feat(embedding): 知识库支持文本(bge-m3 /txte)/多媒体(CLIP /mme)双向量引擎

- create_kb/new_kb_form 加向量引擎选择(文本 bge-m3 / 多媒体 clip-vith14)
- upload_file/batch_ingest/search_result/init.py 按 embedding_engine 路由到 /txte 或 /mme
- 文本知识库上传媒体文件友好拒绝(不再崩溃报错)

rag/init.py

@@ -113,7 +113,7 @@ async def search_handler(request, params_kw, *args, **kwargs):
         # Process query + media → embedding vector
         query_vec = None
         if query or file_data:
-            query_vec = await _build_search_vector(query, file_data, file_name)
+            query_vec = await _build_search_vector(query, file_data, file_name, env, kb_id)
 
         if not query_vec:
             return json.dumps({"status": "SUCCEEDED", "data": {"results": [], "total": 0, "message": "no query or file provided"}}, ensure_ascii=False)
@@ -181,8 +181,8 @@ async def _resolve_search_kbs(env, userorgid, kb_id):
         return [r.id for r in recs]
 
 
-async def _build_search_vector(query, file_data, file_name):
+async def _build_search_vector(query, file_data, file_name, env=None, kb_id=''):
-    """Build search embedding from text query + media file"""
+    """Build search embedding from text query + media file（按 kb 向量引擎选端点）"""
     texts = []
     if query:
         texts.append(query)
@@ -216,11 +216,23 @@ async def _build_search_vector(query, file_data, file_name):
 
     combined = " ".join(texts)
     try:
-        resp = await _call_uapi("rag-embedding", "embed", {
+        emb_engine = 'clip-vith14'
-            "texts": [combined],
+        if env is not None and kb_id:
-            "model": "CLIP-ViT-H-14"
+            async with get_sor_context(env, 'rag') as sor:
-        })
+                krecs = await sor.sqlExe("SELECT embedding_engine FROM knowledge_bases WHERE id=${kb_id}$", {"kb_id": kb_id})
-        vecs = resp.get("embeddings", []) if isinstance(resp, dict) else []
+                if krecs:
+                    emb_engine = (getattr(krecs[0], 'embedding_engine', '') or 'clip-vith14').strip()
+        if emb_engine == 'bge-m3':
+            emb_url = 'https://embedding.opencomputing.net:10443/txte/api/embed'
+            emb_model = 'bge-m3'
+        else:
+            emb_url = 'https://embedding.opencomputing.net:10443/mme/api/embed'
+            emb_model = 'CLIP-ViT-H-14'
+        import aiohttp
+        async with aiohttp.ClientSession(timeout=aiohttp.ClientTimeout(total=15)) as s:
+            async with s.post(emb_url, json={"texts": [combined], "model": emb_model}) as resp:
+                emb_resp = await resp.json()
+        vecs = emb_resp.get("text_embeddings", emb_resp.get("embeddings", [])) if isinstance(emb_resp, dict) else []
         return vecs[0] if vecs else None
     except Exception as e:
         exception(f"query embedding failed: {e}")
@@ -480,11 +492,24 @@ async def _rag_ingest_async(env, text, kb_id, doc_id):
         return {"chunks": 0}
     chunk_count = len(chunks)
 
-    # 1. Embedding
+    # 1. Embedding（按知识库向量引擎选文本/多模态端点）
     try:
-        emb_resp = await _call_uapi("rag-embedding", "embed",
+        emb_engine = 'clip-vith14'
-                                    {"texts": chunks, "model": "CLIP-ViT-H-14"})
+        async with get_sor_context(env, 'rag') as sor:
-        embeddings = emb_resp.get("embeddings", []) if isinstance(emb_resp, dict) else []
+            krecs = await sor.sqlExe("SELECT embedding_engine FROM knowledge_bases WHERE id=${kb_id}$", {"kb_id": kb_id})
+            if krecs:
+                emb_engine = (getattr(krecs[0], 'embedding_engine', '') or 'clip-vith14').strip()
+        if emb_engine == 'bge-m3':
+            emb_url = 'https://embedding.opencomputing.net:10443/txte/api/embed'
+            emb_model = 'bge-m3'
+        else:
+            emb_url = 'https://embedding.opencomputing.net:10443/mme/api/embed'
+            emb_model = 'CLIP-ViT-H-14'
+        import aiohttp
+        async with aiohttp.ClientSession(timeout=aiohttp.ClientTimeout(total=15)) as s:
+            async with s.post(emb_url, json={"texts": chunks, "model": emb_model}) as resp:
+                emb_resp = await resp.json()
+        embeddings = emb_resp.get("text_embeddings", emb_resp.get("embeddings", [])) if isinstance(emb_resp, dict) else []
     except Exception as e:
         exception(f"embedding failed: {e}")
         embeddings = []

