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* 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 <noreply@anthropic.com> * 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 <joshchesser97@gmail.com> Co-authored-by: Claude <noreply@anthropic.com>