=mime_type, + ) + except Exception as exc: + raise HTTPException(status_code=404, detail=str(exc)) from exc + + +# ═══════════════════════════════════════════════════════════════════════════ +# File Upload & Ingestion +# ═══════════════════════════════════════════════════════════════════════════ + + +@router.post("/upload", response_model=RAGIngestResponse) +@router.post("/upload/{collection_name}", response_model=RAGIngestResponse) +async def upload_document( + file: UploadFile, + collection_name: str = "documents", + replace: bool = Query(True, description="Replace existing document with same name"), + session: AsyncSession = Depends(get_db_session), +): + """Upload a file for RAG ingestion. + + Supported formats: PDF, DOCX, TXT, MD (based on configured parser). + The file is validated, stored, tracked in the database, and queued for + background ingestion via ARQ. + """ + if not file.filename: + raise HTTPException(status_code=400, detail="No filename provided") + + file_data = await file.read() + service = RAGDocumentService(db=session) + store = _get_vector_store() + + try: + result = await service.dispatch_upload( + collection_name=collection_name, + file_data=file_data, + filename=file.filename, + replace=replace, + vector_store=store, + ) + return result + except Exception as exc: + raise HTTPException(status_code=400, detail=str(exc)) from exc + + +# ═══════════════════════════════════════════════════════════════════════════ +# Sync Operations +# ═══════════════════════════════════════════════════════════════════════════ + + +@router.get("/sync-logs", response_model=RAGSyncLogList) +async def list_sync_logs( + collection_name: str | None = Query(None), + limit: int = Query(20, ge=1, le=100), + session: AsyncSession = Depends(get_db_session), +) -> RAGSyncLogList: + """List RAG sync operation logs.""" + service = RAGSyncService(db=session) + return await service.list_sync_logs(collection_name=collection_name, limit=limit) + + +@router.post("/sync", response_model=RAGSyncResponse) +async def trigger_sync( + body: RAGSyncRequest, + session: AsyncSession = Depends(get_db_session), +) -> RAGSyncResponse: + """Trigger a sync operation for a local path.""" + service = RAGSyncService(db=session) + try: + sync_log = await service.start_local_sync( + collection_name=body.collection_name, + mode=body.mode, + path=body.path or None, + ) + return RAGSyncResponse( + id=str(sync_log.id), + status=sync_log.status, + message=f"Sync started for collection '{body.collection_name}'", + ) + except Exception as exc: + raise HTTPException(status_code=400, detail=str(exc)) from exc + + +@router.post("/sync/{sync_id}/cancel", response_model=RAGMessageResponse) +async def cancel_sync( + sync_id: str, + session: AsyncSession = Depends(get_db_session), +) -> RAGMessageResponse: + """Cancel a running sync operation.""" + service = RAGSyncService(db=session) + try: + await service.cancel_sync(sync_id) + return RAGMessageResponse(message=f"Sync '{sync_id}' cancelled") + except ValueError as exc: + raise HTTPException(status_code=400, detail=str(exc)) from exc + + +# ═══════════════════════════════════════════════════════════════════════════ +# SSE Status Streaming +# ═══════════════════════════════════════════════════════════════════════════ + + +@router.get("/status") +async def stream_rag_status(): + """SSE endpoint that streams RAG ingestion status events. + + Events are consumed from a Redis pub/sub channel ``rag_status``. + Each event has ``event: status`` and JSON data. + """ + status_service = RAGStatusService() + return StreamingResponse( + status_service.stream_events(), + media_type="text/event-stream", + headers={ + "Cache-Control": "no-cache", + "Connection": "keep-alive", + "X-Accel-Buffering": "no", + }, + ) + + +# ═══════════════════════════════════════════════════════════════════════════ +# Connectors +# ═══════════════════════════════════════════════════════════════════════════ + + +@router.get("/connectors", response_model=ConnectorList) +async def list_connectors() -> ConnectorList: + """List available sync source connector types.""" + return SyncSourceService.list_connectors() + + +# ═══════════════════════════════════════════════════════════════════════════ +# Sync Sources (CRUD + trigger) +# ═══════════════════════════════════════════════════════════════════════════ + + +@router.get("/sources", response_model=SyncSourceList) +async def list_sources( + is_active: bool | None = Query(None), + session: AsyncSession = Depends(get_db_session), +) -> SyncSourceList: + """List sync source configurations.""" + service = SyncSourceService(db=session) + return await service.list_sources(is_active=is_active) + + +@router.get("/sources/{source_id}", response_model=SyncSourceRead) +async def get_source( + source_id: str, + session: AsyncSession = Depends(get_db_session), +) -> SyncSourceRead: + """Get a specific sync source.""" + service = SyncSourceService(db=session) + try: + source = await service.get_source(source_id) + return service._to_read(source) # type: ignore[return-value] + except Exception as exc: + raise HTTPException(status_code=404, detail=str(exc)) from exc + + +@router.post("/sources", response_model=SyncSourceRead, status_code=201) +async def create_source( + body: SyncSourceCreate, + session: AsyncSession = Depends(get_db_session), +) -> SyncSourceRead: + """Create a new sync source configuration.""" + service = SyncSourceService(db=session) + try: + return await service.create_source(body) + except Exception as exc: + raise HTTPException(status_code=400, detail=str(exc)) from exc + + +@router.patch("/sources/{source_id}", response_model=SyncSourceRead) +async def update_source( + source_id: str, + body: SyncSourceUpdate, + session: AsyncSession = Depends(get_db_session), +) -> SyncSourceRead: + """Update an existing sync source.""" + service = SyncSourceService(db=session) + try: + return await service.update_source(source_id, body) + except Exception as exc: + raise HTTPException(status_code=400, detail=str(exc)) from exc + + +@router.delete("/sources/{source_id}", response_model=RAGMessageResponse) +async def delete_source( + source_id: str, + session: AsyncSession = Depends(get_db_session), +) -> RAGMessageResponse: + """Delete a sync source.""" + service = SyncSourceService(db=session) + try: + await service.delete_source(source_id) + return RAGMessageResponse(message=f"Source '{source_id}' deleted") + except Exception as exc: + raise HTTPException(status_code=404, detail=str(exc)) from exc + + +@router.post("/sources/{source_id}/sync", response_model=RAGSyncResponse) +async def trigger_source_sync( + source_id: str, + session: AsyncSession = Depends(get_db_session), +) -> RAGSyncResponse: + """Trigger an immediate sync for a configured source.""" + service = SyncSourceService(db=session) + try: + sync_log = await service.trigger_sync(source_id) + return RAGSyncResponse( + id=str(sync_log.id), + status=sync_log.status, + message=f"Sync triggered for source '{source_id}'", + ) + except Exception as exc: + raise HTTPException(status_code=400, detail=str(exc)) from exc diff --git a/backend/schemas/__init__.py b/backend/schemas/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/schemas/rag.py b/backend/schemas/rag.py new file mode 100644 index 0000000..ef8821a --- /dev/null +++ b/backend/schemas/rag.py @@ -0,0 +1,158 @@ +"""RAG API schemas.""" + +from typing import Any + +from pydantic import BaseModel, Field + + +class RAGSearchRequest(BaseModel): + """Parameters for a vector search query.""" + + collection_name: str = Field("documents", description="Target collection for search") + collection_names: list[str] | None = Field( + None, description="Search across multiple collections (overrides collection_name)" + ) + query: str = Field(..., description="Natural language search query") + limit: int = Field(default=4, ge=1, le=20) + min_score: float = Field(default=0.0, ge=0.0, le=1.0) + filter: str | None = Field( + None, description="Scalar filter expression (e.g. 'filetype == \"pdf\"')" + ) + + +class RAGSearchResult(BaseModel): + """A single retrieved chunk with its associated metadata.""" + + content: str + score: float + metadata: dict[str, Any] + parent_doc_id: str + + +class RAGSearchResponse(BaseModel): + """List of results found in the vector store.""" + + results: list[RAGSearchResult] + + +class RAGCollectionInfo(BaseModel): + """Statistical information about a specific collection.""" + + name: str + total_vectors: int + dim: int + indexing_status: str = "complete" + + +class RAGCollectionList(BaseModel): + """List of all available collection names.""" + + items: list[str] + + +class RAGDocumentItem(BaseModel): + """Information about a single document in a collection.""" + + document_id: str = Field(..., description="Unique identifier of the document") + filename: str | None = Field(None, description="Original filename of the document") + filesize: int | None = Field(None, description="Size of the file in bytes") + filetype: str | None = Field(None, description="MIME type of the file") + chunk_count: int = Field(default=0, description="Number of chunks/vectors in the collection") + additional_info: dict[str, Any] | None = Field(None, description="Additional metadata") + + +class RAGDocumentList(BaseModel): + """List of all documents in a collection.""" + + items: list[RAGDocumentItem] + total: int = Field(..., description="Total number of unique documents") + + +class RAGMessageResponse(BaseModel): + """Simple message response.""" + + message: str + + +class RAGTrackedDocumentItem(BaseModel): + """A document tracked in the SQL database.""" + + id: str + collection_name: str + filename: str + filesize: int + filetype: str + status: str + error_message: str | None = None + vector_document_id: str | None = None + chunk_count: int = 0 + has_file: bool = False + created_at: str | None = None + completed_at: str | None = None + + +class RAGTrackedDocumentList(BaseModel): + """List of tracked RAG documents.""" + + items: list[RAGTrackedDocumentItem] + total: int + + +class RAGIngestResponse(BaseModel): + """Response for document ingestion (async or sync).""" + + id: str + status: str + filename: str + collection: str + message: str + document_id: str | None = None + + +class RAGRetryResponse(BaseModel): + """Response for document retry.""" + + id: str + status: str + message: str + + +class RAGSyncRequest(BaseModel): + """Request to trigger a sync operation.""" + + collection_name: str = Field("documents", description="Target collection") + mode: str = Field("full", description="Sync mode: full, new_only, update_only") + path: str = Field("", description="Source path") + + +class RAGSyncLogItem(BaseModel): + """A sync operation log entry.""" + + id: str + source: str + collection_name: str + status: str + mode: str + total_files: int = 0 + ingested: int = 0 + updated: int = 0 + skipped: int = 0 + failed: int = 0 + error_message: str | None = None + started_at: str | None = None + completed_at: str | None = None + + +class RAGSyncLogList(BaseModel): + """List of sync log entries.""" + + items: list[RAGSyncLogItem] + total: int + + +class RAGSyncResponse(BaseModel): + """Response for sync trigger.""" + + id: str + status: str + message: str diff --git a/backend/schemas/sync_source.py b/backend/schemas/sync_source.py new file mode 100644 index 0000000..1c494f3 --- /dev/null +++ b/backend/schemas/sync_source.py @@ -0,0 +1,77 @@ +"""Sync source API schemas.""" + +from __future__ import annotations + +from typing import Any + +from pydantic import BaseModel, Field + + +class ConnectorConfigField(BaseModel): + """Schema for a single connector configuration field.""" + + type: str = "string" + description: str = "" + required: bool = False + default: Any = None + + +class ConnectorInfo(BaseModel): + """Information about an available connector.""" + + type: str + name: str + config_schema: dict[str, ConnectorConfigField] + enabled: bool = True + + +class ConnectorList(BaseModel): + """List of available connectors.""" + + items: list[ConnectorInfo] + + +class SyncSourceCreate(BaseModel): + """Request to create a sync source.""" + + name: str = Field(..., description="Human-readable name") + connector_type: str = Field(..., description="Connector type identifier") + collection_name: str = Field("documents", description="Target RAG collection") + config: dict[str, Any] = Field(default_factory=dict) + sync_mode: str = Field("full", description="Sync mode: full, new_only, update_only") + schedule_minutes: int | None = Field(None, description="Auto-sync interval in minutes") + + +class SyncSourceUpdate(BaseModel): + """Request to update a sync source.""" + + name: str | None = Field(None, description="Human-readable name") + collection_name: str | None = Field(None, description="Target RAG collection") + config: dict[str, Any] | None = Field(None) + sync_mode: str | None = Field(None, description="Sync mode") + schedule_minutes: int | None = Field(None, description="Auto-sync interval") + is_active: bool | None = Field(None, description="Whether the source is active") + + +class SyncSourceRead(BaseModel): + """Sync source read model returned by the API.""" + + id: str + name: str + connector_type: str + collection_name: str + config: dict[str, Any] = Field(default_factory=dict) + sync_mode: str = "full" + schedule_minutes: int | None = None + is_active: bool = True + last_sync_at: str | None = None + last_sync_status: str | None = None + last_error: str | None = None + created_at: str | None = None + + +class SyncSourceList(BaseModel): + """List of sync sources.""" + + items: list[SyncSourceRead] + total: int diff --git a/backend/services/file_storage.py b/backend/services/file_storage.py new file mode 100644 index 0000000..0cfb15d --- /dev/null +++ b/backend/services/file_storage.py @@ -0,0 +1,141 @@ +"""File storage service for chat file uploads. + +Supports local filesystem storage. +Files are organized per-user: {storage_root}/{user_id}/{uuid}_{filename} +""" + +import logging +import re +import uuid +from abc import ABC, abstractmethod +from pathlib import Path + +from backend.core.config import settings + +logger = logging.getLogger(__name__) + +ALLOWED_MIME_TYPES = { + "image/jpeg", + "image/png", + "image/gif", + "image/webp", + "text/plain", + "text/markdown", + "text/csv", + "text/html", + "text/css", + "text/xml", + "text/x-python", + "text/javascript", + "text/x-yaml", + "application/json", + "application/pdf", + "application/vnd.openxmlformats-officedocument.wordprocessingml.document", + "application/x-yaml", +} + +IMAGE_MIME_TYPES = {"image/jpeg", "image/png", "image/gif", "image/webp"} + +MAX_UPLOAD_SIZE = 10 * 1024 * 1024 # 10MB + + +def classify_file(mime_type: str, filename: str) -> str: + """Classify file type based on MIME type and extension.""" + if mime_type in IMAGE_MIME_TYPES: + return "image" + if mime_type == "application/pdf" or filename.lower().endswith(".pdf"): + return "pdf" + ext = filename.lower().rsplit(".", 1)[-1] if "." in filename else "" + if ext == "docx" or "wordprocessingml" in mime_type: + return "docx" + return "text" + + +_UNSAFE_FILENAME_CHARS = re.compile(r"[^\w.