# YAMS v2 Configuration Template
# This file contains all available configuration options with their default values
# Copy this file to ~/.config/yams/config.toml and modify as needed

[version]
config_version = 2
migrated_from = 0
migration_date = "2025-01-16"

[core]
# Data storage directory (~ will be expanded to home directory)
data_dir = "~/.yams/data"
# Storage engine: "local" or "distributed" (future)
storage_engine = "local"
# Enable Write-Ahead Logging for crash recovery
enable_wal = true
# Enable telemetry (anonymous usage statistics)
enable_telemetry = false
# Log level: "trace", "debug", "info", "warn", "error"
log_level = "info"

[auth]
# Path to Ed25519 private key for signing
private_key_path = "~/.config/yams/keys/ed25519.pem"
# Path to Ed25519 public key for verification
public_key_path = "~/.config/yams/keys/ed25519.pub"
# API keys for authentication (optional)
api_keys = []

[performance]
# Number of worker threads for parallel operations
num_worker_threads = 4
# Size of I/O thread pool
io_thread_pool_size = 2
# Maximum concurrent operations
max_concurrent_operations = 100
# Enable memory-mapped file access
enable_memory_mapping = false
# Total cache size in MB
cache_size_mb = 512

[storage]
# Storage engine type
engine = "local"
# Base path for storage
base_path = "~/.yams/data"
# Enable transparent compression
enable_compression = true
# Enable content deduplication
enable_deduplication = true
# Enable content-defined chunking
enable_chunking = true
# CAS object storage directories (relative to base_path)
objects_dir = "objects"
staging_dir = "staging"

[chunking]
# Rolling hash window size
window_size = 48
# Minimum chunk size in bytes
min_chunk_size = 1024
# Maximum chunk size in bytes
max_chunk_size = 16384
# Target average chunk size
average_chunk_size = 8192
# Hash algorithm for chunk identification
hash_algorithm = "sha256"

[compression]
# Enable compression for new content
enable = true
# Compression algorithm: "zstd" or "lzma"
algorithm = "zstd"
# ZSTD compression level (1-22)
zstd_level = 9
# LZMA compression level (0-9)
lzma_level = 6
# Minimum size to consider for compression
chunk_threshold = 1024
# Always compress files larger than this
always_compress_above = 10485760 # 10MB
# Never compress files smaller than this
never_compress_below = 1024 # 1KB
# Compress content older than N days
compress_after_days = 1
# Use LZMA for content older than N days
archive_after_days = 30
# Maximum parallel compression operations
max_concurrent_compressions = 4
# Use background compression
async_compression = true
# Decompression cache entries
decompression_cache_size = 100
# Decompression cache size in bytes
decompression_cache_size_bytes = 536870912 # 512MB

[wal]
# Enable Write-Ahead Logging
enable = true
# Directory for WAL files
wal_directory = "./wal"
# Maximum size per log file
max_log_size = 104857600 # 100MB
# Sync to disk every N entries
sync_interval = 1000
# Maximum time between syncs (ms)
sync_timeout_ms = 100
# Compress rotated log files
compress_old_logs = true
# Maximum open log files
max_open_files = 10
# Batch commits for performance
enable_group_commit = true

[vector_database]
# Enable vector database for embeddings
enable = true
# SQLite database path for vectors
database_path = "vectors.db"
# Table name for embeddings
table_name = "document_embeddings"
# Embedding dimension (model-dependent)
embedding_dim = 384
# Index type: "FLAT", "IVF_PQ", "HNSW"
index_type = "IVF_PQ"
# Number of partitions for IVF index
num_partitions = 256
# Number of sub-quantizers for PQ
num_sub_quantizers = 96
# Enable periodic checkpoints
enable_checkpoints = true
# Operations between checkpoints
checkpoint_frequency = 1000
# Maximum batch size for operations
max_batch_size = 1000
# Default similarity threshold for search
default_similarity_threshold = 0.7
# Use in-memory database (for testing)
use_in_memory = false
# Auto-create database if missing
auto_create = true
# Auto-repair on corruption
auto_repair = true

