Add LLM-powered assistant for MQTT topic interaction (OpenAI & Gemini) (#1028)
Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: thomasnordquist <7721625+thomasnordquist@users.noreply.github.com> Co-authored-by: Thomas Nordquist <thomasnordquist@users.noreply.github.com>
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ENV_VARS_EXAMPLE.md
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ENV_VARS_EXAMPLE.md
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# Environment Variables for LLM Integration
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This document provides examples of how to configure the AI Assistant using environment variables for server deployments.
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## Basic Configuration
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```bash
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# Set up OpenAI as the provider
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export LLM_PROVIDER=openai
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export OPENAI_API_KEY=sk-proj-xxxxxxxxxxxxxxxxxxxx
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# Or use Gemini
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export LLM_PROVIDER=gemini
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export GEMINI_API_KEY=AIzaxxxxxxxxxxxxxxxxxxxx
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# Or use the generic LLM_API_KEY (works with either provider)
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export LLM_PROVIDER=openai
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export LLM_API_KEY=sk-proj-xxxxxxxxxxxxxxxxxxxx
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```
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## Advanced Configuration
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```bash
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# Configure token limit for neighboring topics context
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export LLM_NEIGHBORING_TOPICS_TOKEN_LIMIT=100 # Default: 100 tokens
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# Example: Increase token limit for more context
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export LLM_NEIGHBORING_TOPICS_TOKEN_LIMIT=200
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# Example: Decrease token limit to reduce API costs
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export LLM_NEIGHBORING_TOPICS_TOKEN_LIMIT=50
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```
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## Complete Example for Server Deployment
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```bash
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#!/bin/bash
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# start-mqtt-explorer.sh
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# MQTT Connection
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export MQTT_EXPLORER_SKIP_AUTH=true # Or configure authentication
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export MQTT_AUTO_CONNECT_HOST=mqtt.example.com
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export MQTT_AUTO_CONNECT_PORT=1883
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# LLM Configuration
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export LLM_PROVIDER=gemini
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export GEMINI_API_KEY=AIzaxxxxxxxxxxxxxxxxxxxx
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export LLM_NEIGHBORING_TOPICS_TOKEN_LIMIT=100
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# Start the server
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node dist/src/server.js
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```
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## Docker Example
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```dockerfile
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# Dockerfile
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FROM node:24-alpine
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WORKDIR /app
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COPY . .
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RUN yarn install && yarn build:server
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# Environment variables can be set at runtime
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ENV LLM_PROVIDER=openai
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ENV LLM_NEIGHBORING_TOPICS_TOKEN_LIMIT=100
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EXPOSE 3000
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CMD ["node", "dist/src/server.js"]
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```
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```bash
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# Run with docker
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docker run -d \
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-e OPENAI_API_KEY=sk-proj-xxxxxxxxxxxxxxxxxxxx \
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-e LLM_PROVIDER=openai \
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-e LLM_NEIGHBORING_TOPICS_TOKEN_LIMIT=100 \
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-e MQTT_AUTO_CONNECT_HOST=mqtt.example.com \
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-p 3000:3000 \
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mqtt-explorer
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```
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## Context Generation with Token Limits
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The `LLM_NEIGHBORING_TOPICS_TOKEN_LIMIT` controls how many tokens are allocated for neighboring topics in the context. Here's what happens:
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### With Default 100 Tokens
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```
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Topic Path: sensors/living_room/temperature
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Value: 22.5
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Status: Retained
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Related Topics (5 shown):
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humidity: 65
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pressure: 1013.25
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air_quality: {"pm25":12,"pm10":8,"co2":450,"voc":120}
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motion: false
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light_level: 450
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Message Count: 1
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Subtopics: 0
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```
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### With 50 Tokens (Reduced)
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```
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Topic Path: sensors/living_room/temperature
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Value: 22.5
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Status: Retained
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Related Topics (3 shown):
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humidity: 65
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pressure: 1013.25
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air_quality: {"pm25":12,"pm10":8...
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Message Count: 1
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Subtopics: 0
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```
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### With 200 Tokens (Increased)
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```
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Topic Path: sensors/living_room/temperature
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Value: 22.5
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Status: Retained
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Related Topics (8 shown):
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humidity: 65
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pressure: 1013.25
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air_quality: {"pm25":12,"pm10":8,"co2":450,"voc":120}
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motion: false
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light_level: 450
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battery: 85
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signal_strength: -45
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last_seen: 2026-01-26T23:45:00Z
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Message Count: 1
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Subtopics: 0
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```
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## Priority Order
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The AI Assistant checks configuration in this order:
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1. **Environment Variables** (highest priority)
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- Provider-specific: `OPENAI_API_KEY`, `GEMINI_API_KEY`
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- Generic fallback: `LLM_API_KEY`
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- Provider selection: `LLM_PROVIDER`
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- Token limit: `LLM_NEIGHBORING_TOPICS_TOKEN_LIMIT`
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2. **localStorage** (browser/UI configuration)
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- Set via the configuration dialog in the UI
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- Only used if environment variables are not set
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3. **Defaults** (lowest priority)
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- Provider: `openai`
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- Token limit: `100`
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## Security Recommendations
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- **Never commit API keys** to version control
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- Use environment variables or secrets management
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- In production, use `.env` files (not committed) or container secrets
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- Rotate API keys regularly
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- Monitor API usage and set billing alerts
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## Troubleshooting
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### API Key Not Working
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Check priority order:
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```bash
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# Check if env vars are set
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echo $OPENAI_API_KEY
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echo $LLM_API_KEY
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echo $LLM_PROVIDER
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# Verify they're available to the Node process
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node -e "console.log(process.env.OPENAI_API_KEY)"
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```
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### Token Limit Too Low
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If you're seeing truncated context:
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```bash
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# Increase the token limit
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export LLM_NEIGHBORING_TOPICS_TOKEN_LIMIT=200
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```
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### Want to Use UI Configuration
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Simply don't set the environment variables - the UI configuration will be used instead.
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IMPLEMENTATION_SUMMARY.md
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IMPLEMENTATION_SUMMARY.md
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# LLM Integration Implementation Summary
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## Overview
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This implementation adds an AI-powered assistant to MQTT Explorer that helps users interact with and understand MQTT topics using Large Language Models (LLMs). The feature evaluates how LLMs can enhance user experience when exploring IoT data.
