494 lines
36 KiB
Markdown
494 lines
36 KiB
Markdown
# Plan: Add Mistral AI as Alternative Chat Provider
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## Context
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bDS currently routes all AI chat through the OpenCode Zen gateway (`opencode.ai/zen/v1/...`) with two code paths: Anthropic Messages API and OpenAI-compatible. The user wants Mistral AI added as a direct alternative provider with frontier models that support chat completion, tool use, and vision. Mistral's API is OpenAI-compatible (`api.mistral.ai/v1/chat/completions`), making integration straightforward.
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**Important architecture facts:**
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- HTTP requests are currently **non-streaming** (full response body collected, text emitted after each complete call) — to be converted to SSE streaming as part of this work
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- Neither `sendAnthropicMessage()` nor `sendOpenAIMessage()` currently sets `tool_choice`
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- `sendOpenAIMessage()` does **not** convert `view_image` results to `image_url` format — they are JSON-stringified
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- `generateConversationTitle()` is hardcoded to `claude-haiku-4-5` via `ZEN_ANTHROPIC_URL`
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- `analyzeMediaImage()` is hardcoded to `claude-sonnet-4-5` via `ZEN_ANTHROPIC_URL`
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- `checkReady()` only checks the OpenCode key — blocks `sendMessage()` for keyless users
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## Target Models
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| Model ID | Display Name | Vision | Tools | Context Window | Context Budget |
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|----------|-------------|--------|-------|----------------|----------------|
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| `mistral-large-2512` | Mistral Large 3 | yes | yes | 40k | 35,000 |
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| `mistral-medium-2508` | Mistral Medium 3.1 | yes | yes | 40k | 35,000 |
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| `mistral-small-2506` | Mistral Small 3.2 | yes | yes | 128k | 120,000 |
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| `devstral-small-2505` | Devstral Small | no | yes | 128k | 120,000 |
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| `devstral-large-2506` | Devstral 2 | no | yes | 256k | 240,000 |
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## Files to Modify
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### 1. `src/main/engine/OpenCodeManager.ts` - Core provider logic
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**A. Add Mistral constants** (near lines 23-25)
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- `MISTRAL_API_URL = 'https://api.mistral.ai/v1/chat/completions'`
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- `MISTRAL_MODELS_URL = 'https://api.mistral.ai/v1/models'`
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**B. Add Mistral models to `MODEL_DISPLAY_NAMES`** (lines 28-69)
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```
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'mistral-large-2512': 'Mistral Large 3'
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'mistral-medium-2508': 'Mistral Medium 3.1'
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'mistral-small-2506': 'Mistral Small 3.2'
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'devstral-small-2505': 'Devstral Small'
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'devstral-large-2506': 'Devstral 2'
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```
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**C. Update `detectProvider()`** (lines 1839-1845)
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- Add: `if (id.startsWith('mistral') || id.startsWith('ministral') || id.startsWith('devstral') || id.startsWith('codestral') || id.startsWith('pixtral')) return 'mistral';`
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- This covers all current and foreseeable Mistral model prefixes (Mistral, Ministral, Devstral, Codestral, Pixtral)
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**C2. Update `formatModelName()` and `UPPERCASE_PREFIXES`**
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- `formatModelName()` (L1869) first checks `MODEL_DISPLAY_NAMES`, then auto-formats via hyphen splitting + capitalization
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- All 5 Mistral models are in `MODEL_DISPLAY_NAMES`, so auto-format is a fallback for future unknown models — no changes needed
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- `UPPERCASE_PREFIXES` (L72) contains `['gpt', 'glm']` — no Mistral prefixes need uppercasing, so no changes needed
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**D. Add Mistral API key storage**
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- New field: `private mistralApiKey: string = ''`
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- New methods: `setMistralApiKey()`, `getMistralApiKey()`, `validateMistralApiKey()`
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- Load on init from settings key `'mistral_api_key'`
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**E. Update `checkReady()`**
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- Return `ready: true` if **either** OpenCode key or Mistral key is set
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- Extend `ChatReadyStatus` to report per-provider availability, e.g. `providers: { opencode: boolean, mistral: boolean }`
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- Callers (`Sidebar.tsx`, `sendMessage()`) must gate on the relevant provider, not a single boolean
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**F. Parameterize `sendOpenAIMessage()` for Mistral (no separate method)**
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- Mistral uses the identical OpenAI-compatible chat/completions format — creating a separate `sendMistralRequest()` would be a near-duplicate
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- Instead, parameterize `sendOpenAIMessage()` to accept URL, API key, and provider-specific options:
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- Add params: `apiUrl: string`, `apiKey: string`, `providerOptions?: { parallelToolCalls?: boolean }`
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- `sendMessage()` determines provider via `detectProvider()` and calls `sendOpenAIMessage()` with the correct URL/key/options
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- For OpenCode OpenAI path: URL = `ZEN_OPENAI_URL`, key = `this.apiKey`
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- For Mistral: URL = `MISTRAL_API_URL`, key = `this.mistralApiKey`, `parallelToolCalls: false`
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- `tool_choice`: omit entirely for all OpenAI-compatible providers (default `"auto"` is correct)
