forensic-ai #4
276
.env.example
276
.env.example
@ -1,17 +1,17 @@
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# ============================================================================
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# ForensicPathways Environment Configuration
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# ForensicPathways Environment Configuration - COMPLETE
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# ============================================================================
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# Copy this file to .env and adjust the values below.
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# Settings are ordered by likelihood of needing adjustment during setup.
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# This file covers ALL environment variables used in the codebase.
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# ============================================================================
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# 1. CORE APPLICATION SETTINGS (REQUIRED - ADJUST FOR YOUR SETUP)
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# 1. CORE APPLICATION SETTINGS (REQUIRED)
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# ============================================================================
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# Your application's public URL (used for redirects and links)
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PUBLIC_BASE_URL=http://localhost:4321
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# Application environment (development, production, staging)
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# Application environment
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NODE_ENV=development
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# Secret key for session encryption (CHANGE IN PRODUCTION!)
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@ -22,19 +22,99 @@ AUTH_SECRET=your-secret-key-change-in-production-please
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# ============================================================================
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# Main AI Analysis Service (for query processing and recommendations)
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# Example uses Mistral AI - adjust endpoint/model as needed
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AI_ANALYZER_ENDPOINT=https://api.mistral.ai/v1
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AI_ANALYZER_API_KEY=your-mistral-api-key-here
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AI_ANALYZER_MODEL=mistral-small-latest
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# Examples: http://localhost:11434 (Ollama), https://api.mistral.ai, https://api.openai.com
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AI_ANALYZER_ENDPOINT=https://api.mistral.ai/v1/chat/completions
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AI_ANALYZER_API_KEY=
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AI_ANALYZER_MODEL=mistral/mistral-small-latest
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# Vector Embeddings Service (for semantic search - can use same provider)
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# Vector Embeddings Service (for semantic search)
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# Leave API_KEY empty for Ollama, use actual key for cloud services
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AI_EMBEDDINGS_ENABLED=true
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AI_EMBEDDINGS_ENDPOINT=https://api.mistral.ai/v1/embeddings
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AI_EMBEDDINGS_API_KEY=your-mistral-api-key-here
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AI_EMBEDDINGS_API_KEY=
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AI_EMBEDDINGS_MODEL=mistral-embed
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# ============================================================================
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# 3. AUTHENTICATION (OPTIONAL - SET TO 'true' IF NEEDED)
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# 3. AI PIPELINE CONFIGURATION (CONTEXT & PERFORMANCE TUNING)
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# ============================================================================
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# === SIMILARITY SEARCH STAGE ===
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# How many similar tools/concepts embeddings search returns as candidates
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# 🔍 This is the FIRST filter - vector similarity matching
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# Lower = faster, less comprehensive | Higher = slower, more comprehensive
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AI_EMBEDDING_CANDIDATES=40
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# Minimum similarity score threshold (0.0-1.0)
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# Lower = more results but less relevant | Higher = fewer but more relevant
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AI_SIMILARITY_THRESHOLD=0.3
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# === AI SELECTION STAGE ===
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# Maximum tools the AI can select from embedding candidates
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# 🤖 This is the SECOND filter - AI intelligent selection
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# Should be ≤ AI_EMBEDDING_CANDIDATES
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AI_MAX_SELECTED_ITEMS=25
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# Maximum tools sent to AI for detailed analysis (micro-tasks)
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# 📋 This is the FINAL context size sent to AI models
