From a580fdfe661c5c51da8155db200510d7f43c419c Mon Sep 17 00:00:00 2001 From: Josh Date: Sun, 5 Oct 2025 13:49:09 -0500 Subject: [PATCH] Feature/LLM-Providers-UI-Polished (#736) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Add Anthropic and Grok provider support * feat: Add crucial GPT-5 and reasoning model support for OpenRouter - Add requires_max_completion_tokens() function for GPT-5, o1, o3, Grok-3 series - Add prepare_chat_completion_params() for reasoning model compatibility - Implement max_tokens → max_completion_tokens conversion for reasoning models - Add temperature handling for reasoning models (must be 1.0 default) - Enhanced provider validation and API key security in provider endpoints - Streamlined retry logic (3→2 attempts) for faster issue detection - Add failure tracking and circuit breaker analysis for debugging - Support OpenRouter format detection (openai/gpt-5-nano, openai/o1-mini) - Improved Grok provider empty response handling with structured fallbacks - Enhanced contextual embedding with provider-aware model selection Core provider functionality: - OpenRouter, Grok, Anthropic provider support with full embedding integration - Provider-specific model defaults and validation - Secure API connectivity testing endpoints - Provider context passing for code generation workflows 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude * fully working model providers, addressing securtiy and code related concerns, throughly hardening our code * added multiprovider support, embeddings model support, cleaned the pr, need to fix health check, asnyico tasks errors, and contextual embeddings error * fixed contextual embeddings issue * - Added inspect-aware shutdown handling so get_llm_client always closes the underlying AsyncOpenAI / httpx.AsyncClient while the loop is still alive, with defensive logging if shutdown happens late (python/src/server/services/llm_provider_service.py:14, python/src/server/ services/llm_provider_service.py:520). * - Restructured get_llm_client so client creation and usage live in separate try/finally blocks; fallback clients now close without logging spurious Error creating LLM client when downstream code raises (python/src/server/services/llm_provider_service.py:335-556). - Close logic now sanitizes provider names consistently and awaits whichever aclose/close coroutine the SDK exposes, keeping the loop shut down cleanly (python/src/server/services/llm_provider_service.py:530-559). Robust JSON Parsing - Added _extract_json_payload to strip code fences / extra text returned by Ollama before json.loads runs, averting the markdown-induced decode errors you saw in logs (python/src/server/services/storage/code_storage_service.py:40-63). - Swapped the direct parse call for the sanitized payload and emit a debug preview when cleanup alters the content (python/src/server/ services/storage/code_storage_service.py:858-864). * added provider connection support * added provider api key not being configured warning * Updated get_llm_client so missing OpenAI keys automatically fall back to Ollama (matching existing tests) and so unsupported providers still raise the legacy ValueError the suite expects. The fallback now reuses _get_optimal_ollama_instance and rethrows ValueError(OpenAI API key not found and Ollama fallback failed) when it cant connect. Adjusted test_code_extraction_source_id.py to accept the new optional argument on the mocked extractor (and confirm its None when present). * Resolved a few needed code rabbit suggestion - Updated the knowledge API key validation to call create_embedding with the provider argument and removed the hard-coded OpenAI fallback (python/src/server/api_routes/knowledge_api.py). - Broadened embedding provider detection so prefixed OpenRouter/OpenAI model names route through the correct client (python/src/server/ services/embeddings/embedding_service.py, python/src/server/services/llm_provider_service.py). - Removed the duplicate helper definitions from llm_provider_service.py, eliminating the stray docstring that was causing the import-time syntax error. * updated via code rabbit PR review, code rabbit in my IDE found no issues and no nitpicks with the updates! what was done: Credential service now persists the provider under the uppercase key LLM_PROVIDER, matching the read path (no new EMBEDDING_PROVIDER usage introduced). Embedding batch creation stops inserting blank strings, logging failures and skipping invalid items before they ever hit the provider (python/src/server/services/embeddings/embedding_service.py). Contextual embedding prompts use real newline characters everywhereboth when constructing the batch prompt and when parsing the models response (python/src/server/services/embeddings/contextual_embedding_service.py). Embedding provider routing already recognizes OpenRouter-prefixed OpenAI models via is_openai_embedding_model; no further change needed there. Embedding insertion now skips unsupported vector dimensions instead of forcing them into the 1536-column, and the backoff loop uses await asyncio.sleep so we no longer block the event loop (python/src/server/services/storage/code_storage_service.py). RAG settings props were extended to include LLM_INSTANCE_NAME and OLLAMA_EMBEDDING_INSTANCE_NAME, and the debug log no longer prints API-key prefixes (the rest of the TanStack refactor/EMBEDDING_PROVIDER support remains deferred). * test fix * enhanced Openrouters parsing logic to automatically detect reasoning models and parse regardless of json output or not. this commit creates a robust way for archons parsing to work throughly with openrouter automatically, regardless of the model youre using, to ensure proper functionality with out breaking any generation capabilities! * updated ui llm interface, added seprate embeddings provider, made the system fully capabale of mix and matching llm providers (local and non local) for chat & embeddings. updated the ragsettings.tsx ui mainly, along with core functionality * added warning labels and updated ollama health checks * ready for review, fixed som error warnings and consildated ollama status health checks * fixed FAILED test_async_embedding_service.py * code rabbit fixes * Separated the code-summary LLM provider from the embedding provider, so code example storage now forwards a dedicated embedding provider override end-to-end without hijacking the embedding pipeline. this fixes code rabbits (Preserve provider override in create_embeddings_batch) suggesting * - Swapped API credential storage to booleans so decrypted keys never sit in React state (archon-ui-main/src/components/ settings/RAGSettings.tsx). - Normalized Ollama instance URLs and gated the metrics effect on real state changes to avoid mis-counts and duplicate fetches (RAGSettings.tsx). - Tightened crawl progress scaling and indented-block parsing to handle min_length=None safely (python/src/server/ services/crawling/code_extraction_service.py:160, python/src/server/services/crawling/code_extraction_service.py:911). - Added provider-agnostic embedding rate-limit retries so Google and friends back off gracefully (python/src/server/ services/embeddings/embedding_service.py:427). - Made the orchestration registry async + thread-safe and updated every caller to await it (python/src/server/services/ crawling/crawling_service.py:34, python/src/server/api_routes/knowledge_api.py:1291). * Update RAGSettings.tsx - header for 'LLM Settings' is now 'LLM Provider Settings' * (RAG Settings) - Ollama Health Checks & Metrics - Added a 10-second timeout to the health fetch so it doesn't hang. - Adjusted logic so metric refreshes run for embedding-only Ollama setups too. - Initial page load now checks Ollama if either chat or embedding provider uses it. - Metrics and alerts now respect which provider (chat/embedding) is currently selected. - Provider Sync & Alerts - Fixed a sync bug so the very first provider change updates settings as expected. - Alerts now track the active provider (chat vs embedding) rather than only the LLM provider. - Warnings about missing credentials now skip whichever provider is currently selected. - Modals & Types - Normalize URLs before handing them to selection modals to keep consistent data. - Strengthened helper function types (getDisplayedChatModel, getModelPlaceholder, etc.). (Crawling Service) - Made the orchestration registry lock lazy-initialized to avoid issues in Python 3.12 and wrapped registry commands (register, unregister) in async calls. This keeps things thread-safe even during concurrent crawling and cancellation. * - migration/complete_setup.sql:101 seeds Google/OpenRouter/Anthropic/Grok API key rows so fresh databases expose every provider by default. - migration/0.1.0/009_add_provider_placeholders.sql:1 backfills the same rows for existing Supabase instances and records the migration. - archon-ui-main/src/components/settings/RAGSettings.tsx:121 introduces a shared credentialprovider map, reloadApiCredentials runs through all five providers, and the status poller includes the new keys. - archon-ui-main/src/components/settings/RAGSettings.tsx:353 subscribes to the archon:credentials-updated browser event so adding/removing a key immediately refetches credential status and pings the corresponding connectivity test. - archon-ui-main/src/components/settings/RAGSettings.tsx:926 now treats missing Anthropic/OpenRouter/Grok keys as missing, preventing stale connected badges when a key is removed. * - archon-ui-main/src/components/settings/RAGSettings.tsx:90 adds a simple display-name map and reuses one red alert style. - archon-ui-main/src/components/settings/RAGSettings.tsx:1016 now shows exactly one red banner when the active provider - Removed the old duplicate Missing API Key Configuration block, so the panel no longer stacks two warnings. * Update credentialsService.ts default model * updated the google embedding adapter for multi dimensional rag querying * thought this micro fix in the google embedding pushed with the embedding update the other day, it didnt. pushing now --------- Co-authored-by: Chillbruhhh Co-authored-by: Claude --- archon-ui-main/package-lock.json | 1383 +++++++------- archon-ui-main/package.json | 1 + .../src/components/settings/RAGSettings.tsx | 1696 +++++++++++------ .../src/services/credentialsService.ts | 41 +- .../0.1.0/009_add_provider_placeholders.sql | 18 + migration/complete_setup.sql | 5 +- python/src/server/api_routes/knowledge_api.py | 4 +- .../crawling/code_extraction_service.py | 68 +- .../services/crawling/crawling_service.py | 53 +- .../crawling/document_storage_operations.py | 10 +- .../src/server/services/credential_service.py | 68 +- .../services/embeddings/embedding_service.py | 222 ++- .../services/storage/code_storage_service.py | 9 +- .../tests/test_code_extraction_source_id.py | 4 +- 14 files changed, 2194 insertions(+), 1388 deletions(-) create mode 100644 migration/0.1.0/009_add_provider_placeholders.sql diff --git a/archon-ui-main/package-lock.json b/archon-ui-main/package-lock.json index a6653753..b29650ab 100644 --- a/archon-ui-main/package-lock.json +++ b/archon-ui-main/package-lock.json @@ -30,6 +30,7 @@ "react-dnd": "^16.0.1", "react-dnd-html5-backend": "^16.0.1", "react-dom": "^18.3.1", + "react-icons": "^5.5.0", "react-markdown": "^10.1.0", "react-router-dom": "^6.26.2", "tailwind-merge": "latest", @@ -62,9 +63,9 @@ } }, "node_modules/@adobe/css-tools": { - 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"version": "3.25.46", - "resolved": "https://registry.npmjs.org/zod/-/zod-3.25.46.tgz", - "integrity": "sha512-IqRxcHEIjqLd4LNS/zKffB3Jzg3NwqJxQQ0Ns7pdrvgGkwQsEBdEQcOHaBVqvvZArShRzI39+aMST3FBGmTrLQ==", + "version": "3.25.76", + "resolved": "https://registry.npmjs.org/zod/-/zod-3.25.76.tgz", + "integrity": "sha512-gzUt/qt81nXsFGKIFcC3YnfEAx5NkunCfnDlvuBSSFS02bcXu4Lmea0AFIUwbLWxWPx3d9p8S5QoaujKcNQxcQ==", "license": "MIT", "funding": { "url": "https://github.com/sponsors/colinhacks" diff --git a/archon-ui-main/package.json b/archon-ui-main/package.json index 31c07574..a0f86e81 100644 --- a/archon-ui-main/package.json +++ b/archon-ui-main/package.json @@ -50,6 +50,7 @@ "react-dnd": "^16.0.1", "react-dnd-html5-backend": "^16.0.1", "react-dom": "^18.3.1", + "react-icons": "^5.5.0", "react-markdown": "^10.1.0", "react-router-dom": "^6.26.2", "tailwind-merge": "latest", diff --git a/archon-ui-main/src/components/settings/RAGSettings.tsx b/archon-ui-main/src/components/settings/RAGSettings.tsx index ccba61ce..62739fc7 100644 --- a/archon-ui-main/src/components/settings/RAGSettings.tsx +++ b/archon-ui-main/src/components/settings/RAGSettings.tsx @@ -1,9 +1,12 @@ -import React, { useState, useEffect, useRef } from 'react'; -import { Settings, Check, Save, Loader, ChevronDown, ChevronUp, Zap, Database, Trash2 } from 'lucide-react'; +import React, { useState, useEffect, useRef, useCallback } from 'react'; +import { Settings, Check, Save, Loader, ChevronDown, ChevronUp, Zap, Database, Trash2, Cog } from 'lucide-react'; import { Card } from '../ui/Card'; import { Input } from '../ui/Input'; import { Select } from '../ui/Select'; import { Button } from '../ui/Button'; +import { Button as GlowButton } from '../../features/ui/primitives/button'; +import { LuBrainCircuit } from 'react-icons/lu'; +import { PiDatabaseThin } from 'react-icons/pi'; import { useToast } from '../../features/shared/hooks/useToast'; import { credentialsService } from '../../services/credentialsService'; import OllamaModelDiscoveryModal from './OllamaModelDiscoveryModal'; @@ -11,6 +14,9 @@ import OllamaModelSelectionModal from './OllamaModelSelectionModal'; type ProviderKey = 'openai' | 'google' | 'ollama' | 'anthropic' | 'grok' | 'openrouter'; +// Providers that support embedding models +const EMBEDDING_CAPABLE_PROVIDERS: ProviderKey[] = ['openai', 'google', 'ollama']; + interface ProviderModels { chatModel: string; embeddingModel: string; @@ -85,27 +91,53 @@ const colorStyles: Record = { grok: 'border-yellow-500 bg-yellow-500/10', }; -const providerAlertStyles: Record = { - openai: 'bg-green-50 dark:bg-green-900/20 border-green-200 dark:border-green-800 text-green-800 dark:text-green-300', - google: 'bg-blue-50 dark:bg-blue-900/20 border-blue-200 dark:border-blue-800 text-blue-800 dark:text-blue-300', - openrouter: 'bg-cyan-50 dark:bg-cyan-900/20 border-cyan-200 dark:border-cyan-800 text-cyan-800 dark:text-cyan-300', - ollama: 'bg-purple-50 dark:bg-purple-900/20 border-purple-200 dark:border-purple-800 text-purple-800 dark:text-purple-300', - anthropic: 'bg-orange-50 dark:bg-orange-900/20 border-orange-200 dark:border-orange-800 text-orange-800 dark:text-orange-300', - grok: 'bg-yellow-50 dark:bg-yellow-900/20 border-yellow-200 dark:border-yellow-800 text-yellow-800 dark:text-yellow-300', -}; +const providerWarningAlertStyle = 'bg-yellow-50 dark:bg-yellow-900/20 border-yellow-200 dark:border-yellow-800 text-yellow-800 dark:text-yellow-300'; +const providerErrorAlertStyle = 'bg-red-50 dark:bg-red-900/20 border-red-200 dark:border-red-800 text-red-800 dark:text-red-300'; +const providerMissingAlertStyle = providerErrorAlertStyle; -const providerAlertMessages: Record = { - openai: 'Configure your OpenAI API key in the credentials section to use GPT models.', - google: 'Configure your Google API key in the credentials section to use Gemini models.', - openrouter: 'Configure your OpenRouter API key in the credentials section to use models.', - ollama: 'Configure your Ollama instances in this panel to connect local models.', - anthropic: 'Configure your Anthropic API key in the credentials section to use Claude models.', - grok: 'Configure your Grok API key in the credentials section to use Grok models.', +const providerDisplayNames: Record = { + openai: 'OpenAI', + google: 'Google', + openrouter: 'OpenRouter', + ollama: 'Ollama', + anthropic: 'Anthropic', + grok: 'Grok', }; const isProviderKey = (value: unknown): value is ProviderKey => typeof value === 'string' && ['openai', 'google', 'openrouter', 'ollama', 'anthropic', 'grok'].includes(value); +// Default base URL for Ollama instances when not explicitly configured +const DEFAULT_OLLAMA_URL = 'http://host.docker.internal:11434/v1'; + +const PROVIDER_CREDENTIAL_KEYS = [ + 'OPENAI_API_KEY', + 'GOOGLE_API_KEY', + 'ANTHROPIC_API_KEY', + 'OPENROUTER_API_KEY', + 'GROK_API_KEY', +] as const; + +type ProviderCredentialKey = typeof PROVIDER_CREDENTIAL_KEYS[number]; + +const CREDENTIAL_PROVIDER_MAP: Record = { + OPENAI_API_KEY: 'openai', + GOOGLE_API_KEY: 'google', + ANTHROPIC_API_KEY: 'anthropic', + OPENROUTER_API_KEY: 'openrouter', + GROK_API_KEY: 'grok', +}; + +const normalizeBaseUrl = (url?: string | null): string | null => { + if (!url) return null; + const trimmed = url.trim(); + if (!trimmed) return null; + + let normalized = trimmed.replace(/\/+$/, ''); + normalized = normalized.replace(/\/v1$/i, ''); + return normalized || null; +}; + interface RAGSettingsProps { ragSettings: { MODEL_CHOICE: string; @@ -118,6 +150,7 @@ interface RAGSettingsProps { LLM_BASE_URL?: string; LLM_INSTANCE_NAME?: string; EMBEDDING_MODEL?: string; + EMBEDDING_PROVIDER?: string; OLLAMA_EMBEDDING_URL?: string; OLLAMA_EMBEDDING_INSTANCE_NAME?: string; // Crawling Performance Settings @@ -148,6 +181,7 @@ export const RAGSettings = ({ const [showCrawlingSettings, setShowCrawlingSettings] = useState(false); const [showStorageSettings, setShowStorageSettings] = useState(false); const [showModelDiscoveryModal, setShowModelDiscoveryModal] = useState(false); + const [showOllamaConfig, setShowOllamaConfig] = useState(false); // Edit modals state const [showEditLLMModal, setShowEditLLMModal] = useState(false); @@ -160,6 +194,16 @@ export const RAGSettings = ({ // Provider-specific model persistence state const [providerModels, setProviderModels] = useState(() => loadProviderModels()); + // Independent provider selection state + const [chatProvider, setChatProvider] = useState(() => + (ragSettings.LLM_PROVIDER as ProviderKey) || 'openai' + ); + const [embeddingProvider, setEmbeddingProvider] = useState(() => + // Default to openai if no specific embedding provider is set + (ragSettings.EMBEDDING_PROVIDER as ProviderKey) || 'openai' + ); + const [activeSelection, setActiveSelection] = useState<'chat' | 'embedding'>('chat'); + // Instance configurations const [llmInstanceConfig, setLLMInstanceConfig] = useState({ name: '', @@ -215,16 +259,32 @@ export const RAGSettings = ({ } }, [ragSettings.OLLAMA_EMBEDDING_URL, ragSettings.OLLAMA_EMBEDDING_INSTANCE_NAME]); - // Provider model persistence effects + // Provider model persistence effects - separate for chat and embedding useEffect(() => { - // Update provider models when current models change - const currentProvider = ragSettings.LLM_PROVIDER as ProviderKey; - if (currentProvider && ragSettings.MODEL_CHOICE && ragSettings.EMBEDDING_MODEL) { + // Update chat provider models when chat model changes + if (chatProvider && ragSettings.MODEL_CHOICE) { setProviderModels(prev => { const updated = { ...prev, - [currentProvider]: { - chatModel: ragSettings.MODEL_CHOICE, + [chatProvider]: { + ...prev[chatProvider], + chatModel: ragSettings.MODEL_CHOICE + } + }; + saveProviderModels(updated); + return updated; + }); + } + }, [ragSettings.MODEL_CHOICE, chatProvider]); + + useEffect(() => { + // Update embedding provider models when embedding model changes + if (embeddingProvider && ragSettings.EMBEDDING_MODEL) { + setProviderModels(prev => { + const updated = { + ...prev, + [embeddingProvider]: { + ...prev[embeddingProvider], embeddingModel: ragSettings.EMBEDDING_MODEL } }; @@ -232,93 +292,181 @@ export const RAGSettings = ({ return updated; }); } - }, [ragSettings.MODEL_CHOICE, ragSettings.EMBEDDING_MODEL, ragSettings.LLM_PROVIDER]); + }, [ragSettings.EMBEDDING_MODEL, embeddingProvider]); - // Load API credentials for status checking - useEffect(() => { - const loadApiCredentials = async () => { - try { - // Get decrypted values for the API keys we need for status checking - const keyNames = ['OPENAI_API_KEY', 'GOOGLE_API_KEY', 'ANTHROPIC_API_KEY']; - const statusResults = await credentialsService.checkCredentialStatus(keyNames); - - const credentials: {[key: string]: string} = {}; - - for (const [key, result] of Object.entries(statusResults)) { - if (result.has_value && result.value && result.value.trim().length > 0) { - credentials[key] = result.value; - } - } - - console.log('🔑 Loaded API credentials for status checking:', Object.keys(credentials)); - setApiCredentials(credentials); - } catch (error) { - console.error('Failed to load API credentials for status checking:', error); - } - }; - - loadApiCredentials(); - }, []); - - // Reload API credentials when ragSettings change (e.g., after saving) - // Use a ref to track if we've loaded credentials to prevent infinite loops const hasLoadedCredentialsRef = useRef(false); - - // Manual reload function for external calls - const reloadApiCredentials = async () => { + + const reloadApiCredentials = useCallback(async () => { try { - // Get decrypted values for the API keys we need for status checking - const keyNames = ['OPENAI_API_KEY', 'GOOGLE_API_KEY', 'ANTHROPIC_API_KEY']; - const statusResults = await credentialsService.checkCredentialStatus(keyNames); - - const credentials: {[key: string]: string} = {}; - - for (const [key, result] of Object.entries(statusResults)) { - if (result.has_value && result.value && result.value.trim().length > 0) { - credentials[key] = result.value; - } + const statusResults = await credentialsService.checkCredentialStatus( + Array.from(PROVIDER_CREDENTIAL_KEYS), + ); + + const credentials: { [key: string]: boolean } = {}; + + for (const key of PROVIDER_CREDENTIAL_KEYS) { + const result = statusResults[key]; + credentials[key] = !!result?.has_value; } - - console.log('🔄 Reloaded API credentials for status checking:', Object.keys(credentials)); + + console.log( + '🔑 Updated API credential status snapshot:', + Object.keys(credentials), + ); setApiCredentials(credentials); hasLoadedCredentialsRef.current = true; } catch (error) { - console.error('Failed to reload API credentials:', error); + console.error('Failed to load API credentials for status checking:', error); } - }; - + }, []); + useEffect(() => { - // Only reload if we have ragSettings and haven't loaded yet, or if LLM_PROVIDER changed - if (Object.keys(ragSettings).length > 0 && (!hasLoadedCredentialsRef.current || ragSettings.LLM_PROVIDER)) { - reloadApiCredentials(); + void reloadApiCredentials(); + }, [reloadApiCredentials]); + + useEffect(() => { + if (!hasLoadedCredentialsRef.current) { + return; } - }, [ragSettings.LLM_PROVIDER]); // Only depend on LLM_PROVIDER changes - - // Reload credentials periodically to catch updates from other components (like onboarding) + + void reloadApiCredentials(); + }, [ragSettings.LLM_PROVIDER, reloadApiCredentials]); + useEffect(() => { - // Set up periodic reload every 30 seconds when component is active (reduced from 2s) const interval = setInterval(() => { if (Object.keys(ragSettings).length > 0) { - reloadApiCredentials(); + void reloadApiCredentials(); } - }, 30000); // Changed from 2000ms to 30000ms (30 seconds) + }, 30000); return () => clearInterval(interval); - }, [ragSettings.LLM_PROVIDER]); // Only restart interval if provider changes - + }, [ragSettings.LLM_PROVIDER, reloadApiCredentials]); + + useEffect(() => { + const needsDetection = chatProvider === 'ollama' || embeddingProvider === 'ollama'; + + if (!needsDetection) { + setOllamaServerStatus('unknown'); + return; + } + + const baseUrl = ( + ragSettings.LLM_BASE_URL?.trim() || + llmInstanceConfig.url?.trim() || + ragSettings.OLLAMA_EMBEDDING_URL?.trim() || + embeddingInstanceConfig.url?.trim() || + DEFAULT_OLLAMA_URL + ); + + const normalizedUrl = baseUrl.replace('/v1', '').replace(/\/$/, ''); + + let cancelled = false; + + (async () => { + try { + const response = await fetch( + `/api/ollama/instances/health?instance_urls=${encodeURIComponent(normalizedUrl)}`, + { method: 'GET', headers: { Accept: 'application/json' }, signal: AbortSignal.timeout(10000) } + ); + + if (cancelled) return; + + if (!response.ok) { + setOllamaServerStatus('offline'); + return; + } + + const data = await response.json(); + const instanceStatus = data.instance_status?.[normalizedUrl]; + setOllamaServerStatus(instanceStatus?.is_healthy ? 'online' : 'offline'); + } catch (error) { + if (!cancelled) { + setOllamaServerStatus('offline'); + } + } + })(); + + return () => { + cancelled = true; + }; + }, [chatProvider, embeddingProvider, ragSettings.LLM_BASE_URL, ragSettings.OLLAMA_EMBEDDING_URL, llmInstanceConfig.url, embeddingInstanceConfig.url]); + + // Sync independent provider states with ragSettings (one-way: ragSettings -> local state) + useEffect(() => { + if (ragSettings.LLM_PROVIDER && ragSettings.LLM_PROVIDER !