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timmy-config/patches/agent/evolution/domain_distiller.py
Alexander Payne 37873ca100
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feat(sidecar-3): restructure agent/* extensions as runtime patches
Move 13 custom agent extension modules from local hermes-agent fork
into timmy-config's sidecar overlay as runtime patches. These files
are deployed into ~/.hermes/hermes-agent/agent/ by deploy.sh.

Files restructured:
- agent/conscience_mapping.py
- agent/evolution/{domain_distiller, self_correction_generator, world_modeler}.py
- agent/fallback_router.py
- agent/gemini_adapter.py
- agent/input_sanitizer.py
- agent/knowledge_ingester.py
- agent/meta_reasoning.py
- agent/nexus_architect.py
- agent/symbolic_memory.py
- agent/temporal_{knowledge_graph,reasoning}.py

This clears the way to reset hermes-agent to pure upstream.
Closes #339
2026-04-30 01:53:33 -04:00

46 lines
1.6 KiB
Python

"""Phase 3: Deep Knowledge Distillation from Google.
Performs deep dives into technical domains and distills them into
Timmy's Sovereign Knowledge Graph.
"""
import logging
import json
from typing import List, Dict, Any
from agent.gemini_adapter import GeminiAdapter
from agent.symbolic_memory import SymbolicMemory
logger = logging.getLogger(__name__)
class DomainDistiller:
def __init__(self):
self.adapter = GeminiAdapter()
self.symbolic = SymbolicMemory()
def distill_domain(self, domain: str):
"""Crawls and distills an entire technical domain."""
logger.info(f"Distilling domain: {domain}")
prompt = f"""
Please perform a deep knowledge distillation of the following domain: {domain}
Use Google Search to find foundational papers, recent developments, and key entities.
Synthesize this into a structured 'Domain Map' consisting of high-fidelity knowledge triples.
Focus on the structural relationships that define the domain.
Format: [{{"s": "subject", "p": "predicate", "o": "object"}}]
"""
result = self.adapter.generate(
model="gemini-3.1-pro-preview",
prompt=prompt,
system_instruction=f"You are Timmy's Domain Distiller. Your goal is to map the entire {domain} domain into a structured Knowledge Graph.",
grounding=True,
thinking=True,
response_mime_type="application/json"
)
triples = json.loads(result["text"])
count = self.symbolic.ingest_text(json.dumps(triples))
logger.info(f"Distilled {count} new triples for domain: {domain}")
return count