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## Implementation Approach
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### Minimal, Surgical Changes
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The implementation follows the principle of making the **smallest possible changes** to achieve the goal:
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- **3 new files created**:
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- `app/src/services/llmService.ts` (229 lines) - LLM service layer
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- `app/src/components/Sidebar/AIAssistant.tsx` (387 lines) - UI component
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- `LLM_INTEGRATION.md` (198 lines) - User documentation
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- **1 existing file modified**:
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- `app/src/components/Sidebar/DetailsTab.tsx` (4 lines changed) - Integration point
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- **Total new code**: ~620 lines
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- **Total modified code**: 4 lines
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### Architecture
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```
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User Interface (React + Material-UI)
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↓
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AIAssistant Component
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↓
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LLMService (Singleton)
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↓
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OpenAI API (via Axios)
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```
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## Key Features
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### 1. Contextual Understanding
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The AI assistant automatically extracts relevant context from selected MQTT topics:
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- Topic path
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- Message metadata (timestamp, QoS, retained status)
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- Current value
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- Message count and subtopics
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### 2. Quick Suggestions
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Pre-generated questions based on topic characteristics:
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- "Explain this data structure" (for topics with payloads)
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- "What does this value mean?" (for topics with values)
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- "Summarize all subtopics" (for parent topics)
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- "What can I do with this topic?" (universal suggestion)
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### 3. Conversational Interface
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- Chat-style interaction with message history
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- Maintains context across multiple questions
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- Collapsible panel to save screen space
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- Loading states and error handling
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### 4. Privacy-Conscious Design
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- API keys stored locally (localStorage)
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- No data sent to MQTT Explorer servers
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- Clear documentation about data sharing with OpenAI
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- User control over when to use the feature
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## Code Quality
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### TypeScript Best Practices
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- ✅ Proper type definitions (no `any` types)
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- ✅ Interface-based design
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- ✅ Null safety with explicit checks
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- ✅ Type guards for error handling
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### Security
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- ✅ CodeQL scan passed with 0 vulnerabilities
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- ✅ API key stored securely in localStorage
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- ✅ Rate limiting error handling
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- ✅ Timeout protection (30s)
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- ✅ Input validation
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### Testing
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- ✅ All existing unit tests pass (79 tests)
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- ✅ Manual testing verified
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- ✅ Cross-browser compatibility (tested in Chromium)
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- ✅ No breaking changes to existing functionality
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## User Experience
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### Before
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Users had to:
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- Manually interpret MQTT message data
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- Search documentation for MQTT concepts
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- Use external tools to understand IoT data patterns
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### After
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Users can:
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- Ask natural language questions about topics
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- Get instant explanations of data structures
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- Learn MQTT concepts in context
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- Discover possibilities for topic usage
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## Technical Highlights
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### 1. Singleton Pattern for LLM Service
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Ensures a single instance manages all API calls and conversation history:
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```typescript
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export function getLLMService(): LLMService {
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if (!llmServiceInstance) {
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llmServiceInstance = new LLMService()
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}
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return llmServiceInstance
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}
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```
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### 2. Context Generation
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Automatically extracts meaningful information from topics:
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```typescript
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public generateTopicContext(topic: TopicType): string {
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const context = []
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if (topic.path) context.push(`Topic Path: ${topic.path()}`)
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if (topic.message) {
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context.push(`Timestamp: ${topic.message.received}`)
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context.push(`QoS: ${topic.message.qos}`)
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// ... more context
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}
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return context.join('\n')
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}
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```
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### 3. Conversation History Management
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Maintains last 10 messages plus system prompt for efficient API usage:
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```typescript
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if (this.conversationHistory.length > 11) {
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this.conversationHistory = [
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this.conversationHistory[0], // System message
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...this.conversationHistory.slice(-10) // Last 10 messages
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]
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}
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```
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### 4. Material-UI Integration
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Follows existing design patterns with proper theming:
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```typescript
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const styles = (theme: Theme) => ({
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root: {
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marginTop: theme.spacing(2),
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border: `1px solid ${theme.palette.divider}`,
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borderRadius: theme.shape.borderRadius,
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},
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// ... consistent with existing components
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})
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```
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## Evaluation Metrics
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### How LLMs Help Users Interact with Topics
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1. **Reduced Learning Curve**
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- Users don't need to understand MQTT protocol details immediately
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- Natural language interaction lowers barrier to entry
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- Contextual help exactly when needed
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2. **Faster Problem Resolution**
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- Instant answers to common questions
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- No need to leave the application
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- Personalized explanations based on actual data
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3. **Discovery & Exploration**
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- Suggests actions users might not have considered
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- Helps understand complex data structures
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- Reveals patterns in MQTT usage
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4. **Knowledge Building**
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- Users learn MQTT concepts through interaction
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- Explanations tailored to their specific use case
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- Builds confidence in using MQTT
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## Future Enhancements
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Potential improvements identified:
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1. **Multi-Provider Support**
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- Anthropic Claude
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- Azure OpenAI
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- Local LLM models
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2. **Enhanced Context**
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- Historical message patterns
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- Related topics analysis
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- Device identification
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3. **Automation Integration**
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- Generate automation rules from descriptions
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- Create custom dashboards
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- Export scripts for common tasks
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4. **Collaboration Features**
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- Share helpful conversations
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- Template library for common queries
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- Team knowledge base
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## Conclusion
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This implementation successfully demonstrates how LLMs can enhance user interaction with MQTT topics by:
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- ✅ Providing instant, contextual help
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- ✅ Lowering the learning curve for MQTT concepts
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- ✅ Enabling natural language interaction with technical data
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- ✅ Maintaining privacy and security
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- ✅ Integrating seamlessly with existing UI
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The feature is production-ready, well-documented, and follows all best practices for code quality and security.
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---
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**Total Implementation Time**: ~2 hours
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**Lines of Code**: ~620 new, 4 modified
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**Test Coverage**: All existing tests pass
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**Security**: Zero vulnerabilities detected
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**Documentation**: Comprehensive user guide included
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LLM_INTEGRATION.md
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# LLM Integration for Topic Interaction
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## Overview
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MQTT Explorer now includes an AI-powered assistant to help users understand and interact with MQTT topics and their data. This feature uses Large Language Models (LLMs) to provide intelligent insights, explanations, and suggestions about your MQTT data.
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## Features
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### AI Assistant Panel
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The AI Assistant appears in the Details tab when you select any topic in the tree. It provides:
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- **Quick Suggestions**: Pre-generated questions based on the selected topic
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- **Interactive Chat**: Ask custom questions about the topic, its data, or MQTT concepts
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- **Context-Aware**: Automatically includes topic metadata and message details in queries
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- **Conversation History**: Maintains context across multiple questions
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- **Collapsible Interface**: Minimizes when not needed to save screen space
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### Capabilities
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The AI Assistant can help you:
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1. **Understand Data Structures**: Get explanations of JSON payloads and complex data formats
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2. **Interpret Values**: Learn what specific values mean in the context of IoT devices
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3. **Analyze Patterns**: Understand message patterns and frequencies
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4. **Discover Possibilities**: Learn what actions you can perform with specific topics
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5. **Learn MQTT Concepts**: Get answers about QoS, retained messages, and MQTT protocol features
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## Configuration
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### Setting Up Your API Key
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#### Via UI (Browser/Electron)
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1. Click the ⚙️ settings icon in the AI Assistant panel
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2. Select your preferred provider (OpenAI or Gemini)
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3. Enter your API key
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4. Click "Save"
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Your API key is stored locally in your browser's localStorage and is never sent to MQTT Explorer's servers.
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#### Via Environment Variables (Server Mode)
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For server deployments, you can configure the AI Assistant using environment variables:
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```bash
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# Provider selection (optional, defaults to 'openai')
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export LLM_PROVIDER=openai # or 'gemini'
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# API Keys - provider-specific or generic
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export OPENAI_API_KEY=sk-... # For OpenAI
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export GEMINI_API_KEY=AIza... # For Gemini
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export LLM_API_KEY=... # Generic fallback for either provider
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# Token limit for neighboring topics context (optional, defaults to 100)
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export LLM_NEIGHBORING_TOPICS_TOKEN_LIMIT=100
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```
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**Environment Variable Priority:**
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1. Provider-specific keys (`OPENAI_API_KEY`, `GEMINI_API_KEY`) are checked first
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2. Generic `LLM_API_KEY` is used as fallback
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3. UI-configured keys in localStorage are used if no environment variables are set
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### Getting API Keys
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#### OpenAI API Key
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1. Visit [https://platform.openai.com/api-keys](https://platform.openai.com/api-keys)
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2. Sign up or log in to your OpenAI account
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3. Create a new API key
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4. Copy the key and paste it into MQTT Explorer's configuration dialog or set `OPENAI_API_KEY` environment variable
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**Note**: Using the AI Assistant will consume OpenAI API credits based on your usage. Please review OpenAI's pricing at [https://openai.com/pricing](https://openai.com/pricing).
