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from fastapi import FastAPI, HTTPException |
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from pydantic import BaseModel |
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import logging, json |
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from pathlib import Path |
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logging.basicConfig(level=logging.INFO) |
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STATUS_FILE = Path('/tmp/setup_status.json') |
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READY_FLAG = Path('/tmp/faiss_ready') |
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def get_status(): |
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if not STATUS_FILE.exists(): |
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return {'status': 'init', 'progress': 0} |
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with open(STATUS_FILE) as f: |
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return json.load(f) |
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query_engine = None |
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def get_engine(): |
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global query_engine |
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if query_engine is None: |
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if not READY_FLAG.exists(): |
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raise HTTPException(503, "Em construção") |
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from query_engine import QueryEngine |
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query_engine = QueryEngine() |
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return query_engine |
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app = FastAPI(title="Para.AI RAG", version="3.5.0") |
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class SearchRequest(BaseModel): |
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query: str |
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top_k: int = 10 |
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@app.get("/") |
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async def root(): |
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return {"status": "online", "rag_ready": READY_FLAG.exists(), "setup": get_status()} |
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@app.get("/setup/status") |
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async def status(): |
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return get_status() |
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@app.get("/setup/logs") |
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async def logs(): |
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try: |
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with open('/tmp/setup_debug.log') as f: |
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return {"logs": f.read()} |
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except: |
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return {"logs": "N/A"} |
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@app.post("/search/embedding") |
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async def search(req: SearchRequest): |
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return get_engine().search_by_embedding(req.query, req.top_k) |
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