feat: import raw ChatGPT export (new sharded format)

OpenAI's export changed: conversations.json is now sharded into
conversations-000.json..NNN.json, each a JSON array of conversations with the
mapping tree and per-message create_time.

ingest now reads that format directly (supersedes the old convert/trim/split
scripts): walks each conversation's mapping ordered by create_time, keeps text
and multimodal_text (drops thoughts/reasoning_recap), captures real per-message
timestamps, and imports idempotently by conversation_id. `lyra-import <dir>`
auto-detects raw-export vs legacy {title,messages} dirs; optional limit arg.

Verified on 15 conversations: real dates, correct ordering, recall returns
dated poker history.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

lyra/ingest.py

@@ -21,6 +21,10 @@ from lyra import llm, logbus, memory
 EMBED_BATCH = 64
 EMBED_CHAR_CAP = 6000  # cap embed input size; full content is still stored
 
+# Message content types worth keeping from a raw ChatGPT export. We drop
+# 'thoughts' (internal chain-of-thought) and 'reasoning_recap' (meta).
+KEEP_CONTENT_TYPES = {"text", "multimodal_text"}
+
 
 def _session_id(path: Path) -> str:
     """Stable id derived from the filename, so re-imports don't duplicate."""
@@ -80,11 +84,98 @@ def import_dir(dirpath: str | Path, created_at: str | None = None) -> dict:
     return {"files": len(files), "sessions_imported": sessions, "exchanges": exchanges}
 
 
+# --- Raw ChatGPT export (sharded conversations-*.json with timestamps) ---
+
+
+def _ts_to_iso(ts: float | None, fallback: str) -> str:
+    if not ts:
+        return fallback
+    return datetime.fromtimestamp(ts, tz=timezone.utc).isoformat()
+
+
+def _message_text(msg: dict) -> str | None:
+    """Extract plain text from a ChatGPT message node, or None to skip it."""
+    content = msg.get("content") or {}
+    if content.get("content_type") not in KEEP_CONTENT_TYPES:
+        return None
+    parts = [p for p in (content.get("parts") or []) if isinstance(p, str) and p.strip()]
+    text = "\n".join(parts).strip()
+    return text or None
+
+
+def _convo_rows(convo: dict) -> list[tuple[float, str, str]]:
+    """(create_time, role, text) for each keepable message, chronologically."""
+    rows: list[tuple[float, str, str]] = []
+    conv_ct = convo.get("create_time") or 0
+    for node in convo.get("mapping", {}).values():
+        msg = node.get("message")
+        if not msg:
+            continue
+        role = (msg.get("author") or {}).get("role")
+        if role not in ("user", "assistant"):
+            continue
+        text = _message_text(msg)
+        if text is None:
+            continue
+        rows.append((msg.get("create_time") or conv_ct, role, text))
+    rows.sort(key=lambda r: r[0] or 0)
+    return rows
+
+
+def import_conversation(convo: dict) -> int:
+    """Import one raw-export conversation. Idempotent by conversation_id."""
+    session_id = convo.get("conversation_id") or convo.get("id")
+    if not session_id or memory.history(session_id):
+        return 0
+    rows = _convo_rows(convo)
+    if not rows:
+        return 0
+
+    memory.ensure_session(session_id, name=convo.get("title") or "untitled")
+    fallback = datetime.now(timezone.utc).isoformat()
+    exchanges: list[tuple[str, str, list[float], str]] = []
+    for i in range(0, len(rows), EMBED_BATCH):
+        batch = rows[i : i + EMBED_BATCH]
+        embeddings = llm.embed([text[:EMBED_CHAR_CAP] for _, _, text in batch])
+        for (ts, role, text), emb in zip(batch, embeddings):
+            exchanges.append((role, text, emb, _ts_to_iso(ts, fallback)))
+    return memory.add_exchanges_bulk(session_id, exchanges)
+
+
+def import_export(export_dir: str | Path, limit: int | None = None) -> dict:
+    """Import a raw ChatGPT export directory (sharded conversations-*.json)."""
+    shards = sorted(Path(export_dir).glob("conversations-*.json"))
+    convos, exchanges, seen = 0, 0, 0
+    for shard in shards:
+        for convo in json.loads(shard.read_text(encoding="utf-8")):
+            if limit is not None and seen >= limit:
+                break
+            seen += 1
+            added = import_conversation(convo)
+            if added:
+                convos += 1
+                exchanges += added
+        if limit is not None and seen >= limit:
+            break
+    logbus.log(
+        "info", "export import complete",
+        shards=len(shards), conversations=convos, exchanges=exchanges,
+    )
+    return {"shards": len(shards), "conversations_imported": convos, "exchanges": exchanges}
+
+
 def main() -> int:
     if len(sys.argv) < 2:
-        print("usage: lyra-import <dir-of-chat-json>", file=sys.stderr)
+        print("usage: lyra-import <dir> [limit]", file=sys.stderr)
         return 2
-    report = import_dir(sys.argv[1])
+    path = Path(sys.argv[1])
+    limit = int(sys.argv[2]) if len(sys.argv) > 2 else None
+    # A raw ChatGPT export has sharded conversations-*.json; otherwise treat the
+    # directory as legacy {title, messages} files.
+    if list(path.glob("conversations-*.json")):
+        report = import_export(path, limit=limit)
+    else:
+        report = import_dir(path)
     print(report)
     return 0