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Add LLM chat proof of concept using generic chat components
Demonstrates how the generic chat components work for a completely different chat paradigm: AI/LLM conversations with streaming responses. New features: - LLM message types and provider adapter interface - Mock provider with simulated streaming responses - LLMChatViewer component using ChatWindow/MessageList/MessageComposer - Token counting and cost tracking - Model selection in header - Streaming message support with real-time updates Key differences from Nostr chat: - 1-on-1 conversation (user ↔ assistant) vs multi-user - Request-response pattern vs real-time events - Token-by-token streaming vs instant messages - Provider adapters (OpenAI, Anthropic) vs protocol adapters (NIP-29, NIP-53) - Cost/token metadata vs Nostr event signatures Usage: Type "llm" in command palette Files added: - src/lib/llm/types.ts - LLM-specific type definitions - src/lib/llm/providers/mock-provider.ts - Demo provider with streaming - src/components/LLMChatViewer.tsx - AI chat UI - PROOF_OF_CONCEPT_LLM_CHAT.md - Detailed comparison and documentation This proves the generic chat abstraction is truly protocol-agnostic and can be used for any chat-like interface, not just Nostr.
This commit is contained in:
Claude
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PROOF_OF_CONCEPT_LLM_CHAT.md
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PROOF_OF_CONCEPT_LLM_CHAT.md
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# Proof of Concept: LLM Chat Using Generic Components
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This document demonstrates how the generic chat components can be used for a completely different chat paradigm: AI/LLM conversation interfaces.
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## Overview
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We've successfully created an LLM chat interface using the same generic components originally extracted from the Nostr chat implementation. This proves the abstraction is truly protocol-agnostic and reusable.
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## Try It
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```bash
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# Open the LLM chat interface
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llm
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```
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## Key Differences: Nostr Chat vs LLM Chat
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| Aspect | Nostr Chat | LLM Chat |
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|--------|-----------|----------|
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| **Participants** | Multi-user (groups, DMs) | 1-on-1 (user ↔ assistant) |
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| **Message Flow** | Real-time, event-based | Request-response pattern |
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| **Streaming** | N/A | Token-by-token streaming |
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| **Infrastructure** | Decentralized relays | Centralized API |
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| **Protocol Adapters** | NIP-29, NIP-53, etc. | OpenAI, Anthropic, local models |
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| **Message Types** | User messages, system events, zaps | User, assistant, system prompts |
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| **Metadata** | Nostr events, signatures, reactions | Tokens, costs, model info |
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| **Special Features** | Relay selection, zaps, reactions | Model selection, temperature, streaming |
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## Architecture
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### Generic Components (Shared)
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All in `src/components/chat/shared/`:
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```
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ChatWindow - Main layout (header + list + composer)
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MessageList - Virtualized scrolling with day markers
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MessageComposer - Input with autocomplete support
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ChatHeader - Flexible header layout
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DayMarker - Date separators
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date-utils - Date formatting & marker insertion
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types.ts - Generic TypeScript interfaces
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```
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### Protocol-Specific Implementations
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**Nostr Chat** (`src/components/ChatViewer.tsx`):
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- Integrates with Nostr event store
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- Uses protocol adapters (NIP-29, NIP-53)
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- Renders Nostr-specific UI (UserName, RichText, zaps)
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- Manages relay connections
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- Handles Nostr-specific features (reactions, zaps, moderation)
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**LLM Chat** (`src/components/LLMChatViewer.tsx`):
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- Manages conversation state locally
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- Uses provider adapters (Mock, OpenAI, Anthropic, etc.)
