Astraea v1.0 — multi-agent WA family-law assistant

Eight specialist agents over a 16-book verified WA law corpus (RAG with citations),
per-user document vault, WA court-form PDF auto-fill, comms missions with DV
safety guard, no-KYC auth, TTS. Self-hosted: Flask + SQLite + Ollama, stdlib-only RAG.

Includes README, LICENSE (MIT + not-legal-advice notice), DEPLOY runbook, .gitignore.

benchmark_models.py

@@ -0,0 +1,92 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""Astraea model benchmark — feed identical retrieved facts to each candidate model
+and compare grounded, practical legal answers. Picks the best RAG answer lane.
+"""
+import json
+import os
+import sys
+import time
+import urllib.request
+
+sys.path.insert(0, "/opt/astraea")
+import rag  # reuse the retrieval pipeline (embed + cosine top-k)
+
+OLLAMA = "http://10.30.20.29:11434"
+OUT = "/opt/astraea/benchmark-results.json"
+
+MODELS = [
+    "granite4.2:latest",
+    "qwen3.8fast:latest",
+    "ornith-1.5:9b",
+    "gemma4:26b-a4b-it-q4_K_M",
+    "nemotron-3.5-lightning:30b-a3b-q4_K_M",
+]
+
+# (agent_id, question) — each has a real answer in the current index.
+QUESTIONS = [
+    ("property", "My spouse bought a house before we married, but we paid the mortgage from our joint account for 15 years. When we divorce, is the house community or separate property?"),
+    ("maintenance", "We're divorcing after a 22-year marriage. I stayed home to raise our kids and have no income. Am I entitled to spousal maintenance, and what factors does the court weigh?"),
+    ("divorce", "What factors does a Washington court consider when deciding how to divide our property and debts, and can one spouse get more than half?"),
+    ("divorce", "I just moved to Washington three weeks ago and want to file for divorce from my spouse who lives in Oregon. Can I file here, and is there a waiting period?"),
+]
+
+SYSTEM = (
+    "You are a licensed Washington State family-law attorney giving a real client direct, "
+    "practical legal advice. Base every legal claim on the reference documents. Cite the RCW "
+    "section AND any controlling case law by name. Be specific and actionable: state the rule, "
+    "apply it to the client's facts, and tell them what to do next. Do not hedge or refuse to "
+    "answer. If the exact scenario is not in the documents, give the closest governing principle "
+    "and note it needs attorney review."
+)
+
+
+def chat(model, system, user):
+    payload = {
+        "model": model,
+        "messages": [{"role": "system", "content": system},
+                     {"role": "user", "content": user}],
+        "stream": False,
+        "think": False,
+        "options": {"temperature": 0.2, "num_predict": 1400, "num_ctx": 16384},
+    }
+    req = urllib.request.Request(f"{OLLAMA}/api/chat", data=json.dumps(payload).encode(),
+                                 headers={"Content-Type": "application/json"})
+    op = urllib.request.build_opener(urllib.request.ProxyHandler({}))
+    with op.open(req, timeout=300) as r:
+        d = json.loads(r.read().decode("utf-8"))
+    m = d.get("message", {})
+    return (m.get("content") or m.get("thinking") or "").strip()
+
+
+def main():
+    if not rag._INDEX:
+        print("loading index from cache...", flush=True)
+        rag._INDEX.extend(rag.build_index())
+    print(f"index: {len(rag._INDEX)} chunks", flush=True)
+    results = []
+    for agent_id, q in QUESTIONS:
+        top = rag.retrieve(q, agent_id, top_k=5)
+        ctx = "\n\n".join(
+            f"[{i+1}] ({c['source']} — {c['title']}):\n{c['text']}"
+            for i, (s, c) in enumerate(top)
+        )
+        print(f"\n=== Q: {q[:70]}... ({len(top)} chunks) ===", flush=True)
+        for model in MODELS:
+            t0 = time.time()
+            try:
+                ans = chat(model, SYSTEM, f"REFERENCE DOCUMENTS:\n\n{ctx}\n\nQUESTION: {q}")
+                dt = round(time.time() - t0, 1)
+                print(f"  {model}: {len(ans)} chars in {dt}s", flush=True)
+                results.append({"question": q, "agent": agent_id, "model": model,
+                                "answer": ans, "latency_s": dt,
+                                "sources": [c["source"] for s, c in top]})
+            except Exception as e:
+                print(f"  {model}: ERROR {e}", flush=True)
+                results.append({"question": q, "agent": agent_id, "model": model, "error": str(e)})
+    json.dump(results, open(OUT, "w"), indent=2)
+    print(f"\n=== done -> {OUT} ===")
+
+
+if __name__ == "__main__":
+    main()