rock 721589d251
CI / CI (pull_request) Successful in 11m41s
feat: LLM-based fact extraction + robust entity parsing
Entity extraction fixes:
- clean_llm_response() strips <think> tags, markdown fences, extracts JSON
- Handle array responses (wrap in {"entities": [...]})
- EntityType custom Deserialize: unknown variants map to Unknown (not crash)
- Increase timeout to 90s for reasoning models
- Increase max_tokens to 1500 for reasoning model overhead

Fact extraction (new):
- LlmFactExtractor: LLM-based relationship extraction between entities
- Validates source/target against known entity list (no hallucinated edges)
- Same clean_llm_response() for reasoning model + Ollama compatibility
- Graceful fallback: returns empty on LLM error (no pipeline crash)
- IngestWorker uses LlmFactExtractor when LLM_ENDPOINT set

K8s deployment:
- Add LLM_ENDPOINT, LLM_API_BASE, LLM_MODEL env vars
- Points to in-cluster reasoning-predictor service

Tested E2E with local Ollama (qwen2.5:3b):
- 12 entities extracted (person, tool, concept, organization)
- 5 edges with meaningful relationships and facts
- 781 tests pass
2026-09-09 17:53:44 +09:00
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
Readme
2.3 MiB
Languages
Rust 98.5%
Shell 0.8%
Python 0.4%
PLpgSQL 0.2%