Spaces:
Sleeping
Sleeping
Created using Colaboratory
Browse files
notebooks/05-Improve_Prompts_+_Add_Source.ipynb
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"<a href=\"https://colab.research.google.com/github/towardsai/ai-tutor-rag-system/blob/main/notebooks/05-Improve_Prompts_%2B_Add_Source.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
|
| 1057 |
+
]
|
| 1058 |
+
},
|
| 1059 |
+
{
|
| 1060 |
+
"cell_type": "markdown",
|
| 1061 |
+
"source": [
|
| 1062 |
+
"# Install Packages and Setup Variables"
|
| 1063 |
+
],
|
| 1064 |
+
"metadata": {
|
| 1065 |
+
"id": "5BGJ3fxhOk2V"
|
| 1066 |
+
}
|
| 1067 |
+
},
|
| 1068 |
+
{
|
| 1069 |
+
"cell_type": "code",
|
| 1070 |
+
"execution_count": null,
|
| 1071 |
+
"metadata": {
|
| 1072 |
+
"id": "QPJzr-I9XQ7l",
|
| 1073 |
+
"colab": {
|
| 1074 |
+
"base_uri": "https://localhost:8080/"
|
| 1075 |
+
},
|
| 1076 |
+
"outputId": "c31cde74-f2a8-4c1b-adce-a8cce4268ec2"
|
| 1077 |
+
},
|
| 1078 |
+
"outputs": [
|
| 1079 |
+
{
|
| 1080 |
+
"output_type": "stream",
|
| 1081 |
+
"name": "stdout",
|
| 1082 |
+
"text": [
|
| 1083 |
+
"\u001b[2K \u001b[90mββββββββββββββββββββββββββββββββββββββββ\u001b[0m \u001b[32m15.7/15.7 MB\u001b[0m \u001b[31m52.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
| 1084 |
+
"\u001b[2K \u001b[90mββββββββββββββββββββββββββββββββββββββββ\u001b[0m \u001b[32m225.4/225.4 kB\u001b[0m \u001b[31m22.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
| 1085 |
+
"\u001b[2K \u001b[90mββββββββββββββββββββββββββββββββββββββββ\u001b[0m \u001b[32m51.7/51.7 kB\u001b[0m \u001b[31m3.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
| 1086 |
+
"\u001b[2K \u001b[90mββββββββββββββββββββββββββββββββββββββββ\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m47.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
| 1087 |
+
"\u001b[2K \u001b[90mββββββββββββββββββββββββββββββββββββββββ\u001b[0m \u001b[32m508.6/508.6 kB\u001b[0m \u001b[31m23.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
| 1088 |
+
"\u001b[2K \u001b[90mββββββββββββββββββββββββββββββββββββββββ\u001b[0m \u001b[32m79.9/79.9 MB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
| 1089 |
+
"\u001b[2K \u001b[90mββββββββββββββββββββββββββββββββββββββββ\u001b[0m \u001b[32m45.7/45.7 kB\u001b[0m \u001b[31m4.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
| 1090 |
+
"\u001b[2K \u001b[90mββββββββββββββββββββββββββββββββββββββββ\u001b[0m \u001b[32m147.9/147.9 kB\u001b[0m \u001b[31m15.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
| 1091 |
+
"\u001b[2K \u001b[90mββββββββββββββββββββββββββββββββββββββββ\u001b[0m \u001b[32m75.9/75.9 kB\u001b[0m \u001b[31m7.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
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+
"\u001b[2K \u001b[90mββββββββββββββββββββββββββββββββββββββββ\u001b[0m \u001b[32m3.1/3.1 MB\u001b[0m \u001b[31m79.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
| 1093 |
+
