148 lines
8.0 KiB
Python
148 lines
8.0 KiB
Python
from pathlib import Path
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PROJECT_ROOT = Path(__file__).resolve().parent.parent
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TEMPLATES_DIR = PROJECT_ROOT / "templates"
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STORAGE_DIR = PROJECT_ROOT / "storage"
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FRONTEND_DIST = PROJECT_ROOT / "frontend" / "dist"
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DB_PATH = STORAGE_DIR / "creator.db"
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PROJECTS_DIR = PROJECT_ROOT / "projects"
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UNI_DIR = PROJECT_ROOT / "uni"
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MAX_CONCURRENT_GENERATIONS = 10
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# Readability gate: deterministic checker (small German complexity model,
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# scale 1–7). Sections that are too hard go into the read-exam revision.
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# If transformers/torch or the model are missing → gate silently off.
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READABILITY_ACTIVE = True
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READABILITY_MODEL = "MiriUll/distilbert-german-text-complexity"
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# Anchors on the 1–7 scale (TextComplexityDE): plain language ~1.2; Wikipedia average
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# ~3.22; from MOS > 4 a sentence counts as "truly complex" (the paper's simplification cutoff).
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READABILITY_MAX = 3.5 # section too hard when the sentence average is above this
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READABILITY_HARD = 4.0 # an individual sentence is "hard" from here on
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READABILITY_HARD_SHARE = 0.30 # … OR when this share of sentences is hard
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# Block consolidation: semantic embedding clustering instead of an LLM list merge.
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# A small multilingual sentence embedding (mean-pool) builds the candidate clusters
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# GLOBALLY (no chunk loss) via cosine + union-find. Title variants of the same concept
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# ("Vertex Cover" / "Vertex Cover Definition") merge; the consensus then counts the
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# real readers per cluster (≥2 = consensus). If transformers/torch are missing or the model
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# won't load → embedding silently off, `_consolidate` falls back to the old panel-judge path.
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EMBEDDING_AKTIV = True
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EMBEDDING_MODELL = "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2" # CPU, multilingual, ~470 MB
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# Stronger (larger) CPU alternative if needed: "BAAI/bge-m3".
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# Consolidation = two-stage: (1) the embedding builds COARSE similarity blocks (high recall),
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# (2) an LLM judge groups EACH block into the real blocks (merge paraphrases, split
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# over-merges). Pure threshold blocking creates a giant component (everything chained) →
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# hence "capped blocking": greedily merge by cosine, but cap the block size. This keeps the
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# LLM lists short and stable (evidenced: embedding block + LLM judge ≈ 95% precision).
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EMBEDDING_BLOCK_FLOOR = 0.5 # minimum cosine for two candidates to share ONE block
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EMBEDDING_BLOCK_CAP = 25 # max. titles per block (keep the LLM list short/stable)
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# Subblock dedup: purely deterministic (no LLM). Subblocks are short statements IN THE SAME
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# block context — from this cosine on two are the same statement (checked on aak: ≥0.88 are
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# without exception true duplicates). Conservative 0.90 so different aspects (∈NP ≠ NP-hard) stay separate.
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EMBEDDING_SUB_DUP = 0.90
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# Cap for concurrent CLI agent processes (across all generations).
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# Own lane for interactive calls (chat, elements) so they don't hang behind
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# running writers in the queue.
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MAX_CONCURRENT_AGENTS = 10
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MAX_CONCURRENT_INTERACTIVE = 8
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# Grace window of the consensus races (blocks, guide, OnePager): after the first
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# valid result the remaining agents may still become done for this many seconds
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# (kill only once the minimum is already in).
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CONSENSUS_GRACE = 300
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# Research race: longer grace window. Research drives the whole block count;
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# with slow providers (e.g. MiniMax) ALL 5 agents should become done, not just
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# the quorum of 3. The per-agent timeout (TIMEOUTS["research"]=1800s) caps real hangs.
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RESEARCH_GRACE = 900
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# Cap of the clarification and check loops: maximum rounds until everything must be
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# decided. In the last round the mapping agent MUST decide every entry;
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# check loops leave any remaining objections standing after that.
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CONSENSUS_MAX_ROUNDS = 3
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# Crawler triage (content/noise) — deterministic rule filter instead of an LLM.
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# Match: substring (lowercase) against URL AND file name. Order: keep > noise > min_chars > keep.