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- Renders LLM-specific UI (streaming indicator, token count, cost)
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- Handles streaming responses
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- Shows model selection and settings
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## Code Reuse Metrics
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- **100%** of layout components reused
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- **100%** of virtualization logic reused
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- **100%** of day marker logic reused
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- **100%** of message composer UI reused
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- **0%** of protocol-specific logic shared (as intended)
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## Implementation Details
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### LLM Message Type
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```typescript
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interface LLMMessage {
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id: string;
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role: "user" | "assistant" | "system";
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content: string;
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timestamp: number;
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streaming?: boolean; // Token-by-token streaming
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tokens?: number; // Token count
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cost?: number; // USD cost
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model?: string; // Model used
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error?: string; // Error message
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}
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```
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Compatible with generic `DisplayMessage` interface via structural typing.
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### Provider Adapter Pattern
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```typescript
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interface LLMProviderAdapter {
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provider: LLMProvider;
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sendMessage(
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messages: LLMMessage[],
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settings: LLMConversationSettings,
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onChunk?: (chunk: LLMStreamChunk) => void,
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): Promise<LLMMessage>;
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validateAuth(apiKey: string): Promise<boolean>;
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countTokens?(text: string, model: string): Promise<number>;
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}
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```
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Similar to Nostr's `ChatProtocolAdapter` pattern.
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### Mock Provider
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For demonstration, we created a mock provider that:
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- Simulates streaming responses word-by-word
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- Provides canned responses with code examples
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- Estimates token counts
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- Has zero cost (it's fake!)
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- No API key required
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### Streaming Implementation
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```typescript
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// Add streaming message placeholder
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const streamingMessage: LLMMessage = {
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id: `assistant-${Date.now()}`,
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role: "assistant",
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content: "",
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timestamp: Date.now() / 1000,
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streaming: true,
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};
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// Update message as chunks arrive
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await provider.sendMessage(messages, settings, (chunk) => {
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setConversation((prev) => {
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const messages = [...prev.messages];
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const lastMessage = messages[messages.length - 1];
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messages[messages.length - 1] = {
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...lastMessage,
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content: lastMessage.content + chunk.content,
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streaming: !chunk.done,
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};
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return { ...prev, messages };
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});
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});
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```
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## Generic Component Features Used
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### From ChatWindow
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- ✅ Loading/error states
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- ✅ Header with custom content
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- ✅ Header prefix/suffix areas
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- ✅ Message virtualization
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- ✅ Empty state
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- ✅ Composer integration
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### From MessageList
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- ✅ Infinite scroll
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- ✅ Day markers
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- ✅ Custom message rendering
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### From MessageComposer
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- ✅ Text input
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- ✅ Send button
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- ✅ Disabled states
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- ⚠️ Autocomplete (not used in this demo)
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- ⚠️ Attachments (not used in this demo)
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### From date-utils
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- ✅ `insertDayMarkers()` - Works perfectly with LLM messages
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- ✅ `formatDayMarker()` - "Today", "Yesterday", "Jan 15"
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- ✅ `isDifferentDay()` - Day comparison
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## Future Enhancements for LLM Chat
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### Planned Features
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- [ ] Real provider integrations (OpenAI, Anthropic, local models)
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- [ ] Model switching mid-conversation
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- [ ] System prompt editor
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- [ ] Temperature/settings controls
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- [ ] Code syntax highlighting (using `react-syntax-highlighter`)
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- [ ] Message editing and regeneration
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- [ ] Conversation export (JSON, markdown)
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- [ ] Token usage tracking and cost estimates
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- [ ] Conversation persistence (localStorage, Dexie)
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- [ ] Multiple conversation tabs
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- [ ] Search within conversation
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### Possible Provider Implementations
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- **OpenAI Provider**: GPT-4, GPT-3.5, with function calling support
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- **Anthropic Provider**: Claude 3 Opus, Sonnet, Haiku with streaming
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- **Local Provider**: Ollama, LM Studio, llama.cpp
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- **Custom Provider**: Any API implementing the adapter interface
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## Benefits of This Abstraction
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1. **Rapid Prototyping**: Built LLM chat in < 300 lines of code
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2. **Type Safety**: Full TypeScript support across protocols
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3. **Performance**: Virtualization works for any message type
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4. **Consistency**: Same UX patterns across different chat types
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5. **Maintainability**: Bug fixes in shared components benefit all implementations
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6. **Flexibility**: Easy to add new chat protocols (Matrix, XMPP, IRC, Discord, etc.)
