From 322383406970b4d39a53cebca9a91e98ea9f5164 Mon Sep 17 00:00:00 2001 From: qitan Date: Tue, 9 Apr 2024 14:46:51 +0800 Subject: [PATCH] add technical solution 109 --- README-CN.md | 25 +- README.md | 9 +- .../solution/ai/exclusive-qa-service.yml | 584 ++++++++++++++++++ 3 files changed, 602 insertions(+), 16 deletions(-) create mode 100644 documents/solution/ai/exclusive-qa-service.yml diff --git a/README-CN.md b/README-CN.md index c6daee2b..614efc99 100644 --- a/README-CN.md +++ b/README-CN.md @@ -440,18 +440,19 @@ ROS 模板的示例和最佳实践。模板分类如下： - ai -| 模板 | 说明 | -|----------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------| -| [use-function-compute-to-deploy-stable-diffusion-for-AI-painting.yml](documents/solution/ai/use-function-compute-to-deploy-stable-diffusion-for-ai-painting.yml) | 使用函数计算部署Stable Diffusion进行AI绘画。支持自定义模型。 | -| [aigc-text-generation-3d-model-with-ecs.yml](documents/solution/ai/aigc-text-generation-3d-model-with-ecs.yml) | AIGC文本生成3D模型。 ｜ [解决方案](https://aliyun.com/solution/tech-solution/tg3dm) | -| [use-pai-to-deploy-stable-diffusion-for-AI-painting.yml](documents/solution/ai/use-pai-to-deploy-stable-diffusion-for-AI-painting.yml) | 如何在阿里云快速启动Stable Diffusion轻松玩转AI绘画。 | -| [lingjun-LLAMA-2-best-practice.yml](documents/solution/ai/lingjun-LLAMA-2-best-practice.yml) | LLAMA-2全托管灵骏最佳实践。 ｜ [解决方案](https://aliyun.com/solution/tech-solution/pai_lingjun) | -| [pai-ai-painting-solution.yml](documents/solution/ai/pai-ai-painting-solution.yml) | PAI AI绘画解决方案。 ｜ [解决方案](https://www.aliyun.com/solution/tech-solution/pai_eas) | -| [pai-lingjun-serverless-LLM-best-practice.yml](documents/solution/ai/upai-lingjun-serverless-LLM-best-practice.yml) | PAI灵骏智算资源（Serverless版）大模型最佳实践。 | -| [use-gpu-ecs-to-deploy-chatGLM.yaml](documents/solution/ai/use-gpu-ecs-to-deploy-chatGLM.yaml) | 向量数据库构建企业智能知识库。 ｜ [解决方案](https://aliyun.com/solution/tech-solution/baeeikb) | -| [build-a-dialogue-model-based-on-ChatGLM-and-LangChain.yml](documents/solution/ai/build-a-dialogue-model-based-on-ChatGLM-and-LangChain.yml) | 基于ChatGLM和LangChain搭建对话模型。｜ [解决方案](https://www.aliyun.com/solution/tech-solution/calbadm) | -| [build-a-dialogue-model-based-on-Qwen-and-LangChain.yml](documents/solution/ai/build-a-dialogue-model-based-on-Qwen-and-LangChain.yml) | 基于通义千问和LangChain搭建对话模型。 | -| [ack-ai-fine-tuning.yml](documents/solution/ai/ack-ai-fine-tuning.yml) | 使用云原生AI套件提交模型微调训练任务与部署GPU共享推理服务。 ｜ [解决方案](https://www.aliyun.com/solution/tech-solution/one_gpu) | +| 模板 | 说明 | +|----------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------| +| [use-function-compute-to-deploy-stable-diffusion-for-AI-painting.yml](documents/solution/ai/use-function-compute-to-deploy-stable-diffusion-for-ai-painting.yml) | 使用函数计算部署Stable Diffusion进行AI绘画。支持自定义模型。 | +| [aigc-text-generation-3d-model-with-ecs.yml](documents/solution/ai/aigc-text-generation-3d-model-with-ecs.yml) | AIGC文本生成3D模型。 ｜ [解决方案](https://aliyun.com/solution/tech-solution/tg3dm) | +| [use-pai-to-deploy-stable-diffusion-for-AI-painting.yml](documents/solution/ai/use-pai-to-deploy-stable-diffusion-for-AI-painting.yml) | 如何在阿里云快速启动Stable Diffusion轻松玩转AI绘画。 | +| [lingjun-LLAMA-2-best-practice.yml](documents/solution/ai/lingjun-LLAMA-2-best-practice.yml) | LLAMA-2全托管灵骏最佳实践。 ｜ [解决方案](https://aliyun.com/solution/tech-solution/pai_lingjun) | +| [pai-ai-painting-solution.yml](documents/solution/ai/pai-ai-painting-solution.yml) | PAI AI绘画解决方案。 ｜ [解决方案](https://www.aliyun.com/solution/tech-solution/pai_eas) | +| [pai-lingjun-serverless-LLM-best-practice.yml](documents/solution/ai/upai-lingjun-serverless-LLM-best-practice.yml) | PAI灵骏智算资源（Serverless版）大模型最佳实践。 | +| [use-gpu-ecs-to-deploy-chatGLM.yaml](documents/solution/ai/use-gpu-ecs-to-deploy-chatGLM.yaml) | 向量数据库构建企业智能知识库。 ｜ [解决方案](https://aliyun.com/solution/tech-solution/baeeikb) | +| [build-a-dialogue-model-based-on-ChatGLM-and-LangChain.yml](documents/solution/ai/build-a-dialogue-model-based-on-ChatGLM-and-LangChain.yml) | 基于ChatGLM和LangChain搭建对话模型。｜ [解决方案](https://www.aliyun.com/solution/tech-solution/calbadm) | +| [build-a-dialogue-model-based-on-Qwen-and-LangChain.yml](documents/solution/ai/build-a-dialogue-model-based-on-Qwen-and-LangChain.yml) | 基于通义千问和LangChain搭建对话模型。 | +| [ack-ai-fine-tuning.yml](documents/solution/ai/ack-ai-fine-tuning.yml) | 使用云原生AI套件提交模型微调训练任务与部署GPU共享推理服务。 ｜ [解决方案](https://www.aliyun.com/solution/tech-solution/one_gpu) | +| [exclusive-qa-service.yml](exclusive-qa-service.yml) | 向量检索与通义千问搭建专属问答服务。 | - data-analysis diff --git a/README.md b/README.md index 69538dcb..edefc0b1 100644 --- a/README.md +++ b/README.md @@ -446,14 +446,15 @@ Examples and best practices of ROS templates. The templates are categorized as f |--------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------| | [use-function-compute-to-deploy-stable-diffusion-for-AI-painting.yml](documents/solution/ai/use-function-compute-to-deploy-stable-diffusion-for-ai-painting.yml) | Use Function Compute to deploy Stable Diffusion for AI Painting. Custom models is supported. | | [lingjun-LLAMA-2-best-practice.yml](documents/solution/ai/lingjun-LLAMA-2-best-practice.yml) | LingJun LLAMA-2 Best Practice. | -| [pai-ai-painting-solution.yml](documents/solution/ai/pai-ai-painting-solution.yml) | PAI AI painting solution | +| [pai-ai-painting-solution.yml](documents/solution/ai/pai-ai-painting-solution.yml) | PAI AI painting solution | | [pai-lingjun-serverless-LLM-best-practice.yml](documents/solution/ai/pai-lingjun-serverless-LLM-best-practice.yml) | PAI LingJun Serverless LLM Best Practice. | | [aigc-text-generation-3d-model-with-ecs.yml](documents/solution/ai/aigc-text-generation-3d-model-with-ecs.yml) | AIGC Text Generation 3D Model. | | [use-pai-to-deploy-stable-diffusion-for-AI-painting.yml](documents/solution/ai/use-pai-to-deploy-stable-diffusion-for-AI-painting.yml) | How to quickly start Stable Diffusion on Alibaba Cloud and easily play with AI painting. | | [use-gpu-ecs-to-deploy-chatGLM.yaml](documents/solution/ai/use-gpu-ecs-to-deploy-chatGLM.yaml) | Large model combines the AnalyticDB to build Chatbot. | -| [build-a-dialogue-model-based-on-ChatGLM-and-LangChain.yml](documents/solution/ai/build-a-dialogue-model-based-on-ChatGLM-and-LangChain.yml) | Build a dialogue model based on ChatGLM and LangChain. | -| [build-a-dialogue-model-based-on-Qwen-and-LangChain.yml](documents/solution/ai/build-a-dialogue-model-based-on-Qwen-and-LangChain.yml) | Build a dialogue model based on Qwen and LangChain. | -| [ack-ai-fine-tuning.yml](documents/solution/ai/ack-ai-fine-tuning.yml) | Use