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refactor(agent): apply model-source data without an LLM

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`_build_prompt_context()` was only reached when the LLM was configured,
so a user with no provider got nothing at all from a linked model source
— no preview, no example images, no author summary, no tags — even
though all of that is deterministic data from a public API.

Split the model-card fetch into `_load_source_card()`, which runs for
every source-backed enrichment, and have the post-processor apply its
result whether or not the LLM runs. The prompt is then built from the
already-fetched card rather than re-fetching it.

Invoking "Enrich Metadata with AI" still always calls the provider; a
model source supplying a description, images and tags is not treated as
a reason to skip it, since the LLM's summary and notes are richer and an
action that silently does not call out to the provider would be
unpredictable. The site data acts as a fallback for the gaps the LLM
leaves.

Add `base_model_resolver.resolve_base_model()` to map the site's own
names (`krea/Krea-2-Turbo`, `KREA_2_TURBO`) onto the canonical
vocabulary, used only when the LLM returns no base model. It is strictly
conservative — exact normalised matching plus a bounded set of variant
suffixes, and it only ever returns a name that is already in the
vocabulary — so an uncertain hint defers to the LLM instead of writing a
plausible-looking wrong value.
This commit is contained in:
Will Miao
2026-09-14 20:39:10 +08:00
parent 35b291ab19
commit 4064ea7d3a
5 changed files with 756 additions and 23 deletions
Download Patch File Download Diff File Expand all files Collapse all files
py/services/agent/agent_service.py
+134 -18
View File
@@ -27,11 +27,13 @@ from typing import Any, Dict, List, Optional
from ...config import config
from ..llm_service import LLMService
from ..model_sources import (
ModelCardContext,
get_source,
resolve_source_ref,
source_label,
)
from ..websocket_manager import ws_manager
from .base_model_resolver import resolve_base_model
from .post_processor import PostProcessor
from .skill_registry import SkillRegistry
from .skills.enrich_hf_metadata.readme_processor import (
@@ -282,14 +284,37 @@ class AgentService:
skip_model = True
if not skip_model:
prompt_vars: Dict[str, Any] = {"model_path": model_path}
if skill.llm_required and llm_configured:
prompt_vars = await self._build_prompt_context(
skill_name, model_path, metadata, registry, llm,
# The site's own data is deterministic and must land whether
# or not an LLM is available: a user without a key still gets
# the author summary, the example images and the tags.
source_vars, source_context = await self._load_source_card(
model_path, metadata,
)
resolved_base_model = ""
if skill_name == "enrich_hf_metadata" and not (
metadata.get("base_model") or ""
).strip():
resolved_base_model = await self._resolve_site_base_model(
source_context,
)
llm_response: Optional[Dict[str, Any]] = None
if skill.llm_required and llm_configured:
if skill.llm_required and not llm_configured:
# Without a provider the deterministic model-source data
# still lands; the LLM-only fields simply stay untouched.
logger.info(
"[%s] No LLM configured for %s — applying %s data only",
skill_name, model_filename,
"model-source"
if not source_context.is_empty()
else "README",
)
elif skill.llm_required:
prompt_vars = await self._build_prompt_context(
skill_name, model_path, metadata, registry, llm,
source_vars=source_vars,
source_context=source_context,
)
prompt_template = registry.load_prompt(skill_name)
rendered = _render_prompt(prompt_template, prompt_vars)
llm_response = await llm.chat_completion_json(
@@ -312,7 +337,9 @@ class AgentService:
model_path=model_path,
llm_output=llm_response or {},
metadata=metadata,
readme_content=prompt_vars.get("readme_content_full", ""),
readme_content=source_vars.get("readme_content_full", ""),
source_context=source_context,
resolved_base_model=resolved_base_model,
)
if model_result.get("success", True):
@@ -395,6 +422,77 @@ class AgentService:
"""
return "\n".join(f"- {m}" for m in models)
async def _load_source_card(
self, model_path: str, metadata: Dict[str, Any]
) -> tuple[Dict[str, Any], ModelCardContext]:
"""Fetch the model card and site-published extras for one model.
