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06b876bdea3fde7841629da6b7bbf6f657c65172
matrix-ai-agent/bot.py
Christian Gick 1c8d45c31b fix: harden Matrix ecosystem — pool recovery, parallel queries, voice persistence 
- Memory service: asyncpg pool auto-reconnect on connection loss, IVFFlat lists 10→100
- Bot: parallel RAG/memory/chunk queries (asyncio.gather), parallel tool execution
- Bot: skip memory extraction for trivial messages (<20 chars, no personal facts)
- Bot: persist voice call transcripts as searchable conversation chunks
- RAG: JSON parse safety in AI metadata, embedding_status tracking, fetch timeouts
- Drive sync: token refresh mutex to prevent race conditions, fetch timeouts

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-08 18:10:22 +02:00

3019 lines
138 KiB
Python
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import os
import json
import asyncio
import base64
import hashlib
import io
import logging
import re
import time
import uuid
import sentry_sdk
import docx
import fitz # pymupdf
import httpx
from openai import AsyncOpenAI
from olm import sas as olm_sas
from rag_key_manager import RAGKeyManager
from nio import (
AsyncClient,
AsyncClientConfig,
LoginResponse,
InviteMemberEvent,
MegolmEvent,
RoomEncryptedFile,
RoomEncryptedImage,
RoomMessageFile,
RoomMessageImage,
RoomMessageText,
RoomMessageUnknown,
SyncResponse,
UnknownEvent,
KeyVerificationStart,
KeyVerificationCancel,
KeyVerificationKey,
KeyVerificationMac,
ToDeviceError,
)
from nio.crypto.attachments import decrypt_attachment
from livekit import api
from voice import VoiceSession
from article_summary import ArticleSummaryHandler
BOT_DEVICE_ID = "AIBOT"
CALL_MEMBER_TYPE = "org.matrix.msc3401.call.member"
ENCRYPTION_KEYS_TYPE = "io.element.call.encryption_keys"
MODEL_STATE_TYPE = "ai.agiliton.model"
RENAME_STATE_TYPE = "ai.agiliton.auto_rename"
logger = logging.getLogger("matrix-ai-bot")
# Sentry error tracking
_sentry_dsn = os.environ.get("SENTRY_DSN", "")
if _sentry_dsn:
sentry_sdk.init(dsn=_sentry_dsn, traces_sample_rate=0.1, environment=os.environ.get("SENTRY_ENV", "production"))
logger.info("Sentry initialized for bot")
HOMESERVER = os.environ["MATRIX_HOMESERVER"]
BOT_USER = os.environ["MATRIX_BOT_USER"]
BOT_PASS = os.environ["MATRIX_BOT_PASSWORD"]
LK_URL = os.environ["LIVEKIT_URL"]
LK_KEY = os.environ["LIVEKIT_API_KEY"]
LK_SECRET = os.environ["LIVEKIT_API_SECRET"]
AGENT_NAME = os.environ.get("AGENT_NAME", "matrix-ai")
STORE_PATH = os.environ.get("CRYPTO_STORE_PATH", "/data/crypto_store")
CREDS_FILE = os.path.join(STORE_PATH, "credentials.json")
LITELLM_URL = os.environ.get("LITELLM_BASE_URL", "")
LITELLM_KEY = os.environ.get("LITELLM_API_KEY", "not-needed")
DEFAULT_MODEL = os.environ.get("DEFAULT_MODEL", "claude-sonnet")
BASE_MODEL = os.environ.get("BASE_MODEL", "claude-haiku")
ESCALATION_MODEL = os.environ.get("ESCALATION_MODEL", "claude-sonnet")
MEMORY_SERVICE_URL = os.environ.get("MEMORY_SERVICE_URL", "http://memory-service:8090")
MEMORY_SERVICE_TOKEN = os.environ.get("MEMORY_SERVICE_TOKEN", "")
CONFLUENCE_URL = os.environ.get("CONFLUENCE_BASE_URL", "")
CONFLUENCE_USER = os.environ.get("CONFLUENCE_USER", "")
CONFLUENCE_TOKEN = os.environ.get("CONFLUENCE_TOKEN", "")
PORTAL_URL = os.environ.get("PORTAL_URL", "")
BOT_API_KEY = os.environ.get("BOT_API_KEY", "")
RAG_ENDPOINT = os.environ.get("RAG_ENDPOINT", "") # Customer-VM RAG service (e.g. http://127.0.0.1:8765)
RAG_AUTH_TOKEN = os.environ.get("RAG_AUTH_TOKEN", "") # Bearer token for local RAG
BRAVE_API_KEY = os.environ.get("BRAVE_API_KEY", "")
ELEVENLABS_API_KEY = os.environ.get("ELEVENLABS_API_KEY", "")
ELEVENLABS_VOICE_ID = os.environ.get("ELEVENLABS_VOICE_ID", "ML23UVoFL5mI6APbRAeR")
FIRECRAWL_URL = os.environ.get("FIRECRAWL_URL", "")
MAX_TOOL_ITERATIONS = 5
SYSTEM_PROMPT = """You are a helpful AI assistant in a Matrix chat room.
Keep answers concise but thorough. Use markdown formatting when helpful.
Always respond in the same language the user writes in. Never switch languages unless the user explicitly asks you to. If a user writes in German, reply in German. If they write in English, reply in English. Do not offer to translate or change language on your own.
IMPORTANT RULES — FOLLOW THESE STRICTLY:
- When document context is provided below, use it to answer. Always include any links.
- NEVER tell the user to run commands or type anything special. No commands exist.
- NEVER mention "!ai", "!ai search", "!ai read", or any slash/bang commands.
- NEVER say you cannot access files, documents, or links.
- NEVER ask the user where documents are stored, how they were uploaded, or under what filename.
- NEVER suggest contacting an administrator, using a web interface, or checking another system.
- NEVER ask follow-up questions about document storage or file locations.
- If no relevant documents were found, simply say you don't have information on that topic and ask if you can help with something else. Do NOT speculate about why or suggest the user look elsewhere.
- You can see and analyze images that users send. Describe what you see when asked about an image.
- You can read and analyze PDF documents that users send. Summarize content and answer questions about them.
- Always focus on the user's most recent message — whether it was text or voice. Do not automatically continue or summarize previous conversations.
- When a user greets you or starts a new conversation after a pause, respond with a brief greeting and wait for their instructions.
- You can generate images when asked — use the generate_image tool for any image creation, drawing, or illustration requests.
- You can search the web using the web_search tool. Use it when users ask about current events, facts, or anything that needs up-to-date information.
- You can open and read web pages using browse_url. Use it when a user shares a link, or when you need more detail from a search result. Summarize the key content concisely.
- When you use web_search, embed source links INLINE in the text where the information appears, e.g. "Laut [Cyprus Mail](url) hat..." or "([Quelle](url))". Do NOT collect links in a separate section at the bottom. Every claim from a search result must have its source linked right there in the sentence.
- Keep formatting compact. STRICT rules: NEVER use headings (no #, ##, ###). Use **bold text** for section titles instead. Use --- sparingly to separate major sections. NEVER add blank lines between list items or between a section title and its content. Maximum one blank line between sections.
- You can search Confluence and Jira using tools. When users ask about documentation, wiki pages, tickets, or tasks, use the appropriate tool. Use confluence_recent_pages FIRST to show recently edited pages before searching.
- When creating Jira issues, always confirm the project key and summary with the user before creating.
- If a user's Atlassian account is not connected, tell them to connect it at matrixhost.eu/settings and provide the link.
- If user memories are provided, use them to personalize responses. Address users by name if known.
- When asked to translate, provide ONLY the translation with no explanation."""
IMAGE_GEN_TOOLS = [{
"type": "function",
"function": {
"name": "generate_image",
"description": "Generate an image from a text description. Use when the user asks to create, draw, generate, design, or make an image/picture/photo/illustration.",
"parameters": {
"type": "object",
"properties": {
"prompt": {"type": "string", "description": "Detailed image generation prompt"}
},
"required": ["prompt"]
}
}
}]
ATLASSIAN_TOOLS = [
{
"type": "function",
"function": {
"name": "confluence_recent_pages",
"description": "List recently modified Confluence pages. Use this FIRST when the user mentions Confluence, documents, or wiki pages — shows the last 5 recently edited pages so the user can pick one directly without searching.",
"parameters": {
"type": "object",
"properties": {
"limit": {"type": "integer", "description": "Max pages to return (default 5)", "default": 5},
},
},
},
},
{
"type": "function",
"function": {
"name": "confluence_search",
"description": "Search Confluence wiki pages using CQL (Confluence Query Language). Use when the user asks about documentation, wiki, or knowledge base content.",
"parameters": {
"type": "object",
"properties": {
"query": {"type": "string", "description": "Search query text"},
"limit": {"type": "integer", "description": "Max results to return (default 5)", "default": 5},
},
"required": ["query"],
},
},
},
{
"type": "function",
"function": {
"name": "confluence_read_page",
"description": "Read the full content of a Confluence page by its ID. Use after searching to get page details.",
"parameters": {
"type": "object",
"properties": {
"page_id": {"type": "string", "description": "The Confluence page ID"},
},
"required": ["page_id"],
},
},
},
{
"type": "function",
"function": {
"name": "confluence_update_page",
"description": "Update a section of a Confluence page by heading. Use when the user asks to change, edit, or update part of a wiki page.",
"parameters": {
"type": "object",
"properties": {
"page_id": {"type": "string", "description": "The Confluence page ID"},
"section_heading": {"type": "string", "description": "The heading text of the section to update"},
"new_content": {"type": "string", "description": "New content for the section (paragraphs separated by newlines)"},
},
"required": ["page_id", "section_heading", "new_content"],
},
},
},
{
"type": "function",
"function": {
"name": "confluence_create_page",
"description": "Create a new Confluence page. Use when the user asks to create a new wiki page or document.",
"parameters": {
"type": "object",
"properties": {
"title": {"type": "string", "description": "Page title"},
"content": {"type": "string", "description": "Page body text (paragraphs separated by newlines)"},
"space_key": {"type": "string", "description": "Confluence space key (default: AI)", "default": "AI"},
},
"required": ["title", "content"],
},
},
},
{
"type": "function",
"function": {
"name": "jira_search",
"description": "Search Jira issues using JQL (Jira Query Language). Use when the user asks about tickets, tasks, bugs, or project issues.",
"parameters": {
"type": "object",
"properties": {
"jql": {"type": "string", "description": "JQL query string"},
"limit": {"type": "integer", "description": "Max results (default 10)", "default": 10},
},
"required": ["jql"],
},
},
},
{
"type": "function",
"function": {
"name": "jira_get_issue",
"description": "Get detailed information about a specific Jira issue by its key (e.g. CF-123).",
"parameters": {
"type": "object",
"properties": {
"issue_key": {"type": "string", "description": "Jira issue key like CF-123"},
},
"required": ["issue_key"],
},
},
},
{
"type": "function",
"function": {
"name": "jira_create_issue",
"description": "Create a new Jira issue. Always confirm project and summary with user before creating.",
"parameters": {
"type": "object",
"properties": {
"project": {"type": "string", "description": "Project key (e.g. CF)"},
"summary": {"type": "string", "description": "Issue title/summary"},
"issue_type": {"type": "string", "description": "Issue type (default: Task)", "default": "Task"},
"description": {"type": "string", "description": "Issue description (optional)"},
},
"required": ["project", "summary"],
},
},
},
{
"type": "function",
"function": {
"name": "jira_add_comment",
"description": "Add a comment to an existing Jira issue.",
"parameters": {
"type": "object",
"properties": {
"issue_key": {"type": "string", "description": "Jira issue key like CF-123"},
"comment": {"type": "string", "description": "Comment text"},
},
"required": ["issue_key", "comment"],
},
},
},
{
"type": "function",
"function": {
"name": "jira_transition",
"description": "Change the status of a Jira issue (e.g. move to 'In Progress', 'Done').",
"parameters": {
"type": "object",
"properties": {
"issue_key": {"type": "string", "description": "Jira issue key like CF-123"},
"status": {"type": "string", "description": "Target status name (e.g. 'In Progress', 'Done')"},
},
"required": ["issue_key", "status"],
},
},
},
]
WEB_SEARCH_TOOLS = [{
"type": "function",
"function": {
"name": "web_search",
"description": "Search the web for current information. Use when the user asks about recent events, facts, or anything that needs up-to-date information from the internet.",
"parameters": {
"type": "object",
"properties": {
"query": {"type": "string", "description": "Search query"},
"count": {"type": "integer", "description": "Number of results (default 5)", "default": 5},
},
"required": ["query"],
},
},
}, {
"type": "function",
"function": {
"name": "browse_url",
"description": "Open a web page and read its text content. Use when the user shares a URL, or when you need more detail from a search result link.",
"parameters": {
"type": "object",
"properties": {
"url": {"type": "string", "description": "Full URL to fetch (https://...)"},
},
"required": ["url"],
},
},
}]
ROOM_TOOLS = [{
"type": "function",
"function": {
"name": "search_room_history",
"description": (
"Search the current chat room's message history for specific content. "
"Use when the user asks about something said earlier in the conversation that is beyond "
"the recent messages visible to you, e.g. 'what did I say about X last week', "
"'find the message where I mentioned Y', 'what was the first thing I asked today'."