the cloud-native AI suite to submit model fine-tuning training tasks and deploy GPU shared inference services. | +| [build-a-dialogue-model-based-on-ChatGLM-and-LangChain.yml](documents/solution/ai/build-a-dialogue-model-based-on-ChatGLM-and-LangChain.yml) | Build a dialogue model based on ChatGLM and LangChain. | +| [build-a-dialogue-model-based-on-Qwen-and-LangChain.yml](documents/solution/ai/build-a-dialogue-model-based-on-Qwen-and-LangChain.yml) | Build a dialogue model based on Qwen and LangChain. | +| [ack-ai-fine-tuning.yml](documents/solution/ai/ack-ai-fine-tuning.yml) | Use the cloud-native AI suite to submit model fine-tuning training tasks and deploy GPU shared inference services. | +| [exclusive-qa-service.yml](exclusive-qa-service.yml) | DashVector and Tongyi QianWen build exclusive Q&A service. | diff --git a/documents/solution/ai/exclusive-qa-service.yml b/documents/solution/ai/exclusive-qa-service.yml new file mode 100644 index 00000000..ab28ec11 --- /dev/null +++ b/documents/solution/ai/exclusive-qa-service.yml @@ -0,0 +1,584 @@ +ROSTemplateFormatVersion: '2015-09-01' +Description: + en: DashVector and Tongyi QianWen build exclusive Q&A service. + zh-cn: 向量检索与通义千问搭建专属问答服务。 +Parameters: + DashScopeApiKey: + Type: String + NoEcho: true + AssociationProperty: 'ALIYUN::DashScope::ApiKey::ApiKey' + Label: + en: DashScope API-KEY + zh-cn: DashScope API-KEY + Description: + zh-cn: >- + 开通灵积模型服务，并获得 API-KEY。请参考： + 开通DashScope并创建API-KEY。 + en: >- + Activate DashScope and obtain the API-KEY. Please refer to: + Activate DashScope and create API-KEY. + DashVectorApiKey: + Type: String + NoEcho: true + AssociationProperty: 'ALIYUN::DashVector::ApiKey::ApiKey' + Label: + en: DashVector API-KEY + zh-cn: DashVector API-KEY + Description: + zh-cn: >- + 开通DashVector向量检索服务，并获得 API-KEY。请参考： + 开通DashScope并创建API-KEY。 + en: >- + Activate DashVector and obtain the API-KEY. Please refer to: + Activate DashVector and create API-KEY. + ClusterName: + Type: String + Label: + en: Name of DashVector Cluster + zh-cn: DashVector集群名称 + AllowedPattern: '[-a-z0-9_]{3,32}' + AssociationProperty: AutoCompleteInput + AssociationPropertyMetadata: + Length: 5 + Prefix: exclusive-qa-server- + CharacterClasses: + - Class: lowercase + min: 1 + ConstraintDescription: + zh-cn: 由大小写字母、数字、下划线(_)、中划线(-)组成，长度[3, 32]。 + en: It consists of uppercase and lowercase letters, numbers, underline (_), and dash (-). The length is [3, 32] + Description: + zh-cn: 本方案会创建一个 S.small 规格集群。 + en: This solution will create a S.small cluster. + ZoneId: + Type: String + AssociationProperty: 'ALIYUN::ECS::Instance::ZoneId' + AssociationPropertyMetadata: + AutoSelectFirst: true + Label: + en: Availability Zone + zh-cn: 可用区 + InstanceType: + Type: String + AssociationProperty: 'ALIYUN::ECS::Instance::InstanceType' + AssociationPropertyMetadata: + SpotStrategy: SpotAsPriceGo + InstanceChargeType: PostPaid + SystemDiskCategory: cloud_essd + ZoneId: ${ZoneId} + Label: + en: Instance Type + zh-cn: 实例规格 + Description: + zh-cn: 本方案会创建一个抢占式实例，并且自动部署专属问答服务。 + en: >- + This solution will create a spot instance and automatically deploy a + exclusive question and answer service. + Default: ecs.c7.large + InstancePassword: + NoEcho: true + Type: String + Description: + en: >- + Server login password, Length 8-30, must contain three(Capital letters, + lowercase letters, numbers, ()`~!