Set default embedding & llm

.github/workflows/docker.yml

@@ -36,7 +36,7 @@ jobs:
 
       - name: Build and push base image
         env:
-          IMAGE_TAG: 0.0.1
+          IMAGE_TAG: 0.0.2
         run: |
           docker build -t ${{ env.REGISTRY }}/mybigpai/pairag:$IMAGE_TAG .
           docker tag ${{ env.REGISTRY }}/mybigpai/pairag:$IMAGE_TAG ${{ env.REGISTRY_HZ }}/mybigpai/pairag:$IMAGE_TAG
@@ -45,7 +45,7 @@ jobs:
 
       - name: Build and push GPU image
         env:
-          IMAGE_TAG: 0.0.1_gpu
+          IMAGE_TAG: 0.0.2_gpu
         run: |
           docker build -t ${{ env.REGISTRY }}/mybigpai/pairag:$IMAGE_TAG -f Dockerfile_gpu .
           docker tag ${{ env.REGISTRY }}/mybigpai/pairag:$IMAGE_TAG ${{ env.REGISTRY_HZ }}/mybigpai/pairag:$IMAGE_TAG

.github/workflows/main.yml

@@ -50,6 +50,9 @@ jobs:
         env:
           DASHSCOPE_API_KEY: ${{ secrets.TESTDASHSCOPEKEY }}
           IS_PAI_RAG_CI_TEST: true
+          PAIRAG_RAG__embedding__source: "DashScope"
+          PAIRAG_RAG__llm__source: "DashScope"
+          PAIRAG_RAG__llm__name: "qwen-turbo"
 
       - name: Get Cover
         uses: orgoro/coverage@v3.1

src/pai_rag/app/web/tabs/vector_db_panel.py

@@ -113,9 +113,9 @@ def create_vector_db_panel(
                     label="Table",
                     elem_id="hologres_table",
                 )
-                hologres_pre_delete = gr.Dropdown(
-                    ["True", "False"],
-                    label="Pre Delete",
+                hologres_pre_delete = gr.Checkbox(
+                    label="Yes",
+                    info="Clear hologres table on connection.",
                     elem_id="hologres_pre_delete",
                 )
 

src/pai_rag/app/web/view_model.py

@@ -32,7 +32,7 @@ class ViewModel(BaseModel):
     llm: str = "PaiEas"
     llm_eas_url: str = None
     llm_eas_token: str = None
-    llm_eas_model_name: str = None
+    llm_eas_model_name: str = "PAI-EAS-LLM"
     llm_api_key: str = None
     llm_api_model_name: str = None
     llm_temperature: float = 0.1

src/pai_rag/config/settings.toml

@@ -20,7 +20,8 @@ persist_path = "localdata/storage"
 type = "SimpleDirectoryReader"
 
 [rag.embedding]
-source = "DashScope"
+source = "HuggingFace"
+model_name = "bge-small-zh-v1.5"
 
 [rag.evaluation]
 retrieval = ["mrr", "hit_rate"]
@@ -32,8 +33,9 @@ persist_path = "localdata/storage"
 vector_store.type = "FAISS"
 
 [rag.llm]
-source = "DashScope"
-name = "qwen-turbo"
+source = "PaiEas"
+endpoint = ""
+token = ""
 
 [rag.llm_chat_engine]
 type = "SimpleChatEngine"

src/pai_rag/modules/llm/llm_module.py

@@ -60,7 +60,7 @@ def _create_new_instance(self, new_params: Dict[str, Any]):
                 model_name=model_name, temperature=config.get("temperature", 0.1)
             )
         elif source == "paieas":
-            model_name = config["name"]
+            model_name = config.get("name", "PAI-EAS-LLM")
             endpoint = config["endpoint"]
             token = config["token"]
             logger.info(