Add ManufacturingAgentSuite example

.pre-commit-config.yaml

@@ -82,6 +82,7 @@ repos:
     rev: v1.7.7
     hooks:
       - id: docformatter
+        language_version: python3.12
         args: [
             --in-place,
             --wrap-summaries=0, # 0 means disable wrap

EdgeCraftRAG/cli/README.md

@@ -329,12 +329,14 @@ The CLI will display error messages from the API in JSON format. Network errors
 ## Tips
 
 - Use `--help` with any command to see detailed help:
+
   ```bash
   ecrag pipeline --help
   ecrag pipeline create --help
   ```
 
 - Pipe JSON output to other tools:
+
   ```bash
   ecrag kb list | jq '.[]' | head -n 20
   ```

EdgeCraftRAG/cli/client.py

@@ -2,17 +2,18 @@
 # SPDX-License-Identifier: Apache-2.0
 
 import json
-import requests
-from typing import Optional, Dict, Any
+from typing import Any, Dict, Optional
 from urllib.parse import urljoin
 
+import requests
+
 
 class EcragApiClient:
     """API client for Edge Craft RAG."""
 
     def __init__(self, host: str = "http://localhost", server_port: int = 16010, mega_port: int = 16011):
         """Initialize the API client.
-        
+
         Args:
             host: The host URL (default: http://localhost)
             server_port: The server port (default: 16010)
@@ -21,10 +22,10 @@ def __init__(self, host: str = "http://localhost", server_port: int = 16010, meg
         # Normalize host URL
         if not host.startswith(("http://", "https://")):
             host = f"http://{host}"
-        
+
         # Remove trailing slash if present
         host = host.rstrip("/")
-        
+
         self.server_url = f"{host}:{server_port}"
         self.mega_url = f"{host}:{mega_port}"
 
@@ -170,12 +171,16 @@ def delete_knowledge_base(self, kb_name: str) -> Dict[str, Any]:
     def add_files_to_kb(self, kb_name: str, local_paths: list) -> Dict[str, Any]:
         """Add files to a knowledge base."""
         url = urljoin(self.server_url, f"/v1/knowledge/{kb_name}/files")
-        return self._request("POST", url, json={"local_paths": local_paths}, headers={"Content-Type": "application/json"})
+        return self._request(
+            "POST", url, json={"local_paths": local_paths}, headers={"Content-Type": "application/json"}
+        )
 
     def delete_files_from_kb(self, kb_name: str, local_paths: list) -> Dict[str, Any]:
         """Delete files from a knowledge base."""
         url = urljoin(self.server_url, f"/v1/knowledge/{kb_name}/files")
-        return self._request("DELETE", url, json={"local_paths": local_paths}, headers={"Content-Type": "application/json"})
+        return self._request(
+            "DELETE", url, json={"local_paths": local_paths}, headers={"Content-Type": "application/json"}
+        )
 
     # Experience Management
     def get_experiences(self) -> Dict[str, Any]:

EdgeCraftRAG/cli/config.py

@@ -1,6 +1,5 @@
 # Copyright (C) 2024 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
-
 """Configuration for EdgeCraft RAG CLI."""
 
 import os

EdgeCraftRAG/cli/main.py

@@ -2,10 +2,11 @@
 # SPDX-License-Identifier: Apache-2.0
 
 import json
-import click
 import os
 from pathlib import Path
 from typing import Optional
+
+import click
 from cli.client import EcragApiClient
 from cli.config import get_config
 
@@ -34,26 +35,26 @@ def run_chatqna_query(client: EcragApiClient, query: str, top_n: int, max_tokens
 @click.pass_context
 def cli(ctx, host: Optional[str], port: Optional[int], mega_port: Optional[int]):
     """EdgeCraft RAG CLI Tool.
-    
+
     Configure server connection via command-line options or environment variables:
     - ECRAG_HOST: Server host (default: http://localhost)
     - ECRAG_PORT: Server port (default: 16010)
     - ECRAG_MEGA_PORT: Mega service port (default: 16011)
     """
     ctx.ensure_object(dict)
-    
+
     # Get defaults from config
     config = get_config()
-    
+
     # Use provided options or environment/defaults
     final_host = host or config.host
     final_port = port or config.port
     final_mega_port = mega_port or config.mega_port
-    
+
     # Normalize host URL
     if not final_host.startswith(("http://", "https://")):
         final_host = f"http://{final_host}"
-    
+
     ctx.obj["client"] = EcragApiClient(host=final_host, server_port=final_port, mega_port=final_mega_port)
 
 

EdgeCraftRAG/cli/quickstart.py

@@ -1,25 +1,25 @@
 # Copyright (C) 2024 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
-
 """Quick start guide for EdgeCraft RAG CLI."""
 