@#$%^&*_-+=|{}[]:;'<>,.?/ Special + symbol in) + zh-cn: >- + 服务器登录密码,长度8-30，必须包含三项（大写字母、小写字母、数字、 ()`~!@#$%^&*_-+=|{}[]:;'<>,.?/ + 中的特殊符号） + Label: + en: Instance Password + zh-cn: 实例密码 + ConstraintDescription: + en: >- + Length 8-30, must contain three(Capital letters, lowercase letters, + numbers, ()`~!@#$%^&*_-+=|{}[]:;'<>,.?/ Special symbol in) + zh-cn: '长度8-30，必须包含三项（大写字母、小写字母、数字、 ()`~!@#$%^&*_-+=|{}[]:;''<>,.?/ 中的特殊符号）' + AssociationProperty: 'ALIYUN::ECS::Instance::Password' + Default: null +Resources: + Vpc: + Type: 'ALIYUN::ECS::VPC' + Properties: + CidrBlock: 192.168.0.0/16 + VSwitch: + Type: 'ALIYUN::ECS::VSwitch' + Properties: + VpcId: + Ref: Vpc + CidrBlock: 192.168.0.0/24 + ZoneId: + Ref: ZoneId + SecurityGroup: + Type: 'ALIYUN::ECS::SecurityGroup' + Properties: + VpcId: + Ref: Vpc + SecurityGroupIngress: + - PortRange: 22/22 + SourceCidrIp: 0.0.0.0/0 + IpProtocol: tcp + - PortRange: 5000/5000 + SourceCidrIp: 0.0.0.0/0 + IpProtocol: tcp + EcsInstance: + Type: 'ALIYUN::ECS::InstanceGroup' + Properties: + VpcId: + Ref: Vpc + ZoneId: + Ref: ZoneId + VSwitchId: + Ref: VSwitch + SecurityGroupId: + Ref: SecurityGroup + ImageId: aliyun_3_9_x64_20G_alibase_ + InstanceName: dashVector + InstanceType: + Ref: InstanceType + SystemDiskCategory: cloud_essd + MaxAmount: 1 + InternetMaxBandwidthOut: 100 + SpotStrategy: SpotAsPriceGo + Password: + Ref: InstancePassword + DashVectorCluster: + Type: 'ALIYUN::DashVector::Cluster' + Properties: + IgnoreExisting: true + ReplicaCount: 1 + ClusterName: + Ref: ClusterName + ClusterType: S.small + RunCommand: + Type: 'ALIYUN::ECS::RunCommand' + Properties: + InstanceIds: + Fn::GetAtt: + - EcsInstance + - InstanceIds + Type: RunShellScript + Sync: true + Timeout: 3600 + CommandContent: + Fn::Sub: |- + #!/bin/bash + + # script exit code: + # 0 - success + # 1 - unsupported system + # 2 - network not available + # 3 - failed to git clone + # 4 - failed to init python environment + # 5 - failed to embedding data + # 6 - failed to init git + # 7 - failed to run flask app + + # 环境变量配置 + export PATH=/usr/local/bin:$PATH + export DASHSCOPE_API_KEY=${DashScopeApiKey} + export DASHVECTOR_API_KEY=${DashVectorApiKey} + export DASHVECTOR_CLUSTER_ENDPOINT=${DashVectorCluster.Endpoint} + + # GitHub 仓库 URL + REPO_URL="https://github.com/shijiebei2009/CEC-Corpus.git" + # GitRepoName + REPO_NAME="CEC-Corpus" + # 最大尝试次数 + MAX_RETRIES=10 + # 当前尝试次数 + ATTEMPT_NUM=1 + # 等待时间 (秒) 在重试之前 + SLEEP_SECONDS=10 + # CollectionName + COLLECTION_NAME="news_embeddings" + + function unsupported_system() { + log_fatal 1 "Unsupported System: $1" + } + + function log_info() { + printf "$(date '+%Y-%m-%d %H:%M:%S') [INFO] $1\n" + } + + function log_error() { + printf "$(date '+%Y-%m-%d %H:%M:%S') [ERROR] $1\n" + } + + function log_fatal() { + printf "\n========================================================================\n" + printf "$(date '+%Y-%m-%d %H:%M:%S') [FATAL] $2." + printf "\n========================================================================\n" + exit $1 + } + + function debug_exec(){ + local cmd="$@" + log_info "$cmd" + eval "$cmd" + ret=$? + echo "" + log_info "$cmd, exit code: $ret" + return $ret + } + + function check_network_available() { + log_info "ping github.com ..." + if ! debug_exec ping -c 4 github.com; then + log_fatal 2 "Could not connect to https://github.com" + fi + } + + function clone_repo() { + log_info "init git" + if ! debug_exec yum install -y git && git init; then + log_fatal 6 "failed to init git" + fi + + rm -rf $REPO_NAME + until debug_exec git clone $REPO_URL; do + if [ "$ATTEMPT_NUM" -ge "$MAX_RETRIES" ]; then + log_fatal 3 "Reached maximum number of retries, aborting." + fi + log_error "Attempt $ATTEMPT_NUM failed! Trying again in $SLEEP_SECONDS seconds..." + sleep "$SLEEP_SECONDS" + ATTEMPT_NUM=$((ATTEMPT_NUM+1)) + done + } + + function init_env() { + log_info "init python environment" + yum install -y python3.8 + ln -sf /usr/bin/python3.8 /usr/bin/python + python -m ensurepip + python -m pip install --upgrade pip + python -m pip install -r requirements.txt + } + + function embedding() { + log_info "embedding data" + if ! debug_exec python embedding.py; then + log_fatal 5 "failed to embedding data." + fi + } + + log_info "System Information:" + if ! lsb_release -a; then + unsupported_system + fi; + echo "" + + check_network_available + + cat << 'EOF' > requirements.txt + flask==3.0.2 + PySocks==1.7.1 + dashscope==1.17.0 + dashvector==1.0.14 + + EOF + + if ! debug_exec init_env; then + log_fatal 4 "failed to init python environment" + fi + + cat << EOF > embedding.py + # -*- coding: utf-8 -*- + + import os + import dashscope + from dashscope import TextEmbedding + from dashvector import Client, Doc + + + def prepare_data(path, batch_size=25): + batch_docs = [] + for file in os.listdir(path): + with open(path + '/' + file, 'r', encoding='utf-8') as f: + batch_docs.append(f.read()) + if len(batch_docs) == batch_size: + yield batch_docs + batch_docs = [] + + if batch_docs: + yield batch_docs + + + def generate_embeddings(news): + rsp = TextEmbedding.call( + model=TextEmbedding.Models.text_embedding_v1, + input=news + ) + embeddings = [record['embedding'] for record in rsp.output['embeddings']] + return embeddings if isinstance(news, list) else embeddings[0] + + + if __name__ == '__main__': + dashscope.api_key = os.environ.get('DASHSCOPE_API_KEY') + collection_name = '$COLLECTION_NAME' + # 初始化 dashvector client + client = Client( + api_key=os.environ.get('DASHVECTOR_API_KEY'), + endpoint=os.environ.get('DASHVECTOR_CLUSTER_ENDPOINT') + ) + + try: + del_rsp = client.delete(collection_name) + assert del_rsp, del_rsp + except AssertionError as e: + if e.args[0].code != -2021: + raise + + # 创建集合：指定集合名称和向量维度, text_embedding_v1 模型产生的向量统一为 1536 维 + rsp = client.create(collection_name, 1536) + assert rsp, rsp + + # 加载语料 + id = 0 + collection = client.get(collection_name) + for news in list(prepare_data('CEC-Corpus/raw corpus/allSourceText')): + ids = [id + i for i, _ in enumerate(news)] + id += len(news) + + vectors = generate_embeddings(news) + # 写入 dashvector 构建索引 + rsp = collection.upsert( + [ + Doc(id=str(id), vector=vector, fields={"raw": doc}) + for id, vector, doc in zip(ids, vectors, news) + ] + ) + assert rsp, rsp + + EOF + + + cat << EOF > run.py + # -*- coding: utf-8 -*- + + import os + import uuid + from flask import Flask, request, render_template_string, redirect, url_for, session + import dashscope + from dashscope import TextEmbedding, Generation + from dashvector import Client + + app = Flask(__name__) + app.secret_key = uuid.uuid4().hex + + # HTML 模板 + HTML_TEMPLATE = ''' + + + + DashVector + + + +