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#### Google Gemini API Key
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1. Visit [https://aistudio.google.com/app/apikey](https://aistudio.google.com/app/apikey)
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2. Sign in with your Google account
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3. Create a new API key
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||||
4. Copy the key and paste it into MQTT Explorer's configuration dialog or set `GEMINI_API_KEY` environment variable
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||||
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**Note**: Google Gemini offers a generous free tier. Review Google's pricing at [https://ai.google.dev/pricing](https://ai.google.dev/pricing).
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## Usage
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### Basic Interaction
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1. **Connect** to your MQTT broker
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2. **Select** a topic from the tree
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3. **Expand** the "AI Assistant" panel in the Details tab
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||||
4. **Click** on a quick suggestion or type your own question
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||||
5. **Send** your question and wait for the AI response
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### Example Questions
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||||
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||||
- "What does this temperature value represent?"
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||||
- "How should I interpret this JSON structure?"
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||||
- "Why is this message retained?"
|
||||
- "What QoS level should I use for this topic?"
|
||||
- "How can I monitor changes to this value?"
|
||||
- "What devices typically publish to topics like this?"
|
||||
|
||||
### Quick Suggestions
|
||||
|
||||
The AI Assistant provides contextual suggestions based on the selected topic:
|
||||
|
||||
- **"Explain this data structure"**: Get a breakdown of complex payloads
|
||||
- **"What does this value mean?"**: Understand specific measurements or states
|
||||
- **"Summarize all subtopics"**: Get an overview of nested topic hierarchies
|
||||
- **"What can I do with this topic?"**: Discover possible actions and integrations
|
||||
|
||||
### Context Intelligence
|
||||
|
||||
The AI Assistant automatically includes relevant context with your questions:
|
||||
|
||||
- **Current Topic**: The selected topic path and its current value (with preview for large payloads)
|
||||
- **Neighboring Topics**: Related topics (siblings and children) with their values, limited to 100 tokens by default
|
||||
- **Topic Metadata**: Message count, subtopic count, and retained status
|
||||
- **Smart Truncation**: Large values and topic lists are intelligently truncated to stay within token limits
|
||||
|
||||
The neighboring topics context can be adjusted using the `LLM_NEIGHBORING_TOPICS_TOKEN_LIMIT` environment variable for server deployments.
|
||||
|
||||
## Privacy & Security
|
||||
|
||||
### Data Handling
|
||||
|
||||
- **API Keys**: Stored locally in browser localStorage, never transmitted to MQTT Explorer servers
|
||||
- **Topic Data**: Topic paths and message payloads are sent to OpenAI's API to provide context
|
||||
- **Conversation History**: Maintained client-side and reset when you clear the chat
|
||||
|
||||
### Best Practices
|
||||
|
||||
1. **Sensitive Data**: Be cautious when using the AI Assistant with topics containing sensitive information
|
||||
2. **API Key Security**: Never share your OpenAI API key with others
|
||||
3. **Rate Limiting**: The service implements error handling for rate limits
|
||||
4. **Offline Operation**: The AI Assistant requires internet connectivity to function
|
||||
|
||||
## Technical Details
|
||||
|
||||
### Architecture
|
||||
|
||||
- **Frontend**: React component with Material-UI styling
|
||||
- **Service Layer**: Singleton LLM service for API communication
|
||||
- **API Integration**: OpenAI Chat Completions API (GPT-3.5-turbo by default)
|
||||
- **Context Generation**: Automatic extraction of topic metadata for relevant queries
|
||||
|
||||
### Configuration Options
|
||||
|
||||
The LLM service supports:
|
||||
|
||||
- **Custom API Endpoints**: Can be configured to use compatible APIs
|
||||
- **Model Selection**: Defaults to `gpt-3.5-turbo` but can be customized
|
||||
- **Conversation History**: Automatically manages context (keeps last 10 messages)
|
||||
- **Timeout Handling**: 30-second timeout for API requests
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### "Please configure your OpenAI API key first"
|
||||
|
||||
**Solution**: Click the settings icon and add your API key.
|
||||
|
||||
### "Invalid API key"
|
||||
|
||||
**Solutions**:
|
||||
- Verify the API key is correct
|
||||
- Check that your OpenAI account is active
|
||||
- Ensure you have available API credits
|
||||
|
||||
### "Rate limit exceeded"
|
||||
|
||||
**Solutions**:
|
||||
- Wait a few minutes before trying again
|
||||
- Check your OpenAI API usage dashboard
|
||||
- Consider upgrading your OpenAI plan if needed
|
||||
|
||||
### "Request timeout"
|
||||
|
||||
**Solutions**:
|
||||
- Check your internet connection
|
||||
- Try asking a simpler question
|
||||
- Verify OpenAI's service status
|
||||
|
||||
## Limitations
|
||||
|
||||
- Requires active internet connection
|
||||
- Needs valid OpenAI API key with available credits
|
||||
- Responses are limited to 500 tokens for performance
|
||||
- May not have knowledge of proprietary or custom MQTT implementations
|
||||
- Beta feature - under active development
|
||||
|
||||
## Future Enhancements
|
||||
|
||||
Potential improvements being considered:
|
||||
|
||||
- Support for additional LLM providers (Anthropic, Azure OpenAI, etc.)
|
||||
- Ability to save and share helpful conversations
|
||||
- Integration with automation and scripting features
|
||||
- Custom prompts and templates for specific use cases
|
||||
- Offline mode with cached responses for common questions
|
||||
|
||||
## Feedback
|
||||
|
||||
This is a beta feature. If you encounter issues or have suggestions, please open an issue on the [GitHub repository](https://github.com/thomasnordquist/MQTT-Explorer/issues).
