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function-callable tool the orchestrator can invoke.
Public entry: `build_default_tools(rag_index_dir)` returns the 5 tools.
"""
from __future__ import annotations
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Callable
from pydantic import BaseModel, ValidationError
from src.agents.schemas import (
BBBPipelineInput,
BBBPipelineOutput,
EEGPipelineInput,
EEGPipelineOutput,
MRIPipelineInput,
MRIPipelineOutput,
RetrieveContextInput,
RetrieveContextOutput,
)
from src.fusion.engine import fuse as fuse_engine
from src.fusion.types import FusionInput, FusionOutput
from src.core.logger import get_logger
logger = get_logger(__name__)
@dataclass
class Tool:
"""One callable tool exposed to the orchestrator.
`execute(input_model_instance) -> output_model_instance` is the contract.
`invoke(args_dict)` validates the dict, runs execute, returns a plain dict.
"""
name: str
description: str
input_model: type[BaseModel]
output_model: type[BaseModel]
execute: Callable[[Any], BaseModel]
def openai_schema(self) -> dict[str, Any]:
"""OpenAI/OpenRouter function-calling schema for this tool."""
params = self.input_model.model_json_schema()
# OpenAI doesn't accept top-level $defs / title in some clients —
# strip the cosmetic ones; keep properties/required/type.
cleaned = {
"type": "object",
"properties": params.get("properties", {}),
"required": params.get("required", []),
}
return {
"type": "function",
"function": {
"name": self.name,
"description": self.description,
"parameters": cleaned,
},
}
def invoke(self, args: dict[str, Any]) -> dict[str, Any]:
try:
inp = self.input_model.model_validate(args)
except ValidationError as e:
raise ValueError(f"invalid input for {self.name}: {e}") from e
out = self.execute(inp)
return out.model_dump()
# ---------------------------------------------------------------------------
# Tool implementations — thin wrappers around existing pipelines + RAG.
# Heavy work stays in the underlying modules; these only adapt I/O.
# ---------------------------------------------------------------------------
def _make_bbb_executor() -> Callable[[BBBPipelineInput], BBBPipelineOutput]:
"""Closure factory: BBB permeability prediction + SHAP, translates HTTPException."""
def execute(inp: BBBPipelineInput) -> BBBPipelineOutput:
from src.api import routes as api_routes
from src.api.schemas import BBBPredictRequest
from fastapi import HTTPException
try:
response = api_routes.predict_bbb(
BBBPredictRequest(smiles=inp.smiles, top_k=inp.top_k)
)
except HTTPException as e:
raise ValueError(f"bbb tool failed: {e.detail}") from e
return BBBPipelineOutput(
smiles=inp.smiles,
label=response.label,
label_text=response.label_text,
confidence=response.confidence,
top_features=[f.model_dump() for f in response.top_features],
drift_z=response.drift_z,
)
return execute
def _make_eeg_executor(processed_dir: Path) -> Callable[[EEGPipelineInput], EEGPipelineOutput]:
"""Closure factory: EEG pipeline, writes output under processed_dir."""
def execute(inp: EEGPipelineInput) -> EEGPipelineOutput:
from src.api.schemas import EEGRequest
from src.api import routes as api_routes
from fastapi import HTTPException
# TODO(post-hackathon): per-call output path. Concurrent /agent/run
# invocations race on this file and clobber each other's MLflow runs.
out_path = processed_dir / "eeg_features.parquet"
try:
response = api_routes.run_eeg(
EEGRequest(
input_path=inp.input_path,
output_path=str(out_path),
epoch_duration_s=inp.epoch_duration_s,
)
)
except HTTPException as e:
raise ValueError(f"eeg tool failed: {e.detail}") from e
return EEGPipelineOutput(
input_path=inp.input_path,
output_path=response.output_path,
rows=response.rows,
columns=response.columns,
duration_sec=response.duration_sec,
)
return execute
def _make_mri_executor(processed_dir: Path) -> Callable[[MRIPipelineInput], MRIPipelineOutput]:
"""Closure factory: MRI pipeline, writes output under processed_dir."""
