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langchain4j aideepin

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Productivity & Workflow Automation
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Basic Information

This repository contains the backend service for AIDEEPIN (langchain4j-aideepin), an AI-powered productivity platform intended to assist enterprises and teams with tasks such as technical research and development, product design, HR/finance/IT consultations, system and product consulting, and customer service dialogue support. The project supplies a server component and links to separate frontend repositories for a user web app and an admin web app. The backend integrates with language models and search services to provide multi-session chat, image generation, knowledge base and retrieval-augmented generation, AI workflows, a marketplace for microservices (MCP), and speech capabilities. The README provides deployment and initialization instructions, database setup SQL, model and platform configuration examples, and pointers to detailed documentation and screenshots.

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874
App URL
https://github.com/moyangzhan/langchain4j-aideepin
Github Repository
https://github.com/moyangzhan/langchain4j-aideepin/blob/main/README.md

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App Details

Features
The README documents a set of core features: multi-session and multi-role chat, image generation including text-to-image, image editing and image-to-image, a large-model powered knowledge base with vector search and graph search, web search based RAG, AI workflow orchestration, and an MCP service marketplace. It also supports ASR and TTS to enable text-to-text, text-to-speech, speech-to-text and speech-to-speech interactions. The backend integrates with many model platforms such as DeepSeek, OpenAI, several Chinese model providers, SiliconFlow and Ollama. It supports Google search integration and can be configured to use pgvector and Apache AGE or Neo4j for vector and graph storage. The README shows deployment options including Maven build, jar startup and Docker.
Use Cases
This backend enables organizations to deploy a turnkey AI assistant platform that centralizes model access, retrieval, multimodal generation and voice capabilities for business use cases. It helps teams set up a unified knowledge base with vector and graph search to enable accurate RAG responses, supports image creation and editing for design tasks, and offers workflow orchestration to automate multi-step AI tasks. The MCP marketplace lets teams compose or reuse services. Administrators can configure models and search providers via SQL or the admin frontend. Deployment instructions, supported stacks and example configuration snippets make it practical to integrate into existing infrastructure and run locally or in containers.

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