Semantic Retrieval

Semantic retrieval is the process of fetching context based on meaning, relationships, and intent—not just keyword matches or vector similarity alone. It combines multiple signals: what the query means (semantic similarity), what entities are involved (relationships), what's recent (temporal context), and what's important (ranking).

Unlike keyword search that requires exact matches, or pure vector search that only considers embedding similarity, semantic retrieval reasons over structured knowledge. It answers "show me tasks blocking Project Alpha owned by engineers" by understanding entities (Alpha, engineers), relationships (owns, blocks), and intent—not just matching keywords or similar vectors.

The outcome is precise, context-aware retrieval that agents use to access the right memory at the right time.

Why it matters

• Improves retrieval precision: "Tasks owned by Alice about authentication" is more precise than keyword "Alice authentication" or vector similarity alone.
• Supports complex queries: Multi-constraint queries (entity + relationship + time + topic) work naturally with semantic retrieval.
• Reduces false positives: Keyword search returns irrelevant matches; semantic retrieval filters by meaning, relationships, and context.
• Enables natural language queries: Users ask questions naturally—"what's blocking Alpha?"—semantic retrieval interprets intent.
• Powers agent memory access: Agents need relevant context, not just similar text—semantic retrieval assembles what matters.
• Combines multiple signals: Hybrid retrieval (semantic + structural + temporal) outperforms any single approach.

How it works

Semantic retrieval operates through multi-signal query processing:

• Query Understanding → The system analyzes the query: intent, entities mentioned, relationships implied, time constraints, topic.
• Semantic Similarity → Vector embeddings are compared: query embedding vs. content embeddings—finds semantically related content.
• Entity and Relationship Filtering → Knowledge graph is queried: "owned by Alice" resolves to specific tasks linked to Alice entity.
• Temporal Filtering → Time-based constraints are applied: "last week" filters to events and documents from that range.
• Ranking and Scoring → Results are scored by relevance (semantic similarity), recency (temporal decay), importance (priority, impact), and completeness (matches all constraints).
• Result Assembly → Top-ranked items are returned with context: surrounding facts, related entities, and provenance.

This multi-dimensional retrieval ensures agents get the right context.

Comparison & confusion to avoid

Examples & uses

Multi-constraint query: "Tasks owned by engineers blocking Alpha last week"

• Semantic: "blocking" and "tasks" identify task-related content.
• Entity: "Alpha" resolves to Project Alpha entity.
• Relationship: "owned by engineers" traverses ownership links where owner.role = engineer.
• Temporal: "last week" filters events and changes to that time range.
• Result: 2 tasks matching all constraints.

Natural language query: "What did Alice decide about authentication?"

• Semantic: "decide" and "authentication" identify decision-related content.
• Entity: "Alice" resolves to Alice entity.
• Relationship: Decisions where Alice is the decider.
• Topic: Content tagged or about "authentication."
• Result: "Alice decided to migrate to OAuth on Nov 3."

Contextual retrieval for agent task: "Generate status brief for Project X"

• Entity: "Project X" identifies relevant project.
• Relationship: Tasks, owners, dependencies related to X.
• Temporal: Recent changes (last week) weighted higher.
• Importance: Blockers and decisions prioritized.
• Result: Assembled context for brief generation.

Best practices

• Combine multiple signals: Don't rely on vector similarity alone—add entities, relationships, time, and importance.
• Weight recency appropriately: Recent context usually matters more—implement temporal decay in ranking.
• Support natural language queries: Users shouldn't need to learn query syntax—parse intent from natural questions.
• Provide result provenance: Show where retrieved context came from (document, timestamp, confidence)—enables trust and verification.
• Tune ranking for use cases: Status briefs need recent changes; historical analysis needs broader time ranges—ranking should adapt.
• Monitor retrieval quality: Track precision (are results relevant?) and recall (are we missing important context?)—iterate on signals and weights.

Common pitfalls

• Over-relying on vector similarity: Embeddings alone miss relationships, time, and importance—hybrid retrieval performs better.
• Ignoring temporal signals: Retrieving semantically similar but outdated content leads to wrong conclusions—recency matters.
• No entity resolution: If "Project Alpha" and "Alpha" aren't linked, queries miss relevant content—entity linking is prerequisite.
• One-size-fits-all ranking: Different tasks need different retrieval strategies—adapt scoring to context.
• No query understanding: Treating queries as bag-of-words misses intent—parse entities, relationships, and constraints.

See also

• Context Engine — Intelligent memory assembly using semantic retrieval
• Semantic Memory — Structured knowledge that semantic retrieval accesses
• Knowledge Graph — Entity and relationship structure for retrieval
• Memory Index — Multi-dimensional indexes enabling fast semantic retrieval
• Agent Memory — Persistent context accessed via semantic retrieval

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See how Graphlit implements Semantic Retrieval for agents → Agent Memory Platform