wwwroot/knowledge_bases_list/batch_ingest.dspy

@@ -9,6 +9,14 @@ async with get_sor_context(env, 'rag') as sor:
         "SELECT id, file_name, file_size, file_path FROM documents WHERE kb_id=${kb_id}$ AND (chunk_count IS NULL OR chunk_count=0 OR status='pending') LIMIT 20",
         {"kb_id": kb_id})
     rows = [dict(r) for r in recs]
+    krecs = await sor.sqlExe("SELECT embedding_engine FROM knowledge_bases WHERE id=${kb_id}$", {"kb_id": kb_id})
+    emb_engine = (getattr(krecs[0], 'embedding_engine', '') or 'clip-vith14').strip() if krecs else 'clip-vith14'
+if emb_engine == 'bge-m3':
+    emb_url = 'https://embedding.opencomputing.net:10443/txte/api/embed'
+    emb_model = 'bge-m3'
+else:
+    emb_url = 'https://embedding.opencomputing.net:10443/mme/api/embed'
+    emb_model = 'CLIP-ViT-H-14'
 
 if not rows:
     return json.dumps({"widgettype": "Text", "options": {"text": "No unprocessed documents found"}}, ensure_ascii=False)
@@ -173,8 +181,8 @@ async with aiohttp.ClientSession(timeout=aiohttp.ClientTimeout(total=60)) as ses
 
         # Embed
         try:
-            r2 = await session.post('https://embedding.opencomputing.net:10443/api/embed',
+            r2 = await session.post(emb_url,
-                json={"texts": chunks, "model": "CLIP-ViT-H-14"})
+                json={"texts": chunks, "model": emb_model})
             emb = await r2.json() if r2.status == 200 else {}
             embeddings = emb.get("text_embeddings", emb.get("embeddings", []))
         except:

wwwroot/knowledge_bases_list/create_kb.dspy

@@ -7,10 +7,14 @@ async with db.sqlorContext(dbname) as sor:
     kb_id = uuid()
     name = ns.get("name", "")
     desc = ns.get("description", "")
+    # 向量引擎：clip-vith14=多媒体(CLIP)，bge-m3=文本(bge-m3)。默认 clip-vith14 兼容旧数据
+    emb_type = ns.get("embedding_type", "") or ns.get("embedding_engine", "")
+    if emb_type not in ("bge-m3", "clip-vith14"):
+        emb_type = "clip-vith14"
     await sor.sqlExe(
         "INSERT INTO knowledge_bases (id, name, description, org_id, embedding_engine, vdb_collection, doc_count, total_size, chunk_count, status, created_at) "
-        "VALUES (${id}$, ${name}$, ${desc}$, ${org_id}$, 'CLIP ViT-H-14', 'rag_collection', 0, 0, 0, 'active', NOW())",
+        "VALUES (${id}$, ${name}$, ${desc}$, ${org_id}$, ${emb}$, 'rag_collection', 0, 0, 0, 'active', NOW())",
-        {"id": kb_id, "name": name, "desc": desc, "org_id": userorgid})
+        {"id": kb_id, "name": name, "desc": desc, "org_id": userorgid, "emb": emb_type})
     return {
         "widgettype": "urlwidget",
         "options": {"url": entire_url('/rag/knowledge_bases_list/index.ui')}

wwwroot/knowledge_bases_list/embedding_options.dspy

@@ -0,0 +1,5 @@
+import json
+return json.dumps([
+    {"value": "clip-vith14", "text": "多媒体（图文音视）"},
+    {"value": "bge-m3", "text": "文本（文档检索）"}
+], ensure_ascii=False)

wwwroot/knowledge_bases_list/new_kb_form.ui

@@ -10,7 +10,8 @@
                 "cols": 1,
                 "fields": [
                     {"name": "name", "label": "名称", "uitype": "str", "required": true},
-                    {"name": "description", "label": "描述", "uitype": "text"}
+                    {"name": "description", "label": "描述", "uitype": "text"},
+                    {"name": "embedding_type", "label": "向量引擎", "uitype": "code", "value": "clip-vith14", "dataurl": "{{entire_url('./embedding_options.dspy')}}"}
                 ]
             }
         }

wwwroot/knowledge_bases_list/search_result.dspy

@@ -75,13 +75,28 @@ hits = []
 raw_rows = []
 kw_rows = []
 