"\u001b[2K \u001b[90mββββββββββββββββββββββββββββββββββββββββ\u001b[0m \u001b[32m2.4/2.4 MB\u001b[0m \u001b[31m71.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
| 1094 |
+
"\u001b[2K \u001b[90mββββββββββββββββββββββββββββββββββββββββ\u001b[0m \u001b[32m92.0/92.0 kB\u001b[0m \u001b[31m9.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
| 1095 |
+
"\u001b[2K \u001b[90mββββββββββββββββββββββββββββββββββββββββ\u001b[0m \u001b[32m60.7/60.7 kB\u001b[0m \u001b[31m5.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
| 1096 |
+
"\u001b[2K \u001b[90mββββββββββββββββββββββββββββββββββββββββ\u001b[0m \u001b[32m40.8/40.8 kB\u001b[0m \u001b[31m3.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
| 1097 |
+
"\u001b[2K \u001b[90mββββββββββββββββββββββββββββββββββββββββ\u001b[0m \u001b[32m5.4/5.4 MB\u001b[0m \u001b[31m61.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
| 1098 |
+
"\u001b[2K \u001b[90mββββββββββββββββββββββββββββββββββββββββ\u001b[0m \u001b[32m6.8/6.8 MB\u001b[0m \u001b[31m61.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
| 1099 |
+
"\u001b[2K \u001b[90mββββββββββββββββββββββββββββββββββββββββ\u001b[0m \u001b[32m57.9/57.9 kB\u001b[0m \u001b[31m6.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
| 1100 |
+
"\u001b[2K \u001b[90mββββββββββββββββββββββββββββββββββββββββ\u001b[0m \u001b[32m105.6/105.6 kB\u001b[0m \u001b[31m11.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
| 1101 |
+
"\u001b[2K \u001b[90mββββββββββββββββββββββββββββββββββββββββ\u001b[0m \u001b[32m67.3/67.3 kB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
| 1102 |
+
"\u001b[?25h Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
|
| 1103 |
+
" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
|
| 1104 |
+
" Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
|
| 1105 |
+
"\u001b[2K \u001b[90mββββββββββββββββββββββββββββββββββββββββ\u001b[0m \u001b[32m698.9/698.9 kB\u001b[0m \u001b[31m52.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
| 1106 |
+
"\u001b[2K \u001b[90mββββββββββββββββββββββββββββββββββββββββ\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m64.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
| 1107 |
+
"\u001b[2K \u001b[90mββββββββββββββββββββββββββββββββββββββββ\u001b[0m \u001b[32m67.6/67.6 kB\u001b[0m \u001b[31m6.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
| 1108 |
+
"\u001b[2K \u001b[90mββββββββββββββββββββββββββββββββββββββββ\u001b[0m \u001b[32m71.1/71.1 kB\u001b[0m \u001b[31m7.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
| 1109 |
+
"\u001b[2K \u001b[90mβββββββββββββββββββββοΏ½οΏ½ββββββββββββββββββ\u001b[0m \u001b[32m76.9/76.9 kB\u001b[0m \u001b[31m7.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
| 1110 |
+
"\u001b[2K \u001b[90mββββββββββββββββββββββββββββββββββββββββ\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
| 1111 |
+
"\u001b[2K \u001b[90mββββββββββββββββββββββββββββββββββββββββ\u001b[0m \u001b[32m46.0/46.0 kB\u001b[0m \u001b[31m4.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
