Multi-Source RAG: Integrate Diverse Knowledge Bases and Modalities

TL;DR

Query multiple sources in parallel (internal docs, web, databases, even images), then merge and reconcile evidence before generation.

What Is Multi-Source RAG?

Retrieval from multiple knowledge bases and/or modalities with fusion strategies to improve coverage and robustness.

When to Use Multi-Source RAG

• Assistants that combine company docs with web results
• Cross-referencing tasks needing multiple repositories
• Image+text scenarios and heterogeneous data

Building Multi-Source RAG in N8N

• Set up parallel retrieval nodes for each source
• Normalize and label results by source
• Merge candidates and optionally rerank with an LLM
• Prompt LLM to synthesize and reconcile conflicts

Strengths & Weaknesses

Strengths: improved coverage and bias mitigation. Weaknesses: higher complexity, potential inconsistencies, extra latency and cost.

Metrics to Track

• Synthesis quality and coherence
• Cross-source coverage and trust scoring
• Hallucination count and latency