),
"parameters": {
"type": "object",
"properties": {
"query": {"type": "string", "description": "Search term or phrase to look for in room messages"},
"limit": {"type": "integer", "description": "Max messages to scan (default 200, max 500)", "default": 200},
},
"required": ["query"],
},
},
}]
ALL_TOOLS = IMAGE_GEN_TOOLS + WEB_SEARCH_TOOLS + ATLASSIAN_TOOLS + ROOM_TOOLS
ATLASSIAN_NOT_CONNECTED_MSG = (
"Your Atlassian account is not connected. "
"Please connect it at [matrixhost.eu/settings](https://matrixhost.eu/settings?connect=atlassian) "
"to use Jira and Confluence features."
)
HELP_TEXT = """**AI Bot**
Just write naturally — I'll respond to all messages in DMs.
In group chats, @mention me to get my attention.
I can search your documents, translate messages, and analyze images.
Manage settings at [matrixhost.eu/settings](https://matrixhost.eu/settings)."""
class DocumentRAG:
"""Search documents via customer-VM RAG service or central portal fallback."""
def __init__(self, portal_url: str, bot_api_key: str,
rag_endpoint: str = "", rag_auth_token: str = ""):
self.portal_url = portal_url.rstrip("/")
self.bot_api_key = bot_api_key
self.rag_endpoint = rag_endpoint.rstrip("/") if rag_endpoint else ""
self.rag_auth_token = rag_auth_token
self.use_local_rag = bool(self.rag_endpoint)
self.enabled = bool(self.rag_endpoint) or bool(portal_url and bot_api_key)
async def search(self, query: str, top_k: int = 10, api_key: str | None = None, org_slug: str | None = None, matrix_user_id: str | None = None) -> list[dict]:
if not self.enabled or not matrix_user_id:
return []
# Prefer customer-VM RAG service (encrypted, local)
if self.use_local_rag:
return await self._search_local(query, top_k, matrix_user_id)
# Fallback: central portal API (legacy, unencrypted)
return await self._search_portal(query, top_k, matrix_user_id)
async def _search_local(self, query: str, top_k: int, matrix_user_id: str | None = None) -> list[dict]:
"""Search via customer-VM RAG service (localhost)."""
try:
body = {"query": query, "limit": top_k}
if matrix_user_id:
body["owner_id"] = matrix_user_id
headers: dict[str, str] = {"Content-Type": "application/json"}
if self.rag_auth_token:
headers["Authorization"] = f"Bearer {self.rag_auth_token}"
async with httpx.AsyncClient(timeout=15.0) as client:
resp = await client.post(
f"{self.rag_endpoint}/rag/search",
json=body,
headers=headers,
)
resp.raise_for_status()
return resp.json().get("results", [])
except Exception:
logger.debug("Local RAG search failed", exc_info=True)
return []
async def _search_portal(self, query: str, top_k: int, matrix_user_id: str) -> list[dict]:
"""Search via central portal API (legacy fallback)."""
try:
body = {"query": query, "limit": top_k, "matrix_user_id": matrix_user_id}
async with httpx.AsyncClient(timeout=15.0) as client:
resp = await client.post(
f"{self.portal_url}/api/bot/documents/search",
json=body,
headers={"Authorization": f"Bearer {self.bot_api_key}"},
)
resp.raise_for_status()
return resp.json().get("results", [])
except Exception:
logger.debug("Portal document search failed", exc_info=True)
return []
async def health(self) -> dict | None:
"""Check local RAG service health."""
if not self.use_local_rag:
return None
try:
headers: dict[str, str] = {}
if self.rag_auth_token:
headers["Authorization"] = f"Bearer {self.rag_auth_token}"
async with httpx.AsyncClient(timeout=5.0) as client:
resp = await client.get(f"{self.rag_endpoint}/health", headers=headers)
resp.raise_for_status()
return resp.json()
except Exception:
return None
def format_context(self, results: list[dict]) -> str:
if not results:
return ""
parts = ["The following documents were found in our document archive:\n"]
for i, r in enumerate(results, 1):
title = r.get("title", r.get("filename", "Untitled"))
link = r.get("source_url") or r.get("metadata", {}).get("source_url", "")
category = r.get("category", "")
date = r.get("detected_date", "")
content = r.get("content", "")
summary = r.get("metadata", {}).get("summary", "")
parts.append(f"--- Document {i}: {title} ---")
if category:
parts.append(f"Category: {category}")
if date:
parts.append(f"Date: {date}")
if link:
parts.append(f"Link: {link}")
if summary:
parts.append(f"Summary: {summary}")
if content:
parts.append(f"Content:\n{content}")
parts.append("") # blank line between docs
parts.append("IMPORTANT INSTRUCTIONS FOR DOCUMENT RESPONSES:\n"
"1. Answer the user's question using ALL the documents above.\n"
"2. These are FRESH search results — they override anything from chat history.\n"
" If previous messages said 'only one passport' but documents show more, trust the documents.\n"
"3. TRUST the document TITLE and SUMMARY — they are AI-generated and accurate.\n"
" The Content field may be garbled OCR from scanned PDFs (random characters, broken text).\n"
" If the title says 'Christian's Passport' and summary says 'passport belonging to Christian',\n"
" then it IS a passport — even if the content looks like gibberish.\n"
"4. You MUST include a source link for EVERY document you reference.\n"
"5. Format links as markdown: [Document Title](url)\n"
"6. Place the link right after mentioning or quoting the document.\n"
"7. If a document has no link, skip the link but still reference the title.\n"
"8. Never show raw URLs without markdown formatting.\n"
"9. List ALL matching documents, not just the first one.")
return "\n".join(parts)
class MemoryClient:
"""Async HTTP client for the memory-service."""
def __init__(self, base_url: str, token: str = ""):
self.base_url = base_url.rstrip("/")
self.token = token
self.enabled = bool(base_url)
def _headers(self) -> dict:
h = {}
if self.token:
h["Authorization"] = f"Bearer {self.token}"
return h
async def store(self, user_id: str, fact: str, source_room: str = ""):
if not self.enabled:
return
try:
async with httpx.AsyncClient(timeout=10.0) as client:
await client.post(
f"{self.base_url}/memories/store",
json={"user_id": user_id, "fact": fact, "source_room": source_room},
headers=self._headers(),
)
except Exception:
logger.warning("Memory store failed", exc_info=True)
async def query(self, user_id: str, query: str, top_k: int = 10) -> list[dict]:
if not self.enabled:
return []
try:
async with httpx.AsyncClient(timeout=10.0) as client:
resp = await client.post(
f"{self.base_url}/memories/query",
json={"user_id": user_id, "query": query, "top_k": top_k},
headers=self._headers(),
)
resp.raise_for_status()
return resp.json().get("results", [])
except Exception:
logger.warning("Memory query failed", exc_info=True)
return []
async def delete_user(self, user_id: str) -> int:
if not self.enabled:
return 0
try:
async with httpx.AsyncClient(timeout=10.0) as client:
resp = await client.delete(
f"{self.base_url}/memories/{user_id}",
headers=self._headers(),
)
resp.raise_for_status()
return resp.json().get("deleted", 0)
except Exception:
logger.warning("Memory delete failed", exc_info=True)
return 0
async def list_all(self, user_id: str) -> list[dict]:
if not self.enabled:
return []
try:
async with httpx.AsyncClient(timeout=10.0) as client:
resp = await client.get(
f"{self.base_url}/memories/{user_id}",
headers=self._headers(),
)
resp.raise_for_status()
return resp.json().get("memories", [])
except Exception:
logger.warning("Memory list failed", exc_info=True)
return []
async def store_chunk(self, user_id: str, room_id: str, chunk_text: str,
summary: str, source_event_id: str = "", original_ts: float = 0.0):
if not self.enabled:
return
try:
async with httpx.AsyncClient(timeout=15.0) as client:
await client.post(
f"{self.base_url}/chunks/store",
json={
"user_id": user_id, "room_id": room_id,
"chunk_text": chunk_text, "summary": summary,
"source_event_id": source_event_id, "original_ts": original_ts,
},
headers=self._headers(),
)
except Exception:
logger.warning("Chunk store failed", exc_info=True)
async def query_chunks(self, query: str, user_id: str, room_id: str = "",
top_k: int = 5) -> list[dict]:
if not self.enabled:
return []
if not user_id:
logger.error("query_chunks called with empty user_id — returning empty")
return []
try:
async with httpx.AsyncClient(timeout=10.0) as client:
resp = await client.post(
f"{self.base_url}/chunks/query",
json={"user_id": user_id, "room_id": room_id, "query": query, "top_k": top_k},
headers=self._headers(),
)
resp.raise_for_status()
return resp.json().get("results", [])
except Exception:
logger.warning("Chunk query failed", exc_info=True)
return []
class AtlassianClient:
"""Fetches per-user Atlassian tokens from the portal and calls Atlassian REST APIs."""
def __init__(self, portal_url: str, bot_api_key: str):
self.portal_url = portal_url.rstrip("/")
self.bot_api_key = bot_api_key
self.enabled = bool(portal_url and bot_api_key)
# Cache cloud IDs per user token to avoid repeated lookups
self._cloud_id_cache: dict[str, str] = {}
async def get_token(self, matrix_user_id: str) -> str | None:
"""Fetch user's Atlassian token from the portal."""
if not self.enabled:
return None
try:
async with httpx.AsyncClient(timeout=10.0) as client:
resp = await client.get(
f"{self.portal_url}/api/bot/tokens",
params={"matrix_user_id": matrix_user_id, "provider": "atlassian"},
headers={"Authorization": f"Bearer {self.bot_api_key}"},
)
resp.raise_for_status()
data = resp.json()
return data.get("access_token") if data.get("connected") else None
except Exception:
logger.warning("Failed to fetch Atlassian token for %s", matrix_user_id, exc_info=True)
return None
async def _get_cloud_id(self, token: str) -> str | None:
"""Get the Atlassian Cloud ID for the user's instance."""
if token in self._cloud_id_cache:
return self._cloud_id_cache[token]
try:
async with httpx.AsyncClient(timeout=10.0) as client:
resp = await client.get(
"https://api.atlassian.com/oauth/token/accessible-resources",
headers={"Authorization": f"Bearer {token}"},
)
resp.raise_for_status()
resources = resp.json()
if resources:
cloud_id = resources[0]["id"]
self._cloud_id_cache[token] = cloud_id
return cloud_id
except Exception:
logger.warning("Failed to fetch Atlassian cloud ID", exc_info=True)
return None
async def confluence_recent_pages(self, token: str, limit: int = 5) -> str:
cloud_id = await self._get_cloud_id(token)
if not cloud_id:
return "Error: Could not determine Atlassian Cloud instance."
try:
async with httpx.AsyncClient(timeout=15.0) as client:
resp = await client.get(
f"https://api.atlassian.com/ex/confluence/{cloud_id}/wiki/rest/api/content/search",
params={"cql": "type=page ORDER BY lastmodified DESC", "limit": str(limit)},
headers={"Authorization": f"Bearer {token}"},
)
resp.raise_for_status()
data = resp.json()
results = data.get("results", [])
if not results:
return "No recent Confluence pages found."
lines = ["Recently modified pages:"]
for i, r in enumerate(results, 1):
title = r.get("title", "Untitled")
page_id = r.get("id", "")
space = r.get("_expandable", {}).get("space", "").split("/")[-1]
lines.append(f"{i}. **{title}** (ID: {page_id}, Space: {space})")
return "\n".join(lines)
except Exception as e:
return f"Failed to fetch recent pages: {e}"
async def confluence_search(self, token: str, query: str, limit: int = 5) -> str:
cloud_id = await self._get_cloud_id(token)
if not cloud_id:
return "Error: Could not determine Atlassian Cloud instance."
try:
async with httpx.AsyncClient(timeout=15.0) as client:
resp = await client.get(
f"https://api.atlassian.com/ex/confluence/{cloud_id}/wiki/rest/api/content/search",
params={"cql": f'text ~ "{query}"', "limit": str(limit), "expand": "metadata.labels"},
headers={"Authorization": f"Bearer {token}"},
)
resp.raise_for_status()
data = resp.json()
results = data.get("results", [])
if not results:
return f"No Confluence pages found for: {query}"
lines = []
for r in results:
title = r.get("title", "Untitled")
page_id = r.get("id", "")
space = r.get("_expandable", {}).get("space", "").split("/")[-1]
url = f"https://api.atlassian.com/ex/confluence/{cloud_id}/wiki{r.get('_links', {}).get('webui', '')}"
lines.append(f"- **{title}** (ID: {page_id}, Space: {space})\n {url}")
return f"Found {len(results)} pages:\n" + "\n".join(lines)
except Exception as e:
return f"Confluence search error: {e}"
async def confluence_read_page(self, token: str, page_id: str) -> str:
cloud_id = await self._get_cloud_id(token)
if not cloud_id:
return "Error: Could not determine Atlassian Cloud instance."
try:
async with httpx.AsyncClient(timeout=15.0) as client:
resp = await client.get(
f"https://api.atlassian.com/ex/confluence/{cloud_id}/wiki/api/v2/pages/{page_id}",
params={"body-format": "storage"},
headers={"Authorization": f"Bearer {token}"},
)
resp.raise_for_status()
data = resp.json()
title = data.get("title", "Untitled")
html_body = data.get("body", {}).get("storage", {}).get("value", "")
# Strip HTML tags for a readable summary
from bs4 import BeautifulSoup
text = BeautifulSoup(html_body, "lxml").get_text(separator="\n", strip=True)
# Truncate for LLM context
if len(text) > 8000:
text = text[:8000] + "\n...(truncated)"
return f"**{title}**\n\n{text}"
except Exception as e:
return f"Failed to read Confluence page {page_id}: {e}"
async def confluence_update_page(self, token: str, page_id: str,
section_heading: str, new_content: str) -> str:
cloud_id = await self._get_cloud_id(token)
if not cloud_id:
return "Error: Could not determine Atlassian Cloud instance."
