394 lines
20 KiB
Plaintext
394 lines
20 KiB
Plaintext
import base64, os, subprocess
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ns = params_kw.copy()
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kb_id = ns.get('kb_id', '')
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folder_id = ns.get('folder', '')
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file_name = ns.get('file_name', 'upload.bin')
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if not kb_id:
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return json.dumps({"status": "error", "error": "kb_id required"}, ensure_ascii=False)
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file_data = await request.read()
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if not file_data:
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return json.dumps({"status": "error", "error": "no file data"}, ensure_ascii=False)
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env = request._run_ns
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userorgid = await env.get_userorgid()
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file_size = len(file_data)
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# ---- ORG STORAGE QUOTA CHECK (per-org limit, not global) ----
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def fmt_bytes(n):
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if n < 1024: return str(n) + 'B'
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if n < 1048576: return str(round(n/1024, 1)) + 'KB'
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return str(round(n/1048576, 1)) + 'MB'
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quota_limit = 104857600
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used = 0
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async with get_sor_context(env, 'rag') as sor:
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rec = await sor.sqlExe("SELECT COALESCE(SUM(file_size),0) AS used FROM rag_documents WHERE org_id=${org_id}$", {"org_id": userorgid})
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if rec: used = int(rec[0].used)
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lim = await sor.sqlExe("SELECT limit_bytes FROM rag_org_storage_limits WHERE org_id=${org_id}$", {"org_id": userorgid})
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if lim: quota_limit = int(lim[0].limit_bytes)
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if used + file_size > quota_limit:
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return json.dumps({"status": "error", "error": "storage_quota_exceeded",
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"message": "存储配额超限：机构已用 " + fmt_bytes(used) + "，限额 " + fmt_bytes(quota_limit) + "，本文件 " + fmt_bytes(file_size)}, ensure_ascii=False)
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# Save file via FileStorage (returns web path e.g. /idfile/191/193/197/97/xxx.txt)
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web_path = await env.save_file(file_data, file_name)
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real_path = env.realpath(web_path)
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doc_id = str(uuid()).replace('-', '')[:16]
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ext = '.' + file_name.rsplit('.', 1)[1] if '.' in file_name else '.bin'
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ext_l = ext.lower()
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# ============================================================
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# BACKGROUND INGESTION — runs in a separate asyncio task after
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# the response is returned (background_reco = create_task wrapper).
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# SELF-CONTAINED: never touches env/request (invalid after response),
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# creates its own DBPools connection. All args are primitives.
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# ============================================================
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async def ingest_doc(doc_id, kb_id, file_name, ext_l, real_path):
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db = DBPools()
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# 库名必须经宿主的 get_module_dbname 映射（ragserver→'rag'，pipeline-app→'pipeline'）
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# 后台任务无 request/env，用注入的全局函数解析；禁硬编码库名
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_dbname = get_module_dbname('rag')
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# 读知识库向量引擎：
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# bge-m3 → 在线文本(阿里, 1024维)
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# qwen3-vl-embedding → 在线多模态(阿里, 2560维)
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# clip-vith14 → GPU 本地 CLIP(1024维, 有GPU环境保留)
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emb_engine = 'clip-vith14'
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try:
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async with db.sqlorContext(_dbname) as sor:
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krecs = await sor.sqlExe("SELECT embedding_engine FROM rag_knowledge_bases WHERE id=${kb_id}$", {"kb_id": kb_id})
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if krecs:
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emb_engine = (getattr(krecs[0], 'embedding_engine', '') or 'clip-vith14').strip()
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except: pass
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is_text = emb_engine == 'bge-m3'
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is_vl = emb_engine == 'qwen3-vl-embedding'
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vdb_dim = 2560 if is_vl else 1024
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# VDB 服务地址：读 upapp.rag-vdb（生产已切内网），不硬编码
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try:
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async with db.sqlorContext(_dbname) as sor:
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_u = await sor.sqlExe("SELECT baseurl FROM upapp WHERE id='rag-vdb'", {})
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VDB_BASE = (_u[0].baseurl or '').rstrip('/') if _u else 'https://vectordb.opencomputing.net:10443'
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except:
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VDB_BASE = 'https://vectordb.opencomputing.net:10443'
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if is_text:
