fix jina adding issure and term weight refinement (#974)

### What problem does this PR solve?

#724 #162

### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] New Feature (non-breaking change which adds functionality)

api/apps/llm_app.py

@@ -39,17 +39,18 @@ def factories():
 def set_api_key():
     req = request.json
     # test if api key works
-    chat_passed = False
+    chat_passed, embd_passed, rerank_passed = False, False, False
     factory = req["llm_factory"]
     msg = ""
     for llm in LLMService.query(fid=factory):
-        if llm.model_type == LLMType.EMBEDDING.value:
+        if not embd_passed and llm.model_type == LLMType.EMBEDDING.value:
             mdl = EmbeddingModel[factory](
                 req["api_key"], llm.llm_name, base_url=req.get("base_url"))
             try:
                 arr, tc = mdl.encode(["Test if the api key is available"])
                 if len(arr[0]) == 0 or tc == 0:
                     raise Exception("Fail")
+                embd_passed = True
             except Exception as e:
                 msg += f"\nFail to access embedding model({llm.llm_name}) using this api key." + str(e)
         elif not chat_passed and llm.model_type == LLMType.CHAT.value:
@@ -60,20 +61,21 @@ def set_api_key():
                                  "temperature": 0.9})
                 if not tc:
                     raise Exception(m)
-                chat_passed = True
             except Exception as e:
                 msg += f"\nFail to access model({llm.llm_name}) using this api key." + str(
                     e)
-        elif llm.model_type == LLMType.RERANK:
+            chat_passed = True
+        elif not rerank_passed and llm.model_type == LLMType.RERANK:
             mdl = RerankModel[factory](
                 req["api_key"], llm.llm_name, base_url=req.get("base_url"))
             try:
-                m, tc = mdl.similarity("What's the weather?", ["Is it sunny today?"])
+                arr, tc = mdl.similarity("What's the weather?", ["Is it sunny today?"])
-                if len(arr[0]) == 0 or tc == 0:
+                if len(arr) == 0 or tc == 0:
                     raise Exception("Fail")
             except Exception as e:
                 msg += f"\nFail to access model({llm.llm_name}) using this api key." + str(
                     e)
+            rerank_passed = True
 
     if msg:
         return get_data_error_result(retmsg=msg)

api/db/services/llm_service.py

@@ -147,7 +147,6 @@ class TenantLLMService(CommonService):
                     .where(cls.model.tenant_id == tenant_id, cls.model.llm_name == mdlnm)\
                     .execute()
         except Exception as e:
-            print(e)
             pass
         return num
 

rag/llm/__init__.py

@@ -28,6 +28,7 @@ EmbeddingModel = {
     "FastEmbed": FastEmbed,
     "Youdao": YoudaoEmbed,
     "BaiChuan": BaiChuanEmbed,
+    "Jina": JinaEmbed,
     "BAAI": DefaultEmbedding
 }
 

rag/llm/embedding_model.py

@@ -291,7 +291,7 @@ class JinaEmbed(Base):
             "input": texts,
             'encoding_type': 'float'
         }
-        res = requests.post(self.base_url, headers=self.headers, json=data)
+        res = requests.post(self.base_url, headers=self.headers, json=data).json()
         return np.array([d["embedding"] for d in res["data"]]), res["usage"]["total_tokens"]
 
     def encode_queries(self, text):

rag/llm/rerank_model.py

@@ -91,7 +91,7 @@ class JinaRerank(Base):
             "documents": texts,
             "top_n": len(texts)
         }
-        res = requests.post(self.base_url, headers=self.headers, json=data)
+        res = requests.post(self.base_url, headers=self.headers, json=data).json()
         return np.array([d["relevance_score"] for d in res["results"]]), res["usage"]["total_tokens"]
 
 

rag/nlp/query.py

@@ -44,7 +44,7 @@ class EsQueryer:
 
     def question(self, txt, tbl="qa", min_match="60%"):
         txt = re.sub(
-            r"[ \r\n\t,，。？?/`!！&\^%%]+",
+            r"[ :\r\n\t,，。？?/`!！&\^%%]+",
             " ",
             rag_tokenizer.tradi2simp(
                 rag_tokenizer.strQ2B(

rag/nlp/term_weight.py

@@ -104,7 +104,7 @@ class Dealer:
         while i < len(tks):
             j = i
             if i == 0 and oneTerm(tks[i]) and len(
-                    tks) > 1 and len(tks[i + 1]) > 1:  # 多 工位
+                    tks) > 1 and (len(tks[i + 1]) > 1 and not re.match(r"[0-9a-zA-Z]", tks[i + 1])):  # 多 工位
                 res.append(" ".join(tks[0:2]))
                 i = 2
                 continue