== chatProvider) { + setChatProvider(ragSettings.LLM_PROVIDER as ProviderKey); + } + }, [ragSettings.LLM_PROVIDER]); // Remove chatProvider dependency to avoid loops + + useEffect(() => { + if (ragSettings.EMBEDDING_PROVIDER && ragSettings.EMBEDDING_PROVIDER !== embeddingProvider) { + setEmbeddingProvider(ragSettings.EMBEDDING_PROVIDER as ProviderKey); + } + }, [ragSettings.EMBEDDING_PROVIDER]); // Remove embeddingProvider dependency to avoid loops + + useEffect(() => { + setOllamaManualConfirmed(false); + setOllamaServerStatus('unknown'); + }, [ragSettings.LLM_BASE_URL, ragSettings.OLLAMA_EMBEDDING_URL, chatProvider, embeddingProvider]); + + // Update ragSettings when independent providers change (one-way: local state -> ragSettings) + // Split the “first‐run” guard into two refs so chat and embedding effects don’t interfere. + const updateChatRagSettingsRef = useRef(true); + const updateEmbeddingRagSettingsRef = useRef(true); + + useEffect(() => { + // Only update if this is a user‐initiated change, not a sync from ragSettings + if (updateChatRagSettingsRef.current && chatProvider !== ragSettings.LLM_PROVIDER) { + setRagSettings(prev => ({ + ...prev, + LLM_PROVIDER: chatProvider + })); + } + updateChatRagSettingsRef.current = true; + }, [chatProvider]); + + useEffect(() => { + // Only update if this is a user‐initiated change, not a sync from ragSettings + if (updateEmbeddingRagSettingsRef.current && embeddingProvider && embeddingProvider !== ragSettings.EMBEDDING_PROVIDER) { + setRagSettings(prev => ({ + ...prev, + EMBEDDING_PROVIDER: embeddingProvider + })); + } + updateEmbeddingRagSettingsRef.current = true; + }, [embeddingProvider]); + + // Status tracking const [llmStatus, setLLMStatus] = useState({ online: false, responseTime: null, checking: false }); const [embeddingStatus, setEmbeddingStatus] = useState({ online: false, responseTime: null, checking: false }); + const llmRetryTimeoutRef = useRef(null); + const embeddingRetryTimeoutRef = useRef(null); // API key credentials for status checking - const [apiCredentials, setApiCredentials] = useState<{[key: string]: string}>({}); + const [apiCredentials, setApiCredentials] = useState<{[key: string]: boolean}>({}); // Provider connection status tracking const [providerConnectionStatus, setProviderConnectionStatus] = useState<{ [key: string]: { connected: boolean; checking: boolean; lastChecked?: Date } }>({}); + const [ollamaServerStatus, setOllamaServerStatus] = useState<'unknown' | 'online' | 'offline'>('unknown'); + const [ollamaManualConfirmed, setOllamaManualConfirmed] = useState(false); + + useEffect(() => { + return () => { + if (llmRetryTimeoutRef.current) { + clearTimeout(llmRetryTimeoutRef.current); + llmRetryTimeoutRef.current = null; + } + if (embeddingRetryTimeoutRef.current) { + clearTimeout(embeddingRetryTimeoutRef.current); + embeddingRetryTimeoutRef.current = null; + } + }; + }, []); // Test connection to external providers - const testProviderConnection = async (provider: string): Promise => { + const testProviderConnection = useCallback(async (provider: string): Promise => { setProviderConnectionStatus(prev => ({ ...prev, [provider]: { ...prev[provider], checking: true } @@ -345,7 +493,7 @@ export const RAGSettings = ({ })); return false; } - }; + }, []); // Test provider connections when API credentials change useEffect(() => { @@ -371,7 +519,39 @@ export const RAGSettings = ({ const interval = setInterval(testConnections, 60000); return () => clearInterval(interval); - }, [apiCredentials]); // Test when credentials change + }, [apiCredentials, testProviderConnection]); // Test when credentials change + + useEffect(() => { + const handleCredentialUpdate = (event: Event) => { + const detail = (event as CustomEvent<{ keys?: string[] }>).detail; + const updatedKeys = (detail?.keys ?? []).map(key => key.toUpperCase()); + + if (updatedKeys.length === 0) { + void reloadApiCredentials(); + return; + } + + const touchedProviderKeys = updatedKeys.filter(key => key in CREDENTIAL_PROVIDER_MAP); + if (touchedProviderKeys.length === 0) { + return; + } + + void reloadApiCredentials(); + + touchedProviderKeys.forEach(key => { + const provider = CREDENTIAL_PROVIDER_MAP[key as ProviderCredentialKey]; + if (provider) { + void testProviderConnection(provider); + } + }); + }; + + window.addEventListener('archon:credentials-updated', handleCredentialUpdate); + + return () => { + window.removeEventListener('archon:credentials-updated', handleCredentialUpdate); + }; + }, [reloadApiCredentials, testProviderConnection]); // Ref to track if initial test has been run (will be used after function definitions) const hasRunInitialTestRef = useRef(false); @@ -444,7 +624,14 @@ export const RAGSettings = ({ }; // Manual test function with user feedback using backend proxy - const manualTestConnection = async (url: string, setStatus: React.Dispatch>, instanceName: string) => { +const manualTestConnection = async ( + url: string, + setStatus: React.Dispatch>, + instanceName: string, + context?: 'chat' | 'embedding', + options?: { suppressToast?: boolean } + ): Promise => { + const suppressToast = options?.suppressToast ?? false; setStatus(prev => ({ ...prev, checking: true })); const startTime = Date.now(); @@ -471,31 +658,58 @@ export const RAGSettings = ({ if (instanceStatus?.is_healthy) { const responseTime = Math.round(instanceStatus.response_time_ms || (Date.now() - startTime)); setStatus({ online: true, responseTime, checking: false }); - showToast(`${instanceName} connection successful: ${instanceStatus.models_available || 0} models available (${responseTime}ms)`, 'success'); - + + // Context-aware model count display + let modelCount = instanceStatus.models_available || 0; + let modelType = 'models'; + + if (context === 'chat') { + modelCount = ollamaMetrics.llmInstanceModels?.chat || 0; + modelType = 'chat models'; + } else if (context === 'embedding') { + modelCount = ollamaMetrics.embeddingInstanceModels?.embedding || 0; + modelType = 'embedding models'; + } + + if (!suppressToast) { + showToast(`${instanceName} connection successful: ${modelCount} ${modelType} available (${responseTime}ms)`, 'success'); + } + // Scenario 2: Manual "Test Connection" button - refresh Ollama metrics if Ollama provider is selected - if (ragSettings.LLM_PROVIDER === 'ollama') { + if (ragSettings.LLM_PROVIDER === 'ollama' || embeddingProvider === 'ollama' || context === 'embedding') { console.log('🔄 Fetching Ollama metrics - Test Connection button clicked'); fetchOllamaMetrics(); } + + return true; } else { setStatus({ online: false, responseTime: null, checking: false }); - showToast(`${instanceName} connection failed: ${instanceStatus?.error_message || 'Instance is not healthy'}`, 'error'); + if (!suppressToast) { + showToast(`${instanceName} connection failed: ${instanceStatus?.error_message || 'Instance is not healthy'}`, 'error'); + } + return false; } } else { setStatus({ online: false, responseTime: null, checking: false }); - showToast(`${instanceName} connection failed: Backend proxy error (HTTP ${response.status})`, 'error'); + if (!suppressToast) { + showToast(`${instanceName} connection failed: Backend proxy error (HTTP ${response.status})`, 'error'); + } + return false; } } catch (error: any) { setStatus({ online: false, responseTime: null, checking: false }); - - if (error.name === 'AbortError') { - showToast(`${instanceName} connection failed: Request timeout (>15s)`, 'error'); - } else { - showToast(`${instanceName} connection failed: ${error.message || 'Unknown error'}`, 'error'); + + if (!suppressToast) { + if (error.name === 'AbortError') { + showToast(`${instanceName} connection failed: Request timeout (>15s)`, 'error'); + } else { + showToast(`${instanceName} connection failed: ${error.message || 'Unknown error'}`, 'error'); + } } + + return false; } - };; + }; // Function to handle LLM instance deletion const handleDeleteLLMInstance = () => { @@ -546,11 +760,14 @@ export const RAGSettings = ({ try { setOllamaMetrics(prev => ({ ...prev, loading: true })); - // Prepare instance URLs for the API call - const instanceUrls = []; - if (llmInstanceConfig.url) instanceUrls.push(llmInstanceConfig.url); - if (embeddingInstanceConfig.url && embeddingInstanceConfig.url !== llmInstanceConfig.url) { - instanceUrls.push(embeddingInstanceConfig.url); + // Prepare normalized instance URLs for the API call + const instanceUrls: string[] = []; + const llmUrlBase = normalizeBaseUrl(llmInstanceConfig.url); + const embUrlBase = normalizeBaseUrl(embeddingInstanceConfig.url); + + if (llmUrlBase) instanceUrls.push(llmUrlBase); + if (embUrlBase && embUrlBase !== llmUrlBase) { + instanceUrls.push(embUrlBase); } if (instanceUrls.length === 0) { @@ -574,18 +791,18 @@ export const RAGSettings = ({ // Count models for LLM instance const llmChatModels = allChatModels.filter((model: any) => - model.instance_url === llmInstanceConfig.url + normalizeBaseUrl(model.instance_url) === llmUrlBase ); const llmEmbeddingModels = allEmbeddingModels.filter((model: any) => - model.instance_url === llmInstanceConfig.url + normalizeBaseUrl(model.instance_url) === llmUrlBase ); - + // Count models for Embedding instance const embChatModels = allChatModels.filter((model: any) => - model.instance_url === embeddingInstanceConfig.url + normalizeBaseUrl(model.instance_url) === embUrlBase ); const embEmbeddingModels = allEmbeddingModels.filter((model: any) => - model.instance_url === embeddingInstanceConfig.url + normalizeBaseUrl(model.instance_url) === embUrlBase ); // Calculate totals @@ -624,7 +841,7 @@ export const RAGSettings = ({ // Use refs to prevent infinite connection testing const lastTestedLLMConfigRef = useRef({ url: '', name: '', provider: '' }); const lastTestedEmbeddingConfigRef = useRef({ url: '', name: '', provider: '' }); - const lastMetricsFetchRef = useRef({ provider: '', llmUrl: '', embUrl: '', llmOnline: false, embOnline: false }); + const lastMetricsFetchRef = useRef({ provider: '', embProvider: '', llmUrl: '', embUrl: '', llmOnline: false, embOnline: false }); // Auto-testing disabled to prevent API calls on every keystroke per user request // Connection testing should only happen on manual "Test Connection" or "Save Changes" button clicks @@ -672,91 +889,106 @@ export const RAGSettings = ({ // } // }, [embeddingInstanceConfig.url, embeddingInstanceConfig.name, ragSettings.LLM_PROVIDER]); - // Fetch Ollama metrics only when Ollama provider is initially selected (not on URL changes during typing) React.useEffect(() => { - if (ragSettings.LLM_PROVIDER === 'ollama') { - const currentProvider = ragSettings.LLM_PROVIDER; - const lastProvider = lastMetricsFetchRef.current.provider; - - // Only fetch if provider changed to Ollama (scenario 1: user clicks on Ollama Provider) - if (currentProvider !== lastProvider) { - lastMetricsFetchRef.current = { - provider: currentProvider, - llmUrl: llmInstanceConfig.url, - embUrl: embeddingInstanceConfig.url, - llmOnline: llmStatus.online, - embOnline: embeddingStatus.online - }; - console.log('🔄 Fetching Ollama metrics - Provider selected'); - fetchOllamaMetrics(); - } + const current = { + provider: ragSettings.LLM_PROVIDER, + embProvider: embeddingProvider, + llmUrl: normalizeBaseUrl(llmInstanceConfig.url) ?? '', + embUrl: normalizeBaseUrl(embeddingInstanceConfig.url) ?? '', + llmOnline: llmStatus.online, + embOnline: embeddingStatus.online, + }; + const last = lastMetricsFetchRef.current; + + const meaningfulChange = + current.provider !== last.provider || + current.embProvider !== last.embProvider || + current.llmUrl !== last.llmUrl || + current.embUrl !== last.embUrl || + current.llmOnline !== last.llmOnline || + current.embOnline !