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emb_url = 'https://embedding.opencomputing.net:10443/txte/api/embed'
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emb_model = 'bge-m3'
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else:
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emb_url = 'https://embedding.opencomputing.net:10443/mme/api/embed'
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emb_model = 'CLIP-ViT-H-14'
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# 在线引擎：配置读取 + key 解密（mm_embedding / mm_rerank）
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_vl_cfg = None
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if is_vl or not is_text:
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try:
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from rag.vl_online import get_mm_cfg
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_vl_cfg = await get_mm_cfg('mm_embedding')
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except:
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_vl_cfg = None
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async def _online_text_embed(texts):
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"""文本向量化：vl 引擎走在线原生API；bge-m3 走在线兼容API（经 engine_configs）"""
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if is_vl and _vl_cfg:
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from rag.vl_online import vl_embed_texts
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return await vl_embed_texts(texts)
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try:
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from rag.init import _online_embed
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from ahserver.serverenv import ServerEnv
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return await _online_embed(ServerEnv(), texts)
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except:
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return []
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async def _media_embed_image(img_b64):
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"""图片向量化：vl 在线(base64) 或 GPU CLIP"""
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if is_vl and _vl_cfg:
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try:
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from rag.vl_online import vl_embed_contents
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v = await vl_embed_contents([{"image": "data:image/jpeg;base64," + img_b64}])
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return v
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except:
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return None
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try:
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client = StreamHttpClient()
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resp = await client.request('POST', emb_url,
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json={"images": [img_b64], "model": emb_model})
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emb_data = json.loads(resp)
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es = emb_data.get("image_embeddings", emb_data.get("embeddings", []))
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return es[0] if es else None
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except:
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return None
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async def ensure_vdb_collection(client, kb_id):
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payload = {"colname": kb_id, "fields": [{"name": "id", "type": "str", "is_primary": True, "max_length": 64}, {"name": "vector", "type": "fvector", "dim": vdb_dim}, {"name": "text", "type": "str", "max_length": 65535}], "description": "RAG kb", "metric": "COSINE"}
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await client.request('POST', VDB_BASE + '/v1/createcollection', json=payload)
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text = ''
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chunks_n = 0
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face_count = 0
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voice_speakers = 0
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meta_parts = {}
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try:
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with open(real_path, 'rb') as f:
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file_data = f.read()
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text_exts = {'.txt', '.md', '.csv', '.json', '.xml', '.html', '.htm', '.py', '.js', '.css', '.yaml', '.yml', '.log', '.rst'}
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image_exts = {'.jpg', '.jpeg', '.png', '.bmp', '.gif', '.webp'}
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audio_exts = {'.mp3', '.wav', '.flac', '.ogg', '.m4a', '.aac'}
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video_exts = {'.mp4', '.avi', '.mov', '.mkv', '.webm'}
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# --- OFFICE DOCS: text extraction (PDF/DOCX/PPTX/XLSX) ---
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if ext_l == '.pdf' and not text:
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import io; from PyPDF2 import PdfReader
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reader = PdfReader(io.BytesIO(file_data))
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text = '\n'.join(p.extract_text() or '' for p in reader.pages)
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elif ext_l == '.docx' and not text:
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import io; from docx import Document
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doc = Document(io.BytesIO(file_data))
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text = '\n'.join(p.text for p in doc.paragraphs)
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elif ext_l == '.pptx' and not text:
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import io; from pptx import Presentation
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prs = Presentation(io.BytesIO(file_data))
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parts = []
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for slide in prs.slides:
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for shape in slide.shapes:
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if hasattr(shape, 'text') and shape.text:
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parts.append(shape.text)
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text = '\n'.join(parts)
