fix raptor bugs (#928)

### What problem does this PR solve?

#922 
### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)

api/apps/api_app.py

         return get_json_result(data=False, retmsg=errors, retcode=RetCode.SERVER_ERROR)
 
     return get_json_result(data=True)
+
+
+@manager.route('/completion_aibotk', methods=['POST'])
+@validate_request("Authorization", "conversation_id", "word")
+def completion_faq():
+    import base64
+    req = request.json
+
+    token = req["Authorization"]
+    objs = APIToken.query(token=token)
+    if not objs:
+        return get_json_result(
+            data=False, retmsg='Token is not valid!"', retcode=RetCode.AUTHENTICATION_ERROR)
+
+    e, conv = API4ConversationService.get_by_id(req["conversation_id"])
+    if not e:
+        return get_data_error_result(retmsg="Conversation not found!")
+    if "quote" not in req: req["quote"] = True
+
+    msg = []
+    msg.append({"role": "user", "content": req["word"]})
+
+    try:
+        conv.message.append(msg[-1])
+        e, dia = DialogService.get_by_id(conv.dialog_id)
+        if not e:
+            return get_data_error_result(retmsg="Dialog not found!")
+        del req["conversation_id"]
+
+        if not conv.reference:
+            conv.reference = []
+        conv.message.append({"role": "assistant", "content": ""})
+        conv.reference.append({"chunks": [], "doc_aggs": []})
+
+        def fillin_conv(ans):
+            nonlocal conv
+            if not conv.reference:
+                conv.reference.append(ans["reference"])
+            else: conv.reference[-1] = ans["reference"]
+            conv.message[-1] = {"role": "assistant", "content": ans["answer"]}
+
+        data_type_picture = {
+            "type": 3,
+            "url": "base64 content"
+        }
+        data = [
+            {
+                "type": 1,
+                "content": ""
+            }
+        ]
+        for ans in chat(dia, msg, stream=False, **req):
+            # answer = ans
+            data[0]["content"] += re.sub(r'##\d\$\$', '', ans["answer"])
+            fillin_conv(ans)
+            API4ConversationService.append_message(conv.id, conv.to_dict())
+
+            chunk_idxs = [int(match[2]) for match in re.findall(r'##\d\$\$', ans["answer"])]
+            for chunk_idx in chunk_idxs[:1]:
+                if ans["reference"]["chunks"][chunk_idx]["img_id"]:
+                    try:
+                        bkt, nm = ans["reference"]["chunks"][chunk_idx]["img_id"].split("-")
+                        response = MINIO.get(bkt, nm)
+                        data_type_picture["url"] = base64.b64encode(response).decode('utf-8')
+                        data.append(data_type_picture)
+                    except Exception as e:
+                        return server_error_response(e)
+            break
+
+        response = {"code": 200, "msg": "success", "data": data}
+        return response
+
+    except Exception as e:
+        return server_error_response(e)

api/apps/chunk_app.py

         v = 0.1 * v[0] + 0.9 * v[1]
         d["q_%d_vec" % len(v)] = v.tolist()
         ELASTICSEARCH.upsert([d], search.index_name(tenant_id))
+
+        DocumentService.increment_chunk_num(
+            doc.id, doc.kb_id, c, 1, 0)
         return get_json_result(data={"chunk_id": chunck_id})
     except Exception as e:
         return server_error_response(e)

api/db/services/document_service.py

                     prg = -1
                     status = TaskStatus.FAIL.value
                 elif finished:
-                    if d["parser_config"].get("raptor") and d["progress_msg"].lower().find(" raptor")<0:
+                    if d["parser_config"].get("raptor", {}).get("use_raptor") and d["progress_msg"].lower().find(" raptor")<0:
                         queue_raptor_tasks(d)
                         prg *= 0.98
                         msg.append("------ RAPTOR -------")

deepdoc/vision/postprocess.py

 import copy
 import re
-
 import numpy as np
 import cv2
 from shapely.geometry import Polygon

rag/llm/chat_model.py

         if system:
             history.insert(0, {"role": "system", "content": system})
         ans = ""
-        tk_count = 0
         try:
             req = {
                 "parameters": {
                 if resp.choices[0].finish_reason == "stop":
                     tk_count = resp.usage.total_tokens
                 yield ans
+
         except Exception as e:
             yield ans + "\n**ERROR**: " + str(e)
         yield tk_count

rag/raptor.py

                 gm.fit(reduced_embeddings)
                 probs = gm.predict_proba(reduced_embeddings)
                 lbls = [np.where(prob > self._threshold)[0] for prob in probs]
+                lbls = [lbl[0] if isinstance(lbl, np.ndarray) else lbl for lbl in lbls]
             lock = Lock()
             with ThreadPoolExecutor(max_workers=12) as executor:
                 threads = []

requirements.txt

 zhipuai==2.0.1
 BCEmbedding
 loguru==0.7.2
+umap-learn
 fasttext==0.9.2

requirements_dev.txt

 ollama==0.1.8
 redis==5.0.4
 fasttext==0.9.2
+umap-learn