[embeddings]
# Enable embedding generation
enable = true
# Automatically generate for new documents
auto_generate = false
# Preferred embedding model
preferred_model = "all-MiniLM-L6-v2"
# Path to model files
model_path = "~/.yams/models"
# Path to tokenizer
tokenizer_path = "models/tokenizer.json"
# Maximum sequence length
max_sequence_length = 512
# Embedding dimension (must match model)
embedding_dim = 384
# Batch size for generation
batch_size = 32
# Normalize embedding vectors
normalize_embeddings = true
# Use GPU acceleration if available
enable_gpu = false
# Number of threads (-1 for auto)
num_threads = -1
# Delay between generation batches (ms)
generation_delay_ms = 1000
# Track content hash for staleness detection
track_content_hash = true
# Model version identifier
model_version = "1.0.0"
# Embedding schema version
embedding_schema_version = 1
# Keep model loaded in memory (hot) for better performance
keep_model_hot = true
# Model unload timeout in seconds (0 = never unload)
model_idle_timeout = 300
# Preload model on startup
preload_on_startup = false
# Maximum memory for model cache (MB)
max_model_memory_mb = 1024

[embeddings.cache]
# Enable embedding cache for queries
enable_query_cache = true
# Cache size (number of queries)
cache_size = 1000
# Cache TTL in seconds
cache_ttl_seconds = 3600
# Use LRU eviction
lru_eviction = true


[vector_index]
# Index type: "FLAT", "HNSW", "IVF_FLAT", "IVF_PQ", "LSH", "ANNOY"
type = "HNSW"
# Vector dimension
dimension = 384
# Distance metric: "COSINE", "L2", "INNER_PRODUCT"
distance_metric = "COSINE"
# HNSW: connections per node
hnsw_m = 16
# HNSW: construction accuracy
hnsw_ef_construction = 200
# HNSW: search accuracy
hnsw_ef_search = 50
# Normalize vectors before indexing
normalize_vectors = true
# Maximum number of vectors
max_elements = 1000000
# Save/load index from disk
enable_persistence = true
# Index file path
index_path = "vector_index.bin"
# Batch size for operations
batch_size = 1000
# Use SIMD instructions
use_simd = true
# Number of threads (0 for auto)
num_threads = 0
# Use delta index for updates
enable_delta_index = true
# Merge delta when it reaches this size
delta_threshold = 1000

[search]
# Default result limit
default_limit = 10
# Maximum allowed limit
max_limit = 100
# Enable search result caching
enable_cache = true
# Cache size (number of queries)
cache_size = 1000
# Cache TTL in minutes
cache_ttl_minutes = 60

[search.hybrid]
# Enable hybrid search
enable = true
# Enable knowledge graph scoring
enable_kg = true
# Weight for vector similarity
vector_weight = 0.50
# Weight for keyword relevance
keyword_weight = 0.30
# Weight for KG entity similarity
kg_entity_weight = 0.10
# Weight for structural prior
structural_weight = 0.05
# Weight for BART classification similarity (when enhanced search enabled)
classification_weight = 0.05
# Number of vector results to retrieve
vector_top_k = 50
# Number of keyword results to retrieve
keyword_top_k = 50
# Final number of results
final_top_k = 10
# Fusion strategy: "LINEAR_COMBINATION", "RECIPROCAL_RANK", "CASCADE"
fusion_strategy = "LINEAR_COMBINATION"
# RRF constant (for RECIPROCAL_RANK)
rrf_k = 60.0
# Enable result reranking
enable_reranking = true
# Number of results to rerank
rerank_top_k = 20
# Minimum score for reranking
rerank_threshold = 0.0
# Enable query expansion
enable_query_expansion = false
# Number of expansion terms
expansion_terms = 5
# Weight for expansion terms
expansion_weight = 0.3
# Maximum KG neighbors to consider
kg_max_neighbors = 32
# Maximum KG hops
kg_max_hops = 1
# Time budget for KG scoring (ms)
kg_budget_ms = 20
# Normalize scores to [0,1]
normalize_scores = true
# Generate search explanations
generate_explanations = true
# Include debug scores
include_debug_scores = false
# Run searches in parallel
parallel_search = true

[knowledge_graph]
# Database path for KG
db_path = "~/.yams/data/yams.db"
# Enable full-text search for aliases
enable_alias_fts = true
# Enable WAL mode
enable_wal = true
# Prefer exact alias matches
prefer_exact_alias_first = true
# Default query limit
default_limit = 1000
# Node cache capacity
node_cache_capacity = 10000
# Alias cache capacity
alias_cache_capacity = 50000
# Embedding cache capacity
embedding_cache_capacity = 10000
# Neighbor cache capacity
neighbor_cache_capacity = 10000

