feat(pipeline): MC Quality Overhaul — 74.5% → 92.8% accuracy, 5.3K → 13.6K MCs
Phase 0: Quality Audit script (Claude Sonnet, 1750 samples) Phase 1: Object ontology expanded 31 → 74 tokens with descriptions + boundaries Phase 2: 174K controls re-classified via Haiku (10 batches, $50) - Generic tokens removed (documentation, procedure, process) - L2 sub-topics added (108K + 64K controls) - Bad subtopics fixed (stakeholder_*, escalation fragments) Phase 3: Re-clustering K=18704 (37K objects → 16.7K groups) Phase 4: Direct MC generation from canonical tokens (gpre2_direct_mc.py) Phase 5: Regulation-source split (gpre3, dry-run tested) New features: - Tenant-isolated document upload API (rag-service) - BAuA crawler (Playwright, 131 PDFs downloaded) - OSHA Technical Manual crawler (23 chapters) - CE obligation extractor (6141 obligations from Qdrant) RAG ingestion: - 126 BAuA PDFs (TRBS/TRGS/ASR): 27,664 chunks - OSHA Technical Manual: 7,241 chunks - OSHA 1910 Subpart O (full): 745 chunks - EuGH C-588/21 P: 216 chunks - EU 2018/1725: 842 chunks Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Benjamin Admin
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"""
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Tenant-isolated document upload, listing, and deletion.
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Each tenant gets their own Qdrant collection (bp_docs_tenant_{short_id}).
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Documents are stored in MinIO under tenant-specific paths.
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No data crosses tenant boundaries.
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Endpoints:
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POST /api/v1/tenant/documents - Upload + process PDF
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GET /api/v1/tenant/documents - List tenant's documents
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DELETE /api/v1/tenant/documents/{doc_id} - Delete document + vectors
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GET /api/v1/tenant/documents/{doc_id}/status - Processing status
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"""
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import json
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import logging
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import uuid
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from typing import Optional
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from fastapi import APIRouter, File, Form, HTTPException, Header, Request, UploadFile
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from pydantic import BaseModel
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from api.auth import optional_jwt_auth
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from embedding_client import embedding_client
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from html_utils import decode_html_bytes, looks_like_html, strip_html
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from minio_client_wrapper import minio_wrapper
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from qdrant_client_wrapper import qdrant_wrapper
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logger = logging.getLogger("rag-service.api.tenant-documents")
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router = APIRouter(prefix="/api/v1/tenant/documents")
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VECTOR_DIM = 1024 # bge-m3 dimension
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MAX_FILE_SIZE = 50 * 1024 * 1024 # 50 MB
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ALLOWED_TYPES = {"application/pdf", "text/html", "text/plain"}
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PDF_MAGIC = b"%PDF"
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def _collection_name(tenant_id: str) -> str:
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"""Derive tenant-specific Qdrant collection name."""
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short = tenant_id.replace("-", "")[:12]
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return f"bp_docs_tenant_{short}"
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def _storage_path(tenant_id: str, document_id: str, filename: str) -> str:
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"""Derive tenant-isolated storage path."""
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short = tenant_id.replace("-", "")[:12]
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return f"tenant_docs/{short}/{document_id}/{filename}"
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def _extract_tenant_id(
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request: Request,
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x_tenant_id: Optional[str] = Header(None),
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) -> str:
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"""Extract tenant ID from header. Required for all tenant endpoints."""
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tid = x_tenant_id or request.headers.get("x-tenant-id", "")
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if not tid:
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raise HTTPException(status_code=400, detail="X-Tenant-ID header required")
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return tid
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# ── Response models ────────────────────────────────────────────────
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class DocumentResponse(BaseModel):
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id: str
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filename: str
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file_size: int
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status: str
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chunk_count: int
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collection: str
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created_at: Optional[str] = None
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class DocumentListResponse(BaseModel):
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documents: list[DocumentResponse]
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total: int
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# ── Endpoints ──────────────────────────────────────────────────────
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@router.post("", response_model=DocumentResponse)
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async def upload_tenant_document(
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request: Request,
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file: UploadFile = File(...),
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x_tenant_id: Optional[str] = Header(None),
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chunk_size: int = Form(default=512),
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chunk_overlap: int = Form(default=50),
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metadata_json: Optional[str] = Form(default=None),
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):
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"""Upload a document, process it, and index in tenant-specific collection."""
