Fusion RAG: Combining Multiple Data Sources for Smarter Retrieval

TL;DR

Fusion RAG enhances standard retrieval-augmented generation by pulling evidence from multiple, heterogeneous data sources—vector databases, keyword search, APIs, and SQL queries—and merging them intelligently before answer generation.

What Is Fusion RAG?

Fusion RAG fuses retrieved data from different systems (semantic search, keyword search, SQL, APIs) into a single ranked list of context snippets. The model then synthesizes an answer that reflects diverse perspectives — effectively ensembling retrieval.

When to Use Fusion RAG

• Finance: Merge market data, news feeds, analyst reports.
• Enterprise knowledge bases: Combine internal docs with public APIs or support portals.
• Scientific summarization: Blend structured (PubMed DB) and unstructured (arXiv PDFs) sources.

Example N8N Workflow

• Parallel Retrieval Nodes: Vector Search node → semantic match from embeddings DB, HTTP Request node → keyword or external API results, Database node → structured query to SQL/Notion.
• Merge results with a Set or Merge node.
• Rerank results by relevance using a ChatGPT node or reranker model.
• Feed top-ranked docs to the LLM generator with labeled citations.

Implementation Patterns

• Hybrid retrieval: combine semantic and keyword search via Reciprocal Rank Fusion.
• Multi-indexing: maintain several databases optimized for different query types.
• Fusion-in-Decoder (FiD): treat each document as a separate token sequence and fuse during decoding.

Strengths & Weaknesses

Strengths: Broader coverage across diverse data domains, reduces single-source bias and improves completeness, flexible architecture adaptable to different industries. Weaknesses: Complexity in data merging and scoring, possible noise or contradictions from multiple sources, higher latency due to multiple API calls.