debug backend API for TAB 'search' (#2389)

### What problem does this PR solve?
#2247

### Type of change

- [x] New Feature (non-breaking change which adds functionality)

api/apps/chunk_app.py

@@ -261,7 +261,7 @@ def retrieval_test():
     kb_id = req["kb_id"]
     if isinstance(kb_id, str): kb_id = [kb_id]
     doc_ids = req.get("doc_ids", [])
-    similarity_threshold = float(req.get("similarity_threshold", 0.2))
+    similarity_threshold = float(req.get("similarity_threshold", 0.0))
     vector_similarity_weight = float(req.get("vector_similarity_weight", 0.3))
     top = int(req.get("top_k", 1024))
 

api/apps/conversation_app.py

@@ -15,8 +15,8 @@
 #
 import json
 import re
+import traceback
 from copy import deepcopy
-
 from api.db.services.user_service import UserTenantService
 from flask import request, Response
 from flask_login import login_required, current_user
@@ -333,6 +333,8 @@ def mindmap():
                            0.3, 0.3, aggs=False)
     mindmap = MindMapExtractor(chat_mdl)
     mind_map = mindmap([c["content_with_weight"] for c in ranks["chunks"]]).output
+    if "error" in mind_map:
+        return server_error_response(Exception(mind_map["error"]))
     return get_json_result(data=mind_map)
 
 

api/db/services/dialog_service.py

@@ -218,7 +218,7 @@ def chat(dialog, messages, stream=True, **kwargs):
         for ans in chat_mdl.chat_streamly(prompt, msg[1:], gen_conf):
             answer = ans
             delta_ans = ans[len(last_ans):]
-            if num_tokens_from_string(delta_ans) < 12:
+            if num_tokens_from_string(delta_ans) < 16:
                 continue
             last_ans = answer
             yield {"answer": answer, "reference": {}, "audio_binary": tts(tts_mdl, delta_ans)}
@@ -404,7 +404,6 @@ def rewrite(tenant_id, llm_id, question):
 
 
 def tts(tts_mdl, text):
-    return
     if not tts_mdl or not text: return
     bin = b""
     for chunk in tts_mdl.tts(text):

graphrag/mind_map_extractor.py

@@ -107,7 +107,7 @@ class MindMapExtractor:
                 res.append(_.result())
 
             if not res:
-                return MindMapResult(output={"root":{}})
+                return MindMapResult(output={"id": "root", "children": []})
 
             merge_json = reduce(self._merge, res)
             if len(merge_json.keys()) > 1:

rag/llm/embedding_model.py

@@ -15,7 +15,7 @@
 #
 import re
 from typing import Optional
-import  threading
+import threading
 import requests
 from huggingface_hub import snapshot_download
 from openai.lib.azure import AzureOpenAI

rag/nlp/search.py

@@ -224,6 +224,8 @@ class Dealer:
     def insert_citations(self, answer, chunks, chunk_v,
                          embd_mdl, tkweight=0.1, vtweight=0.9):
         assert len(chunks) == len(chunk_v)
+        if not chunks:
+            return answer, set([])
         pieces = re.split(r"(```)", answer)
         if len(pieces) >= 3:
             i = 0
@@ -263,7 +265,7 @@ class Dealer:
 
         ans_v, _ = embd_mdl.encode(pieces_)
         assert len(ans_v[0]) == len(chunk_v[0]), "The dimension of query and chunk do not match: {} vs. {}".format(
-            len(ans_v[0]), len(chunk_v[0]))
+                len(ans_v[0]), len(chunk_v[0]))
 
         chunks_tks = [rag_tokenizer.tokenize(self.qryr.rmWWW(ck)).split(" ")
                       for ck in chunks]
@@ -360,29 +362,33 @@ class Dealer:
         ranks = {"total": 0, "chunks": [], "doc_aggs": {}}
         if not question:
             return ranks
-        req = {"kb_ids": kb_ids, "doc_ids": doc_ids, "size": page_size,
+        RERANK_PAGE_LIMIT = 3
+        req = {"kb_ids": kb_ids, "doc_ids": doc_ids, "size": page_size*RERANK_PAGE_LIMIT,
                "question": question, "vector": True, "topk": top,
                "similarity": similarity_threshold,
                "available_int": 1}
+        if page > RERANK_PAGE_LIMIT:
+            req["page"] = page
+            req["size"] = page_size
         sres = self.search(req, index_name(tenant_id), embd_mdl, highlight)
+        ranks["total"] = sres.total
 
-        if rerank_mdl:
-            sim, tsim, vsim = self.rerank_by_model(rerank_mdl,
-                sres, question, 1 - vector_similarity_weight, vector_similarity_weight)
+        if page <= RERANK_PAGE_LIMIT:
+            if rerank_mdl:
+                sim, tsim, vsim = self.rerank_by_model(rerank_mdl,
+                    sres, question, 1 - vector_similarity_weight, vector_similarity_weight)
+            else:
+                sim, tsim, vsim = self.rerank(
+                    sres, question, 1 - vector_similarity_weight, vector_similarity_weight)
+            idx = np.argsort(sim * -1)[(page-1)*page_size:page*page_size]
         else:
-            sim, tsim, vsim = self.rerank(
-                sres, question, 1 - vector_similarity_weight, vector_similarity_weight)
-        idx = np.argsort(sim * -1)
+            sim = tsim = vsim = [1]*len(sres.ids)
+            idx = list(range(len(sres.ids)))
 
         dim = len(sres.query_vector)
-        start_idx = (page - 1) * page_size
         for i in idx:
             if sim[i] < similarity_threshold:
                 break
-            ranks["total"] += 1
-            start_idx -= 1
-            if start_idx >= 0:
-                continue
             if len(ranks["chunks"]) >= page_size:
                 if aggs:
                     continue
@@ -406,7 +412,10 @@ class Dealer:
                 "positions": sres.field[id].get("position_int", "").split("\t")
             }
             if highlight:
-                d["highlight"] = rmSpace(sres.highlight[id])
+                if id in sres.highlight:
+                    d["highlight"] = rmSpace(sres.highlight[id])
+                else:
+                    d["highlight"] = d["content_with_weight"]
             if len(d["positions"]) % 5 == 0:
                 poss = []
                 for i in range(0, len(d["positions"]), 5):