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- `parallel_tool_calls: false` — set explicitly for Mistral only; our tool executor runs tools sequentially, so parallel tool calls would break the execution loop
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**F2. Add `MODEL_CONTEXT_BUDGETS` map**
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- New constant map `MODEL_CONTEXT_BUDGETS: Record<string, number>` with per-model token budgets
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- `truncateToTokenBudget()` (L1654) currently defaults to `maxContextTokens = 150000`
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- In `sendAnthropicMessage()` and `sendOpenAIMessage()`: pass the model's context budget from the map (defaulting to 150,000 for OpenCode models)
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- The parameterized `sendOpenAIMessage()` looks up `MODEL_CONTEXT_BUDGETS[modelId]` for Mistral models and passes to truncation
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- Values from Target Models table (35k, 120k, 240k)
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**G. Fix tool-call message history in OpenAI-compatible path**
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- Within a single `sendMessage()` call, the tool loop correctly tracks tool results across rounds
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- However, `tool` role messages are not persisted to DB-backed conversation history — on conversation resume, the model loses context about prior tool results
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- Ensure `tool` role messages are included when persisting conversation history so cross-session continuity works
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- This affects all OpenAI-compatible providers (OpenCode OpenAI path + Mistral)
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**H. Fix vision in OpenAI-compatible path (affects Mistral too)**
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- `sendOpenAIMessage()` currently JSON-stringifies `view_image` results — no `image_url` conversion
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- Add `image_url` format conversion for `__isImageResult` objects in the OpenAI path:
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`{ type: 'image_url', image_url: { url: 'data:image/webp;base64,...' } }`
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- This fixes vision for all OpenAI-compatible providers, not just Mistral
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**I. Update `getAvailableModels()` — merge from both providers**
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- **Model list merge strategy**: fetch models from each configured provider's API endpoint and merge into a single list. When both keys are configured, return models from both; when only one key is set, return only that provider's models; when no key is set, return an empty list (UI disables the dropdown)
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- OpenCode models: fetched from existing OpenCode API (as today)
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- Mistral models: fetched from `GET https://api.mistral.ai/v1/models` when Mistral key is set; cross-reference returned IDs with `MODEL_DISPLAY_NAMES` to use display names + static `vision`/`contextBudget` metadata
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- Every model entry carries `provider: 'opencode' | 'mistral'` so the UI and engine can resolve the correct API URL + key
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- Invalidate `cachedModels`/`cachedModelsAt` when any provider key is added or removed
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**J. Update `generateConversationTitle()` — make configurable in Preferences**
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- Currently hardcoded to `claude-haiku-4-5` via `ZEN_ANTHROPIC_URL` with OpenCode key
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- Add a **"Title generation model"** preference in Settings so users can pick the cheapest model for this task
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- Default: `claude-haiku-4-5` (OpenCode) or `mistral-small-2506` (Mistral) based on available keys
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- Route to the correct provider URL + API key based on the selected model's provider
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- Must work with any configured provider, not just OpenCode
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**K. Update `analyzeMediaImage()` — make configurable in Preferences** (lines 2066-2192)
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- Currently hardcoded to `claude-sonnet-4-5` via `ZEN_ANTHROPIC_URL`
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- Add an **"Image analysis model"** preference in Settings so users can select a dedicated vision model independent of their chat model (e.g. use Devstral for chat but Mistral Large 3 for images)
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- **Only vision-capable models may be offered** — filter model list by a `vision` capability flag (e.g. Devstral models have no vision and must be excluded)
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- Default: `claude-sonnet-4-5` (OpenCode) or first vision-capable Mistral model based on available keys
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- Route to the correct provider URL + API key based on the selected model's provider
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- When routed to Mistral: use `image_url` format with base64 data URI
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- When routed to OpenCode/Anthropic: keep current Anthropic-native `image` block format
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**L. Update `analyzeTaxonomy()`**
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- Currently uses `this.apiKey` (OpenCode) for both Anthropic and OpenAI paths
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- Has an early-return guard `if (!this.apiKey)` that must become provider-aware — check Mistral key when provider is Mistral
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- When a Mistral model is selected: use Mistral API key + `MISTRAL_API_URL`
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- Must branch on provider to select correct key and URL
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**L2. Update `analyzeMediaImage()` API key guard**
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- Same issue: has `if (!this.apiKey)` early-return guard
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- Must become provider-aware — check the relevant provider's key based on the selected image analysis model
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- When routed to Mistral: check `this.mistralApiKey` instead of `this.apiKey`
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**M. Convert chat HTTP calls to SSE streaming**
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Currently `httpRequest()` buffers the entire response body before any text reaches the UI. Users wait 5–30s per API round with only a bouncing-dots indicator. All three providers (Anthropic, OpenAI, Mistral) support `stream: true` with SSE.