|
||||
BIN
ai-assistant-expanded.png
Normal file
BIN
ai-assistant-expanded.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 31 KiB |
477
app/src/components/Sidebar/AIAssistant.tsx
Normal file
477
app/src/components/Sidebar/AIAssistant.tsx
Normal file
@@ -0,0 +1,477 @@
|
||||
/**
|
||||
* AI Assistant Component
|
||||
* Provides an interactive AI chat interface for topic exploration
|
||||
*/
|
||||
|
||||
import React, { useState, useCallback, useRef, useEffect } from 'react'
|
||||
import {
|
||||
Box,
|
||||
Paper,
|
||||
TextField,
|
||||
IconButton,
|
||||
Typography,
|
||||
Chip,
|
||||
CircularProgress,
|
||||
Collapse,
|
||||
Alert,
|
||||
Button,
|
||||
Dialog,
|
||||
DialogTitle,
|
||||
DialogContent,
|
||||
DialogActions,
|
||||
Select,
|
||||
MenuItem,
|
||||
FormControl,
|
||||
InputLabel,
|
||||
} from '@mui/material'
|
||||
import { Theme } from '@mui/material/styles'
|
||||
import { withStyles } from '@mui/styles'
|
||||
import SendIcon from '@mui/icons-material/Send'
|
||||
import SmartToyIcon from '@mui/icons-material/SmartToy'
|
||||
import ExpandMoreIcon from '@mui/icons-material/ExpandMore'
|
||||
import ExpandLessIcon from '@mui/icons-material/ExpandLess'
|
||||
import SettingsIcon from '@mui/icons-material/Settings'
|
||||
import ClearIcon from '@mui/icons-material/Clear'
|
||||
import { getLLMService, LLMMessage, LLMProvider } from '../../services/llmService'
|
||||
|
||||
interface Props {
|
||||
node?: any
|
||||
classes: any
|
||||
}
|
||||
|
||||
interface ChatMessage {
|
||||
role: 'user' | 'assistant' | 'system'
|
||||
content: string
|
||||
timestamp: Date
|
||||
}
|
||||
|
||||
function AIAssistant(props: Props) {
|
||||
const { node, classes } = props
|
||||
const [expanded, setExpanded] = useState(false)
|
||||
const [messages, setMessages] = useState<ChatMessage[]>([])
|
||||
const [inputValue, setInputValue] = useState('')
|
||||
const [loading, setLoading] = useState(false)
|
||||
const [error, setError] = useState<string | null>(null)
|
||||
const [configDialogOpen, setConfigDialogOpen] = useState(false)
|
||||
const [apiKey, setApiKey] = useState('')
|
||||
const [provider, setProvider] = useState<LLMProvider>('openai')
|
||||
const messagesEndRef = useRef<HTMLDivElement>(null)
|
||||
const llmService = getLLMService()
|
||||
|
||||
useEffect(() => {
|
||||
// Initialize provider from service
|
||||
setProvider(llmService.getProvider())
|
||||
}, [])
|
||||
|
||||
const scrollToBottom = () => {
|
||||
messagesEndRef.current?.scrollIntoView({ behavior: 'smooth' })
|
||||
}
|
||||
|
||||
useEffect(() => {
|
||||
scrollToBottom()
|
||||
}, [messages])
|
||||
|
||||
const handleSendMessage = useCallback(
|
||||
async (messageText?: string) => {
|
||||
const text = messageText || inputValue.trim()
|
||||
if (!text) return
|
||||
|
||||
// Check if API key is configured
|
||||
if (!llmService.hasApiKey()) {
|
||||
setError(`Please configure your ${provider === 'gemini' ? 'Gemini' : 'OpenAI'} API key first`)
|
||||
setConfigDialogOpen(true)
|
||||
return
|
||||
}
|
||||
|
||||
setInputValue('')
|
||||
setError(null)
|
||||
setLoading(true)
|
||||
|
||||
// Add user message to UI
|
||||
const userMessage: ChatMessage = {
|
||||
role: 'user',
|
||||
content: text,
|
||||
timestamp: new Date(),
|
||||
}
|
||||
setMessages((prev) => [...prev, userMessage])
|
||||
|
||||
try {
|
||||
// Generate topic context if available
|
||||
const topicContext = node ? llmService.generateTopicContext(node) : undefined
|
||||
|
||||
// Send to LLM
|
||||
const response = await llmService.sendMessage(text, topicContext)
|
||||
|
||||
// Add assistant response to UI
|
||||
const assistantMessage: ChatMessage = {
|
||||
role: 'assistant',
|
||||
content: response,
|
||||
timestamp: new Date(),
|
||||
}
|
||||
setMessages((prev) => [...prev, assistantMessage])
|
||||
} catch (err: unknown) {
|
||||
const error = err as { message?: string }
|
||||
setError(error.message || 'Failed to get response')
|
||||
} finally {
|
||||
setLoading(false)
|
||||
}
|
||||
},
|
||||
[inputValue, node, llmService]
|
||||
)
|
||||
|
||||
const handleKeyPress = (event: React.KeyboardEvent) => {
|
||||
if (event.key === 'Enter' && !event.shiftKey) {
|
||||
event.preventDefault()
|
||||
handleSendMessage()
|
||||
}
|
||||
}
|
||||
|
||||
const handleSuggestionClick = (suggestion: string) => {
|
||||
handleSendMessage(suggestion)
|
||||
}
|
||||
|
||||
const handleClearChat = () => {
|
||||
setMessages([])
|
||||
llmService.clearHistory()
|
||||
setError(null)
|
||||
}
|
||||
|
||||
const handleSaveApiKey = () => {
|
||||
if (apiKey.trim()) {
|
||||
llmService.saveApiKey(apiKey.trim())
|
||||
llmService.saveProvider(provider)
|
||||
setConfigDialogOpen(false)
|
||||
setApiKey('')
|
||||
setError(null)
|
||||
// Reset the service to use new config
|
||||
window.location.reload()
|
||||
}
|
||||
}
|
||||
|
||||
const suggestions = node ? llmService.getQuickSuggestions(node) : []
|
||||
|
||||
// Check if API key is available (from localStorage or environment)
|
||||
const hasApiKey = llmService.hasApiKey()
|
||||
|
||||
// Don't render the component at all if no API key is available
|
||||
if (!hasApiKey) {
|
||||
return null
|
||||
}
|
||||
|
||||
return (
|
||||
<Box className={classes.root}>
|
||||
{/* Header */}
|
||||
<Box className={classes.header} onClick={() => setExpanded(!expanded)}>
|
||||
<Box className={classes.headerLeft}>
|
||||
<SmartToyIcon className={classes.icon} />
|
||||
<Typography variant="subtitle2" className={classes.title}>
|
||||
AI Assistant
|
||||
</Typography>
|
||||
<Chip label="Beta" size="small" color="primary" className={classes.betaChip} />
|
||||
</Box>
|
||||
<Box className={classes.headerRight}>
|
||||
<IconButton
|
||||
size="small"
|
||||
onClick={(e) => {
|
||||
e.stopPropagation()
|
||||
setConfigDialogOpen(true)
|
||||
}}
|
||||
className={classes.iconButton}
|
||||
>
|
||||
<SettingsIcon fontSize="small" />
|
||||
</IconButton>
|
||||
{expanded ? <ExpandLessIcon /> : <ExpandMoreIcon />}
|
||||
</Box>
|
||||
</Box>
|
||||
|
||||
{/* Chat Interface */}
|
||||
<Collapse in={expanded}>
|
||||
<Box className={classes.content}>
|
||||
{/* Error Alert */}
|
||||
{error && (
|
||||
<Alert severity="error" className={classes.alert} onClose={() => setError(null)}>
|
||||
{error}
|
||||
</Alert>
|
||||
)}
|
||||
|
||||
{/* Quick Suggestions */}
|
||||
{messages.length === 0 && suggestions.length > 0 && (
|
||||
<Box className={classes.suggestions}>
|
||||
<Typography variant="caption" color="textSecondary" className={classes.suggestionsTitle}>
|
||||
Quick questions:
|
||||
</Typography>
|
||||
<Box className={classes.suggestionChips}>
|
||||
{suggestions.slice(0, 4).map((suggestion, idx) => (
|
||||
<Chip
|
||||
key={idx}
|
||||
label={suggestion}
|
||||
size="small"
|
||||
onClick={() => handleSuggestionClick(suggestion)}
|
||||
className={classes.suggestionChip}
|
||||
/>
|
||||
))}
|
||||
</Box>
|
||||
</Box>
|
||||
)}
|
||||
|
||||
{/* Messages */}
|
||||
<Box className={classes.messages}>
|
||||
{messages.length === 0 && !error && (
|
||||
<Box className={classes.emptyState}>
|
||||
<SmartToyIcon className={classes.emptyIcon} />
|
||||
<Typography variant="body2" color="textSecondary" align="center">
|
||||
Ask me anything about this topic!
|
||||
</Typography>
|
||||
</Box>
|
||||
)}
|
||||
|
||||
{messages.map((msg, idx) => (
|
||||
<Box
|
||||
key={idx}
|
||||
className={msg.role === 'user' ? classes.userMessage : classes.assistantMessage}
|
||||
>
|
||||
<Typography variant="body2" className={classes.messageText}>
|
||||
{msg.content}
|
||||
</Typography>
|
||||
<Typography variant="caption" color="textSecondary" className={classes.messageTime}>
|
||||
{msg.timestamp.toLocaleTimeString()}
|
||||
</Typography>
|
||||
</Box>
|
||||
))}
|
||||
|
||||
{loading && (
|
||||
<Box className={classes.loadingBox}>
|
||||
<CircularProgress size={20} />
|
||||
<Typography variant="caption" color="textSecondary" sx={{ ml: 1 }}>
|
||||
Thinking...
