feat: Add Atlassian tools and agentic tool-calling loop
- Add AtlassianClient class: fetches per-user OAuth tokens from portal, calls Jira and Confluence REST APIs on behalf of users - Add 7 Atlassian tools: confluence_search, confluence_read_page, jira_search, jira_get_issue, jira_create_issue, jira_add_comment, jira_transition - Replace single LLM call with agentic loop (max 5 iterations) that feeds tool results back to the model - Add PORTAL_URL and BOT_API_KEY env vars to docker-compose - Update system prompt with Atlassian tool guidance Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Christian Gick
490
bot.py
490
bot.py
@@ -67,6 +67,9 @@ MEMORY_SERVICE_URL = os.environ.get("MEMORY_SERVICE_URL", "http://memory-service
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CONFLUENCE_URL = os.environ.get("CONFLUENCE_BASE_URL", "")
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CONFLUENCE_USER = os.environ.get("CONFLUENCE_USER", "")
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CONFLUENCE_TOKEN = os.environ.get("CONFLUENCE_TOKEN", "")
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PORTAL_URL = os.environ.get("PORTAL_URL", "")
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BOT_API_KEY = os.environ.get("BOT_API_KEY", "")
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MAX_TOOL_ITERATIONS = 5
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SYSTEM_PROMPT = """You are a helpful AI assistant in a Matrix chat room.
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Keep answers concise but thorough. Use markdown formatting when helpful.
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@@ -84,6 +87,9 @@ IMPORTANT RULES — FOLLOW THESE STRICTLY:
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- You can see and analyze images that users send. Describe what you see when asked about an image.
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- You can read and analyze PDF documents that users send. Summarize content and answer questions about them.
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- You can generate images when asked — use the generate_image tool for any image creation, drawing, or illustration requests.
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- You can search Confluence and Jira using tools. When users ask about documentation, wiki pages, tickets, or tasks, use the appropriate tool.
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- When creating Jira issues, always confirm the project key and summary with the user before creating.
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- If a user's Atlassian account is not connected, tell them to connect it at matrixhost.eu/settings and provide the link.
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- If user memories are provided, use them to personalize responses. Address users by name if known.
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- When asked to translate, provide ONLY the translation with no explanation."""
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@@ -102,6 +108,122 @@ IMAGE_GEN_TOOLS = [{
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}
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}]
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ATLASSIAN_TOOLS = [
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{
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"type": "function",
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"function": {
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"name": "confluence_search",
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"description": "Search Confluence wiki pages using CQL (Confluence Query Language). Use when the user asks about documentation, wiki, or knowledge base content.",
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"parameters": {
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"type": "object",
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"properties": {
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"query": {"type": "string", "description": "Search query text"},
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"limit": {"type": "integer", "description": "Max results to return (default 5)", "default": 5},
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},
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"required": ["query"],
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},
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},
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},
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{
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"type": "function",
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"function": {
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"name": "confluence_read_page",
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"description": "Read the full content of a Confluence page by its ID. Use after searching to get page details.",
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"parameters": {
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"type": "object",
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"properties": {
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"page_id": {"type": "string", "description": "The Confluence page ID"},
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},
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"required": ["page_id"],
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},
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},
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},
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{
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"type": "function",
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"function": {
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"name": "jira_search",
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"description": "Search Jira issues using JQL (Jira Query Language). Use when the user asks about tickets, tasks, bugs, or project issues.",
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"parameters": {
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"type": "object",
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"properties": {
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"jql": {"type": "string", "description": "JQL query string"},
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"limit": {"type": "integer", "description": "Max results (default 10)", "default": 10},
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},
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"required": ["jql"],
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},
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},
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},
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{
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"type": "function",
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"function": {
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"name": "jira_get_issue",
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"description": "Get detailed information about a specific Jira issue by its key (e.g. CF-123).",
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"parameters": {
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"type": "object",
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"properties": {
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"issue_key": {"type": "string", "description": "Jira issue key like CF-123"},
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},
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"required": ["issue_key"],
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},
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},
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},
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{
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"type": "function",
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"function": {
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"name": "jira_create_issue",
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"description": "Create a new Jira issue. Always confirm project and summary with user before creating.",
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"parameters": {
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"type": "object",
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"properties": {
