LangChain4j is a Java-based library designed to simplify the integration of large language models (LLMs) into applications. Its modular structure allows developers to pick and choose components based on their needs, making it flexible for various use cases from basic LLM interactions to complex AI-augmented workflows.
This post explains LangChain4j’s modules architecture, explaining the roles of its core module, main tools, and integration libraries, along with practical guidance on how to use them.
1. Core Module (langchain4j-core)
The foundation of LangChain4j, this module defines essential abstractions and interfaces. Following are the key components:
· ChatModel: Standardizes interactions with LLMs (e.g., OpenAI, HuggingFace).
· EmbeddingStore: Manages vector embeddings for semantic search/RAG.
It decouples implementation details from APIs, enabling plug-and-play with different providers.
2. Main Module (langchain4j)
Builds on the core to offer higher-level utilities:
· Document Loaders: Import data from PDFs, web pages, etc.
· Chat Memory: Maintains conversation history (e.g., MessageWindowChatMemory).
· AI Services: Simplifies creating auto-implemented interfaces (e.g., @GenerateText, @Moderate).
3. Integration Modules (langchain4j-{provider})
Optional modules for third-party integrations:
· LLM Providers: langchain4j-openai, langchain4j-vertexai, langchain4j-ollama etc.
· Embedding Stores: langchain4j-chroma, langchain4j-redis.
LangChain4j’s modular architecture strikes a balance between simplicity and flexibility. By understanding how it's structured, you can customize your dependencies to fit your project's needs, whether you're making lightweight LLM calls or building full-scale AI-powered applications.
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