AI Agent 实时知识图谱记忆系统设计模板

You are a knowledge graph and AI memory systems architect.

I need to design a temporal knowledge graph memory system for my AI agent. The system should track how facts change over time, support incremental updates, and enable hybrid retrieval.

My Agent Context

• [Agent type: personal assistant, customer service, research agent, etc.]
• [Data sources: user conversations, documents, APIs, structured databases]
• [Scale: expected number of entities and relationships]
• [Query patterns: what questions will the agent need to answer?]

Please Design

1. Data Model

Define the temporal knowledge graph schema:

• Entity types and their attributes
• Relationship types with temporal validity windows
• Episode model (raw source data linkage)
• Fact versioning: how to track when facts become true/false

Example:

Entity: Person { name, summary, created_at, updated_at }
Relationship: WORKS_AT { valid_from, valid_until, confidence }
Episode: { source_type, content, timestamp, processed_at }

2. Ingestion Pipeline

• How to extract entities and relationships from unstructured text
• Conflict resolution: when new facts contradict existing ones
• Incremental update strategy (no full recomputation)
• Entity deduplication and merging

3. Hybrid Retrieval Strategy

Design a retrieval system combining:

• Semantic search: vector similarity on entity/relationship embeddings
• Keyword search: BM25 on entity names and descriptions
• Graph traversal: multi-hop reasoning through relationships
• Temporal filtering: what was true as of date X?

Provide the query pipeline with ranking and fusion strategy.

4. Technology Stack

Recommend specific tools:

• Graph database (Neo4j, FalkorDB, etc.)
• Vector store (embedded or external)
• LLM for entity extraction
• Framework (Graphiti, LightRAG, custom)

5. Implementation Code Skeleton

Provide a Python code skeleton showing:

• Graph schema initialization
• Adding an episode and extracting entities
• Querying with temporal awareness
• Updating facts when new information arrives

Be specific and practical. Include error handling and edge cases.