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May 23, 2026
Merged

fix(operator): configure embeddings first; SDK-direct catalog refresh #401

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a5a5efb
fix(operator): configure embeddings first; refresh catalog via SDK
aaronsb May 21, 2026
97c0872
fix(operator): set_model_default works with RealDictCursor connections
aaronsb May 21, 2026
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33 changes: 17 additions & 16 deletions api/app/lib/model_catalog.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -145,26 +145,26 @@ def set_model_default(conn, catalog_id: int) -> bool:
"""
Set a model as the default for its provider+category.

Clears existing default for that provider+category first.
Clears existing default for that provider+category first. Uses a
subquery rather than fetchone() + tuple-unpack so the function works
regardless of the connection's cursor_factory — RealDictCursor returns
dict-like rows that silently yield column *names* on tuple unpacking.
"""
with conn.cursor() as cur:
# Get the provider and category for this model
cur.execute(
"SELECT provider, category FROM kg_api.provider_model_catalog WHERE id = %s",
(catalog_id,),
)
row = cur.fetchone()
if not row:
return False

provider, category = row

# Clear existing default
# Clear any existing default that shares the new model's
# (provider, category), excluding the new model itself in case it
# is already the default (so this call is idempotent).
cur.execute(
"""UPDATE kg_api.provider_model_catalog
SET is_default = FALSE, updated_at = NOW()
WHERE provider = %s AND category = %s AND is_default = TRUE""",
(provider, category),
WHERE is_default = TRUE
AND id <> %s
AND (provider, category) = (
SELECT provider, category
FROM kg_api.provider_model_catalog
WHERE id = %s
)""",
(catalog_id, catalog_id),
)

# Set new default (also ensures enabled)
Expand All @@ -174,8 +174,9 @@ def set_model_default(conn, catalog_id: int) -> bool:
WHERE id = %s""",
(catalog_id,),
)
updated = cur.rowcount > 0
conn.commit()
return True
return updated


def update_model_pricing(
Expand Down
85 changes: 81 additions & 4 deletions operator/configure.py
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -187,6 +187,7 @@ def cmd_embedding(self, args):
"""Configure embedding provider by activating a pre-configured profile"""
profile_id = getattr(args, 'profile_id', None)
provider_name = getattr(args, 'provider', None)
device = getattr(args, 'device', None)

# If no profile_id or provider specified, list available profiles
if profile_id is None and provider_name is None:
Expand Down Expand Up @@ -229,9 +230,21 @@ def cmd_embedding(self, args):
# Activate selected profile (use profile['id'] from query, not profile_id arg)
cur.execute("UPDATE kg_api.embedding_profile SET active = true WHERE id = %s", (profile['id'],))

# Optionally update the compute device on the activated profile.
# The wizard maps its GPU_MODE choice (mac/nvidia/amd/cpu) to a
# PyTorch device string here so the API container loads the
# model on the right accelerator at startup.
effective_device = profile['device']
if device:
cur.execute(
"UPDATE kg_api.embedding_profile SET device = %s WHERE id = %s",
(device, profile['id']),
)
effective_device = device

conn.commit()

device_info = f" ({profile['device']})" if profile['device'] else ""
device_info = f" ({effective_device})" if effective_device else ""
print(f"✅ Activated: [{profile['id']}] {profile['provider']} / {profile['model_name']} ({profile['embedding_dimensions']} dims, {profile['precision']}){device_info}")
return True

Expand Down Expand Up @@ -323,6 +336,71 @@ def _validate_provider_key(self, provider, key):
return False
return None

def _fetch_catalog_via_sdk(self, provider):
"""Fetch a provider's model catalog without instantiating the full
AIProvider class.

AnthropicProvider and OllamaProvider eagerly construct an OpenAI
embedding provider in __init__ when none is supplied — but the
operator container has no loaded EmbeddingModelManager (only the API
container initializes one at startup), so get_embedding_provider()
returns None and the eager fallback runs and fails. fetch_model_catalog
itself only needs self.client (or self.api_key for OpenRouter), so we
construct the SDK client directly and bypass __init__ via __new__,
reusing the existing fetch_model_catalog implementation rather than
duplicating per-provider pricing/feature dicts.
"""
from api.app.lib.ai_providers import (
_load_api_key,
OpenAIProvider,
AnthropicProvider,
OpenRouterProvider,
)

if provider == "openai":
from openai import OpenAI
key = _load_api_key("openai", None, "OPENAI_API_KEY")
if not key:
raise RuntimeError(
"OpenAI API key not configured. Store it first via "
"`configure.py api-key openai`."
)
prov = OpenAIProvider.__new__(OpenAIProvider)
prov.client = OpenAI(api_key=key)
return prov.fetch_model_catalog()

if provider == "anthropic":
from anthropic import Anthropic
key = _load_api_key("anthropic", None, "ANTHROPIC_API_KEY")
if not key:
raise RuntimeError(
"Anthropic API key not configured. Store it first via "
"`configure.py api-key anthropic`."
)
prov = AnthropicProvider.__new__(AnthropicProvider)
prov.client = Anthropic(api_key=key)
return prov.fetch_model_catalog()

if provider == "openrouter":
from openai import OpenAI
key = _load_api_key("openrouter", None, "OPENROUTER_API_KEY")
if not key:
raise RuntimeError(
"OpenRouter API key not configured. Store it first via "
"`configure.py api-key openrouter`."
)
prov = OpenRouterProvider.__new__(OpenRouterProvider)
# OpenRouter's fetch_model_catalog uses self.api_key for the
# Authorization header and OPENROUTER_BASE_URL from the class.
prov.api_key = key
return prov.fetch_model_catalog()