\-]+") + + +def _sanitize_filename(filename: str) -> str: + """Strip path separators, NULL bytes, and unsafe chars from a filename. + + The result is always a single path component with no traversal segments. + Empty results fall back to ``"file"`` to preserve a non-empty name. + """ + base = Path(filename).name.replace("\x00", "") + cleaned = _UNSAFE_FILENAME_CHARS.sub("_", base).strip("._") + return cleaned or "file" + + +def make_storage_filename(filename: str) -> str: + """Create a unique storage filename to prevent collisions and path traversal.""" + safe = _sanitize_filename(filename) + return f"{uuid.uuid4().hex[:12]}_{safe}" + + +class BaseFileStorage(ABC): + """Abstract file storage backend.""" + + @abstractmethod + async def save(self, user_id: str, filename: str, data: bytes) -> str: + """Save file and return storage path/key.""" + + @abstractmethod + async def load(self, storage_path: str) -> bytes: + """Load file bytes by storage path.""" + + @abstractmethod + async def delete(self, storage_path: str) -> None: + """Delete file by storage path.""" + + def get_full_path(self, storage_path: str) -> Path | None: + """Return absolute filesystem path if available (local storage only).""" + return None # pragma: no cover + + +class LocalFileStorage(BaseFileStorage): + """Store files on local filesystem.""" + + def __init__(self, base_dir: str | Path = "media"): + self.base_dir = Path(base_dir) + self.base_dir.mkdir(parents=True, exist_ok=True) + + def _resolve_safe_path(self, storage_path: str) -> Path: + """Resolve a storage path under base_dir, rejecting traversal attempts.""" + base = self.base_dir.resolve() + candidate = (base / storage_path).resolve() + if base != candidate and base not in candidate.parents: + raise ValueError(f"Path escapes storage root: {storage_path}") + return candidate + + async def save(self, user_id: str, filename: str, data: bytes) -> str: + safe_user = _sanitize_filename(user_id) + user_dir = self.base_dir / safe_user + user_dir.mkdir(parents=True, exist_ok=True) + storage_name = make_storage_filename(filename) + file_path = user_dir / storage_name + file_path.write_bytes(data) + return f"{safe_user}/{storage_name}" + + async def load(self, storage_path: str) -> bytes: + file_path = self._resolve_safe_path(storage_path) + if not file_path.exists(): + raise FileNotFoundError(f"File not found: {storage_path}") + return file_path.read_bytes() + + async def delete(self, storage_path: str) -> None: + file_path = self._resolve_safe_path(storage_path) + if file_path.exists(): + file_path.unlink() + + def get_full_path(self, storage_path: str) -> Path | None: + """Return absolute filesystem path for local files.""" + try: + file_path = self._resolve_safe_path(storage_path) + except ValueError: + return None + return file_path if file_path.exists() else None + + +def get_file_storage() -> BaseFileStorage: + """Factory: create file storage backend based on settings.""" + media_dir = getattr(settings, "media_dir", "media") + return LocalFileStorage(base_dir=media_dir) diff --git a/backend/services/file_upload.py b/backend/services/file_upload.py new file mode 100644 index 0000000..06ba654 --- /dev/null +++ b/backend/services/file_upload.py @@ -0,0 +1,156 @@ +"""File upload service (PostgreSQL async). + +Contains business logic for file validation, content parsing, and chat file +creation. Moves parsing helpers and file classification out of the route layer. +""" + +import logging +from typing import Any + +from sqlalchemy.ext.asyncio import AsyncSession + +from backend.db.models.chat_file import ChatFile +from backend.services.file_storage import ( + ALLOWED_MIME_TYPES, + MAX_UPLOAD_SIZE, + classify_file, +) + +logger = logging.getLogger(__name__) + + +class FileUploadService: + """Service for file upload validation, parsing, and persistence.""" + + ALLOWED_MIME_TYPES = ALLOWED_MIME_TYPES + MAX_UPLOAD_SIZE = MAX_UPLOAD_SIZE + + def __init__(self, db: AsyncSession): + self.db = db + + @staticmethod + def validate_upload(content_type: str | None, size: int) -> tuple[bool, str | None]: + """Validate file type and size. + + Returns: + Tuple of (is_valid, error_message). + """ + if content_type not in ALLOWED_MIME_TYPES: + return False, f"File type '{content_type}' is not supported." + if size > MAX_UPLOAD_SIZE: + return False, f"File too large. Maximum size is {MAX_UPLOAD_SIZE // (1024 * 1024)}MB." + return True, None + + @staticmethod + def classify_file(mime_type: str, filename: str) -> str: + """Classify file type based on MIME type and extension.""" + return classify_file(mime_type, filename) + + async def parse_content( + self, + data: bytes, + file_type: str, + mime_type: str = "", + ) -> str | None: + """Parse file content based on file type. + + Returns extracted text content or None if parsing fails. + """ + if file_type == "text": + return self._parse_text_content(data, mime_type) + elif file_type == "pdf": + return self._parse_pdf_content(data) + elif file_type == "docx": + return self._parse_docx_content(data) + return None + + @staticmethod + def _parse_text_content(data: bytes, mime_type: str) -> str | None: + """Extract text content from text-based files.""" + try: + return data.decode("utf-8") + except (UnicodeDecodeError, ValueError): + return None + + @staticmethod + def _parse_pdf_content(data: bytes) -> str | None: + """Extract text from PDF using PyMuPDF.""" + try: + import pymupdf + + doc: Any = pymupdf.open(stream=data, filetype="pdf") # type: ignore[no-untyped-call,unused-ignore] + texts = [] + for page in doc: + blocks = page.get_text("blocks") + for b in blocks: + if b[6] == 0: + text = b[4].strip() + if text: + texts.append(text) + try: + tables = page.find_tables() + if tables and tables.tables: + for table in tables.tables: + df = table.to_pandas() + if not df.empty: + texts.append(df.to_markdown(index=False)) + except Exception: + pass + doc.close() + return "\n\n".join(texts) if texts else None + except Exception as e: + logger.warning(f"PDF parsing failed: {e}") + return None + + @staticmethod + def _parse_docx_content(data: bytes) -> str | None: + """Extract text from DOCX.""" + try: + import io + + from docx import Document as DOCXDocument + + doc: Any = DOCXDocument(io.BytesIO(data)) + return "\n".join(p.text for p in doc.paragraphs if p.text.strip()) + except Exception as e: + logger.warning(f"DOCX parsing failed: {e}") + return None + + async def get_user_file(self, file_id: Any, user_id: Any) -> ChatFile: + """Get a file by ID, verifying ownership. + + Raises: + NotFoundError: If file does not exist or user has no access. + """ + from backend.core.exceptions import NotFoundError + from backend.repositories import chat_file_repo + + chat_file = await chat_file_repo.get_by_id(self.db, file_id) + if not chat_file or str(chat_file.user_id) != str(user_id): + raise NotFoundError(message="File not found") + return chat_file + + async def create_chat_file( + self, + *, + user_id: Any, + filename: str, + mime_type: str, + size: int, + storage_path: str, + file_type: str, + parsed_content: str | None = None, + ) -> ChatFile: + """Create a chat file record in the database.""" + from backend.repositories import chat_file_repo + + return await chat_file_repo.create( + self.db, + user_id=user_id, + filename=filename, + mime_type=mime_type, + size=size, + storage_path=storage_path, + file_type=file_type, + parsed_content=parsed_content, + ) diff --git a/backend/services/rag/config.py b/backend/services/rag/config.py new file mode 100644 index 0000000..50f28c8 --- /dev/null +++ b/backend/services/rag/config.py @@ -0,0 +1,164 @@ +"""RAG configuration.""" + +from enum import StrEnum + +from pydantic import BaseModel, Field, model_validator + + +class DocumentExtensions(StrEnum): + """Extensions supported by the RAG ingestion pipeline.""" + + PDF = ".pdf" + DOCX = ".docx" + MD = ".md" + TXT = ".txt" + + +# Supported file formats per parser +PYMUPDF_FORMATS: set[str] = {".pdf", ".docx", ".txt", ".md"} + +LITEPARSE_FORMATS: set[str] = { + ".pdf", + ".docx", + ".xlsx", + ".pptx", + ".txt", + ".md", + ".jpg", + ".jpeg", + ".png", + ".tiff", +} + +LLAMAPARSE_FORMATS: set[str] = { + # Documents + ".pdf", + ".docx", + ".doc", + ".pptx", + ".ppt", + ".rtf", + ".txt", + ".md", + ".epub", + # Images + ".jpg", + ".jpeg", + ".png", + ".gif", + ".bmp", + ".tiff", + ".webp", + # Spreadsheets + ".xlsx", + ".xls", + ".csv", + ".tsv", + ".ods", + # Audio + ".mp3", + ".mp4", + ".wav", + ".m4a", + ".webm", + # Web + ".html", + ".htm", + ".xml", +} + +PARSER_FORMATS: dict[str, set[str]] = { + "pymupdf": PYMUPDF_FORMATS, + "liteparse": LITEPARSE_FORMATS, + "llamaparse": LLAMAPARSE_FORMATS, +} + + +def get_supported_formats(parser_name: str = "pymupdf") -> set[str]: + """Get supported file formats for a given parser.""" + return PARSER_FORMATS.get(parser_name, PYMUPDF_FORMATS) + + +# Known embedding models and their output dimensions. +# Used to auto-set vector store dimension from model name. +EMBEDDING_DIMENSIONS: dict[str, int] = { + # OpenAI + "text-embedding-3-small": 1536, + "text-embedding-3-large": 3072, + "text-embedding-ada-002": 1536, + # Voyage AI + "voyage-3": 1024, + "voyage-3-lite": 512, + "voyage-code-3": 1024, + # Google Gemini + "gemini-embedding-exp-03-07": 3072, + # SentenceTransformers (local) + "all-MiniLM-L6-v2": 384, + "all-mpnet-base-v2": 768, + "bge-small-en-v1.5": 384, + "bge-base-en-v1.5": 768, + "bge-large-en-v1.5": 1024, +} + + +class EmbeddingsConfig(BaseModel): + """Embeddings configuration. Dimension is auto-derived from model name.""" + + model: str = "text-embedding-3-small" + dim: int = 1536 + + @model_validator(mode="after") + def set_dim_from_model(self) -> "EmbeddingsConfig": + if self.model in EMBEDDING_DIMENSIONS: + self.dim = EMBEDDING_DIMENSIONS[self.model] + return self + + +class RerankerConfig(BaseModel): + """Reranker configuration.""" + + model: str = "cross_encoder" + + +class DocumentParser(BaseModel): + """Document parsing settings (non-PDF files).""" + + method: str = "python_native" + + +class PdfParser(BaseModel): + """PDF parsing settings.""" + + method: str = "pymupdf" + + +class RAGSettings(BaseModel): + """RAG pipeline configuration.""" + + collection_name: str = "documents" + + allowed_extensions: list[DocumentExtensions] = Field( + default_factory=lambda: list(DocumentExtensions) + ) + + # Chunking + chunk_size: int = 512 + chunk_overlap: int = 50 + chunking_strategy: str = "recursive" + enable_hybrid_search: bool = False + enable_ocr: bool = False + + # Embeddings + embeddings_config: EmbeddingsConfig = Field(default_factory=EmbeddingsConfig) + # Reranker + reranker_config: RerankerConfig = Field(default_factory=RerankerConfig) + + # Parsers + document_parser: DocumentParser = Field(default_factory=DocumentParser) + pdf_parser: PdfParser = Field(default_factory=PdfParser) + # Image description + enable_image_description: bool = True + image_description_model: str = "" + + # Sources + gdrive_ingestion: bool = False diff --git a/backend/services/rag/documents.py b/backend/services/rag/documents.py new file mode 100644 index 0000000..e9f3f61 --- /dev/null +++ b/backend/services/rag/documents.py @@ -0,0 +1,467 @@ +import logging +from abc import ABC, abstractmethod +from pathlib import Path +from typing import Any + +import pymupdf +from docx import Document as DOCXDocument +from langchain_text_splitters import RecursiveCharacterTextSplitter + +from backend.services.rag.config import DocumentExtensions, RAGSettings +from backend.services.rag.models import ( + Document, + DocumentImage, + DocumentMetadata, + DocumentPage, + DocumentPageChunk, +) + +logger = logging.getLogger(__name__) + + +class BaseDocumentParser(ABC): + """Abstract base class for document parsing strategies. + Defines the interface that all document parsers must implement. + Supports parsing of various document formats (PDF, DOCX, TXT, MD). + """ + + allowed = [f"{ext.value}" for ext in DocumentExtensions] + + def is_file_existing(self, filepath: Path) -> bool: + """Check if file exists at the given path. + Args: + filepath: Path to the file to check. + Returns: + True if the file exists, False otherwise. + """ + return Path.exists(filepath) + + def is_extension_allowed(self, filepath: Path) -> bool: + """Check whether document extension is allowed for parsing. + Args: + filepath: Path to the file to check. + Returns: + True if the extension is supported and file exists. + """ + return filepath.suffix.lower() in self.allowed and self.is_file_existing(filepath) + + def get_document_metadata(self, filepath: Path) -> DocumentMetadata: + """Collect metadata about a given document. + Args: + filepath: Path to the document file. + Returns: + DocumentMetadata object containing file information. + """ + import hashlib + + content_hash = hashlib.sha256(filepath.read_bytes()).hexdigest() + return DocumentMetadata( + filename=filepath.name, + filesize=filepath.stat().st_size, + filetype=filepath.suffix.replace(".", ""), + source_path=str(filepath.resolve()), + content_hash=content_hash, + ) + + @abstractmethod + async def parse(self, filepath: Path) -> Document: + """Parse a file and read its content into a Document object. + Args: + filepath: Path to the file to parse. + Returns: + Document object with parsed content and metadata. + """ + pass + + +class TextDocumentParser(BaseDocumentParser): + """Parser for text-based documents (TXT, MD). + Uses Python's built-in file reading capabilities to extract + text content from plain text and Markdown files. + """ + + def _parse_text_file(self, filepath: Path) -> Document: + """Extract raw text from a TXT or MD file. + Args: + filepath: Path to the text file. + Returns: + Document object with the file content. + """ + with open(filepath, encoding="utf-8") as f: + page = DocumentPage(page_num=1, content=f.read()) + + return Document(pages=[page], metadata=self.get_document_metadata(filepath)) + + async def parse(self, filepath: Path) -> Document: + """Parse a text file (TXT or MD). + + Args: + filepath: Path to the text file. + + Returns: + Document object with parsed content. + + Raises: + ValueError: If the file extension is not supported. + """ + if not self.is_extension_allowed(filepath): + raise ValueError(f"Extension {filepath.suffix} not supported by TextDocumentParser") + + if filepath.suffix in (".txt", ".md"): + return self._parse_text_file(filepath) + else: + raise ValueError(f"Unsupported file extension. Allowed extensions: {self.allowed}") + + +class DocxDocumentParser(BaseDocumentParser): + """Parser