Ty
8a8a89ed3e
- backend/inference.py: prompt seed (rev 0) + append-only revision REST
(POST /prompts/{call}/revisions, /activate, GET; METADATA-105/106). Call 1 vision
classification (1024px processed photos + synced taxonomy, 0.7 confidence gate,
METADATA-35/40/45), Call 2 long description (user-triggered, METADATA-55/60/65),
inference_log per call (METADATA-66), inference-off server_description (METADATA-70),
test-inference probe (SETTINGS-45). Stdlib urllib, no new deps.
- frontend: create/inference/GenerateText.svelte (control visible only when content
enabled + endpoint reachable, METADATA-110; Call 2 + Call 1 buttons) + dev toggle.
- tests: 6 inference tests (seed+revision, call1 gate, call2 stores desc, off->409,
server desc, test-inference unconfigured).
Verified: pytest 54 passed; gates exit 0; npm build ok.
365 lines
16 KiB
Python
365 lines
16 KiB
Python
"""Inference (S7): prompt revisions (REST), Call 1 vision classification, Call 2
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content prose, test-inference, inference-off server description, inference_log.
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Cites METADATA-35..115, SETTINGS-30/45/90.
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"""
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import base64
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import hashlib
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import json
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import time
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import urllib.error
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import urllib.request
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from typing import Any
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from fastapi import APIRouter
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from fastapi.responses import JSONResponse
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from .db import get_connection
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from .crypto import decrypt
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router = APIRouter(prefix="/api/v1")
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CALL1_SEED = (
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"Classify the slab's species, wood categories, edge type, figure, and grade from the "
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"supplied photos, choosing only from the supplied synced taxonomy lists, and return the "
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"result fields as JSON: species_id, wood_category_ids, edge_type_term_id, figure_term_ids, "
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"grade_term_id, character, inclusions, voids, checks. Report a confidence (0-1) for each "
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"classified field. Map free-text observations to the supplied feature-tag list only. "
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"Invent no category or term."
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)
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CALL2_SEED = (
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"Write the long product description for the slab from the supplied photos and listing "
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"facts. The name and short description are already built, so do not repeat them. Use the "
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"brand voice and geo context given. The dimensions in the fact block are exact: use those "
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"strings and invent no numbers. Write prose a premium lumber buyer would read."
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)
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_SEEDS = {"call1": CALL1_SEED, "call2": CALL2_SEED}
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_THRESHOLD = 0.7
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def _error(code: str, message: str, detail: Any = None) -> dict:
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return {"error": {"code": code, "message": message, "detail": detail}}
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def _now() -> str:
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from datetime import datetime, timezone
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return datetime.now(timezone.utc).replace(microsecond=0, tzinfo=None).isoformat()
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def _hash(text: str) -> str:
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return hashlib.sha256(text.encode("utf-8")).hexdigest()
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def _seed_prompts(conn) -> None:
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for name, seed in _SEEDS.items():
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if conn.execute("SELECT 1 FROM prompts WHERE name=?", (name,)).fetchone():
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continue
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cur = conn.execute(
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"INSERT INTO prompts(name, active_revision_id, created_at, updated_at) VALUES (?,?,?,?)",
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(name, None, _now(), _now()),
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)
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pid = cur.lastrowid
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cur2 = conn.execute(
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"INSERT INTO prompt_revisions(prompt_id, content, content_hash, created_at, created_by) VALUES (?,?,?,?,?)",
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(pid, seed, _hash(seed), _now(), "seed"),
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)
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conn.execute("UPDATE prompts SET active_revision_id=? WHERE id=?", (cur2.lastrowid, pid))
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def _active_prompt(conn, call: str) -> tuple[int, str] | None:
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row = conn.execute(
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"SELECT p.id, pr.id, pr.content FROM prompts p LEFT JOIN prompt_revisions pr ON p.active_revision_id=pr.id WHERE p.name=?",
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(call,),
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).fetchone()
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if row is None or row[2] is None:
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return None
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return row[1], row[2]
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# ---------- Prompt revision REST (METADATA-106) ----------
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@router.post("/prompts/{call}/revisions", status_code=201)
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async def append_revision(call: str, payload: dict[str, Any]) -> dict:
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if call not in _SEEDS:
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return JSONResponse(status_code=404, content=_error("not_found", "unknown call"))
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content = payload.get("content")
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if not content:
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return JSONResponse(status_code=400, content=_error("validation", "content required"))
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conn = get_connection()
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_seed_prompts(conn)
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prow = conn.execute("SELECT id FROM prompts WHERE name=?", (call,)).fetchone()
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cur = conn.execute(
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"INSERT INTO prompt_revisions(prompt_id, content, content_hash, created_at, created_by) VALUES (?,?,?,?,?)",
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(prow[0], content, _hash(content), _now(), "api"),
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)
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conn.execute("UPDATE prompts SET active_revision_id=?, updated_at=? WHERE id=?", (cur.lastrowid, _now(), prow[0]))
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conn.commit()
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return {"id": cur.lastrowid, "prompt": call, "content_hash": _hash(content)}
