101 lines
3.1 KiB
Python
Executable File
101 lines
3.1 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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Graph Self-Healing Tool
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Automatically retrieves "Missing Entity Pages" from the wiki and generates
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comprehensive definition pages for them using the LLM.
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It resolves broken entity links by scanning existing contexts where the entity is referenced.
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Usage:
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python tools/heal.py
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"""
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import os
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import sys
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from pathlib import Path
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try:
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from litellm import completion
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except ImportError:
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print("Error: litellm not installed. Run: pip install litellm")
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sys.exit(1)
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# Ensure tools can be imported
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sys.path.insert(0, str(Path(__file__).parent.parent))
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from tools.lint import find_missing_entities, all_wiki_pages
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REPO_ROOT = Path(__file__).parent.parent
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WIKI_DIR = REPO_ROOT / "wiki"
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ENTITIES_DIR = WIKI_DIR / "entities"
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def call_llm(prompt: str, max_tokens: int = 1500) -> str:
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# Use litellm standard environment variables
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# e.g., GEMINI_API_KEY, ANTHROPIC_API_KEY, OPENAI_API_KEY
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model = os.getenv("LLM_MODEL", "claude-3-5-haiku-latest") # default to fast model
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response = completion(
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model=model,
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messages=[{"role": "user", "content": prompt}],
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max_tokens=max_tokens
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)
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return response.choices[0].message.content
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def search_sources(entity: str, pages: list[Path]) -> list[Path]:
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"""Find up to 15 pages where this entity is mentioned natively."""
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sources = []
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for p in pages:
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if "entities" not in str(p.parent) and "concepts" not in str(p.parent):
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content = p.read_text(encoding="utf-8")
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if entity.lower() in content.lower():
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sources.append(p)
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return sources[:15]
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def heal_missing_entities():
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pages = all_wiki_pages()
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missing_entities = find_missing_entities(pages)
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if not missing_entities:
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print("Graph is fully connected. No missing entities found!")
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return
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ENTITIES_DIR.mkdir(exist_ok=True, parents=True)
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print(f"Found {len(missing_entities)} missing entity nodes. Commencing auto-heal...")
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for entity in missing_entities:
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print(f"Healing entity page for: {entity}")
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sources = search_sources(entity, pages)
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context = ""
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for s in sources:
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context += f"\n\n### {s.name}\n{s.read_text(encoding='utf-8')[:800]}"
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prompt = f"""You are filling a data gap in the Personal LLM Wiki.
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Create an Entity definition page for "{entity}".
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Here is how the entity appears in the current sources:
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{context}
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Format:
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---
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title: "{entity}"
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type: entity
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tags: []
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sources: {[s.name for s in sources]}
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---
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# {entity}
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Write a comprehensive paragraph defining what `{entity}` means in the context of this wiki, its main significance, and any actions or associations related to it.
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"""
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try:
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result = call_llm(prompt)
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out_path = ENTITIES_DIR / f"{entity}.md"
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out_path.write_text(result, encoding="utf-8")
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print(f" -> Saved to {out_path.relative_to(REPO_ROOT)}")
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except Exception as e:
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print(f" [!] Failed to generate {entity}: {e}")
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if __name__ == "__main__":
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heal_missing_entities()
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