161 lines
4.1 KiB
Plaintext
161 lines
4.1 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Embedding Model"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Launch A Server"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Embedding server is ready. Proceeding with the next steps.\n"
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]
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}
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],
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"source": [
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"import subprocess\n",
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"import time\n",
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"import requests\n",
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"\n",
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"# Equivalent to running this in the shell:\n",
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"# python -m sglang.launch_server --model-path Alibaba-NLP/gte-Qwen2-7B-instruct --port 30010 --host 0.0.0.0 --is-embedding --log-level error\n",
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"embedding_process = subprocess.Popen(\n",
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" [\n",
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" \"python\",\n",
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" \"-m\",\n",
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" \"sglang.launch_server\",\n",
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" \"--model-path\",\n",
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" \"Alibaba-NLP/gte-Qwen2-7B-instruct\",\n",
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" \"--port\",\n",
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" \"30010\",\n",
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" \"--host\",\n",
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" \"0.0.0.0\",\n",
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" \"--is-embedding\",\n",
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" \"--log-level\",\n",
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" \"error\",\n",
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" ],\n",
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" text=True,\n",
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" stdout=subprocess.DEVNULL,\n",
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" stderr=subprocess.DEVNULL,\n",
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")\n",
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"\n",
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"while True:\n",
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" try:\n",
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" response = requests.get(\n",
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" \"http://localhost:30010/v1/models\",\n",
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" headers={\"Authorization\": \"Bearer None\"},\n",
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" )\n",
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" if response.status_code == 200:\n",
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" break\n",
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" except requests.exceptions.RequestException:\n",
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" time.sleep(1)\n",
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"\n",
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"print(\"Embedding server is ready. Proceeding with the next steps.\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Use Curl"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[0.0083160400390625, 0.0006804466247558594, -0.00809478759765625, -0.0006995201110839844, 0.0143890380859375, -0.0090179443359375, 0.01238250732421875, 0.00209808349609375, 0.0062103271484375, -0.003047943115234375]\n"
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]
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}
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],
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"source": [
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"# Get the first 10 elements of the embedding\n",
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"\n",
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"! curl -s http://localhost:30010/v1/embeddings \\\n",
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" -H \"Content-Type: application/json\" \\\n",
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" -H \"Authorization: Bearer None\" \\\n",
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" -d '{\"model\": \"Alibaba-NLP/gte-Qwen2-7B-instruct\", \"input\": \"Once upon a time\"}' \\\n",
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" | python3 -c \"import sys, json; print(json.load(sys.stdin)['data'][0]['embedding'][:10])\""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Using OpenAI Compatible API"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[0.00603485107421875, -0.0190582275390625, -0.01273345947265625, 0.01552581787109375, 0.0066680908203125, -0.0135955810546875, 0.01131439208984375, 0.0013713836669921875, -0.0089874267578125, 0.021759033203125]\n"
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]
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}
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],
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"source": [
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"import openai\n",
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"\n",
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"client = openai.Client(\n",
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" base_url=\"http://127.0.0.1:30010/v1\", api_key=\"None\"\n",
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")\n",
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"\n",
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"# Text embedding example\n",
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"response = client.embeddings.create(\n",
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" model=\"Alibaba-NLP/gte-Qwen2-7B-instruct\",\n",
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" input=\"How are you today\",\n",
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")\n",
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"\n",
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"embedding = response.data[0].embedding[:10]\n",
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"print(embedding)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "AlphaMeemory",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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