rag: Design & UI for AI Agents | OpenAgentSkill

Overview

Implements document chunking, embedding generation, vector storage, and retrieval pipelines for Retrieval-Augmented Generation systems. Use when building RAG applications, creating document Q&A systems, or integrating AI with knowledge bases.

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RAG Implementation

Build Retrieval-Augmented Generation systems that extend AI capabilities with external knowledge sources.

Overview

This skill covers: document processing, embedding generation, vector storage, retrieval configuration, and RAG pipeline implementation.

When to Use

Building Q&A systems over proprietary documents
Creating chatbots with factual information from knowledge bases
Implementing semantic search with natural language queries
Reducing hallucinations with grounded, sourced responses
Building documentation assistants and research tools
Enabling AI systems to access domain-specific knowledge

Instructions

Step 1: Choose Vector Database

Select based on your requirements:

Requirement	Recommended
Production scalability	Pinecone, Milvus
Open-source	Weaviate, Qdrant
Local development	Chroma, FAISS
Hybrid search	Weaviate with BM25

Step 2: Select Embedding Model

Use Case	Model
General purpose	text-embedding-ada-002
Fast and lightweight	all-MiniLM-L6-v2
Multilingual	e5-large-v2
Best performance	bge-large-en-v1.5

Step 3: Implement Document Processing Pipeline

Load documents from source (file system, database, API)
Clean and preprocess (remove formatting, normalize text)
Split documents into chunks with appropriate strategy
Generate embeddings for each chunk
Store embeddings in vector database with metadata

Validation: Verify embeddings were generated successfully:

List<Embedding> embeddings = embeddingModel.embedAll(segments);
if (embeddings.isEmpty() || embeddings.get(0).dimension() != expectedDim) {
    throw new IllegalStateException("Embedding generation failed");
}

Step 4: Configure Retrieval Strategy

Choose the appropriate strategy:

Dense Retrieval: Semantic similarity via embeddings (default for most cases)
Hybrid Search: Dense + sparse retrieval for better coverage
Metadata Filtering: Filter by document attributes
Reranking: Cross-encoder reranking for high-precision requirements

Step 5: Build RAG Pipeline

Create content retriever with your embedding store
Configure AI service with retriever and chat memory
Implement prompt template with context injection
Add response validation and grounding checks

Validation: Test with known queries to verify context injection works correctly.

Error Handling: For batch ingestion, wrap in retry logic:

for (Document doc : documents) {
    int attempts = 0;
    while (attempts < 3) {
        try {
            store.add(embeddingModel.embed(doc).content(), doc.toTextSegment());
            break;
        } catch (EmbeddingException e) {
            attempts++;
            if (attempts == 3) throw new RuntimeException("Failed after 3 retries", e);
        }
    }
}

Step 6: Evaluate and Optimize

Measure retrieval metrics: precision@k, recall@k, MRR
Evaluate answer quality: faithfulness, relevance
Monitor performance and user feedback
Iterate on chunking, retrieval, and prompt parameters

Examples

Example 1: Basic Document Q&A

List<Document> documents = FileSystemDocumentLoader.loadDocuments("/docs");

InMemoryEmbeddingStore<TextSegment> store = new InMemoryEmbeddingStore<>();
EmbeddingStoreIngestor.ingest(documents, store);

DocumentAssistant assistant = AiServices.builder(DocumentAssistant.class)
    .chatModel(chatModel)
    .contentRetriever(EmbeddingStoreContentRetriever.from(store))
    .build();

String answer = assistant.answer("What is the company policy on remote work?");

Example 2: Metadata-Filtered Retrieval

EmbeddingStoreContentRetriever retriever = EmbeddingStoreContentRetriever.builder()
    .embeddingStore(store)
    .embeddingModel(embeddingModel)
    .maxResults(5)
    .minScore(0.7)
    .filter(metadataKey("category").isEqualTo("technical"))
    .build();

Example 3: Multi-Source RAG Pipeline

ContentRetriever webRetriever = EmbeddingStoreContentRetriever.from(webStore);
ContentRetriever docRetriever = EmbeddingStoreContentRetriever.from(docStore);

List<Content> results = new ArrayList<>();
results.addAll(webRetriever.retrieve(query));
results.addAll(docRetriever.retrieve(query));

List<Content> topResults = reranker.reorder(query, results).subList(0, 5);

Example 4: RAG with Chat Memory

Assistant assistant = AiServices.builder(Assistant.class)
    .chatModel(chatModel)
    .chatMemory(MessageWindowChatMemory.withMaxMessages(10))
    .contentRetriever(retriever)
    .build();

assistant.chat("Tell me about the product features");
assistant.chat("What about pricing for those features?");  // Maintains context

Best Practices

Document Preparation

Clean documents before ingestion; remove irrelevant content and formatting
Add relevant metadata for filtering and context

Chunking Strategy

Use 500-1000 tokens per chunk for optimal balance
Include 10-20% overlap to preserve context at boundaries
Test different sizes for your specific use case

Retrieval Optimization

Start with high k values (10-20), then filter/rerank
Use metadata filtering to improve relevance
Monitor retrieval quality and iterate based on user feedback

