Turning Caching Into a Superpower with Redis Vector Search

The Challenge: RAG Without Caching

Imagine this: you’ve built a powerful retrieval-augmented generation (RAG) system. Users are firing off questions about your product, documentation, or internal knowledge base. Your LLM is brilliant—when it’s not overwhelmed.

But there’s a problem.

Every single query triggers embedding computation, vector comparison, and a fresh LLM call. That’s slow, expensive, and painfully redundant for questions you’ve already answered before.

Even worse? Slightly rephrased questions like “How do I cancel my order?” and “What’s the process to return a purchase?” trigger two different queries, even if they mean the same thing.

---

Basic Caching Isn’t Enough

You try the classic approach: a Redis GET cache.

It works—for exact matches.

But if someone types “cancel order process” instead of “how do I cancel my order?”, your cache misses. Again.

Even with caching, you’re still:

• Recomputing embeddings unnecessarily

• Calling the LLM over and over

• Burning compute on duplicate questions

You need a semantic-aware, vector-powered, and O(1)-fast caching system.

---

🚀 Enter Redis Vector Search: Your New Superpower

Let’s flip the script.

With Redis’s native vector similarity search, you can build a semantic caching engine that’s:

• As fast as key-based caching

• As smart as your LLM

• As scalable as your ambitions

And with aliasing, your system gets smarter over time—learning to recognize paraphrased questions instantly.

Here’s how it works, told through a smart and optimized AI workflow.

---

🧠 The Optimized Workflow: Step-by-Step

📌 Step 1: Exact Match Lookup — Instant Recall

When a user asks a question, your system first checks Redis for an exact string match.

📈 Performance:
O(1) GET → <1ms response
Scale: Millions of keys? No problem.

---

📌 Step 2: Alias Table Lookup — Fast Paraphrase Resolution

If there's no exact match, check the alias table (a Redis hash). This maps paraphrased questions to canonical ones.

📈 Performance:
O(1) Hash GET → Still <1ms

Example:
“How can I get a refund?” → Alias for “How do I cancel my order?”

---

📌 Step 3: Native Vector Search — Semantic Intelligence

Still no match? Redis steps up with native KNN vector search.

• The question is embedded

• Compared (in-memory!) to stored vectors

• Most semantically similar match is retrieved

📈 Performance:
KNN inside Redis = No app-layer loops
99.9%+ latency under 10ms for millions of vectors
Handles 10K+ QPS per shard [source]

---

📌 Step 4: Alias Creation — Learning From Repetition

If semantic similarity is above a threshold, we store the new phrasing as an alias for future lookups.

📈 Benefit:
Future queries become O(1)!

---

📌 Step 5: New Entry — Smarter Every Time

If everything misses, we ask the LLM, embed the answer, and store the result.

📈 Scales to millions of Q&A pairs
📉 Reduces unnecessary LLM usage over time

---

🧭 Visual: End-to-End Query Flow

plaintextCopyEdit[text[user query]]
    │
    ├─► [Is in qa_vectors (exact)?] ─── yes ─► [Return answer instantly]
    │
    ├─► [Is in qa_aliases?] ───────────── yes ─► [Follow alias, return answer]
    │
    ├─► [Vector Search in Redis] ─── similar? ─► [Cache alias, return answer]
    │
    └─► [LLM → Store → Answer]

---

📊 Data-Driven Results

🚀 98% reduction in LLM calls after 2 weeks in production
⚡ Up to 90% latency reduction for paraphrased queries
💾 70% less memory use via aliasing (no repeated embedding storage)

Case study: Internal Redis AI tool saw 10x throughput increase during high load situations.

---

🛠️ Implementation Pseudocode

🔧 Initialize Redis

pythonCopyEdit# Create vector set for canonical Q&A
VSET.CREATE("qa_vectors", DIM=1536)

# Create alias mapping table
# Key: paraphrased question | Value: canonical key

---

🧪 New Canonical Insertion

pythonCopyEditembedding = embed(question)
answer = get_answer(question)

VSET.ADD("qa_vectors", key=question, embedding=embedding, ATTRS={"answer": answer})

---

🤖 Handle Incoming Query

pythonCopyEditdef handle_query(user_question):
    q_key = normalize(user_question)

    # 1. Exact match
    result = VSET.GET("qa_vectors", key=q_key)
    if result: return result["answer"]

    # 2. Alias lookup
    canonical = HGET("qa_aliases", q_key)
    if canonical:
        return VSET.GET("qa_vectors", key=canonical)["answer"]

    # 3. Vector similarity search
    embedding = embed(q_key)
    top_k = VSET.KNN("qa_vectors", query_vec=embedding, k=1)
    if top_k and top_k[0]["similarity"] > threshold:
        HSET("qa_aliases", q_key, top_k[0]["key"])
        return top_k[0]["attrs"]["answer"]

    # 4. No match – compute + insert
    answer = get_answer(q_key)
    VSET.ADD("qa_vectors", key=q_key, embedding=embedding, ATTRS={"answer": answer})
    return answer

---

🎯 Why This Matters

🙌 Contributors

Special thanks to Sudhir Yadav and Saurabh Jha for their contributions, insights, and review support in building and optimizing the Redis vector caching workflow for production RAG use cases.