Runs for every source-backed enrichment regardless of LLM
availability, because everything it returns is deterministic data that
should be applied even without a configured provider.
"""
variables: Dict[str, Any] = {
"asset_base_url": "",
"source_description": "",
"source_base_model": "",
"source_official_tags": "",
"source_example_images": "",
"source_trigger_words": "",
"readme_content": "(README not available)",
"readme_content_full": "",
}
ref = resolve_source_ref(metadata)
source = get_source(ref.platform) if ref is not None else None
if ref is None or source is None or not source.supports_enrichment:
return variables, ModelCardContext()
raw_basename = os.path.splitext(os.path.basename(model_path))[0]
variables["asset_base_url"] = source.asset_base_url(ref.source_id)
readme = await source.fetch_model_card(ref.source_id)
# Sites such as ModelScope keep part of the model card outside the
# README (author summary, curated tags, per-file example images).
card_context = await source.fetch_model_card_context(
ref.source_id, os.path.basename(model_path)
)
variables["source_description"] = card_context.description
variables["source_base_model"] = card_context.base_model
variables["source_official_tags"] = "\n".join(
f"- {tag}" for tag in card_context.official_tags
)
variables["source_example_images"] = "\n".join(
f"- {url}" for url in card_context.example_images
)
variables["source_trigger_words"] = ", ".join(card_context.trigger_words)
# Trim README to the section relevant to this model file
# (collection repos often have multiple models in one README).
if readme and raw_basename:
trimmed = extract_relevant_section(readme, raw_basename)
cleaned = clean_readme_for_llm(trimmed) if trimmed else ""
else:
cleaned = clean_readme_for_llm(readme) if readme else ""
variables["readme_content"] = cleaned if cleaned else "(README not available)"
variables["readme_content_full"] = readme or ""
return variables, card_context
async def _resolve_site_base_model(self, source_context: ModelCardContext) -> str:
"""Resolve the site's base-model hints to a canonical name, or ``""``."""
from ...metadata_ops import list_base_models
hints = [*source_context.base_model_aliases, source_context.base_model]
if not any(hints):
return ""
try:
known_names = await list_base_models()
except Exception as exc:
logger.debug("Failed to list base models for site resolution: %s", exc)
return ""
return resolve_base_model(hints, known_names)
async def _build_prompt_context(
self,
skill_name: str,
@@ -402,16 +500,25 @@ class AgentService:
metadata: Dict[str, Any],
registry: SkillRegistry,
llm: Any,
*,
source_vars: Optional[Dict[str, Any]] = None,
source_context: Optional[ModelCardContext] = None,
) -> Dict[str, Any]:
"""Gather variables for the skill's prompt template.
Reads metadata, fetches the HF README (if applicable), lists available
Reads metadata, fetches the model card (unless a pre-fetched
*source_vars* / *source_context* pair is supplied), lists available
base models, loads user priority tags, and returns a dict that maps to
``{{variable}}`` placeholders in ``prompt.md``.
"""
from ...metadata_ops import identify_model_type, list_base_models
from ..settings_manager import SettingsManager
if source_vars is None or source_context is None:
source_vars, source_context = await self._load_source_card(
model_path, metadata,
)
context: Dict[str, Any] = {
"model_path": model_path,
"model_basename": "",
@@ -421,6 +528,15 @@ class AgentService:
