Sync from GitHub a1e7660

app.py

@@ -45,6 +45,7 @@ Module layout:
 
 import base64
 import io
+import json
 import logging
 import os
 import shutil
@@ -56,6 +57,8 @@ from fastapi.responses import FileResponse, HTMLResponse, Response
 from huggingface_hub import HfApi, snapshot_download
 
 from core.constants import (
+    AGENT_MODELS,
+    DEFAULT_AGENT_MODEL,
     DEFAULT_TOP_K,
     GROUND_ENABLE_THINKING,
     LIBRARY_DATASET_ID,
@@ -114,6 +117,8 @@ def _build_libraries():
 
 
 VISUAL_STORE, PARSED_STORE, PIPELINE = _build_libraries()
+# Valid agent-brain keys, for validating the per-request `agent_model`.
+_AGENT_MODEL_KEYS = {m["key"] for m in AGENT_MODELS}
 # method -> store, for the picker / pdf lookups. In mock both keys map to the
 # one MockStore, so every mock manual reads as indexed under both methods.
 _METHOD_STORES = {"visual": VISUAL_STORE, "parsed": PARSED_STORE}
@@ -293,6 +298,7 @@ def api_find(
     pages: list = None,
     history: list = None,
     think: bool = GROUND_ENABLE_THINKING,
+    agent_model: str = DEFAULT_AGENT_MODEL,
 ) -> dict:  # the per-yield type: Server.api infers outputs from this annotation
     """One agent turn (one ZeroGPU call), streamed as events (see
     pipelines/agent_ask.py for the protocol). page/section are what the viewer
@@ -325,14 +331,19 @@ def api_find(
     )
     viewer = {"page": int(page or 0), "section": str(section or ""), "pages": shown}
     options = _router_options(manual, request)
+    # Unknown model key → default (the pipeline falls back too; validate here so
+    # the log reflects what actually ran).
+    if agent_model not in _AGENT_MODEL_KEYS:
+        agent_model = DEFAULT_AGENT_MODEL
     log.info(
-        "find: manual=%s k=%s think=%s viewer=%s hist=%d opts=%d q=%r",
-        manual, k, bool(think), viewer, len(history or []), len(options), request[:200],
+        "find: manual=%s k=%s think=%s model=%s viewer=%s hist=%d opts=%d q=%r",
+        manual, k, bool(think), agent_model, viewer, len(history or []),
+        len(options), request[:200],
     )
     try:
         events = PIPELINE.run_find(
             VISUAL_STORE, PARSED_STORE, request, [manual], int(k), options,
-            viewer, history, bool(think),
+            viewer, history, bool(think), agent_model,
         )
         for ev in events:
             if ev.get("type") == "tool_result" and "gallery" in ev:
@@ -367,8 +378,8 @@ _FRONTEND_DIR = os.path.join(os.path.dirname(__file__), "frontend")
 @app.get("/")
 def index():
     """Serve the single-page UI, injecting the small bit of server config the
-    frontend needs (default/max k, default grounding-thinking) so it needs no
-    extra round-trip on load."""
+    frontend needs (default/max k, default grounding-thinking, the agent-model
+    list + default) so it needs no extra round-trip on load."""
     with open(os.path.join(_FRONTEND_DIR, "index.html")) as f:
         html = f.read()
     html = (
@@ -376,6 +387,13 @@ def index():
         .replace("__MAX_K__", str(MAX_TOP_K))
         # Initial state of the settings-panel "thinking" toggle (a JS bool).
         .replace("__GROUND_THINK__", "true" if GROUND_ENABLE_THINKING else "false")
+        # Agent-brain picker: the selectable models (key+label) and the default,
+        # so the settings dropdown needs no extra round-trip on load.
+        .replace(
+            "__AGENT_MODELS_JSON__",
+            json.dumps([{"key": m["key"], "label": m["label"]} for m in AGENT_MODELS]),
+        )
+        .replace("__AGENT_MODEL__", DEFAULT_AGENT_MODEL)
         # Cache-bust key for /page images: changing the render DPI changes the
         # served page size, so it must change the URL too — otherwise a browser
         # could keep an old-resolution page (cached up to a day) under the same

