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The goal of LangChain4j is to simplify the integration of AI and LLM capabilities into Java applications. The project lives on GitHub, and has a separate repository with demo applications. I first learned about LangChain4j at the Devoxx conference in Antwerp in October last year. Lize Raes gave an impressive presentation with 12 demos. In the last demo, she asked the application to give some answers based on a provided text. And that was exactly what I was looking for to be able to interact with an existing dataset.
The goal of LangChain4j is to simplify the integration of AI and LLM capabilities into Java applications. The project lives on GitHub, and has a separate repository with demo applications. I first learned about LangChain4j at the Devoxx conference in Antwerp in October last year. Lize Raes gave an impressive presentation with 12 demos. In the last demo, she asked the application to give some answers based on a provided text. And that was exactly what I was looking for to be able to interact with an existing dataset.
The full sources are available on GitHub. Most of the code is identical to the JavaFX demo application described above, which you can find in the LangChain4j examples repository. The main differences can be found in DocsAnswerService.java. Here, all methods to load the documentation JSON and interact with OpenAI, are bundled.