File size: 11,395 Bytes
1d9bd9b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 | """Private project-document extraction and optional Qdrant vector publishing."""
from __future__ import annotations
import hashlib
import json
import os
import re
import uuid
from pathlib import Path
from urllib.parse import quote
import numpy as np
import requests
from document_text import extract_document
from project_store import ProjectStore
class KnowledgeServiceError(Exception):
pass
def _chunks(text: str, *, size: int = 1_400, overlap: int = 180, limit: int = 64) -> list[str]:
paragraphs = [re.sub(r"\s+", " ", value).strip() for value in re.split(r"\n\s*\n", text) if value.strip()]
chunks: list[str] = []
current = ""
for paragraph in paragraphs:
pending = paragraph
while pending:
room = size - len(current)
if room <= 80:
chunks.append(current.strip())
current = current[-overlap:].lstrip()
room = size - len(current)
take = pending[:room]
split = take.rfind(" ") if len(pending) > room else len(take)
if split < max(80, room // 2):
split = len(take)
current = (current + " " + pending[:split]).strip()
pending = pending[split:].lstrip()
if len(chunks) >= limit:
return chunks[:limit]
if current and len(chunks) < limit:
chunks.append(current.strip())
return chunks[:limit]
class KnowledgeService:
def __init__(self, projects: ProjectStore, corpus):
self.projects = projects
self.corpus = corpus
self.qdrant_url = os.environ.get("QDRANT_URL", "").rstrip("/")
self.qdrant_key = os.environ.get("QDRANT_API_KEY", "")
self.collection = os.environ.get("QDRANT_KNOWLEDGE_COLLECTION", "moonley_tenant_knowledge")
self.supabase_url = os.environ.get("SUPABASE_URL", "").rstrip("/")
self.supabase_key = os.environ.get("SUPABASE_SERVICE_ROLE_KEY", "")
self.supabase_bucket = os.environ.get("SUPABASE_STORAGE_BUCKET", "")
@property
def qdrant_configured(self) -> bool:
return bool(self.qdrant_url and self.qdrant_key)
@property
def supabase_configured(self) -> bool:
return bool(self.supabase_url and self.supabase_key and self.supabase_bucket)
def status(self) -> dict:
return {
"source_provider": "supabase_private" if self.supabase_configured else ("mounted_volume" if self.projects.configured else "unconfigured"),
"vector_provider": "qdrant" if self.qdrant_configured else "private_mounted_volume",
"qdrant_configured": self.qdrant_configured,
"supabase_configured": self.supabase_configured,
"ocr": "tesseract",
"chat_embedding_default": False,
}
@staticmethod
def _owner_key(owner_id: str) -> str:
return hashlib.sha256(owner_id.encode("utf-8")).hexdigest()
def source_text(self, owner_id: str, project_id: str, document_id: str) -> tuple[str, dict]:
knowledge_dir = self.projects.knowledge_dir(owner_id, project_id, document_id)
cache_path = knowledge_dir / "content.json"
if cache_path.exists():
payload = json.loads(cache_path.read_text(encoding="utf-8"))
return str(payload.get("text") or ""), dict(payload.get("extraction") or {})
document = self.projects.document_record(owner_id, project_id, document_id)
result = extract_document(
self.projects.document_path(owner_id, project_id, document_id),
str(document.get("media_type") or ""),
)
extraction = result.public_dict()
temp = knowledge_dir / f".content-{uuid.uuid4().hex}.tmp"
temp.write_text(
json.dumps({"version": 1, "text": result.text, "extraction": extraction}, ensure_ascii=False),
encoding="utf-8",
)
os.replace(temp, cache_path)
self.projects.record_extraction(owner_id, project_id, document_id, extraction, status="extracted")
return result.text, extraction
def ingest(self, owner_id: str, project_id: str, document_id: str) -> None:
try:
if self.supabase_configured:
self._publish_supabase(owner_id, project_id, document_id)
text, extraction = self.source_text(owner_id, project_id, document_id)
chunks = _chunks(text)
if not chunks:
raise ValueError("No readable text was found in the document.")
