Trainable tools with sourced evidence and owned weights.

python -m pip install 'tensorcode[tools]'

import torch
from tensorcode import training
from tensorcode.tools.investigator import Investigator

torch.manual_seed(0)
model = Investigator({"vocabulary": ["database", "network", "connection", "refused", "packet", "loss"],
                      "dimensions": 16, "slots": 2, "steps": 1})
trainer = training.Trainer.from_tool(model, optimizer=torch.optim.AdamW(model.parameters(), lr=0.01))

hypotheses = [{"id": "database", "text": "database connection refused"},
              {"id": "network", "text": "network packet loss"}]

def case(log_line):
    return {"question": "which component failed",
            "evidence": [{"source_id": "log:1", "text": log_line}],
            "hypotheses": hypotheses}

# Reviewed feedback, with explicit provenance, becomes training experience.
experiences = [trainer.capture(case("connection refused"), "database", source="review:1"),
               trainer.capture(case("packet loss"), "network", source="review:2")]
losses = trainer.fit(experiences, epochs=30)

model.save_pretrained("./investigator")
restored = Investigator.from_pretrained("./investigator")
print(restored(case("packet loss"))["selected_id"])  # network

npm install tensorcode

import { AdamW, manualSeed } from 'tensorcode/nn';
import { Investigator } from 'tensorcode/tools';
import { Trainer } from 'tensorcode/training';

manualSeed(0);
const model = new Investigator({
  vocabulary: ['database', 'network', 'connection', 'refused', 'packet', 'loss'],
  dimensions: 16, slots: 2, steps: 1,
});
const trainer = Trainer.fromTool(model, { optimizer: (params) => new AdamW(params, { lr: 0.01 }) });

const hypotheses = [
  { id: 'database', text: 'database connection refused' },
  { id: 'network', text: 'network packet loss' },
];
const incident = (logLine: string) => ({
  question: 'which component failed',
  evidence: [{ source_id: 'log:1', text: logLine }],
  hypotheses,
});

// Reviewed feedback, with explicit provenance, becomes training experience.
const experiences = [
  trainer.capture(incident('connection refused'), 'database', { source: 'review:1' }),
  trainer.capture(incident('packet loss'), 'network', { source: 'review:2' }),
];
const losses = trainer.fit(experiences, { epochs: 30 });

await model.savePretrained('./investigator');
const restored = await Investigator.fromPretrained('./investigator');
console.log(restored.call(incident('packet loss')).selected_id); // network