LangChain in java

LangChain is primarily a Python and JavaScript/TypeScript project, but there are Java alternatives and integrations that allow similar capabilities.

If you're looking for LangChain-like functionality in Java, here’s a practical overview:

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🔧 1. Use LangChain4j (LangChain for Java)

LangChain4j is the official LangChain-style library for Java, maintained by the LangChain ecosystem.

✨ Features:

• Prompt templating

• Memory

• LLM integrations (OpenAI, Azure, Hugging Face, Ollama, etc.)

• Tools and Chains

• Retrieval Augmented Generation (RAG)

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✅ Example (LangChain4j)

1. Add Maven Dependency

<dependency>
    <groupId>dev.langchain4j</groupId>
    <artifactId>langchain4j</artifactId>
    <version>0.26.0</version>
</dependency>

Versions change frequently. Always check: Maven Central Repository Search

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2. Basic Chat Example (OpenAI)

import dev.langchain4j.model.openai.OpenAiChatModel;
import dev.langchain4j.service.SystemMessage;
import dev.langchain4j.service.UserMessage;
import dev.langchain4j.service.AiService;

public class LangChain4jExample {

    interface Assistant {
        @SystemMessage("You are a helpful assistant.")
        String chat(@UserMessage String message);
    }

    public static void main(String[] args) {
        var model = OpenAiChatModel.withApiKey("YOUR_OPENAI_API_KEY");

        Assistant assistant = AiService.create(Assistant.class, model);
        String response = assistant.chat("What is the capital of France?");
        System.out.println(response);
    }
}

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3. File or Document Q&A (RAG)

You can load PDF/text/CSV files and use embeddings + vector stores (like FAISS, Chroma, or Redis) for semantic search.

LangChain4j supports:

• Embedding models (OpenAI, HuggingFace)

• Vector stores (in-memory, Redis, Qdrant)

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🚀 Summary

Feature	Support in Java
Prompt templating	✅ LangChain4j
Chains and tools	✅ LangChain4j
Memory support	✅ LangChain4j
Vector store + RAG	✅ LangChain4j
OpenAI/LLM support	✅ LangChain4j
Ollama/Local model	✅ via REST