Memory Retrieval Strategies
Master how AI agents retrieve relevant memories to support intelligent decision-making and personalized responses
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0 / 5 completedOptimization & Fine-Tuning
Beyond basic retrieval and ranking, advanced techniques help optimize memory retrieval for speed, quality, and cost. These strategies balance trade-offs between recall (finding all relevant memories) and precision (avoiding irrelevant ones).
Interactive: Top-K & Threshold Tuning
Adjust parameters to see how they affect retrieved memories:
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🚀 Advanced Optimization Techniques
🔄Query Expansion
Generate multiple reformulations of the query using LLM or synonyms to improve recall.
📦Hierarchical Retrieval
Retrieve at multiple levels: documents → sections → chunks. Enables context-aware retrieval.
🎭Hypothetical Document Embeddings (HyDE)
Generate hypothetical answer to query, embed it, then search for similar memories. Improves semantic match.
⚡Caching & Memoization
Cache recent queries and results. If similar query arrives, return cached results instantly.
⚡ Performance Optimization
Compression
Use Product Quantization or Scalar Quantization to reduce vector storage size by 8-16x with minimal accuracy loss.
Load Balancing
Distribute queries across multiple vector DB instances. Use consistent hashing for efficient shard routing.
Index Tuning
Adjust HNSW parameters: M (connections per node) and efConstruction (build quality) for speed vs accuracy.
Batch Processing
Batch multiple queries together for embedding generation and search to maximize GPU/CPU utilization.