# Other providers (ollama, llamacpp) — fall back to the original
# construction. They don't currently appear in the guided wizard, and
# their catalog refresh has different requirements (base_url, etc.).
from api.app.lib.ai_providers import get_provider
return get_provider(provider).fetch_model_catalog()

def cmd_api_key(self, args):
"""Store encrypted API key"""
provider = args.provider
Expand Down Expand Up @@ -469,11 +547,9 @@ def cmd_models(self, args):

print(f"🔄 Fetching model catalog from {provider}...")
try:
from api.app.lib.ai_providers import get_provider
from api.app.lib.model_catalog import upsert_catalog_entries

prov = get_provider(provider)
entries = prov.fetch_model_catalog()
entries = self._fetch_catalog_via_sdk(provider.lower())

if not entries:
print(f"⚠️ No models returned from {provider}")
Expand Down Expand Up @@ -695,6 +771,7 @@ def main():
embed_parser = subparsers.add_parser('embedding', help='List or activate embedding profile')
embed_parser.add_argument('profile_id', nargs='?', type=int, help='Profile ID to activate (omit to list profiles)')
embed_parser.add_argument('--provider', help='Select profile by provider name (local, openai)')
embed_parser.add_argument('--device', help='Set compute device on the activated profile (cpu, cuda, mps)')

# api-key
key_parser = subparsers.add_parser('api-key', help='Store encrypted API key')
Expand Down
42 changes: 27 additions & 15 deletions operator/lib/guided-init.sh
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -343,9 +343,30 @@ fi
docker exec kg-operator python /workspace/operator/configure.py admin --password "$ADMIN_PASSWORD"
echo ""

# Step 4: Configure AI provider (interactive selection)
# Step 4: Configure local embedding profile (GPU_MODE-aware)
# This runs BEFORE AI provider selection because the embedding model is
# system-level infrastructure that the API container loads at startup. It
# is also activated against the device chosen at the very start of the
# wizard, so the user's GPU/CPU intent is honored end-to-end.
echo -e "${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}"
echo -e "${BOLD}Step 4/9: Choosing AI extraction provider${NC}"
echo -e "${BOLD}Step 4/9: Configuring local embedding profile${NC}"
echo -e "${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}"
echo ""

case "$GPU_MODE" in
mac) EMBEDDING_DEVICE="mps" ;;
nvidia) EMBEDDING_DEVICE="cuda" ;;
amd|amd-host) EMBEDDING_DEVICE="cuda" ;; # PyTorch ROCm presents as cuda
cpu|*) EMBEDDING_DEVICE="cpu" ;;
esac

echo "Activating local embeddings (nomic-ai/nomic-embed-text-v1.5) on device: ${EMBEDDING_DEVICE}"
docker exec kg-operator python /workspace/operator/configure.py embedding --provider local --device "$EMBEDDING_DEVICE"
echo ""

# Step 5: Configure AI provider (interactive selection)
echo -e "${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}"
echo -e "${BOLD}Step 5/9: Choosing AI extraction provider${NC}"
echo -e "${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}"
echo ""
echo "Choose your AI extraction provider:"
Expand Down Expand Up @@ -392,9 +413,9 @@ case "$REPLY" in
esac
echo ""

# Step 5: Store API key (skip for Ollama)
# Step 6: Store API key (skip for Ollama)
echo -e "${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}"
echo -e "${BOLD}Step 5/9: Validating API key${NC}"
echo -e "${BOLD}Step 6/9: Validating API key${NC}"
echo -e "${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}"
echo ""

Expand Down Expand Up @@ -427,9 +448,9 @@ while [ "$API_KEY_STORED" = false ]; do
fi
done

# Step 6: Refresh model catalog and select model
# Step 7: Refresh model catalog and select model
echo -e "${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}"
echo -e "${BOLD}Step 6/9: Selecting extraction model${NC}"
echo -e "${BOLD}Step 7/9: Selecting extraction model${NC}"
echo -e "${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}"
echo ""

Expand Down Expand Up @@ -625,15 +646,6 @@ else
fi
echo ""

# Step 7: Configure embeddings
echo -e "${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}"
echo -e "${BOLD}Step 7/9: Configuring embedding provider${NC}"
echo -e "${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}"
echo ""
echo "Activating local embeddings (nomic-ai/nomic-embed-text-v1.5)..."
docker exec kg-operator python /workspace/operator/configure.py embedding --provider local
echo ""

# Step 8: Configure Garage credentials
echo -e "${BLUE}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}"
echo -e "${BOLD}Step 8/9: Configuring Garage object storage${NC}"
Expand Down
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