Real-Time RAG

Real-Time RAG Pipeline

Build RAG pipelines with always-fresh data. RisingWave continuously updates structured context from streaming sources — keeping vector attributes current, reducing hallucinations, and enabling retrieval-augmented generation with live business data.

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Always-Fresh

Context Layer

Materialized views continuously aggregate and enrich data from streaming sources into RAG-ready shapes

Incremental

Metadata Updates

Only changed rows trigger recomputation — fresh context without the cost of full dataset reprocessing

Hybrid

Retrieval

Combine fresh structured data from RisingWave with vector similarity scores for grounded retrieval

PostgreSQL

Integration

Works with LangChain, LlamaIndex, and any framework that supports PostgreSQL retrieval

The Problem

Why do static RAG pipelines produce stale and inaccurate results?

Most RAG pipelines ingest data in batches — indexing documents hourly or daily. Between indexing runs, the retrieval context becomes stale. Products go out of stock, prices change, user preferences shift, but the RAG pipeline returns outdated information, causing hallucinations and incorrect recommendations.

Factor	Batch RAG Pipeline	Streaming RAG (RisingWave)
Context Freshness	Hours (batch reindex)	Sub-second (streaming)
Reindex Cost	Full dataset each time	Incremental (changed rows only)
Metadata Accuracy	Drifts between batches	Always current
Hallucination Risk	High (stale attributes)	Low (fresh context)

→Hourly or daily reindexing means retrieved context can be hours old
→Full dataset reindexing is expensive and slow, so teams batch less frequently
→Vector embeddings stay fixed while underlying business data changes
→LLMs confidently present stale retrieved data as current facts

Architectures

What RAG architectures work best with streaming data?

The most effective real-time RAG architectures separate vector similarity search from structured context retrieval. RisingWave handles the structured layer — maintaining fresh business data that enriches vector results — while your vector database handles embedding-based similarity search.

Hybrid Retrieval

Vector search finds semantically similar documents. RisingWave enriches results with fresh structured attributes like price, availability, and user context

Pre-Filtered RAG

Query RisingWave first to narrow the candidate set (e.g., in-stock items only), then run vector similarity on the filtered subset

Streaming Context Injection

Inject real-time computed features (rolling averages, trend signals) from materialized views directly into LLM prompts alongside retrieved documents

Event-Driven Reindexing

Use RisingWave's change stream to trigger targeted vector embedding updates when source records change, eliminating scheduled batch reindexing

How It Works

How does RisingWave keep RAG context fresh in real-time?

RisingWave sits between your streaming data sources and the RAG pipeline, continuously computing structured context via materialized views. As source data changes, views update incrementally. Applications query these views to get fresh metadata, then combine it with vector similarity results for grounded, accurate retrieval.

→Materialized views continuously aggregate and enrich data from Kafka, CDC, and event streams into RAG-ready shapes
→Only changed rows trigger recomputation — fresh context without the cost of full dataset reprocessing
→Combine RisingWave's fresh structured data with vector similarity scores for relevant and current retrieval
→Query RisingWave to find which records changed, then selectively update only affected embeddings
→PostgreSQL-compatible interface integrates with LangChain, LlamaIndex, and custom Python code

Frequently Asked Questions

How does RisingWave improve RAG pipeline accuracy?

Does RisingWave replace a vector database?

Can I use RisingWave with LangChain or LlamaIndex?

How does streaming SQL help with RAG reindexing?

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