for DOCX documents using python-docx. + + Extracts text content from Microsoft Word documents by reading + all paragraphs and joining them with newline characters. + """ + + def _parse_docx_file(self, filepath: Path) -> Document: + """Extract raw text from the DOCX file. + + Args: + filepath: Path to the DOCX file. + + Returns: + Document object with the file content. + """ + file: Any = DOCXDocument(str(filepath)) + page = DocumentPage(page_num=1, content="\n".join([p.text for p in file.paragraphs])) + return Document(pages=[page], metadata=self.get_document_metadata(filepath)) + + async def parse(self, filepath: Path) -> Document: + """Parse a DOCX file. + + Args: + filepath: Path to the DOCX file. + + Returns: + Document object with parsed content. + + Raises: + ValueError: If the file is not a DOCX file. + """ + if not self.is_extension_allowed(filepath): + raise ValueError(f"Extension {filepath.suffix} not supported by DocxDocumentParser") + + if filepath.suffix == ".docx": + return self._parse_docx_file(filepath) + else: + raise ValueError(f"Unsupported file extension. Allowed extensions: {self.allowed}") + + +class PyMuPDFParser(BaseDocumentParser): + """Smart PDF parser using PyMuPDF. + + Features: + - Text extraction with layout preservation (blocks) + - Table detection → markdown tables + - Header/footer detection and removal + - OCR fallback for scanned pages (optional, requires tesseract) + - Image extraction for LLM-based description + - Document metadata (author, title, TOC) + """ + + MIN_TEXT_LENGTH = 50 # below this → likely a scan, try OCR + + def __init__(self, enable_ocr: bool = False, image_describer: Any = None): + self.enable_ocr = enable_ocr + self._image_describer = image_describer + + def _detect_repeated_content(self, doc: Any) -> set[str]: + """Detect headers/footers — text appearing on >70% of pages.""" + if len(doc) < 3: + return set() + text_counts: dict[str, int] = {} + for page in doc: + for b in page.get_text("blocks"): + if b[6] != 0: # skip image blocks + continue + y_ratio = b[1] / page.rect.height if page.rect.height else 0 + if y_ratio < 0.15 or y_ratio > 0.85: + text = b[4].strip() + if text and len(text) < 200: + text_counts[text] = text_counts.get(text, 0) + 1 + threshold = len(doc) * 0.7 + return {t for t, c in text_counts.items() if c >= threshold} + + def _extract_text(self, page: Any, repeated: set[str]) -> str: + """Extract text blocks, filtering headers/footers.""" + texts = [] + for b in page.get_text("blocks"): + if b[6] != 0: # skip image blocks + continue + text = b[4].strip() + if text and text not in repeated: + texts.append(text) + return str("\n\n".join(texts)) + + def _extract_tables(self, page: Any) -> str: + """Extract tables as markdown.""" + try: + tables = page.find_tables() + if not tables or not tables.tables: + return "" + parts = [] + for table in tables.tables: + df = table.to_pandas() + if not df.empty: + parts.append(df.to_markdown(index=False)) + return "\n\n".join(parts) + except Exception: + return "" + + def _ocr_page(self, page: Any, image_describer: Any = None) -> str: + """OCR a scanned page by rendering it as image and sending to LLM vision.""" + if not image_describer: + return "" + try: + import asyncio + + pix = page.get_pixmap(dpi=200) + image_bytes = pix.tobytes("png") + loop = asyncio.new_event_loop() + try: + return str( + loop.run_until_complete(image_describer.describe(image_bytes, "image/png")) + ) + finally: + loop.close() + except Exception as e: + logger.warning(f"LLM OCR failed for page {page.number + 1}: {e}") + return "" + + def _extract_images(self, doc: Any, page: Any) -> list["DocumentImage"]: + """Extract images from page for LLM description.""" + images = [] + for img_info in page.get_images(full=True): + xref = img_info[0] + try: + base = doc.extract_image(xref) + if base and base["image"] and len(base["image"]) > 1000: + ext = base.get("ext", "png") + mime_map = {"png": "image/png", "jpeg": "image/jpeg", "jpg": "image/jpeg"} + images.append( + DocumentImage( + page_num=page.number + 1, + image_bytes=base["image"], + mime_type=mime_map.get(ext, f"image/{ext}"), + ) + ) + except Exception: + pass + return images + + def _parse_pdf_file(self, filepath: Path) -> Document: + """Parse PDF with smart extraction pipeline.""" + doc: Any = pymupdf.open(filepath) # type: ignore[no-untyped-call] + + # Doc-level metadata + meta = doc.metadata or {} + toc = doc.get_toc() + + # Detect repeated headers/footers + repeated = self._detect_repeated_content(doc) + + pages = [] + for page in doc: + # 1. Text with layout (skip image blocks, filter headers/footers) + text = self._extract_text(page, repeated) + + # 2. Tables → markdown + tables_md = self._extract_tables(page) + if tables_md: + text = text + "\n\n" + tables_md if text.strip() else tables_md + + # 3. OCR fallback for scans/empty pages + if self.enable_ocr and len(text.strip()) < self.MIN_TEXT_LENGTH: + ocr_text = self._ocr_page(page, self._image_describer) + if len(ocr_text.strip()) > len(text.strip()): + text = ocr_text + logger.info(f"OCR fallback used for page {page.number + 1}") + # 4. Images + images = self._extract_images(doc, page) + + pages.append( + DocumentPage( + page_num=page.number + 1, + content=text, + images=images, + ) + ) + + doc.close() + + # Enrich metadata + additional: dict[str, Any] = {} + if meta.get("title"): + additional["pdf_title"] = meta["title"] + if meta.get("author"): + additional["pdf_author"] = meta["author"] + if toc: + additional["toc"] = [{"level": t[0], "title": t[1], "page": t[2]} for t in toc[:20]] + + doc_meta = self.get_document_metadata(filepath) + if additional: + doc_meta.additional_info = {**(doc_meta.additional_info or {}), **additional} + + return Document(pages=pages, metadata=doc_meta) + + async def parse(self, filepath: Path) -> Document: + if not self.is_extension_allowed(filepath): + raise ValueError(f"Extension {filepath.suffix} not supported by PyMuPDFParser") + if filepath.suffix == ".pdf": + return self._parse_pdf_file(filepath) + raise ValueError(f"Unsupported: {filepath.suffix}") + + +class DocumentProcessor: + """Orchestrates parsing and chunking of files into Document objects. + + Manages the document processing pipeline: + 1. Route to appropriate parser based on file extension + 2. Parse document content + 3. Chunk document pages using RecursiveCharacterTextSplitter + Supported file types: + - TXT, MD: TextDocumentParser (Python native) + - DOCX: DocxDocumentParser (Python native) + - PDF: PyMuPDFParser (local, tables, OCR fallback) + """ + + def __init__(self, settings: RAGSettings): + """Initialize the document processor. + + Args: + settings: RAG configuration settings. + """ + self.settings = settings + self.splitter = self._create_splitter(settings) + + # Always use Python native parser for plain text + self.text_parser = TextDocumentParser() + self.docx_parser = DocxDocumentParser() + # Image describer for LLM-based image descriptions and OCR fallback + self.image_describer = ( + self._init_image_describer(settings) if settings.enable_image_description else None + ) + self.pdf_parser = PyMuPDFParser( + enable_ocr=settings.enable_ocr, + image_describer=self.image_describer, + ) + + @staticmethod + def _create_splitter(settings: RAGSettings) -> Any: + """Create text splitter based on chunking strategy.""" + from langchain_text_splitters import ( + MarkdownHeaderTextSplitter, + ) + + strategy = settings.chunking_strategy + + if strategy == "markdown": + # Split by markdown headers, then by size + return MarkdownHeaderTextSplitter( + headers_to_split_on=[ + ("#", "h1"), + ("##", "h2"), + ("###", "h3"), + ], + strip_headers=False, + ) + + if strategy == "fixed": + # Simple fixed-size chunks with no smart splitting + return RecursiveCharacterTextSplitter( + chunk_size=settings.chunk_size, + chunk_overlap=settings.chunk_overlap, + length_function=len, + separators=["\n"], + ) + + # Default: recursive (smart splitting by paragraphs, sentences, words) + return RecursiveCharacterTextSplitter( + chunk_size=settings.chunk_size, + chunk_overlap=settings.chunk_overlap, + length_function=len, + is_separator_regex=False, + ) + + @staticmethod + def _init_image_describer(settings: RAGSettings) -> Any: + """Initialize the image describer using the configured AI framework.""" + from backend.core.config import settings as app_settings + + model_name = ( + getattr(app_settings, "rag_image_description_model", None) or app_settings.ai_model + ) + from backend.services.rag.image_describer import PydanticAIImageDescriber + + return PydanticAIImageDescriber(model_name=model_name) + + async def _describe_images(self, document: Document) -> None: + """Generate text descriptions for all images in document pages.""" + for page in document.pages: + if not page.images: + continue + for image in page.images: + image.description = await self.image_describer.describe( + image.image_bytes, image.mime_type + ) + img_descriptions = [ + f"[Image: {img.description}]" for img in page.images if img.description + ] + if img_descriptions: + page.content = f"{page.content}\n\n{chr(10).join(img_descriptions)}" + + async def process_file(self, filepath: Path) -> Document: + """Main entry point: filepath -> Document with chunks. + + Args: + filepath: Path to the file to process. + + Returns: + Document object with parsed pages and chunked content. + + Raises: + ValueError: If the file type is not supported. + """ + # Route to appropriate parser based on file extension + if filepath.suffix in (".txt", ".md"): + document = await self.text_parser.parse(filepath) + elif filepath.suffix == ".docx": + document = await self.docx_parser.parse(filepath) + elif filepath.suffix == ".pdf": + document = await self.pdf_parser.parse(filepath) + else: + raise ValueError(f"Unsupported file type: {filepath.suffix}") + # Describe images using LLM vision before chunking + await self._describe_images(document) + + pages = document.pages + + chunked_pages: list[DocumentPageChunk] = [] + is_markdown_splitter = self.settings.chunking_strategy == "markdown" + for page in pages: + if is_markdown_splitter: + # MarkdownHeaderTextSplitter returns Document objects + md_docs = self.splitter.split_text(page.content) + chunks = [doc.page_content for doc in md_docs] + else: + chunks = self.splitter.split_text(page.content) + for chunk_num, chunk in enumerate(chunks): + chunked_pages.append( + DocumentPageChunk( + chunk_content=chunk, + chunk_num=chunk_num, + parent_doc_id=document.id, + **page.model_dump(exclude={"parent_doc_id"}), + ) + ) + + # Add chunked pages to original document + document.chunked_pages = chunked_pages + return document diff --git a/backend/services/rag/embeddings.py b/backend/services/rag/embeddings.py new file mode 100644 index 0000000..9a3691a --- /dev/null +++ b/backend/services/rag/embeddings.py @@ -0,0 +1,153 @@ +from abc import ABC, abstractmethod + +from openai import OpenAI + +from backend.services.rag.config import RAGSettings +from backend.services.rag.models import Document + + +class BaseEmbeddingProvider(ABC): + """Abstract base class for embedding providers. + + Defines the interface that all embedding providers must implement. + """ + + @abstractmethod + def embed_queries(self, texts: list[str]) -> list[list[float]]: + """Embed a list of query texts. + + Args: + texts: List of text strings to embed. + + Returns: + List of embedding vectors, one for each input text. + """ + pass + + @abstractmethod + def embed_document(self, document: Document) -> list[list[float]]: + """Embed all chunks of a document. + + Args: + document: Document object containing chunked pages to embed. + + Returns: + List of embedding vectors, one for each chunk in the document. + """ + pass + + @abstractmethod + def warmup(self) -> None: + """Ensures the model is loaded and ready for inference.""" + pass + + +class OpenAIEmbeddingProvider(BaseEmbeddingProvider): + """OpenAI embedding provider using the OpenAI API. + + Uses OpenAI's embedding models to generate text embeddings. + """ + + def __init__(self, model: str, base_url: str | None = None) -> None: + """Initialize the OpenAI embedding provider. + + Args: + model: The OpenAI embedding model name (e.g., 'text-embedding-3-small'). + base_url: Explicit base URL for the OpenAI API. If not provided, + defaults to the standard OpenAI API, explicitly ignoring any + OPENAI_BASE_URL env var (which may point to a chat-only local + server that doesn't support embeddings). + """ + self.model = model + # Explicitly pass base_url to avoid picking up OPENAI_BASE_URL from env + self.client = OpenAI(base_url=base_url or "https://api.openai.com/v1") + + def embed_queries(self, texts: list[str]) -> list[list[float]]: + """Embed a list of query texts using OpenAI. + + Args: + texts: List of text strings to embed. + + Returns: + List of embedding vectors. + """ + response = self.client.embeddings.create(model=self.model, input=texts) + return [data.embedding for data in response.data] + + def embed_document(self, document: Document) -> list[list[float]]: + """Embed all chunks of a document using OpenAI. + + Args: + document: Document object containing chunked pages. + + Returns: + List of embedding vectors for each chunk. + """ + texts = [ + doc.chunk_content if doc.chunk_content else "" for doc in (document.chunked_pages or []) + ] + return self.embed_queries(texts) + + def warmup(self) -> None: + """Warmup method for OpenAI client. + + OpenAI API is a remote service, so this is a no-op. + """ + pass + + +# Embedding orchestrator +class EmbeddingService: + """Service for managing text embeddings. + + Orchestrates embedding operations using a configured embedding provider. + Supports multiple backends: OpenAI, Voyage AI, and Sentence Transformers. + """ + + def __init__(self, settings: RAGSettings): + """Initialize the embedding service. + + Args: + settings: RAG configuration settings. + """ + config = settings.embeddings_config + self.expected_dim = config.dim + self.provider = OpenAIEmbeddingProvider(model=config.model) + + def embed_query(self, query: str) -> list[float]: + """Embed a single query text. + + Args: + query: The text query to embed. + + Returns: + Embedding vector for the query. + """ + result = self.provider.embed_queries([query])[0] + if len(result) != self.expected_dim: + raise ValueError( + f"Embedding dimension mismatch: expected {self.expected_dim}, " + f"got {len(result)}. Check your embedding model configuration." + ) + return result + + def embed_document(self, document: Document) -> list[list[float]]: + """Embed all chunks of a document. + + Args: + document: Document object containing chunked pages. + + Returns: + List of embedding vectors for each chunk. + """ + results = self.provider.embed_document(document) + if results and len(results[0]) != self.expected_dim: + raise