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OpenTriage AI Engine - Full-Featured Backend
Lift-and-shift deployment of the original Python AI backend.
All service logic is preserved exactly as-is from the original codebase.
Designed for Hugging Face Spaces deployment.
Build: 2026-02-09 v2.1 - Fixed import issues, added README cache
"""
import logging
import os
from contextlib import asynccontextmanager
from fastapi import FastAPI, HTTPException, Request, Depends
from fastapi.middleware.cors import CORSMiddleware
# Import authentication middleware
from middleware import require_api_key_or_auth, get_optional_user
from pydantic import BaseModel
from typing import List, Dict, Any, Optional
from datetime import datetime, timezone
# Configure logging
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
)
logger = logging.getLogger(__name__)
# Import original services (unchanged logic)
from services.ai_service import ai_triage_service, ai_chat_service
from services.rag_chatbot_service import rag_chatbot_service
from services.mentor_matching_service import mentor_matching_service
from services.hype_generator_service import hype_generator_service
from services.rag_data_prep import rag_data_prep
from services.sentiment_analysis_service import sentiment_analysis_service
from services.mentor_leaderboard_service import mentor_leaderboard_service
# Import models for request/response types
from models.issue import Issue
from models.mentor_leaderboard import (
MentorLeaderboardEntry,
LeaderboardResponse,
LeaderboardEdit
)
@asynccontextmanager
async def lifespan(app: FastAPI):
"""Application lifespan handler."""
logger.info("Starting OpenTriage AI Engine (Full Backend)...")
logger.info(f"Environment: {os.getenv('ENVIRONMENT', 'development')}")
yield
logger.info("Shutting down OpenTriage AI Engine...")
app = FastAPI(
title="OpenTriage AI Engine",
description="Full-featured AI backend for issue triage, RAG chatbot, mentor matching, and hype generation",
version="2.1.0",
lifespan=lifespan
)
# CORS configuration
app.add_middleware(
CORSMiddleware,
allow_origins=os.getenv("CORS_ORIGINS", "http://localhost:3000,http://localhost:5173,https://open-triage.vercel.app,https://opentriage.onrender.com").split(","),
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# Import and include data routes (contributor, messaging, auth)
from routes.data_routes import router as data_router
app.include_router(data_router)
# =============================================================================
# Request Models (matching original service expectations)
# =============================================================================
class TriageRequest(BaseModel):
"""Request for issue triage - matches ai_service.classify_issue()"""
title: str
body: Optional[str] = ""
authorName: str = "unknown"
isPR: bool = False
# Full Issue object fields for compatibility
id: Optional[str] = None
githubIssueId: Optional[int] = None
number: Optional[int] = None
repoId: Optional[str] = None
repoName: Optional[str] = None
class ChatRequest(BaseModel):
"""Request for AI chat - matches ai_chat_service.chat()"""
message: str
history: Optional[List[Dict[str, str]]] = None
context: Optional[Dict[str, Any]] = None
class RAGChatRequest(BaseModel):
"""Request for RAG chatbot - matches rag_chatbot_service.answer_question()"""
question: str
repo_name: Optional[str] = None
top_k: int = 5
github_access_token: Optional[str] = None
class MentorMatchRequest(BaseModel):
"""Request for mentor matching - matches mentor_matching_service.find_mentors_for_user()"""
user_id: str
username: str
limit: int = 5
skill_filter: Optional[List[str]] = None
class HypeRequest(BaseModel):
"""Request for hype generation - matches hype_generator_service"""
pr_title: str
pr_body: Optional[str] = ""
files_changed: Optional[List[str]] = None
additions: int = 0
deletions: int = 0
repo_name: Optional[str] = None
class ImpactSummaryRequest(BaseModel):
"""Request for impact summary generation"""
pr_title: str