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optional_jwt_auth(request)
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tenant_id = _extract_tenant_id(request, x_tenant_id)
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# Read + validate
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file_bytes = await file.read()
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if len(file_bytes) == 0:
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raise HTTPException(status_code=400, detail="Empty file")
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if len(file_bytes) > MAX_FILE_SIZE:
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raise HTTPException(status_code=413, detail=f"File too large (max {MAX_FILE_SIZE // 1024 // 1024} MB)")
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filename = file.filename or "document.pdf"
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content_type = file.content_type or "application/octet-stream"
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# PDF magic bytes check
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if filename.lower().endswith(".pdf") and not file_bytes[:4].startswith(PDF_MAGIC):
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raise HTTPException(status_code=400, detail="File claims to be PDF but magic bytes don't match")
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document_id = str(uuid.uuid4())
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collection = _collection_name(tenant_id)
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object_name = _storage_path(tenant_id, document_id, filename)
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# Ensure collection exists
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await qdrant_wrapper.create_collection(collection, VECTOR_DIM)
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# Store in MinIO
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try:
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await minio_wrapper.upload_document(
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object_name=object_name,
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data=file_bytes,
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content_type=content_type,
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metadata={"document_id": document_id, "tenant_id": tenant_id},
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)
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except Exception as exc:
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logger.error("MinIO upload failed for tenant %s: %s", tenant_id, exc)
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raise HTTPException(status_code=500, detail="Storage failed")
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# Extract text
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try:
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text = await _extract_text(file_bytes, filename, content_type)
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except Exception as exc:
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logger.error("Text extraction failed: %s", exc)
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raise HTTPException(status_code=500, detail=f"Text extraction failed: {exc}")
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if not text or not text.strip():
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raise HTTPException(status_code=400, detail="No text could be extracted")
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# Chunk
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chunk_result = await embedding_client.chunk_text(
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text=text, strategy="recursive",
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chunk_size=chunk_size, overlap=chunk_overlap,
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)
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chunks = chunk_result.chunks
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chunks_meta = chunk_result.chunks_with_metadata
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if not chunks:
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raise HTTPException(status_code=400, detail="Chunking produced zero chunks")
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# Embed
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embeddings = await embedding_client.generate_embeddings(chunks)
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# Parse extra metadata
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extra_metadata = {}
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if metadata_json:
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try:
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extra_metadata = json.loads(metadata_json)
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except json.JSONDecodeError:
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pass
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# Build payloads with tenant isolation
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_STRUCT_FIELDS = ("section", "section_title", "paragraph", "paragraph_num", "page")
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payloads = []
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for i, chunk in enumerate(chunks):
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payload = {
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"document_id": document_id,
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"tenant_id": tenant_id,
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"filename": filename,
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"chunk_index": i,
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"chunk_text": chunk,
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**extra_metadata,
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}
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if i < len(chunks_meta):
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for field in _STRUCT_FIELDS:
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value = chunks_meta[i].get(field)
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if value is not None and value != "":
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payload[field] = value
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payloads.append(payload)
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# Index in tenant collection
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indexed = await qdrant_wrapper.index_documents(
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collection=collection, vectors=embeddings, payloads=payloads,
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)
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logger.info(
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"Tenant %s: uploaded %s (%d chunks, %d vectors) to %s",
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tenant_id[:8], filename, len(chunks), indexed, collection,
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)
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return DocumentResponse(
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id=document_id, filename=filename,
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file_size=len(file_bytes), status="indexed",
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chunk_count=len(chunks), collection=collection,
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)
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@router.get("", response_model=DocumentListResponse)
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async def list_tenant_documents(
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request: Request,
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x_tenant_id: Optional[str] = Header(None),
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):
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"""List all documents for this tenant."""
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optional_jwt_auth(request)
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tenant_id = _extract_tenant_id(request, x_tenant_id)
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collection = _collection_name(tenant_id)
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try:
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# Get unique document_ids from Qdrant
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docs = await qdrant_wrapper.get_unique_documents(collection)
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except Exception:
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# Collection doesn't exist yet → no documents
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docs = []
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return DocumentListResponse(documents=docs, total=len(docs))
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@router.delete("/{doc_id}")
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async def delete_tenant_document(
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doc_id: str,
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request: Request,
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x_tenant_id: Optional[str] = Header(None),
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):
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"""Delete a document and all its vectors from tenant collection."""
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optional_jwt_auth(request)
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tenant_id = _extract_tenant_id(request, x_tenant_id)
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collection = _collection_name(tenant_id)
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errors = []
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# Delete vectors from Qdrant
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try:
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await qdrant_wrapper.delete_by_filter(
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collection=collection,
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filter_conditions={"document_id": doc_id},
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)
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except Exception as exc:
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errors.append(f"Qdrant: {exc}")
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# Delete file from MinIO
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try:
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prefix = f"tenant_docs/{tenant_id.replace('-', '')[:12]}/{doc_id}/"
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await minio_wrapper.delete_by_prefix(prefix)
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except Exception as exc:
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errors.append(f"MinIO: {exc}")
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if errors:
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logger.warning("Partial delete for %s/%s: %s", tenant_id[:8], doc_id[:8], errors)
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return {"deleted": True, "warnings": errors}
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logger.info("Tenant %s: deleted document %s", tenant_id[:8], doc_id[:8])
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return {"deleted": True, "document_id": doc_id}
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@router.get("/{doc_id}/status")
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async def document_status(
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doc_id: str,
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request: Request,
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x_tenant_id: Optional[str] = Header(None),
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):
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"""Get processing status for a document."""
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optional_jwt_auth(request)
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tenant_id = _extract_tenant_id(request, x_tenant_id)
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collection = _collection_name(tenant_id)
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try:
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count = await qdrant_wrapper.count_by_filter(
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collection=collection,
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filter_conditions={"document_id": doc_id},
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)
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status = "indexed" if count > 0 else "not_found"
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except Exception:
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count = 0
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status = "not_found"
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return {"document_id": doc_id, "status": status, "chunk_count": count}
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# ── Helpers ────────────────────────────────────────────────────────
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async def _extract_text(file_bytes: bytes, filename: str, content_type: str) -> str:
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"""Extract text from PDF, HTML, or plain text."""
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if content_type == "application/pdf" or filename.lower().endswith(".pdf"):
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return await embedding_client.extract_pdf(file_bytes)
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if filename.lower().endswith((".html", ".htm")):
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text = decode_html_bytes(file_bytes)
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return strip_html(text)
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text = file_bytes.decode("utf-8", errors="replace")
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if looks_like_html(text):
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return strip_html(text)
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return text
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