ValueError( + f"Embedding dimension mismatch: expected {self.expected_dim}, " + f"got {len(results[0])}. Check your embedding model configuration." + ) + return results + + def warmup(self) -> None: + """Ensures the provider is ready for usage.""" + self.provider.warmup() diff --git a/backend/services/rag/image_describer.py b/backend/services/rag/image_describer.py new file mode 100644 index 0000000..f49250e --- /dev/null +++ b/backend/services/rag/image_describer.py @@ -0,0 +1,64 @@ +"""LLM-based image description for RAG document processing. + +Uses the configured AI framework (PydanticAI, LangChain, etc.) to describe +images extracted from documents. Descriptions are appended to page content +before chunking, making image content searchable via text embeddings. + +Configuration: + RAG_IMAGE_DESCRIPTION_MODEL — LLM model to use (defaults to AI_MODEL from .env) +""" + +import base64 +import logging +from abc import ABC, abstractmethod + +logger = logging.getLogger(__name__) + +IMAGE_DESCRIPTION_PROMPT = ( + "Describe this image in detail. Focus on any text, data, charts, diagrams, " + "or visual information that would be useful for document search and retrieval. " + "Be concise but comprehensive." +) + + +class BaseImageDescriber(ABC): + """Abstract base for LLM-based image description.""" + + @abstractmethod + async def describe(self, image_bytes: bytes, mime_type: str = "image/png") -> str: + """Generate a text description of an image.""" + + +def _b64_encode(image_bytes: bytes) -> str: + """Base64-encode raw image bytes.""" + return base64.b64encode(image_bytes).decode("utf-8") + + +class PydanticAIImageDescriber(BaseImageDescriber): + """Image description using PydanticAI (supports all providers).""" + + def __init__(self, model_name: str | None = None): + from backend.core.config import settings + + self.model_name = ( + model_name + or getattr(settings, "rag_image_description_model", None) + or settings.ai_model + ) + + async def describe(self, image_bytes: bytes, mime_type: str = "image/png") -> str: + try: + from pydantic_ai import Agent + from pydantic_ai.messages import BinaryContent + + agent = Agent(self.model_name) + result = await agent.run( + [ + BinaryContent(data=image_bytes, media_type=mime_type), + IMAGE_DESCRIPTION_PROMPT, + ] + ) + return result.output if hasattr(result, "output") else str(result.data) + except Exception as e: + logger.error(f"PydanticAI image description failed: {e}") + return "" diff --git a/backend/services/rag/ingestion.py b/backend/services/rag/ingestion.py new file mode 100644 index 0000000..a7fd136 --- /dev/null +++ b/backend/services/rag/ingestion.py @@ -0,0 +1,199 @@ +from __future__ import annotations + +import logging +from collections.abc import Awaitable, Callable +from pathlib import Path + +from backend.services.rag.documents import DocumentProcessor +from backend.services.rag.models import Document, IngestionResult, IngestionStatus +from backend.services.rag.vectorstore import BaseVectorStore + +logger = logging.getLogger(__name__) + + +class IngestionService: + """ + Orchestrates the data flow: + File Path -> Parse/Chunk -> Deduplicate -> Embed/Store -> Query-Ready + """ + + def __init__( + self, + processor: DocumentProcessor, + vector_store: BaseVectorStore, + on_event: Callable[..., Awaitable[None]] | None = None, + ): + self.processor = processor + self.store = vector_store + self._on_event = on_event + + @classmethod + def from_settings( + cls, + on_event: Callable[..., Awaitable[None]] | None = None, + ) -> IngestionService: + """Build an IngestionService using the application's RAG settings.""" + from backend.core.config import settings + from backend.services.rag.embeddings import EmbeddingService + from backend.services.rag.vectorstore import MilvusVectorStore as VectorStore + + rag_settings = settings.rag + embed_service = EmbeddingService(settings=rag_settings) + vector_store = VectorStore(settings=rag_settings, embedding_service=embed_service) + processor = DocumentProcessor(settings=rag_settings) + return cls(processor=processor, vector_store=vector_store, on_event=on_event) + + async def _emit(self, event: str, data: dict[str, object]) -> None: + """Emit a webhook event if callback is configured.""" + if self._on_event: + try: + await self._on_event(event, data) + except Exception as e: + logger.warning(f"Webhook event dispatch failed: {e}") + + async def _find_existing_by_source(self, collection_name: str, source_path: str) -> str | None: + """Find an existing document by source_path. + + Returns the document_id if found, None otherwise. + """ + try: + docs = await self.store.get_documents(collection_name) + for doc in docs: + meta = doc.additional_info or {} + if meta.get("source_path") == source_path: + return doc.document_id + # Also check top-level metadata fields + # (source_path is stored in metadata dict per chunk) + # Fallback: check filename match + for doc in docs: + if doc.filename and doc.filename == Path(source_path).name: + return doc.document_id + except Exception: + pass + return None + + async def _find_existing_by_hash(self, collection_name: str, content_hash: str) -> str | None: + """Find an existing document by content hash (exact duplicate check).""" + try: + docs = await self.store.get_documents(collection_name) + for doc in docs: + meta = doc.additional_info or {} + if meta.get("content_hash") == content_hash: + return doc.document_id + except Exception: + pass + return None + + async def ingest_file( + self, + filepath: Path, + collection_name: str, + replace: bool = True, + source_path: str = "", + ) -> IngestionResult: + """Processes a file and pushes it into the vector database. + + Args: + filepath: Path to the file to process. + collection_name: Target collection name. + replace: If True, replace existing document with same source_path. + source_path: Override source path (e.g., gdrive://id, s3://bucket/key). + """ + try: + # Processing (Parsing + Chunking) + document: Document = await self.processor.process_file(filepath) + + # Set source_path override if provided (e.g., from GDrive/S3) + if source_path: + document.metadata.source_path = source_path + document.metadata.filename = Path(source_path).name + + # Deduplication check + existing_id = None + if replace: + # Check by source_path first + if document.metadata.source_path: + existing_id = await self._find_existing_by_source( + collection_name, document.metadata.source_path + ) + # If not found by path, check by content hash (exact duplicate) + if not existing_id and document.metadata.content_hash: + existing_id = await self._find_existing_by_hash( + collection_name, document.metadata.content_hash + ) + + if existing_id: + # Remove old version before inserting new + await self.store.delete_document(collection_name, existing_id) + logger.info(f"Replaced existing document {existing_id} for '{filepath.name}'") + + # Storage (Embedding + Insertion) + await self.store.insert_document( + collection_name=collection_name, + document=document, + ) + + action = "replaced" if existing_id else "ingested" + + await self._emit( + "rag.document.ingested", + { + "document_id": document.id, + "filename": filepath.name, + "collection": collection_name, + "action": action, + "chunks": len(document.chunked_pages or []), + "source_path": document.metadata.source_path, + }, + ) + + return IngestionResult( + status=IngestionStatus.DONE, + document_id=document.id, + message=f"Successfully {action} '{filepath.name}'", + ) + + except Exception as e: + logger.error(f"Ingestion error for {filepath.name}: {e!s}") + return IngestionResult( + status=IngestionStatus.ERROR, + error_message=str(e), + message=f"Failed to process {filepath.name}", + ) + + async def find_existing(self, collection_name: str, source_path: str) -> str | None: + """Check if a document with this source_path already exists. Returns document_id or None.""" + return await self._find_existing_by_source(collection_name, source_path) + + async def get_existing_hash(self, collection_name: str, source_path: str) -> str | None: + """Get content_hash of existing document by source_path.""" + doc_id = await self._find_existing_by_source(collection_name, source_path) + if not doc_id: + return None + try: + docs = await self.store.get_documents(collection_name) + for doc in docs: + if doc.document_id == doc_id and doc.additional_info: + return doc.additional_info.get("content_hash") + except Exception: + pass + return None + + async def remove_document(self, collection_name: str, document_id: str) -> bool: + """Wipes all traces of a document from the vector store.""" + try: + await self.store.delete_document( + collection_name=collection_name, + document_id=document_id, + ) + await self._emit( + "rag.document.deleted", + { + "document_id": document_id, + "collection": collection_name, + }, + ) + return True + except Exception as e: + logger.error(f"Failed to delete document {document_id}: {e!s}") + return False diff --git a/backend/services/rag/models.py b/backend/services/rag/models.py new file mode 100644 index 0000000..60d9b79 --- /dev/null +++ b/backend/services/rag/models.py @@ -0,0 +1,116 @@ +"""RAG Data Models. + +Structures used to interface with the RAG feature.""" + +import uuid +from enum import StrEnum +from typing import Any + +from pydantic import BaseModel, Field, computed_field, model_validator + + +class DocumentImage(BaseModel): + """An image extracted from a document page.""" + + image_id: str = Field(default_factory=lambda: str(uuid.uuid4())) + page_num: int = 0 + image_bytes: bytes = b"" + description: str = "" + mime_type: str = "image/png" + + +class DocumentPage(BaseModel): + """Content of document's page.""" + + page_id: str = Field(default_factory=lambda: str(uuid.uuid4())) + page_num: int + content: str + parent_doc_id: str | None = None + images: list[DocumentImage] = Field(default_factory=list) + + +class DocumentPageChunk(DocumentPage): + """Content of chunked document's page.""" + + chunk_content: str + chunk_id: str = Field(default_factory=lambda: str(uuid.uuid4())) + chunk_num: int = 0 + + +class DocumentMetadata(BaseModel): + """Metadata of a document.""" + + filename: str + filesize: int + filetype: str + source_path: str = "" # original path: local path, s3://bucket/key, gdrive://file_id + content_hash: str = "" # SHA256 hash for deduplication + additional_info: dict[str, Any] | None = None + + +class Document(BaseModel): + """A Document object that describes an ingested file.""" + + id: str = Field(default_factory=lambda: str(uuid.uuid4())) + pages: list[DocumentPage] + chunked_pages: list[DocumentPageChunk] | None = None + metadata: DocumentMetadata + + @computed_field # type: ignore[prop-decorator] + @property + def num_pages(self) -> int: + return len(self.pages) + + @model_validator(mode="after") + def connect_pages(self) -> "Document": + for page in self.pages: + page.parent_doc_id = self.id + return self + + +class SearchResult(BaseModel): + """A schema of vector store query output.""" + + content: str + score: float + metadata: dict[str, Any] = Field(default_factory=dict) + parent_doc_id: str | None = None + + +class IngestionStatus(StrEnum): + """A collection of available ingestion statuses.""" + + NEW = "new" + PROCESSING = "processing" + ADDING = "adding" + DONE = "done" + ERROR = "error" + + +class IngestionResult(BaseModel): + """A schema to handle document ingestion results.""" + + status: IngestionStatus = IngestionStatus.NEW + message: str | None = None + error_message: str | None = None + document_id: str | None = None + + +class CollectionInfo(BaseModel): + """Collection of information about given collection.""" + + name: str + total_vectors: int + dim: int + indexing_status: str = "complete" + + +class DocumentInfo(BaseModel): + """Information about a document stored in a collection.""" + + document_id: str + filename: str | None = None + filesize: int | None = None + filetype: str | None = None + chunk_count: int = 0 + additional_info: dict[str, Any] | None = None diff --git a/backend/services/rag/reranker.py b/backend/services/rag/reranker.py new file mode 100644 index 0000000..9b976bd --- /dev/null +++ b/backend/services/rag/reranker.py @@ -0,0 +1,271 @@ +"""Reranker implementations for improving RAG retrieval quality. + +This module provides reranking functionality to improve the relevance of +search results. It supports both API-based rerankers (Cohere) and local +models (Cross Encoder). +""" + +import logging +import time +from abc import ABC, abstractmethod + +from backend.core.config import settings +from backend.services.rag.config import RAGSettings +from backend.services.rag.models import SearchResult + +logger = logging.getLogger(__name__) + + +class BaseReranker(ABC): + """Abstract base class for reranking implementations. + + Defines the interface that all reranker providers must implement. + Rerankers take an initial set of search results and reorder them + based on semantic relevance to the query. + """ + + @abstractmethod + async def rerank( + self, + query: str, + results: list[SearchResult], + top_k: int, + ) -> list[SearchResult]: + """Rerank search results based on query relevance. + + Args: + query: The original search query. + results: Initial search results from vector search. + top_k: Number of top results to return after reranking. + + Returns: + Reranked list of SearchResult objects, sorted by relevance. + """ + pass + + @abstractmethod + def warmup(self) -> None: + """Ensure the reranker model is loaded and ready for inference. + + For API-based rerankers, this may validate credentials. + For local models, this triggers model download and loading. + """ + pass + + @property + @abstractmethod + def name(self) -> str: + """Return the name of the reranker for logging purposes.""" + pass + + +from sentence_transformers import CrossEncoder + + +class CrossEncoderReranker(BaseReranker): + """Cross Encoder reranker using local Sentence Transformers model. + + Uses a cross-encoder model to score query-document pairs for relevance. + Runs entirely locally - no API calls required. + + Default model: cross-encoder/ms-marco-MiniLM-L6-v2 (lightweight, fast) + """ + + # Default cross-encoder model for reranking + DEFAULT_MODEL = settings.cross_encoder_model + + def __init__(self, model: str | None = None, cache_dir: str | None = None): + """Initialize the Cross Encoder reranker. + + Args: + model: Cross-encoder model name from Sentence Transformers. + Defaults to cross-encoder/ms-marco-MiniLM-L6-v2 if not specified. + cache_dir: Directory to cache the model. Defaults to app models cache. + """ + self.model_name = model or self.DEFAULT_MODEL + self.cache_dir = cache_dir + self._model = None + + @property + def model(self) -> CrossEncoder: + """Lazy load the cross-encoder model.""" + if self._model is None: + from backend.core.config