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**M1. Core streaming infrastructure — `httpRequestStream()`**
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- New method (~100 lines) — uses Node.js `https.request()` but reads `res` as a readable stream
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- Returns an async iterable of parsed SSE events (or accepts an `onEvent` callback)
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- SSE line protocol: lines separated by `\n\n`, each line prefixed with `event: ` or `data: `
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- Must handle:
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- Buffering partial lines across `data` chunks (TCP may split mid-line)
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- Empty `data:` lines (keep-alive pings)
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- `data: [DONE]` sentinel — terminates the stream for OpenAI/Mistral (do NOT try to JSON.parse this)
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- Multiple `data:` lines between double-newlines (concatenate per SSE spec)
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- Supports `AbortSignal` — calls `req.destroy()` to terminate immediately
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- 120-second timeout matching existing `httpRequest()`
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- On non-2xx status: collect the error body (not streamed) and throw with parsed error message
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**M2. SSE parser for OpenAI/Mistral format** (~50 lines)
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OpenAI and Mistral use identical SSE event structure:
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```
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data: {"id":"...","choices":[{"delta":{"content":"Hello"}}]}
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data: {"id":"...","choices":[{"delta":{"tool_calls":[{"index":0,"id":"call_...","function":{"name":"search_posts","arguments":""}}]}}]}
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data: {"id":"...","choices":[{"delta":{"tool_calls":[{"index":0,"function":{"arguments":"{\"query\""}}]}}]}
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...
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data: {"id":"...","usage":{"prompt_tokens":150,"completion_tokens":42,"total_tokens":192}}
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data: [DONE]
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```
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- **Text deltas**: `choices[0].delta.content` — emit via `onDelta(content)` immediately
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- **Tool call start**: `delta.tool_calls[i]` with `id` + `function.name` — begin accumulating arguments for tool call at index `i`
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- **Tool call argument fragments**: `delta.tool_calls[i].function.arguments` — append to argument accumulator string for index `i`
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- **Finish reason**: `choices[0].finish_reason === 'tool_calls'` or `'stop'` — signals end of this chunk
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- **Token usage**: arrives in the **final chunk before `[DONE]`** only if `stream_options: { include_usage: true }` is set in the request body — parse `usage.prompt_tokens`, `usage.completion_tokens`, `usage.total_tokens`
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- **`[DONE]` sentinel**: stop iteration, do NOT JSON.parse
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- After stream ends: if tool calls were accumulated, JSON.parse each tool's assembled arguments string and execute
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**M3. SSE parser for Anthropic format** (~60 lines)
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Anthropic uses named event types:
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```
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event: message_start
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data: {"type":"message_start","message":{"id":"...","model":"...","usage":{"input_tokens":150}}}
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event: content_block_start
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data: {"type":"content_block_start","index":0,"content_block":{"type":"text","text":""}}
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event: content_block_delta
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data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"Hello"}}
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event: content_block_start
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data: {"type":"content_block_start","index":1,"content_block":{"type":"tool_use","id":"toolu_...","name":"search_posts"}}
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event: content_block_delta
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data: {"type":"content_block_delta","index":1,"delta":{"type":"input_json_delta","partial_json":"{\"query\""}}
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event: content_block_stop
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data: {"type":"content_block_stop","index":1}
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event: message_delta
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data: {"type":"message_delta","delta":{"stop_reason":"tool_use"},"usage":{"output_tokens":42}}
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event: message_stop
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data: {"type":"message_stop"}
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```
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- **`message_start`**: extract `usage.input_tokens` (prompt tokens) + `usage.cache_read_input_tokens` + `usage.cache_creation_input_tokens`
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- **`content_block_start`** with `type: 'text'`: no-op (empty initial text)
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- **`content_block_start`** with `type: 'tool_use'`: record tool call `id` and `name` at block index
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- **`content_block_delta`** with `type: 'text_delta'`: emit via `onDelta(delta.text)` immediately
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- **`content_block_delta`** with `type: 'input_json_delta'`: append `delta.partial_json` to argument accumulator