== last.embOnline; + + if ((current.provider === 'ollama' || current.embProvider === 'ollama') && meaningfulChange) { + lastMetricsFetchRef.current = current; + console.log('🔄 Fetching Ollama metrics - state changed'); + fetchOllamaMetrics(); } - }, [ragSettings.LLM_PROVIDER]); // Only watch provider changes, not URL changes + }, [ragSettings.LLM_PROVIDER, embeddingProvider, llmStatus.online, embeddingStatus.online]); + + const hasApiCredential = (credentialKey: ProviderCredentialKey): boolean => { + if (credentialKey in apiCredentials) { + return Boolean(apiCredentials[credentialKey]); + } + + const fallbackKey = Object.keys(apiCredentials).find( + key => key.toUpperCase() === credentialKey, + ); + + return fallbackKey ? Boolean(apiCredentials[fallbackKey]) : false; + }; // Function to check if a provider is properly configured const getProviderStatus = (providerKey: string): 'configured' | 'missing' | 'partial' => { switch (providerKey) { case 'openai': - // Check if OpenAI API key is configured (case insensitive) - const openAIKey = Object.keys(apiCredentials).find(key => key.toUpperCase() === 'OPENAI_API_KEY'); - const keyValue = openAIKey ? apiCredentials[openAIKey] : undefined; - // Don't consider encrypted placeholders as valid API keys for connection testing - const hasOpenAIKey = openAIKey && keyValue && keyValue.trim().length > 0 && !keyValue.includes('[ENCRYPTED]'); - + const hasOpenAIKey = hasApiCredential('OPENAI_API_KEY'); + // Only show configured if we have both API key AND confirmed connection const openAIConnected = providerConnectionStatus['openai']?.connected || false; const isChecking = providerConnectionStatus['openai']?.checking || false; - - console.log('🔍 OpenAI status check:', { - openAIKey, - keyValue: keyValue ? `${keyValue.substring(0, 10)}...` : keyValue, - hasValue: !!keyValue, - hasOpenAIKey, - openAIConnected, - isChecking, - allCredentials: Object.keys(apiCredentials) - }); - + + // Intentionally avoid logging API key material. + if (!hasOpenAIKey) return 'missing'; if (isChecking) return 'partial'; return openAIConnected ? 'configured' : 'missing'; case 'google': - // Check if Google API key is configured (case insensitive) - const googleKey = Object.keys(apiCredentials).find(key => key.toUpperCase() === 'GOOGLE_API_KEY'); - const googleKeyValue = googleKey ? apiCredentials[googleKey] : undefined; - // Don't consider encrypted placeholders as valid API keys for connection testing - const hasGoogleKey = googleKey && googleKeyValue && googleKeyValue.trim().length > 0 && !googleKeyValue.includes('[ENCRYPTED]'); + const hasGoogleKey = hasApiCredential('GOOGLE_API_KEY'); // Only show configured if we have both API key AND confirmed connection const googleConnected = providerConnectionStatus['google']?.connected || false; const googleChecking = providerConnectionStatus['google']?.checking || false; - + if (!hasGoogleKey) return 'missing'; if (googleChecking) return 'partial'; return googleConnected ? 'configured' : 'missing'; case 'ollama': - // Check if both LLM and embedding instances are configured and online - if (llmStatus.online && embeddingStatus.online) return 'configured'; - if (llmStatus.online || embeddingStatus.online) return 'partial'; - return 'missing'; + { + if (ollamaManualConfirmed || llmStatus.online || embeddingStatus.online) { + return 'configured'; + } + + if (ollamaServerStatus === 'online') { + return 'partial'; + } + + if (ollamaServerStatus === 'offline') { + return 'missing'; + } + + return 'missing'; + } case 'anthropic': - // Use server-side connection status + const hasAnthropicKey = hasApiCredential('ANTHROPIC_API_KEY'); const anthropicConnected = providerConnectionStatus['anthropic']?.connected || false; const anthropicChecking = providerConnectionStatus['anthropic']?.checking || false; + if (!hasAnthropicKey) return 'missing'; if (anthropicChecking) return 'partial'; return anthropicConnected ? 'configured' : 'missing'; case 'grok': - // Use server-side connection status + const hasGrokKey = hasApiCredential('GROK_API_KEY'); const grokConnected = providerConnectionStatus['grok']?.connected || false; const grokChecking = providerConnectionStatus['grok']?.checking || false; + if (!hasGrokKey) return 'missing'; if (grokChecking) return 'partial'; return grokConnected ? 'configured' : 'missing'; case 'openrouter': - // Use server-side connection status + const hasOpenRouterKey = hasApiCredential('OPENROUTER_API_KEY'); const openRouterConnected = providerConnectionStatus['openrouter']?.connected || false; const openRouterChecking = providerConnectionStatus['openrouter']?.checking || false; + if (!hasOpenRouterKey) return 'missing'; if (openRouterChecking) return 'partial'; return openRouterConnected ? 'configured' : 'missing'; default: @@ -764,20 +996,147 @@ export const RAGSettings = ({ } }; - const selectedProviderKey = isProviderKey(ragSettings.LLM_PROVIDER) - ? (ragSettings.LLM_PROVIDER as ProviderKey) + const resolvedProviderForAlert = activeSelection === 'chat' ? chatProvider : embeddingProvider; + const activeProviderKey = isProviderKey(resolvedProviderForAlert) + ? (resolvedProviderForAlert as ProviderKey) : undefined; - const selectedProviderStatus = selectedProviderKey ? getProviderStatus(selectedProviderKey) : undefined; - const shouldShowProviderAlert = Boolean( - selectedProviderKey && selectedProviderStatus === 'missing' - ); - const providerAlertClassName = shouldShowProviderAlert && selectedProviderKey - ? providerAlertStyles[selectedProviderKey] - : ''; - const providerAlertMessage = shouldShowProviderAlert && selectedProviderKey - ? providerAlertMessages[selectedProviderKey] - : ''; + const selectedProviderStatus = activeProviderKey ? getProviderStatus(activeProviderKey) : undefined; + + let providerAlertMessage: string | null = null; + let providerAlertClassName = ''; + + if (activeProviderKey === 'ollama') { + if (ollamaServerStatus === 'offline') { + providerAlertMessage = 'Local Ollama service is not running. Start the Ollama server and ensure it is reachable at the configured URL.'; + providerAlertClassName = providerErrorAlertStyle; + } else if (selectedProviderStatus === 'partial' && ollamaServerStatus === 'online') { + providerAlertMessage = 'Local Ollama service detected. Click "Test Connection" to confirm model availability.'; + providerAlertClassName = providerWarningAlertStyle; + } + } else if (activeProviderKey && selectedProviderStatus === 'missing') { + const providerName = providerDisplayNames[activeProviderKey] ?? activeProviderKey; + providerAlertMessage = `${providerName} API key is not configured. Add it in Settings > API Keys.`; + providerAlertClassName = providerMissingAlertStyle; + } + + const shouldShowProviderAlert = Boolean(providerAlertMessage); + useEffect(() => { + if (chatProvider !== 'ollama') { + if (llmRetryTimeoutRef.current) { + clearTimeout(llmRetryTimeoutRef.current); + llmRetryTimeoutRef.current = null; + } + return; + } + + const baseUrl = ( + ragSettings.LLM_BASE_URL?.trim() || + llmInstanceConfig.url?.trim() || + DEFAULT_OLLAMA_URL + ); + + if (!baseUrl) { + return; + } + + const instanceName = llmInstanceConfig.name?.trim().length + ? llmInstanceConfig.name + : 'LLM Instance'; + + let cancelled = false; + + const runTest = async () => { + if (cancelled) return; + + const success = await manualTestConnection( + baseUrl, + setLLMStatus, + instanceName, + 'chat', + { suppressToast: true } + ); + + if (!success && chatProvider === 'ollama' && !cancelled) { + llmRetryTimeoutRef.current = window.setTimeout(runTest, 5000); + } + }; + + if (llmRetryTimeoutRef.current) { + clearTimeout(llmRetryTimeoutRef.current); + llmRetryTimeoutRef.current = null; + } + + setLLMStatus(prev => ({ ...prev, checking: true })); + runTest(); + + return () => { + cancelled = true; + if (llmRetryTimeoutRef.current) { + clearTimeout(llmRetryTimeoutRef.current); + llmRetryTimeoutRef.current = null; + } + }; + }, [chatProvider, ragSettings.LLM_BASE_URL, ragSettings.LLM_INSTANCE_NAME, llmInstanceConfig.url, llmInstanceConfig.name]); + + useEffect(() => { + if (embeddingProvider !== 'ollama') { + if (embeddingRetryTimeoutRef.current) { + clearTimeout(embeddingRetryTimeoutRef.current); + embeddingRetryTimeoutRef.current = null; + } + return; + } + + const baseUrl = ( + ragSettings.OLLAMA_EMBEDDING_URL?.trim() || + embeddingInstanceConfig.url?.trim() || + DEFAULT_OLLAMA_URL + ); + + if (!baseUrl) { + return; + } + + const instanceName = embeddingInstanceConfig.name?.trim().length + ? embeddingInstanceConfig.name + : 'Embedding Instance'; + + let cancelled = false; + + const runTest = async () => { + if (cancelled) return; + + const success = await manualTestConnection( + baseUrl, + setEmbeddingStatus, + instanceName, + 'embedding', + { suppressToast: true } + ); + + if (!success && embeddingProvider === 'ollama' && !cancelled) { + embeddingRetryTimeoutRef.current = window.setTimeout(runTest, 5000); + } + }; + + if (embeddingRetryTimeoutRef.current) { + clearTimeout(embeddingRetryTimeoutRef.current); + embeddingRetryTimeoutRef.current = null; + } + + setEmbeddingStatus(prev => ({ ...prev, checking: true })); + runTest(); + + return () => { + cancelled = true; + if (embeddingRetryTimeoutRef.current) { + clearTimeout(embeddingRetryTimeoutRef.current); + embeddingRetryTimeoutRef.current = null; + } + }; + }, [embeddingProvider, ragSettings.OLLAMA_EMBEDDING_URL, ragSettings.OLLAMA_EMBEDDING_INSTANCE_NAME, embeddingInstanceConfig.url, embeddingInstanceConfig.name]); + // Test Ollama connectivity when Settings page loads (scenario 4: page load) // This useEffect is placed after function definitions to ensure access to manualTestConnection useEffect(() => { @@ -792,35 +1151,74 @@ export const RAGSettings = ({ }); // Only run once when data is properly loaded and not run before - if (!hasRunInitialTestRef.current && - ragSettings.LLM_PROVIDER === 'ollama' && - Object.keys(ragSettings).length > 0 && - (llmInstanceConfig.url || embeddingInstanceConfig.url)) { + if ( + !hasRunInitialTestRef.current && + (ragSettings.LLM_PROVIDER === 'ollama' || embeddingProvider === 'ollama') && + Object.keys(ragSettings).length > 0 + ) { hasRunInitialTestRef.current = true; console.log('🔄 Settings page loaded with Ollama - Testing connectivity'); - - // Test LLM instance if configured (use URL presence as the key indicator) - // Only test if URL is explicitly set in ragSettings, not just using the default - if (llmInstanceConfig.url && ragSettings.LLM_BASE_URL) { + + // Test LLM instance if a URL is available (either saved or default) + if (llmInstanceConfig.url) { setTimeout(() => { const instanceName = llmInstanceConfig.name || 'LLM Instance'; console.log('🔍 Testing LLM instance on page load:', instanceName, llmInstanceConfig.url); - manualTestConnection(llmInstanceConfig.url, setLLMStatus, instanceName); + manualTestConnection( + llmInstanceConfig.url, + setLLMStatus, + instanceName, + 'chat', + { suppressToast: true } + ); }, 1000); // Increased delay to ensure component is fully ready } - + // If no saved URL, run tests against default endpoint + else { + setTimeout(() => { + const defaultInstanceName = 'Local Ollama (Default)'; + console.log('🔍 Testing default Ollama chat instance on page load:', DEFAULT_OLLAMA_URL); + manualTestConnection( + DEFAULT_OLLAMA_URL, + setLLMStatus, + defaultInstanceName, + 'chat', + { suppressToast: true } + ); + }, 1000); + } + // Test Embedding instance if configured and different from LLM instance - // Only test if URL is explicitly set in ragSettings, not just using the default - if (embeddingInstanceConfig.url && ragSettings.OLLAMA_EMBEDDING_URL && + if (embeddingInstanceConfig.url && embeddingInstanceConfig.url !== llmInstanceConfig.url) { setTimeout(() => { const instanceName = embeddingInstanceConfig.name || 'Embedding Instance'; console.log('🔍 Testing Embedding instance on page load:', instanceName, embeddingInstanceConfig.url); - manualTestConnection(embeddingInstanceConfig.url, setEmbeddingStatus, instanceName); + manualTestConnection( + embeddingInstanceConfig.url, + setEmbeddingStatus, + instanceName, + 'embedding', + { suppressToast: true } + ); }, 1500); // Stagger the tests } - + // If embedding provider is also Ollama but no specific URL is set, test default as fallback + else if (embeddingProvider === 'ollama' && !embeddingInstanceConfig.url) { + setTimeout(() => { + const defaultEmbeddingName = 'Local Ollama (Default)'; + console.log('🔍 Testing default Ollama embedding instance on page load:', DEFAULT_OLLAMA_URL); + manualTestConnection( + DEFAULT_OLLAMA_URL, + setEmbeddingStatus, + defaultEmbeddingName, + 'embedding', + { suppressToast: true } + ); + }, 1500); + } + // Fetch Ollama metrics after testing connections setTimeout(() => { console.log('📊 Fetching Ollama metrics on page load'); @@ -838,12 +1236,63 @@ export const RAGSettings = ({ knowledge retrieval.