import settings as app_settings + + cache_path = self.cache_dir or str(app_settings.models_cache_dir) + # Ensure cache directory exists + app_settings.models_cache_dir.mkdir(exist_ok=True, parents=True) + + logger.info(f"[RERANKER] Loading Cross Encoder model: {self.model_name}") + self._model = CrossEncoder( + self.model_name, + cache_folder=cache_path, + token=settings.hf_token, + ) + logger.info("[RERANKER] Cross Encoder model loaded successfully") + return self._model + + @property + def name(self) -> str: + return f"CrossEncoderReranker({self.model_name})" + + async def rerank( + self, + query: str, + results: list[SearchResult], + top_k: int, + ) -> list[SearchResult]: + """Rerank results using local Cross Encoder model. + + Args: + query: The search query. + results: Initial search results. + top_k: Number of results to return. + + Returns: + Reranked results sorted by relevance score. + """ + if not results: + return [] + + print( + f"[RERANKER] Cross Encoder reranking {len(results)} documents, " + f"query: '{query[:50]}...', top_k: {top_k}" + ) + + start_time = time.time() + + try: + # Prepare query-document pairs for scoring + # CrossEncoder expects list of [query, document] pairs + pairs = [[query, result.content] for result in results] + + # Get relevance scores (higher = more relevant) + scores = self.model.predict(pairs) + + elapsed = time.time() - start_time + logger.info(f"[RERANKER] Cross Encoder reranking completed in {elapsed:.3f}s") + + # Create new results with cross-encoder scores + scored_results = [] + for i, (result, score) in enumerate(zip(results, scores)): + logger.debug( + f"[RERANKER] CrossEncoder score for doc {i}: {score:.4f} " + f"(original: {result.score:.4f}) - '{result.content[:30]}...'" + ) + scored_results.append( + SearchResult( + content=result.content, + score=float(score), # Use cross-encoder score + metadata=result.metadata, + parent_doc_id=result.parent_doc_id, + ) + ) + + # Sort by cross-encoder score (descending) + scored_results.sort(key=lambda x: x.score, reverse=True) + + # Log top results + for i, r in enumerate(scored_results[:3]): + logger.debug( + f"[RERANKER] Rank #{i + 1}: score={r.score:.4f}, content='{r.content[:50]}...'" + ) + + return scored_results[:top_k] + + except Exception as e: + logger.error(f"[RERANKER] Cross Encoder reranking failed: {e!s}") + return results[:top_k] + + def warmup(self) -> None: + """Trigger model download and loading.""" + logger.info(f"[RERANKER] Cross Encoder warmup: loading model {self.model_name}") + _ = self.model + logger.info(f"[RERANKER] Cross Encoder ready: {self.model_name}") + + +class RerankService: + """Service for managing reranking operations. + + Orchestrates reranking using a configured reranker provider. + Supports both Cohere API and local Cross Encoder models. + """ + + def __init__(self, settings: RAGSettings): + """Initialize the rerank service. + + Args: + settings: RAG configuration settings containing reranker config. + """ + self.settings = settings + config = settings.reranker_config # type: ignore[attr-defined] + self._reranker: BaseReranker | None = None + if config.model == "cross_encoder": + self._reranker = CrossEncoderReranker() + logger.info("[RERANKER] Using Cross Encoder reranker") + + if self._reranker is None: + logger.warning( + f"[RERANKER] No reranker configured (model: {config.model}). " + "Reranking will be skipped." + ) + + @property + def reranker(self) -> BaseReranker | None: + """Return the configured reranker, if any.""" + return self._reranker + + @property + def is_enabled(self) -> bool: + """Check if reranking is enabled.""" + return self._reranker is not None + + async def rerank( + self, + query: str, + results: list[SearchResult], + top_k: int, + ) -> list[SearchResult]: + """Rerank search results if a reranker is configured. + + Args: + query: The search query. + results: Initial search results to rerank. + top_k: Number of results to return. + + Returns: + Reranked results if reranker is configured, otherwise original results. + """ + if not self._reranker: + logger.debug("[RERANKER] No reranker configured, returning original results") + return results[:top_k] + + print( + f"[RERANKER] Starting reranking with {self._reranker.name}, " + f"query: '{query[:50]}...', results: {len(results)}, top_k: {top_k}" + ) + + # Log pre-reranking scores + for i, r in enumerate(results[:5]): + logger.debug( + f"[RERANKER] Pre-rerank #{i + 1}: score={r.score:.4f}, " + f"content='{r.content[:50]}...'" + ) + + reranked = await self._reranker.rerank(query, results, top_k) + + # Log post-reranking scores + for i, r in enumerate(reranked[:5]): + logger.debug( + f"[RERANKER] Post-rerank #{i + 1}: score={r.score:.4f}, " + f"content='{r.content[:50]}...'" + ) + + return reranked + + def warmup(self) -> None: + """Initialize the reranker model if configured.""" + if self._reranker: + logger.info(f"[RERANKER] Warming up {self._reranker.name}") + self._reranker.warmup() + logger.info(f"[RERANKER] {self._reranker.name} warmup complete") diff --git a/backend/services/rag/retrieval.py b/backend/services/rag/retrieval.py new file mode 100644 index 0000000..aab75b1 --- /dev/null +++ b/backend/services/rag/retrieval.py @@ -0,0 +1,381 @@ +from __future__ import annotations + +import hashlib +import logging +import time +from abc import ABC, abstractmethod + +from backend.services.rag.config import RAGSettings +from backend.services.rag.models import SearchResult +from backend.services.rag.reranker import RerankService +from backend.services.rag.vectorstore import BaseVectorStore + +logger = logging.getLogger(__name__) + + +class BaseRetrievalService(ABC): + """Abstract base class for retrieval service implementations. + + Defines the interface for querying the vector store and retrieving + relevant document chunks based on a query. + """ + + @abstractmethod + async def retrieve( + self, + query: str, + collection_name: str, + limit: int = 5, + min_score: float = 0.0, + filter: str = "", + ) -> list[SearchResult]: + """Execute the retrieval pipeline to find relevant chunks. + + Args: + query: The search query text. + collection_name: Name of the collection to search in. + limit: Maximum number of results to return. + min_score: Minimum similarity score threshold (0.0 to 1.0). + filter: Optional filter expression for the search. + + Returns: + List of SearchResult objects sorted by relevance. + """ + pass + + @abstractmethod + async def retrieve_by_document( + self, query: str, collection_name: str, document_id: str, limit: int = 3 + ) -> list[SearchResult]: + """Specialized retrieval restricted to a single document. + + Useful for "Chat with this PDF" functionality where results + should only come from a specific document. + + Args: + query: The search query text. + collection_name: Name of the collection to search in. + document_id: ID of the document to restrict search to. + limit: Maximum number of results to return. + + Returns: + List of SearchResult objects from the specified document. + """ + pass + + +class RetrievalService(BaseRetrievalService): + """High-level retrieval service with multi-stage pipeline. + + Handles query execution against any vector store backend, including + vector search, hybrid BM25 fusion, score filtering, and reranking. + """ + + def __init__( + self, + vector_store: BaseVectorStore, + settings: RAGSettings, + rerank_service: RerankService | None = None, + ): + """Initialize the Milvus retrieval service. + + Args: + vector_store: The vector store to query. + settings: RAG configuration settings. + rerank_service: Optional reranking service for improved results. + """ + self.store = vector_store + self.settings = settings + self.rerank_service = rerank_service + self._reranker_enabled = rerank_service is not None and rerank_service.is_enabled + self._hybrid_enabled = settings.enable_hybrid_search + self._bm25_index: dict[str, object] = {} # collection_name -> BM25 index + + @staticmethod + def _rrf_fuse( + vector_results: list[SearchResult], + bm25_results: list[SearchResult], + k: int = 60, + ) -> list[SearchResult]: + """Reciprocal Rank Fusion of vector and BM25 results.""" + scores: dict[str, float] = {} + result_map: dict[str, SearchResult] = {} + + for rank, r in enumerate(vector_results): + key = ( + f"{r.parent_doc_id}:{r.metadata.get('chunk_num', '')}" + if r.parent_doc_id + else hashlib.md5(r.content.encode()).hexdigest() + ) + scores[key] = scores.get(key, 0) + 1.0 / (k + rank + 1) + result_map[key] = r + + for rank, r in enumerate(bm25_results): + key = ( + f"{r.parent_doc_id}:{r.metadata.get('chunk_num', '')}" + if r.parent_doc_id + else hashlib.md5(r.content.encode()).hexdigest() + ) + scores[key] = scores.get(key, 0) + 1.0 / (k + rank + 1) + if key not in result_map: + result_map[key] = r + + sorted_keys = sorted(scores, key=lambda x: scores[x], reverse=True) + return [ + SearchResult( + content=result_map[key].content, + score=scores[key], + metadata=result_map[key].metadata, + parent_doc_id=result_map[key].parent_doc_id, + ) + for key in sorted_keys + ] + + async def _bm25_search( + self, query: str, collection_name: str, limit: int + ) -> list[SearchResult]: + """BM25 keyword search over stored documents.""" + try: + from rank_bm25 import BM25Okapi + except ImportError: + logger.warning("rank-bm25 not installed, skipping BM25 search") + return [] + + # Get all documents for BM25 (cached per collection) + docs = await self.store.get_documents(collection_name) + if not docs: + return [] + + # Build corpus from stored content via vector store search with high limit + all_results = await self.store.search( + collection_name=collection_name, query=query, limit=min(limit * 10, 100) + ) + if not all_results: + return [] + + corpus = [r.content.lower().split() for r in all_results] + bm25 = BM25Okapi(corpus) + query_tokens = query.lower().split() + bm25_scores = bm25.get_scores(query_tokens) + + scored = sorted(zip(all_results, bm25_scores), key=lambda x: x[1], reverse=True) + return [ + SearchResult( + content=r.content, + score=float(s), + metadata=r.metadata, + parent_doc_id=r.parent_doc_id, + ) + for r, s in scored[:limit] + if s > 0 + ] + + async def retrieve( + self, + query: str, + collection_name: str, + limit: int = 5, + min_score: float = 0.0, + filter: str = "", + use_reranker: bool = False, + ) -> list[SearchResult]: + """Execute the retrieval pipeline: Vector Search + Reranking (optional) + Filtering. + + Args: + query: The search query text. + collection_name: Name of the collection to search in. + limit: Maximum number of results to return. + min_score: Minimum similarity score threshold (0.0 to 1.0). + filter: Optional filter expression for the search. + use_reranker: Whether to use reranking (if configured). + + Returns: + List of SearchResult objects sorted by relevance. + """ + # Determine if we should actually use reranking + should_rerank = use_reranker and self._reranker_enabled + + # Fetch more results if reranking is enabled (reranker will reduce) + # We fetch 3x results to give reranker room to pick best ones + fetch_multiplier = 3 if should_rerank else 2 + + logger.info( + f"[RETRIEVAL] Query: '{query[:50]}...', collection: {collection_name}, " + f"limit: {limit}, filter: '{filter}', rerank: {should_rerank}" + ) + + start_time = time.time() + + # Step 1: Execute Vector Search via the Vector Store + raw_results = await self.store.search( + collection_name=collection_name, + query=query, + filter=filter, + limit=limit * fetch_multiplier, + ) + + search_time = time.time() - start_time + logger.info( + f"[RETRIEVAL] Vector search completed in {search_time:.3f}s, " + f"found {len(raw_results)} results" + ) + + # Step 1b: Hybrid search (BM25 + vector fusion) if enabled + if self._hybrid_enabled: + bm25_results = await self._bm25_search(query, collection_name, limit * fetch_multiplier) + if bm25_results: + raw_results = self._rrf_fuse(raw_results, bm25_results) + logger.info(f"[RETRIEVAL] Hybrid search: fused {len(raw_results)} results") + + # Log initial results + for i, r in enumerate(raw_results[:3]): + logger.debug( + f"[RETRIEVAL] Initial result #{i + 1}: score={r.score:.4f}, " + f"content='{r.content[:50]}...'" + ) + + results = raw_results + + # Step 2: Apply reranking if enabled and requested + if should_rerank and self.rerank_service: + logger.info("[RETRIEVAL] Applying reranking...") + rerank_start = time.time() + + # Rerank the results - fetches more initially so reranker can pick best + results = await self.rerank_service.rerank( + query=query, + results=raw_results, + top_k=limit * 2, # Get more from reranker before filtering + ) + + rerank_time = time.time() - rerank_start + logger.info( + f"[RETRIEVAL] Reranking completed in {rerank_time:.3f}s, " + f"returned {len(results)} results" + ) + elif use_reranker and not self._reranker_enabled: + logger.warning("[RETRIEVAL] Reranking requested but not configured - skipping") + + # Step 3: Post-processing: Filter by score + # Cosine similarity is higher = better. + filtered_results = [res for res in results if res.score >= min_score] + + # Step 4: Deduplicate — keep highest-scored result per unique chunk + seen_keys: set[str] = set() + deduped_results: list[SearchResult] = [] + for r in filtered_results: + key = ( + f"{r.parent_doc_id}:{r.metadata.get('chunk_num', '')}" + if r.parent_doc_id + else hashlib.md5(r.content.encode()).hexdigest() + ) + if key not in seen_keys: + seen_keys.add(key) + deduped_results.append(r) + + if len(deduped_results) < len(filtered_results): + logger.info( + f"[RETRIEVAL] Deduplicated: {len(filtered_results)} -> {len(deduped_results)} results" + ) + + # Log final results + for i, r in enumerate(deduped_results[:3]): + logger.debug( + f"[RETRIEVAL] Final result #{i + 1}: score={r.score:.4f}, " + f"content='{r.content[:50]}...'" + ) + + # Apply final limit + final_results = deduped_results[:limit] + + total_time = time.time() - start_time + logger.info( + f"[RETRIEVAL] Total retrieval time: {total_time:.3f}s, " + f"returning {len(final_results)} results" + ) + + return final_results + + async def retrieve_multi( + self, + query: str, + collection_names: list[str], + limit: int = 5, + min_score: float = 0.0, + use_reranker: bool = False, + ) -> list[SearchResult]: + """Search across multiple collections and merge results. + + Searches each collection, merges results, sorts by score. + """ + all_results: list[SearchResult] = [] + for name in collection_names: + try: + results = await self.retrieve( + query=query, + collection_name=name, + limit=limit, + min_score=min_score, + use_reranker=use_reranker, + ) + # Tag results with collection name in metadata + for r in results: + r.metadata["collection"] = name + all_results.extend(results) + except Exception as e: + logger.warning(f"[RETRIEVAL] Failed to search collection '{name}': {e}") + + all_results.sort(key=lambda