Memory Retrieval Strategies
Master how AI agents retrieve relevant memories to support intelligent decision-making and personalized responses
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Review and check off these essential concepts from the Memory Retrieval module. Complete all items to finish this module and unlock the next one!
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Memory retrieval is the process of searching stored memories and selecting the most relevant ones for context
Effective retrieval balances speed, quality, and cost—retrieving too much wastes tokens, too little misses context
Keyword matching is fast but literal, missing semantically similar content without exact word matches
Semantic search uses embeddings and cosine similarity to understand meaning, finding related content intelligently
Hybrid search combines keyword precision with semantic understanding for optimal retrieval results
Multi-factor ranking combines relevance (semantic similarity), recency (temporal decay), and importance (priority)
Ranking formula: final_score = (α × relevance) + (β × recency) + (γ × importance), with weights summing to 1.0
Top-K limits max results to save tokens; threshold filters low-quality results below a similarity cutoff
Query expansion generates multiple reformulations to improve recall by searching with synonyms and variations
Reranking uses a more expensive model (cross-encoder) to reorder top candidates for maximum accuracy
Caching recent queries reduces latency and cost by returning stored results for similar repeated queries
Metadata filtering pre-filters memories by date, source, or category before semantic search to narrow results
Diversity penalty (MMR) reduces scores of similar memories to avoid redundancy in retrieved results
Monitor retrieval latency (P50, P95, P99) and optimize slow queries with profiling and index tuning
Good retrieval enables contextual, personalized agent responses; poor retrieval leads to irrelevant or generic answers