Simple

agentscope-extensions-rag-simple is the “DIY end-to-end” RAG implementation: it bundles document readers, chunking strategies, embedding adapters, and five out-of-the-box vector store adapters. Use it when: you’re happy to run embeddings + vector store yourself and don’t want a third-party RAG platform.

Add the dependency

<dependency>
    <groupId>io.agentscope</groupId>
    <artifactId>agentscope-extensions-rag-simple</artifactId>
    <version>${agentscope.version}</version>
</dependency>

Quickstart

import io.agentscope.core.embedding.dashscope.DashScopeTextEmbedding;
import io.agentscope.core.rag.knowledge.SimpleKnowledge;
import io.agentscope.core.rag.store.InMemoryStore;
import io.agentscope.core.rag.model.RetrieveConfig;

// 1) Embedding model
EmbeddingModel embeddings = DashScopeTextEmbedding.builder()
    .apiKey(System.getenv("DASHSCOPE_API_KEY"))
    .modelName("text-embedding-v3")
    .dimensions(1024)
    .build();

// 2) Vector store (in-process here)
VDBStoreBase store = InMemoryStore.builder().dimensions(1024).build();

// 3) Assemble Knowledge
SimpleKnowledge knowledge = SimpleKnowledge.builder()
    .embeddingModel(embeddings)
    .embeddingStore(store)
    .build();

// 4) Ingest documents
List<Document> docs = new TikaReader().read(input).block();
knowledge.addDocuments(docs).block();

// 5) Retrieve
List<Document> hits = knowledge.retrieve(
    "What is AgentScope?",
    RetrieveConfig.builder().limit(5).scoreThreshold(0.5).build()
).block();

Built-in document readers

The io.agentscope.core.rag.reader package contains readers for common formats; each produces List<Document>:

Reader	Input
`TextReader`	Plain text
`PDFReader`	PDF (PDFBox-backed)
`WordReader`	Microsoft Word documents
`ImageReader`	Images, paired with multimodal embeddings
`TikaReader`	Generic Apache Tika fallback
`ExternalApiReader`	External parser APIs (OCR / custom pipelines)

The resulting Document objects already carry metadata; pair with TextChunker and SplitStrategy for chunking.

Built-in embedding providers

Class	Service	Mode
`DashScopeTextEmbedding`	Alibaba Cloud DashScope	Text
`DashScopeMultiModalEmbedding`	Alibaba Cloud DashScope	Multimodal (text/image)
`OpenAITextEmbedding`	OpenAI-compatible API	Text
`OllamaTextEmbedding`	Local Ollama	Text

Implement EmbeddingModel to add your own. EmbeddingModel.embed(ContentBlock) returns Mono<EmbeddingResponse>; the response carries the embedding vector and, when the provider reports it, EmbeddingUsage token counts. Pass a usageListener to SimpleKnowledge.builder() to receive each non-null embedding usage report. The listener runs on the embedding response thread and should be non-blocking and thread-safe; providers that omit usage do not invoke it. OpenAITextEmbedding lazily creates one SDK client and reuses it for all requests, including concurrent calls and retries. Reuse the model for the lifetime of your RAG service. After all embedding operations have completed, call close() (for example, from your application shutdown hook), or use try-with-resources for a scoped model. SimpleKnowledge does not close a shared embedding model for you. Closing an unused model does not create a client; a closed model cannot be used again. Subscribing to a closed model fails immediately without retrying, including when the Mono was created before the model was closed.

Built-in vector stores

Implementation	Deployment
`InMemoryStore`	In-process (dev / testing)
`PgVectorStore`	PostgreSQL + pgvector
`MilvusStore`	Milvus
`QdrantStore`	Qdrant
`ElasticsearchStore`	Elasticsearch (`dense_vector`)

Switching stores is a one-line change: pass a different VDBStoreBase to SimpleKnowledge.builder().embeddingStore(...).

Retrieval parameters

RetrieveConfig controls retrieval:

Field	Notes
`limit`	Top-K
`scoreThreshold`	Minimum score (0–1)
`metadata`	Filter by document metadata

Wire into an Agent

ReActAgent agent = ReActAgent.builder()
    .name("Assistant")
    .model(model)
    .knowledge(knowledge)
    .ragMode(RAGMode.AGENTIC)   // Agent decides when to retrieve
    .build();

Integration

Channels

Distributed storage

Add the dependency

Quickstart

Built-in document readers

Built-in embedding providers

Built-in vector stores

Retrieval parameters

Wire into an Agent

​Add the dependency

​Quickstart

​Built-in document readers

​Built-in embedding providers

​Built-in vector stores

​Retrieval parameters

​Wire into an Agent

Add the dependency

Quickstart

Built-in document readers

Built-in embedding providers

Built-in vector stores

Retrieval parameters

Wire into an Agent