langchain4j
LangChain4j is an open-source Java library that simplifies the integration of LLMs into Java applications through a unified API, providing access to popular LLMs and vector databases. It makes implementing RAG, tool calling (including support for MCP), and agents easy. LangChain4j integrates seamlessly with various enterprise Java frameworks.
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LangChain for Java simplifies integrating Large Language Models (LLMs) into Java applications by offering unified APIs for various LLM providers and embedding stores. It provides a comprehensive toolbox with tools for prompt templating, chat memory management, function calling, and high-level patterns like Agents and RAG. The library supports 15+ popular LLM providers and 15+ embedding stores, offering numerous examples to help users quickly start building LLM-powered applications. LangChain4j is a fusion of ideas from various projects and actively incorporates new techniques and integrations to keep users up-to-date. The project is under active development, with core functionality already in place for users to start building LLM-powered apps.
README:
Welcome!
The goal of LangChain4j is to simplify integrating LLMs into Java applications.
Here's how:
- Unified APIs: LLM providers (like OpenAI or Google Vertex AI) and embedding (vector) stores (such as Pinecone or Milvus) use proprietary APIs. LangChain4j offers a unified API to avoid the need for learning and implementing specific APIs for each of them. To experiment with different LLMs or embedding stores, you can easily switch between them without the need to rewrite your code. LangChain4j currently supports 20+ popular LLM providers and 30+ embedding stores.
- Comprehensive Toolbox: Since early 2023, the community has been building numerous LLM-powered applications, identifying common abstractions, patterns, and techniques. LangChain4j has refined these into practical code. Our toolbox includes tools ranging from low-level prompt templating, chat memory management, and function calling to high-level patterns like Agents and RAG. For each abstraction, we provide an interface along with multiple ready-to-use implementations based on common techniques. Whether you're building a chatbot or developing a RAG with a complete pipeline from data ingestion to retrieval, LangChain4j offers a wide variety of options.
- Numerous Examples: These examples showcase how to begin creating various LLM-powered applications, providing inspiration and enabling you to start building quickly.
LangChain4j began development in early 2023 amid the ChatGPT hype. We noticed a lack of Java counterparts to the numerous Python and JavaScript LLM libraries and frameworks, and we had to fix that! Although "LangChain" is in our name, the project is a fusion of ideas and concepts from LangChain, Haystack, LlamaIndex, and the broader community, spiced up with a touch of our own innovation.
We actively monitor community developments, aiming to quickly incorporate new techniques and integrations, ensuring you stay up-to-date. The library is under active development. While some features are still being worked on, the core functionality is in place, allowing you to start building LLM-powered apps now!
Documentation can be found here.
The documentation chatbot (experimental) can be found here.
Getting started guide can be found here.
Please see examples of how LangChain4j can be used in langchain4j-examples repo:
- Examples in plain Java
- Examples with Quarkus (uses quarkus-langchain4j dependency)
- Example with Spring Boot
- Examples with Helidon (uses io.helidon.integrations.langchain4j dependency)
- Examples with Micronaut (uses micronaut-langchain4j dependency)
Useful materials can be found here.
Please use Discord or GitHub discussions to get help.
Please let us know what features you need by opening an issue.
Contribution guidelines can be found here.
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