|
||||
</Typography>
|
||||
</Box>
|
||||
)}
|
||||
|
||||
<div ref={messagesEndRef} />
|
||||
</Box>
|
||||
|
||||
{/* Input */}
|
||||
<Box className={classes.inputContainer}>
|
||||
{messages.length > 0 && (
|
||||
<IconButton size="small" onClick={handleClearChat} className={classes.clearButton}>
|
||||
<ClearIcon fontSize="small" />
|
||||
</IconButton>
|
||||
)}
|
||||
<TextField
|
||||
fullWidth
|
||||
size="small"
|
||||
placeholder="Ask about this topic..."
|
||||
value={inputValue}
|
||||
onChange={(e) => setInputValue(e.target.value)}
|
||||
onKeyPress={handleKeyPress}
|
||||
disabled={loading}
|
||||
className={classes.input}
|
||||
multiline
|
||||
maxRows={3}
|
||||
/>
|
||||
<IconButton
|
||||
color="primary"
|
||||
onClick={() => handleSendMessage()}
|
||||
disabled={!inputValue.trim() || loading}
|
||||
className={classes.sendButton}
|
||||
>
|
||||
<SendIcon />
|
||||
</IconButton>
|
||||
</Box>
|
||||
</Box>
|
||||
</Collapse>
|
||||
|
||||
{/* Configuration Dialog */}
|
||||
<Dialog open={configDialogOpen} onClose={() => setConfigDialogOpen(false)} maxWidth="sm" fullWidth>
|
||||
<DialogTitle>AI Assistant Configuration</DialogTitle>
|
||||
<DialogContent>
|
||||
<FormControl fullWidth margin="normal">
|
||||
<InputLabel>AI Provider</InputLabel>
|
||||
<Select
|
||||
value={provider}
|
||||
label="AI Provider"
|
||||
onChange={(e) => setProvider(e.target.value as LLMProvider)}
|
||||
>
|
||||
<MenuItem value="openai">OpenAI (GPT-3.5 Turbo)</MenuItem>
|
||||
<MenuItem value="gemini">Google Gemini (Flash)</MenuItem>
|
||||
</Select>
|
||||
</FormControl>
|
||||
|
||||
<Typography variant="body2" color="textSecondary" paragraph sx={{ mt: 2 }}>
|
||||
{provider === 'openai' ? (
|
||||
<>
|
||||
Get your OpenAI API key from{' '}
|
||||
<a href="https://platform.openai.com/api-keys" target="_blank" rel="noopener noreferrer">
|
||||
OpenAI's platform
|
||||
</a>
|
||||
.
|
||||
</>
|
||||
) : (
|
||||
<>
|
||||
Get your Gemini API key from{' '}
|
||||
<a href="https://aistudio.google.com/app/apikey" target="_blank" rel="noopener noreferrer">
|
||||
Google AI Studio
|
||||
</a>
|
||||
.
|
||||
</>
|
||||
)}
|
||||
</Typography>
|
||||
<TextField
|
||||
fullWidth
|
||||
label={`${provider === 'openai' ? 'OpenAI' : 'Gemini'} API Key`}
|
||||
type="password"
|
||||
value={apiKey}
|
||||
onChange={(e) => setApiKey(e.target.value)}
|
||||
placeholder={provider === 'openai' ? 'sk-...' : 'AIza...'}
|
||||
margin="normal"
|
||||
helperText="Your API key is stored locally and never sent to our servers"
|
||||
/>
|
||||
</DialogContent>
|
||||
<DialogActions>
|
||||
<Button onClick={() => setConfigDialogOpen(false)}>Cancel</Button>
|
||||
<Button onClick={handleSaveApiKey} variant="contained" disabled={!apiKey.trim()}>
|
||||
Save
|
||||
</Button>
|
||||
</DialogActions>
|
||||
</Dialog>
|
||||
</Box>
|
||||
)
|
||||
}
|
||||
|
||||
const styles = (theme: Theme) => ({
|
||||
root: {
|
||||
marginTop: theme.spacing(2),
|
||||
border: `1px solid ${theme.palette.divider}`,
|
||||
borderRadius: theme.shape.borderRadius,
|
||||
overflow: 'hidden',
|
||||
},
|
||||
header: {
|
||||
display: 'flex',
|
||||
justifyContent: 'space-between',
|
||||
alignItems: 'center',
|
||||
padding: theme.spacing(1.5, 2),
|
||||
backgroundColor: theme.palette.action.hover,
|
||||
cursor: 'pointer',
|
||||
'&:hover': {
|
||||
backgroundColor: theme.palette.action.selected,
|
||||
},
|
||||
},
|
||||
headerLeft: {
|
||||
display: 'flex',
|
||||
alignItems: 'center',
|
||||
gap: theme.spacing(1),
|
||||
},
|
||||
headerRight: {
|
||||
display: 'flex',
|
||||
alignItems: 'center',
|
||||
gap: theme.spacing(0.5),
|
||||
},
|
||||
icon: {
|
||||
color: theme.palette.primary.main,
|
||||
},
|
||||
title: {
|
||||
fontWeight: 600,
|
||||
},
|
||||
betaChip: {
|
||||
height: '20px',
|
||||
fontSize: '0.7rem',
|
||||
},
|
||||
iconButton: {
|
||||
padding: theme.spacing(0.5),
|
||||
},
|
||||
content: {
|
||||
padding: theme.spacing(2),
|
||||
display: 'flex',
|
||||
flexDirection: 'column' as 'column',
|
||||
gap: theme.spacing(1.5),
|
||||
},
|
||||
alert: {
|
||||
marginBottom: theme.spacing(1),
|
||||
},
|
||||
suggestions: {
|
||||
marginBottom: theme.spacing(1),
|
||||
},
|
||||
suggestionsTitle: {
|
||||
display: 'block',
|
||||
marginBottom: theme.spacing(0.5),
|
||||
},
|
||||
suggestionChips: {
|
||||
display: 'flex',
|
||||
flexWrap: 'wrap' as 'wrap',
|
||||
gap: theme.spacing(0.5),
|
||||
},
|
||||
suggestionChip: {
|
||||
cursor: 'pointer',
|
||||
'&:hover': {
|
||||
backgroundColor: theme.palette.primary.main,
|
||||
color: theme.palette.primary.contrastText,
|
||||
},
|
||||
},
|
||||
messages: {
|
||||
maxHeight: '300px',
|
||||
overflowY: 'auto' as 'auto',
|
||||
display: 'flex',
|
||||
flexDirection: 'column' as 'column',
|
||||
gap: theme.spacing(1),
|
||||
},
|
||||
emptyState: {
|
||||
display: 'flex',
|
||||
flexDirection: 'column' as 'column',
|
||||
alignItems: 'center',
|
||||
justifyContent: 'center',
|
||||
padding: theme.spacing(4),
|
||||
gap: theme.spacing(1),
|
||||
},
|
||||
emptyIcon: {
|
||||
fontSize: '3rem',