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"project": {"type": "string", "description": "Project key (e.g. CF)"},
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"summary": {"type": "string", "description": "Issue title/summary"},
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"issue_type": {"type": "string", "description": "Issue type (default: Task)", "default": "Task"},
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"description": {"type": "string", "description": "Issue description (optional)"},
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},
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"required": ["project", "summary"],
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},
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},
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},
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{
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"type": "function",
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"function": {
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"name": "jira_add_comment",
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"description": "Add a comment to an existing Jira issue.",
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"parameters": {
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"type": "object",
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"properties": {
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"issue_key": {"type": "string", "description": "Jira issue key like CF-123"},
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"comment": {"type": "string", "description": "Comment text"},
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},
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"required": ["issue_key", "comment"],
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},
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},
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},
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{
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"type": "function",
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"function": {
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"name": "jira_transition",
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"description": "Change the status of a Jira issue (e.g. move to 'In Progress', 'Done').",
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"parameters": {
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"type": "object",
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"properties": {
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"issue_key": {"type": "string", "description": "Jira issue key like CF-123"},
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"status": {"type": "string", "description": "Target status name (e.g. 'In Progress', 'Done')"},
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},
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"required": ["issue_key", "status"],
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},
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},
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},
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]
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ALL_TOOLS = IMAGE_GEN_TOOLS + ATLASSIAN_TOOLS
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ATLASSIAN_NOT_CONNECTED_MSG = (
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"Your Atlassian account is not connected. "
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"Please connect it at [matrixhost.eu/settings](https://matrixhost.eu/settings?connect=atlassian) "
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"to use Jira and Confluence features."
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)
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HELP_TEXT = """**AI Bot Commands**
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- `!ai help` — Show this help
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- `!ai models` — List available models
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@@ -254,6 +376,281 @@ class MemoryClient:
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return []
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class AtlassianClient:
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"""Fetches per-user Atlassian tokens from the portal and calls Atlassian REST APIs."""
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def __init__(self, portal_url: str, bot_api_key: str):
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self.portal_url = portal_url.rstrip("/")
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self.bot_api_key = bot_api_key
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self.enabled = bool(portal_url and bot_api_key)
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# Cache cloud IDs per user token to avoid repeated lookups
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self._cloud_id_cache: dict[str, str] = {}
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async def get_token(self, matrix_user_id: str) -> str | None:
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"""Fetch user's Atlassian token from the portal."""
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if not self.enabled:
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return None
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try:
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async with httpx.AsyncClient(timeout=10.0) as client:
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resp = await client.get(
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f"{self.portal_url}/api/bot/tokens",
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params={"matrix_user_id": matrix_user_id, "provider": "atlassian"},
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headers={"Authorization": f"Bearer {self.bot_api_key}"},
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)
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resp.raise_for_status()
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data = resp.json()
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return data.get("access_token") if data.get("connected") else None
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except Exception:
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logger.warning("Failed to fetch Atlassian token for %s", matrix_user_id, exc_info=True)
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return None
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async def _get_cloud_id(self, token: str) -> str | None:
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"""Get the Atlassian Cloud ID for the user's instance."""
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if token in self._cloud_id_cache:
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return self._cloud_id_cache[token]
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try:
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async with httpx.AsyncClient(timeout=10.0) as client:
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resp = await client.get(
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"https://api.atlassian.com/oauth/token/accessible-resources",
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headers={"Authorization": f"Bearer {token}"},
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)
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resp.raise_for_status()
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resources = resp.json()
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if resources:
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cloud_id = resources[0]["id"]
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self._cloud_id_cache[token] = cloud_id
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return cloud_id
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except Exception:
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logger.warning("Failed to fetch Atlassian cloud ID", exc_info=True)
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return None
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async def confluence_search(self, token: str, query: str, limit: int = 5) -> str:
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cloud_id = await self._get_cloud_id(token)
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if not cloud_id:
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return "Error: Could not determine Atlassian Cloud instance."