try:
base_url = f"https://api.atlassian.com/ex/confluence/{cloud_id}/wiki"
headers = {"Authorization": f"Bearer {token}"}
async with httpx.AsyncClient(timeout=15.0) as client:
# Fetch current page
resp = await client.get(
f"{base_url}/rest/api/content/{page_id}",
params={"expand": "body.storage,version"},
headers=headers,
)
resp.raise_for_status()
page = resp.json()
title = page["title"]
version = page["version"]["number"]
body_html = page["body"]["storage"]["value"]
# Find and replace section
from confluence_collab.parser import parse_sections, find_section, replace_section_content
sections = parse_sections(body_html)
section = find_section(sections, section_heading)
if section is None:
return f"Section '{section_heading}' not found on page '{title}'"
paragraphs = [p.strip() for p in new_content.split("\n") if p.strip()]
new_html = "".join(f"<p>{p}</p>" for p in paragraphs) if paragraphs else f"<p>{new_content}</p>"
new_body = replace_section_content(body_html, section, new_html)
# Update page
resp = await client.put(
f"{base_url}/rest/api/content/{page_id}",
json={
"version": {"number": version + 1},
"title": title,
"type": "page",
"body": {"storage": {"value": new_body, "representation": "storage"}},
},
headers=headers,
)
resp.raise_for_status()
return f"Section '{section_heading}' updated successfully on '{title}'"
except Exception as e:
return f"Failed to update Confluence page: {e}"
async def confluence_create_page(self, token: str, title: str, content: str,
space_key: str = "AI") -> str:
cloud_id = await self._get_cloud_id(token)
if not cloud_id:
return "Error: Could not determine Atlassian Cloud instance."
try:
headers = {"Authorization": f"Bearer {token}"}
async with httpx.AsyncClient(timeout=15.0) as client:
# Resolve space key to space ID (v2 API requires ID)
space_resp = await client.get(
f"https://api.atlassian.com/ex/confluence/{cloud_id}/wiki/api/v2/spaces",
params={"keys": space_key},
headers=headers,
)
space_resp.raise_for_status()
spaces = space_resp.json().get("results", [])
if not spaces:
return f"Space '{space_key}' not found."
space_id = spaces[0]["id"]
paragraphs = [p.strip() for p in content.split("\n") if p.strip()]
body_html = "".join(f"<p>{p}</p>" for p in paragraphs) if paragraphs else f"<p>{content}</p>"
resp = await client.post(
f"https://api.atlassian.com/ex/confluence/{cloud_id}/wiki/api/v2/pages",
json={
"spaceId": space_id,
"status": "current",
"title": title,
"body": {"representation": "storage", "value": body_html},
},
headers=headers,
)
if resp.status_code == 401:
logger.warning("Confluence v2 create returned 401, trying v1 API")
# Fallback to v1 API which may accept classic scopes
resp_v1 = await client.post(
f"https://api.atlassian.com/ex/confluence/{cloud_id}/wiki/rest/api/content",
json={
"type": "page",
"title": title,
"space": {"key": space_key},
"body": {"storage": {"value": body_html, "representation": "storage"}},
},
headers=headers,
)
if resp_v1.status_code in (200, 201):
data = resp_v1.json()
page_id = data["id"]
return f"Page created: **{title}** (ID: {page_id})"
logger.error("Confluence v1 fallback also failed: %s %s", resp_v1.status_code, resp_v1.text[:500])
return f"Failed to create page: Unauthorized. Please re-connect Atlassian at matrixhost.eu/settings to grant write permissions."
resp.raise_for_status()
data = resp.json()
page_id = data["id"]
return f"Page created: **{title}** (ID: {page_id})"
except Exception as e:
return f"Failed to create Confluence page: {e}"
async def jira_search(self, token: str, jql: str, limit: int = 10) -> str:
cloud_id = await self._get_cloud_id(token)
if not cloud_id:
return "Error: Could not determine Atlassian Cloud instance."
try:
async with httpx.AsyncClient(timeout=15.0) as client:
resp = await client.get(
f"https://api.atlassian.com/ex/jira/{cloud_id}/rest/api/3/search/jql",
params={"jql": jql, "maxResults": str(limit), "fields": "summary,status,assignee,priority,created,updated"},
headers={"Authorization": f"Bearer {token}"},
)
resp.raise_for_status()
data = resp.json()
issues = data.get("issues", [])
if not issues:
return f"No Jira issues found for: {jql}"
lines = []
for issue in issues:
key = issue["key"]
fields = issue.get("fields", {})
summary = fields.get("summary", "")
status = fields.get("status", {}).get("name", "")
assignee = fields.get("assignee", {})
assignee_name = assignee.get("displayName", "Unassigned") if assignee else "Unassigned"
priority = fields.get("priority", {}).get("name", "") if fields.get("priority") else ""
lines.append(f"- **{key}**: {summary} [{status}] (Assignee: {assignee_name}, Priority: {priority})")
return f"Found {data.get('total', len(issues))} issues:\n" + "\n".join(lines)
except Exception as e:
return f"Jira search error: {e}"
async def jira_get_issue(self, token: str, issue_key: str) -> str:
cloud_id = await self._get_cloud_id(token)
if not cloud_id:
return "Error: Could not determine Atlassian Cloud instance."
try:
async with httpx.AsyncClient(timeout=15.0) as client:
resp = await client.get(
f"https://api.atlassian.com/ex/jira/{cloud_id}/rest/api/3/issue/{issue_key}",
params={"fields": "summary,status,assignee,priority,description,created,updated,comment"},
headers={"Authorization": f"Bearer {token}"},
)
resp.raise_for_status()
data = resp.json()
fields = data.get("fields", {})
summary = fields.get("summary", "")
status = fields.get("status", {}).get("name", "")
assignee = fields.get("assignee", {})
assignee_name = assignee.get("displayName", "Unassigned") if assignee else "Unassigned"
priority = fields.get("priority", {}).get("name", "") if fields.get("priority") else ""
desc = fields.get("description")
# ADF description — extract text nodes
desc_text = ""
if desc and isinstance(desc, dict):
desc_text = self._extract_adf_text(desc)
elif isinstance(desc, str):
desc_text = desc
if len(desc_text) > 4000:
desc_text = desc_text[:4000] + "...(truncated)"
comments = fields.get("comment", {}).get("comments", [])
comment_lines = []
for c in comments[-5:]: # Last 5 comments
author = c.get("author", {}).get("displayName", "Unknown")
body = self._extract_adf_text(c.get("body", {})) if isinstance(c.get("body"), dict) else str(c.get("body", ""))
comment_lines.append(f" - **{author}**: {body[:500]}")
result = f"**{issue_key}: {summary}**\nStatus: {status} | Assignee: {assignee_name} | Priority: {priority}"
if desc_text:
result += f"\n\nDescription:\n{desc_text}"
if comment_lines:
result += f"\n\nRecent comments:\n" + "\n".join(comment_lines)
return result
except Exception as e:
return f"Failed to fetch {issue_key}: {e}"
async def jira_create_issue(self, token: str, project: str, summary: str,
issue_type: str = "Task", description: str = "") -> str:
cloud_id = await self._get_cloud_id(token)
if not cloud_id:
return "Error: Could not determine Atlassian Cloud instance."
try:
body: dict = {
"fields": {
"project": {"key": project},
"summary": summary,
"issuetype": {"name": issue_type},
}
}
if description:
body["fields"]["description"] = {
"type": "doc",
"version": 1,
"content": [{"type": "paragraph", "content": [{"type": "text", "text": description}]}],
}
async with httpx.AsyncClient(timeout=15.0) as client:
resp = await client.post(
f"https://api.atlassian.com/ex/jira/{cloud_id}/rest/api/3/issue",
json=body,
headers={"Authorization": f"Bearer {token}", "Content-Type": "application/json"},
)
resp.raise_for_status()
data = resp.json()
return f"Created **{data['key']}**: {summary}"
except httpx.HTTPStatusError as e:
return f"Failed to create issue: {e.response.text}"
except Exception as e:
return f"Failed to create issue: {e}"
async def jira_add_comment(self, token: str, issue_key: str, comment: str) -> str:
cloud_id = await self._get_cloud_id(token)
if not cloud_id:
return "Error: Could not determine Atlassian Cloud instance."
try:
body = {
"body": {
"type": "doc",
"version": 1,
"content": [{"type": "paragraph", "content": [{"type": "text", "text": comment}]}],
}
}
async with httpx.AsyncClient(timeout=15.0) as client:
resp = await client.post(
f"https://api.atlassian.com/ex/jira/{cloud_id}/rest/api/3/issue/{issue_key}/comment",
json=body,
headers={"Authorization": f"Bearer {token}", "Content-Type": "application/json"},
)
resp.raise_for_status()
return f"Comment added to {issue_key}."
except Exception as e:
return f"Failed to add comment to {issue_key}: {e}"
async def jira_transition(self, token: str, issue_key: str, status: str) -> str:
cloud_id = await self._get_cloud_id(token)
if not cloud_id:
return "Error: Could not determine Atlassian Cloud instance."
try:
async with httpx.AsyncClient(timeout=15.0) as client:
# First, get available transitions
resp = await client.get(
f"https://api.atlassian.com/ex/jira/{cloud_id}/rest/api/3/issue/{issue_key}/transitions",
headers={"Authorization": f"Bearer {token}"},
)
resp.raise_for_status()
transitions = resp.json().get("transitions", [])
# Find matching transition (case-insensitive)
target = None
for t in transitions:
if t["name"].lower() == status.lower() or t["to"]["name"].lower() == status.lower():
target = t
break
if not target:
available = ", ".join(t["name"] for t in transitions)
return f"Cannot transition {issue_key} to '{status}'. Available transitions: {available}"
# Execute transition
resp = await client.post(
f"https://api.atlassian.com/ex/jira/{cloud_id}/rest/api/3/issue/{issue_key}/transitions",
json={"transition": {"id": target["id"]}},
headers={"Authorization": f"Bearer {token}", "Content-Type": "application/json"},
)
resp.raise_for_status()
return f"Transitioned {issue_key} to **{target['to']['name']}**."
except Exception as e:
return f"Failed to transition {issue_key}: {e}"
@staticmethod
def _extract_adf_text(adf: dict) -> str:
"""Recursively extract text from Atlassian Document Format."""
if not isinstance(adf, dict):
return str(adf)
parts = []
if adf.get("type") == "text":
parts.append(adf.get("text", ""))
for child in adf.get("content", []):
parts.append(AtlassianClient._extract_adf_text(child))
return " ".join(parts).strip()
class Bot:
def __init__(self):
config = AsyncClientConfig(
max_limit_exceeded=0,
max_timeouts=0,
store_sync_tokens=True,
encryption_enabled=True,
)
self.client = AsyncClient(
HOMESERVER,
BOT_USER,
store_path=STORE_PATH,
config=config,
)
self.lkapi = None
self.voice_sessions: dict[str, VoiceSession] = {}
self.active_calls = set() # rooms where we've sent call member event
self.active_callers: dict[str, set[str]] = {} # room_id → set of caller user IDs
self.rag = DocumentRAG(PORTAL_URL, BOT_API_KEY,
rag_endpoint=RAG_ENDPOINT, rag_auth_token=RAG_AUTH_TOKEN)
self.key_manager = RAGKeyManager(self.client, PORTAL_URL, BOT_API_KEY)
self.memory = MemoryClient(MEMORY_SERVICE_URL, token=MEMORY_SERVICE_TOKEN)
self.atlassian = AtlassianClient(PORTAL_URL, BOT_API_KEY)
self.llm = AsyncOpenAI(base_url=LITELLM_URL, api_key=LITELLM_KEY) if LITELLM_URL else None
self._documents_cache: dict[str, str | None] = {} # matrix_user_id -> connected status
self.room_models: dict[str, str] = {} # room_id -> model name
self.auto_rename_rooms: set[str] = set() # rooms with auto-rename enabled
self._recent_images: dict[str, tuple[str, str, float]] = {} # room_id -> (b64, mime, timestamp)
self.renamed_rooms: dict[str, float] = {} # room_id -> timestamp of last rename
self._loaded_rooms: set[str] = set() # rooms where we've loaded state
self._sync_token_received = False
self._verifications: dict[str, dict] = {} # txn_id -> verification state
self._room_document_context: dict[str, list[dict]] = {} # room_id -> [{type, filename, text, timestamp}, ...]
# Article summary handler (Blinkist-style audio summaries)
if self.llm and ELEVENLABS_API_KEY:
self.article_handler = ArticleSummaryHandler(
llm_client=self.llm,
model=DEFAULT_MODEL,
elevenlabs_key=ELEVENLABS_API_KEY,
voice_id=ELEVENLABS_VOICE_ID,
firecrawl_url=FIRECRAWL_URL or None,
)
else:
self.article_handler = None
async def _has_documents(self, matrix_user_id: str) -> bool:
"""Check if user has documents via local RAG or MatrixHost portal API.
Results are cached per session.
