File size: 4,947 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 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 | """Helper script to add /rlm/query endpoint to main.py"""
rlm_endpoint = '''
# ─────────────────────────────────────────────────────────────────────────────
# Phase 4.5: Recursive Synthesis Engine Endpoint
# ─────────────────────────────────────────────────────────────────────────────
class RLMQueryRequest(BaseModel):
"""Request model for Phase 4.5 recursive memory query."""
query: str = Field(..., min_length=1, max_length=4096, description="The query to synthesize (can be complex/multi-topic)")
context_text: Optional[str] = Field(None, max_length=500000, description="Optional large external text (Ripple environment)")
project_id: Optional[str] = Field(None, max_length=128, description="Optional project scope for isolation masking")
max_depth: Optional[int] = Field(None, ge=0, le=5, description="Max recursion depth (0-5, default 3)")
max_sub_queries: Optional[int] = Field(None, ge=1, le=10, description="Max sub-queries to decompose into (1-10, default 5)")
top_k: Optional[int] = Field(None, ge=1, le=50, description="Final results to return (default 10)")
class RLMQueryResponse(BaseModel):
"""Response model for Phase 4.5 recursive memory query."""
ok: bool
query: str
sub_queries: List[str]
results: List[Dict[str, Any]]
synthesis: str
max_depth_hit: int
elapsed_ms: float
ripple_snippets: List[str]
stats: Dict[str, Any]
@app.post(
"/rlm/query",
response_model=RLMQueryResponse,
dependencies=[Depends(get_api_key), Depends(QueryRateLimiter())],
tags=["Phase 4.5"],
summary="Recursive Synthesis Query",
description=(
"Phase 4.5: Recursive Language Model (RLM) query. "
"Decomposes complex queries into sub-questions, searches MnemoCore in parallel, "
"recursively analyzes low-confidence clusters, and synthesizes a final answer. "
"Implements the MIT CSAIL RLM paradigm to eliminate Context Rot."
),
)
@track_async_latency(API_REQUEST_LATENCY, {"method": "POST", "endpoint": "/rlm/query"})
async def rlm_query(
req: RLMQueryRequest,
engine: HAIMEngine = Depends(get_engine),
):
"""
Phase 4.5 Recursive Synthesis Engine.
Instead of a single flat search, this endpoint:
1. Decomposes your query into focused sub-questions
2. Searches MnemoCore in PARALLEL for each sub-question
3. Recursively drills into low-confidence clusters
4. Synthesizes all results into a coherent answer
Rate limit: 500/minute (shared with /query).
"""
API_REQUEST_COUNT.labels(method="POST", endpoint="/rlm/query", status="200").inc()
from mnemocore.core.recursive_synthesizer import RecursiveSynthesizer, SynthesizerConfig
from mnemocore.core.ripple_context import RippleContext
# Build config from request overrides
synth_config = SynthesizerConfig(
max_depth=req.max_depth if req.max_depth is not None else 3,
max_sub_queries=req.max_sub_queries if req.max_sub_queries is not None else 5,
final_top_k=req.top_k if req.top_k is not None else 10,
)
# Build RippleContext if external text provided
ripple_ctx = None
if req.context_text and req.context_text.strip():
ripple_ctx = RippleContext(text=req.context_text, source_label="api_context")
# Run recursive synthesis (no LLM wired at API level — use heuristic mode)
# To enable LLM synthesis, configure via RLMIntegrator in your application code
synthesizer = RecursiveSynthesizer(engine=engine, config=synth_config)
result = await synthesizer.synthesize(
query=req.query,
ripple_context=ripple_ctx,
project_id=req.project_id,
)
return {
"ok": True,
"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,
}
'''
path = "src/api/main.py"
content = open(path, "r", encoding="utf-8").read()
# Insert before the if __name__ == "__main__" block
marker = '\nif __name__ == "__main__":'
idx = content.find(marker)
if idx == -1:
print("ERROR: marker not found")
else:
new_content = content[:idx] + rlm_endpoint + content[idx:]
open(path, "w", encoding="utf-8").write(new_content)
print(f"OK: /rlm/query endpoint inserted at position {idx}")
print(f"New length: {len(new_content)}")
|