[file_detection]
# Use libmagic if available
use_lib_magic = true
# Use built-in patterns
use_builtin_patterns = true
# Use custom patterns
use_custom_patterns = true
# Path to custom patterns file
patterns_file = ""
# Maximum bytes to read for detection
max_bytes_to_read = 512
# Cache detection results
cache_results = true
# Cache size
cache_size = 1000

[mcp_server]
# Enable MCP server
enable = false
# Transport: "stdio", "websocket"
transport = "stdio"
# WebSocket host
host = "localhost"
# WebSocket port
port = 8080
# WebSocket path
path = "/mcp"
# Use SSL/TLS
use_ssl = false
# Connection timeout (ms)
connect_timeout_ms = 5000
# Receive timeout (ms)
receive_timeout_ms = 30000
# Maximum message size
max_message_size = 1048576 # 1MB
# Enable WebSocket ping/pong
enable_ping_pong = true
# Ping interval (seconds)
ping_interval_s = 30

[daemon]
# Enable daemon for background embedding generation
# This eliminates ONNX model loading overhead on every command
enable = true
# Auto-start daemon if not running when needed
auto_start = true
# Unix domain socket path for IPC
socket_path = "/tmp/yams-daemon.sock"
# PID file for daemon process management
pid_file = "/tmp/yams-daemon.pid"
# Number of worker threads for request handling
worker_threads = 4
# Maximum memory usage in GB
max_memory_gb = 4
# Daemon log level: "trace", "debug", "info", "warn", "error"
log_level = "info"
# Connection timeout in milliseconds
connect_timeout_ms = 1000
# Request timeout in milliseconds
request_timeout_ms = 5000

[daemon.models]
# Maximum number of models to keep loaded in memory
max_loaded_models = 4
# Number of models to keep always loaded (hot pool)
hot_pool_size = 2
# Models to preload on daemon startup
preload_models = ["all-MiniLM-L6-v2", "bart-large-mnli"]
# Load models only when first requested
lazy_loading = false
# Unload models after being idle for N seconds
model_idle_timeout_s = 300
# Enable GPU acceleration if available
enable_gpu = false
# Number of threads for ONNX inference
num_threads = 4
# Model eviction policy: "lru" (least recently used), "lfu", "fifo"
eviction_policy = "lru"

[daemon.resource_pool]
# Minimum ONNX sessions per model
min_sessions = 1
# Maximum ONNX sessions per model
max_sessions = 3
# Session idle timeout in seconds
idle_timeout_s = 300
# Validate session health before use
validate_on_acquire = true

[daemon.circuit_breaker]
# Enable circuit breaker for resilience
enable = true
# Number of failures before opening circuit
failure_threshold = 5
# Number of successes to close circuit
success_threshold = 2
# Time to wait before trying again (seconds)
open_timeout_s = 30
# Time to test recovery (seconds)
half_open_timeout_s = 10

[daemon.client]
# Use daemon for embedding generation
use_daemon = true
# Fall back to local generation if daemon fails
fallback_to_local = true
# Maximum retry attempts
max_retries = 3
# Batch size for embedding requests
batch_size = 32

[experimental]
# Enable experimental features
enable = false

[experimental.bert_ner]
# Enable BERT Named Entity Recognition
enable = false
# Path to BERT model
model_path = ""
# Model name
model_name = "bert-base-NER"
# Batch size for processing
batch_size = 16
# Minimum confidence threshold
confidence_threshold = 0.8
# Automatically tag documents
auto_tag = false
# Update knowledge graph with entities
update_knowledge_graph = false
# Entity types to extract
entity_types = ["PERSON", "ORG", "LOC", "MISC"]

[experimental.auto_tagging]
# Enable automatic tagging
enable = false
# Use BERT NER for tagging
use_bert_ner = false
# Use keyword extraction
use_keyword_extraction = true
# Use topic modeling
use_topic_modeling = false
# Minimum tag confidence
min_tag_confidence = 0.7
# Maximum tags per document
max_tags_per_document = 10
# Update frequency (hours)
tag_update_frequency_hours = 24

[experimental.smart_chunking]
# Enable smart chunking
enable = false
# Use semantic boundaries
use_semantic_boundaries = false
# Use sentence detection
use_sentence_detection = true
# Preserve code blocks
preserve_code_blocks = true
# Preserve markdown sections
preserve_markdown_sections = true

