Recursive knowledge

Recursive Knowledge Processing

Process arbitrarily large document sets through knowledge graph construction and stateful multi-hop queries. Based on RLM research but with proper state management and termination logic.

Core Concept

Instead of stuffing documents into context (which causes degradation), this skill:

1. Indexes documents into a knowledge graph (entities, relationships)
2. Answers queries by traversing the graph
3. Tracks state to avoid redundant exploration
4. Uses confidence thresholds to know when to stop

Workflow

Phase 1: Indexing

For a new corpus, run the indexer:

python3 scripts/index_corpus.py --input /path/to/documents --output /path/to/graph.json

This extracts:

• Entities: People, organizations, concepts, dates, locations
• Relationships: References, mentions, contradicts, supports, relates_to
• Metadata: Source document, position, extraction confidence

For details on entity/relationship schema, see references/graph-schema.md.

Phase 2: Querying

For user queries against an indexed corpus:

python3 scripts/query.py --graph /path/to/graph.json --query "user question here"

The query engine:

1. Parses query into target entities/relationships
2. Finds entry points in graph
3. Traverses with state tracking
4. Stops when confidence threshold met
5. Returns answer with provenance

Phase 3: Incremental Updates

Add new documents to existing graph:

python3 scripts/index_corpus.py --input /path/to/new_docs --output /path/to/graph.json --append

State Management (Critical)

The key improvement over naive recursive approaches is stateful traversal. See references/state-management.md for full details.

During query execution, track:

State	Purpose
visited_nodes	Prevent re-exploring same entities
visited_edges	Prevent re-traversing same relationships
findings	Accumulated evidence with sources
confidence	Current certainty level (0-1)
depth	Current traversal depth

Termination conditions:

STOP if:
  - confidence >= 0.85 (high certainty)
  - len(corroborating_sources) >= 3 (multiple agreement)
  - depth > max_depth (prevent infinite exploration)
  - all relevant paths exhausted

Multi-Hop Reasoning

For questions requiring connection across documents:

1. Identify query components (what entities/facts needed)
2. Find entry points for each component
3. Traverse from each entry point
4. Look for path intersections
5. Synthesize findings at intersection points

Example: "Who worked with X on project Y?"

• Entry point 1: Entity "X" → relationships → projects
• Entry point 2: Entity "Project Y" → relationships → people
• Intersection: People connected to both X and Project Y

See references/traversal-patterns.md for patterns.

When NOT to Use This Skill

• Small document sets that fit in context (<50k tokens) - just use direct context
• Simple keyword search - use grep/search tools instead
• No multi-hop reasoning needed - simpler approaches work
• Real-time streaming data - this is for static corpora

File Reference

• scripts/index_corpus.py - Build graph from documents
• scripts/query.py - Execute queries with state management
• scripts/graph_ops.py - Graph CRUD utilities
• references/graph-schema.md - Entity and relationship types
• references/state-management.md - Termination and confidence logic
• references/traversal-patterns.md - Multi-hop query patterns