If you've ever scaled a RAG pipeline, you know the feeling. You increase the chunk size to catch more semantics, or you increase the retrieval count to ensure coverage, and suddenly your token costs spike while the model starts hallucinating or getting confused by repetitive noise.
The fundamental issue isn't always the embeddings; often, it's that your retriever is grabbing three different versions of the same fact. You end up stuffing your context window with essentially the same information rewritten slightly differently. You're paying for those extra thousands of tokens just to watch the LLM process redundancy instead of signal.
Most people try to fix this by tweaking chunking strategies or playing with similarity thresholds. That's reactive engineering. It's guesswork. To do this properly, you need a deterministic way to measure exactly how much overlap exists in your retrieved set before you send it to the prompt.
I wanted a way to quantify this without building a custom NLP preprocessing layer every single time I ship an agentic workflow. That’s why we built the Context Redundancy Deduplicator.
The math behind the noise
Standard semantic search doesn't care if two chunks say nearly the same thing as long as they aren't mathematically identical in vector space. But for an LLM, highly similar text is cognitive clutter.
The tool uses N-gram analysis—specifically looking at contiguous sequences of characters or words—to find exact overlaps across your document sets. Unlike purely semantic approaches which can be fuzzy and expensive, this is deterministic. It tells you exactly what percentage of your payload is repetitive.
It exposes three core capabilities that move beyond simple string matching:
- analyze_redundancy: You provide a set of documents and an N-gram size (like 5-grams). It returns a redundancy percentage. If a document contains content that is heavily superseded by other parts of your set, it flags it.
- find_duplicate_segments: Instead of just giving you a percentage, it isolates the specific text blocks that are identical across documents. This lets you see exactly where your data ingestion logic is failing to create unique boundaries.(Note: In professional RAG setups, finding these segments is usually the difference between a clean prompt and one that triggers self-contradiction in models.)
- calculate_savings_projection: This is probably the most practical part for anyone managing cloud budgets or strict window limits (like Gemini's huge but costly windows). You tell it the current byte size and the detected redundant size, and it gives you a concrete projection of how many bytes you can strip out.
A rule of thumb we use: when any document has an overlap exceeding 70%, it should be flagged immediately. At that point, you aren't retrieving new info; you're just feeding the model echoes.
Why standard "cleaning" fails engineers
You might think, "Can't I just run a deduplication script during indexing?"
You can, but indexing deduplication is different from runtime retrieval deduplication.
during indexing, you want uniqueness across your entire database. But during retrieval (the RAG phase), your vector DB might return five chunks that happen to share heavy boilerplate or overlapping sentences due to how they were sliced during ingestion. An indexer won't help you here because those pieces are technically distinct entries in your vector store.
You need to solve this at the orchestration layer—after retrieval but before prompting.
The Context Redundancy Deduplicator acts as that middleman via MCP (Model Context Protocol). Because it operates as an MCP server, an agent running in Claude Desktop or Cursor can call these tools autonomously once it realizes its retrieved context is becoming bloated or inefficient.
The implementation under Vinkius handles all the heavy lifting around isolation and execution stability through our MCPFusion framework. We focus on making sure these tools behave predictably in production environments—running them in isolated V8 sandboxes so they don't interfere with your main application logic even though they are performing intensive text analysis tasks.
Moving toward efficient context management
The goal isn't just to save money on tokens—though saving 24% on a massive corpus certainly helps hit those KPIs—it's about precision. High-performing agents require high-density information per token spent.
If you are struggling with noisy retrievals or wondering why your agent keeps looping on certain topics despite having "all" the context, stop adjusting temperatures and start looking at your N-gram overlap rates. Sometimes being smarter means sending less stuff through the pipe.
iof course,
the nuances of chunk boundary optimization remain another separate beast entirely (\//vinkius.com/mcp/rag-chunk-boundary-optimizer works well alongside this for checking semantic continuity),\kbut solving redundancy is often lower hanging fruit for immediate performance gains.\r
furthermore,
it makes sense to pair this with something like our Keyword Extractor to validate whether your remaining non-redundant chunks actually contain the terms necessary for answering queries efficiently.\r\setupside note:
these tools aren't meant for casual chat users;
they are intended for developers building automated pipelines where reliability and cost predictable are requirements.,not luxuries..\r\stop
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