core/constants.py

@@ -42,6 +42,46 @@ GROUND_THINK_MAX_NEW_TOKENS = 512
 # remote code to drift) but overridable; pin a commit before a real deploy.
 MINICPM_AGENT_MODEL_ID = os.environ.get("MINICPM_AGENT_MODEL_ID", "openbmb/MiniCPM5-1B")
 MINICPM_AGENT_REVISION = os.environ.get("MINICPM_AGENT_REVISION", "") or None
+
+# Selectable agent brains, offered in the UI settings panel. ONE model is
+# resident in VRAM at a time — switching evicts the previous and loads the next
+# (models/minicpm_agent.use_model). All load as a plain AutoModelForCausalLM.
+# `thinking` flags whether the chat template accepts enable_thinking (Qwen3 and
+# MiniCPM do — tool routing passes it False; Cohere's template does not, so the
+# kwarg is omitted there). The FIRST entry is the default at boot and tracks the
+# MINICPM_AGENT_MODEL_ID/REVISION env overrides, so existing config still
+# applies. All four stay well under the hackathon's 32B total-params budget.
+AGENT_MODELS = [
+    {
+        "key": "minicpm5-1b",
+        "label": "MiniCPM5 1B",
+        "model_id": MINICPM_AGENT_MODEL_ID,
+        "revision": MINICPM_AGENT_REVISION,
+        "thinking": True,
+    },
+    {
+        "key": "qwen3-1.7b",
+        "label": "Qwen3 1.7B",
+        "model_id": "Qwen/Qwen3-1.7B",
+        "revision": None,
+        "thinking": True,
+    },
+    {
+        "key": "qwen3-0.6b",
+        "label": "Qwen3 0.6B",
+        "model_id": "Qwen/Qwen3-0.6B",
+        "revision": None,
+        "thinking": True,
+    },
+    {
+        "key": "tiny-aya",
+        "label": "Tiny Aya 3.35B (Cohere)",
+        "model_id": "CohereLabs/tiny-aya-global",
+        "revision": None,
+        "thinking": False,
+    },
+]
+DEFAULT_AGENT_MODEL = AGENT_MODELS[0]["key"]
 # A tool-call decision is short JSON; a rerank reply is a single number. 96 was
 # too tight — the 1B writes verbose search queries and was getting CUT OFF
 # mid-string (unterminated JSON → parse fail → wasted retry), seen live.

core/pdf.py

@@ -57,9 +57,9 @@ def pdf_outline(pdf_path: str) -> list[dict]:
     doc = fitz.open(pdf_path)
     try:
         entries = [
-            {"title": " ".join(title.split()), "page_start": max(1, page)}
+            {"title": " ".join(title.split()), "page_start": page}
             for _, title, page in doc.get_toc()
-            if title.strip()
+            if title.strip() and page >= 1
         ]
         for i, e in enumerate(entries):
             e["page_end"] = (

frontend/index.html

@@ -345,6 +345,21 @@
                class="w-full accent-brand-600">
       </div>
 