"""
if matrix_user_id in self._documents_cache:
return self._documents_cache[matrix_user_id] is not None
# Check local RAG service first (customer-VM encrypted RAG)
if self.rag.use_local_rag:
health = await self.rag.health()
if health and health.get("document_count", 0) > 0:
self._documents_cache[matrix_user_id] = "connected"
return True
# Fallback: check via central portal
if self.atlassian.enabled:
try:
async with httpx.AsyncClient(timeout=10.0) as client:
resp = await client.get(
f"{self.atlassian.portal_url}/api/bot/tokens",
params={"matrix_user_id": matrix_user_id, "provider": "documents"},
headers={"Authorization": f"Bearer {self.atlassian.bot_api_key}"},
)
resp.raise_for_status()
data = resp.json()
if data.get("connected"):
self._documents_cache[matrix_user_id] = "connected"
return True
except Exception:
logger.debug("Portal document check failed for %s", matrix_user_id, exc_info=True)
self._documents_cache[matrix_user_id] = None
return False
async def start(self):
# Restore existing session or create new one
if os.path.exists(CREDS_FILE):
with open(CREDS_FILE) as f:
creds = json.load(f)
self.client.restore_login(
user_id=creds["user_id"],
device_id=creds["device_id"],
access_token=creds["access_token"],
)
self.client.load_store()
logger.info("Restored session as %s (device %s)", creds["user_id"], creds["device_id"])
else:
resp = await self.client.login(BOT_PASS, device_name="ai-voice-bot")
if not isinstance(resp, LoginResponse):
logger.error("Login failed: %s", resp)
return
# Persist credentials for next restart
with open(CREDS_FILE, "w") as f:
json.dump({
"user_id": resp.user_id,
"device_id": resp.device_id,
"access_token": resp.access_token,
}, f)
logger.info("Logged in as %s (device %s) — credentials saved", resp.user_id, resp.device_id)
if self.client.should_upload_keys:
await self.client.keys_upload()
self.lkapi = api.LiveKitAPI(LK_URL, LK_KEY, LK_SECRET)
self.client.add_event_callback(self.on_invite, InviteMemberEvent)
self.client.add_event_callback(self.on_megolm, MegolmEvent)
self.client.add_event_callback(self.on_unknown, UnknownEvent)
self.client.add_event_callback(self.on_text_message, RoomMessageText)
self.client.add_event_callback(self.on_image_message, RoomMessageImage)
self.client.add_event_callback(self.on_encrypted_image_message, RoomEncryptedImage)
self.client.add_event_callback(self.on_file_message, RoomMessageFile)
self.client.add_event_callback(self.on_encrypted_file_message, RoomEncryptedFile)
self.client.add_event_callback(self.on_room_unknown, RoomMessageUnknown)
self.client.add_response_callback(self.on_sync, SyncResponse)
self.client.add_to_device_callback(self.on_key_verification, KeyVerificationStart)
self.client.add_to_device_callback(self.on_key_verification, KeyVerificationKey)
self.client.add_to_device_callback(self.on_key_verification, KeyVerificationMac)
self.client.add_to_device_callback(self.on_key_verification, KeyVerificationCancel)
await self.client.sync_forever(timeout=30000, full_state=True)
async def _inject_rag_key(self):
"""Load document encryption key from Matrix and inject into RAG service."""
try:
seed_key = os.environ.get("RAG_ENCRYPTION_KEY_SEED")
success = await self.key_manager.ensure_rag_key(seed_key_hex=seed_key)
if success:
logger.info("RAG encryption key loaded from Matrix E2EE")
if seed_key:
logger.info("Migration complete - RAG_ENCRYPTION_KEY_SEED can now be removed from env")
else:
logger.warning("Failed to load RAG encryption key - documents will be inaccessible")
except Exception as e:
logger.error("RAG key injection failed: %s", e, exc_info=True)
async def on_invite(self, room, event: InviteMemberEvent):
if event.state_key != BOT_USER:
return
logger.info("Invited to %s, joining room", room.room_id)
await self.client.join(room.room_id)
async def on_sync(self, response: SyncResponse):
"""After each sync, trust all devices in our rooms."""
if not self._sync_token_received:
self._sync_token_received = True
logger.info("Initial sync complete, text handler active")
# Inject RAG encryption key from Matrix E2EE room
asyncio.create_task(self._inject_rag_key())
for user_id in list(self.client.device_store.users):
for device in self.client.device_store.active_user_devices(user_id):
if not device.verified:
self.client.verify_device(device)
logger.info("Auto-trusted device %s of %s", device.device_id, user_id)
async def on_unknown(self, room, event: UnknownEvent):
"""Handle call member state events and in-room verification."""
# Route verification events
if event.type.startswith("m.key.verification."):
if event.sender != BOT_USER:
await self._route_verification(room, event)
return
# Forward encryption key events to active voice sessions (skip our own)
if event.type == ENCRYPTION_KEYS_TYPE:
if event.sender == BOT_USER:
return # ignore our own key events
room_id = room.room_id
content = event.source.get("content", {})
device_id = content.get("device_id", "")
keys_list = content.get("keys", [])
logger.info("Got encryption_keys timeline event from %s in %s (device=%s, keys=%d, content_keys=%s)",
event.sender, room_id, device_id, len(keys_list), list(content.keys()))
vs = self.voice_sessions.get(room_id)
if vs:
for k in keys_list:
if "key" in k and "index" in k:
key_b64 = k["key"]
key_b64 += "=" * (-len(key_b64) % 4)
key_bytes = base64.urlsafe_b64decode(key_b64)
vs.on_encryption_key(event.sender, device_id, key_bytes, k["index"])
else:
logger.warning("encryption_keys event missing key/index: %s", k)
if not keys_list:
logger.warning("encryption_keys event has empty keys list, full content: %s", content)
else:
logger.warning("No voice session for room %s to deliver encryption key", room_id)
return
if event.type != CALL_MEMBER_TYPE:
return
if event.sender == BOT_USER:
return # ignore our own events
# Non-empty content means someone started/is in a call
if event.source.get("content", {}):
room_id = room.room_id
if room_id in self.active_calls:
return
logger.info("Call detected in %s from %s, joining...", room_id, event.sender)
self.active_calls.add(room_id)
self.active_callers.setdefault(room_id, set()).add(event.sender)
# Get the foci_preferred from the caller's event
content = event.source["content"]
foci = content.get("foci_preferred", [{
"type": "livekit",
"livekit_service_url": f"{HOMESERVER}/livekit-jwt-service",
"livekit_alias": room_id,
}])
# Compute LiveKit room name using same hash as lk-jwt-service
# SHA256(room_id + "|" + "m.call#ROOM") encoded as unpadded base64
lk_room_hash = hashlib.sha256((room_id + "|m.call#ROOM").encode()).digest()
lk_room_name = base64.b64encode(lk_room_hash).decode().rstrip("=")
logger.info("LiveKit room name: %s (hashed from %s)", lk_room_name, room_id)
# Send our own call member state event FIRST so Element Call
# sends encryption_keys in response (before we start VoiceSession)
call_content = {
"application": "m.call",
"call_id": "",
"scope": "m.room",
"device_id": BOT_DEVICE_ID,
"expires": 7200000,
"focus_active": {
"type": "livekit",
"focus_selection": "oldest_membership",
},
"foci_preferred": foci,
"m.call.intent": "audio",
}
state_key = f"_{BOT_USER}_{BOT_DEVICE_ID}_m.call"
try:
resp = await self.client.room_put_state(
room_id, CALL_MEMBER_TYPE, call_content, state_key=state_key,
)
logger.info("Sent call member event in %s: %s", room_id, resp)
except Exception:
logger.exception("Failed to send call member event in %s", room_id)
# Now create VoiceSession — encryption_keys may arrive via sync
# while VoiceSession waits for key (up to 10s)
if room_id not in self.voice_sessions:
try:
model = self.room_models.get(room_id, DEFAULT_MODEL)
caller_device_id = content.get("device_id", "")
# Generate bot's own E2EE key (16 bytes like Element Call)
import secrets
bot_key = secrets.token_bytes(16)
# Collect all recent document contexts (< 1 hour)
doc_entries = [e for e in self._room_document_context.get(room_id, [])
if time.time() - e["timestamp"] < 3600]
document_context = None
if doc_entries:
parts = []
for e in doc_entries:
label = {"pdf": "PDF", "image": "Bild", "text": "Datei"}.get(e["type"], "Dokument")
text = e["text"][:40000] if e["type"] != "image" else e["text"][:2000]
parts.append(f"[{label}: {e['filename']}]\n{text}")
document_context = "\n\n".join(parts)
logger.info("Passing %d document context(s) to voice session (%d chars total)",
len(doc_entries), len(document_context))
vs = VoiceSession(
nio_client=self.client,
room_id=room_id,
device_id=BOT_DEVICE_ID,
lk_url=LK_URL,
model=model,
bot_key=bot_key,
publish_key_cb=lambda key, rid=room_id: asyncio.ensure_future(
self._publish_encryption_key(rid, key)),
memory=self.memory,
caller_user_id=event.sender,
document_context=document_context,
)
# Check timeline for caller's key
caller_key = await self._get_call_encryption_key(room_id, event.sender, caller_device_id)
if caller_key:
vs.on_encryption_key(event.sender, caller_device_id, caller_key, 0)
# Store BEFORE start so on_unknown handler can forward keys via sync
self.voice_sessions[room_id] = vs
await vs.start()
logger.info("Voice session started for room %s (e2ee_key=%s)",
room_id, "yes" if caller_key else "waiting for sync")
except Exception:
logger.exception("Voice session start failed for %s", room_id)
self.voice_sessions.pop(room_id, None)
else:
# Empty content = someone left the call
room_id = room.room_id
if room_id in self.active_calls:
# Remove this caller from active set
callers = self.active_callers.get(room_id, set())
callers.discard(event.sender)
if callers:
logger.info("Participant %s left %s but %d other(s) still in call — keeping session",
event.sender, room_id, len(callers))
return
# No callers left — stop voice session
logger.info("Last caller %s left %s — stopping session", event.sender, room_id)
self.active_callers.pop(room_id, None)
vs = self.voice_sessions.pop(room_id, None)
if vs:
transcript = vs.get_transcript()
doc_context = vs.get_document_context()
confluence_page_id = vs.get_confluence_page_id()
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)
# Persist voice transcript as conversation chunks in memory service
try:
caller = event.sender
for entry in transcript:
if entry["role"] == "user":
user_text = entry["text"]
# Find the next assistant response
idx = transcript.index(entry)
ai_text = ""
if idx + 1 < len(transcript) and transcript[idx + 1]["role"] == "assistant":
ai_text = transcript[idx + 1]["text"]
if user_text and ai_text:
await self._store_conversation_chunk(
user_text, ai_text, caller, room_id
)
logger.info("Stored %d voice transcript chunks for %s", len(transcript) // 2, room_id)
except Exception:
logger.warning("Failed to store voice transcript chunks for %s", room_id, exc_info=True)
# Extract and post document annotations if a document was discussed
if doc_context:
try:
annotations = await self._extract_document_annotations(
transcript, doc_context, room_id)
if annotations:
await self._send_text(room_id,
f"**Dokument-Aenderungen:**\n\n{annotations}")
except Exception:
logger.exception("Failed to post document annotations for %s", room_id)
# Leave the call too
self.active_calls.discard(room_id)
state_key = f"_{BOT_USER}_{BOT_DEVICE_ID}_m.call"
try:
await self.client.room_put_state(
room_id, CALL_MEMBER_TYPE, {}, state_key=state_key,
)
logger.info("Left call in %s", room_id)
except Exception:
logger.exception("Failed to leave call in %s", room_id)
async def _load_room_settings(self, room_id: str):
"""Load persisted model and auto-rename settings from room state."""
if room_id in self._loaded_rooms:
return
self._loaded_rooms.add(room_id)
for state_type, target in [
(MODEL_STATE_TYPE, "model"),
(RENAME_STATE_TYPE, "rename"),
]:
try:
resp = await self.client.room_get_state_event(room_id, state_type, "")
if hasattr(resp, "content"):
content = resp.content
if target == "model" and "model" in content:
self.room_models[room_id] = content["model"]
elif target == "rename" and content.get("enabled"):
self.auto_rename_rooms.add(room_id)
except Exception:
pass # State event doesn't exist yet
# --- User memory helpers ---
@staticmethod
def _format_memories(memories: list[dict]) -> str:
"""Format memory query results as a system prompt section."""
if not memories:
return ""
facts = [m["fact"] for m in memories]
return "You have these memories about this user:\n" + "\n".join(f"- {f}" for f in facts)
@staticmethod
def _format_chunks(chunks: list[dict]) -> str:
"""Format conversation chunk results as a system prompt section."""
if not chunks:
return ""
parts = ["Relevant past conversations:"]
for c in chunks:
ts = c.get("original_ts", 0)
date_str = time.strftime("%Y-%m-%d", time.gmtime(ts)) if ts else "unknown"
summary = c.get("summary", "")
text = c.get("chunk_text", "")
# Truncate chunk text to ~500 chars for context window efficiency
if len(text) > 500:
text = text[:500] + "..."
parts.append(f"\n### {summary} ({date_str})\n{text}")
return "\n".join(parts)
async def _store_conversation_chunk(self, user_message: str, ai_reply: str,
sender: str, room_id: str):
"""Store a user-assistant exchange as a conversation chunk for RAG."""
if not self.llm or not self.memory.enabled:
return
chunk_text = f"User: {user_message}\nAssistant: {ai_reply}"
try:
resp = await self.llm.chat.completions.create(
model="gemini-flash",
messages=[
{"role": "system", "content": (
"Summarize this conversation exchange in 1-2 sentences for search indexing. "
"Focus on the topic and key information discussed. Be concise. "
"Write the summary in the same language as the conversation."