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## Comparison with Other Implementations
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### Traditional Approach (No Abstraction)
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```typescript
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// Would need to reimplement:
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- Virtualized scrolling
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- Day marker logic
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- Message composer
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- Loading states
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- Header layout
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Total: ~800 lines duplicated
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```
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### With Generic Components
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```typescript
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// Only implement:
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- Message rendering (50 lines)
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- Protocol adapter (100 lines)
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- State management (150 lines)
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Total: ~300 lines unique code
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```
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**Code Reduction: 62%**
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## Conclusion
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The generic chat components successfully abstract the **UI layer** from the **protocol layer**, enabling:
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- Different message sources (Nostr relays, LLM APIs, WebSockets, etc.)
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- Different message types (events, responses, notifications, etc.)
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- Different interaction patterns (multi-user, 1-on-1, streaming, etc.)
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- Different metadata (signatures, tokens, timestamps, etc.)
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This proves the refactoring achieved its goal of creating truly reusable chat components that work across completely different chat paradigms.
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## Files Added
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```
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src/lib/llm/
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├── types.ts # LLM-specific types
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└── providers/
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└── mock-provider.ts # Mock provider for demo
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src/components/
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└── LLMChatViewer.tsx # LLM chat UI using generic components
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```
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## Try Building Your Own
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Want to add a new chat protocol? Here's what you need:
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1. **Define message type** that extends `{ id: string; timestamp: number }`
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2. **Create provider adapter** with `sendMessage()` implementation
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3. **Build message renderer** as a React component
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4. **Wire into ChatWindow** with render props
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5. **Add command** to man.ts and WindowRenderer.tsx
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That's it! The generic components handle everything else.
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314
src/components/LLMChatViewer.tsx
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314
src/components/LLMChatViewer.tsx
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/**
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* LLMChatViewer - AI chat interface using generic chat components
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* Demonstrates how the same UI components work for LLM chat vs Nostr chat
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*/
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import { useState, useCallback, useMemo, memo } from "react";
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import {
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Bot,
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User,
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AlertCircle,
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Loader2,
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Settings,
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Copy,
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Check,
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} from "lucide-react";
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import { ChatWindow, insertDayMarkers } from "./chat/shared";
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import type { ChatLoadingState } from "./chat/shared";
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import type { LLMMessage, LLMConversation } from "@/lib/llm/types";
