Add story-to-pack research and a structured situation review

Research toward building a content pack from a story corpus, kept on its own branch and independent of the game. Records the selection experiments against blind labels, and settles selection as gate G2 followed by a human review: review.py writes REVIEW.md and a review.json form, apply_review.py checks the filled form and writes situations.json for the next stage. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01C6UDQ9o6L6Ey173U7XVou6
2026-09-15 06:59:35 -04:00
parent d84ae495f4
commit fa3769d0fe
112 changed files with 79868 additions and 0 deletions
@@ -0,0 +1,49 @@
+"""The one place that talks to an inference host.
+
+The host is shared with other development work, so this is deliberately
+gentle: one request at a time, a fixed pause between requests, and a short
+keep_alive so the model is not left occupying the GPU after a run.
+
+Configuration comes from the environment, never from a file in this tree:
+  STP_OLLAMA   base URL of an Ollama server, e.g. http://inference.lan:11434
+  STP_PAUSE    seconds between requests (default 0.5)
+"""
+import json, os, sys, time, urllib.request
+
+BASE = os.environ.get('STP_OLLAMA', '').rstrip('/')
+PAUSE = float(os.environ.get('STP_PAUSE', '0.5'))
+KEEP_ALIVE = '2m'
+
+if not BASE:
+    sys.exit('STP_OLLAMA is not set -- refusing to guess an inference host')
+
+def post(route, body, timeout=300):
+    req = urllib.request.Request(BASE + route, data=json.dumps(body).encode(),
+                                 headers={'Content-Type': 'application/json'})
+    for attempt in range(4):
+        try:
+            with urllib.request.urlopen(req, timeout=timeout) as r:
+                result = json.load(r)
+            time.sleep(PAUSE)
+            return result
+        except Exception as e:
+            if attempt == 3: raise
+            print(f'  retry {attempt + 1}: {e}', flush=True)
+            time.sleep(5 * (attempt + 1))
+
+def embed(model, texts):
+    return post('/api/embed', {'model': model, 'input': texts, 'keep_alive': KEEP_ALIVE})['embeddings']
+
+def chat(model, system, user, num_ctx=2048, num_predict=80, fmt=None, think=None):
+    # num_ctx is pinned small on purpose: the 2026-09-10 hang was a 16k-context
+    # variant prefilling on CPU. A scene is ~450 tokens with the prompt.
+    body = {'model': model, 'stream': False, 'keep_alive': KEEP_ALIVE,
+            'messages': [{'role': 'system', 'content': system},
+                         {'role': 'user', 'content': user}],
+            'options': {'num_ctx': num_ctx, 'num_predict': num_predict, 'temperature': 0}}
+    if fmt: body['format'] = fmt
+    # Thinking models (qwen3) otherwise spend the token budget in a separate thinking
+    # channel and return empty content.
+    if think is not None: body['think'] = think
+    d = post('/api/chat', body)
+    return d['message']['content'].strip(), d.get('eval_duration', 0), d.get('prompt_eval_duration', 0)