+      <!-- agent brain: which model drives the find-and-point loop -->
+      <div class="mt-5">
+        <label class="block text-xs font-semibold uppercase tracking-wide text-brand-500/80 mb-1.5">Agent brain</label>
+        <div class="relative">
+          <select x-model="agentModel"
+            class="w-full appearance-none rounded-xl border border-brand-200 bg-brand-50/50 px-3.5 py-2.5 pr-9 text-sm font-medium text-navy focus:border-brand-400 focus:ring-2 focus:ring-brand-100 outline-none transition">
+            <template x-for="m in agentModels" :key="m.key">
+              <option :value="m.key" x-text="m.label"></option>
+            </template>
+          </select>
+          <i data-lucide="chevron-down" class="pointer-events-none absolute right-3 top-1/2 -translate-y-1/2 w-4 h-4 text-brand-400"></i>
+        </div>
+        <p class="mt-1 text-xs text-brand-400">The model that finds pages and points. Switching loads it fresh — the first turn after a change is slower.</p>
+      </div>
+
       <!-- think: let the VLM reason before committing to a circle box -->
       <div class="mt-5 flex items-start justify-between gap-3">
         <div>
@@ -396,6 +411,11 @@ function repairGuy() {
     // helps diagram-callout targets). Sent as `think` with each /find turn;
     // initialized from the server default.
     think:__GROUND_THINK__,
+    // The agent "brain" picker: the selectable models (key+label) and the
+    // current choice, sent as `agent_model` each turn. Switching one in evicts
+    // the previous from VRAM server-side (load-on-switch), so the FIRST turn
+    // after a change is slower while the new model loads.
+    agentModels:__AGENT_MODELS_JSON__, agentModel:'__AGENT_MODEL__',
     input:'', loading:false, status:'',
     settingsOpen:false,
     ICONS,
@@ -486,7 +506,7 @@ function repairGuy() {
 
     // --- viewer state machine -----------------------------------------
     sectionTitle(p){ let t=''; for(const s of this.sections){ if(s.page_start<=p) t=s.title; else break; } return t; },
-    sectionIndex(p){ let i=-1; this.sections.forEach((s,j)=>{ if(s.page_start<=p) i=j; }); return i; },
+    sectionIndex(p){ let i=-1; for(let j=0;j<this.sections.length;j++){ if(this.sections[j].page_start<=p) i=j; else break; } return i; },
     tocActive(j){ return j===this.sectionIndex(this.viewPage); },
 
     setPage(p, push=false){
@@ -645,7 +665,7 @@ function repairGuy() {
         mono:this.history.map((h,i)=>`${i+1}. asked: ${h.request}\n   did:   ${h.action}`).join('\n')});
       try{
         const job = this.client.submit('/find', {
-          request:q, manual:this.manual, k:this.k, think:this.think,
+          request:q, manual:this.manual, k:this.k, think:this.think, agent_model:this.agentModel,
           page: this.viewDoc ? this.viewPage : 0, section: this.sectionTitle(this.viewPage),
           pages: this.spreadPages,  // every page currently on screen — the agent may circle on any
           history: this.history,
@@ -776,6 +796,14 @@ function repairGuy() {
       //    loaded as injected <script>s so they stay out of initial page load.
       //    Single-threaded → no SharedArrayBuffer, so no cross-origin isolation
       //    is needed inside the HF Spaces iframe.
+      //
+      //    VERSION COUPLING: vad/bundle.min.js has its OWN onnxruntime-web baked
+      //    in (currently 1.22.0) and fetches the wasm binary from onnxWASMBasePath
+      //    below (/vendor/onnx/). The .wasm/.mjs files there MUST match that ORT
+      //    version — a mismatch dies at session creation with cryptic glue errors
+      //    ("t.getValue is not a function" was 1.22 JS + 1.19 wasm). If you ever
+      //    re-vendor vad's bundle, re-pull onnxruntime-web@<same-version>/dist/
+      //    {ort-wasm-simd-threaded.wasm,ort-wasm-simd-threaded.mjs,ort.wasm.min.js}.
       await this.loadScript('/vendor/onnx/ort.wasm.min.js');
       window.ort.env.wasm.numThreads = 1;
       window.ort.env.wasm.wasmPaths = '/vendor/onnx/';

frontend/vendor/onnx/ort-wasm-simd-threaded.mjs

@@ -1,4 +1,3 @@
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+var isPthread = globalThis.self?.name?.startsWith('em-pthread');
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 if (isNode) isPthread = (await import('worker_threads')).workerData === 'em-pthread';
 

frontend/vendor/onnx/ort-wasm-simd-threaded.wasm

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-size 11018731
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+size 11210254

frontend/vendor/onnx/ort.wasm.min.js

@@ -1,8 +1,8 @@
 /*!
- * ONNX Runtime Web v1.19.2
+ * ONNX Runtime Web v1.22.0
  * Copyright (c) Microsoft Corporation. All rights reserved.
  * Licensed under the MIT License.
  */
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models/minicpm_agent.py