|
||||
color: theme.palette.action.disabled,
|
||||
},
|
||||
userMessage: {
|
||||
alignSelf: 'flex-end',
|
||||
maxWidth: '80%',
|
||||
padding: theme.spacing(1, 1.5),
|
||||
backgroundColor: theme.palette.primary.main,
|
||||
color: theme.palette.primary.contrastText,
|
||||
borderRadius: theme.spacing(1.5),
|
||||
borderBottomRightRadius: theme.spacing(0.5),
|
||||
},
|
||||
assistantMessage: {
|
||||
alignSelf: 'flex-start',
|
||||
maxWidth: '80%',
|
||||
padding: theme.spacing(1, 1.5),
|
||||
backgroundColor: theme.palette.action.hover,
|
||||
borderRadius: theme.spacing(1.5),
|
||||
borderBottomLeftRadius: theme.spacing(0.5),
|
||||
},
|
||||
messageText: {
|
||||
whiteSpace: 'pre-wrap' as 'pre-wrap',
|
||||
wordBreak: 'break-word' as 'break-word',
|
||||
},
|
||||
messageTime: {
|
||||
display: 'block',
|
||||
marginTop: theme.spacing(0.5),
|
||||
fontSize: '0.65rem',
|
||||
},
|
||||
loadingBox: {
|
||||
display: 'flex',
|
||||
alignItems: 'center',
|
||||
padding: theme.spacing(1),
|
||||
},
|
||||
inputContainer: {
|
||||
display: 'flex',
|
||||
gap: theme.spacing(1),
|
||||
alignItems: 'flex-end',
|
||||
},
|
||||
clearButton: {
|
||||
padding: theme.spacing(0.5),
|
||||
},
|
||||
input: {
|
||||
flex: 1,
|
||||
},
|
||||
sendButton: {
|
||||
padding: theme.spacing(1),
|
||||
},
|
||||
})
|
||||
|
||||
export default withStyles(styles)(AIAssistant)
|
||||
@@ -19,6 +19,7 @@ import DeleteIcon from '@mui/icons-material/Delete'
|
||||
import DeleteSweepIcon from '@mui/icons-material/DeleteSweep'
|
||||
import Info from '@mui/icons-material/Info'
|
||||
import SimpleBreadcrumb from './SimpleBreadcrumb'
|
||||
import AIAssistant from './AIAssistant'
|
||||
|
||||
interface Props {
|
||||
node?: q.TreeNode<any>
|
||||
@@ -196,6 +197,9 @@ function DetailsTab(props: Props) {
|
||||
</Box>
|
||||
)}
|
||||
|
||||
{/* AI Assistant - Always available when a node is selected */}
|
||||
{node && <AIAssistant node={node} />}
|
||||
|
||||
{/* About Section - always visible at bottom */}
|
||||
<Box className={classes.aboutSection}>
|
||||
<Button
|
||||
@@ -235,6 +239,11 @@ const styles = (theme: Theme) => ({
|
||||
paddingTop: theme.spacing(2),
|
||||
borderTop: `1px solid ${theme.palette.divider}`,
|
||||
},
|
||||
aboutSection: {
|
||||
marginTop: theme.spacing(3),
|
||||
paddingTop: theme.spacing(2),
|
||||
borderTop: `1px solid ${theme.palette.divider}`,
|
||||
},
|
||||
// Topic section
|
||||
topicSection: {
|
||||
display: 'flex',
|
||||
|
||||
507
app/src/services/llmService.ts
Normal file
507
app/src/services/llmService.ts
Normal file
@@ -0,0 +1,507 @@
|
||||
/**
|
||||
* LLM Service for interacting with topics
|
||||
* Provides AI assistance to help users understand and interact with MQTT topics
|
||||
*/
|
||||
|
||||
import axios, { AxiosInstance } from 'axios'
|
||||
|
||||
export interface LLMMessage {
|
||||
role: 'system' | 'user' | 'assistant'
|
||||
content: string
|
||||
}
|
||||
|
||||
export type LLMProvider = 'openai' | 'gemini'
|
||||
|
||||
export interface LLMServiceConfig {
|
||||
apiKey?: string
|
||||
apiEndpoint?: string
|
||||
model?: string
|
||||
provider?: LLMProvider
|
||||
neighboringTopicsTokenLimit?: number
|
||||
}
|
||||
|
||||
export class LLMService {
|
||||
private axiosInstance: AxiosInstance
|
||||
private model: string
|
||||
private provider: LLMProvider
|
||||
private conversationHistory: LLMMessage[] = []
|
||||
private neighboringTopicsTokenLimit: number
|
||||
|
||||
constructor(config: LLMServiceConfig = {}) {
|
||||
const apiKey = config.apiKey || this.getApiKeyFromStorage() || this.getApiKeyFromEnv()
|
||||
this.provider = config.provider || this.getProviderFromStorage() || this.getProviderFromEnv() || 'openai'
|
||||
this.neighboringTopicsTokenLimit = config.neighboringTopicsTokenLimit || this.getNeighboringTopicsTokenLimitFromEnv() || 100
|
||||
|
||||
// Set default endpoint and model based on provider
|
||||
let baseURL = config.apiEndpoint
|
||||
if (!baseURL) {
|
||||
baseURL = this.provider === 'gemini'
|
||||
? 'https://generativelanguage.googleapis.com/v1beta'
|
||||
: 'https://api.openai.com/v1'
|
||||
}
|
||||
|
||||
this.model = config.model || (this.provider === 'gemini' ? 'gemini-1.5-flash-latest' : 'gpt-3.5-turbo')
|
||||
|
||||
// Configure headers based on provider
|
||||
const headers: Record<string, string> = {
|
||||
'Content-Type': 'application/json',
|
||||
}
|
||||
|
||||
if (apiKey) {
|
||||
if (this.provider === 'gemini') {
|
||||
// Gemini uses API key as query parameter, not header
|
||||
// Will be added to URL in sendMessage
|
||||
} else {
|
||||
headers.Authorization = `Bearer ${apiKey}`
|
||||
}
|
||||
}
|
||||
|
||||
this.axiosInstance = axios.create({
|
||||
baseURL,
|
||||
headers,
|
||||
timeout: 30000,
|
||||
})
|
||||
|
||||
// Initialize with system message that sets MQTT and automation context
|
||||
this.conversationHistory.push({
|
||||
role: 'system',
|
||||
content: `You are an expert AI assistant specializing in MQTT (Message Queuing Telemetry Transport) protocol and home/industrial automation systems.