pr_body: Optional[str] = ""
repo_name: str
files_changed: int = 0
additions: int = 0
deletions: int = 0
class RAGIndexRequest(BaseModel):
"""Request for RAG indexing - matches rag_chatbot_service.index_repository()"""
repo_name: str
github_access_token: Optional[str] = None
class RAGDataPrepRequest(BaseModel):
"""Request for RAG data prep - matches rag_data_prep.prepare_documents()"""
doc_types: Optional[List[str]] = ["issue", "pr", "comment"]
repo_names: Optional[List[str]] = None
collection_name: str = "rag_chunks"
class CommentSentimentRequest(BaseModel):
"""Request for sentiment analysis of a single comment"""
comment_id: str
body: str
author: Optional[str] = "unknown"
force_recalc: bool = False
class BatchCommentSentimentRequest(BaseModel):
"""Request for sentiment analysis of multiple comments"""
comments: List[Dict[str, Any]]
# Each comment dict should have: id, body, author (optional)
class LeaderboardEditRequest(BaseModel):
"""Request to edit a leaderboard entry"""
mentor_id: str
edited_by: str # Maintainer username
reason: Optional[str] = None
# Can update:
custom_notes: Optional[str] = None
sentiment_score: Optional[float] = None
expertise_score: Optional[float] = None
engagement_score: Optional[float] = None
best_language: Optional[str] = None
# =============================================================================
# Health & Status Endpoints
# =============================================================================
@app.get("/health")
async def health_check():
"""Health check endpoint for container orchestration."""
return {
"status": "healthy",
"service": "ai-engine-full",
"version": "2.0.0",
"timestamp": datetime.now(timezone.utc).isoformat(),
"api_key_configured": bool(os.environ.get('API_KEY', ''))
}
@app.get("/debug/env")
async def debug_env(auth: dict = Depends(require_api_key_or_auth)):
"""Debug endpoint to show environment variable configuration."""
return {
"api_key_set": bool(os.environ.get('API_KEY', '')),
"api_key_value": os.environ.get('API_KEY', 'NOT_SET'),
"jwt_secret_set": bool(os.environ.get('JWT_SECRET', '')),
}
@app.post("/debug/test-openrouter")
async def test_openrouter(auth: dict = Depends(require_api_key_or_auth)):
"""Test OpenRouter API connectivity."""
try:
from openai import OpenAI
from config.settings import settings
api_key = settings.OPENROUTER_API_KEY
if not api_key:
return {
"status": "error",
"message": "OPENROUTER_API_KEY not configured",
"api_key_configured": False
}
client = OpenAI(
base_url="https://openrouter.ai/api/v1",
api_key=api_key
)
# Try a simple completion
response = client.chat.completions.create(
model="meta-llama/llama-3.3-70b-instruct:free",
messages=[
{"role": "user", "content": "Say 'test successful' in one word"}
],
temperature=0.7,
max_tokens=10
)
return {
"status": "success",
"message": "OpenRouter API is working",
"response": response.choices[0].message.content,
"api_key_configured": True
}
except Exception as e:
return {
"status": "error",
"message": str(e),
"api_key_configured": bool(settings.OPENROUTER_API_KEY)
}
@app.get("/")
async def root():
"""Root endpoint with service info."""
return {
"service": "OpenTriage AI Engine (Full)",
"version": "2.0.0",
"description": "Full-featured AI backend lifted from original Python codebase",
"endpoints": {
"triage": "POST /triage - Issue classification",
"chat": "POST /chat - AI chat assistant",
"rag_chat": "POST /rag/chat - RAG-based Q&A",
"rag_index": "POST /rag/index - Index repository for RAG",
"rag_suggestions": "GET /rag/suggestions - Get suggested questions",
"mentor_match": "POST /mentor-match - Find mentor matches",
"hype": "POST /hype - Generate PR hype"
}
}
# =============================================================================
# Triage Endpoints
# =============================================================================
@app.post("/triage")
async def triage_issue(request: TriageRequest, auth: dict = Depends(require_api_key_or_auth)):
"""
Classify and triage a GitHub issue using AI.
