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EdgeCraftRAG/docker_compose/intel/gpu/arc/README.md

@@ -95,7 +95,8 @@ For more advanced env variables and configurations, please refer to [Prepare env
 
 ### 5. Deploy the Service on Intel GPU Using Docker Compose
 
-set Milvus DB and chat history round for inference: 
+set Milvus DB and chat history round for inference:
+
 ```bash
 # EC-RAG support Milvus as persistent database, by default milvus is disabled, you can choose to set MILVUS_ENABLED=1 to enable it
 export MILVUS_ENABLED=0
@@ -123,7 +124,7 @@ docker compose -f docker_compose/intel/gpu/arc/compose_vllm.yaml up -d
 #### option b. Deploy the Service on Arc B60 Using Docker Compose
 
 ```bash
-# Besides MILVUS_ENABLED and CHAT_HISTORY_ROUND, below enviroments are exposed for vLLM config, you can change them to your preference:
+# Besides MILVUS_ENABLED and CHAT_HISTORY_ROUND, below environments are exposed for vLLM config, you can change them to your preference:
 # export VLLM_SERVICE_PORT_B60=8086
 # export DTYPE=float16
 # export TP=1 # for multi GPU, you can change TP value

EdgeCraftRAG/docker_compose/intel/gpu/arc/compose_vllm_b60.yaml

@@ -183,4 +183,4 @@ services:
       -dp=$${DP}"
 networks:
   default:
-    driver: bridge
+    driver: bridge

EdgeCraftRAG/docs/API_Guide.md

@@ -219,4 +219,4 @@ curl -X POST http://${HOST_IP}:16010/v1/retrieval -H "Content-Type: application/
 
 ```bash
 curl -X POST http://${HOST_IP}:16011/v1/chatqna -H "Content-Type: application/json" -d '{"messages":"#REPLACE WITH YOUR QUESTION HERE#", "top_n":5, "max_tokens":512}' | jq '.'
-```
+```

EdgeCraftRAG/docs/Advanced_Setup.md

@@ -208,6 +208,6 @@ Model preparation is the same as vLLM inference section, please refer to [Prepar
 
 This section is the same as default vLLM inference section, please refer to [Prepare env variables and configurations](../docker_compose/intel/gpu/arc/README.md#prepare-env-variables-and-configurations) and [Start Edge Craft RAG Services with Docker Compose](../docker_compose/intel/gpu/arc/README.md#deploy-the-service-on-arc-a770-using-docker-compose)
 
-### 2. Access Kbadmin UI 
+### 2. Access Kbadmin UI
 
 please refer to [ChatQnA with Kbadmin in UI](./Explore_Edge_Craft_RAG.md#chatqna-with-kbadmin-in-ui)

EdgeCraftRAG/docs/Explore_Edge_Craft_RAG.md

@@ -34,7 +34,7 @@ After knowledge base creation, you can upload the documents for retrieval.
 Then, you can submit messages in the chat box in `Chat` page.
 ![chat_with_rag](../assets/img/chatqna.png)
 
-## ChatQnA with Kbadmin in UI 
+## ChatQnA with Kbadmin in UI
 
 ### Kbadmin Pipeline
 
@@ -52,4 +52,4 @@ Please select 'kbadmin' in `Type`and select kb name from the kbs you created in
 ![upload_data](../assets/img/kbadmin_kb.png)
 
 Then, you can submit messages in the chat box in `Chat` page.
-![chat_with_rag](../assets/img/chatqna.png)
+![chat_with_rag](../assets/img/chatqna.png)

EdgeCraftRAG/edgecraftrag/api/v1/chatqna.py

@@ -38,7 +38,7 @@ async def chatqna(request: ChatCompletionRequest):
         active_kb = ctx.knowledgemgr.get_active_knowledge_base()
         request.user = active_kb if active_kb else None
         if experience_kb:
-            request.tool_choice = 'auto' if experience_kb.experience_active else 'none'
+            request.tool_choice = "auto" if experience_kb.experience_active else "none"
         generator = ctx.get_pipeline_mgr().get_active_pipeline().generator
         if generator:
             request.model = generator.model_id
@@ -62,7 +62,7 @@ async def ragqna(request: ChatCompletionRequest):
         active_kb = ctx.knowledgemgr.get_active_knowledge_base()
         request.user = active_kb if active_kb else None
         if experience_kb:
-            request.tool_choice = 'auto' if experience_kb.experience_active else 'none'
+            request.tool_choice = "auto" if experience_kb.experience_active else "none"
         generator = ctx.get_pipeline_mgr().get_active_pipeline().generator
         if generator:
             request.model = generator.model_id

EdgeCraftRAG/edgecraftrag/api/v1/data.py

@@ -35,7 +35,7 @@ async def redindex_data():
     pl = ctx.get_pipeline_mgr().get_active_pipeline()
     kb = ctx.get_knowledge_mgr().get_active_knowledge_base()
     if kb:
-        kb_name  = kb.name
+        kb_name = kb.name
         docs_name = kb_name + pl.name + str(pl.indexer.d)
     else:
         kb_name = None
@@ -45,7 +45,7 @@ async def redindex_data():
     pl.update_indexer_to_retriever()
 
     all_docs = []
-    docs_list =ctx.get_file_mgr().get_kb_files_by_name(docs_name)
+    docs_list = ctx.get_file_mgr().get_kb_files_by_name(docs_name)
     for docs_file in docs_list:
         all_docs.extend(docs_file.documents)
     nodelist = ctx.get_pipeline_mgr().run_data_prepare(docs=all_docs)
@@ -99,9 +99,10 @@ async def delete_file(kb_name, file_path):
         if nodelist is not None and len(nodelist) > 0:
             ctx.get_node_mgr().add_nodes(pl.node_parser.idx, nodelist)
 
-        return f"File is deleted"
+        return "File is deleted"
     else:
-        return f"File not found"
+        return "File not found"
+
 
 # DELETE a file
 @data_app.delete(path="/v1/data/all_files/{name}")
@@ -118,6 +119,7 @@ async def delete_all_file(name):
     else:
         return f"File {name} not found"
 
+
 # Upload & save a file from UI
 @data_app.post(path="/v1/data/file/{file_name}")
 async def upload_file(file_name: str, file: UploadFile = File(...)):
@@ -145,4 +147,4 @@ async def upload_file(file_name: str, file: UploadFile = File(...)):
     except Exception as e:
         raise HTTPException(
             status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail=f"Failed to upload file: {str(e)}"
-        )
+        )