Build a Local AI Q&A System with Java, Ollama, and LangChain4J

1. Large Language Models (LLM)

Large language models (LLM) are a major advancement in natural language processing (NLP). They contain billions to trillions of parameters and can generate and understand human language.

Definition : Models with massive numbers of parameters.

Architecture : Typically based on the Transformer architecture with self‑attention.

Training : Trained on massive text corpora using large compute resources.

Applications : Text generation, QA, translation, summarisation, dialogue, etc.

Trends : Ongoing research to improve efficiency and adaptability.

Learning Path : Start with basic Transformers, then study GPT, BERT, LLaMA, Alpaca, etc.

Community Resources : Hugging Face provides many open‑source models and tools.

2. Embedding

Embedding converts text into numerical vectors that capture semantic similarity. Common methods include Word2Vec, GloVe, FastText, BERT, ELMo, and Sentence‑Transformers. Embeddings enable tasks such as classification, sentiment analysis, NER, machine translation, and QA.

3. Vector Database

A vector database stores and queries high‑dimensional vectors for similarity search. Key features include high‑dimensional storage, ANN search, specialised indexes, hybrid queries, scalability, real‑time updates, and cloud‑native deployment. Examples: FAISS, Pinecone, Weaviate, Qdrant, Milvus.

4. Retrieval‑Augmented Generation (RAG)

RAG combines retrieval and generation to enhance LLM responses with external knowledge. It first retrieves relevant documents (often via vector similarity) and then feeds them to the generation model, reducing hallucinations and improving factuality.

Advantages: mitigates knowledge limits, reduces hallucinations, improves safety, enhances domain expertise. Typical use cases: QA systems, conversational agents, document summarisation, text completion.

5. AI Application Development Frameworks

LangChain

LangChain is a framework for building end‑to‑end LLM applications. It provides chains, agents, memory, loaders, prompt engineering, a hub of reusable components, and integrations with external systems.

LangChain4J

LangChain4J brings similar capabilities to the Java ecosystem, offering modular design, support for multiple LLM providers, memory mechanisms, tool integration, and chain execution.

6. Setting Up a Local QA System

6.1 Start a local model with Ollama

Install Ollama, pull models such as llama3 or qwen, and run them locally (default port 11434).

6.2 Launch a local vector store (ChromaDB)

Install with pip install chromadb and start the service.

7. Implementing the System in Java

7.1 Maven dependencies

<properties>
    <maven.compiler.source>8</maven.compiler.source>
    <maven.compiler.target>8</maven.compiler.target>
    <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
    <langchain4j.version>0.31.0</langchain4j.version>
</properties>
<dependencies>
    <dependency>
        <groupId>dev.langchain4j</groupId>
        <artifactId>langchain4j-core</artifactId>
        <version>${langchain4j.version}</version>
    </dependency>
    <!-- other LangChain4j modules, Ollama, and Chroma client -->
</dependencies>

7.2 Core code

Load a local text file, split it into segments, embed each segment with OllamaEmbeddingModel, store embeddings in Chroma, retrieve relevant segments, and generate answers using OllamaChatModel. Example query “What did the polar bear do?” returns the appropriate answer from the sample story.

Conclusion

The guide demonstrates a minimal AI Q&A pipeline that can be extended into a Spring Boot web application. LangChain offers many additional features such as advanced prompting, tool calling, and memory storage.