Passes directly to ai_triage_service.classify_issue()
Requires authentication (API key or JWT token).
Implements Redis caching with 24-hour TTL.
"""
try:
# Import Redis utilities (lazy import to avoid startup dependencies)
from config.redis import generate_cache_key, cache_get, cache_set
# Generate cache key from request data
cache_data = {
"title": request.title,
"body": request.body or "",
"isPR": request.isPR
}
cache_key = generate_cache_key("triage", cache_data)
# Check cache first
cached_result = cache_get(cache_key)
if cached_result is not None:
logger.info(f"Cache HIT for triage request: {cache_key}")
# Add cache metadata
cached_result["_cached"] = True
cached_result["_cache_key"] = cache_key
return cached_result
logger.info(f"Cache MISS for triage request: {cache_key}")
# Create Issue object matching the original service expectation
issue = Issue(
id=request.id or "temp-id",
githubIssueId=request.githubIssueId or 0,
number=request.number or 0,
title=request.title,
body=request.body or "",
authorName=request.authorName,
repoId=request.repoId or "temp-repo",
repoName=request.repoName or "unknown/repo",
isPR=request.isPR
)
# Call AI service (cache miss)
result = await ai_triage_service.classify_issue(issue)
# Cache the result with 24-hour TTL (86400 seconds)
cache_set(cache_key, result, ttl=86400)
logger.info(f"Cached triage result: {cache_key}")
# Add cache metadata
result["_cached"] = False
result["_cache_key"] = cache_key
return result
except Exception as e:
logger.error(f"Triage error: {e}")
raise HTTPException(status_code=500, detail=str(e))
# =============================================================================
# Chat Endpoints
# =============================================================================
@app.post("/chat")
async def chat(request: ChatRequest, auth: dict = Depends(require_api_key_or_auth)):
"""
AI chat endpoint for general assistance.
Passes directly to ai_chat_service.chat()
Requires authentication (API key or JWT token).
"""
try:
response = await ai_chat_service.chat(
message=request.message,
history=request.history,
context=request.context
)
return {"response": response}
except Exception as e:
logger.error(f"Chat error: {e}")
raise HTTPException(status_code=500, detail=str(e))
# =============================================================================
# RAG Chatbot Endpoints
# =============================================================================
@app.post("/rag/chat")
async def rag_chat(request: RAGChatRequest, auth: dict = Depends(require_api_key_or_auth)):
"""
Answer questions using RAG (Retrieval-Augmented Generation).
Passes directly to rag_chatbot_service.answer_question()
Requires authentication.
"""
try:
result = await rag_chatbot_service.answer_question(
question=request.question,
repo_name=request.repo_name,
top_k=request.top_k,
github_access_token=request.github_access_token
)
return result
except Exception as e:
logger.error(f"RAG chat error: {e}")
raise HTTPException(status_code=500, detail=str(e))
@app.post("/rag/index")
async def rag_index(request: RAGIndexRequest, auth: dict = Depends(require_api_key_or_auth)):
"""
Index a repository for RAG search.
Passes directly to rag_chatbot_service.index_repository()
Requires authentication.
"""
try:
result = await rag_chatbot_service.index_repository(
repo_name=request.repo_name,
github_access_token=request.github_access_token
)
return {"success": True, "message": result}
except Exception as e:
logger.error(f"RAG index error: {e}")
raise HTTPException(status_code=500, detail=str(e))
@app.get("/rag/suggestions")
async def rag_suggestions(repo_name: Optional[str] = None):
"""Get suggested questions for RAG chatbot."""
try:
suggestions = await rag_chatbot_service.get_suggested_questions(repo_name)
return {"suggestions": suggestions}
except Exception as e:
logger.error(f"RAG suggestions error: {e}")
raise HTTPException(status_code=500, detail=str(e))
@app.get("/rag/check-index")
async def check_rag_index(
repo_name: str,
auth: dict = Depends(require_api_key_or_auth)
):
"""
Check how many chunks are indexed for a repository.