@@ -32,7 +32,9 @@ and the message builders live here so the wording stays with the model.
 
 from __future__ import annotations
 
+import gc
 import json
+import logging
 import re
 
 import torch
@@ -40,10 +42,12 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 
 from core.constants import (
     AGENT_MAX_NEW_TOKENS,
-    MINICPM_AGENT_MODEL_ID,
-    MINICPM_AGENT_REVISION,
+    AGENT_MODELS,
+    DEFAULT_AGENT_MODEL,
 )
 
+log = logging.getLogger("repairguy.agent")
+
 # The tools the agent may emit, and the JSON shape of each. Kept here so the
 # prompt and the parser can't drift apart.
 TOOLS = ("search", "find_answer", "go_to_section", "go_to_page", "circle", "done")
@@ -228,31 +232,81 @@ def assistant_action_message(tool: dict) -> dict:
     return {"role": "assistant", "content": json.dumps(tool, separators=(",", ":"))}
 
 
-_TOKENIZER = AutoTokenizer.from_pretrained(
-    MINICPM_AGENT_MODEL_ID, revision=MINICPM_AGENT_REVISION
-)
-_MODEL = (
-    AutoModelForCausalLM.from_pretrained(
-        MINICPM_AGENT_MODEL_ID,
-        revision=MINICPM_AGENT_REVISION,
-        dtype=torch.bfloat16,
-        attn_implementation="sdpa",
+# --- the resident brain: ONE model in VRAM, swapped on demand ---------------
+# The agent model is selectable from the UI (core.constants.AGENT_MODELS). Only
+# one is held on the GPU at a time: use_model() evicts the current one before
+# loading the next ("load-on-switch"), so VRAM stays flat as the user A/Bs
+# models. The model/tokenizer live in module globals for the same ZeroGPU reason
+# as the other models — module-level CUDA tensors are shared with the GPU worker.
+_REGISTRY = {m["key"]: m for m in AGENT_MODELS}
+_active_key: str | None = None
+_MODEL = None
+_TOKENIZER = None
+# Whether the active model's chat template accepts enable_thinking (Qwen3 /
+# MiniCPM yes, Cohere no) — drives whether we pass the kwarg below.
+_THINKING = False
+
+
+def _spec(key: str | None) -> dict:
+    return _REGISTRY.get(key or "", _REGISTRY[DEFAULT_AGENT_MODEL])
+
+
+def use_model(key: str | None = None) -> str:
+    """Make `key` the resident agent brain, loading it (and evicting the
+    previous one) when it isn't already active — one model in VRAM at a time.
+    Unknown keys fall back to the default. Called once per turn by the pipeline,
+    inside the GPU context. Returns the active key. Must run on GPU."""
+    global _active_key, _MODEL, _TOKENIZER, _THINKING
+    spec = _spec(key)
+    if spec["key"] == _active_key and _MODEL is not None:
+        return _active_key
+    # Drop the current model first so VRAM holds only one brain at a time.
+    if _MODEL is not None:
+        log.info("agent model: evicting %s", _active_key)
+        _MODEL = _TOKENIZER = None
+        gc.collect()
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+    log.info("agent model: loading %s (%s)", spec["key"], spec["model_id"])
+    _TOKENIZER = AutoTokenizer.from_pretrained(
+        spec["model_id"], revision=spec["revision"]
     )
-    .to("cuda")
-    .eval()
-)
+    _MODEL = (
+        AutoModelForCausalLM.from_pretrained(
+            spec["model_id"],
+            revision=spec["revision"],
+            dtype=torch.bfloat16,
+            attn_implementation="sdpa",
+        )
+        .to("cuda")
+        .eval()
+    )
+    _active_key, _THINKING = spec["key"], spec["thinking"]
+    return _active_key
+
+
+# Load the default brain eagerly at import — same as the other models, so the
+# ZeroGPU startup packing covers it and the common (no-switch) path pays no
+# load cost on the first turn.
+use_model(DEFAULT_AGENT_MODEL)
+
+
+def _template_kwargs() -> dict:
+    """Extra apply_chat_template kwargs for the active model. Tool routing wants
+    a terse decision, not a reasoning trace, so disable thinking — but only for
+    templates that accept the kwarg (others would ignore or choke on it)."""
+    return {"enable_thinking": False} if _THINKING else {}
 