[classification]
# Enable BART-based document classification for intelligent tagging
enable = false
# Path to BART-MNLI model
model_path = "~/.yams/models/bart-large-mnli"
# Confidence threshold for tag assignment
confidence_threshold = 0.7
# Maximum tags per document
max_tags_per_document = 5
# Maximum sequence length for classification
max_sequence_length = 1024
# Enable batch processing for efficiency
enable_batch_processing = true
# Batch size for classification
batch_size = 16
# Model inference timeout (milliseconds)
inference_timeout_ms = 5000
# Automatically classify documents during indexing
auto_classify_on_index = true
# Enable real-time classification during search
enable_search_time_classification = false

[knowledge_graph]
# Knowledge graph endpoints for taxonomy/ontology retrieval
endpoints = ["http://localhost:7200/repositories/yams"]
# Default taxonomy domains for classification
default_domains = ["computer_science", "business", "research"]
# Cache taxonomy for N minutes
taxonomy_cache_minutes = 60
# Maximum related concepts to fetch per query
max_related_concepts = 20
# Maximum concept relationship depth to explore
max_concept_depth = 2
# HTTP timeout for KG requests (milliseconds)
request_timeout_ms = 3000
# Enable automatic domain detection from document content
enable_domain_detection = true
# SPARQL endpoint authentication (if required)
auth_username = ""
auth_password = ""
# Enable concept relationship caching
enable_relationship_cache = true
# Relationship cache TTL (minutes)
relationship_cache_ttl_minutes = 120

[search.hotzones]
# Enable hotzone-based result boosting
enable = false
# Decay factor for hotzone relevance over time (0.0-1.0)
decay_factor = 0.95
# Minimum access frequency to maintain hotzone
min_frequency = 3
# Hotzone decay interval (hours)
decay_interval_hours = 24
# Maximum boost factor for hot concepts
max_boost_factor = 2.0
# Enable persistence of hotzone data across restarts
enable_persistence = true
# Hotzone data file path
data_file = "~/.yams/data/hotzones.db"
# Background decay processing interval (minutes)
decay_processing_interval_minutes = 60
# Enable cross-user hotzone sharing
enable_shared_hotzones = false

[search.enhanced]
# Enable enhanced search pipeline with classification and KG
enable = false
# Weight for BART classification scoring in final results
classification_weight = 0.15
# Weight for knowledge graph expansion in final results
kg_expansion_weight = 0.10
# Weight for hotzone boosting in final results
hotzone_weight = 0.05
# Enable cross-domain concept discovery
enable_cross_domain = true
# Maximum concepts for expansion per search result
max_expansion_concepts = 10
# Enable search result re-ranking based on concept similarity
enable_concept_reranking = true
# Timeout for enhanced search pipeline (milliseconds)
enhanced_search_timeout_ms = 2000

[migrations]
# Automatically migrate on version change
auto_migrate = true
# Create backup before migration
backup_before_migrate = true
# Migration log file path
migration_log_path = "migrations.log"

[downloader]
# Default concurrency for parallel connections
default_concurrency = 4
# Default chunk size in bytes
default_chunk_size_bytes = 8388608
# Per-connection timeout in ms
default_timeout_ms = 60000
# Follow HTTP redirects
follow_redirects = true
# Enable resume using HTTP Range/If-Range
resume = true
# Global rate limit (bytes/sec, 0 = unlimited)
rate_limit_global_bps = 0
# Per-connection rate limit (bytes/sec, 0 = unlimited)
rate_limit_per_conn_bps = 0
# Retry/backoff policy
max_retry_attempts = 5
retry_backoff_ms = 500
retry_backoff_multiplier = 2.0
retry_max_backoff_ms = 15000
# Default checksum algorithm when provided
checksum_algo = "sha256"
# Temporary file extension for staging
temp_extension = ".part"
# Store directly into CAS by default; no user path writes unless export requested
store_only = true
# Maximum allowed file size in bytes (0 = unlimited)
max_file_bytes = 0

[downloader.tls]
# Verify TLS certificates (recommended)
verify = true
# Path to CA bundle (empty = system default)
ca_path = ""

[downloader.proxy]
# Proxy URL (e.g., http://user:pass@host:port) or empty
url = ""