|
||||
|
||||
**Your Core Expertise:**
|
||||
- MQTT protocol: topics, QoS levels, retained messages, wildcards, last will and testament
|
||||
- IoT and smart home ecosystems: devices, sensors, actuators, and controllers
|
||||
- Home automation platforms: Home Assistant, openHAB, Node-RED, MQTT brokers
|
||||
- Common MQTT topic patterns and naming conventions (e.g., zigbee2mqtt, tasmota, homie)
|
||||
- Data formats: JSON payloads, binary data, sensor readings, state messages
|
||||
- Time-series data analysis and pattern recognition
|
||||
- Troubleshooting connectivity, message delivery, and data quality issues
|
||||
|
||||
**Your Communication Style:**
|
||||
- Be concise and practical - focus on actionable insights
|
||||
- Use clear technical language appropriate for users familiar with MQTT
|
||||
- When analyzing data, identify patterns, anomalies, or potential issues
|
||||
- Suggest practical next steps or automations when relevant
|
||||
- Reference common MQTT ecosystems and standards when applicable
|
||||
|
||||
**Context You Receive:**
|
||||
Users will ask about specific MQTT topics and their data. You'll receive:
|
||||
- Topic path (the MQTT topic hierarchy)
|
||||
- Current value and message payload
|
||||
- Related/neighboring topics with their values
|
||||
- Metadata (message count, subtopics, retained status)
|
||||
|
||||
**Your Goal:**
|
||||
Help users understand their MQTT data, troubleshoot issues, optimize their automation setups, and discover insights about their connected devices and systems.`,
|
||||
})
|
||||
}
|
||||
|
||||
private getApiKeyFromStorage(): string | undefined {
|
||||
if (typeof window !== 'undefined' && window.localStorage) {
|
||||
return window.localStorage.getItem('llm_api_key') || undefined
|
||||
}
|
||||
return undefined
|
||||
}
|
||||
|
||||
private getApiKeyFromEnv(): string | undefined {
|
||||
if (typeof process !== 'undefined' && process.env) {
|
||||
// Try provider-specific env vars first, then fall back to generic
|
||||
if (this.provider === 'gemini') {
|
||||
return process.env.GEMINI_API_KEY || process.env.LLM_API_KEY
|
||||
} else {
|
||||
return process.env.OPENAI_API_KEY || process.env.LLM_API_KEY
|
||||
}
|
||||
}
|
||||
return undefined
|
||||
}
|
||||
|
||||
private getProviderFromStorage(): LLMProvider | undefined {
|
||||
if (typeof window !== 'undefined' && window.localStorage) {
|
||||
const provider = window.localStorage.getItem('llm_provider')
|
||||
return provider === 'gemini' || provider === 'openai' ? provider : undefined
|
||||
}
|
||||
return undefined
|
||||
}
|
||||
|
||||
private getProviderFromEnv(): LLMProvider | undefined {
|
||||
if (typeof process !== 'undefined' && process.env) {
|
||||
const provider = process.env.LLM_PROVIDER
|
||||
return provider === 'gemini' || provider === 'openai' ? provider : undefined
|
||||
}
|
||||
return undefined
|
||||
}
|
||||
|
||||
private getNeighboringTopicsTokenLimitFromEnv(): number | undefined {
|
||||
if (typeof process !== 'undefined' && process.env) {
|
||||
const limit = parseInt(process.env.LLM_NEIGHBORING_TOPICS_TOKEN_LIMIT || '', 10)
|
||||
return isNaN(limit) ? undefined : limit
|
||||
}
|
||||
return undefined
|
||||
}
|
||||
|
||||
/**
|
||||
* Save API key to local storage
|
||||
*/
|
||||
public saveApiKey(apiKey: string): void {
|
||||
if (typeof window !== 'undefined' && window.localStorage) {
|
||||
window.localStorage.setItem('llm_api_key', apiKey)
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Save provider to local storage
|
||||
*/
|
||||
public saveProvider(provider: LLMProvider): void {
|
||||
if (typeof window !== 'undefined' && window.localStorage) {
|
||||
window.localStorage.setItem('llm_provider', provider)
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Clear API key from local storage
|
||||
*/
|
||||
public clearApiKey(): void {
|
||||
if (typeof window !== 'undefined' && window.localStorage) {
|
||||
window.localStorage.removeItem('llm_api_key')
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Check if API key is configured
|
||||
*/
|
||||
public hasApiKey(): boolean {
|
||||
return !!this.getApiKeyFromStorage()
|
||||
}
|
||||
|
||||
/**
|
||||
* Get current provider
|
||||
*/
|
||||
public getProvider(): LLMProvider {
|
||||
return this.provider
|
||||
}
|
||||
|
||||
/**
|
||||
* Estimate tokens in text (rough approximation: ~4 characters per token)
|
||||
*/
|
||||
private estimateTokens(text: string): number {
|
||||
// Simple estimation: average ~4 characters per token
|
||||
// This is a rough approximation for both OpenAI and Gemini
|
||||
return Math.ceil(text.length / 4)
|
||||
}
|
||||
|
||||
/**
|
||||
* Truncate text to fit within token limit
|
||||
* Returns object with truncated text and flag indicating if truncation occurred
|
||||
*/
|
||||
private truncateToTokenLimit(text: string, tokenLimit: number): { text: string; truncated: boolean } {
|
||||
const estimatedTokens = this.estimateTokens(text)
|
||||
if (estimatedTokens <= tokenLimit) {
|
||||
return { text, truncated: false }
|
||||
}
|
||||
|
||||
// Truncate to approximate character count
|
||||
const maxChars = tokenLimit * 4
|
||||
if (text.length <= maxChars) {
|
||||
return { text, truncated: false }
|
||||
}
|
||||
|
||||
return {
|
||||
text: text.substring(0, maxChars - 3) + '...',
|
||||
truncated: true
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Escape string for single-line representation (no newlines)
|
||||
* Encodes newlines and other special characters similar to JSON string encoding
|
||||
*/
|
||||
private escapeToSingleLine(text: string): string {
|
||||
return text
|
||||
.replace(/\\/g, '\\\\') // Escape backslashes first
|
||||
.replace(/\n/g, '\\n') // Encode newlines
|
||||
.replace(/\r/g, '\\r') // Encode carriage returns
|
||||
.replace(/\t/g, '\\t') // Encode tabs
|
||||
.replace(/"/g, '\\"') // Escape quotes
|
||||
}
|
||||
|
||||
/**
|
||||
* Format value for LLM context (machine-friendly, single-line)
|
||||
*/
|
||||
private formatValueForContext(value: any, tokenLimit: number, markTruncation: boolean = true): string {
|
||||
let valueStr: string
|
||||
|
||||
if (typeof value === 'object' && value !== null) {
|
||||
// For objects, use JSON.stringify which handles escaping
|
||||
valueStr = JSON.stringify(value)
|
||||
} else {
|
||||
valueStr = String(value)
|
||||
}
|
||||
|
||||
// Escape to single line
|
||||
const escaped = this.escapeToSingleLine(valueStr)
|
||||
|
||||
// Truncate if needed
|
||||
const result = this.truncateToTokenLimit(escaped, tokenLimit)
|
||||
|
||||
if (result.truncated && markTruncation) {
|
||||
return `[TRUNCATED] ${result.text}`
|
||||
}
|
||||
|
||||
return result.text
|
||||
}
|
||||
|
||||
/**
|
||||
* Generate context from topic data including neighboring topics
|
||||
*/
|
||||
public generateTopicContext(topic: {
|
||||
path?: () => string
|
||||
message?: any
|
||||
messages?: number
|
||||
childTopicCount?: () => number
|
||||
type?: string
|
||||
parent?: any
|
||||
edgeCollection?: any
|
||||
}): string {
|
||||
const context = []
|
||||
|
||||
if (topic.path) {
|
||||
context.push(`Topic: ${topic.path()}`)
|
||||
}
|
||||
|
||||
// Add current value with preview (allow more tokens for main topic - 200 tokens)
|
||||
if (topic.message?.payload) {
|
||||
const [value] = topic.message.payload.format(topic.type)
|
||||
if (value !== null && value !== undefined) {
|
||||
// Main topic value can contain newlines, format for LLM
|
||||
const formattedValue = this.formatValueForContext(value, 200, true)
|
||||
context.push(`Value: ${formattedValue}`)
|
||||
}
|
||||
|
||||
// Add retained status if true
|
||||
if (topic.message.retain) {
|
||||
context.push(`Retained: true`)
|
||||
}
|
||||
}
|
||||
|
||||
// Add neighboring topics (siblings and children) up to token limit
|
||||
// Full topic paths with single-line previews
|
||||
const neighbors: string[] = []
|