)},
{"role": "user", "content": chunk_text[:2000]},
],
max_tokens=100,
)
summary = resp.choices[0].message.content.strip()
except Exception:
logger.warning("Chunk summarization failed, using truncated message", exc_info=True)
summary = user_message[:200]
await self.memory.store_chunk(
user_id=sender, room_id=room_id, chunk_text=chunk_text,
summary=summary, original_ts=time.time(),
)
# Regex for detecting personal facts worth extracting (pronouns, possessives, identity markers)
_PERSONAL_FACT_RE = re.compile(
r"\b(ich|mein|meine|meinem|i'm|i am|my |mine|we |our |"
r"name is|hei[sß]e|wohne|arbeite|lebe|studier|born|live|work|"
r"prefer|favorite|hobby|birthday|family|wife|husband|partner|child|dog|cat)\b",
re.IGNORECASE,
)
def _is_trivial_message(self, text: str) -> bool:
"""Return True if the message is too trivial for memory extraction."""
if len(text) >= 20:
return False
if self._PERSONAL_FACT_RE.search(text):
return False
return True
async def _extract_and_store_memories(self, user_message: str, ai_reply: str,
existing_facts: list[str], model: str,
sender: str, room_id: str):
"""Use LLM to extract memorable facts, then store each via memory-service."""
if not self.llm:
return
# Skip extraction for trivial messages (saves ~2-3s + 1 LLM call)
if self._is_trivial_message(user_message):
logger.debug("Skipping memory extraction for trivial message: %s", user_message[:40])
return
existing_text = "\n".join(f"- {f}" for f in existing_facts) if existing_facts else "(none)"
logger.info("Memory extraction: user_msg=%s... (%d existing facts)", user_message[:80], len(existing_facts))
try:
resp = await self.llm.chat.completions.create(
model=model,
messages=[
{"role": "system", "content": (
"You extract memorable facts about the USER from conversations. "
"Return a JSON array of strings — each string is a concise fact worth remembering. "
"Include: name, language preference, location, occupation, interests, preferences, "
"family, pets, projects, important dates, or any personal detail shared. "
"Do NOT include: the current question/topic, temporary info, or things the AI said. "
"Do NOT extract facts from documents or search results mentioned in the AI reply — "
"those are about OTHER people/entities, not the user. Only extract facts the user "
"directly states about themselves. "
"Do NOT duplicate existing memories (rephrase or skip if already known). "
"Return [] if nothing new is worth remembering."
)},
{"role": "user", "content": (
f"Existing memories:\n{existing_text}\n\n"
f"User message: {user_message[:500]}\n"
# Only include first 200 chars of AI reply to avoid document content pollution
f"AI reply (summary only): {ai_reply[:200]}\n\n"
"New facts to remember (JSON array of strings):"
)},
],
max_tokens=300,
)
raw = resp.choices[0].message.content.strip()
logger.info("Memory extraction raw response: %s", raw[:200])
if raw.startswith("```"):
raw = re.sub(r"^```\w*\n?", "", raw)
raw = re.sub(r"\n?```$", "", raw)
match = re.search(r"\[.*\]", raw, re.DOTALL)
if match:
raw = match.group(0)
new_facts = json.loads(raw)
if not isinstance(new_facts, list):
logger.warning("Memory extraction returned non-list: %s", type(new_facts))
return
logger.info("Memory extraction found %d new facts", len(new_facts))
for fact in new_facts:
if isinstance(fact, str) and fact.strip():
await self.memory.store(sender, fact.strip(), room_id)
except json.JSONDecodeError:
logger.warning("Memory extraction JSON parse failed, raw: %s", raw[:200])
except Exception:
logger.warning("Memory extraction failed", exc_info=True)
async def on_text_message(self, room, event: RoomMessageText):
"""Handle text messages: commands and AI responses."""
if event.sender == BOT_USER:
return
if not self._sync_token_received:
return # ignore messages from initial sync / backfill
# Ignore old messages (>30s) to avoid replaying history
server_ts = event.server_timestamp / 1000
if time.time() - server_ts > 30:
return
await self._load_room_settings(room.room_id)
body = event.body.strip()
# In DMs (2 members), respond to all messages; in groups, require @mention
is_dm = room.member_count == 2
if not is_dm:
bot_display = self.client.user_id.split(":")[0].lstrip("@")
mentioned = (
BOT_USER in body
or f"@{bot_display}" in body.lower()
or bot_display.lower() in body.lower()
)
if not mentioned:
return
if not self.llm:
await self._send_text(room.room_id, "LLM not configured (LITELLM_BASE_URL not set).")
return
sender = event.sender
# Check if a recent image was sent in this room (within 60s)
image_data = None
cached = self._recent_images.get(room.room_id)
if cached:
b64, mime, ts = cached
if time.time() - ts < 60:
image_data = (b64, mime)
del self._recent_images[room.room_id]
# Detect Confluence page links → store page ID for voice session context
confluence_page_id = None
conf_long = re.search(r'agiliton\.atlassian\.net/wiki/.*?pages/(\d+)', body)
conf_short = re.search(r'agiliton\.atlassian\.net/wiki/x/([A-Za-z0-9_-]+)', body)
if conf_long:
confluence_page_id = conf_long.group(1)
elif conf_short and CONFLUENCE_URL and CONFLUENCE_USER and CONFLUENCE_TOKEN:
# Resolve short link via Confluence API
tiny_id = conf_short.group(1)
try:
async with httpx.AsyncClient(timeout=10.0) as hc:
resp = await hc.get(
f"{CONFLUENCE_URL}/rest/api/content",
params={"type": "page", "expand": "version",
"limit": "1", "start": "0"},
auth=(CONFLUENCE_USER, CONFLUENCE_TOKEN),
follow_redirects=True,
)
# Try the tiny link redirect approach
resp2 = await hc.get(
f"{CONFLUENCE_URL}/x/{tiny_id}",
auth=(CONFLUENCE_USER, CONFLUENCE_TOKEN),
follow_redirects=True,
)
# Extract page ID from the redirected URL
redir_match = re.search(r'pages/(\d+)', str(resp2.url))
if redir_match:
confluence_page_id = redir_match.group(1)
else:
# Parse page ID from HTML response
id_match = re.search(r'"pageId"\s*:\s*"?(\d+)', resp2.text)
if id_match:
confluence_page_id = id_match.group(1)
logger.info("Resolved Confluence short link /x/%s → page %s",
tiny_id, confluence_page_id)
except Exception as exc:
logger.warning("Confluence short link resolution failed: %s", exc)
if confluence_page_id:
# Fetch page with section structure for targeted editing
conf_text = f"confluence_page_id:{confluence_page_id}"
conf_title = f"Confluence page {confluence_page_id}"
if CONFLUENCE_URL and CONFLUENCE_USER and CONFLUENCE_TOKEN:
try:
from confluence_collab.client import Auth, get_page
from confluence_collab.parser import parse_sections
auth = Auth(base_url=CONFLUENCE_URL, username=CONFLUENCE_USER, api_token=CONFLUENCE_TOKEN)
page = await get_page(confluence_page_id, auth)
conf_title = page.title
sections = parse_sections(page.body_html)
section_outline = "\n".join(
f"{' ' * (s.level - 1)}h{s.level}: {s.heading}" for s in sections
)
# Strip HTML for plain text context
plain = re.sub(r"<[^>]+>", " ", page.body_html)
plain = re.sub(r"\s+", " ", plain).strip()
conf_text = (
f"confluence_page_id:{confluence_page_id}\n\n"
f"Title: {page.title}\n\n"
f"Sections:\n{section_outline}\n\n"
f"{plain}"
)
logger.info("Fetched Confluence page %s: %s (%d chars, %d sections)",
confluence_page_id, page.title, len(plain), len(sections))
except Exception as exc:
logger.warning("Confluence page fetch failed for %s: %s",
confluence_page_id, exc)
# Fallback to voice.py reader
try:
from voice import _confluence_read_page
title, plain, _ver = await _confluence_read_page(confluence_page_id)
conf_title = title
conf_text = f"confluence_page_id:{confluence_page_id}\n\nTitle: {title}\n\n{plain}"
except Exception:
pass
docs = self._room_document_context.setdefault(room.room_id, [])
docs.append({
"type": "confluence",
"filename": conf_title,
"text": conf_text,
"timestamp": time.time(),
})
logger.info("Confluence page %s detected in room %s",
confluence_page_id, room.room_id)
# Check article summary FSM (Blinkist-style audio summaries)
if self.article_handler:
summary_response = await self.article_handler.handle_message(
room.room_id, sender, body
)
if summary_response is not None:
if summary_response == "__GENERATE__":
await self.client.room_typing(room.room_id, typing_state=True)
try:
await self.article_handler.generate_and_post(
self, room.room_id, sender
)
finally:
await self.client.room_typing(room.room_id, typing_state=False)
elif summary_response:
await self._send_text(room.room_id, summary_response)
return
await self.client.room_typing(room.room_id, typing_state=True)
try:
await self._respond_with_ai(room, body, sender=sender, image_data=image_data)
finally:
await self.client.room_typing(room.room_id, typing_state=False)
async def on_image_message(self, room, event: RoomMessageImage):
"""Handle image messages: download, encode, and send to AI for analysis."""
if event.sender == BOT_USER:
return
if not self._sync_token_received:
return
server_ts = event.server_timestamp / 1000
if time.time() - server_ts > 30:
return
await self._load_room_settings(room.room_id)
# In DMs respond to all images; in groups only if bot was recently @mentioned
is_dm = room.member_count == 2
if not is_dm:
# Check if bot was @mentioned in the image body (caption) or skip
body = (event.body or "").strip()
bot_display = self.client.user_id.split(":")[0].lstrip("@")
mentioned = (
BOT_USER in body
or f"@{bot_display}" in body.lower()
or bot_display.lower() in body.lower()
)
if not mentioned:
return
if not self.llm:
await self._send_text(room.room_id, "LLM not configured (LITELLM_BASE_URL not set).")
return
# Download image from Matrix homeserver
mxc_url = event.url
if not mxc_url:
return
try:
resp = await self.client.download(mxc=mxc_url)
if not hasattr(resp, "body"):
logger.warning("Image download failed for %s", mxc_url)
return
img_bytes = resp.body
except Exception:
logger.exception("Failed to download image %s", mxc_url)
return
# Determine MIME type
mime_type = getattr(event, "mimetype", None) or "image/png"
b64_data = base64.b64encode(img_bytes).decode("utf-8")
caption = (event.body or "").strip()
# Treat filenames (contain dots or are very long) as no caption
is_filename = not caption or caption == "image" or "." in caption or len(caption) > 100
text = "What's in this image?" if is_filename else caption
# Cache image for follow-up text messages
self._recent_images[room.room_id] = (b64_data, mime_type, time.time())
await self.client.room_typing(room.room_id, typing_state=True)
try:
reply = await self._respond_with_ai(room, text, sender=event.sender, image_data=(b64_data, mime_type))
if reply:
docs = self._room_document_context.setdefault(room.room_id, [])
docs.append({"type": "image", "filename": caption or "image",
"text": reply, "timestamp": time.time()})
if len(docs) > 5:
del docs[:-5]
finally:
await self.client.room_typing(room.room_id, typing_state=False)
async def on_encrypted_image_message(self, room, event: RoomEncryptedImage):
"""Handle encrypted image messages: decrypt, encode, and send to AI."""
if event.sender == BOT_USER:
return
if not self._sync_token_received:
return
server_ts = event.server_timestamp / 1000
if time.time() - server_ts > 30:
return
await self._load_room_settings(room.room_id)
is_dm = room.member_count == 2
if not is_dm:
body = (event.body or "").strip()
bot_display = self.client.user_id.split(":")[0].lstrip("@")
mentioned = (
BOT_USER in body
or f"@{bot_display}" in body.lower()
or bot_display.lower() in body.lower()
)
if not mentioned:
return
if not self.llm:
await self._send_text(room.room_id, "LLM not configured (LITELLM_BASE_URL not set).")
return
mxc_url = event.url
if not mxc_url:
return
try:
resp = await self.client.download(mxc=mxc_url)
if not hasattr(resp, "body"):
logger.warning("Encrypted image download failed for %s", mxc_url)
return
# Decrypt the attachment
img_bytes = decrypt_attachment(resp.body, event.key["k"], event.hashes["sha256"], event.iv)
except Exception:
logger.exception("Failed to download/decrypt encrypted image %s", mxc_url)
return
mime_type = getattr(event, "mimetype", None) or "image/png"
b64_data = base64.b64encode(img_bytes).decode("utf-8")
caption = (event.body or "").strip()
is_filename = not caption or caption == "image" or "." in caption or len(caption) > 100
text = "What's in this image?" if is_filename else caption
# Cache image for follow-up text messages
self._recent_images[room.room_id] = (b64_data, mime_type, time.time())
await self.client.room_typing(room.room_id, typing_state=True)
try:
reply = await self._respond_with_ai(room, text, sender=event.sender, image_data=(b64_data, mime_type))
if reply:
docs = self._room_document_context.setdefault(room.room_id, [])
docs.append({"type": "image", "filename": caption or "image",
"text": reply, "timestamp": time.time()})
if len(docs) > 5:
del docs[:-5]
finally:
await self.client.room_typing(room.room_id, typing_state=False)
# Supported text-based file extensions
_TEXT_EXTENSIONS = frozenset({
".txt", ".md", ".csv", ".json", ".xml", ".html", ".yaml", ".yml", ".log",
})
async def on_file_message(self, room, event: RoomMessageFile):
"""Handle file messages: extract text from PDFs, docx, and text files."""
if event.sender == BOT_USER:
return
if not self._sync_token_received:
return
server_ts = event.server_timestamp / 1000
if time.time() - server_ts > 30:
return
source = event.source or {}
content = source.get("content", {})
info = content.get("info", {})
mime_type = info.get("mimetype", "")
filename = content.get("body", "file")
ext = os.path.splitext(filename.lower())[1]
# Determine file type
is_pdf = mime_type == "application/pdf" or ext == ".pdf"
is_docx = mime_type == "application/vnd.openxmlformats-officedocument.wordprocessingml.document" or ext == ".docx"
is_text = ext in self._TEXT_EXTENSIONS or mime_type.startswith("text/")
if not (is_pdf or is_docx or is_text):
return
await self._load_room_settings(room.room_id)
# In DMs respond to all files; in groups only if bot was recently @mentioned
is_dm = room.member_count == 2
if not is_dm:
body = (event.body or "").strip()
bot_display = self.client.user_id.split(":")[0].lstrip("@")
mentioned = (
BOT_USER in body
or f"@{bot_display}" in body.lower()
or bot_display.lower() in body.lower()
)
if not mentioned:
return
if not self.llm:
await self._send_text(room.room_id, "LLM not configured (LITELLM_BASE_URL not set).")
return
# Download file
mxc_url = event.url
if not mxc_url:
return
try:
resp = await self.client.download(mxc=mxc_url)
if not hasattr(resp, "body"):
logger.warning("File download failed for %s", mxc_url)
return
file_bytes = resp.body
except Exception:
logger.exception("Failed to download file %s", mxc_url)
return
# Extract text based on file type
if is_pdf:
extracted = self._extract_pdf_text(file_bytes)
doc_type = "pdf"
elif is_docx:
extracted = self._extract_docx_text(file_bytes)
doc_type = "text"
else:
extracted = self._extract_text_file(file_bytes)
doc_type = "text"
if not extracted:
await self._send_text(room.room_id, f"I couldn't extract any text from that file ({filename}).")
return
# Truncate to avoid token limits (roughly 50k chars ≈ 12k tokens)
if len(extracted) > 50000:
extracted = extracted[:50000] + "\n\n[... truncated, file too long ...]"