| 1112 |
+
"\u001b[2K \u001b[90mββββββββββββββββββββββββββββββββββββββββ\u001b[0m \u001b[32m50.8/50.8 kB\u001b[0m \u001b[31m4.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
| 1113 |
+
"\u001b[2K \u001b[90mββββββββββββββββββββββββββββββββββββββββ\u001b[0m \u001b[32m341.4/341.4 kB\u001b[0m \u001b[31m30.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
| 1114 |
+
"\u001b[2K \u001b[90mββββββββββββββββββββββββββββββββββββββββ\u001b[0m \u001b[32m3.4/3.4 MB\u001b[0m \u001b[31m59.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
| 1115 |
+
"\u001b[2K \u001b[90mββββββββββββββββββββββββββββββββββββββββ\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m70.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
| 1116 |
+
"\u001b[2K \u001b[90mββββββββββββββββββββββββββββββββββββββββ\u001b[0m \u001b[32m130.2/130.2 kB\u001b[0m \u001b[31m13.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
| 1117 |
+
"\u001b[2K \u001b[90mββββββββββββββββββββββββββββββββββββββββ\u001b[0m \u001b[32m49.4/49.4 kB\u001b[0m \u001b[31m5.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
| 1118 |
+
"\u001b[2K \u001b[90mββββββββββββββββββββββββββββββββββββββββ\u001b[0m \u001b[32m86.8/86.8 kB\u001b[0m \u001b[31m8.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
| 1119 |
+
"\u001b[?25h Building wheel for pypika (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
|
| 1120 |
+
"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
|
| 1121 |
+
"tensorflow-probability 0.22.0 requires typing-extensions<4.6.0, but you have typing-extensions 4.9.0 which is incompatible.\u001b[0m\u001b[31m\n",
|
| 1122 |
+
"\u001b[0m"
|
| 1123 |
+
]
|
| 1124 |
+
}
|
| 1125 |
+
],
|
| 1126 |
+
"source": [
|
| 1127 |
+
"!pip install -q llama-index==0.9.21 openai==1.6.0 cohere==4.39 tiktoken==0.5.2 chromadb==0.4.21 kaleido==0.2.1 python-multipart==0.0.6 html2text==2020.1.16"
|
| 1128 |
+
]
|
| 1129 |
+
},
|
| 1130 |
+
{
|
| 1131 |
+
"cell_type": "code",
|
| 1132 |
+
"source": [
|
| 1133 |
+
"import os\n",
|
| 1134 |
+
"\n",
|
| 1135 |
+
"# Set the \"OPENAI_API_KEY\" in the Python environment. Will be used by OpenAI client later.\n",
|
| 1136 |
+
"os.environ[\"OPENAI_API_KEY\"] = \"<YOUR_OPENAI_KEY>\""
|
| 1137 |
+
],
|
| 1138 |
+
"metadata": {
|
| 1139 |
+
"id": "riuXwpSPcvWC"
|
| 1140 |
+
},
|
| 1141 |
+
"execution_count": null,
|
| 1142 |
+
"outputs": []
|
| 1143 |
+
},
|
| 1144 |
+
{
|
| 1145 |
+
"cell_type": "code",
|
| 1146 |
+
"source": [
|
| 1147 |
+
"import nest_asyncio\n",
|
| 1148 |
+
"\n",
|
| 1149 |
+
"nest_asyncio.apply()"
|
| 1150 |
+
],
|
| 1151 |
+
"metadata": {
|
| 1152 |
+
"id": "km-KQOrgr3VB"
|
| 1153 |
+
},
|
| 1154 |
+
"execution_count": null,
|
| 1155 |
+
"outputs": []
|
| 1156 |
+
},
|
| 1157 |
+
{
|
| 1158 |
+
"cell_type": "markdown",
|
| 1159 |
+
"source": [
|
| 1160 |
+
"# Load a Model"
|
| 1161 |
+
],
|
| 1162 |
+
"metadata": {
|
| 1163 |
+
"id": "Bkgi2OrYzF7q"
|
| 1164 |
+
}
|
| 1165 |
+
},
|
| 1166 |
+
{
|