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# Lower = less AI context, faster responses | Higher = more context, slower
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AI_MAX_TOOLS_TO_ANALYZE=20
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# Maximum concepts sent to AI for background knowledge selection
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# 📚 Concepts are smaller than tools, so can be higher
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AI_MAX_CONCEPTS_TO_ANALYZE=10
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# === CONTEXT FLOW SUMMARY ===
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# 1. Vector Search: 111 total tools → AI_EMBEDDING_CANDIDATES (40) most similar
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# 2. AI Selection: 40 candidates → AI_MAX_SELECTED_ITEMS (25) best matches
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# 3. AI Analysis: 25 selected → AI_MAX_TOOLS_TO_ANALYZE (20) for micro-tasks
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# 4. Final Output: Recommendations based on analyzed subset
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# ============================================================================
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# 4. AI PERFORMANCE & RATE LIMITING
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# ============================================================================
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# === USER RATE LIMITS (per minute) ===
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# Main queries per user per minute
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AI_RATE_LIMIT_MAX_REQUESTS=4
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# Total AI micro-task calls per user per minute (across all micro-tasks)
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AI_MICRO_TASK_TOTAL_LIMIT=30
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# === PIPELINE TIMING ===
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# Delay between micro-tasks within a single query (milliseconds)
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# Higher = gentler on AI service | Lower = faster responses
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AI_MICRO_TASK_DELAY_MS=500
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# Delay between queued requests (milliseconds)
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AI_RATE_LIMIT_DELAY_MS=2000
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# === EMBEDDINGS BATCH PROCESSING ===
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# How many embeddings to generate per API call
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AI_EMBEDDINGS_BATCH_SIZE=10
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# Delay between embedding batches (milliseconds)
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AI_EMBEDDINGS_BATCH_DELAY_MS=1000
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# ============================================================================
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# 5. AI CONTEXT & TOKEN MANAGEMENT
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# ============================================================================
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# Maximum context tokens to maintain across micro-tasks
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# Controls how much conversation history is preserved between AI calls
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AI_MAX_CONTEXT_TOKENS=3000
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# Maximum tokens per individual AI prompt
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# Larger = more context per call | Smaller = faster responses
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AI_MAX_PROMPT_TOKENS=1200
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# Timeout for individual micro-tasks (milliseconds)
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AI_MICRO_TASK_TIMEOUT_MS=25000
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# Maximum size of the processing queue
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AI_QUEUE_MAX_SIZE=50
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# ============================================================================
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# 6. AUTHENTICATION & AUTHORIZATION (OPTIONAL)
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# ============================================================================
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# Enable authentication for different features
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@ -48,30 +128,47 @@ OIDC_CLIENT_ID=your-client-id
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OIDC_CLIENT_SECRET=your-client-secret
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# ============================================================================
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# 4. ADVANCED AI CONFIGURATION (FINE-TUNING - DEFAULT VALUES USUALLY WORK)
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# 7. FILE UPLOADS - NEXTCLOUD INTEGRATION (OPTIONAL)
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# ============================================================================
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# Pipeline Performance Settings
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AI_MAX_SELECTED_ITEMS=60 # Tools analyzed per micro-task
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AI_EMBEDDING_CANDIDATES=60 # Vector search candidates
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AI_MICRO_TASK_DELAY_MS=500 # Delay between AI micro-tasks
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# Nextcloud server for file uploads (knowledgebase contributions)
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# Leave empty to disable file upload functionality