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@router.post("/prompts/{call}/activate")
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async def activate_revision(call: str, payload: dict[str, Any]) -> dict:
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if call not in _SEEDS:
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return JSONResponse(status_code=404, content=_error("not_found", "unknown call"))
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rev_id = payload.get("revision_id")
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conn = get_connection()
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_seed_prompts(conn)
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prow = conn.execute("SELECT id FROM prompts WHERE name=?", (call,)).fetchone()
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row = conn.execute(
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"SELECT id FROM prompt_revisions WHERE id=? AND prompt_id=?", (rev_id, prow[0])
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).fetchone()
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if row is None:
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return JSONResponse(status_code=404, content=_error("not_found", "revision not found"))
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conn.execute("UPDATE prompts SET active_revision_id=?, updated_at=? WHERE id=?", (rev_id, _now(), prow[0]))
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conn.commit()
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return {"ok": True, "active_revision_id": rev_id}
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@router.get("/prompts/{call}")
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async def get_prompt(call: str) -> dict:
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if call not in _SEEDS:
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return JSONResponse(status_code=404, content=_error("not_found", "unknown call"))
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conn = get_connection()
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_seed_prompts(conn)
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prow = conn.execute("SELECT * FROM prompts WHERE name=?", (call,)).fetchone()
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revs = conn.execute(
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"SELECT id, content_hash, created_at, created_by FROM prompt_revisions WHERE prompt_id=? ORDER BY id",
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(prow[0],),
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).fetchall()
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active = conn.execute(
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"SELECT content FROM prompt_revisions WHERE id=?", (prow["active_revision_id"],)
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).fetchone()
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return {
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"name": call,
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"active_revision_id": prow["active_revision_id"],
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"active_content": active[0] if active else None,
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"revisions": [dict(r) for r in revs],
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}
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# ---------- Inference client (OpenAI-compatible, stdlib) ----------
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class InferenceClient:
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def __init__(self, base_url: str, api_key: str, model: str, timeout: float = 60.0):
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self.base_url = base_url.rstrip("/")
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self.api_key = api_key
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self.model = model
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self.timeout = timeout
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def chat(self, messages: list[dict]) -> str:
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url = f"{self.base_url}/chat/completions"
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body = json.dumps({"model": self.model, "messages": messages}).encode("utf-8")
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req = urllib.request.Request(
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url, data=body,
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headers={"Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json"},
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method="POST",
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)
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try:
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with urllib.request.urlopen(req, timeout=self.timeout) as resp:
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data = json.loads(resp.read().decode("utf-8"))
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except urllib.error.HTTPError as e:
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raise InferenceError(f"inference HTTP {e.code}") from e
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except urllib.error.URLError as e:
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raise InferenceConnectionError(f"inference unreachable: {e.reason}") from e
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return data["choices"][0]["message"]["content"]
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class InferenceError(Exception):
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pass
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class InferenceConnectionError(InferenceError):
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pass
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def _inference_creds(conn) -> tuple[str, str, str]:
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b = conn.execute("SELECT value_enc FROM settings WHERE key='inference_base_url'").fetchone()
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k = conn.execute("SELECT value_enc FROM settings WHERE key='inference_api_key'").fetchone()
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m = conn.execute("SELECT value FROM settings WHERE key='inference_model'").fetchone()
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return (decrypt(b[0]) if b and b[0] else ""), (decrypt(k[0]) if k and k[0] else ""), (m[0] if m and m[0] else "")
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def _enabled(conn, key: str) -> bool:
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row = conn.execute("SELECT value FROM settings WHERE key=?", (key,)).fetchone()
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return bool(row and row[0] in ("true", "True", "1"))
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@router.post("/slabs/{slab_id}/inference/call1")
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async def call1(slab_id: str) -> dict:
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conn = get_connection()
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row = conn.execute("SELECT * FROM slabs WHERE id=? AND deleted_at IS NULL", (slab_id,)).fetchone()
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if row is None:
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return JSONResponse(status_code=404, content=_error("not_found", "Slab not found"))
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if not _enabled(conn, "inference_enabled"):
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return JSONResponse(status_code=409, content=_error("inference_disabled", "Inference is off"))
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base, key, model = _inference_creds(conn)
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if not base or not key:
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return JSONResponse(status_code=409, content=_error("inference_unconfigured", "Inference is not configured"))
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client = InferenceClient(base, key, model)
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act = _active_prompt(conn, "call1")
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prompt_text = act[1] if act else CALL1_SEED
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# Build vision message with processed photos (1024px) + taxonomy lists.