 import json
 import sys
+
 from cli.client import EcragApiClient
 
 
 def test_connection(host: str = "http://localhost", port: int = 16010):
     """Test connection to EdgeCraft RAG server."""
     client = EcragApiClient(host=host, server_port=port)
-    
+
     try:
         print(f"Testing connection to {client.server_url}...")
         result = client.get_system_info()
-        
+
         if "error" in result:
             print(f"❌ Connection failed: {result['error']}")
             return False
-        
+
         print("✓ Connection successful!")
         print(f"  System Info: {json.dumps(result, indent=2)}")
         return True
@@ -62,7 +62,7 @@ def quick_start_guide():
     export ECRAG_MEGA_PORT=16011
 
 COMMON COMMANDS:
-  
+
   Pipeline Management:
     ecrag pipeline list
     ecrag pipeline get --name <name>

EdgeCraftRAG/cli/setup.py

@@ -1,12 +1,10 @@
 #!/usr/bin/env python3
 # Copyright (C) 2024 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
-
 """Canonical setup script for EdgeCraft RAG CLI."""
 
 from setuptools import setup
 
-
 setup(
     name="ecrag-cli",
     version="0.1.0",
@@ -35,4 +33,4 @@
         "Programming Language :: Python :: 3.10",
         "Programming Language :: Python :: 3.11",
     ],
-)
+)

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

@@ -20,19 +20,22 @@ This section describes how to quickly deploy and test the EdgeCraftRAG service m
 
 ### 1. Prerequisites
 
-EC-RAG supports vLLM deployment(default method) and local OpenVINO deployment for Intel Arc GPU and Core Ultra Platform. Prerequisites are shown as below:  
+EC-RAG supports vLLM deployment(default method) and local OpenVINO deployment for Intel Arc GPU and Core Ultra Platform. Prerequisites are shown as below:
 
 #### Core Ultra
+
 **OS**: Ubuntu 24.04 or newer  
 **Driver & libraries**: Please refer to [Installing Client GPUs on Ubuntu Desktop](https://dgpu-docs.intel.com/driver/client/overview.html#installing-client-gpus-on-ubuntu-desktop)  
 **Available Inferencing Framework**: openVINO
 
 #### Intel Arc B60
-**OS**: Ubuntu 25.04 Desktop (for Core Ultra and Xeon-W), Ubuntu 25.04 Server (for Xeon-SP).   
+
+**OS**: Ubuntu 25.04 Desktop (for Core Ultra and Xeon-W), Ubuntu 25.04 Server (for Xeon-SP).  
 **Driver & libraries**: Please refer to [Install Bare Metal Environment](https://github.com/intel/llm-scaler/tree/main/vllm#11-install-bare-metal-environment) for detailed setup  
 **Available Inferencing Framework**: openVINO, vLLM
 
 #### Intel Arc A770
+
 **OS**: Ubuntu Server 22.04.1 or newer (at least 6.2 LTS kernel)  
 **Driver & libraries**: Please refer to [Installing GPUs Drivers](https://dgpu-docs.intel.com/driver/client/overview.html#ubuntu-22.04) for detailed driver & libraries setup  
 **Available Inferencing Framework**: openVINO, vLLM
@@ -48,9 +51,9 @@ cd GenAIExamples/EdgeCraftRAG
 
 > **NOTE**: If you want to checkout a released version, such as v1.5:
 >
->```
->git checkout v1.5
->```
+> ```
+> git checkout v1.5
+> ```
 
 ### 3. Run quick_start.sh
 

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

@@ -23,16 +23,19 @@
 EC-RAG 支持 vLLM 部署（默认方式）以及面向 Intel Arc GPU 和 Core Ultra 平台的本地 OpenVINO 部署。前置条件如下：
 
 #### Core Ultra
+
 **操作系统**：Ubuntu 24.04 或更高版本  
 **驱动与库**：请参考 [Installing Client GPUs on Ubuntu Desktop](https://dgpu-docs.intel.com/driver/client/overview.html#installing-client-gpus-on-ubuntu-desktop)  
 **可用推理框架**：openVINO
 
 #### Intel Arc B60
+
 **操作系统**：Ubuntu 25.04 Desktop（适用于 Core Ultra 和 Xeon-W），Ubuntu 25.04 Server（适用于 Xeon-SP）。  
 **驱动与库**：详细安装请参考 [Install Bare Metal Environment](https://github.com/intel/llm-scaler/tree/main/vllm#11-install-bare-metal-environment)  
 **可用推理框架**：openVINO、vLLM
 