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elif ext_l == '.xlsx' and not text:
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import io; from openpyxl import load_workbook
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wb = load_workbook(io.BytesIO(file_data), data_only=True)
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parts = []
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for sheet in wb.worksheets:
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for row in sheet.iter_rows(values_only=True):
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parts.append('\t'.join(str(c or '') for c in row))
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text = '\n'.join(parts)
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# --- TEXT EXTRACTION ---
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if ext_l in text_exts:
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text = file_data.decode('utf-8', errors='replace')
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# --- 文本知识库不支持媒体文件 ---
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if is_text and (ext_l in image_exts or ext_l in audio_exts or ext_l in video_exts):
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try:
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async with db.sqlorContext(_dbname) as sor:
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await sor.sqlExe(
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"UPDATE rag_documents SET status='failed', metadata=${meta}$, updated_at=NOW() WHERE id=${id}$",
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{"id": doc_id, "meta": json.dumps({"error": "文本知识库不支持媒体文件，请上传文本类文件(txt/md/pdf/docx等)或改用多媒体知识库"}, ensure_ascii=False)})
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except: pass
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return
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# --- IMAGE: face detection + 图片向量化入库 ---
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if ext_l in image_exts:
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img_b64 = base64.b64encode(file_data).decode()
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try:
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client = StreamHttpClient()
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resp = await client.request('POST', 'https://media.opencomputing.net:10443/face/api/detect', json={"images": [img_b64]})
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fd = json.loads(resp)
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results = fd.get("results", [])
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if results and isinstance(results[0], dict):
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faces = results[0].get("faces", results[0].get("detections", []))
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face_count = len(faces)
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if faces and isinstance(faces[0], dict):
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meta_parts['face_bboxes'] = [f.get("bbox", {}) for f in faces[:10]]
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meta_parts['face'] = face_count
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except: pass
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# 图片本身向量化（vl在线 或 GPU CLIP），存入 VDB 供以文搜图
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try:
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img_vec = await _media_embed_image(img_b64)
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if img_vec:
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client2 = StreamHttpClient()
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await ensure_vdb_collection(client2, kb_id)
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vdb_data = {"colname": kb_id, "data": [
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{"id": doc_id + "_c0", "vector": img_vec, "text": file_name}]}
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await client2.request('POST', VDB_BASE + '/v1/upsert', json=vdb_data)
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async with db.sqlorContext(_dbname) as sor:
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await sor.sqlExe(
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"INSERT INTO rag_document_chunks (id, doc_id, kb_id, chunk_index, content, vector_id, created_at) "
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"VALUES (${id}$, ${doc_id}$, ${kb_id}$, 0, ${content}$, ${vid}$, NOW())",
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{"id": doc_id + "_c0", "doc_id": doc_id, "kb_id": kb_id,
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"content": file_name, "vid": doc_id + "_c0"})
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chunks_n = 1
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meta_parts['image'] = 'embedded'
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except: pass
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# --- AUDIO: voiceprint ---
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if ext_l in audio_exts:
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try:
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client = StreamHttpClient()
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resp = await client.request('POST', 'https://media.opencomputing.net:10443/voiceprint/extract/submit',
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files={'file': (file_name, file_data)})
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vd = json.loads(resp)
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voice_speakers = vd.get('speakers', 1) if vd.get('status') == 'SUCCEEDED' else (1 if vd.get('embedding') else 0)
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meta_parts['voiceprint'] = voice_speakers
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except: pass
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# --- VIDEO: frame extraction + voiceprint ---
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if ext_l in video_exts:
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meta_parts['video'] = 'pending'
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video_ok = False
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try:
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tmp_img = '/tmp/' + doc_id + '_frame.jpg'
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subprocess.run(['ffmpeg', '-y', '-i', real_path, '-vframes', '1', '-q:v', '2', tmp_img],
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capture_output=True, timeout=30)
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if os.path.exists(tmp_img):
