[FEATURE]: Native RAG (Retrieval-Augmented Generation) Integration

[vinisdl]

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Feature Request: Native RAG Support

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Is your feature request related to a problem? Please describe.

When building knowledge-aware agents (docs, repositories, internal KB, compliance data), RAG must currently be implemented externally and manually integrated into Laddr.

This causes:

• Repeated boilerplate for embeddings + vector search
• Manual context injection into prompts
• Reduced observability of retrieval quality
• Increased architectural complexity

Given Laddr’s focus on scalable multi-agent orchestration, native RAG support would significantly improve developer experience and enterprise readiness.

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Describe the solution you'd like

Introduce optional built-in RAG support as a first-class agent capability.

Minimal scope proposal:

• Pluggable vector store interface
  • Azure AI Search (vector + hybrid search)
  • pgvector
  • Qdrant / Pinecone (optional future support)
• Configurable embedding provider (OpenAI, Azure OpenAI, Ollama)
• Automatic Top-K retrieval before agent execution
• Context injection middleware
• Retrieval traces visible in dashboard/logs

Example configuration:

rag:
  enabled: true
  vector_store:
    provider: azure-ai-search
    endpoint: ${AZURE_SEARCH_ENDPOINT}
    index_name: ${AZURE_SEARCH_INDEX}
  embedding_provider: azure-openai
  top_k: 5
  hybrid_search: true

Execution flow:

User Input
→ Embed query
→ Azure AI Search (Vector or Hybrid Search)
→ Top-K retrieval
→ Inject context into agent prompt
→ Agent execution

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Describe alternatives you've considered

• External RAG microservice
• LangChain / LlamaIndex integration
• Manual retrieval per agent

These approaches increase complexity and reduce cohesion with Laddr’s orchestration and observability model.

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Additional context

Azure AI Search support is particularly relevant for:

• Enterprise deployments
• Secure VNet/private endpoint setups
• Hybrid keyword + vector retrieval
• RBAC-based index access

Native RAG would enable:

• Knowledge-aware agents
• Persistent memory patterns
• Documentation/codebase assistants
• Enterprise-grade agent systems

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