 #### Intel Arc A770
+
 **操作系统**：Ubuntu Server 22.04.1 或更高版本（至少 6.2 LTS 内核）  
 **驱动与库**：详细驱动与库安装请参考 [Installing GPUs Drivers](https://dgpu-docs.intel.com/driver/client/overview.html#ubuntu-22.04)  
 **可用推理框架**：openVINO、vLLM
@@ -48,9 +51,9 @@ cd GenAIExamples/EdgeCraftRAG
 
 > **注意**：如果你想切换到某个发布版本，例如 v1.5：
 >
->```
->git checkout v1.5
->```
+> ```
+> git checkout v1.5
+> ```
 
 ### 3. 运行 quick_start.sh
 
@@ -112,11 +115,11 @@ If you are accessing from another machine, replace ${HOST_IP} with your server's
 
 下表全面概述了示例 Docker Compose 文件中各类部署所使用的 EdgeCraftRAG 服务。表中每一行代表一个独立服务，详细说明了可用镜像及其在部署架构中的功能描述。
 
-| 服务名称            | 可选镜像名称                             | 可选 | 描述                                                                                             |
-| ------------------- | ---------------------------------------- | ---- | ------------------------------------------------------------------------------------------------ |
-| etcd                | quay.io/coreos/etcd:v3.5.5               | 否   | 提供分布式键值存储，用于服务发现和配置管理。                                                     |
-| minio               | minio/minio:RELEASE.2023-03-20T20-16-18Z | 否   | 提供对象存储服务，用于存储文档和模型文件。                                                       |
-| milvus-standalone   | milvusdb/milvus:v2.4.6                   | 否   | 提供向量数据库能力，用于管理 embedding 和相似度检索。                                            |
-| edgecraftrag-server | opea/edgecraftrag-server:latest          | 否   | 作为 EdgeCraftRAG 服务后端，具体形态随部署方式不同而变化。                                       |
-| edgecraftrag-ui     | opea/edgecraftrag-ui:latest              | 否   | 提供 EdgeCraftRAG 服务的用户界面。                                                               |
-| ecrag               | opea/edgecraftrag:latest                 | 否   | 作为反向代理，管理 UI 与后端服务之间的流量。                                                     |
+| 服务名称            | 可选镜像名称                             | 可选 | 描述                                                       |
+| ------------------- | ---------------------------------------- | ---- | ---------------------------------------------------------- |
+| etcd                | quay.io/coreos/etcd:v3.5.5               | 否   | 提供分布式键值存储，用于服务发现和配置管理。               |
+| minio               | minio/minio:RELEASE.2023-03-20T20-16-18Z | 否   | 提供对象存储服务，用于存储文档和模型文件。                 |
+| milvus-standalone   | milvusdb/milvus:v2.4.6                   | 否   | 提供向量数据库能力，用于管理 embedding 和相似度检索。      |
+| edgecraftrag-server | opea/edgecraftrag-server:latest          | 否   | 作为 EdgeCraftRAG 服务后端，具体形态随部署方式不同而变化。 |
+| edgecraftrag-ui     | opea/edgecraftrag-ui:latest              | 否   | 提供 EdgeCraftRAG 服务的用户界面。                         |
+| ecrag               | opea/edgecraftrag:latest                 | 否   | 作为反向代理，管理 UI 与后端服务之间的流量。               |

EdgeCraftRAG/docs/API_Guide.md

@@ -1,6 +1,7 @@
 # Edge Craft Retrieval-Augmented Generation API Guide
 
 > **Base URLs**
+>
 > - EC-RAG Server: `http://${HOST_IP}:16010`
 > - EC-RAG Mega Service: `http://${HOST_IP}:16011`
 

EdgeCraftRAG/docs/Advanced_Setup.md

@@ -42,6 +42,7 @@ optimum-cli export openvino -m BAAI/bge-reranker-large ${MODEL_PATH}/BAAI/bge-re
 #### LLM
 
 ##### openVINO
+
 If you have Core Ultra platform only, please prepare openVINO models:  
 You can also run openVINO models on discrete GPU.
 