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NEXTCLOUD_ENDPOINT=https://your-nextcloud.com
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# Rate Limiting (requests per minute)
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AI_RATE_LIMIT_MAX_REQUESTS=6 # Main query rate limit
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AI_MICRO_TASK_RATE_LIMIT=15 # Micro-task rate limit
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AI_RATE_LIMIT_DELAY_MS=3000 # Delay between rate-limited calls
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# Nextcloud credentials (app password recommended)
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NEXTCLOUD_USERNAME=your-username
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NEXTCLOUD_PASSWORD=your-app-password
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# Embeddings Batch Processing
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AI_EMBEDDINGS_BATCH_SIZE=20 # Embeddings processed per batch
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AI_EMBEDDINGS_BATCH_DELAY_MS=1000 # Delay between embedding batches
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# Upload directory on Nextcloud (will be created if doesn't exist)
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NEXTCLOUD_UPLOAD_PATH=/kb-media
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# Timeouts and Limits
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AI_MICRO_TASK_TIMEOUT_MS=25000 # Max time per micro-task
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AI_QUEUE_MAX_SIZE=50 # Max queued requests
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AI_SIMILARITY_THRESHOLD=0.3 # Vector similarity threshold
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# Public URL base for sharing uploaded files
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# Usually your Nextcloud base URL + share path
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NEXTCLOUD_PUBLIC_URL=https://your-nextcloud.com/s/
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# ============================================================================
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# 5. FORENSIC AUDIT SYSTEM (OPTIONAL - FOR TRANSPARENCY AND DEBUGGING)
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# 8. GIT CONTRIBUTIONS - ISSUE CREATION (OPTIONAL)
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# ============================================================================
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# Git provider: gitea, github, or gitlab
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GIT_PROVIDER=gitea
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# Repository URL (used to extract owner/name)
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# Example: https://git.example.com/owner/forensic-pathways.git
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GIT_REPO_URL=https://git.example.com/owner/forensic-pathways.git
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# API endpoint for your git provider
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# Gitea: https://git.example.com/api/v1
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# GitHub: https://api.github.com
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# GitLab: https://gitlab.example.com/api/v4
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GIT_API_ENDPOINT=https://git.example.com/api/v1
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# Personal access token or API token for creating issues
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# Generate this in your git provider's settings
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GIT_API_TOKEN=your-git-api-token
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# ============================================================================
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# 9. AUDIT & DEBUGGING (OPTIONAL)
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# ============================================================================
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# Enable detailed audit trail of AI decision-making
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@ -80,38 +177,49 @@ FORENSIC_AUDIT_ENABLED=false
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# Audit detail level: minimal, standard, verbose
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FORENSIC_AUDIT_DETAIL_LEVEL=standard
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# Audit retention and limits
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FORENSIC_AUDIT_RETENTION_HOURS=72 # Keep audit data for 3 days
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FORENSIC_AUDIT_MAX_ENTRIES=50 # Max entries per request
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# Audit retention time (hours)
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FORENSIC_AUDIT_RETENTION_HOURS=24
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# Maximum audit entries per request
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FORENSIC_AUDIT_MAX_ENTRIES=50
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# Enable detailed AI pipeline logging
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AI_PIPELINE_DEBUG=false
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# Enable performance metrics collection
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AI_PERFORMANCE_METRICS=false
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# Enable detailed micro-task debugging
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AI_MICRO_TASK_DEBUG=false
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# ============================================================================
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# 6. QUALITY CONTROL AND BIAS DETECTION (OPTIONAL - ADVANCED FEATURES)
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# 10. QUALITY CONTROL & BIAS DETECTION (ADVANCED)
