6bbb24e633
- Reorganize project into monorepo structure - backend/app/ - New FastAPI backend (modular with src/) - backend/legacy/ - Legacy database modules (relational & vector) - frontend/ - React text editor application - Add launcher.py for easy full-stack startup - Complete documentation in README.md - Quick start guide - API endpoints reference - Development setup - Troubleshooting - Refactor main.py to 35 lines (app configuration only) - Update .gitignore for full-stack project - Add CHANGELOG.md with version history (v0.1.0-v0.1.1) Structure is now clean and ready for team collaboration.
135 lines
3.9 KiB
Python
135 lines
3.9 KiB
Python
import sqlite3
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import os
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import numpy as np
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from fastapi import FastAPI, Body, HTTPException
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from fastapi.middleware.cors import CORSMiddleware
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from sentence_transformers import SentenceTransformer
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import uvicorn
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app = FastAPI()
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_methods=["*"],
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allow_headers=["*"],
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)
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BASE_DIR = os.path.dirname(os.path.abspath(__file__))
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DB_FILE = os.path.join(BASE_DIR, "assets.db")
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MODEL_DIR = os.path.join(BASE_DIR, "local_model_miniLM")
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if not os.path.exists(MODEL_DIR):
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model = SentenceTransformer('all-MiniLM-L6-v2')
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model.save(MODEL_DIR)
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else:
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model = SentenceTransformer(MODEL_DIR)
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def get_db_connection():
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conn = sqlite3.connect(DB_FILE)
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conn.execute("PRAGMA journal_mode=WAL;")
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conn.row_factory = sqlite3.Row
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return conn
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def init_db():
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with get_db_connection() as conn:
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conn.execute("""
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CREATE TABLE IF NOT EXISTS documents
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(
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id
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INTEGER
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PRIMARY
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KEY
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AUTOINCREMENT,
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title
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TEXT
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UNIQUE,
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content
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BLOB,
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content_type
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TEXT,
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embedding
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BLOB
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)
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""")
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conn.commit()
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init_db()
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@app.post("/save-document")
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async def save_document(
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title: str = Body(...),
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content: str = Body(...),
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content_type: str = Body("text/plain")
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):
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vector = model.encode(f"{title} {content}").astype(np.float32).tobytes()
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try:
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with get_db_connection() as conn:
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conn.execute("""
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INSERT INTO documents (title, content, content_type, embedding)
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VALUES (?, ?, ?, ?) ON CONFLICT(title) DO
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UPDATE SET
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content=excluded.content,
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content_type=excluded.content_type,
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embedding=excluded.embedding
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""", (title, content.encode('utf-8'), content_type, vector))
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conn.commit()
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return {"status": "success", "message": f"Dokument '{title}' zapisany."}
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except Exception as e:
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raise HTTPException(status_code=500, detail=str(e))
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@app.post("/search")
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async def search_similar(query: str = Body(..., embed=True), top_k: int = 3):
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"""Wyszukiwanie semantyczne (Vector Search)"""
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query_vector = model.encode(query).astype(np.float32)
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with get_db_connection() as conn:
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cursor = conn.execute("SELECT title, content, embedding FROM documents")
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rows = cursor.fetchall()
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results = []
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for row in rows:
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db_vector = np.frombuffer(row['embedding'], dtype=np.float32)
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score = np.dot(query_vector, db_vector) / (np.linalg.norm(query_vector) * np.linalg.norm(db_vector))
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results.append({
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"title": row['title'],
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"content": row['content'].decode('utf-8', errors='ignore'),
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"score": float(score)
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})
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results = sorted(results, key=lambda x: x['score'], reverse=True)[:top_k]
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return {"results": results}
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@app.get("/load-document")
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async def load_document(title: str = None):
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with get_db_connection() as conn:
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if title:
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row = conn.execute("SELECT title, content FROM documents WHERE title = ?", (title,)).fetchone()
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else:
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row = conn.execute("SELECT title, content FROM documents ORDER BY id DESC LIMIT 1").fetchone()
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if row:
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return {
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"title": row['title'],
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"content": row['content'].decode('utf-8', errors='ignore')
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}
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return {"error": "Nie znaleziono dokumentu"}
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if __name__ == "__main__":
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uvicorn.run(app, host="127.0.0.1", port=8000)
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