- {/* Provider Selection - 6 Button Layout */} + {/* LLM Provider Settings Header */} +
+

+ LLM Provider Settings +

+
+ + {/* Provider Selection Buttons */} +
+ setActiveSelection('chat')} + variant="ghost" + className={`min-w-[180px] px-5 py-3 font-semibold text-white dark:text-white + border border-emerald-400/70 dark:border-emerald-400/40 + bg-black/40 backdrop-blur-md + shadow-[inset_0_0_16px_rgba(15,118,110,0.38)] + hover:bg-emerald-500/12 dark:hover:bg-emerald-500/20 + hover:border-emerald-300/80 hover:shadow-[0_0_22px_rgba(16,185,129,0.5)] + ${(activeSelection === 'chat') + ? 'shadow-[0_0_25px_rgba(16,185,129,0.5)] ring-2 ring-emerald-400/50' + : 'shadow-[0_0_15px_rgba(16,185,129,0.25)]'} + `} + > + + + + setActiveSelection('embedding')} + variant="ghost" + className={`min-w-[180px] px-5 py-3 font-semibold text-white dark:text-white + border border-purple-400/70 dark:border-purple-400/40 + bg-black/40 backdrop-blur-md + shadow-[inset_0_0_16px_rgba(109,40,217,0.38)] + hover:bg-purple-500/12 dark:hover:bg-purple-500/20 + hover:border-purple-300/80 hover:shadow-[0_0_24px_rgba(168,85,247,0.52)] + ${(activeSelection === 'embedding') + ? 'shadow-[0_0_26px_rgba(168,85,247,0.55)] ring-2 ring-purple-400/60' + : 'shadow-[0_0_15px_rgba(168,85,247,0.25)]'} + `} + > + + + +
+ + {/* Context-Aware Provider Grid */}
-
+
{[ { key: 'openai', name: 'OpenAI', logo: '/img/OpenAI.png', color: 'green' }, { key: 'google', name: 'Google', logo: '/img/google-logo.svg', color: 'blue' }, @@ -851,39 +1300,50 @@ export const RAGSettings = ({ { key: 'ollama', name: 'Ollama', logo: '/img/Ollama.png', color: 'purple' }, { key: 'anthropic', name: 'Anthropic', logo: '/img/claude-logo.svg', color: 'orange' }, { key: 'grok', name: 'Grok', logo: '/img/Grok.png', color: 'yellow' } - ].map(provider => ( + ] + .filter(provider => + activeSelection === 'chat' || EMBEDDING_CAPABLE_PROVIDERS.includes(provider.key as ProviderKey) + ) + .map(provider => (
-{(() => { + {(() => { const status = getProviderStatus(provider.key); - const isSelected = ragSettings.LLM_PROVIDER === provider.key; - + if (status === 'configured') { return (
@@ -919,394 +1378,93 @@ export const RAGSettings = ({ ))}
- - {/* Provider-specific configuration */} - {ragSettings.LLM_PROVIDER === 'ollama' && ( -
-
-
-

Ollama Configuration

-

Configure separate Ollama instances for LLM and embedding models

-
-
- {(llmStatus.online && embeddingStatus.online) ? "2 / 2 Online" : - (llmStatus.online || embeddingStatus.online) ? "1 / 2 Online" : "0 / 2 Online"} -
-
- - {/* LLM Instance Card */} -
-
-
-

LLM Instance

-

For chat completions and text generation

-
-
- {llmStatus.checking ? ( - Checking... - ) : llmStatus.online ? ( - Online ({llmStatus.responseTime}ms) - ) : ( - Offline - )} - {llmInstanceConfig.name && llmInstanceConfig.url && ( - - )} -
-
- -
-
- {llmInstanceConfig.name && llmInstanceConfig.url ? ( - <> -
-
{llmInstanceConfig.name}
-
{llmInstanceConfig.url}
-
- -
-
Model:
-
{getDisplayedChatModel(ragSettings)}
-
- -
- {llmStatus.checking ? ( - - ) : null} - {ollamaMetrics.loading ? 'Loading...' : `${ollamaMetrics.llmInstanceModels.total} models available`} -
- - ) : ( -
-
No LLM instance configured
-
Configure an instance to use LLM features
- - {/* Quick setup for single host users */} - {!embeddingInstanceConfig.url && ( -
- -
Sets up both LLM and Embedding for one host
-
- )} - - -
- )} -
- - {llmInstanceConfig.name && llmInstanceConfig.url && ( -
- - - -
- )} -
-
- - {/* Embedding Instance Card */} -
-
-
-

Embedding Instance

-

For generating text embeddings and vector search

-
-
- {embeddingStatus.checking ? ( - Checking... - ) : embeddingStatus.online ? ( - Online ({embeddingStatus.responseTime}ms) - ) : ( - Offline - )} - {embeddingInstanceConfig.name && embeddingInstanceConfig.url && ( - - )} -
-
- -
-
- {embeddingInstanceConfig.name && embeddingInstanceConfig.url ? ( - <> -
-
{embeddingInstanceConfig.name}
-
{embeddingInstanceConfig.url}
-
- -
-
Model:
-
{getDisplayedEmbeddingModel(ragSettings)}
-
- -
- {embeddingStatus.checking ? ( - - ) : null} - {ollamaMetrics.loading ? 'Loading...' : `${ollamaMetrics.embeddingInstanceModels.total} models available`} -
- - ) : ( -
-
No Embedding instance configured
-
Configure an instance to use embedding features
- -
- )} -
- - {embeddingInstanceConfig.name && embeddingInstanceConfig.url && ( -
- - - -
- )} -
-
- - {/* Single Host Indicator */} - {llmInstanceConfig.url && embeddingInstanceConfig.url && - llmInstanceConfig.url === embeddingInstanceConfig.url && ( -
-
- - - - Single Host Setup -
-

- Both LLM and Embedding instances are using the same Ollama host ({llmInstanceConfig.name}) -

-
- )} - - {/* Configuration Summary */} -
-

Configuration Summary

- - {/* Instance Comparison Table */} -
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
ConfigurationLLM InstanceEmbedding Instance
Instance Name - {llmInstanceConfig.name || Not configured} - - {embeddingInstanceConfig.name || Not configured} -
Status - - {llmStatus.checking ? "Checking..." : llmStatus.online ? `Online (${llmStatus.responseTime}ms)` : "Offline"} - - - - {embeddingStatus.checking ? "Checking..." : embeddingStatus.online ? `Online (${embeddingStatus.responseTime}ms)` : "Offline"} - -
Selected Model - {getDisplayedChatModel(ragSettings) || No model selected} - - {getDisplayedEmbeddingModel(ragSettings) || No model selected} -
Available Models - {ollamaMetrics.loading ? ( - - ) : ( -
-
{ollamaMetrics.llmInstanceModels.total} Total Models
- {ollamaMetrics.llmInstanceModels.total > 0 && ( -
- - {ollamaMetrics.llmInstanceModels.chat} Chat - - - {ollamaMetrics.llmInstanceModels.embedding} Embedding - -
- )} -
- )} - 		- {ollamaMetrics.loading ? ( - - ) : ( -
-
{ollamaMetrics.embeddingInstanceModels.total} Total Models
- {ollamaMetrics.embeddingInstanceModels.total > 0 && ( -
- - {ollamaMetrics.embeddingInstanceModels.chat} Chat - - - {ollamaMetrics.embeddingInstanceModels.embedding} Embedding - -
- )} -
- )} -
- - {/* System Readiness Summary */} -
-
- System Readiness: - - {(llmStatus.online && embeddingStatus.online) ? "✓ Ready (Both Instances Online)" : - (llmStatus.online || embeddingStatus.online) ? "⚠ Partial (1 of 2 Online)" : "✗ Not Ready (No Instances Online)"} - -
- - {/* Overall Model Metrics */} -
-
- - - - Overall Available: - - {ollamaMetrics.loading ? ( - - ) : ( - `${ollamaMetrics.totalModels} total (${ollamaMetrics.chatModels} chat, ${ollamaMetrics.embeddingModels} embedding)` - )} - -
-
-
-
-
-
- )} - {shouldShowProviderAlert && (

{providerAlertMessage}

)} -
- + )} + + {/* Save Settings Button */} +
+ + {/* Expandable Ollama Configuration Container */} + {showOllamaConfig && ((activeSelection === 'chat' && chatProvider === 'ollama') || + (activeSelection === 'embedding' && embeddingProvider === 'ollama')) && ( +
+
+
+

+ {activeSelection === 'chat' ? 'LLM Chat Configuration' : 'Embedding Configuration'} +

+

+ {activeSelection === 'chat' + ? 'Configure Ollama instance for chat completions' + : 'Configure Ollama instance for text embeddings'} +

+
+
+ {(activeSelection === 'chat' ? llmStatus.online : embeddingStatus.online) + ? "Online" : "Offline"} +
+
+ + {/* Configuration Content */} +
+ {activeSelection === 'chat' ? ( + // Chat Model Configuration +
+ {llmInstanceConfig.name && llmInstanceConfig.url ? ( + <> +
+
{llmInstanceConfig.name}
+
{llmInstanceConfig.url}
+
+ +
+
Model:
+
{getDisplayedChatModel(ragSettings)}
+
+ +
+ {llmStatus.checking ? ( + + ) : null} + {ollamaMetrics.loading ? 'Loading...' : `${ollamaMetrics.llmInstanceModels?.chat || 0} chat models available`} +
+ +
+ + + +
+ + ) : ( +
+
No LLM instance configured
+
Configure an instance to use LLM chat features
+ +
+ )} +
+ ) : ( + // Embedding Model Configuration +
+ {embeddingInstanceConfig.name && embeddingInstanceConfig.url ? ( + <> +
+
{embeddingInstanceConfig.name}
+
{embeddingInstanceConfig.url}
+
+ +
+
Model:
+
{getDisplayedEmbeddingModel(ragSettings)}
+
+ +
+ {embeddingStatus.checking ? ( + + ) : null} + {ollamaMetrics.loading ? 'Loading...' : `${ollamaMetrics.embeddingInstanceModels?.embedding || 0} embedding models available`} +
+ +
+ + + +
+ + ) : ( +
+
No Embedding instance configured
+
Configure an instance to use embedding features
+ +
+ )} +
+ )} +
+ + {/* Context-Aware Configuration Summary */} +
+

+ {activeSelection === 'chat' ? 'LLM Instance Summary' : 'Embedding Instance Summary'} +

