f3e5840af5
* feat(relevant-notes): dedicated pane with redesigned populated view
Move Relevant Notes out of the chat into its own command-opened pane
("Open Relevant Notes") and redesign the populated view: a toolbar with
the active-note context line + Build index, color-graded relevance
meters/percentages, a "Best" tag, hover quick actions, a live indexing
overlay, and a restyled hover preview card. Add-to-Chat routes a wikilink
into the open chat/agent view via a new INSERT_TEXT_TO_CHAT event.
Rank relevant notes by semantic similarity only so the displayed
percentages stay monotonic — links no longer boost the score (link-only
notes still appear, without a meter). Remove the old in-chat Relevant
Notes block and its showRelevantNotes setting; update docs.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* refine(relevant-notes): linear meter, link badges, excluded state, preview fixes
- Meter width now maps 1:1 to the similarity score (70% → 70%).
- Add an excluded-file empty state when the active note is outside the
index inclusion/exclusion settings (takes priority over build/list).
- Replace the "Best" badge with Outgoing-link / Backlink badges; the
badge+percentage cluster swaps for the action buttons on hover.
- Move row action buttons into the flex flow so the title never overlaps
them and truncates with an ellipsis in both states.
- Render preview snippet line breaks (whitespace-pre-line).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* fix(relevant-notes): route Add to Chat to the last-focused chat view
The Relevant Notes "Add to Chat" action hardcoded agent-first when picking
the target chat (getLeavesOfType(CHAT_AGENT_VIEWTYPE)[0] ?? legacy), so with
both chats open the wikilink landed in the wrong one. Reuse the existing
lastActiveChatViewType tracking (added for the add-to-context commands in
#2555) via a shared pickContextChatViewType() helper, so the chat we reveal
and the chat we type into always agree.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* refine(relevant-notes): show full note content in a scrollable preview
The hover-card preview truncated content two ways: the loaded text was
sliced to 1000 chars and the paragraph was clamped to 4 lines. Drop the
slice and replace line-clamp with a bounded, scrollable area (max-h-64 +
overflow-y-auto) so the full note is reachable in a slightly larger box.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* refine(relevant-notes): replace mount-timing setTimeouts with reliable signals
Address PR review on the Relevant Notes work:
- insertTextIntoActiveChat: drop the 50ms guess. The chat views' event bus
(ChatViewEventTarget) now latches queued "insert text"; the view drains it
on mount and ChatInputContext buffers/flushes it when the Lexical editor
registers, so delivery no longer depends on mount timing.
- RelevantNotesView: drop the 50ms initial dispatch. useActiveFile now seeds
from the current active file on mount, so the pane populates immediately and
still updates via the active-leaf-change listener.
- RelevanceMeter: route the score-driven width/color through CSS variables
(.copilot-relevance-meter-fill) instead of an inline style.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
6.9 KiB
Models and Parameters
This guide explains how to manage chat models, embedding models, and the parameters that control how the AI behaves.
Chat Models
Built-In Models
Copilot comes with a set of built-in models across many providers. Some are always included ("core" models); others can be enabled or disabled.
| Model | Provider | Capabilities |
|---|---|---|
| copilot-plus-flash | Copilot Plus | Vision (Plus exclusive) |
| google/gemini-2.5-flash | OpenRouter | Vision |
| google/gemini-2.5-pro | OpenRouter | Vision |
| google/gemini-3.5-flash | OpenRouter | Vision, Reasoning |
| google/gemini-3.1-pro-preview | OpenRouter | Vision, Reasoning |
| openai/gpt-5.4 | OpenRouter | Vision |
| openai/gpt-5-mini | OpenRouter | Vision |
| gpt-5.4 | OpenAI | Vision |
| gpt-5-mini | OpenAI | Vision |
| gpt-4.1 | OpenAI | Vision |
| gpt-4.1-mini | OpenAI | Vision |
| claude-opus-4-6 | Anthropic | Vision, Reasoning |
| claude-sonnet-4-5-20250929 | Anthropic | Vision, Reasoning |
| gemini-2.5-pro | Vision | |
| gemini-2.5-flash | Vision | |
| gemini-3.5-flash | Vision, Reasoning | |
| grok-4-1-fast | XAI | Vision |
| deepseek-chat | DeepSeek | — |
| deepseek-reasoner | DeepSeek | Reasoning |
Model Capability Badges
Models may show capability badges:
- Reasoning — Extended internal thinking before responding; better for complex tasks
- Vision — Can process images (e.g., screenshots, diagrams embedded in notes)
- Web Search — Can access the internet directly (model-native feature)
Managing Models
Go to Settings → Copilot → Model to see the full model list.