+

基于专属知识的问答服务

+
+ + + + +
+
+ {% if output %} +

回答：

+ + {% endif %} +
+ + + + + ''' + + + @app.route('/', methods=['GET', 'POST']) + def home(): + output_text = None + input_text = "海南安定追尾事故，发生在哪里？原因是什么？人员伤亡情况如何" + if 'output' in session: + output_text = session.pop('output', None) + input_text = session.get('input', None) + + if request.method == 'POST': + input_text = request.form.get('inputText') + output_text = get_answers(input_text) + session['output'] = output_text + session['input'] = input_text + return redirect(url_for('home')) + + return render_template_string(HTML_TEMPLATE, output=output_text, input=input_text) + + + def get_answers(input_text): + dashscope.api_key = os.environ.get('DASHSCOPE_API_KEY') + # question = '海南安定追尾事故，发生在哪里？原因是什么？人员伤亡情况如何？' + context = search_relevant_news(input_text) + return answer_question(input_text, context) + + + def search_relevant_news(question): + # 初始化 dashvector client + client = Client( + api_key=os.environ.get('DASHVECTOR_API_KEY'), + endpoint=os.environ.get('DASHVECTOR_CLUSTER_ENDPOINT') + ) + + # 获取刚刚存入的集合 + collection = client.get('$COLLECTION_NAME') + assert collection, collection + + # 向量检索：指定 topk = 1 + rsp = collection.query(generate_embeddings(question), output_fields=['raw'], topk=1) + assert rsp, rsp + return rsp.output[0].fields['raw'] + + + def generate_embeddings(news): + rsp = TextEmbedding.call( + model=TextEmbedding.Models.text_embedding_v1, + input=news + ) + embeddings = [record['embedding'] for record in rsp.output['embeddings']] + return embeddings if isinstance(news, list) else embeddings[0] + + + def answer_question(question, context): + prompt = f'''请基于\`\`\`内的内容回答问题。" + \`\`\` + {context} + \`\`\` + 我的问题是：{question}。 + ''' + + rsp = Generation.call(model='qwen-turbo', prompt=prompt) + return rsp.output.text + + + if __name__ == '__main__': + app.run(host='0.0.0.0', debug=True) + + EOF + + clone_repo + embedding + + if ! debug_exec "nohup python run.py > output.log 2>&1 &"; then + log_fatal "failed to run flask app" + fi +Outputs: + WebUrl: + Description: + zh-cn: Web 访问地址。 + en: The Addresses of Web. + Value: + 'Fn::Sub': + - 'http://${ServerAddress}:5000' + - ServerAddress: + 'Fn::Select': + - 0 + - 'Fn::GetAtt': + - EcsInstance + - PublicIps + EcsLoginAddress: + Description: + zh-cn: ECS登陆地址。 + en: Ecs login address. + Value: + 'Fn::Sub': + - >- + https://ecs-workbench.aliyun.com/?from=EcsConsole&instanceType=ecs®ionId=${Region}&instanceId=${InstanceId} + - InstanceId: + 'Fn::Select': + - 0 + - 'Fn::GetAtt': + - EcsInstance + - InstanceIds + Region: + Ref: 'ALIYUN::Region' +Metadata: + 'ALIYUN::ROS::Interface': + ParameterGroups: + - Parameters: + - DashScopeApiKey + - DashVectorApiKey + - ClusterName + Label: + default: + en: ApiKey + zh-cn: ApiKey配置 + - Parameters: + - ZoneId + - InstanceType + - InstancePassword + Label: + default: + en: Ecs Instance + zh-cn: ECS实例配置 + TemplateTags: + - acs:technical-solution:ai:向量检索与通义千问搭建专属问答服务-tech_solu_109