🧠 AI Memory Optimizer
Drastically reduce RAG costs and latency while improving retrieval accuracy through advanced memory architecture.
- Increase RAG recall accuracy by implementing semantic chunking strategies.
- Reduce embedding and storage costs by up to 80% with dimension reduction.
- Lower query latency using quantized HNSW indices and semantic caching.
$7
· or 35 creditsSecure checkout via Stripe
Included in download
- Lower query latency using quantized HNSW indices and semantic caching.
- terminal automation included
Sample input
Optimize our RAG setup: 850k docs in Pinecone, using text-embedding-3-large, fixed 1024 chunks, and no caching. We have poor recall (0.72) and high costs.
Sample output
Optimization Report
- Recall@5: 0.72 -> 0.93 (+29%)
- Latency: 450ms -> 85ms (-81%)
- Monthly Cost: $2,450 -> $950 (-61%)
Top Actions:
- Switch to Semantic Chunking (512 tokens).
- Reduce Embedding dimensions to 256 using PCA.
- Deploy HNSW SQ8 Index + Redis Semantic Cache.
Drastically reduce RAG costs and latency while improving retrieval accuracy through advanced memory architecture.
$7
· or 35 creditsSecure checkout via Stripe
Included in download
- Lower query latency using quantized HNSW indices and semantic caching.
- terminal automation included
- Instant install
Sample input
Optimize our RAG setup: 850k docs in Pinecone, using text-embedding-3-large, fixed 1024 chunks, and no caching. We have poor recall (0.72) and high costs.
Sample output
Optimization Report
- Recall@5: 0.72 -> 0.93 (+29%)
- Latency: 450ms -> 85ms (-81%)
- Monthly Cost: $2,450 -> $950 (-61%)
Top Actions:
- Switch to Semantic Chunking (512 tokens).
- Reduce Embedding dimensions to 256 using PCA.
- Deploy HNSW SQ8 Index + Redis Semantic Cache.
About This Skill
What it does
The AI Memory Optimizer is a comprehensive toolkit for developers and agencies building large-scale RAG (Retrieval-Augmented Generation) systems. It analyzes your AI's memory architecture—including chunking strategies, embedding models, vector database indices, and context window usage—to significantly improve retrieval quality while slashing operational costs.
Why use this skill
Standard prompting and basic RAG setups often fail at scale, leading to high latency, poor recall, and ballooning costs. This skill applies data-science-driven optimizations like semantic segmenting and PCA-based dimension reduction. It doesn't just suggest improvements; it provides a structured report with predicted metrics (Recall@k, P99 Latency, Cost-per-Query) and a prioritized action plan.
Supported tools & frameworks
- Vector Databases: Pinecone, Weaviate, Qdrant, Milvus, pgvector.
- Embedding Models: OpenAI (v3), Cohere, Voyage, and open-source models like BGE-M3 or Jina.
- RAG Frameworks: LangChain, LlamaIndex, and custom Python implementations.
- Caching: Redis-based semantic and exact-match caching strategies.
The Output
You receive a detailed Memory Optimization Report. This includes a status audit (Critical/High/Low) for your current stack, a side-by-side comparison of current vs. optimized metrics, and a step-by-step implementation guide with suggested parameters for your specific data scale.
Use Cases
- Increase RAG recall accuracy by implementing semantic chunking strategies.
- Reduce embedding and storage costs by up to 80% with dimension reduction.
- Lower query latency using quantized HNSW indices and semantic caching.
- Manage context windows efficiently for long-context models like GPT-4o.
How to Install
mkdir -p ~/.claude/skills && curl -sL https://www.agensi.io/api/install/ai-memory-optimizer -o /tmp/ai-memory-optimizer.zip && unzip -o /tmp/ai-memory-optimizer.zip -d ~/.claude/skills && rm /tmp/ai-memory-optimizer.zipFree skills install directly. Paid skills require purchase - use the download button above after buying.
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