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try:
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async with httpx.AsyncClient(timeout=15.0) as client:
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resp = await client.get(
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f"https://api.atlassian.com/ex/confluence/{cloud_id}/wiki/rest/api/content/search",
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params={"cql": f'text ~ "{query}"', "limit": str(limit), "expand": "metadata.labels"},
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headers={"Authorization": f"Bearer {token}"},
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)
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resp.raise_for_status()
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data = resp.json()
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results = data.get("results", [])
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if not results:
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return f"No Confluence pages found for: {query}"
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lines = []
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for r in results:
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title = r.get("title", "Untitled")
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page_id = r.get("id", "")
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space = r.get("_expandable", {}).get("space", "").split("/")[-1]
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url = f"https://api.atlassian.com/ex/confluence/{cloud_id}/wiki{r.get('_links', {}).get('webui', '')}"
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lines.append(f"- **{title}** (ID: {page_id}, Space: {space})\n {url}")
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return f"Found {len(results)} pages:\n" + "\n".join(lines)
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except Exception as e:
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return f"Confluence search error: {e}"
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async def confluence_read_page(self, token: str, page_id: str) -> str:
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cloud_id = await self._get_cloud_id(token)
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if not cloud_id:
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return "Error: Could not determine Atlassian Cloud instance."
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try:
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async with httpx.AsyncClient(timeout=15.0) as client:
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resp = await client.get(
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f"https://api.atlassian.com/ex/confluence/{cloud_id}/wiki/rest/api/content/{page_id}",
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params={"expand": "body.storage,version"},
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headers={"Authorization": f"Bearer {token}"},
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)
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resp.raise_for_status()
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data = resp.json()
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title = data.get("title", "Untitled")
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html_body = data.get("body", {}).get("storage", {}).get("value", "")
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# Strip HTML tags for a readable summary
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from bs4 import BeautifulSoup
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text = BeautifulSoup(html_body, "lxml").get_text(separator="\n", strip=True)
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# Truncate for LLM context
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if len(text) > 8000:
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text = text[:8000] + "\n...(truncated)"
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return f"**{title}**\n\n{text}"
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except Exception as e:
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return f"Failed to read Confluence page {page_id}: {e}"
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async def jira_search(self, token: str, jql: str, limit: int = 10) -> str:
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cloud_id = await self._get_cloud_id(token)
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if not cloud_id:
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return "Error: Could not determine Atlassian Cloud instance."
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try:
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async with httpx.AsyncClient(timeout=15.0) as client:
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resp = await client.get(
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f"https://api.atlassian.com/ex/jira/{cloud_id}/rest/api/3/search/jql",
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params={"jql": jql, "maxResults": str(limit), "fields": "summary,status,assignee,priority,created,updated"},
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headers={"Authorization": f"Bearer {token}"},
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)
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resp.raise_for_status()
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data = resp.json()
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issues = data.get("issues", [])
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if not issues:
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return f"No Jira issues found for: {jql}"
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lines = []
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for issue in issues:
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key = issue["key"]
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fields = issue.get("fields", {})
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summary = fields.get("summary", "")
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status = fields.get("status", {}).get("name", "")
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assignee = fields.get("assignee", {})
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assignee_name = assignee.get("displayName", "Unassigned") if assignee else "Unassigned"
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priority = fields.get("priority", {}).get("name", "") if fields.get("priority") else ""
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lines.append(f"- **{key}**: {summary} [{status}] (Assignee: {assignee_name}, Priority: {priority})")
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return f"Found {data.get('total', len(issues))} issues:\n" + "\n".join(lines)
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except Exception as e:
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return f"Jira search error: {e}"
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async def jira_get_issue(self, token: str, issue_key: str) -> str:
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cloud_id = await self._get_cloud_id(token)
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if not cloud_id:
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return "Error: Could not determine Atlassian Cloud instance."