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# ============================================================================
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# Confidence Scoring Weights (must sum to 1.0)
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# Confidence scoring weights (must sum to 1.0)
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CONFIDENCE_EMBEDDINGS_WEIGHT=0.3
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CONFIDENCE_CONSENSUS_WEIGHT=0.25
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CONFIDENCE_DOMAIN_MATCH_WEIGHT=0.25
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CONFIDENCE_FRESHNESS_WEIGHT=0.2
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# Confidence Thresholds (0-100)
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# Confidence thresholds (0-100)
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CONFIDENCE_MINIMUM_THRESHOLD=40
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CONFIDENCE_MEDIUM_THRESHOLD=60
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CONFIDENCE_HIGH_THRESHOLD=80
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# Bias Detection Settings
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# Bias detection settings
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BIAS_DETECTION_ENABLED=false
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BIAS_POPULARITY_THRESHOLD=0.7 # Detect over-popular tools
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BIAS_DIVERSITY_MINIMUM=0.6 # Require recommendation diversity
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BIAS_CELEBRITY_TOOLS="Volatility 3,Wireshark,Autopsy,Maltego"
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BIAS_POPULARITY_THRESHOLD=0.7
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BIAS_DIVERSITY_MINIMUM=0.6
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BIAS_CELEBRITY_TOOLS=""
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# Quality Control Thresholds
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QUALITY_MIN_RESPONSE_LENGTH=50 # Minimum AI response length
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QUALITY_MIN_SELECTION_COUNT=1 # Minimum tools selected
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QUALITY_MAX_PROCESSING_TIME_MS=30000 # Max processing time
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# Quality control thresholds
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QUALITY_MIN_RESPONSE_LENGTH=50
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QUALITY_MIN_SELECTION_COUNT=1
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QUALITY_MAX_PROCESSING_TIME_MS=30000
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# ============================================================================
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# 7. USER INTERFACE PREFERENCES (OPTIONAL - UI DEFAULTS)
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# 11. USER INTERFACE DEFAULTS (OPTIONAL)
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# ============================================================================
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# Default UI behavior (users can override)
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@ -121,34 +229,76 @@ UI_SHOW_BIAS_WARNINGS=true
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UI_AUDIT_TRAIL_COLLAPSIBLE=true
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# ============================================================================
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# 8. EXTERNAL INTEGRATIONS (OPTIONAL - ONLY IF USING THESE SERVICES)
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# 12. CACHING & PERFORMANCE (OPTIONAL)
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# ============================================================================
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# Nextcloud Integration (for file uploads)
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# NEXTCLOUD_ENDPOINT=https://your-nextcloud.com
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# NEXTCLOUD_USERNAME=your-username
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# NEXTCLOUD_PASSWORD=your-password
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# NEXTCLOUD_UPLOAD_PATH=/kb-media
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# NEXTCLOUD_PUBLIC_URL=https://your-nextcloud.com/s/
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# Cache AI responses (milliseconds)
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AI_RESPONSE_CACHE_TTL_MS=3600000
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# Queue cleanup interval (milliseconds)
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AI_QUEUE_CLEANUP_INTERVAL_MS=300000
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# ============================================================================
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# 9. PERFORMANCE AND MONITORING (OPTIONAL - FOR PRODUCTION OPTIMIZATION)
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# PERFORMANCE TUNING PRESETS
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# ============================================================================
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# Caching and Queue Management
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AI_RESPONSE_CACHE_TTL_MS=3600000 # Cache responses for 1 hour
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AI_QUEUE_CLEANUP_INTERVAL_MS=300000 # Cleanup queue every 5 minutes
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# 🚀 FOR FASTER RESPONSES (less comprehensive):
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# AI_EMBEDDING_CANDIDATES=20
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# AI_MAX_SELECTED_ITEMS=15
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# AI_MAX_TOOLS_TO_ANALYZE=10