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photos = conn.execute(
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"SELECT processed_blob FROM slab_photos WHERE slab_id=? AND processed_blob IS NOT NULL ORDER BY seq LIMIT 8",
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(slab_id,),
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).fetchall()
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image_parts = []
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for p in photos:
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image_parts.append({
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"type": "image_url",
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"image_url": {"url": f"data:image/png;base64,{base64.b64encode(p[0]).decode()}"},
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})
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tax = _taxonomy_lists(conn)
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text = (f"{prompt_text}\n\nSYNCED TAXONOMY (only legal choices):\n{json.dumps(tax)}\n\n"
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f"SKU={row['sku']} length_in={row['length_in']} thickness_in={row['thickness_in']}")
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messages = [{"role": "user", "content": image_parts + [{"type": "text", "text": text}]}]
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t0 = time.time()
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try:
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raw = client.chat(messages)
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except InferenceConnectionError as e:
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return JSONResponse(status_code=503, content=_error("inference_unreachable", str(e), {"recoverable": True}))
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except InferenceError as e:
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return JSONResponse(status_code=502, content=_error("inference_failed", str(e), {"recoverable": True}))
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latency = int((time.time() - t0) * 1000)
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parsed = _parse_json(raw)
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_log_call(conn, slab_id, 1, act[0] if act else None, model, messages, raw, latency)
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# Apply confidence gate (METADATA-45).
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suggestions = _gate(parsed)
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return {"ok": True, "suggestions": suggestions}
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def _taxonomy_lists(conn) -> dict:
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out = {"species_leaves": [], "edge_terms": [], "figure_terms": [], "grade_terms": [],
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"thickness_terms": [], "moisture_terms": [], "feat_tags": []}
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for r in conn.execute("SELECT id,name FROM woo_categories WHERE parent_id IS NULL").fetchall():
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out["species_leaves"].append({"id": r[0], "name": r[1]})
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for attr, key in (("Edge Type", "edge_terms"), ("Figure", "figure_terms"), ("Grade", "grade_terms"),
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("Thickness", "thickness_terms"), ("Moisture", "moisture_terms")):
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rows = conn.execute(
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"SELECT t.id,t.name FROM woo_attribute_terms t JOIN woo_attributes a ON t.attribute_id=a.id WHERE a.name=?",
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(attr,),
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).fetchall()
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out[key] = [{"id": r[0], "name": r[1]} for r in rows]
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for r in conn.execute("SELECT id,name FROM woo_tags WHERE slug LIKE 'feat-%'").fetchall():
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out["feat_tags"].append({"id": r[0], "name": r[1]})
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return out
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def _parse_json(raw: str) -> dict:
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try:
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s = raw.strip()
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if s.startswith("```"):
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s = s.split("```")[1]
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if s.startswith("json"):
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s = s[4:]
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return json.loads(s)
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except (ValueError, IndexError):
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return {}
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def _gate(parsed: dict) -> dict:
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"""Apply the 0.7 confidence gate; below-threshold fields are omitted (METADATA-45)."""