# Store document context for voice session pickup
docs = self._room_document_context.setdefault(room.room_id, [])
docs.append({
"type": doc_type,
"filename": filename,
"text": extracted,
"timestamp": time.time(),
})
if len(docs) > 5:
del docs[:-5]
label = "PDF" if is_pdf else "Word document" if is_docx else "file"
user_message = f'The user sent a {label} named "{filename}". Here is the extracted text:\n\n{extracted}\n\nPlease summarize or answer questions about this document.'
await self.client.room_typing(room.room_id, typing_state=True)
try:
await self._respond_with_ai(room, user_message, sender=event.sender)
finally:
await self.client.room_typing(room.room_id, typing_state=False)
async def on_encrypted_file_message(self, room, event: RoomEncryptedFile):
"""Handle encrypted file messages: decrypt and process like on_file_message."""
if event.sender == BOT_USER:
return
if not self._sync_token_received:
return
server_ts = event.server_timestamp / 1000
if time.time() - server_ts > 30:
return
source = event.source or {}
content = source.get("content", {})
filename = content.get("body", "file")
ext = os.path.splitext(filename.lower())[1]
is_pdf = ext == ".pdf"
is_docx = ext == ".docx"
is_text = ext in self._TEXT_EXTENSIONS
if not (is_pdf or is_docx or is_text):
return
await self._load_room_settings(room.room_id)
is_dm = room.member_count == 2
if not is_dm:
body = (event.body or "").strip()
bot_display = self.client.user_id.split(":")[0].lstrip("@")
mentioned = (
BOT_USER in body
or f"@{bot_display}" in body.lower()
or bot_display.lower() in body.lower()
)
if not mentioned:
return
if not self.llm:
await self._send_text(room.room_id, "LLM not configured (LITELLM_BASE_URL not set).")
return
mxc_url = event.url
if not mxc_url:
return
try:
resp = await self.client.download(mxc=mxc_url)
if not hasattr(resp, "body"):
logger.warning("Encrypted file download failed for %s", mxc_url)
return
file_bytes = decrypt_attachment(resp.body, event.key["k"], event.hashes["sha256"], event.iv)
except Exception:
logger.exception("Failed to download/decrypt encrypted file %s", mxc_url)
return
if is_pdf:
extracted = self._extract_pdf_text(file_bytes)
doc_type = "pdf"
elif is_docx:
extracted = self._extract_docx_text(file_bytes)
doc_type = "text"
else:
extracted = self._extract_text_file(file_bytes)
doc_type = "text"
if not extracted:
await self._send_text(room.room_id, f"I couldn't extract any text from that file ({filename}).")
return
if len(extracted) > 50000:
extracted = extracted[:50000] + "\n\n[... truncated, file too long ...]"
docs = self._room_document_context.setdefault(room.room_id, [])
docs.append({
"type": doc_type,
"filename": filename,
"text": extracted,
"timestamp": time.time(),
})
if len(docs) > 5:
del docs[:-5]
label = "PDF" if is_pdf else "Word document" if is_docx else "file"
user_message = f'The user sent a {label} named "{filename}". Here is the extracted text:\n\n{extracted}\n\nPlease summarize or answer questions about this document.'
await self.client.room_typing(room.room_id, typing_state=True)
try:
await self._respond_with_ai(room, user_message, sender=event.sender)
finally:
await self.client.room_typing(room.room_id, typing_state=False)
@staticmethod
def _extract_pdf_text(pdf_bytes: bytes) -> str:
"""Extract text from PDF bytes using pymupdf."""
try:
doc = fitz.open(stream=pdf_bytes, filetype="pdf")
pages = []
for i, page in enumerate(doc):
text = page.get_text().strip()
if text:
pages.append(f"--- Page {i + 1} ---\n{text}")
doc.close()
return "\n\n".join(pages)
except Exception:
logger.exception("PDF text extraction failed")
return ""
@staticmethod
def _extract_docx_text(docx_bytes: bytes) -> str:
"""Extract text from .docx bytes using python-docx."""
try:
doc = docx.Document(io.BytesIO(docx_bytes))
return "\n".join(p.text for p in doc.paragraphs if p.text.strip())
except Exception:
logger.exception("DOCX text extraction failed")
return ""
@staticmethod
def _extract_text_file(file_bytes: bytes) -> str:
"""Decode text file bytes as UTF-8 with fallback to latin-1."""
try:
return file_bytes.decode("utf-8")
except UnicodeDecodeError:
try:
return file_bytes.decode("latin-1")
except Exception:
logger.exception("Text file decode failed")
return ""
async def _brave_search(self, query: str, count: int = 5) -> str:
"""Call Brave Search API and return formatted results."""
if not BRAVE_API_KEY:
return "Web search unavailable (no API key configured)."
try:
async with httpx.AsyncClient(timeout=10.0) as client:
resp = await client.get(
"https://api.search.brave.com/res/v1/web/search",
headers={"Accept": "application/json", "X-Subscription-Token": BRAVE_API_KEY},
params={"q": query, "count": count, "text_decorations": False},
)
resp.raise_for_status()
data = resp.json()
results = data.get("web", {}).get("results", [])
if not results:
return "No results found."
lines = []
for r in results[:count]:
title = r.get("title", "")
desc = r.get("description", "")
url = r.get("url", "")
lines.append(f"- [{title}]({url}): {desc}")
return "\n".join(lines)
except Exception as exc:
logger.warning("Brave search error: %s", exc)
return f"Search failed: {exc}"
async def _fetch_webpage(self, url: str, max_chars: int = 8000) -> str:
"""Fetch a URL and extract clean text content using BeautifulSoup."""
try:
from bs4 import BeautifulSoup
async with httpx.AsyncClient(timeout=15.0, follow_redirects=True,
headers={"User-Agent": "Mozilla/5.0 (compatible; AgilitonBot/1.0)"}) as client:
resp = await client.get(url)
resp.raise_for_status()
ct = resp.headers.get("content-type", "")
if "html" not in ct and "text" not in ct:
return f"URL returned non-text content ({ct})."
soup = BeautifulSoup(resp.text, "lxml")
for tag in soup(["script", "style", "nav", "footer", "header", "aside", "iframe"]):
tag.decompose()
main = soup.find("article") or soup.find("main") or soup.find("body")
text = main.get_text(separator="\n", strip=True) if main else soup.get_text(separator="\n", strip=True)
text = re.sub(r'\n{3,}', '\n\n', text)
if len(text) > max_chars:
text = text[:max_chars] + "\n\n[... truncated]"
return text if text.strip() else "Page loaded but no readable text content found."
except httpx.HTTPStatusError as exc:
return f"HTTP error {exc.response.status_code} fetching {url}"
except Exception as exc:
logger.warning("Webpage fetch error for %s: %s", url, exc)
return f"Failed to fetch page: {exc}"
async def _execute_tool(self, tool_name: str, args: dict, sender: str, room_id: str) -> str:
"""Execute a tool call and return the result as a string."""
# Image generation — no Atlassian token needed
if tool_name == "generate_image":
await self._generate_and_send_image(room_id, args.get("prompt", ""))
return "Image generated and sent to the room."
# Web search — no auth needed
if tool_name == "web_search":
return await self._brave_search(args.get("query", ""), args.get("count", 5))
# Browse URL — no auth needed, with DNS fallback via web search
if tool_name == "browse_url":
url = args.get("url", "")
result = await self._fetch_webpage(url)
if "Name or service not known" in result or "Failed to fetch" in result:
from urllib.parse import urlparse
domain = urlparse(url).netloc or url
logger.info("Browse DNS fail for %s — trying web search", domain)
search_result = await self._brave_search(f"{domain} website", count=3)
if search_result and "No results" not in search_result:
url_match = re.search(r'https?://[^\s\)]+', search_result)
if url_match:
corrected_url = url_match.group(0).rstrip('.,;')
retry = await self._fetch_webpage(corrected_url)
if "Failed to fetch" not in retry:
return f"[Original URL {url} unreachable. Found correct site at {corrected_url}]\n\n{retry}"
return f"Could not reach {url}. The domain may be misspelled — ask the user to clarify."
return result
# Room history search — no auth needed
if tool_name == "search_room_history":
return await self._search_room_history(
room_id, args.get("query", ""), args.get("limit", 200)
)
# Atlassian tools — need per-user token
token = await self.atlassian.get_token(sender) if sender else None
if not token:
return ATLASSIAN_NOT_CONNECTED_MSG
if tool_name == "confluence_recent_pages":
return await self.atlassian.confluence_recent_pages(token, args.get("limit", 5))
elif tool_name == "confluence_search":
return await self.atlassian.confluence_search(token, args["query"], args.get("limit", 5))
elif tool_name == "confluence_read_page":
return await self.atlassian.confluence_read_page(token, args["page_id"])
elif tool_name == "confluence_update_page":
return await self.atlassian.confluence_update_page(
token, args["page_id"], args["section_heading"], args["new_content"])
elif tool_name == "confluence_create_page":
return await self.atlassian.confluence_create_page(
token, args["title"], args["content"], args.get("space_key", "AG"))
elif tool_name == "jira_search":
return await self.atlassian.jira_search(token, args["jql"], args.get("limit", 10))
elif tool_name == "jira_get_issue":
return await self.atlassian.jira_get_issue(token, args["issue_key"])
elif tool_name == "jira_create_issue":
return await self.atlassian.jira_create_issue(
token, args["project"], args["summary"],
args.get("issue_type", "Task"), args.get("description", ""),
)
elif tool_name == "jira_add_comment":
return await self.atlassian.jira_add_comment(token, args["issue_key"], args["comment"])
elif tool_name == "jira_transition":
return await self.atlassian.jira_transition(token, args["issue_key"], args["status"])
else:
return f"Unknown tool: {tool_name}"
async def _search_room_history(self, room_id: str, query: str, limit: int = 200) -> str:
"""Search room message history for messages matching a query string."""
limit = min(limit, 500)
query_lower = query.lower()
try:
# Paginate through room history
matches = []
token = self.client.next_batch or ""
fetched = 0
while fetched < limit:
batch_size = min(100, limit - fetched)
resp = await self.client.room_messages(
room_id, start=token, limit=batch_size
)
if not hasattr(resp, "chunk") or not resp.chunk:
break
for evt in resp.chunk:
if not hasattr(evt, "body"):
continue
if query_lower in evt.body.lower():
ts = evt.server_timestamp / 1000
date_str = time.strftime("%Y-%m-%d %H:%M", time.gmtime(ts))
sender_name = evt.sender.split(":")[0].lstrip("@")
matches.append(f"[{date_str}] {sender_name}: {evt.body[:500]}")
fetched += len(resp.chunk)
token = resp.end
if not token:
break
if not matches:
return f"No messages found matching '{query}' in the last {fetched} messages."
# Return newest first (matches are in reverse chronological from pagination)
result = f"Found {len(matches)} message(s) matching '{query}' (scanned {fetched} messages):\n\n"
result += "\n\n---\n\n".join(matches[:20]) # cap at 20 results
if len(matches) > 20:
result += f"\n\n... and {len(matches) - 20} more matches."
return result
except Exception:
logger.warning("Room history search failed", exc_info=True)
return "Failed to search room history."