Query params:
repo_name: Repository name (owner/repo format)
Returns:
{"repo_name": str, "chunk_count": int}
"""
from config.database import db
try:
# Count documents in MongoDB rag_chunks collection
count = await db.rag_chunks.count_documents({"sourceRepo": repo_name})
return {
"repo_name": repo_name,
"chunk_count": count
}
except Exception as e:
logger.error(f"Failed to check RAG index for {repo_name}: {e}")
return {
"repo_name": repo_name,
"chunk_count": 0,
"error": str(e)
}
# =============================================================================
# Mentor Matching Endpoints
# =============================================================================
@app.post("/mentor-match")
async def mentor_match(request: MentorMatchRequest, auth: dict = Depends(require_api_key_or_auth)):
"""
Find mentor matches for a user.
Passes directly to mentor_matching_service.find_mentors_for_user()
Requires authentication.
"""
try:
matches = mentor_matching_service.find_mentors_for_user(
user_id=request.user_id,
username=request.username,
limit=request.limit,
skill_filter=request.skill_filter
)
return {"matches": matches}
except Exception as e:
logger.error(f"Mentor match error: {e}")
raise HTTPException(status_code=500, detail=str(e))
# =============================================================================
# Hype Generator Endpoints
# =============================================================================
@app.post("/hype")
async def generate_hype(request: HypeRequest, auth: dict = Depends(require_api_key_or_auth)):
"""
Generate hype/celebration message for a PR.
Passes directly to hype_generator_service.generate_hype()
Requires authentication.
"""
try:
result = hype_generator_service.generate_hype(
pr_title=request.pr_title,
pr_body=request.pr_body or "",
files_changed=request.files_changed or [],
additions=request.additions,
deletions=request.deletions,
repo_name=request.repo_name
)
return result
except Exception as e:
logger.error(f"Hype generation error: {e}")
raise HTTPException(status_code=500, detail=str(e))
@app.post("/hype/impact-summary")
async def generate_impact_summary(request: ImpactSummaryRequest, auth: dict = Depends(require_api_key_or_auth)):
"""
Generate a short impact summary for a merged PR.
Returns a motivating one-liner to show in the celebration popup.
Requires authentication.
"""
try:
summary = await hype_generator_service.generate_impact_summary(
pr_title=request.pr_title,
pr_body=request.pr_body or "",
repo_name=request.repo_name,
files_changed=request.files_changed,
additions=request.additions,
deletions=request.deletions
)
return {"impact_summary": summary}
except Exception as e:
logger.error(f"Impact summary generation error: {e}")
raise HTTPException(status_code=500, detail=str(e))
# =============================================================================
# RAG Data Preparation Endpoints
# =============================================================================
@app.post("/rag/prepare")
async def rag_prepare(request: RAGDataPrepRequest):
"""
Prepare documents for RAG vector database.
Passes directly to rag_data_prep.prepare_documents()
"""
try:
result = rag_data_prep.prepare_documents(
doc_types=request.doc_types,
repo_names=request.repo_names,
collection_name=request.collection_name
)
return {"success": True, "chunks_created": result}
except Exception as e:
logger.error(f"RAG prepare error: {e}")
raise HTTPException(status_code=500, detail=str(e))
@app.get("/rag/chunks")
async def get_rag_chunks(batch_size: int = 100, skip_embedded: bool = True):
"""Get chunks ready for embedding."""
try:
chunks = rag_data_prep.get_chunks_for_embedding(
batch_size=batch_size,
skip_embedded=skip_embedded
)
return {"chunks": chunks, "count": len(chunks)}
except Exception as e:
logger.error(f"RAG chunks error: {e}")
raise HTTPException(status_code=500, detail=str(e))
# =============================================================================
# Sentiment Analysis Endpoints (Stage 3 Integration)
# =============================================================================
@app.post("/sentiment/analyze")
async def analyze_comment_sentiment(request: CommentSentimentRequest):
"""
Analyze sentiment of a single PR comment using DistilBERT.
