ducoterra
022da8e2bc
Foundation (phase 01, verified): - FastAPI app: /api/health, /api/suggestions, /api/chat (placeholder), static frontend served locally (no CDN) - Postgres 17 + pgvector via db/Containerfile + compose.yaml (podman compose up -d db), Alembic initial migration (documents, chunks with vector(768), query_log) - LLM client targeting https://aipi.reeseapps.com/v1 (turbo/embed); scripts/llm_probe.py verified models + 768-dim embeddings live - Conditional debugpy: imported only when DEBUGPY=1 (attach on demand, :5678); logging config for clean single-line logs - Frontend shell: mobile-first chat + Sources pages, tokens, a11y baselines - Tests: 24 unit+integration (99% coverage on app/), ruff + pyright clean, Playwright smoke E2E (3 tests) against a deterministic mock LLM - Planning: .agent/PLAN.md (architecture + LOCKED decisions), AGENTS.md, 6 user stories, 7 phase files (one story / one phase / one Playwright suite each)
64 lines
2.0 KiB
Python
64 lines
2.0 KiB
Python
"""Probe the self-hosted LLM endpoint (aipi).
|
|
|
|
Lists available models and verifies the embedding dimension of the
|
|
configured ``embed`` model against ``BOR_EMBEDDING_DIM`` (default 768).
|
|
Run this before the first import if the LLM backend ever changes:
|
|
|
|
uv run python -m scripts.llm_probe
|
|
"""
|
|
from __future__ import annotations
|
|
|
|
import os
|
|
import sys
|
|
|
|
import httpx
|
|
from dotenv import load_dotenv
|
|
|
|
load_dotenv()
|
|
|
|
|
|
def main() -> int:
|
|
base_url = os.environ.get("BOR_LLM_BASE_URL", "https://aipi.reeseapps.com/v1").rstrip("/")
|
|
api_key = (
|
|
os.environ.get("BOR_LLM_API_KEY")
|
|
or os.environ.get("AIPI_KEY")
|
|
or "not-needed"
|
|
)
|
|
embed_model = os.environ.get("BOR_LLM_EMBED_MODEL", "embed")
|
|
chat_model = os.environ.get("BOR_LLM_CHAT_MODEL", "turbo")
|
|
expected_dim = int(os.environ.get("BOR_EMBEDDING_DIM", "768"))
|
|
|
|
headers = {"Authorization": f"Bearer {api_key}"}
|
|
with httpx.Client(base_url=base_url, headers=headers, timeout=30.0) as client:
|
|
r = client.get("/models")
|
|
r.raise_for_status()
|
|
models = [m["id"] for m in r.json()["data"]]
|
|
print(f"[probe] base_url : {base_url}")
|
|
print(f"[probe] models : {', '.join(models)}")
|
|
|
|
for needed in (chat_model, embed_model):
|
|
if needed not in models:
|
|
print(f"[probe] ERROR: required model '{needed}' not available")
|
|
return 1
|
|
|
|
r = client.post(
|
|
"/embeddings",
|
|
json={"model": embed_model, "input": "brain of reese dimension probe"},
|
|
)
|
|
r.raise_for_status()
|
|
dims = sorted({len(d["embedding"]) for d in r.json()["data"]})
|
|
print(f"[probe] dims({embed_model}): {dims}")
|
|
|
|
if dims != [expected_dim]:
|
|
print(
|
|
f"[probe] MISMATCH: expected {expected_dim}, got {dims}. "
|
|
"Update BOR_EMBEDDING_DIM and recreate the chunks table (see README)."
|
|
)
|
|
return 1
|
|
print("[probe] OK — models present, embedding dimension matches configuration.")
|
|
return 0
|
|
|
|
|
|
if __name__ == "__main__":
|
|
sys.exit(main())
|