 
 def _generate(messages: list[dict], max_new_tokens: int) -> str:
-    """Greedy decode the assistant's next message. enable_thinking=False — tool
-    routing wants a terse decision, not a reasoning trace."""
+    """Greedy decode the assistant's next message."""
     inputs = _TOKENIZER.apply_chat_template(
         messages,
         tokenize=True,
         add_generation_prompt=True,
-        enable_thinking=False,
         return_dict=True,
         return_tensors="pt",
+        **_template_kwargs(),
     ).to(_MODEL.device)
     # apply_chat_template emits token_type_ids, which this LlamaForCausalLM's
     # generate() rejects as an unused kwarg.
@@ -274,7 +328,7 @@ def render_prompt(messages: list[dict]) -> str:
         messages,
         tokenize=False,
         add_generation_prompt=True,
-        enable_thinking=False,
+        **_template_kwargs(),
     )
 
 

pipelines/agent_ask.py

@@ -88,11 +88,18 @@ def agent_events(
     viewer: dict | None = None,
     history: list | None = None,
     ground_thinking: bool | None = None,
+    agent_model: str | None = None,
 ):
     """Yield the events of one agent turn (see module docstring). sections is the
     numbered table of contents shown to the agent ([{title, page}]); the agent's
     go_to_section index is 1-based into it. ground_thinking toggles MiniCPM-V's
-    reasoning for the circle grounding (None → server default)."""
+    reasoning for the circle grounding (None → server default); agent_model picks
+    which brain drives the loop (None → default), loaded on switch inside this
+    GPU window."""
+    # Swap in the selected brain (evicts the previous one) before any decide/
+    # rerank. Inside this @spaces.GPU window, so the load happens on the GPU.
+    active = minicpm_agent.use_model(agent_model)
+    log.info("agent brain: %s", active)
     doc_id = doc_ids[0]
     manual = names[doc_id]
     viewer = viewer or {}
@@ -335,6 +342,7 @@ class AgentPipeline:
         viewer: dict | None = None,
         history: list | None = None,
         ground_thinking: bool | None = None,
+        agent_model: str | None = None,
     ):
         """One streamed agent turn (the event generator of agent_events)."""
         request = (request or "").strip()
@@ -347,4 +355,5 @@ class AgentPipeline:
         return agent_events(
             request, visual_store, parsed_store, doc_ids or list(names),
             int(top_k), names, sections, viewer, history, ground_thinking,
+            agent_model,
         )

pipelines/mock_ask.py

@@ -144,11 +144,12 @@ class MockAskPipeline:
         viewer: dict | None = None,
         history: list | None = None,
         ground_thinking: bool | None = None,
+        agent_model: str | None = None,
     ):
         """Yield the same event sequence as pipelines/agent_ask.py, with a
         keyword-driven stand-in for the agent's tool choice. Both stores are the
-        one MockStore; history and ground_thinking are ignored. MOCK_DELAY
-        (seconds) paces events."""
+        one MockStore; history, ground_thinking and agent_model are ignored.
+        MOCK_DELAY (seconds) paces events."""
         request = (request or "").strip()
         if not request:
             raise ValueError("Tell me what to find.")