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try:
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async with httpx.AsyncClient(timeout=15.0) as client:
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resp = await client.get(
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f"https://api.atlassian.com/ex/jira/{cloud_id}/rest/api/3/issue/{issue_key}",
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params={"fields": "summary,status,assignee,priority,description,created,updated,comment"},
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headers={"Authorization": f"Bearer {token}"},
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)
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resp.raise_for_status()
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data = resp.json()
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fields = data.get("fields", {})
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summary = fields.get("summary", "")
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status = fields.get("status", {}).get("name", "")
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assignee = fields.get("assignee", {})
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assignee_name = assignee.get("displayName", "Unassigned") if assignee else "Unassigned"
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priority = fields.get("priority", {}).get("name", "") if fields.get("priority") else ""
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desc = fields.get("description")
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# ADF description — extract text nodes
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desc_text = ""
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if desc and isinstance(desc, dict):
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desc_text = self._extract_adf_text(desc)
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elif isinstance(desc, str):
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desc_text = desc
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if len(desc_text) > 4000:
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desc_text = desc_text[:4000] + "...(truncated)"
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comments = fields.get("comment", {}).get("comments", [])
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comment_lines = []
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for c in comments[-5:]: # Last 5 comments
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author = c.get("author", {}).get("displayName", "Unknown")
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body = self._extract_adf_text(c.get("body", {})) if isinstance(c.get("body"), dict) else str(c.get("body", ""))
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comment_lines.append(f" - **{author}**: {body[:500]}")
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result = f"**{issue_key}: {summary}**\nStatus: {status} | Assignee: {assignee_name} | Priority: {priority}"
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if desc_text:
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result += f"\n\nDescription:\n{desc_text}"
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if comment_lines:
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result += f"\n\nRecent comments:\n" + "\n".join(comment_lines)
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return result
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except Exception as e:
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return f"Failed to fetch {issue_key}: {e}"
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async def jira_create_issue(self, token: str, project: str, summary: str,
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issue_type: str = "Task", description: str = "") -> str:
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cloud_id = await self._get_cloud_id(token)
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if not cloud_id:
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return "Error: Could not determine Atlassian Cloud instance."
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try:
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body: dict = {
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"fields": {
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"project": {"key": project},
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"summary": summary,
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"issuetype": {"name": issue_type},
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}
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}
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if description:
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body["fields"]["description"] = {
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"type": "doc",
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"version": 1,
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"content": [{"type": "paragraph", "content": [{"type": "text", "text": description}]}],
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}
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async with httpx.AsyncClient(timeout=15.0) as client:
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resp = await client.post(
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f"https://api.atlassian.com/ex/jira/{cloud_id}/rest/api/3/issue",
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json=body,
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headers={"Authorization": f"Bearer {token}", "Content-Type": "application/json"},
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)
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resp.raise_for_status()
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data = resp.json()
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return f"Created **{data['key']}**: {summary}"
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except httpx.HTTPStatusError as e:
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return f"Failed to create issue: {e.response.text}"
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except Exception as e:
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return f"Failed to create issue: {e}"
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async def jira_add_comment(self, token: str, issue_key: str, comment: str) -> str:
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cloud_id = await self._get_cloud_id(token)
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if not cloud_id:
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return "Error: Could not determine Atlassian Cloud instance."
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try:
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body = {
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"body": {
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"type": "doc",
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"version": 1,
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"content": [{"type": "paragraph", "content": [{"type": "text", "text": comment}]}],
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}
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}
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async with httpx.AsyncClient(timeout=15.0) as client:
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resp = await client.post(
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f"https://api.atlassian.com/ex/jira/{cloud_id}/rest/api/3/issue/{issue_key}/comment",
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json=body,
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headers={"Authorization": f"Bearer {token}", "Content-Type": "application/json"},
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)
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resp.raise_for_status()
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return f"Comment added to {issue_key}."
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except Exception as e:
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return f"Failed to add comment to {issue_key}: {e}"
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async def jira_transition(self, token: str, issue_key: str, status: str) -> str:
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cloud_id = await self._get_cloud_id(token)
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if not cloud_id:
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return "Error: Could not determine Atlassian Cloud instance."