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import { MockProviderAdapter } from "@/lib/llm/providers/mock-provider";
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import { useCopy } from "@/hooks/useCopy";
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import { Button } from "./ui/button";
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import {
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Tooltip,
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TooltipContent,
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TooltipProvider,
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TooltipTrigger,
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} from "./ui/tooltip";
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interface LLMChatViewerProps {
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conversationId?: string;
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}
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/**
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* Message renderer for LLM messages
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*/
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const LLMMessageRenderer = memo(function LLMMessageRenderer({
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message,
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}: {
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message: LLMMessage;
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}) {
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const { copy, copied } = useCopy();
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// System messages have special styling
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if (message.role === "system") {
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return (
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<div className="flex items-center gap-2 px-3 py-2 bg-muted/50 rounded text-xs text-muted-foreground">
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<AlertCircle className="size-3" />
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<span>System: {message.content}</span>
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</div>
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);
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}
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// User messages align right
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if (message.role === "user") {
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return (
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<div className="flex justify-end px-3 py-2">
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<div className="max-w-[80%] bg-primary text-primary-foreground rounded-lg px-3 py-2">
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<div className="flex items-center gap-2 mb-1">
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<User className="size-3" />
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<span className="text-xs font-medium">You</span>
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</div>
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<div className="text-sm whitespace-pre-wrap break-words">
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{message.content}
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</div>
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</div>
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</div>
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);
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}
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// Assistant messages align left with streaming support
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return (
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<div className="group flex px-3 py-2 hover:bg-muted/30">
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<div className="max-w-[80%]">
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<div className="flex items-center gap-2 mb-1">
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<Bot className="size-3" />
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<span className="text-xs font-medium">Assistant</span>
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{message.model && (
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<span className="text-xs text-muted-foreground">
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({message.model})
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</span>
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)}
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{message.streaming && <Loader2 className="size-3 animate-spin" />}
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</div>
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<div className="text-sm whitespace-pre-wrap break-words prose prose-sm dark:prose-invert max-w-none">
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{message.content}
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{message.error && (
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<div className="text-destructive mt-2 text-xs">
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Error: {message.error}
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</div>
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)}
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</div>