+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Configuration + {activeSelection === 'chat' ? 'LLM Instance' : 'Embedding Instance'} +
Instance Name + {activeSelection === 'chat' + ? (llmInstanceConfig.name || Not configured) + : (embeddingInstanceConfig.name || Not configured) + } +
Instance URL + {activeSelection === 'chat' + ? (llmInstanceConfig.url || Not configured) + : (embeddingInstanceConfig.url || Not configured) + } +
Status + {activeSelection === 'chat' ? ( + + {llmStatus.checking ? "Checking..." : llmStatus.online ? `Online (${llmStatus.responseTime}ms)` : "Offline"} + + ) : ( + + {embeddingStatus.checking ? "Checking..." : embeddingStatus.online ? `Online (${embeddingStatus.responseTime}ms)` : "Offline"} + + )} +
Selected Model + {activeSelection === 'chat' + ? (getDisplayedChatModel(ragSettings) || No model selected) + : (getDisplayedEmbeddingModel(ragSettings) || No model selected) + } +
Available Models + {ollamaMetrics.loading ? ( + + ) : activeSelection === 'chat' ? ( +
+ {ollamaMetrics.llmInstanceModels?.chat || 0} + chat models +
+ ) : ( +
+ {ollamaMetrics.embeddingInstanceModels?.embedding || 0} + embedding models +
+ )} +
+ + {/* Instance-Specific Readiness */} +
+
+ + {activeSelection === 'chat' ? 'LLM Instance Status:' : 'Embedding Instance Status:'} + + + {activeSelection === 'chat' + ? (llmStatus.online ? "✓ Ready" : "✗ Not Ready") + : (embeddingStatus.online ? "✓ Ready" : "✗ Not Ready") + } + +
+ + {/* Instance-Specific Model Metrics */} +
+
+ + + + Available on this instance: + + {ollamaMetrics.loading ? ( + + ) : activeSelection === 'chat' ? ( + `${ollamaMetrics.llmInstanceModels?.chat || 0} chat models` + ) : ( + `${ollamaMetrics.embeddingInstanceModels?.embedding || 0} embedding models` + )} + +
+
+
+
+
+
+ )}
- {/* Model Settings Row - Only show for non-Ollama providers */} - {ragSettings.LLM_PROVIDER !== 'ollama' && ( -
-
- setRagSettings({ - ...ragSettings, - MODEL_CHOICE: e.target.value - })} - placeholder={getModelPlaceholder(ragSettings.LLM_PROVIDER || 'openai')} - accentColor="green" - /> -
-
- setRagSettings({ - ...ragSettings, - EMBEDDING_MODEL: e.target.value - })} - placeholder={getEmbeddingPlaceholder(ragSettings.LLM_PROVIDER || 'openai')} - accentColor="green" - /> -
-
- )} - + {/* Second row: Contextual Embeddings, Max Workers, and description */}
@@ -1778,7 +2210,16 @@ export const RAGSettings = ({ showToast('LLM instance updated successfully', 'success'); // Wait 1 second then automatically test connection and refresh models setTimeout(() => { - manualTestConnection(llmInstanceConfig.url, setLLMStatus, llmInstanceConfig.name); + manualTestConnection( + llmInstanceConfig.url, + setLLMStatus, + llmInstanceConfig.name, + 'chat', + { suppressToast: true } + ).then((success) => { + setOllamaManualConfirmed(success); + setOllamaServerStatus(success ? 'online' : 'offline'); + }); fetchOllamaMetrics(); // Refresh model metrics after saving }, 1000); }} @@ -1829,7 +2270,16 @@ export const RAGSettings = ({ showToast('Embedding instance updated successfully', 'success'); // Wait 1 second then automatically test connection and refresh models setTimeout(() => { - manualTestConnection(embeddingInstanceConfig.url, setEmbeddingStatus, embeddingInstanceConfig.name); + manualTestConnection( + embeddingInstanceConfig.url, + setEmbeddingStatus, + embeddingInstanceConfig.name, + 'embedding', + { suppressToast: true } + ).then((success) => { + setOllamaManualConfirmed(success); + setOllamaServerStatus(success ? 'online' : 'offline'); + }); fetchOllamaMetrics(); // Refresh model metrics after saving }, 1000); }} @@ -1854,7 +2304,7 @@ export const RAGSettings = ({ ]} currentModel={ragSettings.MODEL_CHOICE} modelType="chat" - selectedInstanceUrl={llmInstanceConfig.url.replace('/v1', '')} + selectedInstanceUrl={normalizeBaseUrl(llmInstanceConfig.url) ?? ''} onSelectModel={(modelName: string) => { setRagSettings({ ...ragSettings, MODEL_CHOICE: modelName }); showToast(`Selected LLM model: ${modelName}`, 'success'); @@ -1873,7 +2323,7 @@ export const RAGSettings = ({ ]} currentModel={ragSettings.EMBEDDING_MODEL} modelType="embedding" - selectedInstanceUrl={embeddingInstanceConfig.url.replace('/v1', '')} + selectedInstanceUrl={normalizeBaseUrl(embeddingInstanceConfig.url) ?? ''} onSelectModel={(modelName: string) => { setRagSettings({ ...ragSettings, EMBEDDING_MODEL: modelName }); showToast(`Selected embedding model: ${modelName}`, 'success'); @@ -1907,7 +2357,7 @@ export const RAGSettings = ({ }; // Helper functions to get provider-specific model display -function getDisplayedChatModel(ragSettings: any): string { +function getDisplayedChatModel(ragSettings: RAGSettingsProps["ragSettings"]): string { const provider = ragSettings.LLM_PROVIDER || 'openai'; const modelChoice = ragSettings.MODEL_CHOICE; @@ -1935,8 +2385,8 @@ function getDisplayedChatModel(ragSettings: any): string { } } -function getDisplayedEmbeddingModel(ragSettings: any): string { - const provider = ragSettings.LLM_PROVIDER || 'openai'; +function getDisplayedEmbeddingModel(ragSettings: RAGSettingsProps["ragSettings"]): string { + const provider = ragSettings.EMBEDDING_PROVIDER || ragSettings.LLM_PROVIDER || 'openai'; const embeddingModel = ragSettings.EMBEDDING_MODEL; // Always prioritize user input to allow editing @@ -1964,7 +2414,7 @@ function getDisplayedEmbeddingModel(ragSettings: any): string { } // Helper functions for model placeholders -function getModelPlaceholder(provider: string): string { +function getModelPlaceholder(provider: ProviderKey): string { switch (provider) { case 'openai': return 'e.g., gpt-4o-mini'; @@ -1983,7 +2433,7 @@ function getModelPlaceholder(provider: string): string { } } -function getEmbeddingPlaceholder(provider: string): string { +function getEmbeddingPlaceholder(provider: ProviderKey): string { switch (provider) { case 'openai': return 'Default: text-embedding-3-small'; diff --git a/archon-ui-main/src/services/credentialsService.ts b/archon-ui-main/src/services/credentialsService.ts index f52d9679..b2d2da52 100644 --- a/archon-ui-main/src/services/credentialsService.ts +++ b/archon-ui-main/src/services/credentialsService.ts @@ -23,6 +23,7 @@ export interface RagSettings { OLLAMA_EMBEDDING_URL?: string; OLLAMA_EMBEDDING_INSTANCE_NAME?: string; EMBEDDING_MODEL?: string; + EMBEDDING_PROVIDER?: string; // Crawling Performance Settings CRAWL_BATCH_SIZE?: number; CRAWL_MAX_CONCURRENT?: number; @@ -75,6 +76,16 @@ import { getApiUrl } from "../config/api"; class CredentialsService { private baseUrl = getApiUrl(); + private notifyCredentialUpdate(keys: string[]): void { + if (typeof window === "undefined") { + return; + } + + window.dispatchEvent( + new CustomEvent("archon:credentials-updated", { detail: { keys } }) + ); + } + private handleCredentialError(error: any, context: string): Error { const errorMessage = error instanceof Error ? error.message : String(error); @@ -182,15 +193,16 @@ class CredentialsService { USE_CONTEXTUAL_EMBEDDINGS: false, CONTEXTUAL_EMBEDDINGS_MAX_WORKERS: 3, USE_HYBRID_SEARCH: true, - USE_AGENTIC_RAG: true, - USE_RERANKING: true, - MODEL_CHOICE: "gpt-4.1-nano", - LLM_PROVIDER: "openai", - LLM_BASE_URL: "", - LLM_INSTANCE_NAME: "", - OLLAMA_EMBEDDING_URL: "", - OLLAMA_EMBEDDING_INSTANCE_NAME: "", - EMBEDDING_MODEL: "", + USE_AGENTIC_RAG: true, + USE_RERANKING: true, + MODEL_CHOICE: "gpt-4.1-nano", + LLM_PROVIDER: "openai", + LLM_BASE_URL: "", + LLM_INSTANCE_NAME: "", + OLLAMA_EMBEDDING_URL: "", + OLLAMA_EMBEDDING_INSTANCE_NAME: "", + EMBEDDING_PROVIDER: "openai", + EMBEDDING_MODEL: "", // Crawling Performance Settings defaults CRAWL_BATCH_SIZE: 50, CRAWL_MAX_CONCURRENT: 10, @@ -221,6 +233,7 @@ class CredentialsService { "LLM_INSTANCE_NAME", "OLLAMA_EMBEDDING_URL", "OLLAMA_EMBEDDING_INSTANCE_NAME", + "EMBEDDING_PROVIDER", "EMBEDDING_MODEL", "CRAWL_WAIT_STRATEGY", ].includes(cred.key) @@ -278,7 +291,9 @@ class CredentialsService { throw new Error(`HTTP ${response.status}: ${errorText}`); } - return response.json(); + const updated = await response.json(); + this.notifyCredentialUpdate([credential.key]); + return updated; } catch (error) { throw this.handleCredentialError( error, @@ -302,7 +317,9 @@ class CredentialsService { throw new Error(`HTTP ${response.status}: ${errorText}`); } - return response.json(); + const created = await response.json(); + this.notifyCredentialUpdate([credential.key]); + return created; } catch (error) { throw this.handleCredentialError( error, @@ -321,6 +338,8 @@ class CredentialsService { const errorText = await response.text(); throw new Error(`HTTP ${response.status}: ${errorText}`); } + + this.notifyCredentialUpdate([key]); } catch (error) { throw this.handleCredentialError(error, `Deleting credential '${key}'`); } diff --git a/migration/0.1.0/009_add_provider_placeholders.sql b/migration/0.1.0/009_add_provider_placeholders.sql new file mode 100644 index 00000000..85d526e6 --- /dev/null +++ b/migration/0.1.0/009_add_provider_placeholders.sql @@ -0,0 +1,18 @@ +-- Migration: 009_add_provider_placeholders.sql +-- Description: Add placeholder API key rows for OpenRouter, Anthropic, and Grok +-- Version: 0.1.0 +-- Author: Archon Team +-- Date: 2025 + +-- Insert provider API key placeholders (idempotent) +INSERT INTO archon_settings (key, encrypted_value, is_encrypted, category, description) +VALUES + ('OPENROUTER_API_KEY', NULL, true, 'api_keys', 'OpenRouter API key for hosted community models. Get from: https://openrouter.ai/keys'), + ('ANTHROPIC_API_KEY', NULL, true, 'api_keys', 'Anthropic API key for Claude models. Get from: https://console.anthropic.com/account/keys'), + ('GROK_API_KEY', NULL, true, 'api_keys', 'Grok API key for xAI models. Get from: https://console.x.ai/') +ON CONFLICT (key) DO NOTHING; + +-- Record migration application for tracking +INSERT INTO archon_migrations (version, migration_name) +VALUES ('0.1.0', '009_add_provider_placeholders') +ON CONFLICT (version, migration_name) DO NOTHING; diff --git a/migration/complete_setup.sql b/migration/complete_setup.sql index 1609060c..801b07b4 100644 --- a/migration/complete_setup.sql +++ b/migration/complete_setup.sql @@ -100,7 +100,10 @@ ON CONFLICT (key) DO NOTHING; -- Add provider API key placeholders INSERT INTO archon_settings (key, encrypted_value, is_encrypted, category, description) VALUES -('GOOGLE_API_KEY', NULL, true, 'api_keys', 'Google API Key for Gemini models. Get from: https://aistudio.google.com/apikey') +('GOOGLE_API_KEY', NULL, true, 'api_keys', 'Google API key for Gemini models. Get from: https://aistudio.google.com/apikey'), +('OPENROUTER_API_KEY', NULL, true, 'api_keys', 'OpenRouter API key for hosted community models. Get from: https://openrouter.ai/keys'), +('ANTHROPIC_API_KEY', NULL, true, 'api_keys', 'Anthropic API key for Claude models. Get from: https://console.anthropic.com/account/keys'), +('GROK_API_KEY', NULL, true, 'api_keys', 'Grok API key for xAI models. Get from: https://console.x.ai/') ON CONFLICT (key) DO NOTHING; -- Code Extraction Settings Migration diff --git a/python/src/server/api_routes/knowledge_api.py b/python/src/server/api_routes/knowledge_api.py index 1f26dace..47a3d9db 100644 --- a/python/src/server/api_routes/knowledge_api.py +++ b/python/src/server/api_routes/knowledge_api.py @@ -1288,7 +1288,7 @@ async def stop_crawl_task(progress_id: str): found = False # Step 1: Cancel the orchestration service - orchestration = get_active_orchestration(progress_id) + orchestration = await get_active_orchestration(progress_id) if orchestration: orchestration.cancel() found = True @@ -1306,7 +1306,7 @@ async def stop_crawl_task(progress_id: str): found = True # Step 3: Remove from active orchestrations registry - unregister_orchestration(progress_id) + await unregister_orchestration(progress_id) # Step 4: Update progress tracker to reflect cancellation (only if we found and cancelled something) if found: diff --git a/python/src/server/services/crawling/code_extraction_service.py b/python/src/server/services/crawling/code_extraction_service.py index 1a540f57..b1705b02 100644 --- a/python/src/server/services/crawling/code_extraction_service.py +++ b/python/src/server/services/crawling/code_extraction_service.py @@ -140,6 +140,7 @@ class CodeExtractionService: progress_callback: Callable | None = None, cancellation_check: Callable[[], None] | None = None, provider: str | None = None, + embedding_provider: str | None = None, ) -> int: """ Extract code examples from crawled documents and store them. @@ -150,6 +151,8 @@ class CodeExtractionService: source_id: The unique source_id for all documents progress_callback: Optional async callback for progress updates cancellation_check: Optional function to check for cancellation + provider: Optional