Spaces:
Runtime error
Runtime error
File size: 10,708 Bytes
649fdfc |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 |
{
"cells": [
{
"cell_type": "markdown",
"id": "a0f21cb1-fbc8-4282-b902-f47d92974df8",
"metadata": {},
"source": [
"# Pre-requisites"
]
},
{
"cell_type": "markdown",
"id": "3102abce-ea42-4da6-8c98-c6dd4edf7f0b",
"metadata": {},
"source": [
"## Start TEI\n",
"Run [TEI](https://github.com/huggingface/text-embeddings-inference#docker), I have this running in a nvidia-docker container, but you can install as you like. \n",
"\n",
"Note that as its running, its always going to pull the latest. Its at a very early stage at the time of writing. \n",
"\n",
"I chose the smaller [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) instead of the large. Its just as good on [mteb/leaderboard](https://huggingface.co/spaces/mteb/leaderboard) but its faster and smaller. TEI is fast, but this will make our life easier for storage and retrieval.\n",
"\n",
"I use the `revision=refs/pr/1` because this has the pull request with [safetensors](https://github.com/huggingface/safetensors) which is required by TEI. Check out the [pull request](https://huggingface.co/BAAI/bge-base-en-v1.5/discussions/1) if you want to use a different embedding model and it doesnt have safetensors."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "7e873652-8257-4aae-92bc-94e1bac54b73",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"%%bash\n",
"\n",
"# volume=$PWD/data\n",
"# model=BAAI/bge-base-en-v1.5\n",
"# revision=refs/pr/1\n",
"# docker run \\\n",
"# --gpus all \\\n",
"# -p 8080:80 \\\n",
"# -v $volume:/data \\\n",
"# --pull always \\\n",
"# ghcr.io/huggingface/text-embeddings-inference:latest \\\n",
"# --model-id $model \\\n",
"# --revision $revision \\\n",
"# --pooling cls \\\n",
"# --max-batch-tokens 65536"
]
},
{
"cell_type": "markdown",
"id": "86a5ff83-1038-4880-8c90-dc3cab75cb49",
"metadata": {},
"source": [
"## Test Endpoint"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "52edfc97-5b6f-44f9-8d89-8578cf79fae9",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"passed\n"
]
}
],
"source": [
"%%bash\n",
"\n",
"response_code=$(curl -s -o /dev/null -w \"%{http_code}\" 127.0.0.1:8080/embed \\\n",
" -X POST \\\n",
" -d '{\"inputs\":\"What is Deep Learning?\"}' \\\n",
" -H 'Content-Type: application/json')\n",
"\n",
"if [ \"$response_code\" -eq 200 ]; then\n",
" echo \"passed\"\n",
"else\n",
" echo \"failed\"\n",
"fi"
]
},
{
"cell_type": "markdown",
"id": "b1b28232-b65d-41ce-88de-fd70b93a528d",
"metadata": {},
"source": [
"# Imports"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "88408486-566a-4791-8ef2-5ee3e6941156",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from IPython.core.interactiveshell import InteractiveShell\n",
"InteractiveShell.ast_node_interactivity = 'all'"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "abb5186b-ee67-4e1e-882d-3d8d5b4575d4",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import asyncio\n",
"from pathlib import Path\n",
"import pickle\n",
"\n",
"import aiohttp\n",
"from tqdm.notebook import tqdm"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "c4b82ea2-8b30-4c2e-99f0-9a30f2f1bfb7",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/ec2-user/RAGDemo\n"
]
}
],
"source": [
"proj_dir = Path.cwd().parent\n",
"print(proj_dir)"
]
},
{
"cell_type": "markdown",
"id": "76119e74-f601-436d-a253-63c5a19d1c83",
"metadata": {},
"source": [
"# Config"
]
},
{
"cell_type": "markdown",
"id": "0d2bcda7-b245-45e3-a347-34166f217e1e",
"metadata": {},
"source": [
"I'm putting the documents in pickle files. The compression is nice, though its important to note pickles are known to be a security risk."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "f6f74545-54a7-4f41-9f02-96964e1417f0",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"file_in = proj_dir / 'data/processed/simple_wiki_processed.pkl'\n",
"file_out = proj_dir / 'data/processed/simple_wiki_embeddings.pkl'"
]
},
{
"cell_type": "markdown",
"id": "d2dd0df0-4274-45b3-9ee5-0205494e4d75",
"metadata": {
"tags": []
},
"source": [
"# Setup\n",
