223 lines
9.8 KiB
Plaintext
223 lines
9.8 KiB
Plaintext
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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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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file_path = '/d/rag/ragserver/pkgs/rag/rag/files/' + doc_id + ext
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with open(file_path, "wb") as f:
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f.write(file_data)
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file_size = len(file_data)
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ext_l = ext.lower()
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text_exts = {'.txt', '.md', '.csv', '.json', '.xml', '.html', '.htm', '.py', '.js', '.css', '.yaml', '.yml', '.log', '.rst'}
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doc_exts = {'.pdf', '.docx', '.pptx', '.xlsx', '.doc', '.ppt', '.xls'}
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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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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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# --- 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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elif ext_l == '.pdf':
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import io; from PyPDF2 import PdfReader
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try:
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r = PdfReader(io.BytesIO(file_data))
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text = '\n'.join(p.extract_text() or '' for p in r.pages)
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except: pass
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elif ext_l == '.docx':
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import io; from docx import Document
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try:
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doc = Document(io.BytesIO(file_data))
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text = '\n'.join(p.text for p in doc.paragraphs if p.text.strip())
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except: pass
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elif ext_l == '.pptx':
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import io; from pptx import Presentation
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try:
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prs = Presentation(io.BytesIO(file_data))
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parts = []
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for s in prs.slides:
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for sh in s.shapes:
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if hasattr(sh, 'text') and sh.text.strip():
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parts.append(sh.text)
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text = '\n'.join(parts)
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except: pass
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elif ext_l in {'.xlsx', '.xls'}:
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import io; from openpyxl import load_workbook
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try:
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wb = load_workbook(io.BytesIO(file_data), read_only=True, data_only=True)
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rows = []
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for ws in wb.worksheets:
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for row in ws.iter_rows(values_only=True):
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r = ' | '.join(str(c) if c is not None else '' for c in row)
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if r.strip(): rows.append(r)
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text = '\n'.join(rows)
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except: pass
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import aiohttp, base64
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# --- IMAGE: face detection + CLIP visual embedding ---
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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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async with aiohttp.ClientSession(timeout=aiohttp.ClientTimeout(total=15)) as s:
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# Face detection
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try:
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r = await s.post('https://media.opencomputing.net:10443/face/api/detect', json={"images": [img_b64]})
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if r.status == 200:
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fd = await r.json()
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results = fd.get("results", [])
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if results and isinstance(results[0], dict):
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face_count = len(results[0].get("faces", results[0].get("detections", [])))
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meta_parts['face'] = face_count
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except: pass
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# --- AUDIO: voiceprint extraction ---
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if ext_l in audio_exts:
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async with aiohttp.ClientSession(timeout=aiohttp.ClientTimeout(total=60)) as s:
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try:
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form = aiohttp.FormData()
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form.add_field('file', file_data, filename=file_name)
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r = await s.post('https://media.opencomputing.net:10443/voiceprint/extract/submit', data=form)
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if r.status == 200:
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vd = await r.json()
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if vd.get('status') == 'SUCCEEDED':
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voice_speakers = vd.get('speakers', 1)
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elif vd.get('embedding'):
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voice_speakers = 1
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meta_parts['voiceprint'] = voice_speakers
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except: pass
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# --- VIDEO: frame extraction + audio extraction (via ffmpeg) ---
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if ext_l in video_exts:
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import subprocess, tempfile
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meta_parts['video'] = 'pending'
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# Extract keyframe for face detection
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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', file_path, '-vframes', '1', '-q:v', '2', tmp_img],
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capture_output=True, timeout=30)
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if __import__('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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async with aiohttp.ClientSession(timeout=aiohttp.ClientTimeout(total=15)) as s:
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try:
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r = await s.post('https://media.opencomputing.net:10443/face/api/detect', json={"images": [img_b64]})
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if r.status == 200:
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fd = await r.json()
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results = fd.get("results", [])
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if results and isinstance(results[0], dict):
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face_count = len(results[0].get("faces", results[0].get("detections", [])))
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except: pass
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__import__('os').remove(tmp_img)
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except: pass
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# Extract audio for voiceprint
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try:
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tmp_audio = '/tmp/' + doc_id + '_audio.wav'
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subprocess.run(['ffmpeg', '-y', '-i', file_path, '-vn', '-acodec', 'pcm_s16le', '-ar', '16000', '-ac', '1', tmp_audio],
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capture_output=True, timeout=60)
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if __import__('os').path.exists(tmp_audio) and __import__('os').path.getsize(tmp_audio) > 44:
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with open(tmp_audio, 'rb') as fa:
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audio_data = fa.read()
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async with aiohttp.ClientSession(timeout=aiohttp.ClientTimeout(total=60)) as s:
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try:
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form = aiohttp.FormData()
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form.add_field('file', audio_data, filename='audio.wav')
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r = await s.post('https://media.opencomputing.net:10443/voiceprint/extract/submit', data=form)
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if r.status == 200:
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vd = await r.json()
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if vd.get('status') == 'SUCCEEDED' or vd.get('embedding'):
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voice_speakers = vd.get('speakers', 1)
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except: pass
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__import__('os').remove(tmp_audio)
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except: pass
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meta_parts['face'] = face_count
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meta_parts['voiceprint'] = voice_speakers
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# --- RAG INGEST (text + image CLIP embedding) ---
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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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async with aiohttp.ClientSession(timeout=aiohttp.ClientTimeout(total=15)) as s:
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try:
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r = await s.post('https://embedding.opencomputing.net:10443/api/embed',
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json={"texts": chunks, "model": "CLIP-ViT-H-14"})
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emb_data = await r.json() if r.status == 200 else {}
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embeddings = emb_data.get("text_embeddings", emb_data.get("embeddings", []))
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except:
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embeddings = []
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vector_ids = []
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if embeddings:
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try:
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vdb_data = {"collection": kb_id, "data": [
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{"id": doc_id + "_" + str(i), "vector": emb, "text": chunks[i]}
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for i, emb in enumerate(embeddings)]}
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await s.post('https://vectordb.opencomputing.net:10443/v1/upsert', json=vdb_data)
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vector_ids = [doc_id + "_" + str(i) for i in range(len(embeddings))]
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except:
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pass
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async with get_sor_context(env, 'rag') 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 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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# --- SAVE TO DB ---
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import json as _json
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meta_json = _json.dumps(meta_parts, ensure_ascii=False)
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status = 'done'
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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 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', ${status}$, ${chunks}$, ${meta}$, ${org_id}$, NOW(), NOW())",
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{"id": doc_id, "kb_id": kb_id, "folder_id": folder_id, "file_name": file_name,
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"file_size": file_size, "file_path": "/idfile/files/" + doc_id + ext,
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"status": status, "chunks": chunks_n, "meta": meta_json, "org_id": userorgid})
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await sor.sqlExe(
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"UPDATE knowledge_bases SET doc_count=doc_count+1, total_size=total_size+" + "${size}$ WHERE id=${kb_id}$",
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{"size": file_size, "kb_id": kb_id})
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if chunks_n:
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await sor.sqlExe(
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"UPDATE 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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result = {"status": "SUCCEEDED", "doc_id": doc_id, "file_name": file_name,
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"file_size": file_size, "folder_id": folder_id,
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"text_len": len(text), "chunks": chunks_n,
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"faces": face_count, "speakers": voice_speakers}
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return _json.dumps(result, ensure_ascii=False, default=str)
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