@@ -51,6 +52,7 @@ optimum-cli export openvino --model Qwen/Qwen3-8B ${MODEL_PATH}/OpenVINO/Qwen3-8
 ```
 
 ##### vLLM
+
 Alternatively, if you have discrete GPU and want to use vLLM, please prepare models for vLLM:
 
 ```bash
@@ -178,5 +180,3 @@ export TP=4 # for multi GPU, you can change TP value
 export ZE_AFFINITY_MASK=0,1,2,3 # for multi GPU, you can export ZE_AFFINITY_MASK=0,1,2...
 docker compose --profile b60 -f docker_compose/intel/gpu/arc/compose.yaml up -d
 ```
-
-

EdgeCraftRAG/docs/Advanced_Setup_zh.md

@@ -42,6 +42,7 @@ optimum-cli export openvino -m BAAI/bge-reranker-large ${MODEL_PATH}/BAAI/bge-re
 #### LLM
 
 ##### openVINO
+
 如果仅使用 Core Ultra 平台，请准备 openVINO 模型：  
 你也可以在独立 GPU 上运行 openVINO 模型。
 
@@ -51,6 +52,7 @@ optimum-cli export openvino --model Qwen/Qwen3-8B ${MODEL_PATH}/OpenVINO/Qwen3-8
 ```
 
 ##### vLLM
+
 如果你有独立 GPU 并希望使用 vLLM，可按如下方式准备模型：
 
 ```bash
@@ -105,7 +107,6 @@ export MILVUS_ENABLED=0
 # export CHAT_HISTORY_ROUND= # 按需修改
 ```
 
-
 ### 使用 Docker Compose 在 Intel GPU 上部署服务
 
 #### 选项 a：为 Core Ultra / Arc B60 / Arc A770 部署基于 openVINO LLM 的 EC-RAG

EdgeCraftRAG/docs/Explore_Edge_Craft_RAG.md

@@ -37,18 +37,17 @@ Then, you can submit messages in the chat box in `Chat` page.
 ![alt text](../assets/img/Explore_Edge_Craft_RAG_08.jpg)
 
 ## ChatQnA with Kbadmin in UI
- 
+
 ### Kbadmin Knowledge Base
- 
+
 Go to `Knowledge Base` page and click `Create Knowledge Base` button to create your knowledge base.  
 Please select 'kbadmin' in `Type`and select kb name from the kbs you created in kbadmin UI page. Loading kb name might be slow ,please wait with patient
- 
+
 ![alt text](../assets/img/Explore_Edge_Craft_RAG_09.png)
- 
-Ten you can select embedding infomation in 'Indexer' page
- 
+
+Ten you can select embedding information in 'Indexer' page
+
 ![alt text](../assets/img/Explore_Edge_Craft_RAG_10.png)
- 
+
 After creation, you can see kbadmin tag in knowledge base then you can submit messages in the chat box in `Chat` page.
 ![alt text](../assets/img/Explore_Edge_Craft_RAG_11.png)
-

EdgeCraftRAG/docs/Explore_Edge_Craft_RAG_zh.md

@@ -42,12 +42,12 @@
 
 流水线创建完成后，前往 `Knowledge Base` 页面，点击 `Create Knowledge Base` 按钮创建知识库。  
 请在 `Type` 中选择 `kbadmin`，并从 kbadmin UI 页面中已创建的知识库列表中选择 kb 名称。加载kb名称可能比较耗时，请耐心等待。
- 
+
 ![alt text](../assets/img/Explore_Edge_Craft_RAG_09.png)
- 
+
 在 `Indexer` 页面，填写 Embedding 服务和向量数据库信息，注意 Embedding 服务端口为 13020，向量数据库端口为 29530。
- 
+
 ![alt text](../assets/img/Explore_Edge_Craft_RAG_10.png)
- 
+
 然后，在 `Chat` 页面的聊天框中提交您的问题。
 ![alt text](../assets/img/Explore_Edge_Craft_RAG_11.png)

EdgeCraftRAG/edgecraftrag/api/v1/chatqna.py

@@ -124,7 +124,9 @@ async def res_gen_json():
                 async def context_suffix_gen():
                     yield '","contexts":' + json.dumps(serialize_retrievals(retrievals)) + "}"
 
-                query_gen = stream_generator('{"query":' + json.dumps(original_query, ensure_ascii=False) + ',"response":"')
+                query_gen = stream_generator(
+                    '{"query":' + json.dumps(original_query, ensure_ascii=False) + ',"response":"'
+                )
                 output_gen = chain_async_generators([query_gen, res_gen_json(), context_suffix_gen()])
 
                 return StreamingResponse(output_gen, media_type="text/plain")
@@ -149,7 +151,7 @@ async def res_gen_json():
                     yield json.dumps(token, ensure_ascii=False)[1:-1]
 
             # Reconstruct RagOut in stream response
-            query_gen = stream_generator('{"query":' + json.dumps(request.messages, ensure_ascii=False) + ',')
+            query_gen = stream_generator('{"query":' + json.dumps(request.messages, ensure_ascii=False) + ",")
 
             s_contexts = json.dumps(serialize_contexts(contexts))
             context_gen = stream_generator('"contexts":' + s_contexts + ',"response":"')