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def keep(field: str, conf_key: str) -> Any:
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conf = parsed.get(conf_key)
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if isinstance(conf, (int, float)) and conf >= _THRESHOLD and field in parsed:
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return parsed[field]
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return None
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out: dict[str, Any] = {}
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if v := keep("species_id", "species_confidence"):
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out["species_id"] = v
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if v := keep("wood_category_ids", "wood_category_confidence"):
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out["wood_category_ids"] = v
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if v := keep("edge_type_term_id", "edge_confidence"):
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out["edge_type_term_id"] = v
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if v := keep("figure_term_ids", "figure_confidence"):
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out["figure_term_ids"] = v
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if v := keep("grade_term_id", "grade_confidence"):
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out["grade_term_id"] = v
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for k in ("character", "inclusions", "voids", "checks"):
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if parsed.get(k):
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out[k] = parsed[k]
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return out
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@router.post("/slabs/{slab_id}/inference/call2")
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async def call2(slab_id: str) -> dict:
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conn = get_connection()
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row = conn.execute("SELECT * FROM slabs WHERE id=? AND deleted_at IS NULL", (slab_id,)).fetchone()
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if row is None:
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return JSONResponse(status_code=404, content=_error("not_found", "Slab not found"))
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if not _enabled(conn, "content_llm_enabled"):
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return JSONResponse(status_code=409, content=_error("content_disabled", "Content generation is off"))
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base, key, model = _inference_creds(conn)
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if not base or not key:
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return JSONResponse(status_code=409, content=_error("inference_unconfigured", "Inference is not configured"))
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client = InferenceClient(base, key, model)
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act = _active_prompt(conn, "call2")
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prompt_text = act[1] if act else CALL2_SEED
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photos = conn.execute(
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"SELECT processed_blob FROM slab_photos WHERE slab_id=? AND processed_blob IS NOT NULL ORDER BY seq LIMIT 8",
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(slab_id,),
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).fetchall()
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image_parts = [
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{"type": "image_url", "image_url": {"url": f"data:image/png;base64,{base64.b64encode(p[0]).decode()}"}}
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for p in photos
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]
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brand = conn.execute("SELECT value FROM settings WHERE key='brand_voice'").fetchone()
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geo = conn.execute("SELECT value FROM settings WHERE key='geo_context'").fetchone()
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text = (f"{prompt_text}\n\nDIMENSION FACT BLOCK (exact, invent no numbers):\n"
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f"length={row['length_in']}in thickness={row['thickness_in']}in "
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f"avg_width={row['width_avg_in']}in width_min={row['width_min_in']}in width_max={row['width_max_in']}in "
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f"bdft={row['bdft']}\n\nBrand voice: {brand[0] if brand else ''}\nGeo: {geo[0] if geo else ''}")
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messages = [{"role": "user", "content": image_parts + [{"type": "text", "text": text}]}]
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t0 = time.time()
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try:
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raw = client.chat(messages)
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except InferenceConnectionError as e:
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return JSONResponse(status_code=503, content=_error("inference_unreachable", str(e), {"recoverable": True}))
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except InferenceError as e:
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return JSONResponse(status_code=502, content=_error("inference_failed", str(e), {"recoverable": True}))
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latency = int((time.time() - t0) * 1000)
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_log_call(conn, slab_id, 2, act[0] if act else None, model, messages, raw, latency)
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# Store as description, mark content_source=llm.
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conn.execute("UPDATE slabs SET description=?, content_source='llm', updated_at=? WHERE id=?",
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(raw.strip(), _now(), slab_id))
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conn.commit()
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return {"ok": True, "description": raw.strip()}
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def server_description(slab: dict) -> str:
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"""Inference-off long description (METADATA-70)."""
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return (
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f"A {slab.get('title', 'slab')} from the mill, measured at "
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f"{slab.get('length_in')}in long, {slab.get('width_avg_in')}in average width, and "
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f"{slab.get('thickness_in')}in thick ({slab.get('bdft')} bdft). "
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"Ready to ship."
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)
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def _log_call(conn, slab_id: str, call: int, rev_id: int | None, model: str,
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request: list, response: str, latency_ms: int) -> None:
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conn.execute(
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"INSERT INTO inference_log(slab_id, call, prompt_revision_id, model, request_payload, response_payload, latency_ms, created_at) "
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"VALUES (?,?,?,?,?,?,?,?)",
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(slab_id, call, rev_id, model, json.dumps(request)[:200000], response, latency_ms, _now()),
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)
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conn.commit()
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@router.post("/admin/test-inference")
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async def test_inference() -> dict:
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conn = get_connection()
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base, key, model = _inference_creds(conn)
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if not base or not key:
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return {"ok": False, "mode": "fail", "vision_capable": False, "detail": "Inference is not configured"}
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client = InferenceClient(base, key, model, timeout=20.0)
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t0 = time.time()
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try:
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client.chat([{"role": "user", "content": "Reply with the single word: ok"}])
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except InferenceConnectionError as e:
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return {"ok": False, "mode": "fail", "vision_capable": False, "detail": str(e)}
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except InferenceError as e:
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return {"ok": False, "mode": "fail", "vision_capable": False, "detail": str(e)}
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detail = f"reachable in {int((time.time()-t0)*1000)}ms"
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return {"ok": True, "mode": "stateless", "vision_capable": True, "detail": detail}
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