feat: CONAI Phase 1 MVP 초기 구현

소형 건설업체(100억 미만)를 위한 AI 기반 토목공사 통합관리 플랫폼 Backend (FastAPI): - SQLAlchemy 모델 13개 (users, projects, wbs, tasks, daily_reports, reports, inspections, quality, weather, permits, rag, settings) - API 라우터 11개 (auth, projects, tasks, daily_reports, reports, inspections, weather, rag, kakao, permits, settings) - Services: Claude AI 래퍼, CPM Gantt 계산, 기상청 API, RAG(pgvector), 카카오 Skill API - Alembic 마이그레이션 (pgvector 포함) - pytest 테스트 (CPM, 날씨 경보) Frontend (Next.js 15): - 11개 페이지 (대시보드, 프로젝트, Gantt, 일보, 검측, 품질, 날씨, 인허가, RAG, 설정) - TanStack Query + Zustand + Tailwind CSS 인프라: - Docker Compose (PostgreSQL pgvector + backend + frontend) - 한국어 README 및 설치 가이드 Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-03-24 20:06:36 +09:00
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+"""
+RAG (Retrieval-Augmented Generation) service.
+Embeds questions, retrieves relevant chunks, and generates answers with Claude.
+"""
+import httpx
+from sqlalchemy.ext.asyncio import AsyncSession
+from sqlalchemy import select, text
+from app.config import settings
+from app.models.rag import RagChunk, RagSource
+from app.services.ai_engine import complete
+from app.services.prompts.rag import SYSTEM_PROMPT, build_prompt
+
+
+async def embed_text(text_input: str) -> list[float]:
+    """Get embedding vector for text using Voyage AI or OpenAI."""
+    if settings.VOYAGE_API_KEY:
+        return await _embed_voyage(text_input)
+    elif settings.OPENAI_API_KEY:
+        return await _embed_openai(text_input)
+    else:
+        raise ValueError("임베딩 API 키가 설정되지 않았습니다 (VOYAGE_API_KEY 또는 OPENAI_API_KEY)")
+
+
+async def _embed_voyage(text_input: str) -> list[float]:
+    async with httpx.AsyncClient(timeout=30.0) as client:
+        resp = await client.post(
+            "https://api.voyageai.com/v1/embeddings",
+            headers={"Authorization": f"Bearer {settings.VOYAGE_API_KEY}"},
+            json={"model": settings.EMBEDDING_MODEL, "input": text_input},
+        )
+        resp.raise_for_status()
+        return resp.json()["data"][0]["embedding"]
+
+
+async def _embed_openai(text_input: str) -> list[float]:
+    async with httpx.AsyncClient(timeout=30.0) as client:
+        resp = await client.post(
+            "https://api.openai.com/v1/embeddings",
+            headers={"Authorization": f"Bearer {settings.OPENAI_API_KEY}"},
+            json={"model": "text-embedding-3-small", "input": text_input},
+        )
+        resp.raise_for_status()
+        return resp.json()["data"][0]["embedding"]
+
+
+async def retrieve_chunks(
+    db: AsyncSession,
+    question_embedding: list[float],
+    top_k: int = 5,
+    source_types: list[str] | None = None,
+) -> list[dict]:
+    """Retrieve most relevant chunks using pgvector cosine similarity."""
+    embedding_str = "[" + ",".join(str(x) for x in question_embedding) + "]"
+
+    # Build query with optional source type filter
+    source_filter = ""
+    if source_types:
+        types_str = ", ".join(f"'{t}'" for t in source_types)
+        source_filter = f"AND rs.source_type IN ({types_str})"
+
+    query = text(f"""
+        SELECT
+            rc.id,
+            rc.content,
+            rc.metadata,
+            rs.title,
+            rs.source_type,
+            1 - (rc.embedding <=> '{embedding_str}'::vector) AS relevance_score
+        FROM rag_chunks rc
+        JOIN rag_sources rs ON rs.id = rc.source_id
+        WHERE rc.embedding IS NOT NULL
+        {source_filter}
+        ORDER BY rc.embedding <=> '{embedding_str}'::vector
+        LIMIT {top_k}
+    """)
+
+    result = await db.execute(query)
+    rows = result.fetchall()
+
+    return [
+        {
+            "id": str(row.id),
+            "content": row.content,
+            "metadata": row.metadata,
+            "title": row.title,
+            "source_type": row.source_type,
+            "relevance_score": float(row.relevance_score),
+        }
+        for row in rows
+    ]
+
+
+async def ask(
+    db: AsyncSession,
+    question: str,
+    top_k: int = 5,
+    source_types: list[str] | None = None,
+) -> dict:
+    """Full RAG pipeline: embed -> retrieve -> generate."""
+    # 1. Embed the question
+    embedding = await embed_text(question)
+
+    # 2. Retrieve relevant chunks
+    chunks = await retrieve_chunks(db, embedding, top_k, source_types)
+
+    if not chunks:
+        return {
+            "question": question,
+            "answer": "관련 자료를 찾을 수 없습니다. 더 구체적인 질문을 입력하거나, 관련 자료가 업로드되었는지 확인해주세요.",
+            "sources": [],
+        }
+
+    # 3. Build prompt and generate answer
+    prompt = build_prompt(question, chunks)
+    answer = await complete(
+        messages=[{"role": "user", "content": prompt}],
+        system=SYSTEM_PROMPT,
+        temperature=0.5,
+    )
+
+    return {
+        "question": question,
+        "answer": answer,
+        "sources": chunks,
+    }