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# AI_MICRO_TASK_DELAY_MS=200
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# AI_MAX_CONTEXT_TOKENS=2000
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# Debug and Monitoring
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AI_MICRO_TASK_DEBUG=false # Enable detailed micro-task logging
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AI_PERFORMANCE_METRICS=false # Enable performance tracking
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# 🎯 FOR BETTER QUALITY (more comprehensive):
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# AI_EMBEDDING_CANDIDATES=60
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# AI_MAX_SELECTED_ITEMS=40
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# AI_MAX_TOOLS_TO_ANALYZE=30
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# AI_MICRO_TASK_DELAY_MS=800
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# AI_MAX_CONTEXT_TOKENS=4000
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# 🔋 FOR LOW-POWER SYSTEMS (minimal resources):
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# AI_EMBEDDING_CANDIDATES=15
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# AI_MAX_SELECTED_ITEMS=10
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# AI_MAX_TOOLS_TO_ANALYZE=8
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# AI_RATE_LIMIT_MAX_REQUESTS=2
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# AI_MICRO_TASK_DELAY_MS=1000
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# ============================================================================
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# SETUP CHECKLIST:
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# FEATURE COMBINATIONS GUIDE
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# ============================================================================
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# 1. Set PUBLIC_BASE_URL to your domain
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# 2. Change AUTH_SECRET to a secure random string
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# 3. Configure AI service endpoints and API keys
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# 4. Set authentication options if needed
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# 5. Test with default advanced settings before adjusting
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# 📝 BASIC SETUP (AI only):
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# - Configure AI_ANALYZER_* and AI_EMBEDDINGS_*
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# - Leave authentication, file uploads, and git disabled
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# 🔐 WITH AUTHENTICATION:
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# - Set AUTHENTICATION_NECESSARY_* to true
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# - Configure OIDC_* settings
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# 📁 WITH FILE UPLOADS:
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# - Configure all NEXTCLOUD_* settings
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# - Test connection before enabling in UI
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# 🔄 WITH CONTRIBUTIONS:
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# - Configure all GIT_* settings
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# - Test API token permissions for issue creation
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# 🔍 WITH FULL MONITORING:
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# - Enable FORENSIC_AUDIT_ENABLED=true
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# - Enable AI_PIPELINE_DEBUG=true
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# - Configure audit retention and detail level
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# ============================================================================
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# SETUP CHECKLIST
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# ============================================================================
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# ✅ 1. Set PUBLIC_BASE_URL to your domain
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# ✅ 2. Change AUTH_SECRET to a secure random string
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# ✅ 3. Configure AI endpoints (Ollama: leave API_KEY empty)
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# ✅ 4. Start with default AI values, tune based on performance
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# ✅ 5. Enable authentication if needed (configure OIDC)
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# ✅ 6. Configure Nextcloud if file uploads needed
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# ✅ 7. Configure Git provider if contributions needed
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# ✅ 8. Test with a simple query to verify pipeline works
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# ✅ 9. Enable audit trail for transparency if desired
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# ✅ 10. Tune performance settings based on usage patterns
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# ============================================================================
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@ -1,4 +1,5 @@
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// src/pages/api/ai/enhance-input.ts - ENHANCED with forensics methodology
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// src/pages/api/ai/enhance-input.ts - Enhanced AI service compatibility
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import type { APIRoute } from 'astro';
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import { withAPIAuth } from '../../../utils/auth.js';
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import { apiError, apiServerError, createAuthErrorResponse } from '../../../utils/api.js';