Performance

Cache embeddings for frequently accessed content
Use batch processing for document ingestion
Optimize vector store indexing for your scale

Constraints and Warnings

System Constraints

Embedding models have maximum token limits per document
Vector databases require proper indexing for performance
Chunk boundaries may lose context for complex documents
Hybrid search requires additional infrastructure

Quality Warnings

Retrieval quality depends heavily on chunking strategy
Embedding models may not capture domain-specific semantics
Metadata filtering requires proper document annotation
Reranking adds latency to query responses

Security Warnings

Never hardcode credentials: Use environment variables for API keys and passwords
Validate external content: Documents from file systems, APIs, or web sources may contain malicious content (prompt injection)
Apply content filtering on retrieved documents before passing to LLM
Restrict allowed data source URLs and file paths using allowlists

Resources

Reference Documentation

View source ↗

File metadata

name: rag
description: Implements document chunking, embedding generation, vector storage, and retrieval pipelines for Retrieval-Augmented Generation systems. Use when building RAG applications, creating document Q&A systems, or integrating AI with knowledge bases.
allowed-tools: Read, Write, Bash

View original text

---
name: rag
description: Implements document chunking, embedding generation, vector storage, and retrieval pipelines for Retrieval-Augmented Generation systems. Use when building RAG applications, creating document Q&A systems, or integrating AI with knowledge bases.
allowed-tools: Read, Write, Bash
---

# RAG Implementation

Build Retrieval-Augmented Generation systems that extend AI capabilities with external knowledge sources.

## Overview

This skill covers: document processing, embedding generation, vector storage, retrieval configuration, and RAG pipeline implementation.

## When to Use

- Building Q&A systems over proprietary documents
- Creating chatbots with factual information from knowledge bases
- Implementing semantic search with natural language queries
- Reducing hallucinations with grounded, sourced responses
- Building documentation assistants and research tools
- Enabling AI systems to access domain-specific knowledge

## Instructions

### Step 1: Choose Vector Database

Select based on your requirements:

| Requirement | Recommended |
|-------------|-------------|
| Production scalability | Pinecone, Milvus |
| Open-source | Weaviate, Qdrant |
| Local development | Chroma, FAISS |
| Hybrid search | Weaviate with BM25 |

### Step 2: Select Embedding Model

| Use Case | Model |
|----------|-------|
| General purpose | text-embedding-ada-002 |
| Fast and lightweight | all-MiniLM-L6-v2 |
| Multilingual | e5-large-v2 |
| Best performance | bge-large-en-v1.5 |

### Step 3: Implement Document Processing Pipeline

1. Load documents from source (file system, database, API)
2. Clean and preprocess (remove formatting, normalize text)
3. Split documents into chunks with appropriate strategy
4. Generate embeddings for each chunk
5. Store embeddings in vector database with metadata

**Validation**: Verify embeddings were generated successfully:
```java
List<Embedding> embeddings = embeddingModel.embedAll(segments);
if (embeddings.isEmpty() || embeddings.get(0).dimension() != expectedDim) {
    throw new IllegalStateException("Embedding generation failed");
}
```

### Step 4: Configure Retrieval Strategy

Choose the appropriate strategy:

- **Dense Retrieval**: Semantic similarity via embeddings (default for most cases)
- **Hybrid Search**: Dense + sparse retrieval for better coverage
- **Metadata Filtering**: Filter by document attributes
- **Reranking**: Cross-encoder reranking for high-precision requirements

### Step 5: Build RAG Pipeline

1. Create content retriever with your embedding store
2. Configure AI service with retriever and chat memory
3. Implement prompt template with context injection
4. Add response validation and grounding checks

**Validation**: Test with known queries to verify context injection works correctly.

**Error Handling**: For batch ingestion, wrap in retry logic:
```java
for (Document doc : documents) {
    int attempts = 0;
    while (attempts < 3) {
        try {
            store.add(embeddingModel.embed(doc).content(), doc.toTextSegment());
            break;
        } catch (EmbeddingException e) {
            attempts++;
            if (attempts == 3) throw new RuntimeException("Failed after 3 retries", e);
        }
    }
}
```

### Step 6: Evaluate and Optimize

1. Measure retrieval metrics: precision@k, recall@k, MRR
2. Evaluate answer quality: faithfulness, relevance
3. Monitor performance and user feedback
4. Iterate on chunking, retrieval, and prompt parameters

## Examples

### Example 1: Basic Document Q&A

```java
List<Document> documents = FileSystemDocumentLoader.loadDocuments("/docs");

InMemoryEmbeddingStore<TextSegment> store = new InMemoryEmbeddingStore<>();
EmbeddingStoreIngestor.ingest(documents, store);

DocumentAssistant assistant = AiServices.builder(DocumentAssistant.class)
    .chatModel(chatModel)
    .contentRetriever(EmbeddingStoreContentRetriever.from(store))
    .build();

String answer = assistant.answer("What is the company policy on remote work?");
```

### Example 2: Metadata-Filtered Retrieval

```java
EmbeddingStoreContentRetriever retriever = EmbeddingStoreContentRetriever.builder()
    .embeddingStore(store)
    .embeddingModel(embeddingModel)
    .maxResults(5)
    .minScore(0.7)
    .filter(metadataKey("category").isEqualTo("technical"))
    .build();
```

### Example 3: Multi-Source RAG Pipeline

```java
ContentRetriever webRetriever = EmbeddingStoreContentRetriever.from(webStore);
ContentRetriever docRetriever = EmbeddingStoreContentRetriever.from(docStore);

List<Content> results = new ArrayList<>();
results.addAll(webRetriever.retrieve(query));
results.addAll(docRetriever.retrieve(query));

List<Content> topResults = reranker.reorder(query, results).subList(0, 5);
```

### Example 4: RAG with Chat Memory

```java
Assistant assistant = AiServices.builder(Assistant.class)
    .chatModel(chatModel)
    .chatMemory(MessageWindowChatMemory.withMaxMessages(10))
    .contentRetriever(retriever)
    .build();

assistant.chat("Tell me about the product features");
assistant.chat("What about pricing for those features?");  // Maintains context
```

## Best Practices

### Document Preparation
- Clean documents before ingestion; remove irrelevant content and formatting
- Add relevant metadata for filtering and context

### Chunking Strategy
- Use 500-1000 tokens per chunk for optimal balance
- Include 10-20% overlap to preserve context at boundaries
- Test different sizes for your specific use case

### Retrieval Optimization
- Start with high k values (10-20), then filter/rerank
- Use metadata filtering to improve relevance
- Monitor retrieval quality and iterate based on user feedback

### Performance
- Cache embeddings for frequently accessed content
- Use batch processing for document ingestion
- Optimize vector store indexing for your scale

## Constraints and Warnings

### System Constraints
- Embedding models have maximum token limits per document
- Vector databases require proper indexing for performance
- Chunk boundaries may lose context for complex documents
- Hybrid search requires additional infrastructure

### Quality Warnings
- Retrieval quality depends heavily on chunking strategy
- Embedding models may not capture domain-specific semantics
- Metadata filtering requires proper document annotation
- Reranking adds latency to query responses

### Security Warnings
- **Never hardcode credentials**: Use environment variables for API keys and passwords
- **Validate external content**: Documents from file systems, APIs, or web sources may contain malicious content (prompt injection)
- **Apply content filtering** on retrieved documents before passing to LLM
- Restrict allowed data source URLs and file paths using allowlists

## Resources

### Reference Documentation
- [Vector Database Comparison](references/vector-databases.md)
- [Embedding Models Guide](references/embedding-models.md)
- [Retrieval Strategies](references/retrieval-strategies.md)
- [Document Chunking](references/document-chunking.md)
- [LangChain4j RAG Guide](references/langchain4j-rag-guide.md)

Review the source

Skill source recorded

Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.

Review before install: Avoid automatic install

License: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
The skill uses Java (LangChain4j) for main examples but references Python (LangChain) in the references directory, which may cause confusion for users expecting a single language focus.
SKILL.md does not explicitly state limitations or scenarios where RAG might not be appropriate, which could lead to misuse.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Stars/forks activity: 339 stars, 39 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

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Source & usage notes

Indexed

Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

Source repository: giuseppe-trisciuoglio/developer-kit
License: MIT
Version: 1.0.0
Last GitHub push: Aug 18, 2026
Registry updated: Sep 5, 2026
Instruction path: plugins/developer-kit-ai/skills/rag/SKILL.md @ 50f0b945bd81

Version reported in registry metadata; check source releases before relying on it.

Quality

72/100

Strong

Trust

57/100

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This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.

Open install API LLM text format Open manifest Auto-resolve Plan

More details

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      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "giuseppe-trisciuoglio-rag",
      "task": "Use rag in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/giuseppe-trisciuoglio-rag",
    "api": "https://www.openagentskill.com/api/agent/skills/giuseppe-trisciuoglio-rag",
    "audit": "https://www.openagentskill.com/skills/giuseppe-trisciuoglio-rag/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=giuseppe-trisciuoglio-rag&task=Use%20rag%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20rag%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20rag%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/giuseppe-trisciuoglio-rag/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/giuseppe-trisciuoglio-rag"
  }
}

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Creator: giuseppe-trisciuoglio
Source: giuseppe-trisciuoglio/developer-kit
Indexed by: OpenAgentSkill community index

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Overview

RAG Implementation

Overview

When to Use

Instructions

Step 1: Choose Vector Database

Step 2: Select Embedding Model

Step 3: Implement Document Processing Pipeline

Step 4: Configure Retrieval Strategy

Step 5: Build RAG Pipeline

Step 6: Evaluate and Optimize

Examples

Example 1: Basic Document Q&A

Example 2: Metadata-Filtered Retrieval

Example 3: Multi-Source RAG Pipeline

Example 4: RAG with Chat Memory

Best Practices

Document Preparation

Chunking Strategy

Retrieval Optimization

Performance

Constraints and Warnings

System Constraints

Quality Warnings

Security Warnings

Resources

Reference Documentation

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Start with one small task

Source & usage notes

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