Spaces:
Runtime error
Runtime error
File size: 5,176 Bytes
97fdba5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 |
import asyncio
import logging
import numpy as np
import time
import json
import os
import tempfile
import requests
from fastapi import FastAPI, Header, HTTPException, BackgroundTasks
from fastapi.responses import FileResponse
from aiohttp import ClientSession
from langchain.text_splitter import SpacyTextSplitter
from datasets import Dataset, load_dataset
from tqdm import tqdm
from tqdm.asyncio import tqdm_asyncio
from src.models import chunk_config, embed_config, WebhookPayload
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
HF_TOKEN = os.getenv("HF_TOKEN")
TEI_URL = os.getenv("TEI_URL")
app = FastAPI()
@app.get("/")
async def home():
return FileResponse("home.html")
@app.post("/webhook")
async def post_webhook(
payload: WebhookPayload,
task_queue: BackgroundTasks
):
if not (
payload.event.action == "update"
and payload.event.scope.startswith("repo.content")
and (
payload.repo.name == embed_config.input_dataset
# or payload.repo.name == chunk_config.input_dataset
)
and payload.repo.type == "dataset"
):
# no-op
logger.info("Update detected, no action taken")
return {"processed": False}
if payload.repo.name == chunk_config.input_dataset:
task_queue.add_task(chunk_dataset)
task_queue.add_task(embed_dataset)
return {"processed": True}
"""
CHUNKING
"""
class Chunker:
def __init__(self, strategy, split_seq, chunk_len):
self.split_seq = split_seq
self.chunk_len = chunk_len
if strategy == "spacy":
self.split = SpacyTextSplitter().split_text
if strategy == "sequence":
self.split = self.seq_splitter
if strategy == "constant":
self.split = self.const_splitter
def seq_splitter(self, text):
return text.split(self.split_seq)
def const_splitter(self, text):
return [
text[i * self.chunk_len:(i + 1) * self.chunk_len]
for i in range(int(np.ceil(len(text) / self.chunk_len)))
]
def chunk_generator(input_dataset, chunker):
for i in tqdm(range(len(input_dataset))):
chunks = chunker.split(input_dataset[i][chunk_config.input_text_col])
for chunk in chunks:
if chunk:
yield {chunk_config.input_text_col: chunk}
def chunk_dataset():
logger.info("Update detected, chunking is scheduled")
input_ds = load_dataset(chunk_config.input_dataset, split=chunk_config.input_splits)
chunker = Chunker(
strategy=chunk_config.strategy,
split_seq=chunk_config.split_seq,
chunk_len=chunk_config.chunk_len
)
dataset = Dataset.from_generator(
chunk_generator,
gen_kwargs={
"input_dataset": input_ds,
"chunker": chunker
}
)
dataset.push_to_hub(
chunk_config.output_dataset,
private=chunk_config.private,
token=HF_TOKEN
)
logger.info("Done chunking")
return {"processed": True}
"""
EMBEDDING
"""
async def embed_sent(sentence, semaphore, tei_url, tmp_file):
async with semaphore:
payload = {
"inputs": sentence,
"truncate": True
}
async with ClientSession(
headers={
"Content-Type": "application/json",
"Authorization": f"Bearer {HF_TOKEN}"
}
) as session:
async with session.post(tei_url, json=payload) as resp:
if resp.status != 200:
raise RuntimeError(await resp.text())
result = await resp.json()
tmp_file.write(
json.dumps({"vector": result[0], chunk_config.input_text_col: sentence}) + "\n"
)
async def embed(input_ds, tei_url, temp_file):
semaphore = asyncio.BoundedSemaphore(embed_config.semaphore_bound)
jobs = [
asyncio.create_task(embed_sent(row[chunk_config.input_text_col], semaphore, tei_url, temp_file))
for row in input_ds if row[chunk_config.input_text_col].strip()
]
logger.info(f"num chunks to embed: {len(jobs)}")
tic = time.time()
await tqdm_asyncio.gather(*jobs)
logger.info(f"embed time: {time.time() - tic}")
def wake_up_endpoint(url):
while requests.get(
url=url,
headers={"Authorization": f"Bearer {HF_TOKEN}"}
).status_code != 200:
time.sleep(2)
logger.info("TEI endpoint is up")
def embed_dataset():
logger.info("Update detected, embedding is scheduled")
wake_up_endpoint(embed_config.tei_url)
input_ds = load_dataset(embed_config.input_dataset, split=embed_config.input_splits)
with tempfile.NamedTemporaryFile(mode="a", suffix=".jsonl") as temp_file:
asyncio.run(embed(input_ds, embed_config.tei_url, temp_file))
dataset = Dataset.from_json(temp_file.name)
dataset.push_to_hub(
embed_config.output_dataset,
private=embed_config.private,
token=HF_TOKEN
)
logger.info("Done embedding")
|