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feat: LLM entity + fact extraction pipeline (Zep paper alignment) (#48)
## Changes

### Entity Extraction
- Switch from WikiLinkFallbackExtractor to LlmEntityExtractor when LLM_ENDPOINT set
- `clean_llm_response()`: strips `<think>` tags, markdown fences, extracts JSON
- Handle array responses (Ollama returns `[...]` not `{entities: [...]}`)
- EntityType custom Deserialize: unknown variants → Unknown (no crash)
- Increase timeout 30s→90s, max_tokens 500→1500 for reasoning models
- Graceful reflection fallback: keep entities if verification fails

### Fact Extraction (NEW)
- LlmFactExtractor: LLM-based relationship extraction between entity pairs
- Validates source/target against known entity list (drops hallucinated edges)
- Same robust JSON cleaning for reasoning models + Ollama
- IngestWorker auto-selects LLM vs Simple based on LLM_ENDPOINT env

### K8s Deployment
- Add `command: ["/app/mem"]` (fix args replacing CMD)
- Add LLM_ENDPOINT, LLM_MODEL env vars for in-cluster LLM

## E2E Tested (local Ollama qwen2.5:3b)
- 12 entities extracted (person, tool, concept, organization)
- 5 edges with relationships and facts
- 781 tests pass

## Zep Paper Alignment (§2.2)
- Entity extraction + resolution (§2.2.1)
- Fact extraction between entity pairs (§2.2.2)
- Temporal edge invalidation ready (t_valid/t_invalid schema)
- Reflection verification (§2.2.1, graceful fallback)

---------

Co-authored-by: rock <[email protected]>
Reviewed-on: #48
Co-authored-by: poimen <[email protected]>
2026-09-11 01:11:15 +00:00
2026-08-22 23:13:42 -07:00

Poimen Memory System

Production-grade knowledge graph RAG system with semantic search, temporal filtering, community detection, path finding, and faceted search.

Quick Start

# Build
cargo build --release

# Run
cargo run --release -- --config config/default.toml

API Documentation

See API.md for complete endpoint specifications, request/response formats, and usage examples.

Core Endpoints

  • POST /memory/query/semantic/entities — Semantic search with optional community detection, path finding, facet discovery
  • POST /memory/query/semantic/edges — Relation search with temporal and facet filters
  • POST /memory/query/hybrid — Combined semantic + lexical search (RRF fusion)

Optional Features (via query parameters)

  • Temporal Filtering: start_time, end_time (ISO 8601 datetime)
  • Community Detection: detect_communities=true, min_community_size=N
  • Path Finding: find_paths=true, target_entity_id=<id>, max_path_depth=N, k_hops=N
  • Faceted Search: discover_facets=true, facet_filters={...}

Architecture

crates/mem-cli/src/
├── query/
│   ├── semantic_retriever.rs    (vector + lexical search)
│   ├── community_detector.rs    (Louvain algorithm)
│   ├── path_finder.rs           (BFS/DFS graph traversal)
│   └── faceted_search.rs        (multi-dimension filtering)
├── handlers/
│   └── semantic.rs              (HTTP endpoints)
└── http_server.rs               (Actix-web server)

crates/mem-core/src/
├── domain.rs                    (data structures)
├── entity.rs, edge.rs           (graph entities)
└── scoring.rs                   (relevance metrics)

crates/mem-store/src/
└── *_repo.rs                    (database persistence)

Testing

# Run all tests
cargo test --lib

# Run specific test suite
cargo test --lib query::semantic
cargo test --lib handlers::semantic

# With output
cargo test --lib -- --nocapture

Configuration

See config/default.toml for:

  • Database connection strings
  • JWT authentication settings
  • Rate limiting thresholds
  • Embeddings model configuration

Production Deployment

  1. Build release binary: cargo build --release
  2. Set environment: JWT_SECRET, DATABASE_URL, OPENAI_API_KEY
  3. Run: ./target/release/mem-cli
  4. Health check: GET http://localhost:8080/health

Development

Quality Standards:

  • CRAP score < 3.2 (low complexity)
  • DRY > 98% (minimal duplication)
  • SOLID 5.0/5 (excellent design)
  • 230+ comprehensive tests (100% pass rate)
  • Performance: P50 latency < 500ms

Adding New Features:

  1. Create core module in crates/mem-cli/src/query/
  2. Add optional parameters to request struct
  3. Extend response with optional field (use skip_serializing_if)
  4. Add handler logic (delegate to core module)
  5. Write 25-35 tests (unit + integration)
  6. Document in API.md

See CLAUDE.md for project context and constraints.

CI test 1788759975

S
Description
Agent-ready Graph-RAG system with hallucination prevention and enterprise RBAC
https://forgejo.riotpiao.com/rock/poimen-memory
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