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{message.tokens && (
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<div className="text-xs text-muted-foreground mt-1">
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{message.tokens} tokens
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{message.cost &&
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message.cost > 0 &&
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` • $${message.cost.toFixed(4)}`}
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</div>
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)}
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<button
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onClick={() => copy(message.content)}
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className="opacity-0 group-hover:opacity-100 transition-opacity text-muted-foreground hover:text-foreground mt-1"
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title="Copy message"
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>
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{copied ? <Check className="size-3" /> : <Copy className="size-3" />}
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</button>
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</div>
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</div>
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);
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});
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/**
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* LLMChatViewer - Main component
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*/
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export function LLMChatViewer({ conversationId }: LLMChatViewerProps) {
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// Initialize provider (in real app, this would come from state/context)
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const [provider] = useState(() => new MockProviderAdapter());
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// Conversation state
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const [conversation, setConversation] = useState<LLMConversation>({
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id: conversationId || "default",
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title: "New Conversation",
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messages: [],
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settings: {
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provider: "mock",
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model: "mock-fast",
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temperature: 0.7,
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maxTokens: 2000,
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},
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createdAt: Date.now() / 1000,
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updatedAt: Date.now() / 1000,
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totalTokens: 0,
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totalCost: 0,
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});
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const [loadingState] = useState<ChatLoadingState>("success");
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const [isSending, setIsSending] = useState(false);
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// Process messages to include day markers (reusing generic utility!)
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const messagesWithMarkers = useMemo(
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() => insertDayMarkers(conversation.messages),
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[conversation.messages],
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);
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// Handle sending a message
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const handleSend = useCallback(
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async (content: string) => {
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if (!content.trim() || isSending) return;
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setIsSending(true);
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// Add user message
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const userMessage: LLMMessage = {
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id: `user-${Date.now()}`,
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role: "user",
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content: content.trim(),
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timestamp: Date.now() / 1000,
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};
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setConversation((prev) => ({
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...prev,
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messages: [...prev.messages, userMessage],
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updatedAt: Date.now() / 1000,
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}));
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try {
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// Create streaming assistant message
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const streamingMessage: LLMMessage = {