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try:
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async with httpx.AsyncClient(timeout=15.0) as client:
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# First, get available transitions
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resp = await client.get(
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f"https://api.atlassian.com/ex/jira/{cloud_id}/rest/api/3/issue/{issue_key}/transitions",
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headers={"Authorization": f"Bearer {token}"},
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)
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resp.raise_for_status()
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transitions = resp.json().get("transitions", [])
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# Find matching transition (case-insensitive)
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target = None
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for t in transitions:
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if t["name"].lower() == status.lower() or t["to"]["name"].lower() == status.lower():
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target = t
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break
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if not target:
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available = ", ".join(t["name"] for t in transitions)
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return f"Cannot transition {issue_key} to '{status}'. Available transitions: {available}"
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# Execute transition
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resp = await client.post(
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f"https://api.atlassian.com/ex/jira/{cloud_id}/rest/api/3/issue/{issue_key}/transitions",
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||||
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(
|
||||
@@ -274,6 +671,7 @@ class Bot:
|
||||
self.active_callers: dict[str, set[str]] = {} # room_id → set of caller user IDs
|
||||
self.rag = DocumentRAG(WILDFILES_BASE_URL, WILDFILES_ORG)
|
||||
self.memory = MemoryClient(MEMORY_SERVICE_URL)
|
||||
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.user_keys: dict[str, str] = self._load_user_keys() # matrix_user_id -> api_key
|
||||
self.room_models: dict[str, str] = {} # room_id -> model name
|
||||
@@ -1495,6 +1893,38 @@ class Bot:
|
||||
finally:
|
||||
self._pending_connects.pop(sender, None)
|
||||
|
||||
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."
|
||||
|
||||
# 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_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 == "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 _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 = self.room_models.get(room.room_id, DEFAULT_MODEL)
|
||||
@@ -1569,24 +1999,54 @@ class Bot:
|
||||
else:
|
||||
messages.append({"role": "user", "content": user_message})
|
||||
|
||||
try:
|
||||
resp = await self.llm.chat.completions.create(
|
||||
model=model,
|
||||
messages=messages,
|
||||
max_tokens=2048,
|
||||
tools=IMAGE_GEN_TOOLS if not image_data else None,
|
||||
)
|
||||
choice = resp.choices[0]
|
||||
reply = choice.message.content or ""
|
||||
# Determine available tools (no tools when analyzing images)
|
||||
tools = ALL_TOOLS if not image_data else None
|
||||
|
||||
if choice.message.tool_calls:
|
||||
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 ""
|
||||
|
||||
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:
|
||||
if tc.function.name == "generate_image":
|
||||
assistant_msg["tool_calls"].append({
|
||||
"id": tc.id,
|
||||
"type": "function",
|
||||
"function": {"name": tc.function.name, "arguments": tc.function.arguments},
|
||||
})
|
||||
messages.append(assistant_msg)
|
||||
|
||||
# Execute each tool and append results
|
||||
for tc in choice.message.tool_calls:
|
||||
try:
|
||||
args = json.loads(tc.function.arguments)
|
||||
await self._generate_and_send_image(room.room_id, args["prompt"])
|
||||
if reply:
|
||||
await self._send_text(room.room_id, reply)
|
||||
else:
|
||||
except json.JSONDecodeError:
|
||||
args = {}
|
||||
result = await self._execute_tool(tc.function.name, args, sender, room.room_id)
|
||||
messages.append({
|
||||
"role": "tool",
|
||||
"tool_call_id": tc.id,
|
||||
"content": result,
|
||||
})
|
||||
logger.info("Tool %s executed (iter %d) for %s", tc.function.name, iteration, sender)
|
||||
|
||||
# Send final reply
|
||||
if reply:
|
||||
await self._send_text(room.room_id, reply)
|
||||
|
||||
# Extract and store new memories (after reply sent, with timeout)
|
||||
|
||||
Reference in New Issue
Block a user