add search TAB backend api (#2375)

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

### Type of change

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

api/apps/chunk_app.py

@@ -58,7 +58,7 @@ def list_chunk():
         }
         if "available_int" in req:
             query["available_int"] = int(req["available_int"])
-        sres = retrievaler.search(query, search.index_name(tenant_id))
+        sres = retrievaler.search(query, search.index_name(tenant_id), highlight=True)
         res = {"total": sres.total, "chunks": [], "doc": doc.to_dict()}
         for id in sres.ids:
             d = {
@@ -259,12 +259,25 @@ def retrieval_test():
     size = int(req.get("size", 30))
     question = req["question"]
     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))
     vector_similarity_weight = float(req.get("vector_similarity_weight", 0.3))
     top = int(req.get("top_k", 1024))
+
     try:
-        e, kb = KnowledgebaseService.get_by_id(kb_id)
+        tenants = UserTenantService.query(user_id=current_user.id)
+        for kid in kb_id:
+            for tenant in tenants:
+                if KnowledgebaseService.query(
+                        tenant_id=tenant.tenant_id, id=kid):
+                    break
+            else:
+                return get_json_result(
+                    data=False, retmsg=f'Only owner of knowledgebase authorized for this operation.',
+                    retcode=RetCode.OPERATING_ERROR)
+
+        e, kb = KnowledgebaseService.get_by_id(kb_id[0])
         if not e:
             return get_data_error_result(retmsg="Knowledgebase not found!")
 
@@ -281,9 +294,9 @@ def retrieval_test():
             question += keyword_extraction(chat_mdl, question)
 
         retr = retrievaler if kb.parser_id != ParserType.KG else kg_retrievaler
-        ranks = retr.retrieval(question, embd_mdl, kb.tenant_id, [kb_id], page, size,
+        ranks = retr.retrieval(question, embd_mdl, kb.tenant_id, kb_id, page, size,
                                similarity_threshold, vector_similarity_weight, top,
-                               doc_ids, rerank_mdl=rerank_mdl)
+                               doc_ids, rerank_mdl=rerank_mdl, highlight=req.get("highlight"))
         for c in ranks["chunks"]:
             if "vector" in c:
                 del c["vector"]

api/apps/conversation_app.py

@@ -14,19 +14,22 @@
 #  limitations under the License.
 #
 import json
+import re
 from copy import deepcopy
 
-from db.services.user_service import UserTenantService
+from api.db.services.user_service import UserTenantService
 from flask import request, Response
 from flask_login import login_required, current_user
 
 from api.db import LLMType
-from api.db.services.dialog_service import DialogService, ConversationService, chat
-from api.db.services.llm_service import LLMBundle, TenantService
-from api.settings import RetCode
+from api.db.services.dialog_service import DialogService, ConversationService, chat, ask
+from api.db.services.knowledgebase_service import KnowledgebaseService
+from api.db.services.llm_service import LLMBundle, TenantService, TenantLLMService
+from api.settings import RetCode, retrievaler
 from api.utils import get_uuid
 from api.utils.api_utils import get_json_result
 from api.utils.api_utils import server_error_response, get_data_error_result, validate_request
+from graphrag.mind_map_extractor import MindMapExtractor
 
 
 @manager.route('/set', methods=['POST'])
@@ -286,3 +289,86 @@ def thumbup():
 
     ConversationService.update_by_id(conv["id"], conv)
     return get_json_result(data=conv)
+
+
+@manager.route('/ask', methods=['POST'])
+@login_required
+@validate_request("question", "kb_ids")
+def ask_about():
+    req = request.json
+    uid = current_user.id
+    def stream():
+        nonlocal req, uid
+        try:
+            for ans in ask(req["question"], req["kb_ids"], uid):
+                yield "data:" + json.dumps({"retcode": 0, "retmsg": "", "data": ans}, ensure_ascii=False) + "\n\n"
+        except Exception as e:
+            yield "data:" + json.dumps({"retcode": 500, "retmsg": str(e),
+                                        "data": {"answer": "**ERROR**: " + str(e), "reference": []}},
+                                       ensure_ascii=False) + "\n\n"
+        yield "data:" + json.dumps({"retcode": 0, "retmsg": "", "data": True}, ensure_ascii=False) + "\n\n"
+
+    resp = Response(stream(), mimetype="text/event-stream")
+    resp.headers.add_header("Cache-control", "no-cache")
+    resp.headers.add_header("Connection", "keep-alive")
+    resp.headers.add_header("X-Accel-Buffering", "no")
+    resp.headers.add_header("Content-Type", "text/event-stream; charset=utf-8")
+    return resp
+
+
+@manager.route('/mindmap', methods=['POST'])
+@login_required
+@validate_request("question", "kb_ids")
+def mindmap():
+    req = request.json
+    kb_ids = req["kb_ids"]
+    e, kb = KnowledgebaseService.get_by_id(kb_ids[0])
+    if not e:
+        return get_data_error_result(retmsg="Knowledgebase not found!")
+
+    embd_mdl = TenantLLMService.model_instance(
+        kb.tenant_id, LLMType.EMBEDDING.value, llm_name=kb.embd_id)
+    chat_mdl = LLMBundle(current_user.id, LLMType.CHAT)
+    ranks = retrievaler.retrieval(req["question"], embd_mdl, kb.tenant_id, kb_ids, 1, 12,
+                           0.3, 0.3, aggs=False)
+    mindmap = MindMapExtractor(chat_mdl)
+    mind_map = mindmap([c["content_with_weight"] for c in ranks["chunks"]]).output
+    return get_json_result(data=mind_map)
+
+
+@manager.route('/related_questions', methods=['POST'])
+@login_required
+@validate_request("question")
+def related_questions():
+    req = request.json
+    question = req["question"]
+    chat_mdl = LLMBundle(current_user.id, LLMType.CHAT)
+    prompt = """
+Objective: To generate search terms related to the user's search keywords, helping users find more valuable information.
+Instructions:
+ - Based on the keywords provided by the user, generate 5-10 related search terms.
+ - Each search term should be directly or indirectly related to the keyword, guiding the user to find more valuable information.
+ - Use common, general terms as much as possible, avoiding obscure words or technical jargon.
+ - Keep the term length between 2-4 words, concise and clear.
+ - DO NOT translate, use the language of the original keywords.
+
+### Example:
+Keywords: Chinese football
+Related search terms:
+1. Current status of Chinese football
+2. Reform of Chinese football
+3. Youth training of Chinese football
+4. Chinese football in the Asian Cup
+5. Chinese football in the World Cup
+
+Reason:
+ - When searching, users often only use one or two keywords, making it difficult to fully express their information needs.
+ - Generating related search terms can help users dig deeper into relevant information and improve search efficiency. 
+ - At the same time, related terms can also help search engines better understand user needs and return more accurate search results.
+ 
+"""
+    ans = chat_mdl.chat(prompt, [{"role": "user", "content": f"""
+Keywords: {question}
+Related search terms:
+    """}], {"temperature": 0.9})
+    return get_json_result(data=[re.sub(r"^[0-9]\. ", "", a) for a in ans.split("\n") if re.match(r"^[0-9]\. ", a)])

