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## Getting Started
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[READ THIS IN ENGLISH](README_en.md)
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**LangChain4j-AIDeepin**
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基于 ChatGPT 等大语言模型与 Langchain4j 等应用框架实现,开源、可离线部署的检索增强生成(RAG)大模型知识库项目。
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基于 ChatGPT 等大语言模型与 Langchain4j 等应用框架实现,开源、可离线部署的检索增强生成(RAG)项目。
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> **该项目如对您有帮助,欢迎点赞**
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@ -167,4 +169,4 @@ docker run -d \
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**额度统计:**
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![1691583329105.png](image%2FREADME%2F1691583329105.png)
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!![1691583329105.png](image%2FREADME%2F1691583329105.png)
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## Getting Started
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[中文](README.md)
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**LangChain4j-AIDeepin**
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Langchain4j-aideepin is an open source, offline deployable Retrieval Enhancement Generation (RAG) project based on large language models such as ChatGPT and application frameworks such as Langchain4j.
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## Website
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[http://www.aideepin.com](http://www.aideepin.com/)
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## Feature
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* Login & Register
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* Multiple Conversation | Multiple character
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* AI Draw
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* Prompt
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* Quota
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* Knowledge base(RAG)
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* AI Search(RAG)
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* Multiple models switch at will
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* Multiple search engine switch at will
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## Support Models
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* ChatGPT 3.5
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* 通义千问
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* 文心一言
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* ollama
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* DALL-E 2
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## Support Search Engines
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Google
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Bing (TODO)
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百度 (TODO)
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## Introduction
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This repository is a back-end project, front-end project in [langchain4j-aideepin-web](https://github.com/moyangzhan/langchain4j-aideepin-web)
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Backend:
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jdk17
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springboot3.0.5
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[langchain4j(Java version of LangChain)](https://github.com/langchain4j/langchain4j)
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**Postgresql(需要安装[pgvector](https://github.com/pgvector/pgvector)扩展)**
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Frontend:
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vue3+typescript+pnpm
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## Build and run this project
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### Init
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**a. Init database**
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* Create database schema: aideepin
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* Run: docs/create.sql
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* Update language model config:
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Openai setting
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```plaintext
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update adi_sys_config set value = '{"secret_key":"my_openai_secret_key","models":["gpt-3.5-turbo"]}' where name = 'openai_setting';
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```
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Dashscope setting
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```plaintext
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update adi_sys_config set value = '{"api_key":"my_dashcope_api_key","models":["my model name,eg:qwen-max"]}' where name = 'dashscope_setting';
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```
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Qianfan setting
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```plaintext
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update adi_sys_config set value = '{"api_key":"my_qianfan_api_key","secret_key":"my_qianfan_secret_key","models":["my model name,eg:ERNIE-Bot"]}' where name = 'qianfan_setting';
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```
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Ollama setting
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```
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update adi_sys_config set value = '{"base_url":"my_ollama_base_url","models":["my model name,eg:tinydolphin"]}' where name = 'ollama_setting';
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```
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* Search engine setting
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Google:
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```
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update adi_sys_config set value = '{"url":"https://www.googleapis.com/customsearch/v1","key":"my key from cloud.google.com","cx":"my cx from programmablesearchengine.google.com"}' where name = 'google_setting';
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```
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**b. Init properties**
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* postgresql: spring.datasource in application-[dev|prod].xml
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* redis: spring.data.redis in application-[dev|prod].xml
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* mail: spring.mail in application.xml
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### Compile & Run
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* Enter the project root directory:
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```plaintext
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cd langchain4j-aideepin
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```
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* Package:
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```
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mvn clean package -Dmaven.test.skip=true
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```
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* Run:
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a. Run by jar:
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```plaintext
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cd adi-bootstrap/target
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nohup java -jar -Xms768m -Xmx1024m -XX:+HeapDumpOnOutOfMemoryError adi-chat-0.0.1-SNAPSHOT.jar --spring.profiles.active=[dev|prod] dev/null 2>&1 &
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```
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b. Run by docker
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```plaintext
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cd adi-bootstrap
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docker build . -t aideepin:0.0.1
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docker run -d \
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--name=aideepin \
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-e APP_PROFILE=[dev|prod] \
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-v="/data/aideepin/logs:/data/logs" \
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aideepin:0.0.1
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```
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## TODO:
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Enhance RAG
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More search engine(BING、百度)
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## Screenshot
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**AI Chat:**
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![1691583184761](image/README/1691583184761.png)
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**AI Draw:**
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![1691583124744](image/README/1691583124744.png "AI绘图")
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**Knowlege base:**
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![kbindex](image/README/kbidx.png)
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![kb01](image/README/kb01.png)
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**Embedding:**
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![kb02](image/README/kb02.png)
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![kb03](image/README/kb03.png)
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**Quota:**
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!![1691583329105.png](image%2FREADME%2F1691583329105.png)
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@ -54,6 +54,9 @@ public class SearchService {
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@Resource
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private AiSearchRecordService aiSearchRecordService;
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@Resource
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private UserDayCostService userDayCostService;
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@Resource
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private AsyncTaskExecutor mainExecutor;
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newRecord.setUserUuid(user.getUuid());
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newRecord.setUserId(user.getId());
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aiSearchRecordService.save(newRecord);
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userDayCostService.appendCostToUser(user, promptMeta.getTokens() + answerMeta.getTokens());
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});
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}
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updateRecord.setAnswer(response);
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updateRecord.setAnswerTokens(answerMeta.getTokens());
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aiSearchRecordService.updateById(updateRecord);
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userDayCostService.appendCostToUser(user, promptMeta.getTokens() + answerMeta.getTokens());
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});
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}
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@Service
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public class UserDayCostService extends ServiceImpl<UserDayCostMapper, UserDayCost> {
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/**
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* Append token cost
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*
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* @param user
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* @param tokens The number of tokens
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*/
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public void appendCostToUser(User user, int tokens) {
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UserDayCost userDayCost = getTodayCost(user);
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UserDayCost saveOrUpdateInst = new UserDayCost();
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