"source_platform": "",
"source_label": "",
"asset_base_url": "",
# Site-provided card extras (see ModelSource.fetch_model_card_context)
"source_description": "",
"source_base_model": "",
"source_official_tags": "",
"source_example_images": "",
"source_trigger_words": "",
# Carrier for the structured context handed to the post-processor;
# never rendered into the prompt.
"source_context": ModelCardContext(),
# Legacy Hugging Face aliases (kept so older prompt templates and
# third-party skills keep rendering)
"hf_url": "",
@@ -458,17 +574,17 @@ class AgentService:
source = get_source(ref.platform) if ref is not None else None
if ref is not None and source is not None and source.supports_enrichment:
context["asset_base_url"] = source.asset_base_url(ref.source_id)
readme = await source.fetch_model_card(ref.source_id)
# Trim README to the section relevant to this model file
# (collection repos often have multiple models in one README).
if readme and raw_basename:
trimmed = extract_relevant_section(readme, raw_basename)
cleaned = clean_readme_for_llm(trimmed) if trimmed else ""
else:
cleaned = clean_readme_for_llm(readme) if readme else ""
context["readme_content"] = cleaned if cleaned else "(README not available)"
context["readme_content_full"] = readme or ""
# Values fetched once by _load_source_card and shared with the
# post-processor, so the network is not hit twice per model.
context["asset_base_url"] = source_vars["asset_base_url"]
context["source_context"] = source_context
context["source_description"] = source_vars["source_description"]
context["source_base_model"] = source_vars["source_base_model"]
context["source_official_tags"] = source_vars["source_official_tags"]
context["source_example_images"] = source_vars["source_example_images"]
context["source_trigger_words"] = source_vars["source_trigger_words"]
context["readme_content"] = source_vars["readme_content"]
context["readme_content_full"] = source_vars["readme_content_full"]
try:
raw_models = await list_base_models()
py/services/agent/base_model_resolver.py
+94
View File
@@ -0,0 +1,94 @@
"""Map a site-reported base model onto this system's canonical vocabulary.
Model sites name base models in their own terms: ModelScope publishes
``krea/Krea-2-Turbo`` and ``KREA_2_TURBO`` where this system expects the
canonical ``Krea 2``. Turning one into the other is normally the LLM's job;
this module resolves the cases that can be decided safely so the canonical
field is still populated when the LLM returns nothing usable for it.
The resolver is deliberately strict, because a wrong base model written with
apparent authority is worse than no value at all:
* it only ever returns a name that is already present in *known_names*;
* matching is on the normalised form (lowercased, non-alphanumerics removed),
so separators and casing are ignored but nothing is inferred;
* a bounded set of published variant suffixes may be stripped, and only when
the remainder still matches a known name exactly.
Anything it cannot decide returns ``""``, and the caller falls back to the LLM.
"""
from __future__ import annotations
import re
from typing import Iterable, Sequence
#: Variant suffixes sites append to a base-model *family* name. Stripping one
#: is only attempted when the remainder matches a known name exactly, so an
#: unrecognised suffix can never produce a bogus match.
_VARIANT_SUFFIXES: tuple[str, ...] = (
"turbo",
"schnell",
"lightning",
"dev",
"beta",
"alpha",
)
_NON_ALNUM = re.compile(r"[^a-z0-9]+")
def _normalize(value: str) -> str:
"""Return the comparison form of *value*.
Lowercases and drops every non-alphanumeric character, so ``KREA_2``,
``Krea 2``, ``krea-2`` and ``krea.2`` all collapse to ``krea2``.
"""
return _NON_ALNUM.sub("", (value or "").lower())
def resolve_base_model(
hints: Iterable[str], known_names: Sequence[str]
) -> str:
"""Return the canonical base model that *hints* refers to, or ``""``.
Args:
hints: Site-reported names, best first (e.g. an architecture enum
before a link-style repository id).
known_names: The canonical vocabulary; only these are ever returned.
Returns:
One of *known_names*, or ``""`` when nothing matches exactly.
"""
normalized: dict[str, str] = {}
for name in known_names:
key = _normalize(name)
if key and key not in normalized:
normalized[key] = name
if not normalized:
return ""
ordered = [hint for hint in hints if hint]
# 1. Exact normalised match — the unambiguous case.
for hint in ordered:
candidate = _normalize(hint)
if candidate in normalized:
return normalized[candidate]
# 2. Drop one published variant suffix and retry exactly.
for hint in ordered:
candidate = _normalize(hint)
for suffix in _VARIANT_SUFFIXES:
if not candidate.endswith(suffix) or candidate == suffix:
continue
stem = candidate[: -len(suffix)]
if stem in normalized:
return normalized[stem]
return ""
__all__ = ["resolve_base_model"]