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with open(tmp_img, 'rb') as fi:
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frame_data = fi.read()
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img_b64 = base64.b64encode(frame_data).decode()
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frame_bboxes = []
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try:
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client = StreamHttpClient()
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resp = await client.request('POST', 'https://media.opencomputing.net:10443/face/api/detect', json={"images": [img_b64]})
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fd = json.loads(resp)
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results = fd.get("results", [])
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if results and isinstance(results[0], dict):
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faces = results[0].get("faces", results[0].get("detections", []))
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face_count = len(faces)
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frame_bboxes = [f.get("bbox", {}) for f in faces[:10]] if faces else []
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except: pass
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# --- 视频帧向量化（vl在线 或 GPU CLIP） ---
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try:
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frame_vec = await _media_embed_image(img_b64)
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img_embeddings = [frame_vec] if frame_vec else []
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except:
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img_embeddings = []
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if img_embeddings:
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try:
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client3 = StreamHttpClient()
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await ensure_vdb_collection(client3, kb_id)
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vdb_data = {"colname": kb_id, "data": [
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{"id": doc_id + "_c0", "vector": img_embeddings[0], "text": file_name}
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]}
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await client3.request('POST', VDB_BASE + '/v1/upsert', json=vdb_data)
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chunk_meta = {"start_time": 0}
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if frame_bboxes:
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chunk_meta["bboxes"] = frame_bboxes
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async with db.sqlorContext(_dbname) as sor:
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await sor.sqlExe(
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"INSERT INTO rag_document_chunks (id, doc_id, kb_id, chunk_index, content, vector_id, metadata, created_at) "
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"VALUES (${id}$, ${doc_id}$, ${kb_id}$, 0, ${content}$, ${vid}$, ${meta}$, NOW())",
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{"id": doc_id + "_c0", "doc_id": doc_id, "kb_id": kb_id,
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"content": file_name, "vid": doc_id + "_c0",
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"meta": json.dumps(chunk_meta, ensure_ascii=False)})
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except:
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pass
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os.remove(tmp_img)
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video_ok = True
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except: pass
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# --- Voiceprint: extract audio from video ---
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if video_ok:
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try:
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tmp_wav = '/tmp/' + doc_id + '_audio.wav'
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subprocess.run(['ffmpeg', '-y', '-i', real_path, '-vn', '-acodec', 'pcm_s16le',
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'-ar', '16000', '-ac', '1', tmp_wav],
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capture_output=True, timeout=60)
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if os.path.exists(tmp_wav) and os.path.getsize(tmp_wav) > 1000:
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with open(tmp_wav, 'rb') as fa:
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audio_data = fa.read()
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try:
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client4 = StreamHttpClient()
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resp4 = await client4.request('POST',
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'https://media.opencomputing.net:10443/voiceprint/extract/submit',
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files={'file': (file_name.rsplit('.', 1)[0] + '.wav', audio_data)})
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vd = json.loads(resp4)
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voice_speakers = vd.get('speakers', 1) if vd.get('status') == 'SUCCEEDED' else (1 if vd.get('embedding') else 0)
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meta_parts['voiceprint'] = voice_speakers
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except: pass
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if os.path.exists(tmp_wav):
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os.remove(tmp_wav)
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except: pass
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if video_ok:
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meta_parts['video'] = 'done'
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# --- RAG INGEST for text ---
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if text and len(text.strip()) > 10:
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paragraphs = text.split('\n')
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chunks = []
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cur = ''
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for p in paragraphs:
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p = p.strip()
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if not p:
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if cur: chunks.append(cur); cur = ''
