Fix keys of Xinference deployed models, especially has the same model name with public hosted models. (#2832)

### What problem does this PR solve?

Fix keys of Xinference deployed models, especially has the same model
name with public hosted models.

### Type of change

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

---------

Co-authored-by: 0000sir <[email protected]>
Co-authored-by: Kevin Hu <[email protected]>

api/apps/llm_app.py

@@ -343,10 +343,10 @@ def list_app():
         for m in llms:
             m["available"] = m["fid"] in facts or m["llm_name"].lower() == "flag-embedding" or m["fid"] in self_deploied
 
-        llm_set = set([m["llm_name"] for m in llms])
+        llm_set = set([m["llm_name"]+"@"+m["fid"] for m in llms])
         for o in objs:
             if not o.api_key:continue
-            if o.llm_name in llm_set:continue
+            if o.llm_name+"@"+o.llm_factory in llm_set:continue
             llms.append({"llm_name": o.llm_name, "model_type": o.model_type, "fid": o.llm_factory, "available": True})
 
         res = {}

api/apps/sdk/doc.py

@@ -494,25 +494,24 @@ def set(tenant_id,dataset_id,document_id,chunk_id):
 
 
 
-@manager.route('/retrieval', methods=['GET'])
+@manager.route('/retrieval', methods=['POST'])
 @token_required
 def retrieval_test(tenant_id):
-    req = request.args
-    req_json = request.json
-    if not req_json.get("datasets"):
+    req = request.json
+    if not req.get("datasets"):
         return get_error_data_result("`datasets` is required.")
-    for id in req_json.get("datasets"):
+    kb_id = req["datasets"]
+    if isinstance(kb_id, str): kb_id = [kb_id]
+    for id in kb_id:
         if not KnowledgebaseService.query(id=id,tenant_id=tenant_id):
             return get_error_data_result(f"You don't own the dataset {id}.")
     if "question" not in req_json:
         return get_error_data_result("`question` is required.")
     page = int(req.get("offset", 1))
     size = int(req.get("limit", 30))
-    question = req_json["question"]
-    kb_id = req_json["datasets"]
-    if isinstance(kb_id, str): kb_id = [kb_id]
-    doc_ids = req_json.get("documents", [])
-    similarity_threshold = float(req.get("similarity_threshold", 0.0))
+    question = req["question"]
+    doc_ids = req.get("documents", [])
+    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))
     if req.get("highlight")=="False" or  req.get("highlight")=="false":

rag/llm/cv_model.py

@@ -453,7 +453,7 @@ class XinferenceCV(Base):
     def __init__(self, key, model_name="", lang="Chinese", base_url=""):
         if base_url.split("/")[-1] != "v1":
             base_url = os.path.join(base_url, "v1")
-        self.client = OpenAI(api_key="xxx", base_url=base_url)
+        self.client = OpenAI(api_key=key, base_url=base_url)
         self.model_name = model_name
         self.lang = lang
 

rag/llm/embedding_model.py

@@ -274,7 +274,7 @@ class XinferenceEmbed(Base):
     def __init__(self, key, model_name="", base_url=""):
         if base_url.split("/")[-1] != "v1":
             base_url = os.path.join(base_url, "v1")
-        self.client = OpenAI(api_key="xxx", base_url=base_url)
+        self.client = OpenAI(api_key=key, base_url=base_url)
         self.model_name = model_name
 
     def encode(self, texts: list, batch_size=32):

rag/llm/rerank_model.py

@@ -162,7 +162,8 @@ class XInferenceRerank(Base):
         self.base_url = base_url
         self.headers = {
             "Content-Type": "application/json",
-            "accept": "application/json"
+            "accept": "application/json",
+            "Authorization": f"Bearer {key}"
         }
 
     def similarity(self, query: str, texts: list):

rag/llm/sequence2txt_model.py

@@ -90,6 +90,7 @@ class XinferenceSeq2txt(Base):
     def __init__(self,key,model_name="whisper-small",**kwargs):
         self.base_url = kwargs.get('base_url', None)
         self.model_name = model_name
+        self.key = key
 
     def transcription(self, audio, language="zh", prompt=None, response_format="json", temperature=0.7):
         if isinstance(audio, str):

sdk/python/ragflow/ragflow.py

@@ -74,6 +74,12 @@ class RAGFlow:
         if res.get("code") != 0:
             raise Exception(res["message"])
 
+    def get_dataset(self,name: str):
+        _list = self.list_datasets(name=name)
+        if len(_list) > 0:
+            return _list[0]
+        raise Exception("Dataset %s not found" % name)
+
     def list_datasets(self, page: int = 1, page_size: int = 1024, orderby: str = "create_time", desc: bool = True,
                       id: str = None, name: str = None) -> \
             List[DataSet]: