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rag

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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Precio sin confirmar★ 339 Estrellas de GitHubRegistro actualizado · 5 sept 2026agent-skill

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Documentación de origen, no instrucciones para este sitio. Revisa los permisos antes de ejecutar comandos.

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

Ver fuente ↗

Metadatos del archivo

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

Ver texto original

---
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)

Revisar el código fuente

Precio y costes de ejecución

Obtener el skill: Precio sin confirmar
Ejecutarlo: Requisitos sin confirmar. Consulta los costes del agente, API y servicios en la fuente.
Licencia: MIT
Precio sin confirmar: No hemos confirmado el precio. Los enlaces existentes al código y a la instalación siguen disponibles.

Obtener gratis no significa ejecutar gratis. El precio no es una evaluación de seguridad. Enviar información de precio →

Fuente del skill registrada

La ruta de instrucciones está registrada. No implica pruebas de ejecución, seguridad ni compatibilidad.

Revisar antes de instalar: Evitar instalación automática

Licencia: 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

Abrir auditoría completa

Las herramientas son indicios de metadatos, no compatibilidad probada. Los prompts son sugerencias.

Empieza con una tarea pequeña

1Lee la fuente y confirma entradas, resultados, dependencias y permisos.
2Pide un plan al agente. Aprueba la configuración y los costes antes de probar en un entorno aislado.
3Comprueba resultados y archivos modificados. Informa solo de lo ejecutado y conserva la revisión de la fuente.

Consulta dependencias, claves API y costes externos en la fuente. Un repositorio público no implica servicios gratuitos.

Fuente y notas de uso

Indexado

Los metadatos y revisiones son orientativos. Popularidad, descubrimiento y ejecución correcta son hechos distintos.

Repositorio fuente: giuseppe-trisciuoglio/developer-kit
Licencia: MIT
Versión: 1.0.0
Último push de GitHub: 18 ago 2026
Registro actualizado: 5 sept 2026
Ruta de instrucciones: plugins/developer-kit-ai/skills/rag/SKILL.md @ 50f0b945bd81

Versión declarada en el registro; consulta las versiones de la fuente.

Calidad

69/100

Prometedor

Confianza

55/100

Do not auto-install

Auditoría

72/100

Requiere revisión

Abrir auditoría completa Ver informe de evaluación

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

Verified installs: —
Resultados: —

Copiar no es instalar. Los recuentos requieren un informe de instalación correcta, no garantizan calidad general.

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      "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"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 69,
    "label": "Promising"
  },
  "supply": {
    "track": "Research and knowledge work",
    "scenario": "RAG and knowledge",
    "maintenance": "2mo since push",
    "risk": "Needs review"
  },
  "alternative_skills": [],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "production agents without a repository review",
    "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.",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "SKILL.md does not explicitly state limitations or scenarios where RAG might not be appropriate, which could lead to misuse.",
    "Quality score needs review"
  ],
  "agent_contract": {
    "task_input": "Use rag in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 63/100 Manual review",
      "Audit: 72/100 Needs review",
      "Safety: 28/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "giuseppe-trisciuoglio-rag (rag)",
      "install_command": "npx skills add giuseppe-trisciuoglio/developer-kit --skill rag",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "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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Creador: giuseppe-trisciuoglio
Fuente: giuseppe-trisciuoglio/developer-kit
Indexado por: Índice comunitario de OpenAgentSkill

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