r: r.score, reverse=True) + + # Deduplicate across collections + seen_keys: set[str] = set() + deduped: list[SearchResult] = [] + for r in all_results: + key = ( + f"{r.parent_doc_id}:{r.metadata.get('chunk_num', '')}" + if r.parent_doc_id + else hashlib.md5(r.content.encode()).hexdigest() + ) + if key not in seen_keys: + seen_keys.add(key) + deduped.append(r) + + return deduped[:limit] + + async def retrieve_by_document( + self, + query: str, + collection_name: str, + document_id: str, + limit: int = 3, + use_reranker: bool = False, + ) -> list[SearchResult]: + """Specialized retrieval restricted to a single document. + + Useful for "Chat with this PDF" functionality where results + should only come from a specific document. + + Args: + query: The search query text. + collection_name: Name of the collection to search in. + document_id: ID of the document to restrict search to. + limit: Maximum number of results to return. + use_reranker: Whether to use reranking (if configured). + + Returns: + List of SearchResult objects from the specified document. + """ + # Sanitize document_id to prevent filter injection + sanitized_id = document_id.replace('"', "").replace("\\", "") + filter_expr = f'parent_doc_id == "{sanitized_id}"' + logger.info( + f"[RETRIEVAL] Retrieve by document: doc_id={document_id}, " + f"query='{query[:30]}...', limit={limit}, rerank={use_reranker}" + ) + return await self.retrieve( + query=query, + collection_name=collection_name, + limit=limit, + filter=filter_expr, + use_reranker=use_reranker, + ) diff --git a/backend/services/rag/vectorstore.py b/backend/services/rag/vectorstore.py new file mode 100644 index 0000000..f7faf83 --- /dev/null +++ b/backend/services/rag/vectorstore.py @@ -0,0 +1,246 @@ +import logging +import re +from abc import ABC, abstractmethod +from typing import Any + +from backend.schemas.rag import RAGDocumentItem, RAGDocumentList +from backend.services.rag.models import ( + CollectionInfo, + Document, + DocumentInfo, + DocumentPageChunk, + SearchResult, +) + +logger = logging.getLogger(__name__) + +_COLLECTION_NAME_RE = re.compile(r"^[a-zA-Z][a-zA-Z0-9_]{0,63}$") +_RESERVED_COLLECTION_NAMES = frozenset({"all"}) + + +class BaseVectorStore(ABC): + """Abstract base class for vector store implementations.""" + + @abstractmethod + async def insert_document(self, collection_name: str, document: Document) -> None: + """Embeds and stores document chunks.""" + + @abstractmethod + async def search( + self, collection_name: str, query: str, limit: int = 4, filter: str = "" + ) -> list[SearchResult]: + """Retrieves similar chunks based on a text query.""" + + @abstractmethod + async def delete_collection(self, collection_name: str) -> None: + """Removes a collection and all its data.""" + + @abstractmethod + async def delete_document(self, collection_name: str, document_id: str) -> None: + """Removes all chunks associated with a document ID.""" + + @abstractmethod + async def get_collection_info(self, collection_name: str) -> CollectionInfo: + """Returns metadata and stats about a collection.""" + + @abstractmethod + async def list_collections(self) -> list[str]: + """Returns list of all collection names.""" + + @abstractmethod + async def get_documents(self, collection_name: str) -> list[DocumentInfo]: + """Returns list of unique documents in a collection.""" + + async def get_document_list(self, collection_name: str) -> RAGDocumentList: + """Returns documents as API-ready list response.""" + docs = await self.get_documents(collection_name) + return RAGDocumentList( + items=[ + RAGDocumentItem( + document_id=doc.document_id, + filename=doc.filename, + filesize=doc.filesize, + filetype=doc.filetype, + chunk_count=doc.chunk_count, + additional_info=doc.additional_info, + ) + for doc in docs + ], + total=len(docs), + ) + + async def create_collection(self, name: str) -> None: + """Validate the name and create the collection. + + Raises: + ValueError: If name is invalid or reserved. + """ + if not _COLLECTION_NAME_RE.match(name): + raise ValueError( + "Collection name must start with a letter and contain only " + "letters, numbers, and underscores (max 64 chars)" + ) + if name.lower() in _RESERVED_COLLECTION_NAMES: + raise ValueError(f"'{name}' is a reserved collection name") + await self._ensure_collection(name) + + def _build_chunk_metadata( + self, chunk: "DocumentPageChunk", document: Document + ) -> dict[str, Any]: + """Build metadata dict for a chunk.""" + meta = { + "page_num": chunk.page_num, + "chunk_num": chunk.chunk_num, + "has_images": bool(getattr(chunk, "images", None)), + "image_count": len(getattr(chunk, "images", [])), + **document.metadata.model_dump(), + } + return meta + + def _sanitize_id(self, document_id: str) -> str: + """Sanitize document_id to prevent filter injection.""" + return document_id.replace('"', "").replace("\\", "") + + def _group_documents(self, results: list[dict[str, Any]]) -> list[DocumentInfo]: + """Group query results by parent_doc_id into DocumentInfo list.""" + doc_map: dict[str, dict[str, Any]] = {} + for item in results: + doc_id = item.get("parent_doc_id") + metadata = item.get("metadata", {}) + if doc_id and doc_id not in doc_map: + doc_map[doc_id] = { + "document_id": doc_id, + "filename": metadata.get("filename"), + "filesize": metadata.get("filesize"), + "filetype": metadata.get("filetype"), + "additional_info": { + "source_path": metadata.get("source_path", ""), + "content_hash": metadata.get("content_hash", ""), + **(metadata.get("additional_info") or {}), + }, + "chunk_count": 0, + } + if doc_id: + doc_map[doc_id]["chunk_count"] += 1 + return [ + DocumentInfo( + document_id=d["document_id"], + filename=d.get("filename"), + filesize=d.get("filesize"), + filetype=d.get("filetype"), + chunk_count=d["chunk_count"], + additional_info=d.get("additional_info"), + ) + for d in doc_map.values() + ] + + +from pymilvus import AsyncMilvusClient, DataType + +from backend.core.config import settings as app_settings +from backend.services.rag.config import RAGSettings +from backend.services.rag.embeddings import EmbeddingService + + +class MilvusVectorStore(BaseVectorStore): + """Milvus vector store implementation.""" + + def __init__(self, settings: RAGSettings, embedding_service: EmbeddingService): + self.settings = settings + self.embedder = embedding_service + self.client = AsyncMilvusClient( + uri=app_settings.milvus_uri, token=app_settings.milvus_token + ) + + async def _ensure_collection(self, name: str) -> None: + if not await self.client.has_collection(name): + schema = self.client.create_schema(auto_id=False) + schema.add_field("id", DataType.VARCHAR, is_primary=True, max_length=100) + schema.add_field("parent_doc_id", DataType.VARCHAR, max_length=100) + schema.add_field("content", DataType.VARCHAR, max_length=65535) + schema.add_field( + "vector", DataType.FLOAT_VECTOR, dim=self.settings.embeddings_config.dim + ) + schema.add_field("metadata", DataType.JSON) + await self.client.create_collection( + name, schema=schema, metric_type="COSINE" + ) + indexes = await self.client.list_indexes(name) + if not indexes: + index_params = self.client.prepare_index_params() + index_params.add_index( + field_name="vector", index_type="AUTOINDEX", metric_type="COSINE" + ) + await self.client.create_index( + collection_name=name, index_params=index_params + ) + await self.client.load_collection(name) + + async def insert_document(self, collection_name: str, document: Document) -> None: + await self._ensure_collection(collection_name) + if not document.chunked_pages: + raise ValueError("Document has no chunked pages.") + vectors = self.embedder.embed_document(document) + data = [ + { + "id": chunk.chunk_id, + "parent_doc_id": chunk.parent_doc_id, + "content": chunk.chunk_content, + "vector": vectors[i], + "metadata": self._build_chunk_metadata(chunk, document), + } + for i, chunk in enumerate(document.chunked_pages) + ] + await self.client.insert(collection_name, data=data) + + async def search( + self, collection_name: str, query: str, limit: int = 4, filter: str = "" + ) -> list[SearchResult]: + query_vector = self.embedder.embed_query(query) + results = await self.client.search( + collection_name=collection_name, + data=[query_vector], + limit=limit, + filter=filter, + output_fields=["content", "parent_doc_id", "metadata"], + ) + return [ + SearchResult( + content=hit["entity"]["content"], + score=hit["distance"], + metadata=hit["entity"]["metadata"], + parent_doc_id=hit["entity"]["parent_doc_id"], + ) + for hit in results[0] + ] + + async def get_collection_info(self, collection_name: str) -> CollectionInfo: + count = await self.client.get_collection_stats(collection_name) + return CollectionInfo( + name=collection_name, + total_vectors=count.get("row_count", 0), + dim=self.settings.embeddings_config.dim, + ) + + async def delete_collection(self, collection_name: str) -> None: + await self.client.drop_collection(collection_name) + + async def delete_document(self, collection_name: str, document_id: str) -> None: + sanitized = self._sanitize_id(document_id) + await self.client.delete( + collection_name=collection_name, filter=f'parent_doc_id == "{sanitized}"' + ) + + async def get_documents(self, collection_name: str) -> list[DocumentInfo]: + await self._ensure_collection(collection_name) + results = await self.client.query( + collection_name=collection_name, + filter="", + output_fields=["parent_doc_id", "metadata"], + limit=10000, + ) + return self._group_documents(results) + + async def list_collections(self) -> list[str]: + result: list[str] = await self.client.list_collections() + return result diff --git a/backend/services/rag_document.py b/backend/services/rag_document.py new file mode 100644 index 0000000..7631342 --- /dev/null +++ b/backend/services/rag_document.py @@ -0,0 +1,280 @@ +"""RAG document service (PostgreSQL async). + +Contains business logic for tracking RAG document ingestion, status updates, +file downloads, cascading deletions across DB+vector store+file storage, and +upload-and-dispatch orchestration for the Celery/Taskiq/ARQ worker. +""" + +import logging +import os +from datetime import UTC, datetime +from pathlib import Path +from typing import Any +from uuid import UUID + +from sqlalchemy.ext.asyncio import AsyncSession + +from backend.core.config import settings +from backend.core.exceptions import BadRequestError, NotFoundError +from backend.db.models.rag_document import RAGDocument +from backend.repositories import rag_document_repo +from backend.schemas.rag import RAGIngestResponse, RAGTrackedDocumentItem, RAGTrackedDocumentList +from backend.services.file_storage import get_file_storage +from backend.services.rag.config import get_supported_formats + +logger = logging.getLogger(__name__) + + +class RAGDocumentService: + """Service for RAG document tracking and lifecycle management.""" + + def __init__(self, db: AsyncSession): + self.db = db + + async def list_documents( + self, + collection_name: str | None = None, + ) -> RAGTrackedDocumentList: + """List tracked RAG documents, optionally filtered by collection.""" + docs = await rag_document_repo.get_all(self.db, collection_name) + return RAGTrackedDocumentList( + items=[ + RAGTrackedDocumentItem( + id=str(d.id), + collection_name=d.collection_name, + filename=d.filename, + filesize=d.filesize, + filetype=d.filetype, + status=d.status, + error_message=d.error_message, + vector_document_id=d.vector_document_id, + chunk_count=d.chunk_count, + has_file=bool(d.storage_path), + created_at=d.created_at.isoformat() if d.created_at else None, + completed_at=d.completed_at.isoformat() if d.completed_at else None, + ) + for d in docs + ], + total=len(docs), + ) + + async def get_document(self, doc_id: str) -> RAGDocument: + """Get a RAG document by ID. + + Raises: + NotFoundError: If document does not exist. + """ + doc = await rag_document_repo.get_by_id(self.db, UUID(doc_id)) + if not doc: + raise NotFoundError( + message="Document not found", + details={"doc_id": doc_id}, + ) + return doc + + async def create_document( + self, + *, + collection_name: str, + filename: str, + filesize: int, + filetype: str, + storage_path: str | None = None, + ) -> RAGDocument: + """Create a new RAG document tracking record.""" + return await rag_document_repo.create( + self.db, + collection_name=collection_name, + filename=filename, + filesize=filesize, + filetype=filetype, + storage_path=storage_path or "", + ) + + async def dispatch_upload( + self, + *, + collection_name: str, + file_data: bytes, + filename: str, + replace: bool, + vector_store: Any, + ) -> RAGIngestResponse: + """Validate, persist, and queue an uploaded file for ingestion. + + Performs: + 1. file-extension and size validation (against ``settings``); + 2. permanent storage via ``FileStorage``; + 3. RAGDocument tracking-record creation; + 4. lazy creation of the target vector collection; + 5. tmp-copy under ``MEDIA_DIR/_rag_tmp`` (shared with worker container); + 6. dispatch of the ingestion task on the configured task backend. + """ + allowed = get_supported_formats(getattr(settings, "pdf_parser", "pymupdf")) + max_size = settings.max_upload_size_mb * 1024 * 1024 + + ext = Path(filename).suffix.lower() + if ext not in allowed: + raise BadRequestError( + message=f"File type '{ext}' not supported", + details={"ext": ext, "allowed": sorted(allowed)}, + ) + if len(file_data) > max_size: + raise BadRequestError( + message=f"File too large. Maximum {settings.max_upload_size_mb}MB.", + details={"size": len(file_data), "max_mb": settings.max_upload_size_mb}, + ) + + storage = get_file_storage() + storage_path = await storage.save(f"rag/{collection_name}", filename, file_data) + rag_doc = await self.create_document( + collection_name=collection_name, + filename=filename, + filesize=len(file_data), + filetype=ext.lstrip("."), + storage_path=storage_path, + ) + doc_id = rag_doc.id + + # Ensure the target collection exists before the worker starts + await vector_store.create_collection(collection_name) + + # Stage the upload in the volume shared with the worker container + tmp_dir = os.path.join(str(settings.media_dir), "_rag_tmp") + os.makedirs(tmp_dir, exist_ok=True) + tmp_path = os.path.join(tmp_dir, f"{doc_id!s}{ext}") + with open(tmp_path, "wb") as f: + f.write(file_data) + from backend.worker.dispatcher import get_dispatcher + + get_dispatcher().delay( + "ingest_document_task", + rag_document_id=str(doc_id), + collection_name=collection_name, + filepath=tmp_path, + source_path=filename, + replace=replace, + ) + + return RAGIngestResponse( + id=str(doc_id), + status="processing", + filename=filename, + collection=collection_name, + message="File accepted. Processing in background.", + ) + + async def complete_ingestion( + self, + doc_id: str, + vector_document_id: str, + chunk_count: int = 0, + ) -> None: + """Mark a document as successfully ingested.""" + doc = await self.get_document(doc_id) + await rag_document_repo.update_status( + self.db, + doc.id, + status="done", + vector_document_id=vector_document_id, + chunk_count=chunk_count, + completed_at=datetime.now(UTC), + ) + + async def fail_ingestion(self, doc_id: str, error_message: str) -> None: + """Mark a document ingestion as failed.""" + doc = await self.get_document(doc_id) + await rag_document_repo.update_status( + self.db, + doc.id, + status="error", + error_message=error_message, + completed_at=datetime.now(UTC), + ) + + async def retry_ingestion(self, doc_id: str) -> RAGDocument: + """Reset a failed document for re-ingestion. + + Raises: + NotFoundError: If document does not exist. + ValueError: If document status is not 'error'. + """ + doc = await self.get_document(doc_id) + if doc.status != "error": + raise ValueError("Only failed documents can be retried") + updated = await rag_document_repo.update_status( + self.db, + doc.id, + status="processing", + error_message="", + completed_at=None, + ) + if updated is None: + raise NotFoundError(message="Document not found", details={"doc_id": doc_id}) + return updated + + async def delete_document( + self, + doc_id: str, + ingestion_service: Any = None, + ) -> None: + """Delete a document with cascading cleanup. + + Removes the record from the database and attempts to clean up + the vector store entry and stored file. Failures in cleanup + are logged but do not prevent the DB deletion. + """ + doc = await self.get_document(doc_id) + + # Cascade: vector store + if doc.vector_document_id and ingestion_service: + try: + await ingestion_service.remove_document(doc.collection_name, doc.vector_document_id) + except Exception as e: + logger.warning(f"Failed to delete from vector store: {e}") + + # Cascade: file storage + if doc.storage_path: + try: + storage = get_file_storage() + await storage.delete(doc.storage_path) + except Exception as e: + logger.warning(f"Failed to delete file: {e}") + + # Cascade: DB record + await rag_document_repo.delete(self.db, doc.id) + + async def delete_by_collection(self, collection_name: str) -> int: + """Delete all RAG document records for a collection. + + Returns: + Number of deleted records. + """ + return await rag_document_repo.delete_by_collection(self.db, collection_name) + + async def get_download_info(self, doc_id: str) -> tuple[str, str, str]: + """Get file download information for a document. + + Returns: + Tuple of (file_path, filename, mime_type). + + Raises: + NotFoundError: If document or its file does not exist. + """ + doc = await self.get_document(doc_id) + if not doc.storage_path: + raise NotFoundError(message="No file stored for this document") + + storage = get_file_storage() + file_path = storage.get_full_path(doc.storage_path) + if not file_path: + raise NotFoundError(message="File not found on disk") + + mime_map = { + "pdf": "application/pdf", + "docx": "application/vnd.openxmlformats-officedocument.wordprocessingml.document", + "txt": "text/plain", + "md": "text/markdown", + } + mime_type = mime_map.get(doc.filetype, "application/octet-stream") + return str(file_path), doc.filename, mime_type diff --git a/backend/services/rag_status.py b/backend/services/rag_status.py new file mode 100644 index 0000000..f4fb250 --- /dev/null +++ b/backend/services/rag_status.py @@ -0,0 +1,49 @@ +"""RAG status streaming — Redis pub/sub fan-out for SSE clients.""" + +import asyncio +import logging +from collections.abc import AsyncIterator + +import redis.asyncio as aioredis +from fastapi.sse import ServerSentEvent + +from backend.core.config import settings + +logger = logging.getLogger(__name__) + + +class RAGStatusService: + """Streams RAG ingestion status events from Redis pub/sub.""" + + CHANNEL = "rag_status" + + async def stream_events(self) -> AsyncIterator[ServerSentEvent]: + """Yield ``ServerSentEvent`` items as they arrive on the ``rag_status`` channel. + + The Redis client is created per-stream (one connection per subscriber). Cleanup is + guaranteed via ``finally`` even if the consumer disconnects mid-stream. + """ + client = aioredis.from_url(settings.valkey_url) # type: ignore[no-untyped-call] + pubsub = client.pubsub() + await pubsub.subscribe(self.CHANNEL) + event_id = 0 + + try: + async for message in pubsub.listen(): + if message["type"] != "message": + continue + payload = message["data"] + data = payload.decode() if isinstance(payload, bytes) else payload + event_id += 1 + yield ServerSentEvent(raw_data=data, event="status", id=str(event_id)) + except asyncio.CancelledError: + # Client disconnected — propagate cancellation cleanly + raise + except Exception as exc: + logger.warning("RAG SSE stream error: %s", exc) + finally: + try: + await pubsub.unsubscribe(self.CHANNEL) + await client.aclose() + except Exception as exc: + logger.debug("RAG SSE cleanup error: %s", exc) diff --git a/backend/services/rag_sync.py b/backend/services/rag_sync.py new file mode 100644 index 0000000..1690dd3 --- /dev/null +++ b/backend/services/rag_sync.py @@ -0,0 +1,152 @@ +"""RAG sync service (PostgreSQL async). + +Contains business logic for managing RAG synchronization operations +and their associated log entries. +""" + +from datetime import UTC, datetime +from uuid import UUID + +from sqlalchemy.ext.asyncio import AsyncSession + +from backend.core.exceptions import NotFoundError +from backend.db.models.sync_log import SyncLog +from backend.repositories import sync_log_repo +from backend.schemas.rag import RAGSyncLogItem, RAGSyncLogList + + +class RAGSyncService: + """Service for RAG sync operation management.""" + + def __init__(self, db: AsyncSession): + self.db = db + + async def list_sync_logs( + self, + collection_name: str | None = None, + limit: int = 20, + ) -> RAGSyncLogList: + """List sync operation logs, optionally filtered by collection.""" + logs = await sync_log_repo.get_all(self.db, collection_name=collection_name, limit=limit) + return RAGSyncLogList( + items=[ + RAGSyncLogItem( + id=str(log.id), + source=log.source, + collection_name=log.collection_name, + status=log.status, + mode=log.mode, + total_files=log.total_files, + ingested=log.ingested, + updated=log.updated, + skipped=log.skipped, + failed=log.failed, + error_message=log.error_message, + started_at=log.started_at.isoformat() if log.started_at else None, + completed_at=log.completed_at.isoformat() if log.completed_at else None, + ) + for log in logs + ], + total=len(logs), + ) + + async def get_sync_log(self, sync_id: str) -> SyncLog: + """Get a sync log by ID. + + Raises: + NotFoundError: If sync log does not exist. + """ + log = await sync_log_repo.get_by_id(self.db, UUID(sync_id)) + if not log: + raise NotFoundError( + message="Sync log not found", + details={"sync_id": sync_id}, + ) + return log + + async def create_sync_log( + self, + *, + source: str, + collection_name: str, + mode: str, + ) -> SyncLog: + """Create a new sync log entry.""" + return await sync_log_repo.create( + self.db, + source=source, + collection_name=collection_name, + mode=mode, + ) + + async def start_local_sync( + self, + *, + collection_name: str, + mode: str, + path: str | None, + ) -> SyncLog: + """Persist a sync log and dispatch the local-sync task on the configured backend.""" + sync_log = await self.create_sync_log( + source="local", + collection_name=collection_name, + mode=mode, + ) + from backend.worker.dispatcher import get_dispatcher + + get_dispatcher().delay( + "sync_collection_task", + sync_log_id=str(sync_log.id), + source="local", + collection_name=collection_name, + mode=mode, + path=path, + ) + return sync_log + + async def complete_sync( + self, + sync_id: str, + *, + status: str, + total_files: int = 0, + ingested: int = 0, + updated: int = 0, + skipped: int = 0, + failed: int = 0, + error_message: str | None = None, + ) -> None: + """Mark a sync operation as completed (done or error).""" + log = await self.get_sync_log(sync_id) + await sync_log_repo.update_status( + self.db, + log.id, + status=status, + total_files=total_files, + ingested=ingested, + updated=updated, + skipped=skipped, + failed=failed, + error_message=error_message, + completed_at=datetime.now(UTC), + ) + + async def cancel_sync(self, sync_id: str) -> SyncLog: + """Cancel a running sync operation. + + Raises: + NotFoundError: If sync log does not exist. + ValueError: If sync is not in 'running' state. + """ + log = await self.get_sync_log(sync_id) + if log.status != "running": + raise ValueError("Sync is not running") + cancelled = await sync_log_repo.update_status( + self.db, + log.id, + status="cancelled", + completed_at=datetime.now(UTC), + ) + if cancelled is None: + raise NotFoundError(message="Sync log not found", details={"sync_id": sync_id}) + return cancelled diff --git a/backend/services/sync_source.py b/backend/services/sync_source.py new file mode 100644 index 0000000..a45caeb --- /dev/null +++ b/backend/services/sync_source.py @@ -0,0 +1,189 @@ +"""Sync source service (PostgreSQL async). + +Contains business logic for managing RAG sync source configurations +and triggering sync operations. +""" + +import json +from datetime import UTC, datetime +from uuid import UUID + +from sqlalchemy.ext.asyncio import AsyncSession + +from backend.core.exceptions import BadRequestError, NotFoundError +from backend.db.models.sync_log import SyncLog +from backend.db.models.sync_source import SyncSource +from backend.repositories import sync_log_repo +from backend.repositories import sync_source_repo +from backend.schemas.sync_source import ( + ConnectorConfigField, + ConnectorInfo, + ConnectorList, + SyncSourceCreate, + SyncSourceList, + SyncSourceRead, + SyncSourceUpdate, +) +from backend.rag.connectors import CONNECTOR_REGISTRY + + +class SyncSourceService: + """Service for managing sync source configurations.""" + + def __init__(self, db: AsyncSession): + self.db = db + + def _to_read(self, s: SyncSource) -> SyncSourceRead: + return SyncSourceRead( + id=str(s.id), + name=s.name, + connector_type=s.connector_type, + collection_name=s.collection_name, + config=s.config + if isinstance(s.config, dict) + else json.loads(s.config) + if s.config + else {}, + sync_mode=s.sync_mode, + schedule_minutes=s.schedule_minutes, + is_active=s.is_active, + last_sync_at=s.last_sync_at.isoformat() if s.last_sync_at else None, + last_sync_status=s.last_sync_status, + last_error=s.last_error, + created_at=s.created_at.isoformat() if s.created_at else None, + ) + + async def list_sources( + self, + is_active: bool | None = None, + ) -> SyncSourceList: + """List all sync sources, optionally filtered by active status.""" + sources = await sync_source_repo.get_all(self.db, is_active=is_active) + return SyncSourceList(items=[self._to_read(s) for s in sources], total=len(sources)) + + async def get_source(self, source_id: str) -> SyncSource: + """Get a sync source by ID. + + Raises: + NotFoundError: If sync source does not exist. + """ + source = await sync_source_repo.get_by_id(self.db, UUID(source_id)) + if not source: + raise NotFoundError( + message="Sync source not found", + details={"source_id": source_id}, + ) + return source + + async def create_source(self, data: SyncSourceCreate) -> SyncSourceRead: + """Create a new sync source. + + Validates the connector type and its configuration before creating. + + Raises: + BadRequestError: If connector type is unknown or config is invalid. + """ + if data.connector_type not in CONNECTOR_REGISTRY: + raise BadRequestError( + message=f"Unknown connector type: {data.connector_type}", + details={"connector_type": data.connector_type}, + ) + + connector_cls = CONNECTOR_REGISTRY[data.connector_type] + connector = connector_cls() + is_valid, error = await connector.validate_config(data.config) + if not is_valid: + raise BadRequestError( + message=f"Invalid connector config: {error}", + details={"connector_type": data.connector_type}, + ) + + source = await sync_source_repo.create( + self.db, + name=data.name, + connector_type=data.connector_type, + collection_name=data.collection_name, + config=data.config, + sync_mode=data.sync_mode, + schedule_minutes=data.schedule_minutes, + ) + return self._to_read(source) + + async def update_source(self, source_id: str, data: SyncSourceUpdate) -> SyncSourceRead: + """Update an existing sync source. + + Raises: + NotFoundError: If sync source does not exist. + """ + await self.get_source(source_id) # verify exists + updates = data.model_dump(exclude_unset=True) + source = await sync_source_repo.update(self.db, UUID(source_id), **updates) + if source is None: + raise NotFoundError(message="Sync source not found", details={"source_id": source_id}) + return self._to_read(source) + + async def delete_source(self, source_id: str) -> None: + """Delete a sync source. + + Raises: + NotFoundError: If sync source does not exist. + """ + await self.get_source(source_id) # verify exists + await sync_source_repo.delete(self.db, UUID(source_id)) + + async def trigger_sync(self, source_id: str) -> SyncLog: + """Trigger a manual sync — persists a SyncLog and dispatches the task. + + Raises: + NotFoundError: If sync source does not exist. + """ + source = await self.get_source(source_id) + sync_log = await sync_log_repo.create( + self.db, + source=source.connector_type, + collection_name=source.collection_name, + mode=source.sync_mode, + sync_source_id=source.id, + ) + from backend.worker.dispatcher import get_dispatcher + + get_dispatcher().delay( + "sync_single_source_task", + source_id=source_id, + sync_log_id=str(sync_log.id), + ) + return sync_log + + async def update_after_sync( + self, + source_id: str, + status: str, + error: str | None = None, + ) -> None: + """Update sync source status after a sync operation completes.""" + await sync_source_repo.update_sync_status( + self.db, + UUID(source_id), + last_sync_at=datetime.now(UTC), + last_sync_status=status, + last_error=error, + ) + + @staticmethod + def list_connectors() -> ConnectorList: + """List available connector types with their config schemas.""" + items = [] + for _connector_type, connector_cls in CONNECTOR_REGISTRY.items(): + schema_fields = { + field_name: ConnectorConfigField(**field_spec) + for field_name, field_spec in connector_cls.CONFIG_SCHEMA.items() + } + items.append( + ConnectorInfo( + type=connector_cls.CONNECTOR_TYPE, + name=connector_cls.DISPLAY_NAME, + config_schema=schema_fields, + enabled=True, + ) + ) + return ConnectorList(items=items) diff --git a/backend/worker/__init__.py b/backend/worker/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/worker/arq_settings.py b/backend/worker/arq_settings.py new file mode 100644 index 0000000..0e93a67 --- /dev/null +++ b/backend/worker/arq_settings.py @@ -0,0 +1,26 @@ +"""Task dispatcher configuration. + +In the