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@ -20,7 +21,7 @@ const AI_ANALYZER_MODEL = getEnv('AI_ANALYZER_MODEL');
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const rateLimitStore = new Map<string, { count: number; resetTime: number }>();
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const RATE_LIMIT_WINDOW = 60 * 1000; // 1 minute
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const RATE_LIMIT_MAX = 5;
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const RATE_LIMIT_MAX = 5;
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function sanitizeInput(input: string): string {
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return input
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@ -93,6 +94,45 @@ ${input}
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`.trim();
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}
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// Enhanced AI service call function
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async function callAIService(prompt: string): Promise<Response> {
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const endpoint = AI_ENDPOINT;
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const apiKey = AI_ANALYZER_API_KEY;
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const model = AI_ANALYZER_MODEL;
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// Simple headers - add auth only if API key exists
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let headers: Record<string, string> = {
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'Content-Type': 'application/json'
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};
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// Add authentication if API key is provided
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if (apiKey) {
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headers['Authorization'] = `Bearer ${apiKey}`;
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console.log('[ENHANCE API] Using API key authentication');
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} else {
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console.log('[ENHANCE API] No API key - making request without authentication');
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}
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// Simple request body
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const requestBody = {
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model,
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messages: [{ role: 'user', content: prompt }],
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max_tokens: 300,
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temperature: 0.7,
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top_p: 0.9,
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frequency_penalty: 0.2,
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presence_penalty: 0.1
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};
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// FIXED: This function is already being called through enqueueApiCall in the main handler
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// So we can use direct fetch here since the queuing happens at the caller level
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return fetch(`${endpoint}/v1/chat/completions`, {
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method: 'POST',
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headers,
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body: JSON.stringify(requestBody)
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});
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}
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export const POST: APIRoute = async ({ request }) => {
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try {
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const authResult = await withAPIAuth(request, 'ai');
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@ -121,31 +161,11 @@ export const POST: APIRoute = async ({ request }) => {
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const systemPrompt = createEnhancementPrompt(sanitizedInput);
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const taskId = `enhance_${userId}_${Date.now()}_${Math.random().toString(36).substr(2, 4)}`;
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const aiResponse = await enqueueApiCall(() =>
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fetch(`${AI_ENDPOINT}/v1/chat/completions`, {
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method: 'POST',
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headers: {
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'Content-Type': 'application/json',
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'Authorization': `Bearer ${AI_ANALYZER_API_KEY}`
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},
|
||||
body: JSON.stringify({
|
||||
model: AI_ANALYZER_MODEL,
|
||||
messages: [
|
||||
{
|
||||
role: 'user',
|
||||
content: systemPrompt
|
||||
}
|
||||
],
|
||||
max_tokens: 300,
|
||||
temperature: 0.7,
|
||||
top_p: 0.9,
|
||||
frequency_penalty: 0.2,
|
||||
presence_penalty: 0.1
|
||||
})
|
||||
}), taskId);
|
||||
const aiResponse = await enqueueApiCall(() => callAIService(systemPrompt), taskId);
|
||||
|
||||
if (!aiResponse.ok) {
|
||||
console.error('AI enhancement error:', await aiResponse.text());
|
||||
const errorText = await aiResponse.text();
|
||||
console.error('[ENHANCE API] AI enhancement error:', errorText, 'Status:', aiResponse.status);
|
||||
return apiServerError.unavailable('Enhancement service unavailable');
|
||||
}
|
||||
|
||||
@ -188,7 +208,7 @@ export const POST: APIRoute = async ({ request }) => {
|
||||
questions = [];
|
||||
}
|
||||
|
||||
console.log(`[AI Enhancement] User: ${userId}, Forensics Questions: ${questions.length}, Input length: ${sanitizedInput.length}`);
|
||||
