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import os
import functools
import numpy as np
import fastapi
import voyageai
vo = voyageai.Client(os.environ['VOYAGE_API_KEY'])
theorems = [
theorem.split('--thm--\n')
for theorem in open('theorems.txt').read().split('----thm----\n')[1:]
]
embed = np.load('embed_arr.npy')
app = fastapi.FastAPI()
top_n = 50
@functools.lru_cache(maxsize=2*1024*1024*1024//(400 + top_n*8))
def lru_search(query):
query_embed = vo.embed([query], model="voyage-large-2-instruct", input_type="query").embeddings[0]
similarities = np.dot(embed, query_embed)
top = np.argpartition(similarities, -top_n)[-top_n:]
top = top[np.argsort(similarities[top])][::-1]
sim = similarities[top]
return top, sim
@app.get("/search")
def search(query: str):
# if query is too long, throw an error
if len(query) > 400:
raise fastapi.HTTPException(status_code=400, detail="Query is too long")
top, sim = lru_search(query)
results = []
for i, similarity in zip(top, sim):
module, name, typ, comment = theorems[i]
results.append({
"module": module,
"name": name,
"type": typ,
"comment": comment,
"similarity": similarity
})
return results
@app.get("/")
def read_root():
# HTML
return fastapi.responses.HTMLResponse(
content="""
<html>
<head>
<title>Search Mathlib</title>
<!-- tailwind css -->
<script src="https://cdn.jsdelivr.net/npm/marked/marked.min.js"></script>
<link href="https://cdn.jsdelivr.net/npm/tailwindcss@2.2.19/dist/tailwind.min.css" rel="stylesheet">
</head>
<body class="overflow-scroll mx-auto w-10/12 my-8 text-lg flex flex-col">
<h1 class="text-3xl mx-0.5">Search Mathlib</h1>
<form onsubmit="search(); return false;" class="flex gap-2 mt-2 mb-3">
<input autofocus type="text" name="query" class="grow border p-2 w-full rounded-lg focus:outline-none border-gray-300 focus:ring-1 focus:ring-gray-100">
<button type="submit" class="bg-green-700 text-white p-2 w-24 rounded-lg">Search</button>
</form>
<ul id="results" class="list-disc">
<div>You can search for any object declared in Mathlib and Lean, including theorems, functions, types, syntax, tactics, and metaprogramming functions.</div>
<div>The neural network sees the name, type, file location, and comment of a declaration. Your search terms should match these as closely as possible. </div>
<div>You can find README.md and the code I used in the Files tab. Thank you!</div>
</ul>
<script>
function removeFirstWord(str) {
return str.split(' ').slice(1).join(' ')
}
// https://leanprover-community.github.io/mathlib4_docs/Mathlib/Tactic.Widget.SelectPanelUtils.html#def%20mkSelectionPanelRPC.match_1
async function search() {
const query = document.querySelector('input[name="query"]').value
const response = await fetch(`/search?query=${query}`)
if (!response.ok) {
alert('Error: ' + (await response.json())['detail'])
return
}
const results = await response.json()
const resultsDiv = document.querySelector('#results')
resultsDiv.innerHTML = ''
for (const result of results) {
const resultDiv = document.createElement('li')
const link = "https://leanprover-community.github.io/mathlib4_docs/" + removeFirstWord(result.module).replaceAll('.', '/') + ".html#" + removeFirstWord(result.name)
resultDiv.innerHTML = `
<div class="flex gap-1">
<a href="${link}"
class="text-xl mr-4 text-green-800 hover:text-green-600
">${result.name}</a>
<div class="flex-grow"></div>
<!-- The similarity info is only shown on larger screens -->
<p class="text-gray-800 hidden md:block">Similarity: ${result.similarity.toFixed(3)}</p>
</div>
<div class="flex flex-col">
<p class="font-mono">: ${result.type}</p>
<p class="text-gray-800 mt-1">${result.module}</p>
<p id="comment"></p>
</div>
<div class="border my-1 border-gray-200"></div>
`
if (result.comment.trim() !== '') {
const comment = resultDiv.querySelector('#comment')
comment.innerHTML = `/-- ${marked.marked(result.comment.trim())} -/`
comment.classList.add('bg-gray-50', 'text-gray-900', 'px-2', 'py-1', 'mt-0.5', 'rounded-lg')
}
resultsDiv.appendChild(resultDiv)
}
}
</script>
</body>
</html>
"""
)
'''
to run this with auto-reload, use the following command:
cd data
uvicorn server:app --reload
'''
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