Fluid Machinery By Jose Francisco Pdf May 2026
app = FastAPI() cache = redis.from_url(os.getenv("REDIS_URL"))
export const MachineViewer = ( modelUrl : modelUrl: string ) => { const container = useRef<HTML Fluid Machinery By Jose Francisco Pdf
// src/api.ts export const search = (query: string) => axios.get('/api/search', params: q: query ); export const getEquation = (eqId: string) => axios.get(`/api/equation/$eqId`); export const summarize = (pageRange: string) => axios.post('/api/ai/summary', pages: pageRange ); export const generateQuiz = (chapter: number) => axios.post('/api/ai/quiz', chapter ); export const exportPack = (payload) => axios.post('/api/export', payload, responseType: 'blob' ); Custom Analyzer – tokenizes on whitespace and on LaTeX delimiters ( $ , \ , , ). Fields – content , equation_latex , page_number . app = FastAPI() cache = redis
# ai_gateway/main.py from fastapi import FastAPI, Body import openai, os, redis Provide four options, indicate the correct one, and
@app.post("/quiz") def quiz(chapter: int = Body(...)): prompt = f"Create 5 multiple‑choice questions about the key concepts in Chapter chapter of *Fluid Machinery*. Provide four options, indicate the correct one, and write a brief explanation." return "quiz": call_llm(prompt) Source : Figures in the PDF that are vector (SVG) are exported by the publisher as EPS/AI. Conversion : svg2gltf → glb → served via CDN.
@app.post("/summary") def summary(pages: dict = Body(...)): text = pages["text"] prompt = f"Summarize the following text from *Fluid Machinery* in ≤ 5 bullet points.\n\nText:\ntext" return "summary": call_llm(prompt)
"mappings": "properties": "content": "type": "text", "analyzer": "standard" , "equation_latex": "type": "text", "analyzer": "latex_analyzer" , "page_number": "type": "integer" , "settings": "analysis": "analyzer": "latex_analyzer": "tokenizer": "standard", "filter": ["lowercase", "latex_symbols"] , "filter": "latex_symbols": "type": "pattern_replace", "pattern": "[^\\\\a-zA-Z0-9]", "replacement": " "