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Model R6 (97% pass rate on 33-probe eval, val_bpb 0.2635): - modal/serve.py + modal/_tools.py: tool-aware streaming with TavilySearchBackend auto-detect, python_start/end state machine, output_start/end forcing; mount tavily secret - modal/serve.py: MODEL_TAG=d24-sft-r6, model path points at new SFT r6 - services/chat-api/routes/messages.py: accept thinking_mode flag, inject samosaChaat system prompt (direct or <think> variant) into first user message before streaming to Modal - services/frontend/components/chat/ChatInput.tsx: Brain toggle 'Think' button next to send; when active, model uses think mode - services/frontend/components/chat/ChatWindow.tsx: track thinkingMode state, pass through to API body as thinking_mode - services/frontend/components/chat/MessageBubble.tsx: parse and render <think>...</think> as collapsible italic blocks; <|python_start|>...<|python_end|> as tool-call cards with icons per tool name; <|output_start|>...<|output_end|> as result cards with expandable JSON - nanochat/tools.py: TavilySearchBackend class + auto-detect - nanochat/ui.html: legacy UI reasoning toggle (kept for parity) Tool execution verified live: query -> web_search via Tavily -> Macron returned with grounded answer. |
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| Dockerfile | ||
| pyproject.toml | ||
| README.md | ||
Chat API Service
Orchestration layer for samosaChaat conversations. Manages conversation state in PostgreSQL, authenticates every request via the auth service, and proxies streaming inference requests via Server-Sent Events.
Endpoints
| Method | Path | Description |
|---|---|---|
| GET | /api/health |
Liveness probe (unauthenticated) |
| GET | /api/conversations |
List the authenticated user's conversations, grouped by date |
| POST | /api/conversations |
Create a new conversation |
| GET | /api/conversations/{id} |
Fetch a conversation + full message history |
| PUT | /api/conversations/{id} |
Update the conversation title |
| DELETE | /api/conversations/{id} |
Delete a conversation (cascade deletes messages) |
| POST | /api/conversations/{id}/messages |
Append a user message and stream the assistant response |
| POST | /api/conversations/{id}/regenerate |
Delete the last assistant message and regenerate it |
| GET | /api/models |
Proxy to inference GET /models |
| POST | /api/models/swap |
Proxy to inference POST /models/swap (admin only) |
All authenticated endpoints expect Authorization: Bearer <jwt>. The chat API
validates the token by calling the auth service POST /auth/validate with the
shared X-Internal-API-Key header and caches the result for 5 minutes.
Environment
| Variable | Default | Purpose |
|---|---|---|
DATABASE_URL |
postgresql+asyncpg://localhost/samosachaat |
PostgreSQL connection string |
AUTH_SERVICE_URL |
http://auth:8001 |
Base URL of the auth service |
INFERENCE_SERVICE_URL |
http://inference:8000 |
Base URL of the inference service |
INTERNAL_API_KEY |
— | Shared key for internal service auth |
MAX_CONVERSATION_HISTORY |
50 |
Max messages included in each inference call |
MAX_TOKEN_BUDGET |
6000 |
Character budget proxy for the above |
FRONTEND_URL |
http://localhost:3000 |
Origin allowed by CORS |
LOG_LEVEL |
INFO |
Python log level |
Running locally
uv pip install -e ".[dev]"
uvicorn src.main:app --reload --port 8002
Running tests
cd services/chat-api
pytest
Tests use SQLite + aiosqlite for a throwaway database, respx to mock the auth service, and hand-crafted httpx mocks for the inference SSE stream.