Precision Diagnosis for
Retrieval & Context Integrity

Transform RAG from alchemy to engineering with quantified metrics. Debug retrieval quality and track context pollution in real-time.

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94.2%
Avg. Retrieval Precision
8.3%
Context Noise Rate
Low
Hallucination Risk

Core Diagnostic Modules

Retrieval Quality Analyzer

Visualize query-chunk alignment with similarity heatmaps and top-K decay curves. Identify retrieval bottlenecks instantly.

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Context Pollution Tracker

Detect noise in your context window with pollution heatmaps and signal-to-noise ratio dashboards.

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The "Needle" Finder

Automated stress testing to find the exact point where your RAG system loses critical information.

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Integrate in Seconds

# Add one line to your RAG pipeline
import ragview

@ragview.trace()
def retrieve_context(query):
    chunks = vector_db.search(query, k=5)
    return chunks

# That's it! Start debugging immediately

Supports Python, Node.js, and REST API integration