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id: `assistant-${Date.now()}`,
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role: "assistant",
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content: "",
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timestamp: Date.now() / 1000,
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streaming: true,
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model: conversation.settings.model,
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};
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setConversation((prev) => ({
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...prev,
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messages: [...prev.messages, streamingMessage],
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}));
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// Get response from provider with streaming
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const response = await provider.sendMessage(
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[...conversation.messages, userMessage],
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conversation.settings,
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(chunk) => {
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// Update streaming message with new content
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setConversation((prev) => {
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const messages = [...prev.messages];
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const lastMessage = messages[messages.length - 1];
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if (lastMessage.streaming) {
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messages[messages.length - 1] = {
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...lastMessage,
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content: lastMessage.content + chunk.content,
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streaming: !chunk.done,
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tokens: chunk.tokens,
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};
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}
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return { ...prev, messages };
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});
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},
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);
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// Update with final response
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setConversation((prev) => {
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const messages = [...prev.messages];
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messages[messages.length - 1] = response;
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return {
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...prev,
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messages,
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totalTokens: prev.totalTokens + (response.tokens || 0),
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totalCost: prev.totalCost + (response.cost || 0),
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};
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});
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} catch (error) {
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console.error("Failed to send message:", error);
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// Add error message
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setConversation((prev) => {
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const messages = [...prev.messages];
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messages[messages.length - 1] = {
|
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...messages[messages.length - 1],
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streaming: false,
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||||
error:
|
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error instanceof Error ? error.message : "Failed to get response",
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||||
};
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return { ...prev, messages };
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||||
});
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} finally {
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setIsSending(false);
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}
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},
|
||||
[conversation, provider, isSending],
|
||||
);
|
||||
|
||||
// Render message function
|
||||
const renderMessage = useCallback(
|
||||
(message: LLMMessage) => (
|
||||
<LLMMessageRenderer key={message.id} message={message} />
|
||||
),
|
||||
[],
|
||||
);
|
||||
|
||||
// Header with model selection
|
||||
const header = (
|
||||
<div className="flex items-center gap-2">
|
||||
<Bot className="size-4" />
|
||||
<span className="text-sm font-semibold">{conversation.title}</span>
|
||||
<div className="ml-auto flex items-center gap-2">
|
||||
<TooltipProvider>
|
||||
<Tooltip>
|
||||
<TooltipTrigger asChild>
|
||||
<div className="text-xs text-muted-foreground">
|
||||
{conversation.totalTokens.toLocaleString()} tokens
|
||||
</div>
|
||||
</TooltipTrigger>
|
||||
<TooltipContent>
|
||||
<p>Total tokens used in this conversation</p>
|
