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RAG & Knowledge Systems

Java Rag

Java Rag is an open-source, framework-free Java toolkit for building retrieval-augmented question-answering flows, with a React visual playground for uploading documents, asking grounded questions, and inspecting retrieved evidence.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Java Rag — OpenViST project profile in RAG & Knowledge Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Java Rag to study or prototype a Java-based RAG pipeline, or to explore document-grounded answers and their retrieval evidence in the local playground.

Who it is for

  • Java developers embedding RAG into an existing application.
  • Developers learning how parsing, chunking, embeddings, retrieval, and grounded answer generation fit together.
  • Teams prototyping document question answering and wanting to inspect source passages behind responses.

Documented uses

  • Parse and chunk documents before retrieval.
  • Rank relevant passages and provide selected context to an answer-generation step.
  • Inspect cited document names, chunk identifiers, similarity scores, and retrieved passages.
  • Replace or inject provider implementations for splitting, embeddings, and chat.
Before you choose

Requirements & limits

The repository is public and licensed under Apache-2.0. Its README documents a local Docker Compose playground whose default mode requires no API key or model download.

  • The core toolkit is pure Java and targets Java 8+; the visual interface uses React 19 and TypeScript.
  • The no-API demonstration uses deterministic feature hashing and extractive output, while an optional Ollama profile is documented for local model generation.
  • The repository lists parsers for PDF, Word, PowerPoint, Excel, Markdown, and HTML.
  • Advanced RAG and Modular RAG pipelines are described as experimental.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Start the local visual playground

Run the documented Docker Compose workspace to upload a document, ask a grounded question, and inspect retrieval evidence.

Before you start

  • Git
  • Docker Compose
  • A Java 8+ compatible project environment is specified for the toolkit.

Steps

  1. Clone the Java Rag repository and change into its directory.

  2. Run the command below.

    Documented command
    docker compose up --build

  3. Open http://localhost:3000 and upload a PDF, Word, or Markdown file.

  4. Ask a question and inspect the answer, cited document, chunk IDs, similarity scores, and retrieved passages.

Expected outcome

A local visual workspace for document-grounded chat and inspection of the retrieved evidence.

Guide limitations

  • The default mode uses an extractive demo generator rather than a production language model.
  • The README documents Ollama as an optional profile for replacing the demo generator with a local language model.
Documentation 1 ↗
Source-based practical guide

Run the no-API CLI demonstration

Exercise the local RAG data flow from the command line without an API key, database, or model download.

Before you start

  • A Java and Maven environment compatible with the project.
  • Bash for the documented script, or PowerShell for the alternative Maven command.

Steps

  1. From the repository, run the command below; on Windows without Bash, use the documented Maven command.

    Documented command
    bash scripts/run-no-api-demo.sh

  2. Review the retrieved passage and extractive answer printed by the demonstration.

Expected outcome

A local example of retrieval, context construction, and cited extractive output.

Guide limitations

  • The demo uses deterministic feature hashing and an intentionally simple extractive generator; the project says it is not a substitute for a production embedding model or LLM.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Retrieval-augmented applications

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
Java RagParse and chunk documents before retrieval.The repository is public and licensed under Apache-2.0. Its README documents a local Docker Compose playground whose default mode requires no API key or model download.
LangsearchCrawl website or local data for a knowledge base.CLI, Self-hosted, Server
Hybrid Search RagCombine document retrieval and language-model generation in a configurable workflow.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
12 October 2026
Original website title
ChinaYiqun/java-rag · Official homepage title · retained in its source language
How we review sourcesSuggest a correction