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- **`content_block_stop`**: if tool block, JSON.parse the accumulated arguments for that block
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- **`message_delta`**: extract `usage.output_tokens` (completion tokens), `delta.stop_reason`
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- **`message_stop`**: stream complete
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- **`ping`**: ignore (keep-alive)
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- **`error`**: throw with `data.error.message` — handles mid-stream server errors (e.g. overloaded)
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**M4. Request body changes**
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- `sendAnthropicMessage()`: add `"stream": true` to request body
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- `sendOpenAIMessage()` (used for both OpenCode OpenAI and Mistral): add `"stream": true` and `"stream_options": { "include_usage": true }` to request body — this is **required** to receive token usage in streaming mode (without it, usage is omitted from streamed responses)
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**M5. Tool call accumulation during streaming**
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- Tool call arguments arrive as partial JSON fragments across many SSE events
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- Maintain a per-stream accumulator: `Map<number, { id: string, name: string, arguments: string }>` keyed by tool call index
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- Append each `arguments` fragment to the accumulator string
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- On stream completion (finish_reason `tool_calls`/`tool_use`, or `content_block_stop` for Anthropic): JSON.parse the full accumulated arguments string and execute the tool
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- If JSON.parse fails on accumulated arguments, report a tool error to the model and continue
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**M6. Error handling during streaming**
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- **Non-2xx status on connection**: do NOT stream; collect the full error body and throw (same as current `httpRequest()` behavior)
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- **Mid-stream TCP disconnect / network error**: `res.on('error')` handler — emit whatever text was accumulated so far, then throw so the tool-call loop can surface the error to the user
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- **Mid-stream API error event**: Anthropic sends `event: error` with error details; OpenAI/Mistral return an error JSON in a `data:` line — detect and throw with parsed error message
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- **Abort during streaming**: `req.destroy()` triggers `res.on('error')` or `res.on('close')` — handle gracefully without surfacing as an error to the user (it's intentional cancellation)
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**What does NOT change:**
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- The renderer pipeline — `onDelta` → IPC `chat-stream-delta` → `appendStreamDelta` → React state → live Markdown rendering already works token-by-token; it just receives one big chunk today
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- `AbortController` abort support — `req.destroy()` stops the stream immediately instead of wasting a buffered response
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- The tool-call loop structure — still max 10 rounds, still sequential
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**What to keep non-streaming:**
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- `generateConversationTitle()` — small one-shot request, buffering is fine
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- `analyzeMediaImage()` — one-shot, no UI streaming needed
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- `analyzeTaxonomy()` — one-shot, no UI streaming needed
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- `validateApiKey()` / `validateMistralApiKey()` — small validation requests
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- Note: `validateMistralApiKey()` must call `GET https://api.mistral.ai/v1/models` with `Authorization: Bearer ${key}`. Mistral returns `{ data: [{ id, object, created, owned_by }] }` — check for HTTP 200 + non-empty `data` array. On 401, return invalid. On success, optionally cross-reference returned model IDs with `MODEL_DISPLAY_NAMES` to verify expected models are available
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**Estimated scope:** ~300 lines of new code in `OpenCodeManager.ts` (streaming adds ~100 lines vs original estimate)
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### 2. `src/main/engine/ChatEngine.ts` - Settings persistence
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**A. Add Mistral key helpers**
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- Use existing generic `getSetting()`/`setSetting()` with key `'mistral_api_key'` — no dedicated methods needed, avoids unnecessary boilerplate
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- ChatEngine already exposes generic helpers for reading/writing the settings table
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**B. Default model is user-driven**
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- `getSelectedModel()` defaults to `'claude-sonnet-4-5'`
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- When user configures providers in Preferences, they explicitly select their default model — no automatic fallback logic needed
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- All surfaces (ChatPanel, AssistantSidebar, ImportAnalysisView) use this preference as default
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- If selected model's provider key is later removed:
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- `sendMessage()` returns a clear error string: "The selected model requires a {provider} API key. Configure it in Settings."
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- `checkReady()` still returns `ready: true` if any other provider is available
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- ChatPanel shows an inline error banner (not a toast) with a link/button to open Settings
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- i18n key: `chat.providerKeyMissing` — "The model '{model}' requires a {provider} API key. Go to Settings to configure it."