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# Just add special rules here.
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CRAWL_KEEP_PATTERNS = ["learn-unit", "learn-course"] # always content
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CRAWL_NOISE_PATTERNS = [ # clearly off-topic → out
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"clubs", "events", "podcasts", "resources", "-u-",
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"academy", "pricing", "/plans", "career", "newsletter", "impressum", "login",
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]
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CRAWL_MIN_CHARS = 400 # too little text → out
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# LLM topic relevance gate (after the rule filter): per content page yes/no against the spec.
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# Separates the subject area (e.g. backend vs frontend), which the global CRAWL_* rules can't.
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QUELLE_RELEVANZ_CHUNK = 12 # pages per rater package (small, since a snippet ships per page)
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QUELLE_RELEVANZ_SNIPPET = 800 # body characters per page in the prompt (URL is the primary signal)
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# Timeouts per agent step: (base seconds, seconds per block/section).
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# Applies equally to all providers — whoever is too slow gets restarted or overtaken.
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TIMEOUTS = {
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"research": (1800, 0), # fixed 30 min
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"research_mapping": (600, 3), # n = pre-merged entries
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"selection_mapping": (600, 2), # n = remaining entries (block inventory)
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"ergaenzung": (900, 0), # subject-field extension for projects (web research)
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"plan": (300, 5),
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"plan_judge": (600, 5), # judge reads up to 5 outlines, n = sections
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"content": (600, 90), # identify content per block in the chunk (web search)
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"content_check": (300, 10), # content exam per block in the package
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"subblock": (900, 45), # find subblocks per block in the chunk (web search)
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"subblock_check": (300, 15), # judge decides contested subblocks in the chunk
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"level": (300, 10), # classify subblocks per chunk
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"level_check": (300, 10), # judge decides contested levels in the chunk
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"relevance": (300, 10), # subblocks relevant/peripheral per chunk
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"relevance_check": (300, 10), # judge decides contested relevance in the chunk
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"question_pattern": (300, 15), # question patterns per block (subblocks × types)
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"question_pattern_check": (300, 10), # critic cleans up the pattern table per block
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"writer": (600, 120), # per section in the chunk
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"lese_check": (300, 10), # per section in the package
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}
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# Purpose per format — flows into the outline judge (what the guide should achieve).
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# German strings: these are inserted verbatim into the judge prompt → kept German on purpose.
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FORMAT_PURPOSE = {
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"Guide": "einen fokussierten Guide — alles Relevante ohne Randthemen",
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"FullGuide": "einen Komplett-Guide — das ganze Thema inkl. Randthemen",
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"Rest": "einen Ergänzungs-Guide — nur die Randthemen",
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}
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# Provider stacks: completely independent, any one can be removed at any time.
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# Roles: "quick" = bulk work (research, classification),
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# "fast" = interaction + voting (chat, exam, clarification, elements),
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# "judge" = mapping/judge/check agents — cold (low temperature,
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# no thinking) for stable verdicts; Claude/local map to "fast",
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# "guide" = large generation (proposals, writer).
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DEFAULT_PROVIDER = "claude"
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PROVIDERS = {
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"claude": {
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"cli": "claude",
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"guide": "claude-opus-4-8[1m]",
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"fast": "claude-sonnet-4-6",
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"judge": "claude-sonnet-4-6", # the CLI has no temperature setting
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"quick": "claude-sonnet-4-6",
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"env_key": None, # auth via CLAUDE_CODE_OAUTH_TOKEN or ~/.claude
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},
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# "minimax-kalt/…" is NOT its own stack, just an opencode provider entry
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# (dev-ops/opencode.json) with low temperature; M3 there without thinking.
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"minimax": {
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"cli": "opencode",
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"guide": "minimax/MiniMax-M3",
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"fast": "minimax-kalt/MiniMax-M2.7-highspeed",
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"judge": "minimax-kalt/MiniMax-M3",
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"quick": "minimax/MiniMax-M2.7-highspeed",
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"env_key": "MINIMAX_API_KEY",
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},
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"lokal": {
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"cli": "opencode",
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"guide": "ollama/qwen3.6:27b",
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"fast": "ollama/qwen3.5:9b",
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"judge": "ollama/qwen3.5:9b",
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"quick": "ollama/qwen3.5:9b",
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"env_key": None,
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"check_url": "http://localhost:11434/api/tags", # Ollama reachable?
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},
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}
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