# -- Escalation patterns for model routing --
_ESCALATION_KEYWORDS = re.compile(
r"\b(debug|architecture|algorithm|regex|sql|refactor|optimize|migration"
r"|explain\s+in\s+detail|explain\s+how|step.by.step)\b",
re.IGNORECASE,
)
_EXPLICIT_ESCALATION = re.compile(
r"\b(think\s+harder|detailed|comprehensive|deep\s+dive|ausf[üu]hrlich|genau\s+erkl[äa]r)\b",
re.IGNORECASE,
)
def _check_escalation(self, user_message: str, image_data: tuple | None) -> str | None:
"""Return escalation reason string, or None if Haiku suffices."""
if image_data:
return "multimodal"
if len(user_message) > 500:
return "long_message"
if "```" in user_message:
return "code_block"
m = self._ESCALATION_KEYWORDS.search(user_message)
if m:
return f"technical_keyword:{m.group(0).lower()}"
if user_message.count("?") >= 3:
return "multi_question"
if self._EXPLICIT_ESCALATION.search(user_message):
return "explicit_request"
return None
def _select_model(self, room_id: str, user_message: str, image_data: tuple | None) -> tuple[str, str]:
"""Pick model: room override > escalation heuristics > BASE_MODEL."""
if room_id in self.room_models:
return self.room_models[room_id], "room_override"
reason = self._check_escalation(user_message, image_data)
if reason:
sentry_sdk.set_tag("escalated", "true")
sentry_sdk.set_tag("escalation_reason", reason)
sentry_sdk.add_breadcrumb(category="model", message=f"Escalated to {ESCALATION_MODEL}: {reason}")
return ESCALATION_MODEL, reason
sentry_sdk.set_tag("escalated", "false")
return BASE_MODEL, "default"
async def _respond_with_ai(self, room, user_message: str, sender: str = None, image_data: tuple = None) -> str | None:
"""Send AI response and return the reply text (or None on failure)."""
model, escalation_reason = self._select_model(room.room_id, user_message, image_data)
sentry_sdk.set_tag("model_used", model)
sentry_sdk.set_context("ai_request", {
"message_length": len(user_message),
"has_images": bool(image_data),
"escalation_reason": escalation_reason,
"room_id": room.room_id[:30],
})
logger.info("Model selected: %s (reason: %s) for room %s", model, escalation_reason, room.room_id[:30])
# Fetch conversation history FIRST (needed for query rewriting)
history = []
try:
resp = await self.client.room_messages(
room.room_id, start=self.client.next_batch or "", limit=30
)
if hasattr(resp, "chunk"):
for evt in reversed(resp.chunk):
if not hasattr(evt, "body"):
continue
role = "assistant" if evt.sender == BOT_USER else "user"
history.append({"role": role, "content": evt.body})
except Exception:
logger.debug("Could not fetch room history, proceeding without context")
# Rewrite query using conversation context for better RAG search
search_query = await self._rewrite_query(user_message, history)
# Run RAG search, memory query, and chunk query in parallel (independent)
if sender:
doc_results_coro = self.rag.search(search_query, matrix_user_id=sender)
memories_coro = self.memory.query(sender, user_message, top_k=10)
chunks_coro = self.memory.query_chunks(search_query, user_id=sender, room_id=room.room_id, top_k=5)
doc_results, memories, chunks = await asyncio.gather(
doc_results_coro, memories_coro, chunks_coro,
return_exceptions=True,
)
# Handle exceptions from gather
if isinstance(doc_results, BaseException):
logger.warning("RAG search failed: %s", doc_results)
doc_results = []
if isinstance(memories, BaseException):
logger.warning("Memory query failed: %s", memories)
memories = []
if isinstance(chunks, BaseException):
logger.warning("Chunk query failed: %s", chunks)
chunks = []
else:
doc_results, memories, chunks = [], [], []
doc_context = self.rag.format_context(doc_results)
if doc_context:
logger.info("RAG found %d docs for: %s (original: %s)", len(doc_results), search_query[:50], user_message[:50])
else:
logger.info("RAG found 0 docs for: %s (original: %s)", search_query[:50], user_message[:50])
memory_context = self._format_memories(memories)
chunk_context = self._format_chunks(chunks)
# Include room document context (PDFs, Confluence pages, images uploaded to room)
room_doc_context = ""
room_docs = [e for e in self._room_document_context.get(room.room_id, [])
if time.time() - e["timestamp"] < 3600]
if room_docs:
parts = []
for e in room_docs:
label = {"pdf": "PDF", "image": "Image", "confluence": "Confluence",
"text": "File", "docx": "Word"}.get(e["type"], "Document")
text = e["text"][:20000] if e["type"] != "image" else e["text"][:2000]
parts.append(f"[{label}: {e['filename']}]\n{text}")
room_doc_context = (
"Documents available in this room (uploaded or linked by the user):\n\n"
+ "\n\n---\n\n".join(parts)
+ "\n\nUse these documents to answer questions. "
"You CAN access and read these documents — never say you cannot."
)
# Build conversation context
messages = [{"role": "system", "content": SYSTEM_PROMPT}]
if memory_context:
messages.append({"role": "system", "content": memory_context})
if chunk_context:
messages.append({"role": "system", "content": chunk_context})
if room_doc_context:
messages.append({"role": "system", "content": room_doc_context})
# When RAG returns documents, limit history to 4 messages (2 exchanges) to prevent
# stale answer patterns from overriding fresh search results
history_limit = 4 if doc_context else 10
messages.extend(history[-history_limit:])
if doc_context:
messages.append({"role": "system", "content": doc_context})
# Add current user message (multimodal if image provided)
if image_data:
b64_str, mime_type = image_data
user_content = [
{"type": "text", "text": user_message},
{"type": "image_url", "image_url": {"url": f"data:{mime_type};base64,{b64_str}"}}
]
messages.append({"role": "user", "content": user_content})
else:
messages.append({"role": "user", "content": user_message})
# Determine available tools (no tools when analyzing images)
tools = ALL_TOOLS if not image_data else None
try:
reply = ""
# Agentic tool-calling loop: iterate up to MAX_TOOL_ITERATIONS
for iteration in range(MAX_TOOL_ITERATIONS):
resp = await self.llm.chat.completions.create(
model=model,
messages=messages,
max_tokens=2048,
tools=tools,
)
choice = resp.choices[0]
reply = choice.message.content or ""
sentry_sdk.add_breadcrumb(
category="llm",
message=f"LLM response: {model}",
data={
"tokens_in": getattr(resp.usage, "prompt_tokens", 0) if resp.usage else 0,
"tokens_out": getattr(resp.usage, "completion_tokens", 0) if resp.usage else 0,
"tool_calls": len(choice.message.tool_calls or []),
"iteration": iteration,
},
)
if not choice.message.tool_calls:
# No tool calls — final text response
break
# Process tool calls and feed results back
# Append the assistant message with tool_calls
assistant_msg = {"role": "assistant", "content": reply or None, "tool_calls": []}
for tc in choice.message.tool_calls:
assistant_msg["tool_calls"].append({
"id": tc.id,
"type": "function",
"function": {"name": tc.function.name, "arguments": tc.function.arguments},
})
messages.append(assistant_msg)
# Execute tools in parallel when multiple are requested
async def _run_tool(tc):
try:
args = json.loads(tc.function.arguments)
except json.JSONDecodeError:
args = {}
result = await self._execute_tool(tc.function.name, args, sender, room.room_id)
logger.info("Tool %s executed (iter %d) for %s", tc.function.name, iteration, sender)
return {"role": "tool", "tool_call_id": tc.id, "content": result}
tool_results = await asyncio.gather(
*[_run_tool(tc) for tc in choice.message.tool_calls]
)
messages.extend(tool_results)
# Tag whether tools were used during the loop
if iteration > 0:
sentry_sdk.set_tag("used_tools", "true")
# Send final reply
if reply:
await self._send_text(room.room_id, reply)
# Extract and store new memories + conversation chunk (after reply sent)
if sender and reply:
existing_facts = [m["fact"] for m in memories]
try:
await asyncio.wait_for(
asyncio.gather(
self._extract_and_store_memories(
user_message, reply, existing_facts, model, sender, room.room_id
),
self._store_conversation_chunk(
user_message, reply, sender, room.room_id
),
),
timeout=20.0,
)
except asyncio.TimeoutError:
logger.warning("Memory/chunk extraction timed out for %s", sender)
except Exception:
logger.warning("Memory/chunk save failed", exc_info=True)
# Auto-rename: only for group rooms with explicit opt-in (not DMs)
if room.room_id in self.auto_rename_rooms:
last_rename = self.renamed_rooms.get(room.room_id, 0)
gap_seconds = time.time() - last_rename if last_rename else float("inf")
if gap_seconds > 300:
await self._auto_rename_room(room, user_message, reply)
return reply
except Exception:
logger.exception("LLM call failed")
await self._send_text(room.room_id, "Sorry, I couldn't generate a response.")
return None
async def _rewrite_query(self, user_message: str, history: list[dict]) -> str:
"""Rewrite user message into a standalone search query using conversation context."""
if not history or not self.llm:
return user_message
# Build a compact history summary (last 4 messages max)
recent = history[-4:]
context_lines = []
for msg in recent:
prefix = "User" if msg["role"] == "user" else "Assistant"
context_lines.append(f"{prefix}: {msg['content'][:200]}")
context_text = "\n".join(context_lines)
try:
resp = await self.llm.chat.completions.create(
model=BASE_MODEL,
messages=[
{"role": "system", "content": (
"You are a search query rewriter. Given conversation history and a new user message, "
"produce a single standalone search query that resolves all pronouns and references "
"(like 'this house', 'that document', 'it') using context from the conversation. "
"Reply with ONLY the rewritten search query in the same language as the user message. "
"No explanation, no quotes. If the message is already self-contained, return it as-is."
)},
{"role": "user", "content": f"Conversation:\n{context_text}\n\nNew message: {user_message}"},
],
max_tokens=100,
)
rewritten = resp.choices[0].message.content.strip().strip('"\'')
if rewritten and len(rewritten) < 500:
logger.info("Query rewritten: '%s' -> '%s'", user_message[:50], rewritten[:50])
return rewritten
except Exception:
logger.debug("Query rewrite failed, using original", exc_info=True)
return user_message
async def _auto_rename_room(self, room, user_message: str, ai_reply: str):
"""Generate a short topic title and set it as the room name (Open WebUI style)."""
# Skip rename check — always generate fresh title based on current conversation
try:
resp = await self.llm.chat.completions.create(
model=BASE_MODEL,
messages=[
{"role": "user", "content": user_message},
{"role": "assistant", "content": ai_reply[:300]},
{"role": "user", "content": (
"Generate a concise, 3-5 word title with an emoji as prefix "
"that summarizes the conversation above. "
"Use the same language as the conversation. "
"Do not use quotation marks or formatting. "
"Respond with ONLY the title, nothing else."
)},
],
max_tokens=30,
)
title = resp.choices[0].message.content.strip().strip('"\'')
if not title or len(title) > 80 or len(title) < 3:
return
await self.client.room_put_state(
room.room_id, "m.room.name",
{"name": title}, state_key="",
)
self.renamed_rooms[room.room_id] = time.time()
logger.info("Auto-renamed room %s to: %s", room.room_id, title)
except Exception:
logger.debug("Auto-rename failed", exc_info=True)
@staticmethod
def _md_to_html(text: str) -> str:
"""Minimal markdown to HTML for Matrix formatted_body."""
import html as html_mod
safe = html_mod.escape(text)
# Code blocks (```...```)
safe = re.sub(r"```(\w*)\n(.*?)```", r"<pre><code>\2</code></pre>", safe, flags=re.DOTALL)
# Inline code
safe = re.sub(r"`([^`]+)`", r"<code>\1</code>", safe)
# Bold
safe = re.sub(r"\*\*(.+?)\*\*", r"<strong>\1</strong>", safe)
# Italic
safe = re.sub(r"\*(.+?)\*", r"<em>\1</em>", safe)
# Markdown links [text](url) — must unescape the URL parts first
def _link_repl(m):
import html as _h
label = m.group(1)
url = _h.unescape(m.group(2))
return f'<a href="{url}">{label}</a>'
safe = re.sub(r"\[([^\]]+)\]\(([^)]+)\)", _link_repl, safe)
# Bare URLs (not already in an <a> tag)
safe = re.sub(r'(?<!href=")(?<!">)(https?://[^\s<]+)', r'<a href="\1">\1</a>', safe)
# Headings (### before ## before # to match longest first)
safe = re.sub(r"^### (.+)$", r"<h3>\1</h3>", safe, flags=re.MULTILINE)
safe = re.sub(r"^## (.+)$", r"<h2>\1</h2>", safe, flags=re.MULTILINE)
safe = re.sub(r"^# (.+)$", r"<h1>\1</h1>", safe, flags=re.MULTILINE)
# Horizontal rules (--- on its own line)
safe = re.sub(r"^-{3,}$", r"<hr/>", safe, flags=re.MULTILINE)
# Line breaks
safe = safe.replace("\n", "<br/>")
# Remove redundant <br/> after block elements
safe = re.sub(r"(</h[1-6]>)(<br/>)+", r"\1", safe)
safe = re.sub(r"(</pre>)(<br/>)+", r"\1", safe)
safe = re.sub(r"(<br/>)*(<hr/>)(<br/>)*", r"\2", safe)
return safe
async def _generate_and_send_image(self, room_id: str, prompt: str):
"""Generate an image via LiteLLM and send it to the Matrix room."""
try:
resp = await self.llm.images.generate(
model="dall-e-3", prompt=prompt, n=1, size="1024x1024",
response_format="b64_json",
)
img_b64 = resp.data[0].b64_json
img_bytes = base64.b64decode(img_b64)
await self._send_image(room_id, img_bytes, "image/png", "generated.png")
except Exception:
logger.exception("Image generation failed")
await self._send_text(room_id, "Sorry, I couldn't generate that image.")
async def _send_image(self, room_id: str, image_bytes: bytes, mime_type: str, filename: str):
"""Upload image to Matrix homeserver and send as m.image event."""
from nio import UploadResponse
upload_resp, maybe_keys = await self.client.upload(
data_provider=io.BytesIO(image_bytes),
content_type=mime_type,
filename=filename,
filesize=len(image_bytes),
encrypt=True,
)
if not isinstance(upload_resp, UploadResponse):
logger.error("Image upload failed: %s", upload_resp)
await self._send_text(room_id, "Sorry, I couldn't upload the generated image.")
return
content = {
"msgtype": "m.image",
"body": filename,
"info": {"mimetype": mime_type, "size": len(image_bytes)},
}
if maybe_keys:
content["file"] = {
"url": upload_resp.content_uri,
"key": maybe_keys["key"],
"iv": maybe_keys["iv"],
"hashes": maybe_keys["hashes"],
"v": maybe_keys["v"],
}
else:
content["url"] = upload_resp.content_uri
await self.client.room_send(
room_id,
message_type="m.room.message",
content=content,
)
async def _send_audio(self, room_id: str, audio_bytes: bytes, filename: str, duration_seconds: float):
"""Upload audio to Matrix homeserver and send as m.audio event."""
from nio import UploadResponse
upload_resp, maybe_keys = await self.client.upload(
data_provider=io.BytesIO(audio_bytes),
content_type="audio/mpeg",
filename=filename,
filesize=len(audio_bytes),
encrypt=True,
)
if not isinstance(upload_resp, UploadResponse):
logger.error("Audio upload failed: %s", upload_resp)
await self._send_text(room_id, "Sorry, I couldn't upload the audio file.")
return
content = {
"msgtype": "m.audio",
"body": filename,
"info": {
"mimetype": "audio/mpeg",
"size": len(audio_bytes),
"duration": int(duration_seconds * 1000), # Matrix uses milliseconds
},
}
if maybe_keys:
content["file"] = {
"url": upload_resp.content_uri,
"key": maybe_keys["key"],
"iv": maybe_keys["iv"],
"hashes": maybe_keys["hashes"],
"v": maybe_keys["v"],
}
else:
content["url"] = upload_resp.content_uri
await self.client.room_send(
room_id,
message_type="m.room.message",
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. "
"Keine Ueberschrift, kein Markdown-Heading — beginne direkt mit dem Text."
