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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 | |
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 | |
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 | |
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"> | |
</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 | |
''' | |