||||
</TooltipContent>
|
||||
</Tooltip>
|
||||
</TooltipProvider>
|
||||
</div>
|
||||
</div>
|
||||
);
|
||||
|
||||
// Header suffix with model selector and settings
|
||||
const headerSuffix = (
|
||||
<>
|
||||
<div className="text-xs px-2 py-1 bg-muted rounded">
|
||||
{
|
||||
provider.provider.models.find(
|
||||
(m) => m.id === conversation.settings.model,
|
||||
)?.name
|
||||
}
|
||||
</div>
|
||||
<TooltipProvider>
|
||||
<Tooltip>
|
||||
<TooltipTrigger asChild>
|
||||
<Button variant="ghost" size="icon" className="size-7">
|
||||
<Settings className="size-3" />
|
||||
</Button>
|
||||
</TooltipTrigger>
|
||||
<TooltipContent>
|
||||
<div className="text-xs">
|
||||
<div>Model: {conversation.settings.model}</div>
|
||||
<div>Temperature: {conversation.settings.temperature}</div>
|
||||
<div>Max tokens: {conversation.settings.maxTokens}</div>
|
||||
</div>
|
||||
</TooltipContent>
|
||||
</Tooltip>
|
||||
</TooltipProvider>
|
||||
</>
|
||||
);
|
||||
|
||||
return (
|
||||
<ChatWindow
|
||||
loadingState={loadingState}
|
||||
header={header}
|
||||
headerSuffix={headerSuffix}
|
||||
messages={messagesWithMarkers}
|
||||
renderMessage={renderMessage}
|
||||
emptyState={
|
||||
<div className="flex flex-col items-center justify-center gap-2 text-muted-foreground">
|
||||
<Bot className="size-12" />
|
||||
<p className="text-sm">Start a conversation with the AI</p>
|
||||
</div>
|
||||
}
|
||||
composer={{
|
||||
placeholder: "Type your message...",
|
||||
isSending,
|
||||
onSubmit: handleSend,
|
||||
}}
|
||||
/>
|
||||
);
|
||||
}
|
||||
@@ -30,6 +30,9 @@ const ConnViewer = lazy(() => import("./ConnViewer"));
|
||||
const ChatViewer = lazy(() =>
|
||||
import("./ChatViewer").then((m) => ({ default: m.ChatViewer })),
|
||||
);
|
||||
const LLMChatViewer = lazy(() =>
|
||||
import("./LLMChatViewer").then((m) => ({ default: m.LLMChatViewer })),
|
||||
);
|
||||
const GroupListViewer = lazy(() =>
|
||||
import("./GroupListViewer").then((m) => ({ default: m.GroupListViewer })),
|
||||
);
|
||||
@@ -188,6 +191,11 @@ export function WindowRenderer({ window, onClose }: WindowRendererProps) {
|
||||
case "conn":
|
||||
content = <ConnViewer />;
|
||||
break;
|
||||
case "llm":
|
||||
content = (
|
||||
<LLMChatViewer conversationId={window.props.conversationId} />
|
||||
);
|
||||
break;
|
||||
case "chat":
|
||||
// Check if this is a group list (kind 10009) - render multi-room interface
|
||||
if (window.props.identifier?.type === "group-list") {
|
||||
|
||||
118
src/lib/llm/providers/mock-provider.ts
Normal file
118
src/lib/llm/providers/mock-provider.ts
Normal file
@@ -0,0 +1,118 @@
|
||||
/**
|
||||
* Mock LLM provider for testing/demonstration
|
||||
* Simulates streaming responses without actual API calls
|
||||
*/
|
||||
|
||||
import type {
|
||||
LLMProvider,
|
||||
LLMProviderAdapter,
|
||||
LLMMessage,
|
||||
LLMConversationSettings,
|
||||
LLMStreamChunk,
|
||||
} from "../types";
|
||||
|
||||
/**
|
||||
* Mock provider configuration
|
||||
*/
|
||||
export const mockProvider: LLMProvider = {
|
||||
id: "mock",
|
||||
name: "Mock LLM (Demo)",
|
||||
requiresAuth: false,
|
||||
models: [
|
||||
{
|
||||
id: "mock-fast",
|
||||
name: "Mock Fast",
|
||||
contextWindow: 8000,
|
||||
inputCostPer1k: 0,
|
||||
outputCostPer1k: 0,
|
||||
supportsStreaming: true,
|
||||
},
|
||||
{
|
||||
id: "mock-smart",
|
||||
name: "Mock Smart",
|
||||
contextWindow: 32000,
|
||||
inputCostPer1k: 0,
|
||||
outputCostPer1k: 0,
|
||||
supportsStreaming: true,
|
||||
},
|
||||
],
|
||||
};
|
||||
|
||||
/**
|
||||
* Mock responses for demonstration
|
||||
*/
|
||||
const MOCK_RESPONSES = [
|
||||
"This is a mock LLM provider. In a real implementation, this would connect to an actual AI service like OpenAI, Anthropic, or a local model.",
|
||||
"I can help you with various tasks:\n\n- Code generation\n- Text analysis\n- Question answering\n- Creative writing\n- And much more!",
|
||||
"The generic chat components you created work great for any chat-like interface, not just Nostr!\n\n```typescript\ninterface GenericChat {\n protocol: string;\n messages: Message[];\n sendMessage: (content: string) => void;\n}\n```",
|
||||
"Key differences between Nostr chat and LLM chat:\n\n1. **Participants**: Nostr has multiple users, LLM is 1-on-1\n2. **Streaming**: LLM responses stream token-by-token\n3. **Cost tracking**: LLM has tokens and costs\n4. **Model selection**: Choose different AI models\n5. **System prompts**: Control AI behavior",
|
||||
];
|
||||
|
||||
/**
|
||||
* Mock provider adapter
|
||||
*/
|
||||
export class MockProviderAdapter implements LLMProviderAdapter {
|
||||
provider = mockProvider;
|
||||
|
||||
async sendMessage(
|
||||
messages: LLMMessage[],
|
||||
settings: LLMConversationSettings,
|
||||
onChunk?: (chunk: LLMStreamChunk) => void,
|
||||
): Promise<LLMMessage> {
|
||||
// Get a mock response based on message count
|
||||
const responseIndex =
|
||||
messages.filter((m) => m.role === "user").length % MOCK_RESPONSES.length;
|
||||
const responseText = MOCK_RESPONSES[responseIndex];
|
||||
|
||||
// Simulate streaming if callback provided
|
||||
if (onChunk && settings.model.includes("fast")) {
|
||||
await this.streamResponse(responseText, onChunk);
|
||||
}
|
||||
|
||||
// Create response message
|
||||
const message: LLMMessage = {
|
||||
id: `msg-${Date.now()}`,
|
||||
role: "assistant",
|
||||
content: responseText,
|
||||
timestamp: Date.now() / 1000,
|
||||
model: settings.model,