api/db/services/dialog_service.py

@@ -210,7 +210,7 @@ def chat(dialog, messages, stream=True, **kwargs):
             answer += " Please set LLM API-Key in 'User Setting -> Model Providers -> API-Key'"
         done_tm = timer()
         prompt += "\n### Elapsed\n  - Retrieval: %.1f ms\n  - LLM: %.1f ms"%((retrieval_tm-st)*1000, (done_tm-st)*1000)
-        return {"answer": answer, "reference": refs, "prompt": re.sub(r"\n", "<br/>", prompt)}
+        return {"answer": answer, "reference": refs, "prompt": prompt}
 
     if stream:
         last_ans = ""

api/db/services/llm_service.py

@@ -190,7 +190,7 @@ class LLMBundle(object):
             tenant_id, llm_type, llm_name, lang=lang)
         assert self.mdl, "Can't find mole for {}/{}/{}".format(
             tenant_id, llm_type, llm_name)
-        self.max_length = 512
+        self.max_length = 8192
         for lm in LLMService.query(llm_name=llm_name):
             self.max_length = lm.max_tokens
             break

graphrag/search.py

@@ -23,7 +23,7 @@ from rag.nlp.search import Dealer
 
 
 class KGSearch(Dealer):
-    def search(self, req, idxnm, emb_mdl=None):
+    def search(self, req, idxnm, emb_mdl=None, highlight=False):
         def merge_into_first(sres, title=""):
             df,texts = [],[]
             for d in sres["hits"]["hits"]:

rag/nlp/search.py

@@ -79,9 +79,9 @@ class Dealer:
                     Q("bool", must_not=Q("range", available_int={"lt": 1})))
         return bqry
 
-    def search(self, req, idxnm, emb_mdl=None):
+    def search(self, req, idxnm, emb_mdl=None, highlight=False):
         qst = req.get("question", "")
-        bqry, keywords = self.qryr.question(qst)
+        bqry, keywords = self.qryr.question(qst, min_match="30%")
         bqry = self._add_filters(bqry, req)
         bqry.boost = 0.05
 
@@ -130,7 +130,7 @@ class Dealer:
                 qst, emb_mdl, req.get(
                     "similarity", 0.1), topk)
             s["knn"]["filter"] = bqry.to_dict()
-            if "highlight" in s:
+            if not highlight and "highlight" in s:
                 del s["highlight"]
             q_vec = s["knn"]["query_vector"]
         es_logger.info("【Q】: {}".format(json.dumps(s)))
@@ -356,7 +356,7 @@ class Dealer:
                                            rag_tokenizer.tokenize(inst).split(" "))
 
     def retrieval(self, question, embd_mdl, tenant_id, kb_ids, page, page_size, similarity_threshold=0.2,
-                  vector_similarity_weight=0.3, top=1024, doc_ids=None, aggs=True, rerank_mdl=None):
+                  vector_similarity_weight=0.3, top=1024, doc_ids=None, aggs=True, rerank_mdl=None, highlight=False):
         ranks = {"total": 0, "chunks": [], "doc_aggs": {}}
         if not question:
             return ranks
@@ -364,7 +364,7 @@ class Dealer:
                "question": question, "vector": True, "topk": top,
                "similarity": similarity_threshold,
                "available_int": 1}
-        sres = self.search(req, index_name(tenant_id), embd_mdl)
+        sres = self.search(req, index_name(tenant_id), embd_mdl, highlight)
 
         if rerank_mdl:
             sim, tsim, vsim = self.rerank_by_model(rerank_mdl,
@@ -405,6 +405,8 @@ class Dealer:
                 "vector": self.trans2floats(sres.field[id].get("q_%d_vec" % dim, "\t".join(["0"] * dim))),
                 "positions": sres.field[id].get("position_int", "").split("\t")
             }
+            if highlight:
+                d["highlight"] = rmSpace(sres.highlight[id])
             if len(d["positions"]) % 5 == 0:
                 poss = []
                 for i in range(0, len(d["positions"]), 5):