Returns:
- sentiment_label: "POSITIVE" or "NEGATIVE"
- sentiment_score: Confidence (0.0-1.0)
- prominent_language: Detected language category (technical, positive, negative, etc.)
Used in Stage 3 RAG prompt: "The reviewers' sentiment is {sentiment_label}...
with focus on {prominent_language} aspects"
"""
try:
result = sentiment_analysis_service.analyze_comment(
comment_id=request.comment_id,
comment_text=request.body,
author=request.author,
force_recalc=request.force_recalc
)
return result
except Exception as e:
logger.error(f"Sentiment analysis error: {e}")
raise HTTPException(status_code=500, detail=str(e))
@app.post("/sentiment/analyze-batch")
async def analyze_batch_sentiment(request: BatchCommentSentimentRequest):
"""
Analyze sentiment for multiple comments at once.
Each comment should have:
- id: Comment identifier
- body: Comment text
- author: (optional) Comment author
Returns List of sentiment results + summary stats
"""
try:
results = sentiment_analysis_service.analyze_batch(request.comments)
# Get summary overview
summary = sentiment_analysis_service.get_summary(results)
return {
"comments": results,
"summary": summary,
"total_analyzed": len(results)
}
except Exception as e:
logger.error(f"Batch sentiment analysis error: {e}")
raise HTTPException(status_code=500, detail=str(e))
@app.get("/sentiment/summary")
async def get_sentiment_summary(repo_name: Optional[str] = None):
"""
Get sentiment summary for comments (if you have them cached).
For Stage 3 prompt input, this helps determine:
- Is the review tone supportive or critical?
- Are reviewers focused on technical debt or new features?
"""
try:
# In a real implementation, fetch comments from DB for this repo
# For now, return cache stats
cache_stats = sentiment_analysis_service.get_cache_stats()
return {
"cache_status": cache_stats,
"message": "Sentiment analysis service is ready. Send /sentiment/analyze-batch with comments to get summary."
}
except Exception as e:
logger.error(f"Sentiment summary error: {e}")
raise HTTPException(status_code=500, detail=str(e))
@app.post("/sentiment/clear-cache")
async def clear_sentiment_cache(auth: dict = Depends(require_api_key_or_auth)):
"""
Clear the sentiment analysis cache (admin only).
Useful if you've updated keywords or want fresh analysis.
"""
try:
sentiment_analysis_service.clear_cache()
return {"message": "Sentiment analysis cache cleared", "status": "success"}
except Exception as e:
logger.error(f"Cache clear error: {e}")
raise HTTPException(status_code=500, detail=str(e))
# =============================================================================
# Mentor Leaderboard Endpoints (AI-Powered Rankings with Sentiment)
# =============================================================================
@app.post("/leaderboard/generate")
async def generate_leaderboard(
exclude_maintainer: Optional[str] = None,
auth: dict = Depends(require_api_key_or_auth)
):
"""
Generate the mentor leaderboard from scratch.
This endpoint:
1. Fetches all mentor conversations
2. Analyzes sentiment of each conversation using DistilBERT
3. Detects programming languages mentioned
4. Ranks mentors by: Sentiment (35%) + Expertise (40%) + Engagement (25%)
Returns ranked mentors with scores for each component.
**Parameters:**
- exclude_maintainer: User ID of maintainer to exclude from rankings
**Returns leaderboard with:**
- overall_score: Weighted ranking score (0-100)
- sentiment_score: Quality of mentorship interactions
- expertise_score: Programming language proficiency
- best_language: Top detected language
- rank: Current position
"""
try:
logger.info(f"Generating leaderboard (exclude_maintainer={exclude_maintainer})...")
result = await mentor_leaderboard_service.generate_leaderboard(
exclude_maintainer_id=exclude_maintainer
)
return result
except Exception as e:
logger.error(f"Leaderboard generation error: {e}")
raise HTTPException(status_code=500, detail=str(e))
@app.get("/leaderboard")
async def get_leaderboard(
limit: int = 50,
skip: int = 0,
auth: dict = Depends(require_api_key_or_auth)
):
"""
Get the cached mentor leaderboard.
Returns top mentors with their rankings.
**Query Parameters:**
- limit: Number of entries to return (default: 50)
- skip: Number to skip for pagination (default: 0)
"""
try:
result = await mentor_leaderboard_service.get_leaderboard(
limit=limit,
skip=skip
)
return result
except Exception as e:
logger.error(f"Get leaderboard error: {e}")
raise HTTPException(status_code=500, detail=str(e))
@app.get("/leaderboard/mentor/{mentor_id}")
async def get_mentor_leaderboard_entry(
mentor_id: str,
auth: dict = Depends(require_api_key_or_auth)
):
"""
Get leaderboard entry for a specific mentor.
Returns their ranking, scores, language proficiency, and edit history.
"""
try:
entry = await mentor_leaderboard_service.get_entry(mentor_id)
if not entry:
raise HTTPException(status_code=404, detail=f"Mentor {mentor_id} not in leaderboard")
return entry
except HTTPException:
raise
except Exception as e:
logger.error(f"Get mentor entry error: {e}")
raise HTTPException(status_code=500, detail=str(e))
@app.post("/leaderboard/edit")
async def edit_leaderboard_entry(
request: LeaderboardEditRequest,
auth: dict = Depends(require_api_key_or_auth)
):
"""
Edit a leaderboard entry (maintainer only).
Allows manual adjustments to mentor rankings. All edits are tracked.
**Editable fields:**
- custom_notes: Custom notes about this mentor
- sentiment_score: Adjust sentiment component (0-100)
- expertise_score: Adjust expertise component (0-100)
- engagement_score: Adjust engagement component (0-100)
- best_language: Override detected language
**All edits are recorded in:**
- edit_history: List of all changes with timestamp and reason
- is_custom_edited: Flag marking entry as manually tweaked
- last_edited_by: Who made the edit
"""
try:
# Build update dict from request
updates = {
"edited_by": request.edited_by,
"reason": request.reason
}
if request.custom_notes is not None:
updates["custom_notes"] = request.custom_notes
if request.sentiment_score is not None:
updates["score_sentiment"] = request.sentiment_score
if request.expertise_score is not None:
updates["score_expertise"] = request.expertise_score
if request.engagement_score is not None:
updates["score_engagement"] = request.engagement_score
if request.best_language is not None:
updates["best_language"] = request.best_language
entry = await mentor_leaderboard_service.edit_entry(
request.mentor_id,
**updates
)
return entry
except ValueError as e:
raise HTTPException(status_code=404, detail=str(e))
except Exception as e:
logger.error(f"Edit leaderboard error: {e}")
raise HTTPException(status_code=500, detail=str(e))
@app.get("/leaderboard/export")
async def export_leaderboard(
format: str = "json",
auth: dict = Depends(require_api_key_or_auth)
):
"""
Export leaderboard in various formats.
**Formats:**
- json: Full JSON with all fields
- csv: Simplified CSV for spreadsheets
"""
try:
if format not in ["json", "csv"]:
raise HTTPException(status_code=400, detail="Format must be 'json' or 'csv'")
data = await mentor_leaderboard_service.export_leaderboard(format)
if format == "csv":
return {
"format": "csv",
"data": data,
"message": "Copy this data into a CSV file"
}
return {
"format": "json",
"data": data
}
except HTTPException:
raise
except Exception as e:
logger.error(f"Export leaderboard error: {e}")
raise HTTPException(status_code=500, detail=str(e))
if __name__ == "__main__":
import uvicorn
port = int(os.getenv("PORT", "7860"))
uvicorn.run(
"main:app",
host="0.0.0.0",
port=port,
reload=os.getenv("ENVIRONMENT", "development") != "production"
)
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