vectors = self.corpus.encode_documents(chunks)
knowledge_dir = self.projects.knowledge_dir(owner_id, project_id, document_id)
chunk_payload = {
"version": 1,
"chunks": [{"id": index, "text": value} for index, value in enumerate(chunks)],
}
chunks_temp = knowledge_dir / f".chunks-{uuid.uuid4().hex}.tmp"
chunks_temp.write_text(json.dumps(chunk_payload, ensure_ascii=False), encoding="utf-8")
os.replace(chunks_temp, knowledge_dir / "chunks.json")
provider = "qdrant" if self.qdrant_configured else "private_mounted_volume"
if self.qdrant_configured:
self._publish_qdrant(owner_id, project_id, document_id, vectors)
else:
vector_temp = knowledge_dir / f".vectors-{uuid.uuid4().hex}.npy"
np.save(vector_temp, vectors)
os.replace(vector_temp, knowledge_dir / "vectors.npy")
self.projects.record_extraction(
owner_id,
project_id,
document_id,
{
**extraction,
"chunk_count": len(chunks),
"vector_provider": provider,
"source_provider": "supabase_private" if self.supabase_configured else "mounted_volume",
},
status="ready",
)
except Exception as exc:
try:
self.projects.record_extraction(
owner_id,
project_id,
document_id,
{"method": "failed", "text_chars": 0},
status="failed",
)
except Exception:
pass
print(f"[knowledge] ingestion failed document={document_id}: {type(exc).__name__}", flush=True)
def _supabase_headers(self, media_type: str | None = None) -> dict[str, str]:
headers = {
"apikey": self.supabase_key,
"Authorization": f"Bearer {self.supabase_key}",
"x-upsert": "true",
}
if media_type:
headers["Content-Type"] = media_type
return headers
def _source_object_path(self, owner_id: str, project_id: str, document: dict) -> str:
suffix = Path(str(document.get("stored_name") or "")).suffix.lower()
return f"users/{self._owner_key(owner_id)}/projects/{project_id}/documents/{document['id']}{suffix}"
def _publish_supabase(self, owner_id: str, project_id: str, document_id: str) -> None:
document = self.projects.document_record(owner_id, project_id, document_id)
object_path = self._source_object_path(owner_id, project_id, document)
endpoint = (
f"{self.supabase_url}/storage/v1/object/{quote(self.supabase_bucket, safe='')}/"
f"{quote(object_path, safe='/')}"
)
response = requests.post(
endpoint,
headers=self._supabase_headers(str(document.get("media_type") or "application/octet-stream")),
data=self.projects.document_path(owner_id, project_id, document_id).read_bytes(),
timeout=90,
)
if response.status_code not in {200, 201}:
raise KnowledgeServiceError(f"Private Supabase upload failed ({response.status_code}).")
def delete(self, owner_id: str, project_id: str, document_id: str) -> None:
document = self.projects.document_record(owner_id, project_id, document_id)
if self.qdrant_configured:
tenant_id = self._owner_key(owner_id)
response = requests.post(
f"{self.qdrant_url}/collections/{self.collection}/points/delete?wait=true",
headers=self._headers(),
timeout=45,
json={
"filter": {
"must": [
{"key": "tenant_id", "match": {"value": tenant_id}},
{"key": "project_id", "match": {"value": project_id}},
{"key": "document_id", "match": {"value": document_id}},
]
}
},
)
if response.status_code not in {200, 404}:
raise KnowledgeServiceError(f"Qdrant deletion failed ({response.status_code}).")
if self.supabase_configured:
object_path = self._source_object_path(owner_id, project_id, document)
endpoint = (
f"{self.supabase_url}/storage/v1/object/{quote(self.supabase_bucket, safe='')}/"
f"{quote(object_path, safe='/')}"
)
response = requests.delete(endpoint, headers=self._supabase_headers(), timeout=45)
if response.status_code not in {200, 404}:
raise KnowledgeServiceError(f"Private Supabase deletion failed ({response.status_code}).")
def delete_project(self, owner_id: str, project_id: str) -> None:
project = self.projects.get_project(owner_id, project_id)
for document in project.get("documents") or []:
self.delete(owner_id, project_id, str(document.get("id") or ""))
def _headers(self) -> dict[str, str]:
return {"api-key": self.qdrant_key, "Content-Type": "application/json"}
def _publish_qdrant(self, owner_id: str, project_id: str, document_id: str, vectors: np.ndarray) -> None:
collection_url = f"{self.qdrant_url}/collections/{self.collection}"
response = requests.get(collection_url, headers=self._headers(), timeout=15)
if response.status_code == 404:
response = requests.put(
collection_url,
headers=self._headers(),
timeout=30,
json={"vectors": {"size": int(vectors.shape[1]), "distance": "Cosine"}, "on_disk_payload": True},
)
response.raise_for_status()
tenant_id = self._owner_key(owner_id)
namespace = uuid.UUID("db853e8b-aeb1-47c8-a7fc-680c662ba8ee")
points = [
{
"id": str(uuid.uuid5(namespace, f"{tenant_id}:{project_id}:{document_id}:{index}")),
"vector": vector.tolist(),
"payload": {
"tenant_id": tenant_id,
"project_id": project_id,
"document_id": document_id,
"chunk_id": index,
},
}
for index, vector in enumerate(vectors)
]
for start in range(0, len(points), 32):
result = requests.put(
f"{collection_url}/points?wait=true",
headers=self._headers(),
timeout=90,
json={"points": points[start:start + 32]},
)
result.raise_for_status()
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