+# 读知识库向量引擎，决定用文本(bge-m3 /txte)还是多媒体(CLIP /mme) embedding
+emb_engine = 'clip-vith14'
+try:
+    async with get_sor_context(env, 'rag') as sor:
+        krecs = await sor.sqlExe("SELECT embedding_engine FROM knowledge_bases WHERE id=${kb_id}$", {"kb_id": kb_id})
+        if krecs:
+            emb_engine = (getattr(krecs[0], 'embedding_engine', '') or 'clip-vith14').strip()
+except: pass
+if emb_engine == 'bge-m3':
+    emb_url = 'https://embedding.opencomputing.net:10443/txte/api/embed'
+    emb_model = 'bge-m3'
+else:
+    emb_url = 'https://embedding.opencomputing.net:10443/mme/api/embed'
+    emb_model = 'CLIP-ViT-H-14'
+
 if query:
     # 1. Embed
     vec = []
     try:
         client = StreamHttpClient()
-        resp = await client.request('POST', 'https://embedding.opencomputing.net:10443/api/embed',
+        resp = await client.request('POST', emb_url,
-            json={"texts": [query], "model": "CLIP-ViT-H-14"})
+            json={"texts": [query], "model": emb_model})
         emb = json.loads(resp)
         vec = emb.get("text_embeddings", emb.get("embeddings", [[]]))[0]
     except:

wwwroot/knowledge_bases_list/upload_file.dspy

@@ -48,6 +48,22 @@ ext_l = ext.lower()
 async def ingest_doc(doc_id, kb_id, file_name, ext_l, real_path):
     db = DBPools()
 
+    # 读知识库向量引擎：bge-m3=文本(走 /txte)，clip-vith14=多媒体(走 /mme)
+    emb_engine = 'clip-vith14'
+    try:
+        async with db.sqlorContext('rag') as sor:
+            krecs = await sor.sqlExe("SELECT embedding_engine FROM knowledge_bases WHERE id=${kb_id}$", {"kb_id": kb_id})
+            if krecs:
+                emb_engine = (getattr(krecs[0], 'embedding_engine', '') or 'clip-vith14').strip()
+    except: pass
+    is_text = emb_engine == 'bge-m3'
+    if is_text:
+        emb_url = 'https://embedding.opencomputing.net:10443/txte/api/embed'
+        emb_model = 'bge-m3'
+    else:
+        emb_url = 'https://embedding.opencomputing.net:10443/mme/api/embed'
+        emb_model = 'CLIP-ViT-H-14'
+
     async def ensure_vdb_collection(client, kb_id):
         payload = {"colname": kb_id, "fields": [{"name": "id", "type": "str", "is_primary": True, "max_length": 64}, {"name": "vector", "type": "fvector", "dim": 1024}, {"name": "text", "type": "str", "max_length": 65535}], "description": "RAG kb", "metric": "COSINE"}
         await client.request('POST', 'https://vectordb.opencomputing.net:10443/v1/createcollection', json=payload)
@@ -97,6 +113,16 @@ async def ingest_doc(doc_id, kb_id, file_name, ext_l, real_path):
         if ext_l in text_exts:
             text = file_data.decode('utf-8', errors='replace')
 
+        # --- 文本知识库不支持媒体文件 ---
+        if is_text and (ext_l in image_exts or ext_l in audio_exts or ext_l in video_exts):
+            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": "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)})
+            except: pass
+            return
+
         # --- IMAGE: face detection ---
         if ext_l in image_exts:
             img_b64 = base64.b64encode(file_data).decode()
@@ -150,8 +176,8 @@ async def ingest_doc(doc_id, kb_id, file_name, ext_l, real_path):
                     # --- CLIP image embedding for video frame ---
                     try:
                         client2 = StreamHttpClient()
-                        resp2 = await client2.request('POST', 'https://embedding.opencomputing.net:10443/api/embed',
+                        resp2 = await client2.request('POST', emb_url,
-                            json={"images": [img_b64], "model": "CLIP-ViT-H-14"})
+                            json={"images": [img_b64], "model": emb_model})
                         emb_data = json.loads(resp2)
                         img_embeddings = emb_data.get("image_embeddings", emb_data.get("embeddings", []))
                     except:
@@ -226,8 +252,8 @@ async def ingest_doc(doc_id, kb_id, file_name, ext_l, real_path):
             if chunks:
                 try:
                     client = StreamHttpClient()
-                    resp = await client.request('POST', 'https://embedding.opencomputing.net:10443/api/embed',
+                    resp = await client.request('POST', emb_url,
-                        json={"texts": chunks, "model": "CLIP-ViT-H-14"})
+                        json={"texts": chunks, "model": emb_model})
                     emb_data = json.loads(resp)
                     embeddings = emb_data.get("text_embeddings", emb_data.get("embeddings", []))
                 except: