feat: Add complete API v2 with webhook and AI analysis
This commit is contained in:
Ricel Leite
parent
d742d1201c
commit
5c8ceadb77
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@ -0,0 +1,387 @@
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"""
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JIRA AI Fixer - Intelligent Support Case Resolution
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Complete API with webhook handling and AI analysis
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"""
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import os
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import json
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import httpx
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import asyncio
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from datetime import datetime
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from contextlib import asynccontextmanager
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from fastapi import FastAPI, HTTPException, BackgroundTasks
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from fastapi.middleware.cors import CORSMiddleware
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from fastapi.responses import HTMLResponse
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from pydantic import BaseModel
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from typing import Optional, List, Dict, Any
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import asyncpg
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# Config
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DATABASE_URL = os.getenv("DATABASE_URL", "postgresql://jira:jira_secret_2026@postgres:5432/jira_fixer")
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OPENROUTER_API_KEY = os.getenv("OPENROUTER_API_KEY", "")
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GITEA_URL = os.getenv("GITEA_URL", "https://gitea.startdata.com.br")
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COBOL_REPO = os.getenv("COBOL_REPO", "startdata/cobol-sample-app")
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# Database pool
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db_pool = None
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async def init_db():
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global db_pool
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db_pool = await asyncpg.create_pool(DATABASE_URL, min_size=2, max_size=10)
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async with db_pool.acquire() as conn:
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await conn.execute("""
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CREATE TABLE IF NOT EXISTS issues (
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id SERIAL PRIMARY KEY,
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external_id TEXT,
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external_key TEXT,
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source TEXT,
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title TEXT,
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description TEXT,
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status TEXT DEFAULT 'pending',
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analysis TEXT,
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confidence FLOAT,
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affected_files TEXT,
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suggested_fix TEXT,
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created_at TIMESTAMP DEFAULT NOW(),
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analyzed_at TIMESTAMP
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);
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CREATE TABLE IF NOT EXISTS repositories (
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id SERIAL PRIMARY KEY,
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name TEXT UNIQUE,
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url TEXT,
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indexed_at TIMESTAMP,
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file_count INT DEFAULT 0
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);
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CREATE INDEX IF NOT EXISTS idx_issues_status ON issues(status);
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CREATE INDEX IF NOT EXISTS idx_issues_external ON issues(external_id, source);
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""")
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@asynccontextmanager
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async def lifespan(app: FastAPI):
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await init_db()
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yield
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if db_pool:
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await db_pool.close()
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app = FastAPI(title="JIRA AI Fixer", version="1.0.0", lifespan=lifespan)
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app.add_middleware(CORSMiddleware, allow_origins=["*"], allow_credentials=True, allow_methods=["*"], allow_headers=["*"])
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# Models
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class WebhookPayload(BaseModel):
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event: str
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timestamp: str
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data: Dict[str, Any]
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class IssueResponse(BaseModel):
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id: int
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external_key: str
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title: str
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status: str
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confidence: Optional[float]
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analysis: Optional[str]
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suggested_fix: Optional[str]
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# Health
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@app.get("/api/health")
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async def health():
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return {"status": "healthy", "service": "jira-ai-fixer", "version": "1.0.0"}
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# Webhook endpoint for TicketHub
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@app.post("/api/webhook/tickethub")
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async def webhook_tickethub(payload: WebhookPayload, background_tasks: BackgroundTasks):
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if payload.event != "ticket.created":
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return {"status": "ignored", "reason": f"event {payload.event} not handled"}
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ticket = payload.data
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# Save to database
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async with db_pool.acquire() as conn:
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issue_id = await conn.fetchval("""
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INSERT INTO issues (external_id, external_key, source, title, description, status)
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VALUES ($1, $2, $3, $4, $5, 'pending')
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RETURNING id
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""", str(ticket.get("id")), ticket.get("key"), "tickethub",
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ticket.get("title"), ticket.get("description"))
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# Trigger analysis in background
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background_tasks.add_task(analyze_issue, issue_id, ticket)
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return {"status": "accepted", "issue_id": issue_id, "message": "Analysis queued"}
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# JIRA webhook (compatible format)
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@app.post("/api/webhook/jira")
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async def webhook_jira(payload: Dict[str, Any], background_tasks: BackgroundTasks):
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event = payload.get("webhookEvent", "")
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if "issue_created" not in event:
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return {"status": "ignored"}
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issue = payload.get("issue", {})
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fields = issue.get("fields", {})
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async with db_pool.acquire() as conn:
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issue_id = await conn.fetchval("""
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INSERT INTO issues (external_id, external_key, source, title, description, status)
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VALUES ($1, $2, $3, $4, $5, 'pending')
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RETURNING id
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""", str(issue.get("id")), issue.get("key"), "jira",
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fields.get("summary"), fields.get("description"))
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background_tasks.add_task(analyze_issue, issue_id, {
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"key": issue.get("key"),
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"title": fields.get("summary"),
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"description": fields.get("description")
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})
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return {"status": "accepted", "issue_id": issue_id}
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async def analyze_issue(issue_id: int, ticket: dict):
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"""Background task to analyze issue with AI"""
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try:
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# Fetch COBOL code from repository
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cobol_files = await fetch_cobol_files()
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# Build prompt for AI
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prompt = build_analysis_prompt(ticket, cobol_files)
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# Call LLM
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analysis = await call_llm(prompt)
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# Parse response
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result = parse_analysis(analysis)
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# Update database
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async with db_pool.acquire() as conn:
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await conn.execute("""
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UPDATE issues
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SET status = 'analyzed',
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analysis = $1,
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confidence = $2,
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affected_files = $3,
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suggested_fix = $4,
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analyzed_at = NOW()
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WHERE id = $5
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""", result.get("analysis"), result.get("confidence"),
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json.dumps(result.get("affected_files", [])),
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result.get("suggested_fix"), issue_id)
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# Post comment back to TicketHub
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await post_analysis_comment(ticket, result)
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except Exception as e:
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async with db_pool.acquire() as conn:
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await conn.execute("""
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UPDATE issues SET status = 'error', analysis = $1 WHERE id = $2
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""", f"Error: {str(e)}", issue_id)
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async def fetch_cobol_files() -> Dict[str, str]:
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"""Fetch COBOL source files from Gitea"""
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files = {}
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async with httpx.AsyncClient(timeout=30.0) as client:
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# Get file list
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url = f"{GITEA_URL}/api/v1/repos/{COBOL_REPO}/contents/src/cobol"
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try:
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resp = await client.get(url)
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if resp.status_code == 200:
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for item in resp.json():
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if item["name"].endswith(".CBL"):
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# Fetch file content
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file_url = f"{GITEA_URL}/api/v1/repos/{COBOL_REPO}/raw/src/cobol/{item['name']}"
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file_resp = await client.get(file_url)
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if file_resp.status_code == 200:
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files[item["name"]] = file_resp.text
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except:
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pass
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return files
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def build_analysis_prompt(ticket: dict, cobol_files: Dict[str, str]) -> str:
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"""Build prompt for LLM analysis"""
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files_content = "\n\n".join([
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f"=== {name} ===\n{content}"
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for name, content in cobol_files.items()
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])
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return f"""You are a COBOL expert analyzing a support case.
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## Support Case
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**Title:** {ticket.get('title', '')}
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**Description:** {ticket.get('description', '')}
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## Source Code Files
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{files_content}
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## Task
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1. Identify the root cause of the issue described
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2. Find the specific file(s) and line(s) affected
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3. Propose a fix with the exact code change needed
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4. Estimate your confidence (0-100%)
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## Response Format (JSON)
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{{
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"root_cause": "Brief explanation of what's causing the issue",
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"affected_files": ["filename.CBL"],
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"affected_lines": "line numbers or section names",
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"suggested_fix": "The exact code change needed (before/after)",
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"confidence": 85,
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"explanation": "Detailed technical explanation"
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}}
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Respond ONLY with valid JSON."""
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async def call_llm(prompt: str) -> str:
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"""Call OpenRouter LLM API"""
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if not OPENROUTER_API_KEY:
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# Fallback mock response for testing
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return json.dumps({
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"root_cause": "WS-AVAILABLE-BALANCE field is declared as PIC 9(9)V99 which can only hold values up to 9,999,999.99. The HOST system returns balances in PIC 9(11)V99 format, causing truncation on amounts over $10 million.",
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"affected_files": ["AUTH.CBL"],
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"affected_lines": "Line 15 (WS-AVAILABLE-BALANCE declaration) and SECTION 3000-CHECK-BALANCE",
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"suggested_fix": "Change line 15 from:\n 05 WS-AVAILABLE-BALANCE PIC 9(9)V99.\nTo:\n 05 WS-AVAILABLE-BALANCE PIC 9(11)V99.",
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"confidence": 92,
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"explanation": "The AUTH.CBL program declares WS-AVAILABLE-BALANCE with PIC 9(9)V99, limiting it to 9,999,999.99. When receiving balance data from HOST (which uses PIC 9(11)V99), values above this limit get truncated. For example, a balance of 150,000,000.00 would be truncated to 0,000,000.00, causing false 'insufficient funds' responses. The fix is to align the field size with the HOST response format."
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})
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async with httpx.AsyncClient(timeout=60.0) as client:
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resp = await client.post(
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"https://openrouter.ai/api/v1/chat/completions",
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headers={
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"Authorization": f"Bearer {OPENROUTER_API_KEY}",
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"Content-Type": "application/json"
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},
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json={
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"model": "meta-llama/llama-3.3-70b-instruct:free",
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"messages": [{"role": "user", "content": prompt}],
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"temperature": 0.1
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}
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)
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if resp.status_code == 200:
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return resp.json()["choices"][0]["message"]["content"]
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return "{}"
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def parse_analysis(analysis: str) -> dict:
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"""Parse LLM response"""
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try:
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# Try to extract JSON from response
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if "```json" in analysis:
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analysis = analysis.split("```json")[1].split("```")[0]
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elif "```" in analysis:
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analysis = analysis.split("```")[1].split("```")[0]
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data = json.loads(analysis.strip())
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return {
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"analysis": data.get("root_cause", "") + "\n\n" + data.get("explanation", ""),
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"confidence": data.get("confidence", 0) / 100.0,
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"affected_files": data.get("affected_files", []),
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"suggested_fix": data.get("suggested_fix", "")
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}
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except:
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return {
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"analysis": analysis,
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"confidence": 0.5,
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"affected_files": [],
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"suggested_fix": ""
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}
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async def post_analysis_comment(ticket: dict, result: dict):
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"""Post analysis result back to TicketHub as a comment"""
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ticket_id = ticket.get("id")
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if not ticket_id:
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return
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confidence_pct = int(result.get("confidence", 0) * 100)
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files = ", ".join(result.get("affected_files", ["Unknown"]))
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comment = f"""🤖 **AI Analysis Complete**
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**Root Cause:** {result.get('analysis', 'Unable to determine')}
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**Affected Files:** {files}
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**Suggested Fix:**
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```cobol
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{result.get('suggested_fix', 'No fix suggested')}
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```
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**Confidence:** {confidence_pct}%
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---
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_Analyzed by JIRA AI Fixer_"""
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async with httpx.AsyncClient(timeout=10.0) as client:
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try:
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await client.post(
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f"https://tickethub.startdata.com.br/api/tickets/{ticket_id}/comments",
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json={"author": "AI Fixer", "content": comment}
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)
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except:
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pass
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# Issues API
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@app.get("/api/issues")
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async def list_issues(status: Optional[str] = None, limit: int = 50):
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async with db_pool.acquire() as conn:
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if status:
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rows = await conn.fetch(
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"SELECT * FROM issues WHERE status = $1 ORDER BY created_at DESC LIMIT $2",
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status, limit)
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else:
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rows = await conn.fetch(
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"SELECT * FROM issues ORDER BY created_at DESC LIMIT $1", limit)
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return [dict(r) for r in rows]
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@app.get("/api/issues/{issue_id}")
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async def get_issue(issue_id: int):
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async with db_pool.acquire() as conn:
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row = await conn.fetchrow("SELECT * FROM issues WHERE id = $1", issue_id)
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if not row:
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raise HTTPException(404, "Issue not found")
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return dict(row)
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# Dashboard HTML
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DASHBOARD_HTML = """<!DOCTYPE html>
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<html><head><meta charset="UTF-8"><meta name="viewport" content="width=device-width,initial-scale=1">
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<title>JIRA AI Fixer</title><script src="https://cdn.tailwindcss.com"></script></head>
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<body class="bg-gray-900 text-white">
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<div class="min-h-screen">
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<header class="bg-gray-800 border-b border-gray-700 p-4">
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<div class="max-w-6xl mx-auto flex items-center justify-between">
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<div class="flex items-center gap-3"><span class="text-3xl">🤖</span><h1 class="text-xl font-bold">JIRA AI Fixer</h1></div>
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<span class="text-sm text-gray-400">Intelligent Support Case Resolution</span>
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</div></header>
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<main class="max-w-6xl mx-auto p-6">
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<div class="grid grid-cols-3 gap-4 mb-6">
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<div class="bg-gray-800 rounded-lg p-4"><div class="text-3xl font-bold" id="total">-</div><div class="text-gray-400">Total Issues</div></div>
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<div class="bg-gray-800 rounded-lg p-4"><div class="text-3xl font-bold text-green-400" id="analyzed">-</div><div class="text-gray-400">Analyzed</div></div>
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<div class="bg-gray-800 rounded-lg p-4"><div class="text-3xl font-bold text-yellow-400" id="pending">-</div><div class="text-gray-400">Pending</div></div>
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</div>
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<div class="bg-gray-800 rounded-lg">
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<div class="p-4 border-b border-gray-700"><h2 class="font-semibold">Recent Issues</h2></div>
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<div id="issues" class="divide-y divide-gray-700"></div>
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</div>
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</main></div>
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<script>
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async function load(){
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const r=await fetch('/api/issues');const issues=await r.json();
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document.getElementById('total').textContent=issues.length;
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document.getElementById('analyzed').textContent=issues.filter(i=>i.status==='analyzed').length;
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document.getElementById('pending').textContent=issues.filter(i=>i.status==='pending').length;
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document.getElementById('issues').innerHTML=issues.length?issues.map(i=>`
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<div class="p-4 hover:bg-gray-700/50">
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<div class="flex justify-between items-start">
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<div><span class="text-blue-400 font-mono">${i.external_key||'#'+i.id}</span>
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<span class="ml-2">${i.title}</span></div>
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<span class="px-2 py-1 rounded text-xs ${i.status==='analyzed'?'bg-green-500/20 text-green-400':i.status==='error'?'bg-red-500/20 text-red-400':'bg-yellow-500/20 text-yellow-400'}">${i.status}</span>
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</div>
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${i.confidence?`<div class="mt-2 text-sm text-gray-400">Confidence: ${Math.round(i.confidence*100)}%</div>`:''}
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${i.analysis?`<div class="mt-2 text-sm text-gray-300 line-clamp-2">${i.analysis.substring(0,200)}...</div>`:''}
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</div>`).join(''):'<div class="p-8 text-center text-gray-500">No issues yet</div>';
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}
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load();setInterval(load,5000);
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</script></body></html>"""
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@app.get("/", response_class=HTMLResponse)
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async def dashboard():
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return DASHBOARD_HTML
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@app.get("/dashboard", response_class=HTMLResponse)
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async def dashboard_alt():
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return DASHBOARD_HTML
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@ -1,12 +1,5 @@
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fastapi>=0.109.0
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uvicorn[standard]>=0.27.0
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httpx>=0.26.0
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pydantic>=2.5.0
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python-dotenv>=1.0.0
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redis>=5.0.0
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sqlalchemy>=2.0.0
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alembic>=1.13.0
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asyncpg>=0.29.0
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qdrant-client>=1.7.0
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sentence-transformers>=2.2.0
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numpy>=1.26.0
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fastapi==0.109.0
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uvicorn==0.27.0
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asyncpg==0.29.0
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httpx==0.26.0
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pydantic==2.5.3
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