| 1167 |
+
"cell_type": "code",
|
| 1168 |
+
"source": [
|
| 1169 |
+
"from llama_index.llms import OpenAI\n",
|
| 1170 |
+
"\n",
|
| 1171 |
+
"llm = OpenAI(temperature=0.9, model=\"gpt-3.5-turbo\", max_tokens=512)"
|
| 1172 |
+
],
|
| 1173 |
+
"metadata": {
|
| 1174 |
+
"id": "9oGT6crooSSj"
|
| 1175 |
+
},
|
| 1176 |
+
"execution_count": null,
|
| 1177 |
+
"outputs": []
|
| 1178 |
+
},
|
| 1179 |
+
{
|
| 1180 |
+
"cell_type": "markdown",
|
| 1181 |
+
"source": [
|
| 1182 |
+
"# Create a VectoreStore"
|
| 1183 |
+
],
|
| 1184 |
+
"metadata": {
|
| 1185 |
+
"id": "0BwVuJXlzHVL"
|
| 1186 |
+
}
|
| 1187 |
+
},
|
| 1188 |
+
{
|
| 1189 |
+
"cell_type": "code",
|
| 1190 |
+
"source": [
|
| 1191 |
+
"import chromadb\n",
|
| 1192 |
+
"\n",
|
| 1193 |
+
"# create client and a new collection\n",
|
| 1194 |
+
"# chromadb.EphemeralClient saves data in-memory.\n",
|
| 1195 |
+
"chroma_client = chromadb.PersistentClient(path=\"./mini-llama-articles\")\n",
|
| 1196 |
+
"chroma_collection = chroma_client.create_collection(\"mini-llama-articles\")"
|
| 1197 |
+
],
|
| 1198 |
+
"metadata": {
|
| 1199 |
+
"id": "SQP87lHczHKc"
|
| 1200 |
+
},
|
| 1201 |
+
"execution_count": null,
|
| 1202 |
+
"outputs": []
|
| 1203 |
+
},
|
| 1204 |
+
{
|
| 1205 |
+
"cell_type": "code",
|
| 1206 |
+
"source": [
|
| 1207 |
+
"from llama_index.vector_stores import ChromaVectorStore\n",
|
| 1208 |
+
"\n",
|
| 1209 |
+
"# Define a storage context object using the created vector database.\n",
|
| 1210 |
+
"vector_store = ChromaVectorStore(chroma_collection=chroma_collection)"
|
| 1211 |
+
],
|
| 1212 |
+
"metadata": {
|
| 1213 |
+
"id": "zAaGcYMJzHAN"
|
| 1214 |
+
},
|
| 1215 |
+
"execution_count": null,
|
| 1216 |
+
"outputs": []
|
| 1217 |
+
},
|
| 1218 |
+
{
|
| 1219 |
+
"cell_type": "markdown",
|
| 1220 |
+
"source": [
|
| 1221 |
+
"# Load the Dataset (CSV)"
|
| 1222 |
+
],
|
| 1223 |
+
"metadata": {
|
| 1224 |
+
"id": "I9JbAzFcjkpn"
|
| 1225 |
+
}
|
| 1226 |
+
},
|
| 1227 |
+
{
|
| 1228 |
+
"cell_type": "markdown",
|
| 1229 |
+
"source": [
|
| 1230 |
+
"## Download"
|
| 1231 |
+
],
|
| 1232 |
+
"metadata": {
|
| 1233 |
+
"id": "_Tif8-JoRH68"
|
| 1234 |
+
}
|
| 1235 |
+
},
|
| 1236 |
+
{
|
| 1237 |
+
"cell_type": "markdown",
|
| 1238 |
+
"source": [
|
| 1239 |
+
"The dataset includes several articles from the TowardsAI blog, which provide an in-depth explanation of the LLaMA2 model."
|
| 1240 |
+
],
|
| 1241 |
+
"metadata": {
|
| 1242 |
+
"id": "4fQaa1LN1mXL"
|
| 1243 |
+
}
|
| 1244 |
+
},
|
| 1245 |
+
{
|
| 1246 |
+
"cell_type": "code",
|
| 1247 |
+
"source": [
|
| 1248 |
+
"!wget https://raw.githubusercontent.com/AlaFalaki/tutorial_notebooks/main/data/mini-llama-articles.csv"
|
| 1249 |
+
],
|
| 1250 |
+
"metadata": {
|
| 1251 |
+
"colab": {
|
| 1252 |
+
"base_uri": "https://localhost:8080/"
|
| 1253 |
+
},
|
| 1254 |
+
"id": "fQtpDvUzKNzI",
|
| 1255 |
+
"outputId": "9a62a730-6fe0-4542-cfd1-cbb1f84d889e"
|
| 1256 |
+
},
|
| 1257 |
+
"execution_count": null,
