File size: 3,317 Bytes
dbb04e4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 | """Helper script to insert RLMIntegrator into llm_integration.py"""
import os
rlm_class = '''
class RLMIntegrator:
"""
Phase 4.5: RLM (Recursive Language Models) Integrator.
Bridges HAIMLLMIntegrator with the RecursiveSynthesizer to provide
LLM-powered recursive memory queries.
Usage::
integrator = RLMIntegrator(llm_integrator)
result = await integrator.rlm_query(
"What do we know about X and how does it relate to Y?"
)
print(result["synthesis"])
Without an LLM configured, falls back to heuristic decomposition
and score-based synthesis.
"""
def __init__(self, llm_integrator, config=None):
from mnemocore.core.recursive_synthesizer import RecursiveSynthesizer, SynthesizerConfig
self.llm_integrator = llm_integrator
self.haim = llm_integrator.haim
llm_call = None
if llm_integrator.llm_client is not None:
llm_call = llm_integrator._call_llm
synth_config = config or SynthesizerConfig()
self.synthesizer = RecursiveSynthesizer(
engine=self.haim,
config=synth_config,
llm_call=llm_call,
)
async def rlm_query(self, query, context_text=None, project_id=None):
"""
Execute a Phase 4.5 recursive memory query.
Args:
query: The user question (can be complex/multi-topic).
context_text: Optional large external text (Ripple environment).
project_id: Optional project scope for isolation masking.
Returns:
Dict: query, sub_queries, results, synthesis,
max_depth_hit, elapsed_ms, ripple_snippets, stats
"""
from mnemocore.core.ripple_context import RippleContext
ripple_ctx = None
if context_text and context_text.strip():
ripple_ctx = RippleContext(text=context_text, source_label="api_context")
result = await self.synthesizer.synthesize(
query=query,
ripple_context=ripple_ctx,
project_id=project_id,
)
return {
"query": result.query,
"sub_queries": result.sub_queries,
"results": result.results,
"synthesis": result.synthesis,
"max_depth_hit": result.max_depth_hit,
"elapsed_ms": result.total_elapsed_ms,
"ripple_snippets": result.ripple_snippets,
"stats": result.stats,
}
@classmethod
def from_config(cls, haim_engine, llm_config, synth_config=None):
"""Create an RLMIntegrator directly from an LLMConfig."""
llm_integrator = HAIMLLMIntegrator.from_config(haim_engine, llm_config)
return cls(llm_integrator=llm_integrator, config=synth_config)
'''
path = "src/llm_integration.py"
content = open(path, "r", encoding="utf-8").read()
marker = "\ndef create_demo():"
idx = content.find(marker)
if idx == -1:
print("ERROR: marker not found")
else:
new_content = content[:idx] + rlm_class + content[idx:]
open(path, "w", encoding="utf-8").write(new_content)
print(f"OK: RLMIntegrator inserted at position {idx}")
print(f"New length: {len(new_content)}")
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