feat: api_key support for xinference (#6417)

Signed-off-by: themanforfree <themanforfree@gmail.com>

api/core/model_runtime/model_providers/xinference/llm/llm.py

@@ -453,9 +453,11 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
         if credentials['server_url'].endswith('/'):
             credentials['server_url'] = credentials['server_url'][:-1]
 
+        api_key = credentials.get('api_key') or "abc"
+
         client = OpenAI(
             base_url=f'{credentials["server_url"]}/v1',
-            api_key='abc',
+            api_key=api_key,
             max_retries=3,
             timeout=60,
         )

api/core/model_runtime/model_providers/xinference/rerank/rerank.py

@@ -44,15 +44,23 @@ class XinferenceRerankModel(RerankModel):
                 docs=[]
             )
 
-        if credentials['server_url'].endswith('/'):
+        server_url = credentials['server_url']
-            credentials['server_url'] = credentials['server_url'][:-1]
+        model_uid = credentials['model_uid']
+        api_key = credentials.get('api_key')
+        if server_url.endswith('/'):
+            server_url = server_url[:-1]
+        auth_headers = {'Authorization': f'Bearer {api_key}'} if api_key else {}
 
-        handle = RESTfulRerankModelHandle(credentials['model_uid'], credentials['server_url'],auth_headers={})
+        try:
+            handle = RESTfulRerankModelHandle(model_uid, server_url, auth_headers)
             response = handle.rerank(
                 documents=docs,
                 query=query,
                 top_n=top_n,
             )
+        except RuntimeError as e:
+            raise InvokeServerUnavailableError(str(e))
+
 
         rerank_documents = []
         for idx, result in enumerate(response['results']):

api/core/model_runtime/model_providers/xinference/speech2text/speech2text.py

@@ -121,17 +121,24 @@ class XinferenceSpeech2TextModel(Speech2TextModel):
         :param temperature: The sampling temperature, between 0 and 1. Higher values like 0.8 will make the output mor            e random,while lower values like 0.2 will make it more focused and deterministic.If set to 0, the model wi            ll use log probability to automatically increase the temperature until certain thresholds are hit.
         :return: text for given audio file
         """
-        if credentials['server_url'].endswith('/'):
+        server_url = credentials['server_url']
-            credentials['server_url'] = credentials['server_url'][:-1]
+        model_uid = credentials['model_uid']
+        api_key = credentials.get('api_key')
+        if server_url.endswith('/'):
+            server_url = server_url[:-1]
+        auth_headers = {'Authorization': f'Bearer {api_key}'} if api_key else {}
 
-        handle = RESTfulAudioModelHandle(credentials['model_uid'],credentials['server_url'],auth_headers={})
+        try:
+            handle = RESTfulAudioModelHandle(model_uid, server_url, auth_headers)
             response = handle.transcriptions(
                 audio=file,
-            language = language,
+                language=language,
-            prompt = prompt,
+                prompt=prompt,
-            response_format = response_format,
+                response_format=response_format,
-            temperature = temperature
+                temperature=temperature
             )
+        except RuntimeError as e:
+            raise InvokeServerUnavailableError(str(e))
 
         return response["text"]
 

api/core/model_runtime/model_providers/xinference/text_embedding/text_embedding.py

@@ -43,15 +43,16 @@ class XinferenceTextEmbeddingModel(TextEmbeddingModel):
         """
         server_url = credentials['server_url']
         model_uid = credentials['model_uid']
-
+        api_key = credentials.get('api_key')
         if server_url.endswith('/'):
             server_url = server_url[:-1]
+        auth_headers = {'Authorization': f'Bearer {api_key}'} if api_key else {}
 
         try:
-            handle = RESTfulEmbeddingModelHandle(model_uid, server_url, auth_headers={})
+            handle = RESTfulEmbeddingModelHandle(model_uid, server_url, auth_headers)
             embeddings = handle.create_embedding(input=texts)
         except RuntimeError as e:
-            raise InvokeServerUnavailableError(e)
+            raise InvokeServerUnavailableError(str(e))
 
         """
         for convenience, the response json is like:

api/core/model_runtime/model_providers/xinference/xinference.yaml

@@ -46,3 +46,12 @@ model_credential_schema:
       placeholder:
         zh_Hans: 在此输入您的Model UID
         en_US: Enter the model uid
+    - variable: api_key
+      label:
+        zh_Hans: API密钥
+        en_US: API key
+      type: text-input
+      required: false
+      placeholder:
+        zh_Hans: 在此输入您的API密钥
+        en_US: Enter the api key