LLM provider identifier for summary generation + embedding_provider: Optional embedding provider override for vector creation Returns: Number of code examples stored @@ -158,9 +161,16 @@ class CodeExtractionService: extraction_callback = None if progress_callback: async def extraction_progress(data: dict): - # Scale progress to 0-20% range - raw_progress = data.get("progress", 0) - scaled_progress = int(raw_progress * 0.2) # 0-20% + # Scale progress to 0-20% range with normalization similar to later phases + raw = data.get("progress", data.get("percentage", 0)) + try: + raw_num = float(raw) + except (TypeError, ValueError): + raw_num = 0.0 + if 0.0 <= raw_num <= 1.0: + raw_num *= 100.0 + # 0-20% with clamping + scaled_progress = min(20, max(0, int(raw_num * 0.2))) data["progress"] = scaled_progress await progress_callback(data) extraction_callback = extraction_progress @@ -197,8 +207,15 @@ class CodeExtractionService: if progress_callback: async def summary_progress(data: dict): # Scale progress to 20-90% range - raw_progress = data.get("progress", 0) - scaled_progress = 20 + int(raw_progress * 0.7) # 20-90% + raw = data.get("progress", data.get("percentage", 0)) + try: + raw_num = float(raw) + except (TypeError, ValueError): + raw_num = 0.0 + if 0.0 <= raw_num <= 1.0: + raw_num *= 100.0 + # 20-90% with clamping + scaled_progress = min(90, max(20, 20 + int(raw_num * 0.7))) data["progress"] = scaled_progress await progress_callback(data) summary_callback = summary_progress @@ -216,15 +233,26 @@ class CodeExtractionService: if progress_callback: async def storage_progress(data: dict): # Scale progress to 90-100% range - raw_progress = data.get("progress", 0) - scaled_progress = 90 + int(raw_progress * 0.1) # 90-100% + raw = data.get("progress", data.get("percentage", 0)) + try: + raw_num = float(raw) + except (TypeError, ValueError): + raw_num = 0.0 + if 0.0 <= raw_num <= 1.0: + raw_num *= 100.0 + # 90-100% with clamping + scaled_progress = min(100, max(90, 90 + int(raw_num * 0.1))) data["progress"] = scaled_progress await progress_callback(data) storage_callback = storage_progress # Store code examples in database return await self._store_code_examples( - storage_data, url_to_full_document, storage_callback, provider + storage_data, + url_to_full_document, + storage_callback, + provider, + embedding_provider, ) async def _extract_code_blocks_from_documents( @@ -880,9 +908,20 @@ class CodeExtractionService: current_indent = indent block_start_idx = i current_block.append(line) - elif current_block and len("\n".join(current_block)) >= min_length: + elif current_block: + block_text = "\n".join(current_block) + threshold = ( + min_length + if min_length is not None + else await self._get_min_code_length() + ) + if len(block_text) < threshold: + current_block = [] + current_indent = None + continue + # End of indented block, check if it's code - code_content = "\n".join(current_block) + code_content = block_text # Try to detect language from content language = self._detect_language_from_content(code_content) @@ -1670,12 +1709,20 @@ class CodeExtractionService: url_to_full_document: dict[str, str], progress_callback: Callable | None = None, provider: str | None = None, + embedding_provider: str | None = None, ) -> int: """ Store code examples in the database. Returns: Number of code examples stored + + Args: + storage_data: Prepared code example payloads + url_to_full_document: Mapping of URLs to their full document content + progress_callback: Optional callback for progress updates + provider: Optional LLM provider identifier for summaries + embedding_provider: Optional embedding provider override for vector storage """ # Create progress callback for storage phase storage_progress_callback = None @@ -1713,6 +1760,7 @@ class CodeExtractionService: url_to_full_document=url_to_full_document, progress_callback=storage_progress_callback, provider=provider, + embedding_provider=embedding_provider, ) # Report completion of code extraction/storage phase diff --git a/python/src/server/services/crawling/crawling_service.py b/python/src/server/services/crawling/crawling_service.py index 69c65719..82a98c0c 100644 --- a/python/src/server/services/crawling/crawling_service.py +++ b/python/src/server/services/crawling/crawling_service.py @@ -14,6 +14,7 @@ from typing import Any, Optional from ...config.logfire_config import get_logger, safe_logfire_error, safe_logfire_info from ...utils import get_supabase_client from ...utils.progress.progress_tracker import ProgressTracker +from ..credential_service import credential_service # Import strategies # Import operations @@ -32,22 +33,35 @@ logger = get_logger(__name__) # Global registry to track active orchestration services for cancellation support _active_orchestrations: dict[str, "CrawlingService"] = {} +_orchestration_lock: asyncio.Lock | None = None -def get_active_orchestration(progress_id: str) -> Optional["CrawlingService"]: +def _ensure_orchestration_lock() -> asyncio.Lock: + global _orchestration_lock + if _orchestration_lock is None: + _orchestration_lock = asyncio.Lock() + return _orchestration_lock + + +async def get_active_orchestration(progress_id: str) -> Optional["CrawlingService"]: """Get an active orchestration service by progress ID.""" - return _active_orchestrations.get(progress_id) + lock = _ensure_orchestration_lock() + async with lock: + return _active_orchestrations.get(progress_id) -def register_orchestration(progress_id: str, orchestration: "CrawlingService"): +async def register_orchestration(progress_id: str, orchestration: "CrawlingService"): """Register an active orchestration service.""" - _active_orchestrations[progress_id] = orchestration + lock = _ensure_orchestration_lock() + async with lock: + _active_orchestrations[progress_id] = orchestration -def unregister_orchestration(progress_id: str): +async def unregister_orchestration(progress_id: str): """Unregister an orchestration service.""" - if progress_id in _active_orchestrations: - del _active_orchestrations[progress_id] + lock = _ensure_orchestration_lock() + async with lock: + _active_orchestrations.pop(progress_id, None) class CrawlingService: @@ -246,7 +260,7 @@ class CrawlingService: # Register this orchestration service for cancellation support if self.progress_id: - register_orchestration(self.progress_id, self) + await register_orchestration(self.progress_id, self) # Start the crawl as an async task in the main event loop # Store the task reference for proper cancellation @@ -477,15 +491,27 @@ class CrawlingService: try: # Extract provider from request or use credential service default provider = request.get("provider") + embedding_provider = None + if not provider: try: - from ..credential_service import credential_service provider_config = await credential_service.get_active_provider("llm") provider = provider_config.get("provider", "openai") except Exception as e: - logger.warning(f"Failed to get provider from credential service: {e}, defaulting to openai") + logger.warning( + f"Failed to get provider from credential service: {e}, defaulting to openai" + ) provider = "openai" + try: + embedding_config = await credential_service.get_active_provider("embedding") + embedding_provider = embedding_config.get("provider") + except Exception as e: + logger.warning( + f"Failed to get embedding provider from credential service: {e}. Using configured default." + ) + embedding_provider = None + code_examples_count = await self.doc_storage_ops.extract_and_store_code_examples( crawl_results, storage_results["url_to_full_document"], @@ -493,6 +519,7 @@ class CrawlingService: code_progress_callback, self._check_cancellation, provider, + embedding_provider, ) except RuntimeError as e: # Code extraction failed, continue crawl with warning @@ -548,7 +575,7 @@ class CrawlingService: # Unregister after successful completion if self.progress_id: - unregister_orchestration(self.progress_id) + await unregister_orchestration(self.progress_id) safe_logfire_info( f"Unregistered orchestration service after completion | progress_id={self.progress_id}" ) @@ -567,7 +594,7 @@ class CrawlingService: ) # Unregister on cancellation if self.progress_id: - unregister_orchestration(self.progress_id) + await unregister_orchestration(self.progress_id) safe_logfire_info( f"Unregistered orchestration service on cancellation | progress_id={self.progress_id}" ) @@ -591,7 +618,7 @@ class CrawlingService: await self.progress_tracker.error(error_message) # Unregister on error if self.progress_id: - unregister_orchestration(self.progress_id) + await unregister_orchestration(self.progress_id) safe_logfire_info( f"Unregistered orchestration service on error | progress_id={self.progress_id}" ) diff --git a/python/src/server/services/crawling/document_storage_operations.py b/python/src/server/services/crawling/document_storage_operations.py index 88ed8e80..8bfa4560 100644 --- a/python/src/server/services/crawling/document_storage_operations.py +++ b/python/src/server/services/crawling/document_storage_operations.py @@ -352,6 +352,7 @@ class DocumentStorageOperations: progress_callback: Callable | None = None, cancellation_check: Callable[[], None] | None = None, provider: str | None = None, + embedding_provider: str | None = None, ) -> int: """ Extract code examples from crawled documents and store them. @@ -363,12 +364,19 @@ class DocumentStorageOperations: progress_callback: Optional callback for progress updates cancellation_check: Optional function to check for cancellation provider: Optional LLM provider to use for code summaries + embedding_provider: Optional embedding provider override for code example embeddings Returns: Number of code examples stored """ result = await self.code_extraction_service.extract_and_store_code_examples( - crawl_results, url_to_full_document, source_id, progress_callback, cancellation_check, provider + crawl_results, + url_to_full_document, + source_id, + progress_callback, + cancellation_check, + provider, + embedding_provider, ) return result diff --git a/python/src/server/services/credential_service.py b/python/src/server/services/credential_service.py index 62fbb47a..a8aee849 100644 --- a/python/src/server/services/credential_service.py +++ b/python/src/server/services/credential_service.py @@ -36,42 +36,6 @@ class CredentialItem: description: str | None = None -def _detect_embedding_provider_from_model(embedding_model: str) -> str: - """ - Detect the appropriate embedding provider based on model name. - - Args: - embedding_model: The embedding model name - - Returns: - Provider name: 'google', 'openai', or 'openai' (default) - """ - if not embedding_model: - return "openai" # Default - - model_lower = embedding_model.lower() - - # Google embedding models - google_patterns = [ - "text-embedding-004", - "text-embedding-005", - "text-multilingual-embedding", - "gemini-embedding", - "multimodalembedding" - ] - - if any(pattern in model_lower for pattern in google_patterns): - return "google" - - # OpenAI embedding models (and default for unknown) - openai_patterns = [ - "text-embedding-ada-002", - "text-embedding-3-small", - "text-embedding-3-large" - ] - - # Default to OpenAI for OpenAI models or unknown models - return "openai" class CredentialService: @@ -475,26 +439,24 @@ class CredentialService: # Get the selected provider based on service type if service_type == "embedding": - # Get the LLM provider setting to determine embedding provider - llm_provider = rag_settings.get("LLM_PROVIDER", "openai") - embedding_model = rag_settings.get("EMBEDDING_MODEL", "text-embedding-3-small") + # First check for explicit EMBEDDING_PROVIDER setting (new split provider approach) + explicit_embedding_provider = rag_settings.get("EMBEDDING_PROVIDER") - # Determine embedding provider based on LLM provider - if llm_provider == "google": - provider = "google" - elif llm_provider == "ollama": - provider = "ollama" - elif llm_provider == "openrouter": - # OpenRouter supports both OpenAI and Google embedding models - provider = _detect_embedding_provider_from_model(embedding_model) - elif llm_provider in ["anthropic", "grok"]: - # Anthropic and Grok support both OpenAI and Google embedding models - provider = _detect_embedding_provider_from_model(embedding_model) + # Validate that embedding provider actually supports embeddings + embedding_capable_providers = {"openai", "google", "ollama"} + + if (explicit_embedding_provider and + explicit_embedding_provider != "" and + explicit_embedding_provider in embedding_capable_providers): + # Use the explicitly set embedding provider + provider = explicit_embedding_provider + logger.debug(f"Using explicit embedding provider: '{provider}'") else: - # Default case (openai, or