console.log(`[ENHANCE API] User: ${userId}, Forensics Questions: ${questions.length}, Input length: ${sanitizedInput.length}`);
|
||||
|
||||
return new Response(JSON.stringify({
|
||||
success: true,
|
||||
|
||||
@ -66,6 +66,11 @@ interface AnalysisContext {
|
||||
auditTrail: AuditEntry[];
|
||||
}
|
||||
|
||||
interface SimilarityResult extends EmbeddingData {
|
||||
similarity: number;
|
||||
}
|
||||
|
||||
|
||||
class ImprovedMicroTaskAIPipeline {
|
||||
private config: AIConfig;
|
||||
private maxSelectedItems: number;
|
||||
@ -267,39 +272,62 @@ class ImprovedMicroTaskAIPipeline {
|
||||
userQuery,
|
||||
this.embeddingCandidates,
|
||||
this.similarityThreshold
|
||||
);
|
||||
) as SimilarityResult[]; // Type assertion for similarity property
|
||||
|
||||
const toolNames = new Set<string>();
|
||||
const conceptNames = new Set<string>();
|
||||
console.log(`[IMPROVED PIPELINE] Embeddings found ${similarItems.length} similar items`);
|
||||
|
||||
similarItems.forEach(item => {
|
||||
if (item.type === 'tool') toolNames.add(item.name);
|
||||
if (item.type === 'concept') conceptNames.add(item.name);
|
||||
});
|
||||
// FIXED: Create lookup maps for O(1) access while preserving original data
|
||||
const toolsMap = new Map<string, any>(toolsData.tools.map((tool: any) => [tool.name, tool]));
|
||||
const conceptsMap = new Map<string, any>(toolsData.concepts.map((concept: any) => [concept.name, concept]));
|
||||
|
||||
console.log(`[IMPROVED PIPELINE] Embeddings found: ${toolNames.size} tools, ${conceptNames.size} concepts`);
|
||||
// FIXED: Process in similarity order, preserving the ranking
|
||||
const similarTools = similarItems
|
||||
.filter((item): item is SimilarityResult => item.type === 'tool')
|
||||
.map(item => toolsMap.get(item.name))
|
||||
.filter((tool): tool is any => tool !== undefined); // Proper type guard
|
||||
|
||||
if (toolNames.size >= 15) {
|
||||
candidateTools = toolsData.tools.filter((tool: any) => toolNames.has(tool.name));
|
||||
candidateConcepts = toolsData.concepts.filter((concept: any) => conceptNames.has(concept.name));
|
||||
const similarConcepts = similarItems
|
||||
.filter((item): item is SimilarityResult => item.type === 'concept')
|
||||
.map(item => conceptsMap.get(item.name))
|
||||
.filter((concept): concept is any => concept !== undefined); // Proper type guard
|
||||
|
||||
console.log(`[IMPROVED PIPELINE] Similarity-ordered results: ${similarTools.length} tools, ${similarConcepts.length} concepts`);
|
||||
|
||||
// Log the first few tools to verify ordering is preserved
|
||||
if (similarTools.length > 0) {
|
||||
console.log(`[IMPROVED PIPELINE] Top similar tools (in similarity order):`);
|
||||
similarTools.slice(0, 5).forEach((tool, idx) => {
|
||||
const originalSimilarItem = similarItems.find(item => item.name === tool.name);
|
||||
console.log(` ${idx + 1}. ${tool.name} (similarity: ${originalSimilarItem?.similarity?.toFixed(4) || 'N/A'})`);
|
||||
});
|
||||
}
|
||||
|
||||
if (similarTools.length >= 15) {
|
||||
candidateTools = similarTools;
|
||||
candidateConcepts = similarConcepts;
|
||||
selectionMethod = 'embeddings_candidates';
|
||||
|
||||
console.log(`[IMPROVED PIPELINE] Using embeddings candidates: ${candidateTools.length} tools`);
|
||||
console.log(`[IMPROVED PIPELINE] Using embeddings candidates in similarity order: ${candidateTools.length} tools`);
|
||||
} else {
|
||||
console.log(`[IMPROVED PIPELINE] Embeddings insufficient (${toolNames.size} < 15), using full dataset`);
|
||||
console.log(`[IMPROVED PIPELINE] Embeddings insufficient (${similarTools.length} < 15), using full dataset`);
|
||||
candidateTools = toolsData.tools;
|
||||
candidateConcepts = toolsData.concepts;
|
||||
selectionMethod = 'full_dataset';
|
||||
}
|
||||
|
||||
// NEW: Add Audit Entry for Embeddings Search
|
||||
// NEW: Add Audit Entry for Embeddings Search with ordering verification
|
||||
if (this.auditConfig.enabled) {
|
||||
this.addAuditEntry(null, 'retrieval', 'embeddings-search',
|
||||
{ query: userQuery, threshold: this.similarityThreshold, candidates: this.embeddingCandidates },
|
||||
{ candidatesFound: similarItems.length, toolNames: Array.from(toolNames), conceptNames: Array.from(conceptNames) },
|
||||
similarItems.length >= 15 ? 85 : 60, // Confidence based on result quality
|
||||
{
|
||||
candidatesFound: similarItems.length,
|
||||
toolsInOrder: similarTools.slice(0, 3).map((t: any) => t.name),
|
||||
conceptsInOrder: similarConcepts.slice(0, 3).map((c: any) => c.name),
|
||||
orderingPreserved: true
|
||||
},
|
||||
similarTools.length >= 15 ? 85 : 60,
|
||||
embeddingsStart,
|
||||
{ selectionMethod, embeddingsEnabled: true }
|
||||
{ selectionMethod, embeddingsEnabled: true, orderingFixed: true }
|
||||
);
|
||||
}
|
||||
} else {
|
||||
@ -309,7 +337,7 @@ class ImprovedMicroTaskAIPipeline {
|
||||
selectionMethod = 'full_dataset';
|
||||
}
|
||||
|
||||
console.log(`[IMPROVED PIPELINE] AI will analyze FULL DATA of ${candidateTools.length} candidate tools`);
|
||||
console.log(`[IMPROVED PIPELINE] AI will analyze ${candidateTools.length} candidate tools (ordering preserved: ${selectionMethod === 'embeddings_candidates'})`);
|
||||
const finalSelection = await this.aiSelectionWithFullData(userQuery, candidateTools, candidateConcepts, mode, selectionMethod);
|
||||
|
||||
return {
|
||||
@ -735,33 +763,59 @@ ${JSON.stringify(conceptsWithFullData.slice(0, 10), null, 2)}`;
|
||||
}
|
||||
|
||||
private async callAI(prompt: string, maxTokens: number = 1000): Promise<string> {
|
||||
const response = await fetch(`${this.config.endpoint}/v1/chat/completions`, {
|
||||
method: 'POST',
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
||||
'Authorization': `Bearer ${this.config.apiKey}`
|
||||
},
|
||||
body: JSON.stringify({
|
||||
model: this.config.model,
|
||||
messages: [{ role: 'user', content: prompt }],
|
||||
max_tokens: maxTokens,
|
||||
temperature: 0.3
|
||||
})
|
||||
});
|
||||
|
||||
if (!response.ok) {
|
||||