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- Add this key to all 5 locale files
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**C. Add per-purpose model preferences**
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- `getTitleModel()` / `setTitleModel(modelId)` — settings key `'chat_title_model'`
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- `getImageAnalysisModel()` / `setImageAnalysisModel(modelId)` — settings key `'chat_image_analysis_model'`
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- Both default to `null` (= use hardcoded defaults per provider)
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### 3. `src/main/ipc/chatHandlers.ts` - IPC bridge
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**A. Add Mistral-specific handlers**
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- `chat:setMistralApiKey` - validate + persist Mistral key, invalidate model cache
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- `chat:getMistralApiKey` - return masked key
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- `chat:validateMistralApiKey` - test key against Mistral API
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**B. Update `chat:getAvailableModels`**
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- Include Mistral models when Mistral key is configured
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- Return provider info per model
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**C. Update `chat:checkReady`**
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- Report readiness for both providers independently
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**D. Update `getOpenCodeManager()` init**
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- Load `'mistral_api_key'` from settings on first call (alongside OpenCode key)
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- Call `manager.setMistralApiKey()` during init
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**E. Add per-purpose model preference handlers**
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- `chat:setTitleModel` / `chat:getTitleModel` — persist + load title generation model preference
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- `chat:setImageAnalysisModel` / `chat:getImageAnalysisModel` — persist + load image analysis model preference
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### 4. `src/main/shared/electronApi.ts` - Type definitions
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**A. Extend `ChatModel` interface**
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- Add `provider: 'opencode' | 'mistral'` field (already optional, ensure populated)
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- Add `vision: boolean` field — indicates whether the model supports image inputs (used to filter the image analysis model dropdown)
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**B. Extend `ChatReadyStatus` interface**
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- Add `providers?: { opencode: boolean; mistral: boolean }` for per-provider status
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**C. Add Mistral IPC methods to `ElectronAPI.chat`**
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- `setMistralApiKey(key: string)`
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- `getMistralApiKey()`
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- `validateMistralApiKey(key: string)`
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**D. Add per-purpose model preference methods to `ElectronAPI.chat`**
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- `setTitleModel(modelId: string | null)` / `getTitleModel()`
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- `setImageAnalysisModel(modelId: string | null)` / `getImageAnalysisModel()`
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### 5. `src/renderer/components/SettingsView/SettingsView.tsx` - UI settings
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**A. Add Mistral API key section**
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- Separate input field for Mistral API key (below OpenCode key)
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- Same pattern: masked display, change button, validation on save
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**B. Update model selector**
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- SettingsView uses a native `<select>` element — group models by provider using `<optgroup>` labels ("OpenCode Zen", "Mistral AI")
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- When no API key is configured for any provider, disable the `<select>` dropdown
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- When both keys configured, show merged list from both providers; when only one key set, show only that provider's models
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- **Note**: `availableModels` state is currently typed as `{id: string; name: string}[]` — must be updated to `ChatModel[]` (which includes `provider` and `vision` fields) so provider grouping and vision filtering work
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**C. Add per-purpose model preferences**
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- "Title generation model" dropdown — select cheapest/fastest model for auto-titling conversations
