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feat: async upload — return immediately, ingest in background

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yumoqing 2026-07-31 18:17:47 +08:00
parent cdc02e31e4
commit 8dad4e9b5e
1 changed files with 186 additions and 157 deletions
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71
wwwroot/knowledge_bases_list/upload_file.dspy
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@ -22,16 +22,27 @@ file_size = len(file_data)
ext = '.' + file_name.rsplit('.', 1)[1] if '.' in file_name else '.bin'
ext_l = ext.lower()
text_exts = {'.txt', '.md', '.csv', '.json', '.xml', '.html', '.htm', '.py', '.js', '.css', '.yaml', '.yml', '.log', '.rst'}
image_exts = {'.jpg', '.jpeg', '.png', '.bmp', '.gif', '.webp'}
audio_exts = {'.mp3', '.wav', '.flac', '.ogg', '.m4a', '.aac'}
video_exts = {'.mp4', '.avi', '.mov', '.mkv', '.webm'}
# ============================================================
# BACKGROUND INGESTION — runs in a separate asyncio task after
# the response is returned (background_reco = create_task wrapper).
# SELF-CONTAINED: never touches env/request (invalid after response),
# creates its own DBPools connection. All args are primitives.
# ============================================================
async def ingest_doc(doc_id, kb_id, file_name, ext_l, real_path):
db = DBPools()
text = ''
chunks_n = 0
face_count = 0
voice_speakers = 0
meta_parts = {}
try:
with open(real_path, 'rb') as f:
file_data = f.read()
text_exts = {'.txt', '.md', '.csv', '.json', '.xml', '.html', '.htm', '.py', '.js', '.css', '.yaml', '.yml', '.log', '.rst'}
image_exts = {'.jpg', '.jpeg', '.png', '.bmp', '.gif', '.webp'}
audio_exts = {'.mp3', '.wav', '.flac', '.ogg', '.m4a', '.aac'}
video_exts = {'.mp4', '.avi', '.mov', '.mkv', '.webm'}
# --- OFFICE DOCS: text extraction (PDF/DOCX/PPTX/XLSX) ---
if ext_l == '.pdf' and not text:
@ -123,7 +134,7 @@ if ext_l in video_exts:
{"id": doc_id + "_frame0", "vector": img_embeddings[0], "text": file_name}
]}
await client3.request('POST', 'https://vectordb.opencomputing.net/v1/upsert', json=vdb_data)
async with get_sor_context(env, 'rag') as sor:
async with db.sqlorContext('rag') as sor:
await sor.sqlExe(
"INSERT INTO document_chunks (id, doc_id, kb_id, chunk_index, content, vector_id, created_at) "
"VALUES (${id}$, ${doc_id}$, ${kb_id}$, 0, ${content}$, ${vid}$, NOW())",
@ -173,7 +184,7 @@ if text and len(text.strip()) > 10:
except:
pass
async with get_sor_context(env, 'rag') as sor:
async with db.sqlorContext('rag') as sor:
for i, chunk_text in enumerate(chunks):
vid = vector_ids[i] if i < len(vector_ids) else ''
await sor.sqlExe(
@ -182,27 +193,45 @@ if text and len(text.strip()) > 10:
{"id": doc_id + "_c" + str(i), "doc_id": doc_id, "kb_id": kb_id,
"idx": i, "content": chunk_text[:2000], "vid": vid})
chunks_n = len(chunks)
except Exception as e:
try:
async with db.sqlorContext('rag') as sor:
await sor.sqlExe(
"UPDATE documents SET status='failed', metadata=${meta}$, updated_at=NOW() WHERE id=${id}$",
{"id": doc_id, "meta": json.dumps({"error": str(e)[:300]}, ensure_ascii=False)})
except: pass
return
# --- SAVE TO DB ---
# --- finalize: mark document done + update KB chunk counts ---
meta_json = json.dumps(meta_parts, ensure_ascii=False)
status = 'done'
async with get_sor_context(env, 'rag') as sor:
try:
async with db.sqlorContext('rag') as sor:
await sor.sqlExe(
"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) "
"VALUES (${id}$, ${kb_id}$, ${folder_id}$, ${file_name}$, 'other', ${file_size}$, ${file_path}$, 'application/octet-stream', ${status}$, ${chunks}$, ${meta}$, ${org_id}$, NOW(), NOW())",
{"id": doc_id, "kb_id": kb_id, "folder_id": folder_id, "file_name": file_name,
"file_size": file_size, "file_path": web_path,
"status": status, "chunks": chunks_n, "meta": meta_json, "org_id": userorgid})
await sor.sqlExe(
"UPDATE knowledge_bases SET doc_count=doc_count+1, total_size=total_size+${size}$ WHERE id=${kb_id}$",
{"size": file_size, "kb_id": kb_id})
"UPDATE documents SET status='done', chunk_count=${chunks}$, metadata=${meta}$, updated_at=NOW() WHERE id=${id}$",
{"id": doc_id, "chunks": chunks_n, "meta": meta_json})
if chunks_n:
await sor.sqlExe(
"UPDATE knowledge_bases SET chunk_count=chunk_count+${n}$ WHERE id=${kb_id}$",
{"n": chunks_n, "kb_id": kb_id})
except: pass
# ============================================================
# SYNC PART — record document as 'pending', update KB counts,
# fire background ingestion, return immediately.
# ============================================================
async with get_sor_context(env, 'rag') as sor:
await sor.sqlExe(
"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) "
"VALUES (${id}$, ${kb_id}$, ${folder_id}$, ${file_name}$, 'other', ${file_size}$, ${file_path}$, 'application/octet-stream', 'pending', 0, '{}', ${org_id}$, NOW(), NOW())",
{"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 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,
"text_len": len(text), "chunks": chunks_n,
"faces": face_count, "speakers": voice_speakers}
"file_size": file_size, "folder_id": folder_id, "ingest": "pending"}
return json.dumps(result, ensure_ascii=False, default=str)