unknown providers) + # Fall back to OpenAI as default embedding provider for backward compatibility + if explicit_embedding_provider and explicit_embedding_provider not in embedding_capable_providers: + logger.warning(f"Invalid embedding provider '{explicit_embedding_provider}' doesn't support embeddings, defaulting to OpenAI") provider = "openai" - - logger.debug(f"Determined embedding provider '{provider}' from LLM provider '{llm_provider}' and embedding model '{embedding_model}'") + logger.debug(f"No explicit embedding provider set, defaulting to OpenAI for backward compatibility") else: provider = rag_settings.get("LLM_PROVIDER", "openai") # Ensure provider is a valid string, not a boolean or other type diff --git a/python/src/server/services/embeddings/embedding_service.py b/python/src/server/services/embeddings/embedding_service.py index 4f825f1d..87ce390b 100644 --- a/python/src/server/services/embeddings/embedding_service.py +++ b/python/src/server/services/embeddings/embedding_service.py @@ -5,15 +5,19 @@ Handles all OpenAI embedding operations with proper rate limiting and error hand """ import asyncio +import inspect import os +from abc import ABC, abstractmethod from dataclasses import dataclass, field from typing import Any +import httpx +import numpy as np import openai from ...config.logfire_config import safe_span, search_logger from ..credential_service import credential_service -from ..llm_provider_service import get_embedding_model, get_llm_client, is_google_embedding_model, is_openai_embedding_model +from ..llm_provider_service import get_embedding_model, get_llm_client from ..threading_service import get_threading_service from .embedding_exceptions import ( EmbeddingAPIError, @@ -64,6 +68,167 @@ class EmbeddingBatchResult: return self.success_count + self.failure_count +class EmbeddingProviderAdapter(ABC): + """Adapter interface for embedding providers.""" + + @abstractmethod + async def create_embeddings( + self, + texts: list[str], + model: str, + dimensions: int | None = None, + ) -> list[list[float]]: + """Create embeddings for the given texts.""" + + +class OpenAICompatibleEmbeddingAdapter(EmbeddingProviderAdapter): + """Adapter for providers using the OpenAI embeddings API shape.""" + + def __init__(self, client: Any): + self._client = client + + async def create_embeddings( + self, + texts: list[str], + model: str, + dimensions: int | None = None, + ) -> list[list[float]]: + request_args: dict[str, Any] = { + "model": model, + "input": texts, + } + if dimensions is not None: + request_args["dimensions"] = dimensions + + response = await self._client.embeddings.create(**request_args) + return [item.embedding for item in response.data] + + +class GoogleEmbeddingAdapter(EmbeddingProviderAdapter): + """Adapter for Google's native embedding endpoint.""" + + async def create_embeddings( + self, + texts: list[str], + model: str, + dimensions: int | None = None, + ) -> list[list[float]]: + try: + google_api_key = await credential_service.get_credential("GOOGLE_API_KEY") + if not google_api_key: + raise EmbeddingAPIError("Google API key not found") + + async with httpx.AsyncClient(timeout=30.0) as http_client: + embeddings = await asyncio.gather( + *( + self._fetch_single_embedding(http_client, google_api_key, model, text, dimensions) + for text in texts + ) + ) + + return embeddings + + except httpx.HTTPStatusError as error: + error_content = error.response.text + search_logger.error( + f"Google embedding API returned {error.response.status_code} - {error_content}", + exc_info=True, + ) + raise EmbeddingAPIError( + f"Google embedding API error: {error.response.status_code} - {error_content}", + original_error=error, + ) from error + except Exception as error: + search_logger.error(f"Error calling Google embedding API: {error}", exc_info=True) + raise EmbeddingAPIError( + f"Google embedding error: {str(error)}", original_error=error + ) from error + + async def _fetch_single_embedding( + self, + http_client: httpx.AsyncClient, + api_key: str, + model: str, + text: str, + dimensions: int | None = None, + ) -> list[float]: + if model.startswith("models/"): + url_model = model[len("models/") :] + payload_model = model + else: + url_model = model + payload_model = f"models/{model}" + url = f"https://generativelanguage.googleapis.com/v1beta/models/{url_model}:embedContent" + headers = { + "x-goog-api-key": api_key, + "Content-Type": "application/json", + } + payload = { + "model": payload_model, + "content": {"parts": [{"text": text}]}, + } + + # Add output_dimensionality parameter if dimensions are specified and supported + if dimensions is not None and dimensions > 0: + model_name = payload_model.removeprefix("models/") + if model_name.startswith("textembedding-gecko"): + supported_dimensions = {128, 256, 512, 768} + else: + supported_dimensions = {128, 256, 512, 768, 1024, 1536, 2048, 3072} + + if dimensions in supported_dimensions: + payload["outputDimensionality"] = dimensions + else: + search_logger.warning( + f"Requested dimension {dimensions} is not supported by Google model '{model_name}'. " + "Falling back to the provider default." + ) + + response = await http_client.post(url, headers=headers, json=payload) + response.raise_for_status() + + result = response.json() + embedding = result.get("embedding", {}) + values = embedding.get("values") if isinstance(embedding, dict) else None + if not isinstance(values, list): + raise EmbeddingAPIError(f"Invalid embedding payload from Google: {result}") + + # Normalize embeddings for dimensions < 3072 as per Google's documentation + actual_dimension = len(values) + if actual_dimension > 0 and actual_dimension < 3072: + values = self._normalize_embedding(values) + + return values + + def _normalize_embedding(self, embedding: list[float]) -> list[float]: + """Normalize embedding vector for dimensions < 3072.""" + try: + embedding_array = np.array(embedding, dtype=np.float32) + norm = np.linalg.norm(embedding_array) + if norm > 0: + normalized = embedding_array / norm + return normalized.tolist() + else: + search_logger.warning("Zero-norm embedding detected, returning unnormalized") + return embedding + except Exception as e: + search_logger.error(f"Failed to normalize embedding: {e}") + # Return original embedding if normalization fails + return embedding + + +def _get_embedding_adapter(provider: str, client: Any) -> EmbeddingProviderAdapter: + provider_name = (provider or "").lower() + if provider_name == "google": + return GoogleEmbeddingAdapter() + return OpenAICompatibleEmbeddingAdapter(client) + + +async def _maybe_await(value: Any) -> Any: + """Await the value if it is awaitable, otherwise return as-is.""" + + return await value if inspect.isawaitable(value) else value + # Provider-aware client factory get_openai_client = get_llm_client @@ -185,27 +350,25 @@ async def create_embeddings_batch( "create_embeddings_batch", text_count=len(texts), total_chars=sum(len(t) for t in texts) ) as span: try: - # Intelligent embedding provider routing based on model type - # Get the embedding model first to determine the correct provider - embedding_model = await get_embedding_model(provider=provider) + embedding_config = await _maybe_await( + credential_service.get_active_provider(service_type="embedding") + ) - # Route to correct provider based on model type - if is_google_embedding_model(embedding_model): - embedding_provider = "google" - search_logger.info(f"Routing to Google for embedding model: {embedding_model}") - elif is_openai_embedding_model(embedding_model) or "openai/" in embedding_model.lower(): + embedding_provider = provider or embedding_config.get("provider") + + if not isinstance(embedding_provider, str) or not embedding_provider.strip(): embedding_provider = "openai" - search_logger.info(f"Routing to OpenAI for embedding model: {embedding_model}") - else: - # Keep original provider for ollama and other providers - embedding_provider = provider - search_logger.info(f"Using original provider '{provider}' for embedding model: {embedding_model}") + if not embedding_provider: + search_logger.error("No embedding provider configured") + raise ValueError("No embedding provider configured. Please set EMBEDDING_PROVIDER environment variable.") + + search_logger.info(f"Using embedding provider: '{embedding_provider}' (from EMBEDDING_PROVIDER setting)") async with get_llm_client(provider=embedding_provider, use_embedding_provider=True) as client: # Load batch size and dimensions from settings try: - rag_settings = await credential_service.get_credentials_by_category( - "rag_strategy" + rag_settings = await _maybe_await( + credential_service.get_credentials_by_category("rag_strategy") ) batch_size = int(rag_settings.get("EMBEDDING_BATCH_SIZE", "100")) embedding_dimensions = int(rag_settings.get("EMBEDDING_DIMENSIONS", "1536")) @@ -215,6 +378,8 @@ async def create_embeddings_batch( embedding_dimensions = 1536 total_tokens_used = 0 + adapter = _get_embedding_adapter(embedding_provider, client) + dimensions_to_use = embedding_dimensions if embedding_dimensions > 0 else None for i in range(0, len(texts), batch_size): batch = texts[i : i + batch_size] @@ -243,16 +408,14 @@ async def create_embeddings_batch( try: # Create embeddings for this batch embedding_model = await get_embedding_model(provider=embedding_provider) - - response = await client.embeddings.create( - model=embedding_model, - input=batch, - dimensions=embedding_dimensions, + embeddings = await adapter.create_embeddings( + batch, + embedding_model, + dimensions=dimensions_to_use, ) - # Add successful embeddings - for text, item in zip(batch, response.data, strict=False): - result.add_success(item.embedding, text) + for text, vector in zip(batch, embeddings, strict=False): + result.add_success(vector, text) break # Success, exit retry loop @@ -297,6 +460,17 @@ async def create_embeddings_batch( await asyncio.sleep(wait_time) else: raise # Will be caught by outer try + except EmbeddingRateLimitError as e: + retry_count += 1 + if retry_count < max_retries: + wait_time = 2**retry_count + search_logger.warning( + f"Embedding rate limit for batch {batch_index}: {e}. " + f"Waiting {wait_time}s before retry {retry_count}/{max_retries}" + ) + await asyncio.sleep(wait_time) + else: + raise except Exception as e: # This batch failed - track failures but continue with next batch diff --git a/python/src/server/services/storage/code_storage_service.py b/python/src/server/services/storage/code_storage_service.py index a993bc70..8e237f7e 100644 --- a/python/src/server/services/storage/code_storage_service.py +++ b/python/src/server/services/storage/code_storage_service.py @@ -1091,6 +1091,7 @@ async def add_code_examples_to_supabase( url_to_full_document: dict[str, str] | None = None, progress_callback: Callable | None = None, provider: str | None = None, + embedding_provider: str | None = None, ): """ Add code examples to the Supabase code_examples table in batches. @@ -1105,6 +1106,8 @@ async def add_code_examples_to_supabase( batch_size: Size of each batch for insertion url_to_full_document: Optional mapping of URLs to full document content progress_callback: Optional async callback for progress updates + provider: Optional LLM provider used for summary generation tracking + embedding_provider: Optional embedding provider override for vector generation """ if not urls: return @@ -1183,8 +1186,8 @@ async def add_code_examples_to_supabase( # Use original combined texts batch_texts = combined_texts - # Create embeddings for the batch - result = await create_embeddings_batch(batch_texts, provider=provider) + # Create embeddings for the batch (optionally overriding the embedding provider) + result = await create_embeddings_batch(batch_texts, provider=embedding_provider) # Log any failures if result.has_failures: @@ -1201,7 +1204,7 @@ async def add_code_examples_to_supabase( from ..llm_provider_service import get_embedding_model # Get embedding model name - embedding_model_name = await get_embedding_model(provider=provider) + embedding_model_name = await get_embedding_model(provider=embedding_provider) # Get LLM chat model (used for code summaries and contextual embeddings if enabled) llm_chat_model = None diff --git a/python/tests/test_code_extraction_source_id.py b/python/tests/test_code_extraction_source_id.py index 05405ee7..7899c7fc 100644 --- a/python/tests/test_code_extraction_source_id.py +++ b/python/tests/test_code_extraction_source_id.py @@ -111,8 +111,8 @@ class TestCodeExtractionSourceId: assert args[2] == source_id assert args[3] is None assert args[4] is None - if len(args) > 5: - assert args[5] is None + assert args[5] is None + assert args[6] is None assert result == 5 @pytest.mark.asyncio