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- "Image analysis model" dropdown — select a dedicated vision model for media metadata (independent of chat model, e.g. use Devstral for chat but Mistral Large 3 for images); **only show vision-capable models** (filter out models without vision support like Devstral)
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- Both show available models from all configured providers, grouped by provider
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- Both allow a "Default" option that auto-selects per provider defaults
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### 6. `src/renderer/components/ChatPanel/ChatPanel.tsx` - Chat UI
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**A. Update model selector in chat**
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- ChatPanel uses a custom dropdown (CSS `model-dropdown` with `<button>` elements, not a native `<select>`) — add provider group headers (non-clickable divider labels) within the dropdown to visually separate providers
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- Only show models for configured providers; when no keys configured, hide the model selector entirely
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- When both providers configured, merge models from both with visual grouping
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### 7. `src/renderer/components/AssistantSidebar/` - Assistant UI
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**A. No model selector changes needed**
|
||
- AssistantSidebar has no model selector and no `checkReady()` call of its own
|
||
- It uses whatever default model is set in Preferences (via `getSelectedModel()`)
|
||
- No code changes needed here — provider-awareness is handled at the Preferences and engine level
|
||
|
||
### 8. `src/renderer/components/Sidebar.tsx` - Navigation
|
||
|
||
**A. Update readiness check**
|
||
- Calls `chat.checkReady()` to show/hide chat features
|
||
- Must handle multi-provider readiness (show chat if **any** provider is ready)
|
||
- Note: Zustand store (`src/renderer/store/appStore.ts`) currently only tracks `chatTokenUsage` — no provider/readiness state is stored there. Provider readiness is ephemeral (fetched on mount via `checkReady()`), so no Zustand changes needed. If future features need reactive provider state, consider adding it then
|
||
|
||
### 9. `src/renderer/components/ImportAnalysisView/ImportAnalysisView.tsx` - Taxonomy analysis UI
|
||
|
||
**A. Update model selector**
|
||
- Has its own model selector (`ChatModel[]` state + `getAvailableModels()` call) for taxonomy analysis
|
||
- Currently renders a flat model list with no provider grouping
|
||
- Apply provider grouping matching the component's existing dropdown pattern
|
||
- Default to whatever is set in Preferences as default model (via `getSelectedModel()`)
|
||
|
||
### 9b. Model selector UI approach
|
||
|
||
**Two different dropdown patterns exist** — keep each surface consistent with its current UX:
|
||
- **SettingsView** uses native `<select>` elements → use `<optgroup>` for provider grouping (standard HTML pattern)
|
||
- **ChatPanel** uses a custom CSS dropdown (`model-dropdown` with `<button>` elements) → add non-clickable provider group headers as dividers
|
||
- **ImportAnalysisView** — check which pattern it uses and match accordingly
|
||
- Shared logic (filtering by vision, adding "Default" option, provider grouping) can be extracted into a utility function or hook rather than a full component, since the rendering pattern differs per surface
|
||
- Props for the shared utility: `models: ChatModel[]`, `filterVisionOnly?: boolean`, `includeDefault?: boolean` → returns grouped/filtered model list
|
||
|
||
### 9c. `src/renderer/navigation/useChatMessageSender.ts` - Shared chat hook
|
||
|
||
**A. Verify no provider assumptions**
|
||
- Used by both ChatPanel and AssistantSidebar to send messages
|
||
- Currently delegates to `sendConversationMessage()` from `chatSession.ts` — verify neither has hardcoded provider/model assumptions
|
||
- No code changes expected, but must be verified during implementation
|
||
|
||
### 10. Preload/IPC registration
|
||
|
||
**A. `src/main/preload.ts`**
|
||
- Register new Mistral IPC channels in preload bridge
|
||
- All chat IPC channels are bridged 1:1; new methods need entries here
|
||
|
||
### 11. i18n - All locale files
|
||
|
||
**A. Add Mistral-specific i18n keys** in all 5 locale files:
|
||
- `src/renderer/i18n/locales/en.json`
|
||
- `src/renderer/i18n/locales/de.json`
|
||
- `src/renderer/i18n/locales/fr.json`
|
||
- `src/renderer/i18n/locales/es.json`
|
||
- `src/renderer/i18n/locales/it.json`
|
||
|
||
Keys needed:
|
||
- `settings.ai.mistralApiKeyLabel` — "Mistral API Key"
|
||
- `settings.ai.mistralApiKeyDescription` — description text
|
||
- `settings.ai.mistralApiKeyPlaceholder` — placeholder text
|
||
- `settings.ai.titleModelLabel` — "Title generation model"
|
||
- `settings.ai.titleModelDescription` — description text
|
||
- `settings.ai.imageAnalysisModelLabel` — "Image analysis model"
|
||
- `settings.ai.imageAnalysisModelDescription` — description text
|
||
- `settings.ai.defaultOption` — "Default" (for per-purpose model selectors)
|
||
- `settings.ai.providerGroupOpenCode` — "OpenCode Zen" (provider group label)
|
||
- `settings.ai.providerGroupMistral` — "Mistral AI" (provider group label)
|
||
- `chat.providerKeyMissing` — "The model '{model}' requires a {provider} API key. Go to Settings to configure it."
|
||
- `chat.apiKeyRequiredTitle` — make generic or multi-provider (currently hardcoded to "OpenCode Zen API Key Required")