)},
{"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 _extract_document_annotations(
self, transcript: list[dict], doc_context: str, room_id: str
) -> str | None:
"""Extract document-specific changes/annotations discussed during a voice call.
Returns a structured list of changes, or None if no document changes were discussed.
"""
lines = []
for entry in transcript[-30:]:
role = "Nutzer" if entry["role"] == "user" else "Assistent"
lines.append(f"{role}: {entry['text']}")
transcript_text = "\n".join(lines)
# Truncate document context for the prompt
doc_excerpt = doc_context[:8000]
if not self.llm:
return None
try:
model = self.room_models.get(room_id, DEFAULT_MODEL)
resp = await self.llm.chat.completions.create(
model=model,
messages=[
{"role": "system", "content": (
"Analysiere das Anruf-Transkript im Kontext des besprochenen Dokuments. "
"Extrahiere ALLE konkreten Aenderungen, Korrekturen, Ergaenzungen oder "
"Anmerkungen die der Nutzer zum Dokument gemacht hat.\n\n"
"Format:\n"
"- Jede Aenderung als Bullet Point\n"
"- Zitiere den betroffenen Abschnitt/Satz wenn moeglich\n"
"- Beschreibe was geaendert werden soll\n\n"
"Wenn KEINE Dokument-Aenderungen besprochen wurden, antworte NUR mit: KEINE_AENDERUNGEN\n\n"
"Antworte in der Sprache des Gespraechs."
)},
{"role": "user", "content": (
f"Dokument:\n{doc_excerpt}\n\n---\n\nTranskript:\n{transcript_text}"
)},
],
max_tokens=800,
)
result = resp.choices[0].message.content.strip()
if "KEINE_AENDERUNGEN" in result:
logger.info("No document annotations found in call for %s", room_id)
return None
logger.info("Extracted document annotations for %s: %s", room_id, result[:200])
return result
except Exception:
logger.warning("Document annotation extraction failed", exc_info=True)
return None
async def _send_text(self, room_id: str, text: str):
await self.client.room_send(
room_id,
message_type="m.room.message",
content={
"msgtype": "m.text",
"body": text,
"format": "org.matrix.custom.html",
"formatted_body": self._md_to_html(text),
},
)
async def _get_call_encryption_key(self, room_id: str, sender: str, caller_device_id: str = "") -> bytes | None:
"""Read E2EE encryption key from room timeline messages.
Element Call sends encryption keys as timeline events (NOT state events).
We scan recent room messages for io.element.call.encryption_keys events.
"""
try:
import httpx
token = self.client.access_token
# Fetch recent messages from timeline (where Element Call puts encryption keys)
url = f"{HOMESERVER}/_matrix/client/v3/rooms/{room_id}/messages"
params = {"dir": "b", "limit": "50"}
async with httpx.AsyncClient(timeout=10.0) as http:
resp = await http.get(url, headers={"Authorization": f"Bearer {token}"}, params=params)
resp.raise_for_status()
data = resp.json()
events = data.get("chunk", [])
logger.info("Timeline scan: %d events", len(events))
for evt in events:
evt_type = evt.get("type", "")
if evt_type == ENCRYPTION_KEYS_TYPE:
evt_sender = evt.get("sender", "")
if evt_sender == BOT_USER:
continue # skip our own keys
content = evt.get("content", {})
logger.info("Found encryption_keys timeline event from %s: %s",
evt_sender, list(content.keys()))
for k in content.get("keys", []):
key_b64 = k.get("key", "")
if key_b64:
key_b64 += "=" * (-len(key_b64) % 4)
key = base64.urlsafe_b64decode(key_b64)
logger.info("Got E2EE key from timeline (%s, %d bytes)",
evt_sender, len(key))
return key
# Log event types for debugging
types = [e.get("type", "") for e in events]
logger.info("Timeline event types: %s", types)
except Exception as e:
logger.warning("Timeline scan for encryption keys failed: %s", e)
logger.info("No E2EE encryption key found in timeline for %s in %s", sender, room_id)
return None
async def _publish_encryption_key(self, room_id: str, key: bytes):
"""Publish bot's E2EE encryption key as a timeline event (NOT state).
Element Call distributes encryption keys as timeline events via
io.element.call.encryption_keys, not as state events.
"""
key_b64 = base64.urlsafe_b64encode(key).decode().rstrip("=")
content = {
"call_id": "",
"device_id": BOT_DEVICE_ID,
"keys": [{"index": 0, "key": key_b64}],
}
try:
await self.client.room_send(
room_id,
message_type=ENCRYPTION_KEYS_TYPE,
content=content,
)
logger.info("Published E2EE key as timeline event in %s", room_id)
except Exception:
logger.exception("Failed to publish E2EE key in %s", room_id)
async def _route_verification(self, room, event: UnknownEvent):
"""Route in-room verification events from UnknownEvent."""
source = event.source or {}
verify_type = event.type
logger.info("Verification event: %s from %s", verify_type, event.sender)
if verify_type == "m.key.verification.request":
await self._handle_verification_request(room, source)
elif verify_type == "m.key.verification.start":
await self._handle_verification_start(room, source)
elif verify_type == "m.key.verification.key":
await self._handle_verification_key(room, source)
elif verify_type == "m.key.verification.mac":
await self._handle_verification_mac(room, source)
elif verify_type == "m.key.verification.cancel":
content = source.get("content", {})
txn = content.get("m.relates_to", {}).get("event_id", "")
self._verifications.pop(txn, None)
logger.info("Verification cancelled: %s", txn)
elif verify_type == "m.key.verification.done":
pass # Other side confirmed done
async def on_room_unknown(self, room, event: RoomMessageUnknown):
"""Handle in-room verification events."""
source = event.source or {}
content = source.get("content", {})
event_type = source.get("type", "")
msgtype = content.get("msgtype", "")
logger.info("RoomMessageUnknown: type=%s msgtype=%s sender=%s", event_type, msgtype, event.sender)
# In-room verification events can come as m.room.message with msgtype=m.key.verification.*
# or as direct event types m.key.verification.*
verify_type = ""
if event_type.startswith("m.key.verification."):
verify_type = event_type
elif msgtype.startswith("m.key.verification."):
verify_type = msgtype
if not verify_type:
return
if event.sender == BOT_USER:
return
logger.info("Verification event: %s from %s", verify_type, event.sender)
if verify_type == "m.key.verification.request":
await self._handle_verification_request(room, source)
elif verify_type == "m.key.verification.start":
await self._handle_verification_start(room, source)
elif verify_type == "m.key.verification.key":
await self._handle_verification_key(room, source)
elif verify_type == "m.key.verification.mac":
await self._handle_verification_mac(room, source)
elif verify_type == "m.key.verification.cancel":
txn = content.get("m.relates_to", {}).get("event_id", "")
self._verifications.pop(txn, None)
logger.info("Verification cancelled: %s", txn)
async def _handle_verification_request(self, room, source):
content = source["content"]
txn_id = source["event_id"]
sender = source["sender"]
self._verifications[txn_id] = {"sender": sender, "room_id": room.room_id}
logger.info("Verification request from %s, txn=%s", sender, txn_id)
# Send m.key.verification.ready
await self.client.room_send(
room.room_id,
message_type="m.key.verification.ready",
content={
"m.relates_to": {"rel_type": "m.reference", "event_id": txn_id},
"from_device": self.client.device_id,
"methods": ["m.sas.v1"],
},
)
logger.info("Sent verification ready for %s", txn_id)
async def _handle_verification_start(self, room, source):
content = source["content"]
txn_id = content.get("m.relates_to", {}).get("event_id", "")
v = self._verifications.get(txn_id)
if not v:
logger.warning("Unknown verification start: %s", txn_id)
return
sas_obj = olm_sas.Sas()
v["sas"] = sas_obj
v["commitment"] = content.get("commitment", "")
# Send m.key.verification.accept is NOT needed when we sent "ready"
# and the other side sent "start". We go straight to sending our key.
# Send m.key.verification.key
await self.client.room_send(
room.room_id,
message_type="m.key.verification.key",
content={
"m.relates_to": {"rel_type": "m.reference", "event_id": txn_id},
"key": sas_obj.pubkey,
},
)
v["key_sent"] = True
logger.info("Sent SAS key for %s", txn_id)
async def _handle_verification_key(self, room, source):
content = source["content"]
txn_id = content.get("m.relates_to", {}).get("event_id", "")
v = self._verifications.get(txn_id)
if not v or "sas" not in v:
logger.warning("Unknown verification key: %s", txn_id)
return
sas_obj = v["sas"]
their_key = content["key"]
sas_obj.set_their_pubkey(their_key)
v["their_key"] = their_key
# Auto-confirm SAS (bot trusts the user)
# Generate MAC for our device key and master key
our_user = BOT_USER
our_device = self.client.device_id
their_user = v["sender"]
# Key IDs to MAC
key_id = f"ed25519:{our_device}"
device_key = self.client.olm.account.identity_keys["ed25519"]
# MAC info strings per spec
base_info = (
f"MATRIX_KEY_VERIFICATION_MAC"
f"{our_user}{our_device}"
f"{their_user}{content.get('from_device', '')}"
f"{txn_id}"
)
mac_dict = {}
keys_list = []
# MAC our ed25519 device key
mac_dict[key_id] = sas_obj.calculate_mac(device_key, base_info + key_id)
keys_list.append(key_id)
# MAC the key list
keys_str = ",".join(sorted(keys_list))
keys_mac = sas_obj.calculate_mac(keys_str, base_info + "KEY_IDS")
await self.client.room_send(
room.room_id,
message_type="m.key.verification.mac",
content={
"m.relates_to": {"rel_type": "m.reference", "event_id": txn_id},
"mac": mac_dict,
"keys": keys_mac,
},
)
logger.info("Sent SAS MAC for %s", txn_id)
async def _handle_verification_mac(self, room, source):
content = source["content"]
txn_id = content.get("m.relates_to", {}).get("event_id", "")
v = self._verifications.get(txn_id)
if not v:
return
# Verification complete — send done
await self.client.room_send(
room.room_id,
message_type="m.key.verification.done",
content={
"m.relates_to": {"rel_type": "m.reference", "event_id": txn_id},
},
)
logger.info("Verification complete for %s with %s", txn_id, v["sender"])
self._verifications.pop(txn_id, None)
async def on_megolm(self, room, event: MegolmEvent):
"""Request keys for undecryptable messages."""
logger.warning(
"Undecryptable event %s in %s from %s — requesting keys",
event.event_id, room.room_id, event.sender,
)
try:
await self.client.request_room_key(event)
except Exception:
logger.debug("Key request failed", exc_info=True)
async def on_key_verification(self, event):
"""Auto-accept key verification requests."""
if isinstance(event, KeyVerificationStart):
sas = self.client.key_verifications.get(event.transaction_id)
if sas:
await self.client.accept_key_verification(event.transaction_id)
await self.client.to_device(sas.share_key())
elif isinstance(event, KeyVerificationKey):
sas = self.client.key_verifications.get(event.transaction_id)
if sas:
await self.client.confirm_short_auth_string(event.transaction_id)
elif isinstance(event, KeyVerificationMac):
sas = self.client.key_verifications.get(event.transaction_id)
if sas:
mac = sas.get_mac()
if not isinstance(mac, ToDeviceError):
await self.client.to_device(mac)
async def cleanup(self):
await self.client.close()
if self.lkapi:
await self.lkapi.aclose()
async def main():
os.makedirs(STORE_PATH, exist_ok=True)
bot = Bot()
try:
await bot.start()
finally:
await bot.cleanup()
if __name__ == "__main__":
logging.basicConfig(level=logging.INFO)
asyncio.run(main())
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