current implementation, tasks run in-process via +:class:`backend.worker.dispatcher.TaskDispatcher`. For production +deployments with multiple replicas, replace with a Redis-backed +queue (ARQ, Taskiq, or Celery). + +To switch to ARQ in the future:: + + pip install arq + arq backend.worker.arq_settings.WorkerSettings +""" + +from __future__ import annotations + +from backend.core.config import settings +from backend.worker.tasks.rag_tasks import TASK_REGISTRY + +# List of registered task functions for ARQ/Taskiq configuration +FUNCTIONS = list(TASK_REGISTRY.values()) + +# Redis connection info (for future ARQ/Taskiq worker setup) +REDIS_URL = settings.valkey_url + +__all__ = ["FUNCTIONS", "REDIS_URL"] + diff --git a/backend/worker/dispatcher.py b/backend/worker/dispatcher.py new file mode 100644 index 0000000..590283f --- /dev/null +++ b/backend/worker/dispatcher.py @@ -0,0 +1,91 @@ +"""Lightweight async task dispatcher. + +Dispatches background tasks to the configured backend. +Currently uses in-process ``asyncio.create_task`` for simplicity. +A Redis-backed ARQ/Taskiq worker can replace this for production use. +""" + +from __future__ import annotations + +import asyncio +import logging +from typing import Any + +logger = logging.getLogger(__name__) + + +class Task: + """Represents a background task to be executed.""" + + def __init__(self, name: str, kwargs: dict[str, Any]) -> None: + self.name = name + self.kwargs = kwargs + + +class TaskDispatcher: + """Dispatches background tasks. + + In the current implementation, tasks are executed in-process using + ``asyncio.create_task``. For production, swap the backend to use + Redis (ARQ or Taskiq) for cross-process execution. + """ + + def __init__(self) -> None: + self._running: set[asyncio.Task] = set() + + def delay(self, task_name: str, **kwargs: Any) -> None: + """Enqueue a task for background execution. + + This returns immediately. The task is executed asynchronously + in the event loop. + """ + task = asyncio.create_task(self._execute(task_name, kwargs)) + self._running.add(task) + task.add_done_callback(self._running.discard) + logger.debug("Dispatched task: %s (kwargs=%s)", task_name, kwargs) + + async def _execute(self, name: str, kwargs: dict[str, Any]) -> None: + """Look up the task function and run it.""" + from backend.worker.tasks.rag_tasks import TASK_REGISTRY + + fn = TASK_REGISTRY.get(name) + if fn is None: + logger.error("Unknown task: %s", name) + return + try: + await fn({"dispatcher": self}, **kwargs) + except Exception as exc: + logger.exception("Task %s failed: %s", name, exc) + + async def shutdown(self) -> None: + """Cancel all running tasks (called on app shutdown).""" + for task in self._running: + task.cancel() + if self._running: + await asyncio.gather(*self._running, return_exceptions=True) + self._running.clear() + + +# Module-level singleton +_dispatcher: TaskDispatcher | None = None + + +def get_dispatcher() -> TaskDispatcher: + """Return the shared TaskDispatcher singleton.""" + global _dispatcher + if _dispatcher is None: + _dispatcher = TaskDispatcher() + return _dispatcher + + +def shutdown_dispatcher() -> None: + """Shutdown and clear the dispatcher singleton.""" + global _dispatcher + if _dispatcher is not None: + # Schedule shutdown on the running event loop + try: + loop = asyncio.get_running_loop() + loop.create_task(_dispatcher.shutdown()) + except RuntimeError: + pass # No running loop — nothing to clean up + _dispatcher = None diff --git a/backend/worker/tasks/__init__.py b/backend/worker/tasks/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/worker/tasks/rag_tasks.py b/backend/worker/tasks/rag_tasks.py new file mode 100644 index 0000000..c997268 --- /dev/null +++ b/backend/worker/tasks/rag_tasks.py @@ -0,0 +1,269 @@ +"""Task functions for RAG ingestion and sync operations. + +Each function receives a ``ctx`` dict. Tasks are registered in +``TASK_REGISTRY`` and dispatched by :class:`TaskDispatcher`. +""" + +from __future__ import annotations + +import logging +from pathlib import Path +from typing import Any + +from backend.core.config import settings +from backend.services.rag_document import RAGDocumentService +from backend.services.rag_sync import RAGSyncService + +logger = logging.getLogger(__name__) + +# Registry: maps task name -> async callable(ctx, **kwargs) +TASK_REGISTRY: dict[str, Any] = {} + + +def _register(fn): + """Decorator to register a task function.""" + TASK_REGISTRY[fn.__name__] = fn + return fn + + +@_register +async def ingest_document_task( + ctx: dict, + rag_document_id: str, + collection_name: str, + filepath: str, + source_path: str, + replace: bool = True, +) -> dict: + """Process a single file through the RAG ingestion pipeline. + + Called by :meth:`RAGDocumentService.dispatch_upload`. + + Steps: + 1. Build the IngestionService + 2. Parse, chunk, embed, and store the document + 3. Mark the tracked record as done (or error) + """ + logger.info( + "[WORKER] Starting ingestion: doc=%s, collection=%s, file=%s", + rag_document_id, + collection_name, + filepath, + ) + + from backend.services.rag.embeddings import EmbeddingService + from backend.services.rag.documents import DocumentProcessor + from backend.services.rag.vectorstore import MilvusVectorStore as VectorStore + from backend.services.rag.ingestion import IngestionService + + try: + rag_settings = settings.rag + embed_service = EmbeddingService(settings=rag_settings) + vector_store = VectorStore(settings=rag_settings, embedding_service=embed_service) + processor = DocumentProcessor(settings=rag_settings) + ingestion = IngestionService(processor=processor, vector_store=vector_store) + + result = await ingestion.ingest_file( + filepath=Path(filepath), + collection_name=collection_name, + replace=replace, + source_path=source_path, + ) + + # Create a DB session to update the tracked document record + from backend.core.database import open_session + + async with open_session() as session: + doc_service = RAGDocumentService(db=session) + if result.status.value == "done" and result.document_id: + await doc_service.complete_ingestion( + doc_id=rag_document_id, + vector_document_id=result.document_id, + chunk_count=0, # could be tracked from result + ) + elif result.status.value == "error": + await doc_service.fail_ingestion( + doc_id=rag_document_id, + error_message=result.error_message or "Unknown error", + ) + + logger.info( + "[WORKER] Ingestion complete: doc=%s, status=%s", + rag_document_id, + result.status, + ) + return {"status": result.status.value, "document_id": result.document_id} + + except Exception as exc: + logger.exception("[WORKER] Ingestion failed: doc=%s", rag_document_id) + from backend.core.database import open_session + + async with open_session() as session: + doc_service = RAGDocumentService(db=session) + await doc_service.fail_ingestion( + doc_id=rag_document_id, + error_message=str(exc), + ) + return {"status": "error", "message": str(exc)} + + +@_register +async def sync_collection_task( + ctx: dict, + sync_log_id: str, + source: str, + collection_name: str, + mode: str, + path: str | None = None, +) -> dict: + """Sync a local directory or source into the RAG collection. + + Called by :meth:`RAGSyncService.start_local_sync`. + """ + logger.info( + "[WORKER] Starting sync: log=%s, collection=%s, mode=%s, path=%s", + sync_log_id, + collection_name, + mode, + path, + ) + + from backend.services.rag.embeddings import EmbeddingService + from backend.services.rag.documents import DocumentProcessor + from backend.services.rag.vectorstore import MilvusVectorStore as VectorStore + from backend.services.rag.ingestion import IngestionService + + try: + rag_settings = settings.rag + embed_service = EmbeddingService(settings=rag_settings) + vector_store = VectorStore(settings=rag_settings, embedding_service=embed_service) + processor = DocumentProcessor(settings=rag_settings) + ingestion = IngestionService(processor=processor, vector_store=vector_store) + + ingested = 0 + failed = 0 + skipped = 0 + + if path and Path(path).is_dir(): + allowed_exts = {".pdf", ".docx", ".txt", ".md"} + files = [p for p in Path(path).iterdir() if p.suffix.lower() in allowed_exts] + total = len(files) + logger.info("[WORKER] Found %d files to sync from %s", total, path) + + for filepath in files: + result = await ingestion.ingest_file( + filepath=filepath, + collection_name=collection_name, + replace=(mode == "full"), + source_path=str(filepath), + ) + if result.status.value == "done": + ingested += 1 + elif result.status.value == "error": + failed += 1 + else: + skipped += 1 + else: + total = 0 + logger.warning("[WORKER] No valid path provided for sync") + + from backend.core.database import open_session + + async with open_session() as session: + sync_service = RAGSyncService(db=session) + await sync_service.complete_sync( + sync_id=sync_log_id, + status="done" if failed == 0 else "partial", + total_files=total, + ingested=ingested, + updated=0, + skipped=skipped, + failed=failed, + ) + + logger.info( + "[WORKER] Sync complete: log=%s, ingested=%d, failed=%d", + sync_log_id, + ingested, + failed, + ) + return { + "status": "done", + "total": total, + "ingested": ingested, + "failed": failed, + "skipped": skipped, + } + + except Exception as exc: + logger.exception("[WORKER] Sync failed: log=%s", sync_log_id) + from backend.core.database import open_session + + async with open_session() as session: + sync_service = RAGSyncService(db=session) + await sync_service.complete_sync( + sync_id=sync_log_id, + status="error", + error_message=str(exc), + ) + return {"status": "error", "message": str(exc)} + + +@_register +async def sync_single_source_task( + ctx: dict, + source_id: str, + sync_log_id: str, +) -> dict: + """Sync a single configured sync source. + + Called by :meth:`SyncSourceService.trigger_sync`. + """ + logger.info("[WORKER] Starting source sync: source=%s, log=%s", source_id, sync_log_id) + + from backend.core.database import open_session + from backend.services.sync_source import SyncSourceService + + try: + async with open_session() as session: + source_service = SyncSourceService(db=session) + source = await source_service.get_source(source_id) + + # For local-file connectors, sync the configured path + connector_type = source.connector_type + config = source.config if isinstance(source.config, dict) else {} + + if "path" in config: + result = await sync_collection_task( + ctx, + sync_log_id=sync_log_id, + source=connector_type, + collection_name=source.collection_name, + mode=source.sync_mode, + path=config["path"], + ) + + await source_service.update_after_sync( + source_id=source_id, + status=result.get("status", "error"), + error=result.get("message"), + ) + return result + else: + await source_service.update_after_sync( + source_id=source_id, + status="error", + error="No path configured in source config", + ) + return {"status": "error", "message": "No path configured"} + + except Exception as exc: + logger.exception("[WORKER] Source sync failed: source=%s", source_id) + async with open_session() as session: + source_service = SyncSourceService(db=session) + await source_service.update_after_sync( + source_id=source_id, + status="error", + error=str(exc), + ) + return {"status": "error", "message": str(exc)} diff --git a/docker-compose.yml b/docker-compose.yml index 7f2e542..ba5540c 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -28,6 +28,11 @@ services: - LLM_MODEL=${LLM_MODEL:-llama} - DATABASE_URL=${DATABASE_URL:-postgresql+asyncpg://agent_alpha:agent_alpha@postgres:5432/agent_alpha} - VALKEY_URL=${VALKEY_URL:-redis://valkey:6379/0} + # RAG / Milvus + - MILVUS_URI=${MILVUS_URI:-http://milvus:19530} + - MILVUS_TOKEN=${MILVUS_TOKEN:-} + - MEDIA_DIR=${MEDIA_DIR:-/app/media} + - MAX_UPLOAD_SIZE_MB=${MAX_UPLOAD_SIZE_MB:-50} restart: unless-stopped depends_on: @@ -35,6 +40,8 @@ services: condition: service_healthy valkey: condition: service_healthy + milvus: + condition: service_healthy # Uncomment to use a local .env file instead of env vars: # env_file: # - .env @@ -89,6 +96,68 @@ services: retries: 10 restart: unless-stopped + # ── Milvus Vector Database ────────────────────────────────────── + # Requires etcd (coordinator) and MinIO (storage). + milvus: + image: milvusdb/milvus:v2.5-latest + container_name: agent-alpha-milvus + ports: + - "19530:19530" + environment: + ETCD_ENDPOINTS: etcd:2379 + MINIO_ADDRESS: minio:9000 + MINIO_ACCESS_KEY: minioadmin + MINIO_SECRET_KEY: minioadmin + volumes: + - milvus_data:/var/lib/milvus + depends_on: + etcd: + condition: service_healthy + minio: + condition: service_healthy + healthcheck: + test: ["CMD", "curl", "-f", "http://localhost:9091/health"] + interval: 10s + timeout: 5s + retries: 10 + restart: unless-stopped + + etcd: + image: quay.io/coreos/etcd:v3.5.17 + container_name: agent-alpha-etcd + environment: + ETCD_AUTO_COMPACTION_MODE: revision + ETCD_AUTO_COMPACTION_RETENTION: "1000" + ETCD_QUOTA_BACKEND_BYTES: "4294967296" + ETCD_SNAPSHOT_COUNT: "50000" + volumes: + - etcd_data:/etcd + healthcheck: + test: ["CMD", "etcdctl", "endpoint", "health"] + interval: 10s + timeout: 5s + retries: 10 + restart: unless-stopped + + minio: + image: minio/minio:RELEASE.2024-11-17T22-20-09Z + container_name: agent-alpha-minio + ports: + - "9000:9000" + - "9001:9001" + environment: + MINIO_ACCESS_KEY: minioadmin + MINIO_SECRET_KEY: minioadmin + volumes: + - minio_data:/data + command: server /data --console-address ":9001" + healthcheck: + test: ["CMD", "curl", "-f", "http://localhost:9000/minio/health/live"] + interval: 10s + timeout: 5s + retries: 10 + restart: unless-stopped + # ── Ollama (optional — uncomment to run the LLM locally) ──────── # ollama: # image: ollama/ollama:latest @@ -109,4 +178,7 @@ services: volumes: postgres_data: valkey_data: + milvus_data: + etcd_data: + minio_data: # ollama_data: diff --git a/pyproject.toml b/pyproject.toml index eba11f1..182df00 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -20,4 +20,16 @@ dependencies = [ "bcrypt>=4.2.0", "redis[hiredis]>=5.2.0", "sqlalchemy[asyncio]>=2.0.36", + "pymupdf>=1.27.2.3", + "python-docx>=1.2.0", + "langchain-text-splitters>=1.1.2", + "sentence-transformers>=5.5.1", + # Vector Database + "pymilvus>=2.5.0", + # Async Task Queue (lightweight, redis-based) + # (using redis directly instead of arq to avoid dep conflicts) + # Embeddings + "openai>=1.0.0", + # Hybrid Search + "rank-bm25>=0.2.2", ] diff --git a/uv.lock b/uv.lock index 1d4332b..95c4ef0 100644 --- a/uv.lock +++ b/uv.lock @@ -2,9 +2,15 @@ version = 1 revision = 3 requires-python = ">=3.12" resolution-markers = [ - "python_full_version >= '3.14'", - "python_full_version == '3.13.*'", - "python_full_version < '3.13'", + "python_full_version >= '3.14' and sys_platform == 'win32'", + 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