|
||
- `chat.apiKeyRequiredDescription` — make generic or multi-provider (currently hardcoded to OpenCode-specific text)
|
||
|
||
### 12. MCP Server - `src/main/engine/MCPServer.ts`
|
||
|
||
- No changes needed — MCP server exposes tools for external AI agents to call; no bDS-side AI runs during MCP requests
|
||
|
||
### 13. Python API - `src/main/shared/pythonApiContractV1.ts`
|
||
|
||
- No changes needed — AI/chat features are explicitly not exposed via Python API
|
||
|
||
### 14. Main-process i18n locales - `src/main/shared/i18n/locales/`
|
||
|
||
- No changes expected — chat-related strings are renderer-only
|
||
- Verify no main-process strings reference "OpenCode" in a way that needs updating for multi-provider support
|
||
|
||
## Tests to Update
|
||
|
||
### New tests
|
||
- OpenCodeManager: Mistral key storage, `detectProvider('mistral-*')` + `detectProvider('devstral-*')` + `detectProvider('codestral-*')` + `detectProvider('pixtral-*')`, parameterized `sendOpenAIMessage()` with Mistral URL/key, vision image conversion in OpenAI path, tool-call message persistence in OpenAI path, `generateConversationTitle()` Mistral routing, model cache merge (both providers), `MODEL_CONTEXT_BUDGETS` correctness, SSE line parsing (both OpenAI/Mistral and Anthropic formats), `[DONE]` sentinel handling, tool-call argument accumulation during streaming, mid-stream error handling, `stream_options` in request bodies, provider-aware API key guards in `analyzeTaxonomy()`/`analyzeMediaImage()`
|
||
- ChatEngine: Mistral key via generic `getSetting/setSetting`, `getTitleModel()`/`setTitleModel()`, `getImageAnalysisModel()`/`setImageAnalysisModel()`, default model fallback
|
||
- chatHandlers: new Mistral IPC handlers, per-purpose model preference handlers
|
||
|
||
### Existing tests to update
|
||
- `tests/engine/OpenCodeManagerTools.test.ts` — if mocked manager gains new required fields
|
||
- `tests/engine/ChatEngine.test.ts` — default model fallback logic
|
||
- `tests/ipc/chatHandlers.test.ts` — new handler registration, init flow
|
||
- `electronApiContract.test.ts` — `ElectronAPI.chat` shape now includes Mistral methods
|
||
- 10 renderer test files that mock `window.electronAPI.chat` (12 mock blocks total) — add Mistral method stubs to mocks:
|
||
- `tests/renderer/components/SidebarChat.test.tsx`
|
||
- `tests/renderer/components/SettingsView.test.tsx`
|
||
- `tests/renderer/components/SettingsView.i18n.test.tsx`
|
||
- `tests/renderer/components/TabBar.test.tsx`
|
||
- `tests/renderer/components/EditorDashboardTimeline.test.tsx`
|
||
- `tests/renderer/components/AssistantSidebar.wiring.test.tsx`
|
||
- `tests/renderer/navigation/chatSurfaceUsageGuards.test.ts`
|
||
- `tests/renderer/navigation/chatSurfaceModeUsageGuards.test.ts`
|
||
- `tests/renderer/navigation/assistantSidebarGuards.test.ts`
|
||
- `tests/renderer/a2ui/surfaceActionWiring.test.tsx`
|
||
|
||
## Implementation Order
|
||
|
||
1. Tests first (per AGENTS.md)
|
||
2. Types (`electronApi.ts` — `ChatModel`, `ChatReadyStatus`, `ElectronAPI.chat`)
|
||
3. Engine (`OpenCodeManager.ts` — constants, `MODEL_CONTEXT_BUDGETS`, detection, key storage, `checkReady()`, parameterized `sendOpenAIMessage()`, vision fix, provider-aware guards, title generation fallback, model cache merge)
|
||
4. SSE streaming (`OpenCodeManager.ts` — `httpRequestStream()`, SSE parsers for Anthropic + OpenAI/Mistral formats, `stream: true` + `stream_options` in request bodies)
|
||
5. Persistence (`ChatEngine.ts` — settings helpers, per-purpose model preferences, default model fallback)
|
||
6. IPC (`chatHandlers.ts` — new handlers, init flow update)
|
||
7. Preload (`preload.ts` — bridge new channels)
|
||
8. i18n (all 5 locale files)
|
||
9. Shared utilities (model grouping/filtering utility for provider-aware dropdowns)
|
||
10. UI (`SettingsView/SettingsView.tsx`, `ChatPanel.tsx`, `ImportAnalysisView.tsx`, `Sidebar.tsx` — use shared `ModelSelector`)
|
||
11. Update existing test mocks
|
||
12. Build verification (`npm run build`)
|
||
|
||
## Key Differences to Handle
|
||
|
||
| Aspect | OpenCode/Anthropic | OpenCode/OpenAI-compat | Mistral |
|
||
|--------|-------------------|----------------------|---------|
|
||
| Base URL | `opencode.ai/zen/v1/messages` | `opencode.ai/zen/v1/chat/completions` | `api.mistral.ai/v1/chat/completions` |
|
||
| Auth header | `Bearer ${openCodeKey}` | `Bearer ${openCodeKey}` | `Bearer ${mistralKey}` |
|
||
| Request method | `sendAnthropicMessage()` | `sendOpenAIMessage(url, key)` | `sendOpenAIMessage(url, key, opts)` (same method, parameterized) |
|
||
| Tool choice | not set | not set | not set (default `"auto"`) |
|
||
| Parallel tools | not set | not set | `parallel_tool_calls: false` |
|
||
| Context budget | 150k tokens | 150k tokens | per-model (see Target Models table) |
|
||
| Stream options | `"stream": true` | `"stream": true, "stream_options": {"include_usage": true}` | `"stream": true, "stream_options": {"include_usage": true}` |
|
||
| Vision in tool results | Anthropic `image` block (native) | **BUG: JSON-stringified** | `image_url` block (fix needed) |
|
||
| HTTP mode | SSE streaming (`stream: true`) | SSE streaming (`stream: true`) | SSE streaming (`stream: true`) |
|
||
| Title generation | `claude-haiku-4-5` default | N/A | `mistral-small-2506` default |
|
||
| Image analysis | `claude-sonnet-4-5` default | N/A | user-selected vision model |
|
||
| Model source | fetched from OpenCode API | fetched from OpenCode API | fetched from `api.mistral.ai/v1/models` |