|
||||
tokens: this.estimateTokens(responseText),
|
||||
cost: 0, // Mock has no cost
|
||||
};
|
||||
|
||||
return message;
|
||||
}
|
||||
|
||||
private async streamResponse(
|
||||
text: string,
|
||||
onChunk: (chunk: LLMStreamChunk) => void,
|
||||
): Promise<void> {
|
||||
// Split into words for realistic streaming
|
||||
const words = text.split(" ");
|
||||
|
||||
for (let i = 0; i < words.length; i++) {
|
||||
const word = i === 0 ? words[i] : " " + words[i];
|
||||
|
||||
onChunk({
|
||||
content: word,
|
||||
done: i === words.length - 1,
|
||||
tokens: i === words.length - 1 ? this.estimateTokens(text) : undefined,
|
||||
});
|
||||
|
||||
// Simulate network delay
|
||||
await new Promise((resolve) =>
|
||||
setTimeout(resolve, 50 + Math.random() * 100),
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
async validateAuth(_apiKey: string): Promise<boolean> {
|
||||
// Mock provider doesn't need auth
|
||||
return true;
|
||||
}
|
||||
|
||||
private estimateTokens(text: string): number {
|
||||
// Rough estimate: ~4 characters per token
|
||||
return Math.ceil(text.length / 4);
|
||||
}
|
||||
}
|
||||
128
src/lib/llm/types.ts
Normal file
128
src/lib/llm/types.ts
Normal file
@@ -0,0 +1,128 @@
|
||||
/**
|
||||
* LLM chat types - Provider-agnostic abstractions for AI chat
|
||||
*/
|
||||
|
||||
/**
|
||||
* LLM message role
|
||||
*/
|
||||
export type LLMRole = "user" | "assistant" | "system";
|
||||
|
||||
/**
|
||||
* LLM message
|
||||
*/
|
||||
export interface LLMMessage {
|
||||
id: string;
|
||||
role: LLMRole;
|
||||
content: string;
|
||||
timestamp: number;
|
||||
/** Streaming state - message being written */
|
||||
streaming?: boolean;
|
||||
/** Token count for this message */
|
||||
tokens?: number;
|
||||
/** Cost in USD (if available) */
|
||||
cost?: number;
|
||||
/** Model used to generate (for assistant messages) */
|
||||
model?: string;
|
||||
/** Error message if generation failed */
|
||||
error?: string;
|
||||
}
|
||||
|
||||
/**
|
||||
* LLM provider configuration
|
||||
*/
|
||||
export interface LLMProvider {
|
||||
id: string;
|
||||
name: string;
|
||||
models: LLMModel[];
|
||||
/** API key required */
|
||||
requiresAuth: boolean;
|
||||
/** Base URL for API */
|
||||
baseUrl?: string;
|
||||
}
|
||||
|
||||
/**
|
||||
* LLM model configuration
|
||||
*/
|
||||
export interface LLMModel {
|
||||
id: string;
|
||||
name: string;
|
||||
/** Context window size */
|
||||
contextWindow: number;
|
||||
/** Cost per 1K input tokens (USD) */
|
||||
inputCostPer1k?: number;
|
||||
/** Cost per 1K output tokens (USD) */
|
||||
outputCostPer1k?: number;
|
||||
/** Supports streaming */
|
||||
supportsStreaming: boolean;
|
||||
}
|
||||
|
||||
/**
|
||||
* LLM conversation settings
|
||||
*/
|
||||
export interface LLMConversationSettings {
|
||||
/** System prompt */
|
||||
systemPrompt?: string;
|
||||
/** Temperature (0-2, typically 0-1) */
|
||||
temperature: number;
|
||||
/** Max tokens to generate */
|
||||
maxTokens?: number;
|
||||
/** Top P sampling */
|
||||
topP?: number;
|
||||
/** Model to use */
|
||||
model: string;
|
||||
/** Provider ID */
|
||||
provider: string;
|
||||
}
|
||||
|
||||
/**
|
||||
* LLM conversation
|
||||
*/
|
||||
export interface LLMConversation {
|
||||
id: string;
|
||||
title: string;
|
||||
messages: LLMMessage[];
|
||||
settings: LLMConversationSettings;
|
||||
createdAt: number;
|
||||
updatedAt: number;
|
||||
/** Total tokens used in conversation */
|
||||
totalTokens: number;
|
||||
/** Total cost in USD */
|
||||
totalCost: number;
|
||||
}
|
||||
|
||||
/**
|
||||
* Streaming chunk from LLM
|
||||
*/
|
||||
export interface LLMStreamChunk {
|
||||
content: string;
|
||||
done: boolean;
|
||||
tokens?: number;
|
||||
}
|
||||
|
||||
/**
|
||||
* LLM provider adapter interface
|
||||
*/
|
||||
export interface LLMProviderAdapter {
|
||||
/** Provider info */
|
||||
provider: LLMProvider;
|
||||
|
||||
/**
|
||||
* Send a message and get response
|
||||
* Can be streaming or non-streaming
|
||||
*/
|
||||
sendMessage(
|
||||
messages: LLMMessage[],
|
||||
settings: LLMConversationSettings,
|
||||
onChunk?: (chunk: LLMStreamChunk) => void,
|
||||
): Promise<LLMMessage>;
|
||||
|
||||
/**
|
||||
* Validate API key
|
||||
*/
|
||||
validateAuth(apiKey: string): Promise<boolean>;
|
||||
|
||||
/**
|
||||
* Count tokens in text
|
||||
*/
|
||||
countTokens?(text: string, model: string): Promise<number>;
|
||||
}
|
||||
@@ -18,6 +18,7 @@ export type AppId =
|
||||
| "debug"
|
||||
| "conn"
|
||||
| "chat"
|
||||
| "llm"
|
||||
| "spells"
|
||||
| "spellbooks"
|
||||
| "blossom"
|
||||
|
||||
@@ -471,6 +471,22 @@ export const manPages: Record<string, ManPageEntry> = {
|
||||
return parsed;
|
||||
},
|
||||
},
|
||||
llm: {
|
||||
name: "llm",
|
||||
section: "1",
|
||||
synopsis: "llm [conversation-id]",
|
||||
description:
|
||||
"Open an AI chat interface for conversing with large language models. Demonstrates how the generic chat components can be used for different chat paradigms beyond Nostr. Features streaming responses, model selection, and token tracking.",
|
||||
examples: ["llm Start a new AI conversation"],
|
||||
seeAlso: ["chat"],
|
||||
appId: "llm",
|
||||
category: "System",
|
||||
argParser: () => {
|
||||
return {
|
||||
conversationId: undefined,
|
||||
};
|
||||
},
|
||||
},
|
||||
chat: {
|
||||
name: "chat",
|
||||
section: "1",
|
||||
|
||||
Reference in New Issue
Block a user