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continue
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if len(cur) + len(p) < 500:
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cur = (cur + '\n' + p).strip()
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else:
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if cur: chunks.append(cur)
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cur = p
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if cur: chunks.append(cur)
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if chunks:
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# 在线文本向量化（vl引擎→qwen3-vl原生；bge-m3→在线兼容；GPU CLIP 时代的老路径已移除）
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embeddings = await _online_text_embed(chunks)
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embeddings = [e for e in embeddings if e] or []
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vector_ids = []
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if embeddings:
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try:
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client2 = StreamHttpClient()
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await ensure_vdb_collection(client2, kb_id)
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vdb_data = {"colname": kb_id, "data": [
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{"id": doc_id + "_c" + str(i), "vector": emb, "text": chunks[i]}
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for i, emb in enumerate(embeddings)]}
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resp3 = await client2.request('POST', VDB_BASE + '/v1/upsert', json=vdb_data)
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if json.loads(resp3).get('status') == 'SUCCEEDED':
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vector_ids = [doc_id + "_c" + str(i) for i in range(len(embeddings))]
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except:
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pass
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async with db.sqlorContext(_dbname) as sor:
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for i, chunk_text in enumerate(chunks):
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vid = vector_ids[i] if i < len(vector_ids) else ''
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await sor.sqlExe(
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"INSERT INTO rag_document_chunks (id, doc_id, kb_id, chunk_index, content, vector_id, created_at) "
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"VALUES (${id}$, ${doc_id}$, ${kb_id}$, ${idx}$, ${content}$, ${vid}$, NOW())",
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{"id": doc_id + "_c" + str(i), "doc_id": doc_id, "kb_id": kb_id,
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"idx": i, "content": chunk_text[:2000], "vid": vid})
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chunks_n = len(chunks)
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except Exception as e:
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try:
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async with db.sqlorContext(_dbname) as sor:
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await sor.sqlExe(
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"UPDATE rag_documents SET status='failed', metadata=${meta}$, updated_at=NOW() WHERE id=${id}$",
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{"id": doc_id, "meta": json.dumps({"error": str(e)[:300]}, ensure_ascii=False)})
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except: pass
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return
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# --- finalize: mark document done + update KB chunk counts ---
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meta_json = json.dumps(meta_parts, ensure_ascii=False)
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try:
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async with db.sqlorContext(_dbname) as sor:
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await sor.sqlExe(
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"UPDATE rag_documents SET status='done', chunk_count=${chunks}$, metadata=${meta}$, updated_at=NOW() WHERE id=${id}$",
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{"id": doc_id, "chunks": chunks_n, "meta": meta_json})
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if chunks_n:
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await sor.sqlExe(
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"UPDATE rag_knowledge_bases SET chunk_count=chunk_count+${n}$ WHERE id=${kb_id}$",
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{"n": chunks_n, "kb_id": kb_id})
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except: pass
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# ============================================================
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# SYNC PART — record document as 'pending', update KB counts,
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# fire background ingestion, return immediately.
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# ============================================================
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async with get_sor_context(env, 'rag') as sor:
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await sor.sqlExe(
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"INSERT INTO rag_documents (id, kb_id, folder_id, file_name, file_type, file_size, file_path, mime_type, status, chunk_count, metadata, org_id, created_at, updated_at) "
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"VALUES (${id}$, ${kb_id}$, ${folder_id}$, ${file_name}$, 'other', ${file_size}$, ${file_path}$, 'application/octet-stream', 'pending', 0, '{}', ${org_id}$, NOW(), NOW())",
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{"id": doc_id, "kb_id": kb_id, "folder_id": folder_id, "file_name": file_name,
|
||
"file_size": file_size, "file_path": web_path, "org_id": userorgid})
|
||
await sor.sqlExe(
|
||
"UPDATE rag_knowledge_bases SET doc_count=doc_count+1, total_size=total_size+${size}$ WHERE id=${kb_id}$",
|
||
{"size": file_size, "kb_id": kb_id})
|
||
|
||
# Fire background ingestion — pass primitives only (no env/request/proxy objects)
|
||
background_reco(ingest_doc, doc_id, kb_id, file_name, ext_l, real_path)
|
||
|
||
result = {"status": "SUCCEEDED", "doc_id": doc_id, "file_name": file_name,
|
||
"file_size": file_size, "folder_id": folder_id, "ingest": "pending"}
|
||
return json.dumps(result, ensure_ascii=False, default=str)
|