- Enable/disable — Toggle individual models on or off to control what appears in the model selector
- Reorder — Drag models to change their order in the dropdown
- Delete — Remove custom models you've added
Adding Custom Models
If your provider offers a model that isn't in the built-in list, you can add it manually:
- Go to Settings → Copilot → Model
- Click Add Model
- Enter the model name exactly as the provider expects it (e.g.,
gpt-4-turbo-preview) - Select the provider
- Optionally set a custom base URL (useful for proxies or alternate endpoints)
- Save
Importing Models from Provider
You can automatically import the full list of available models from a provider:
- Go to Settings → Copilot → Model
- Find the Import models button for your provider
- Copilot will fetch the provider's model list and add new ones
Embedding Models
Embedding models convert text into numerical vectors, which powers semantic (meaning-based) search in Vault QA and the "Relevant Notes" feature.
Built-In Embedding Models
| Model | Provider |
|---|---|
| copilot-plus-small | Copilot Plus (Plus exclusive) |
| copilot-plus-large | Copilot Plus (Believer exclusive) |
| copilot-plus-multilingual | Copilot Plus (Plus exclusive) |
| openai/text-embedding-3-small | OpenRouter |
| text-embedding-3-small | OpenAI |
| text-embedding-3-large | OpenAI |
| embed-multilingual-light-v3.0 | Cohere |
| text-embedding-004 | |
| gemini-embedding-001 | |
| Qwen3-Embedding-0.6B | SiliconFlow |
Selecting an Embedding Model
Go to Settings → Copilot → QA → Embedding Model.
If you change embedding models, you must rebuild the vault index because the old vectors are incompatible with the new model. Copilot will prompt you to confirm before rebuilding.
What Embeddings Affect
- Vault QA mode — Uses embeddings to find relevant notes by meaning
- Semantic Search — The "Enable Semantic Search" toggle in QA settings
- Relevant Notes — Shows semantically similar notes in its own pane (command palette: Open Relevant Notes)
Model Parameters
These settings control how the AI responds. Global defaults live in Settings → Copilot → Model. You can override them per-session using the gear icon in the chat panel.
Temperature
Controls how random or creative the responses are.
- Range: 0.0–1.0
- Default: 0.1
- Low (0.0–0.2): Precise, factual, deterministic
- Medium (0.4–0.6): Balanced
- High (0.8–1.0): Creative, varied, less predictable
Max Tokens
Maximum number of tokens in the AI's response. A token is roughly ¾ of a word (so 1,000 tokens ≈ 750 words).
- Default: 6,000
- Higher values allow longer responses but cost more
Conversation Turns in Context
How many past conversation turns to include in each request. More turns = more context but larger requests.
- Default: 15 turns
- Reduce this if you hit context limits or want to lower costs
Auto-Compact Threshold
When the conversation reaches this many tokens, older messages are automatically summarized.
- Default: 128,000 tokens
- Range: 64,000–1,000,000 tokens
- See Chat Interface for details
Reasoning Effort
For reasoning-capable models (like deepseek-reasoner, claude-opus-4-6), controls how much internal reasoning the model does before responding.
- Options: minimal, low, medium, high, xhigh
- Default: low
- Higher effort = better results on complex tasks, slower responses
Verbosity
For models that support it, controls response length and detail.
- Options: low, medium, high
- Default: medium
Top P
An alternative to temperature for controlling randomness. Leave at default unless you have a specific reason to change it.
Frequency Penalty
Reduces the likelihood of the model repeating itself.
Default Model Selection
Your default model is the one Copilot uses when you open a new chat. Set it in: Settings → Copilot → Basic → Default Chat Model
The default is OpenRouter Gemini 2.5 Flash (requires OpenRouter API key).
Related
- LLM Providers — Set up API keys for your provider
- Vault Search and Indexing — How embedding models are used