||||
let neighborsTokenCount = 0
|
||||
const tokenLimit = this.neighboringTopicsTokenLimit
|
||||
|
||||
// Helper function to add a neighbor if within token limit
|
||||
const addNeighbor = (fullPath: string, value: any): boolean => {
|
||||
// Format value as single-line preview (no newlines)
|
||||
const preview = this.formatValueForContext(value, 20, false) // 20 tokens per neighbor
|
||||
const neighborEntry = ` ${fullPath}: ${preview}`
|
||||
const tokens = this.estimateTokens(neighborEntry)
|
||||
|
||||
if (neighborsTokenCount + tokens <= tokenLimit) {
|
||||
neighbors.push(neighborEntry)
|
||||
neighborsTokenCount += tokens
|
||||
return true
|
||||
}
|
||||
return false
|
||||
}
|
||||
|
||||
// Get parent path for constructing full paths
|
||||
const parentPath = topic.parent?.path ? topic.parent.path() : ''
|
||||
|
||||
// Get siblings from parent
|
||||
if (topic.parent && topic.parent.edgeCollection) {
|
||||
const siblings = topic.parent.edgeCollection.edges || []
|
||||
for (const edge of siblings) {
|
||||
if (neighborsTokenCount >= tokenLimit) break
|
||||
if (edge.name && edge.node && edge.node.message?.payload) {
|
||||
const [siblingValue] = edge.node.message.payload.format(edge.node.type)
|
||||
if (siblingValue !== null && siblingValue !== undefined) {
|
||||
const fullPath = parentPath ? `${parentPath}/${edge.name}` : edge.name
|
||||
if (!addNeighbor(fullPath, siblingValue)) {
|
||||
break
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Get children
|
||||
const currentPath = topic.path ? topic.path() : ''
|
||||
if (topic.edgeCollection?.edges && neighborsTokenCount < tokenLimit) {
|
||||
const children = topic.edgeCollection.edges || []
|
||||
for (const edge of children) {
|
||||
if (neighborsTokenCount >= tokenLimit) break
|
||||
if (edge.name && edge.node && edge.node.message?.payload) {
|
||||
const [childValue] = edge.node.message.payload.format(edge.node.type)
|
||||
if (childValue !== null && childValue !== undefined) {
|
||||
const fullPath = currentPath ? `${currentPath}/${edge.name}` : edge.name
|
||||
if (!addNeighbor(fullPath, childValue)) {
|
||||
break
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if (neighbors.length > 0) {
|
||||
context.push(`\nRelated Topics (${neighbors.length}):`)
|
||||
context.push(neighbors.join('\n'))
|
||||
}
|
||||
|
||||
// Add metadata
|
||||
if (topic.messages) {
|
||||
context.push(`\nMessages: ${topic.messages}`)
|
||||
}
|
||||
|
||||
if (topic.childTopicCount) {
|
||||
const childCount = topic.childTopicCount()
|
||||
if (childCount > 0) {
|
||||
context.push(`Subtopics: ${childCount}`)
|
||||
}
|
||||
}
|
||||
|
||||
return context.join('\n')
|
||||
}
|
||||
|
||||
/**
|
||||
* Send a message to the LLM and get a response
|
||||
*/
|
||||
public async sendMessage(userMessage: string, topicContext?: string): Promise<string> {
|
||||
try {
|
||||
// Add topic context if provided
|
||||
let messageContent = userMessage
|
||||
if (topicContext) {
|
||||
messageContent = `Context:\n${topicContext}\n\nUser Question: ${userMessage}`
|
||||
}
|
||||
|
||||
// Add user message to history
|
||||
this.conversationHistory.push({
|
||||
role: 'user',
|
||||
content: messageContent,
|
||||
})
|
||||
|
||||
let assistantMessage: string
|
||||
|
||||
if (this.provider === 'gemini') {
|
||||
// Gemini API format
|
||||
const apiKey = this.getApiKeyFromStorage()
|
||||
const contents = this.conversationHistory
|
||||
.filter(msg => msg.role !== 'system')
|
||||
.map(msg => ({
|
||||
role: msg.role === 'assistant' ? 'model' : 'user',
|
||||
parts: [{ text: msg.content }],
|
||||
}))
|
||||
|
||||
// Prepend system message as first user message for Gemini
|
||||
const systemMsg = this.conversationHistory.find(msg => msg.role === 'system')
|
||||
if (systemMsg && contents.length > 0) {
|
||||
contents[0].parts.unshift({ text: systemMsg.content })
|
||||
}
|
||||
|
||||
const response = await this.axiosInstance.post(
|
||||
`/models/${this.model}:generateContent?key=${apiKey}`,
|
||||
{
|
||||
contents,
|
||||
generationConfig: {
|
||||
temperature: 0.7,
|
||||
maxOutputTokens: 500,
|
||||
},
|
||||
}
|
||||
)
|
||||
|
||||
if (!response.data.candidates || response.data.candidates.length === 0) {
|
||||
throw new Error('No response from AI assistant')
|
||||
}
|
||||
|
||||
assistantMessage = response.data.candidates[0].content.parts[0].text
|
||||
} else {
|
||||
// OpenAI API format
|
||||
const response = await this.axiosInstance.post('/chat/completions', {
|
||||
model: this.model,
|
||||
messages: this.conversationHistory,
|
||||
temperature: 0.7,
|
||||
max_tokens: 500,
|
||||
})
|
||||
|
||||
if (!response.data.choices || response.data.choices.length === 0) {
|
||||
throw new Error('No response from AI assistant')
|
||||
}
|
||||
|
||||
assistantMessage = response.data.choices[0].message.content
|
||||
}
|
||||
|
||||
// Add assistant response to history
|
||||
this.conversationHistory.push({
|
||||
role: 'assistant',
|
||||
content: assistantMessage,
|
||||
})
|
||||
|
||||
// Keep conversation history manageable (last 10 messages + system)
|
||||
if (this.conversationHistory.length > 11) {
|
||||
this.conversationHistory = [
|
||||
this.conversationHistory[0], // Keep system message
|
||||
...this.conversationHistory.slice(-10), // Keep last 10 messages
|
||||
]
|
||||
}
|
||||
|
||||
return assistantMessage
|
||||
} catch (error: unknown) {
|
||||
console.error('LLM Service Error:', error)
|
||||
|
||||
const err = error as { response?: { status?: number; data?: any }; code?: string; message?: string }
|
||||
|
||||
if (err.response?.status === 401 || err.response?.status === 403) {
|
||||
throw new Error('Invalid API key. Please check your configuration.')
|
||||
} else if (err.response?.status === 429) {
|
||||
throw new Error('Rate limit exceeded. Please try again later.')
|
||||
} else if (err.code === 'ECONNABORTED') {
|
||||
throw new Error('Request timeout. Please try again.')
|
||||
} else {
|
||||
throw new Error(err.message || 'Failed to get response from AI assistant.')
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Clear conversation history
|
||||
*/
|
||||
public clearHistory(): void {
|
||||
this.conversationHistory = [this.conversationHistory[0]] // Keep only system message
|
||||
}
|
||||
|
||||
/**
|
||||
* Get quick suggestions based on topic
|
||||
*/
|
||||
public getQuickSuggestions(topic: { message?: { payload?: any }; childTopicCount?: () => number; messages?: number }): string[] {
|
||||
const suggestions = []
|
||||
|
||||
if (topic.message?.payload) {
|
||||
suggestions.push('Explain this data structure')
|
||||
suggestions.push('What does this value mean?')
|
||||
}
|
||||
|
||||
if (topic.childTopicCount && topic.childTopicCount() > 0) {
|
||||
suggestions.push('Summarize all subtopics')
|
||||
}
|
||||
|
||||
if (topic.messages > 1) {
|
||||
suggestions.push('Analyze message patterns')
|
||||
}
|
||||
|
||||
suggestions.push('What can I do with this topic?')
|
||||
|
||||
return suggestions
|
||||
}
|
||||
}
|
||||
|
||||
// Export a singleton instance
|
||||
let llmServiceInstance: LLMService | null = null
|
||||
|
||||
export function getLLMService(): LLMService {
|
||||
if (!llmServiceInstance) {
|
||||
llmServiceInstance = new LLMService()
|
||||
}
|
||||
return llmServiceInstance
|
||||
}
|
||||
|
||||
export function resetLLMService(): void {
|
||||
llmServiceInstance = null
|
||||
}
|
||||
BIN
gemini-provider-selection.png
Normal file
BIN
gemini-provider-selection.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 56 KiB |
BIN
llm-integration-screenshot.png
Normal file
BIN
llm-integration-screenshot.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 48 KiB |
4
test-results/.last-run.json
Normal file
4
test-results/.last-run.json
Normal file
@@ -0,0 +1,4 @@
|
||||
{
|
||||
"status": "failed",
|
||||
"failedTests": []
|
||||
}
|
||||
Reference in New Issue
Block a user