const errorText = await response.text();
|
||||
throw new Error(`AI API error: ${response.status} - ${errorText}`);
|
||||
}
|
||||
|
||||
const data = await response.json();
|
||||
const content = data.choices?.[0]?.message?.content;
|
||||
const endpoint = this.config.endpoint;
|
||||
const apiKey = this.config.apiKey;
|
||||
const model = this.config.model;
|
||||
|
||||
if (!content) {
|
||||
throw new Error('No response from AI model');
|
||||
// Simple headers - add auth only if API key exists
|
||||
let headers: Record<string, string> = {
|
||||
'Content-Type': 'application/json'
|
||||
};
|
||||
|
||||
// Add authentication if API key is provided
|
||||
if (apiKey) {
|
||||
headers['Authorization'] = `Bearer ${apiKey}`;
|
||||
console.log('[AI PIPELINE] Using API key authentication');
|
||||
} else {
|
||||
console.log('[AI PIPELINE] No API key - making request without authentication');
|
||||
}
|
||||
|
||||
// Simple request body
|
||||
const requestBody = {
|
||||
model,
|
||||
messages: [{ role: 'user', content: prompt }],
|
||||
max_tokens: maxTokens,
|
||||
temperature: 0.3
|
||||
};
|
||||
|
||||
try {
|
||||
// FIXED: Use direct fetch since entire pipeline is already queued at query.ts level
|
||||
const response = await fetch(`${endpoint}/v1/chat/completions`, {
|
||||
method: 'POST',
|
||||
headers,
|
||||
body: JSON.stringify(requestBody)
|
||||
});
|
||||
|
||||
return content;
|
||||
if (!response.ok) {
|
||||
const errorText = await response.text();
|
||||
console.error(`[AI PIPELINE] AI API Error ${response.status}:`, errorText);
|
||||
throw new Error(`AI API error: ${response.status} - ${errorText}`);
|
||||
}
|
||||
|
||||
const data = await response.json();
|
||||
const content = data.choices?.[0]?.message?.content;
|
||||
|
||||
if (!content) {
|
||||
console.error('[AI PIPELINE] No response content:', data);
|
||||
throw new Error('No response from AI model');
|
||||
}
|
||||
|
||||
return content;
|
||||
|
||||
} catch (error) {
|
||||
console.error('[AI PIPELINE] AI service call failed:', error.message);
|
||||
throw error;
|
||||
}
|
||||
}
|
||||
|
||||
async processQuery(userQuery: string, mode: string): Promise<AnalysisResult> {
|
||||
|
||||
@ -24,6 +24,10 @@ interface EmbeddingsDatabase {
|
||||
embeddings: EmbeddingData[];
|
||||
}
|
||||
|
||||
interface SimilarityResult extends EmbeddingData {
|
||||
similarity: number;
|
||||
}
|
||||
|
||||
class EmbeddingsService {
|
||||
private embeddings: EmbeddingData[] = [];
|
||||
private isInitialized = false;
|
||||
@ -211,8 +215,9 @@ class EmbeddingsService {
|
||||
return dotProduct / (Math.sqrt(normA) * Math.sqrt(normB));
|
||||
}
|
||||
|
||||
async findSimilar(query: string, maxResults: number = 30, threshold: number = 0.3): Promise<EmbeddingData[]> {
|
||||
async findSimilar(query: string, maxResults: number = 30, threshold: number = 0.3): Promise<SimilarityResult[]> {
|
||||
if (!this.enabled || !this.isInitialized || this.embeddings.length === 0) {
|
||||
console.log('[EMBEDDINGS] Service not available for similarity search');
|
||||
return [];
|
||||
}
|
||||
|
||||
@ -221,18 +226,51 @@ class EmbeddingsService {
|
||||
const queryEmbeddings = await this.generateEmbeddingsBatch([query.toLowerCase()]);
|
||||
const queryEmbedding = queryEmbeddings[0];
|
||||
|
||||
// Calculate similarities
|
||||
const similarities = this.embeddings.map(item => ({
|
||||
console.log(`[EMBEDDINGS] Computing similarities for ${this.embeddings.length} items`);
|
||||
|
||||
// Calculate similarities - properly typed
|
||||
const similarities: SimilarityResult[] = this.embeddings.map(item => ({
|
||||
...item,
|
||||
similarity: this.cosineSimilarity(queryEmbedding, item.embedding)
|
||||
}));
|
||||
|
||||
// Filter by threshold and sort by similarity
|
||||
return similarities
|
||||
// Filter by threshold and sort by similarity (descending - highest first)
|
||||
const results = similarities
|
||||
.filter(item => item.similarity >= threshold)
|
||||
.sort((a, b) => b.similarity - a.similarity)
|
||||
.sort((a, b) => b.similarity - a.similarity) // CRITICAL: Ensure descending order
|
||||
.slice(0, maxResults);
|
||||
|
||||
// ENHANCED: Verify ordering is correct
|
||||
const orderingValid = results.every((item, index) => {
|
||||
if (index === 0) return true;
|
||||
return item.similarity <= results[index - 1].similarity;
|
||||
});
|
||||
|
||||
if (!orderingValid) {
|
||||
console.error('[EMBEDDINGS] CRITICAL: Similarity ordering is broken!');
|
||||
results.forEach((item, idx) => {
|
||||
console.error(` ${idx}: ${item.name} = ${item.similarity.toFixed(4)}`);
|
||||
});
|
||||
}
|
||||
|
||||
// ENHANCED: Log top results for debugging
|
||||
console.log(`[EMBEDDINGS] Found ${results.length} similar items (threshold: ${threshold})`);
|
||||
if (results.length > 0) {
|
||||
console.log('[EMBEDDINGS] Top 5 similarity matches:');
|
||||
results.slice(0, 5).forEach((item, idx) => {
|
||||
console.log(` ${idx + 1}. ${item.name} (${item.type}) = ${item.similarity.toFixed(4)}`);
|
||||
});
|
||||
|
||||
// Verify first result is indeed the highest
|
||||
const topSimilarity = results[0].similarity;
|
||||
const hasHigherSimilarity = results.some(item => item.similarity > topSimilarity);
|
||||
if (hasHigherSimilarity) {
|
||||
console.error('[EMBEDDINGS] CRITICAL: Top result is not actually the highest similarity!');
|
||||
}
|
||||
}
|
||||
|
||||
return results;
|
||||
|
||||
} catch (error) {
|
||||
console.error('[EMBEDDINGS] Failed to find similar items:', error);
|
||||
return [];
|
||||
@ -257,7 +295,7 @@ class EmbeddingsService {
|
||||
// Global instance
|
||||
const embeddingsService = new EmbeddingsService();
|
||||
|
||||
export { embeddingsService, type EmbeddingData };
|
||||
export { embeddingsService, type EmbeddingData, type SimilarityResult };
|
||||
|
||||
// Auto-initialize on import in server environment
|
||||
if (typeof window === 'undefined' && process.env.NODE_ENV !== 'test') {
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user