"Read in our list of dictionaries"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "3c08e039-3686-4eca-9f87-7c469e3f19bc",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 6.24 s, sys: 928 ms, total: 7.17 s\n",
"Wall time: 6.61 s\n"
]
}
],
"source": [
"%%time\n",
"with open(file_in, 'rb') as handle:\n",
" documents = pickle.load(handle)\n",
"\n",
"documents = [document.to_dict() for document in documents]"
]
},
{
"cell_type": "markdown",
"id": "5e73235d-6274-4958-9e57-977afeeb5f1b",
"metadata": {},
"source": [
"# Embed\n",
"## Strategy\n",
"TEI allows multiple concurrent requests, so its important that we dont waste the potential we have. I used the default `max-concurrent-requests` value of `512`, so I want to use that many `MAX_WORKERS`.\n",
"\n",
"Im using an `async` way of making requests that uses `aiohttp` as well as a nice progress bar. "
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "949d6bf8-804f-496b-a59a-834483cc7073",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Constants\n",
"ENDPOINT = \"http://127.0.0.1:8080/embed\"\n",
"HEADERS = {'Content-Type': 'application/json'}\n",
"MAX_WORKERS = 512"
]
},
{
"cell_type": "markdown",
"id": "cf3da8cc-1651-4704-9091-39c2a1b835be",
"metadata": {},
"source": [
"Note that Im using `'truncate':True` as even with our `350` word split earlier, there are always exceptions. Its important that as this scales we have as few issues as possible when embedding. "
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "3353c849-a36c-4047-bb81-93dac6c49b68",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"async def fetch(session, url, document):\n",
" payload = {\"inputs\": [document[\"content\"]], 'truncate':True}\n",
" async with session.post(url, json=payload) as response:\n",
" if response.status == 200:\n",
" resp_json = await response.json()\n",
" # Assuming the server's response contains an 'embedding' field\n",
" document[\"embedding\"] = resp_json[0]\n",
" else:\n",
" print(f\"Error {response.status}: {await response.text()}\")\n",
" # Handle error appropriately if needed\n",
"\n",
"async def main(documents):\n",
" async with aiohttp.ClientSession(headers=HEADERS) as session:\n",
" tasks = [fetch(session, ENDPOINT, doc) for doc in documents]\n",
" await asyncio.gather(*tasks)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "f0d17264-72dc-40be-aa46-17cde38c8189",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f0ff772e915f4432971317e2150b60f2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Processing documents: 0%| | 0/526 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Create a list of async tasks\n",
"tasks = [main(documents[i:i+MAX_WORKERS]) for i in range(0, len(documents), MAX_WORKERS)]\n",
"\n",
"# Add a progress bar for visual feedback and run tasks\n",
"for task in tqdm(tasks, desc=\"Processing documents\"):\n",
" await task"
]
},
{
"cell_type": "markdown",
"id": "f90a0ed7-b5e9-4ae4-9e87-4c04875ebcc9",
"metadata": {},
"source": [
"Lets double check that we got all the embeddings we expected!"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "3950fa88-9961-4b33-9719-d5804509d4cf",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"268980"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"268980"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"count = 0\n",
"for document in documents:\n",
" if len(document['embedding']) == 768:\n",
" count += 1\n",
"count\n",
"len(documents)"
]
},
{
"cell_type": "markdown",
"id": "5b78bfa4-d365-4906-a71c-f444eabf6bf8",
"metadata": {
"tags": []
},
"source": [
"Great, we can see that they match.\n",
"\n",
"Let's write our embeddings to file"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "58d437a5-473f-4eae-9dbf-e8e6992754f6",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 5.68 s, sys: 640 ms, total: 6.32 s\n",
"Wall time: 14.1 s\n"
]
}
],
"source": [
"%%time\n",
"with open(file_out, 'wb') as handle:\n",
" pickle.dump(documents, handle, protocol=pickle.HIGHEST_PROTOCOL)"
]
},
{
"cell_type": "markdown",
"id": "fc1e7cc5-b878-42bb-9fb4-e810f3f5006a",
"metadata": {
"tags": []
},
"source": [
"# Next Steps\n",
"We need to import this into a vector db. "
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|