feat(voice): PDF context in voice calls + call transcript summary (MAT-10)

Pass PDF document context from room to voice session so the voice LLM
can answer questions about uploaded PDFs. Persist call transcripts and
post an LLM-generated summary to the room when the call ends.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

bot.py

@@ -281,6 +281,7 @@ class Bot:
         self._pending_connects: dict[str, str] = {}  # matrix_user_id -> device_code
         self._pending_translate: dict[str, dict] = {}  # sender -> {text, detected_lang, room_id}
         self._pending_reply: dict[str, dict] = {}  # sender -> {target_lang}
+        self._room_pdf_context: dict[str, dict] = {}  # room_id -> {filename, text, timestamp}
 
     @staticmethod
     def _load_user_keys() -> dict[str, str]:
@@ -462,6 +463,14 @@ class Bot:
                     import secrets
                     bot_key = secrets.token_bytes(16)
 
+                    # Get PDF context if recently uploaded (within 1 hour)
+                    pdf_ctx = self._room_pdf_context.get(room_id, {})
+                    pdf_text = None
+                    if pdf_ctx and time.time() - pdf_ctx.get("timestamp", 0) < 3600:
+                        pdf_text = pdf_ctx.get("text")
+                        logger.info("Passing PDF context to voice session: %s (%d chars)",
+                                    pdf_ctx.get("filename", "?"), len(pdf_text) if pdf_text else 0)
+
                     vs = VoiceSession(
                         nio_client=self.client,
                         room_id=room_id,
@@ -473,6 +482,7 @@ class Bot:
                             self._publish_encryption_key(rid, key)),
                         memory=self.memory,
                         caller_user_id=event.sender,
+                        document_context=pdf_text,
                     )
 
                     # Check timeline for caller's key
@@ -506,11 +516,19 @@ class Bot:
                 self.active_callers.pop(room_id, None)
                 vs = self.voice_sessions.pop(room_id, None)
                 if vs:
+                    transcript = vs.get_transcript()
                     try:
                         await vs.stop()
                         logger.info("Voice session stopped for %s", room_id)
                     except Exception:
                         logger.exception("Failed to stop voice session for %s", room_id)
+                    # Post call summary to room
+                    if transcript:
+                        try:
+                            summary = await self._summarize_call(transcript, room_id)
+                            await self._send_text(room_id, f"**Anruf-Zusammenfassung:**\n\n{summary}")
+                        except Exception:
+                            logger.exception("Failed to post call summary for %s", room_id)
 
                 # Leave the call too
                 self.active_calls.discard(room_id)
@@ -965,6 +983,13 @@ class Bot:
         if len(pdf_text) > 50000:
             pdf_text = pdf_text[:50000] + "\n\n[... truncated, PDF too long ...]"
 
+        # Store PDF context for voice session pickup
+        self._room_pdf_context[room.room_id] = {
+            "filename": filename,
+            "text": pdf_text,
+            "timestamp": time.time(),
+        }
+
         user_message = f'The user sent a PDF file named "{filename}". Here is the extracted text:\n\n{pdf_text}\n\nPlease summarize or answer questions about this document.'
 
         await self.client.room_typing(room.room_id, typing_state=True)
@@ -1458,6 +1483,38 @@ class Bot:
             content=content,
         )
 
+    async def _summarize_call(self, transcript: list[dict], room_id: str) -> str:
+        """Generate a concise summary of a voice call transcript via LLM."""
+        # Format transcript for the LLM
+        lines = []
+        for entry in transcript[-30:]:  # last 30 exchanges max
+            role = "Nutzer" if entry["role"] == "user" else "Assistent"
+            lines.append(f"{role}: {entry['text']}")
+        transcript_text = "\n".join(lines)
+
+        if not self.llm:
+            # Fallback: return last 10 exchanges as raw transcript
+            return "\n".join(lines[-20:])
+
+        try:
+            model = self.room_models.get(room_id, DEFAULT_MODEL)
+            resp = await self.llm.chat.completions.create(
+                model=model,
+                messages=[
+                    {"role": "system", "content": (
+                        "Fasse das folgende Anruf-Transkript kurz und praegnant zusammen. "
+                        "Nenne die wichtigsten besprochenen Punkte, Entscheidungen und offene Fragen. "
+                        "Antworte in der Sprache des Gespraechs. Maximal 5-8 Saetze."
+                    )},
+                    {"role": "user", "content": transcript_text},
+                ],
+                max_tokens=500,
+            )
+            return resp.choices[0].message.content.strip()
+        except Exception:
+            logger.warning("Call summary LLM failed, falling back to raw transcript", exc_info=True)
+            return "\n".join(lines[-20:])
+
     async def _send_text(self, room_id: str, text: str):
         await self.client.room_send(
             room_id,