def execute(inp: MRIPipelineInput) -> MRIPipelineOutput:
from src.api.schemas import MRIRequest
from src.api import routes as api_routes
from fastapi import HTTPException
# TODO(post-hackathon): per-call output path. Concurrent /agent/run
# invocations race on this file and clobber each other's MLflow runs.
out_path = processed_dir / "mri_features.parquet"
sites_csv = inp.sites_csv or str(Path(inp.input_dir) / "sites.csv")
try:
response = api_routes.run_mri(
MRIRequest(
input_dir=inp.input_dir,
sites_csv=sites_csv,
output_path=str(out_path),
)
)
except HTTPException as e:
raise ValueError(f"mri tool failed: {e.detail}") from e
return MRIPipelineOutput(
input_dir=inp.input_dir,
output_path=response.output_path,
rows=response.rows,
columns=response.columns,
duration_sec=response.duration_sec,
)
return execute
def _make_retrieve_executor(rag_index_dir: Path | None) -> Callable[[RetrieveContextInput], RetrieveContextOutput]:
"""Closure: capture the index dir; lazy-load the retriever on first call."""
state: dict[str, Any] = {"retriever": None}
def execute(inp: RetrieveContextInput) -> RetrieveContextOutput:
if rag_index_dir is None or not (rag_index_dir / "index.bin").exists():
return RetrieveContextOutput(query=inp.query, chunks=[])
if state["retriever"] is None:
from src.rag.retrieve import RAGRetriever
state["retriever"] = RAGRetriever.load(rag_index_dir)
hits = state["retriever"].search(inp.query, k=inp.k)
return RetrieveContextOutput(query=inp.query, chunks=hits)
return execute
def build_default_tools(
rag_index_dir: Path | None,
processed_dir: Path = Path("data/processed"),
) -> list[Tool]:
"""Return the 5 tools the orchestrator gets by default."""
return [
Tool(
name="run_bbb_pipeline",
description=(
"Predict blood-brain-barrier permeability for a SINGLE SMILES "
"string. Use this when the user input looks like a molecule "
"(short alphanumeric string with no file extension, e.g. 'CCO', "
"'c1ccccc1'). Returns label, confidence, top SHAP features, drift."
),
input_model=BBBPipelineInput,
output_model=BBBPipelineOutput,
execute=_make_bbb_executor(),
),
Tool(
name="run_eeg_pipeline",
description=(
"Run the EEG signal-processing pipeline (bandpass + ICA + "
"epoching + feature extraction) on an EEG recording file. Use "
"when input_path ends in .fif or .edf. Returns row/column "
"counts + duration."
),
input_model=EEGPipelineInput,
output_model=EEGPipelineOutput,
execute=_make_eeg_executor(processed_dir),
),
Tool(
name="run_mri_pipeline",
description=(
"Run the multi-site MRI ComBat-harmonization pipeline. Use "
"when input is a directory containing .nii.gz volumes paired "
"with a sites.csv. Returns row/column counts + duration."
),
input_model=MRIPipelineInput,
output_model=MRIPipelineOutput,
execute=_make_mri_executor(processed_dir),
),
Tool(
name="retrieve_context",
description=(
"Retrieve up to k passages from the curated reference knowledge "
"base. Use AFTER a pipeline tool returns, to ground your final "
"synthesis in cited literature. Formulate a focused query "
"based on the pipeline output (e.g., 'BBB permeability of "
"small lipophilic molecules' or 'ComBat site harmonization')."
),
input_model=RetrieveContextInput,
output_model=RetrieveContextOutput,
execute=_make_retrieve_executor(rag_index_dir),
),
Tool(
name="run_fusion",
description=(
"Combine MRI prediction, EEG prediction, and clinical-test "
"scores (MMSE, MoCA, UPDRS, gait, age) into per-disease "
"(Alzheimer's, Parkinson's, other) confidence with full "
"attribution. Pass whichever modalities are available; "
"missing ones are skipped, not imputed. Does NOT use BBB."
),
input_model=FusionInput,
output_model=FusionOutput,
execute=lambda inp: fuse_engine(inp),
),
]
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