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add mini-context-graph skill (#1580)
* add mini-context-graph skill * remove pycache files * filename case update to SKILL.md * update readme
This commit is contained in:
Nixon Kurian
GitHub
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"""
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template_agent_workflow.py — Template agent script for ingesting + querying the context graph.
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This script demonstrates the complete workflow an agent should follow:
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1. Read markdown guidance files
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2. Extract entities/relations via LLM reasoning
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3. Call Python methods to persist
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4. Query the graph
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5. Handle errors gracefully
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Copy and adapt this template for your agent implementation.
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"""
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import json
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import sys
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from pathlib import Path
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# Add tools to path
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sys.path.insert(0, str(Path(__file__).parent))
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from contextgraph import ContextGraphSkill
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def ingest_document(skill: ContextGraphSkill, document: str) -> dict:
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"""
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Step 1: Agent reads ingestion.md and ontology.md
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Step 2: Agent uses LLM to extract entities and relations
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Step 3: Call Python methods to persist (mimicked here with static extraction)
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In a real agent, replace the static extraction with LLM calls.
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"""
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print(f"\n[INGEST] Processing document:\n{document}\n")
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# --- STEP 1 & 2: LLM EXTRACTION PHASE (Guided by ingestion.md + ontology.md) ---
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# In a real agent, this would use LLM reasoning.
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# For now, we'll mock an extraction result:
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extraction_result = {
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"entities": [
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{"name": "memory leak", "type": "issue"},
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{"name": "system crash", "type": "issue"},
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{"name": "object", "type": "component"},
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],
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"relations": [
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{
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"source": "memory leak",
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"target": "system crash",
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"type": "causes",
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"confidence": 1.0,
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},
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{
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"source": "object",
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"target": "memory leak",
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"type": "contributes to",
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"confidence": 0.9,
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},
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],
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}
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print(f"[LLM] Extracted entities + relations:")
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print(json.dumps(extraction_result, indent=2))
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# --- STEP 3: PERSIST PHASE (Call Python methods) ---
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errors = []
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added_nodes = {}
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for entity in extraction_result["entities"]:
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try:
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node_id = skill.add_node(entity["name"], entity["type"])
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added_nodes[entity["name"]] = node_id
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print(f" ✓ Added node: {entity['name']} (id: {node_id}, type: {entity['type']})")
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except Exception as e:
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errors.append(f"Failed to add node {entity['name']}: {e}")
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print(f" ✗ Error adding node {entity['name']}: {e}")
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for relation in extraction_result["relations"]:
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# Validate both endpoints exist
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if relation["source"] not in added_nodes or relation["target"] not in added_nodes:
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error_msg = f"Cannot add edge: source or target missing"
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errors.append(error_msg)
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print(f" ✗ Skip edge {relation['source']} → {relation['target']}: {error_msg}")
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continue
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# Validate confidence threshold
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if relation["confidence"] < 0.6:
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error_msg = f"Confidence {relation['confidence']} < 0.6 (minimum threshold)"
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errors.append(error_msg)
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print(f" ✗ Skip edge {relation['source']} → {relation['target']}: {error_msg}")
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continue
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try:
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skill.add_edge(
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source_name=relation["source"],
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target_name=relation["target"],
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relation=relation["type"],
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confidence=relation["confidence"],
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)
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print(
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f" ✓ Added edge: {relation['source']} "
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f"--[{relation['type']}]→ {relation['target']} "
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f"(confidence: {relation['confidence']})"
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)
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except Exception as e:
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errors.append(f"Failed to add edge {relation['source']} → {relation['target']}: {e}")
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print(f" ✗ Error adding edge: {e}")
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return {
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"success": len(errors) == 0,
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"nodes_added": len(added_nodes),
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"edges_added": len(extraction_result["relations"]) - len(
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[e for e in errors if "skip edge" in e.lower()]
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),
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"errors": errors,
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}
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def query_graph(skill: ContextGraphSkill, query: str) -> dict:
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"""
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Query the graph for context to answer the user's question.
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Step 1: Read retrieval.md
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Step 2: Call skill.query() which internally handles BFS + subgraph extraction
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Step 3: Return structured context
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"""
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print(f"\n[QUERY] {query}\n")
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try:
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subgraph = skill.query(query)
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if not subgraph["nodes"]:
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print(" ℹ No relevant entities found in graph.")
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return {
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"success": True,
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"query": query,
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"subgraph": subgraph,
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"nodes_found": 0,
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"edges_found": 0,
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}
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print(f" ✓ Retrieved subgraph with {len(subgraph['nodes'])} nodes, {len(subgraph['edges'])} edges")
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print(f"\n Nodes:")
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for node_id, node in subgraph["nodes"].items():
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print(f" - {node['name']} (type: {node['type']}, id: {node_id})")
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print(f"\n Edges:")
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for edge in subgraph["edges"]:
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source_name = subgraph["nodes"][edge["source"]]["name"]
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target_name = subgraph["nodes"][edge["target"]]["name"]
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print(
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f" - {source_name} --[{edge['type']}]→ {target_name} "
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f"(confidence: {edge['confidence']})"
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)
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return {
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"success": True,
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"query": query,
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"subgraph": subgraph,
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"nodes_found": len(subgraph["nodes"]),
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"edges_found": len(subgraph["edges"]),
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}
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except Exception as e:
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error_msg = f"Query failed: {e}"
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print(f" ✗ {error_msg}")
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return {"success": False, "query": query, "error": error_msg}
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def main():
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"""Demo: ingest a document, then query the graph."""
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skill = ContextGraphSkill()
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# ===== INGESTION =====
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document = """
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System crashes due to memory leaks.
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Memory leaks occur when objects are not released.
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"""
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result = ingest_document(skill, document)
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print(f"\n[INGEST RESULT] Nodes added: {result['nodes_added']}, " f"Edges added: {result['edges_added']}")
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if result["errors"]:
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print(f"Errors: {result['errors']}")
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# ===== RETRIEVAL =====
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queries = [
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"Why does the system crash?",
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"What causes memory leaks?",
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]
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for query in queries:
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result = query_graph(skill, query)
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if result["success"]:
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print(f" Nodes found: {result['nodes_found']}, Edges found: {result['edges_found']}")
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else:
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print(f" Error: {result['error']}")
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if __name__ == "__main__":
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main()
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