LangChain4j AI Sample Project

This is a sample AI application project based on LangChain4j, featuring multiple modules that demonstrate how to develop using large language models.

Project Structure

The project consists of multiple modules, each demonstrating a different feature:

langchain4j-ai-helloworld: A beginner example showing how to perform simple conversations using a large language model.
langchain4j-ai-image: An image processing example demonstrating the use of image models.
langchain4j-ai-low-high-api: Demonstrates the use of low-level and high-level APIs.
langchain4j-ai-memory: A memory management example showing how to use chat memory.
langchain4j-ai-model-params: An example of configuring model parameters.
langchain4j-ai-mongodb: An example of storing chat memory using MongoDB.
langchain4j-ai-multimode: An example of calling multiple models.
langchain4j-ai-pinecone: An example of storing embeddings using Pinecone.
langchain4j-ai-prompt: An example of using prompt templates.
langchain4j-ai-rag: An RAG (Retrieval-Augmented Generation) example.
langchain4j-ai-stream: An example of handling streaming responses.
langchain4j-ai-tools: An example of integrating tools, such as weather queries.
langchain4j-ai-xiaoai-agent: A XiaoAI intelligent assistant example integrating multiple features.

Ensure you have Java and Maven installed. Then clone the repository and build the project:

git clone https://gitee.com/pu13398199549/langchain4j-ai-java.git
cd langchain4j-ai-java
mvn clean install

Each module can be run independently. For example, to run the langchain4j-ai-helloworld module:

cd langchain4j-ai-helloworld
mvn spring-boot:run

Visit http://localhost:8080/langchain4j/hello to view the sample output.

Contributions and suggestions are welcome. Please submit a Pull Request or Issue.

This project is licensed under the MIT License. See the LICENSE file for details.