|
||
|
||
## Verification
|
||
|
||
1. Run `npm test` — all existing + new tests pass
|
||
2. Run `npm run build` — clean build
|
||
3. Manual: set Mistral API key in Settings, verify validation
|
||
4. Manual: select Mistral Large 3, send chat message, verify response completes
|
||
5. Manual: use `view_image` tool in chat with Mistral model, verify vision works
|
||
6. Manual: verify tool calling works (search_posts, list_posts, etc.)
|
||
7. Manual: verify OpenCode models still work unchanged
|
||
8. Manual: verify Mistral-only mode (no OpenCode key) — chat works, title generates, readiness shows correctly
|
||
9. Manual: verify `analyzeMediaImage()` and `analyzeTaxonomy()` with Mistral model
|
||
10. Manual: configure title generation model in Settings, verify titles use selected model
|
||
11. Manual: configure image analysis model in Settings, verify media analysis uses selected model (independent of chat model)
|
||
12. Manual: verify SSE streaming — text appears token-by-token (not as a single block after long wait)
|
||
13. Manual: verify abort during streaming — text stops immediately, no wasted response
|
||
|
||
## Resolved Decisions
|
||
|
||
1. **`analyzeMediaImage()`** — Configurable via Settings preference; user selects a dedicated vision model independent of chat model; dropdown only shows vision-capable models
|
||
2. **`generateConversationTitle()`** — Configurable via Settings preference; user selects cheapest/fastest model for auto-titling
|
||
3. **`checkReady()`** — Returns true if any provider key is set; reports per-provider availability
|
||
4. **Default model** — User-driven; set explicitly in Preferences when configuring provider + model; all surfaces (ChatPanel, AssistantSidebar, ImportAnalysisView) use this preference
|
||
5. **Vision in OpenAI path** — Fix `image_url` conversion for all OpenAI-compatible providers (not just Mistral)
|
||
6. **MCP server** — N/A; only exposes tools, no bDS-side AI runs
|
||
7. **Python API** — N/A; AI/chat not exposed via Python API
|
||
8. **Model dropdown grouping** — SettingsView uses native `<select>` with `<optgroup>`; ChatPanel uses custom CSS dropdown with provider header dividers; shared utility extracts grouping/filtering logic while each surface keeps its own rendering pattern
|
||
9. **SSE streaming** — Convert all chat HTTP calls to `stream: true` + SSE parsing; keep one-shot requests (title, image analysis, taxonomy, validation) non-streaming. Renderer needs zero changes — existing `onDelta` pipeline already supports incremental tokens. Token usage requires `stream_options: { include_usage: true }` for OpenAI/Mistral format; Anthropic provides usage in `message_start` + `message_delta` events
|
||
10. **OpenAI tool-call history** — Within a single `sendMessage()` call, tool results are tracked correctly across rounds. The fix is about persisting `tool` role messages to DB-backed conversation history so cross-session resume works
|
||
11. **ImportAnalysisView** — Has its own model selector; apply same provider grouping; default to Preferences model
|
||
12. **AssistantSidebar** — No model selector of its own; uses Preferences default model; no code changes needed
|
||
13. **`tool_choice`** — Do NOT set `tool_choice: "any"` for Mistral (this forces tool use every turn). Omit it entirely; Mistral defaults to `"auto"`, same as OpenCode. Set `parallel_tool_calls: false` explicitly since our tool executor is sequential
|
||
14. **No separate `sendMistralRequest()`** — Parameterize `sendOpenAIMessage()` with URL/key/options instead of creating a near-duplicate method; Mistral uses the identical OpenAI-compatible format
|
||
15. **`detectProvider()` prefixes** — Cover all Mistral model families: `mistral`, `ministral`, `devstral`, `codestral`, `pixtral`
|
||
15. **`formatModelName()` / `UPPERCASE_PREFIXES`** — No changes needed; all 5 Mistral models are in `MODEL_DISPLAY_NAMES`; auto-format fallback handles future unknown models correctly
|
||
16. **Context budgets** — Stored in `MODEL_CONTEXT_BUDGETS` map; passed explicitly to `truncateToTokenBudget()` per provider path; OpenCode defaults to 150k, Mistral per-model (see Target Models table)
|
||
17. **Error UX for removed provider key** — Inline error banner in ChatPanel (not a toast) with link to Settings; `sendMessage()` returns descriptive error string; `checkReady()` stays true if any provider available
|
||
18. **Zustand store** — No changes needed; provider readiness is ephemeral (fetched on mount), token usage tracking is already in store and is provider-agnostic
|
||
19. **`validateMistralApiKey()`** — Calls `GET https://api.mistral.ai/v1/models` with Bearer token; checks for HTTP 200 + non-empty `data` array; Mistral returns `{ data: [{ id, object, created, owned_by }] }` format
|
||
20. **Model cache merge** — `getAvailableModels()` fetches from both provider endpoints when both keys are set, merges into a single list with `provider` field on each model; when only one key is set, only that provider's models are returned; when no keys are set, returns empty list and UI disables the model dropdown
|
||
21. **Provider-aware API key guards** — `analyzeTaxonomy()` and `analyzeMediaImage()` have `if (!this.apiKey)` early-return guards that must become provider-aware (check the relevant provider's key based on the selected model)
|
||
22. **`useChatMessageSender` hook** — Shared by ChatPanel and AssistantSidebar; verify no provider assumptions exist (expected: no changes needed)
|
||
23. **ChatEngine generic settings** — Use existing `getSetting()`/`setSetting()` for Mistral key storage; no dedicated methods needed
|
||
24. **SettingsView model state type** — Currently `{id: string; name: string}[]`; must be updated to `ChatModel[]` to include `provider` and `vision` fields for grouping and filtering
|