|
| 1258 |
+
"outputs": [
|
| 1259 |
+
{
|
| 1260 |
+
"output_type": "stream",
|
| 1261 |
+
"name": "stdout",
|
| 1262 |
+
"text": [
|
| 1263 |
+
"--2024-02-01 14:53:37-- https://raw.githubusercontent.com/AlaFalaki/tutorial_notebooks/main/data/mini-llama-articles.csv\n",
|
| 1264 |
+
"Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
|
| 1265 |
+
"Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
|
| 1266 |
+
"HTTP request sent, awaiting response... 200 OK\n",
|
| 1267 |
+
"Length: 173646 (170K) [text/plain]\n",
|
| 1268 |
+
"Saving to: βmini-llama-articles.csvβ\n",
|
| 1269 |
+
"\n",
|
| 1270 |
+
"\rmini-llama-articles 0%[ ] 0 --.-KB/s \rmini-llama-articles 100%[===================>] 169.58K --.-KB/s in 0.02s \n",
|
| 1271 |
+
"\n",
|
| 1272 |
+
"2024-02-01 14:53:37 (6.64 MB/s) - βmini-llama-articles.csvβ saved [173646/173646]\n",
|
| 1273 |
+
"\n"
|
| 1274 |
+
]
|
| 1275 |
+
}
|
| 1276 |
+
]
|
| 1277 |
+
},
|
| 1278 |
+
{
|
| 1279 |
+
"cell_type": "markdown",
|
| 1280 |
+
"source": [
|
| 1281 |
+
"## Load the Articles"
|
| 1282 |
+
],
|
| 1283 |
+
"metadata": {
|
| 1284 |
+
"id": "zk-4alIxROo8"
|
| 1285 |
+
}
|
| 1286 |
+
},
|
| 1287 |
+
{
|
| 1288 |
+
"cell_type": "code",
|
| 1289 |
+
"source": [
|
| 1290 |
+
"import csv\n",
|
| 1291 |
+
"\n",
|
| 1292 |
+
"rows = []\n",
|
| 1293 |
+
"\n",
|
| 1294 |
+
"# Load the file as a JSON\n",
|
| 1295 |
+
"with open(\"./mini-llama-articles.csv\", mode=\"r\", encoding=\"utf-8\") as file:\n",
|
| 1296 |
+
" csv_reader = csv.reader(file)\n",
|
| 1297 |
+
"\n",
|
| 1298 |
+
" for idx, row in enumerate( csv_reader ):\n",
|
| 1299 |
+
" if idx == 0: continue; # Skip header row\n",
|
| 1300 |
+
" rows.append( row )\n",
|
| 1301 |
+
"\n",
|
| 1302 |
+
"# The number of characters in the dataset.\n",
|
| 1303 |
+
"len( rows )"
|
| 1304 |
+
],
|
| 1305 |
+
"metadata": {
|
| 1306 |
+
"colab": {
|
| 1307 |
+
"base_uri": "https://localhost:8080/"
|
| 1308 |
+
},
|
| 1309 |
+
"id": "_WER5lt0N7c5",
|
| 1310 |
+
"outputId": "3abbb956-12ed-4663-ade9-eb45d0784ee1"
|
| 1311 |
+
},
|
| 1312 |
+
"execution_count": null,
|
| 1313 |
+
"outputs": [
|
| 1314 |
+
{
|
| 1315 |
+
"output_type": "execute_result",
|
| 1316 |
+
"data": {
|
| 1317 |
+
"text/plain": [
|
| 1318 |
+
"14"
|
| 1319 |
+
]
|
| 1320 |
+
},
|
| 1321 |
+
"metadata": {},
|
| 1322 |
+
"execution_count": 27
|
| 1323 |
+
}
|
| 1324 |
+
]
|
| 1325 |
+
},
|
| 1326 |
+
{
|
| 1327 |
+
"cell_type": "code",
|
| 1328 |
+
"source": [
|
| 1329 |
+
"rows[0][3]"
|
| 1330 |
+
],
|
| 1331 |
+
"metadata": {
|
| 1332 |
+
"colab": {
|
| 1333 |
+
"base_uri": "https://localhost:8080/",
|
| 1334 |
+
"height": 35
|
| 1335 |
+
},
|
| 1336 |
+
"id": "NonYMN-Ihx1O",
|
| 1337 |
+
"outputId": "f6fc2829-8a5b-493e-ec97-e1b8d6b66c03"
|
| 1338 |
+
},
|
| 1339 |
+
"execution_count": null,
|
| 1340 |
+
"outputs": [
|
| 1341 |
+
{
|
| 1342 |
+
"output_type": "execute_result",
|
| 1343 |
+
"data": {
|
| 1344 |
+
"text/plain": [
|
| 1345 |
+
"'towards_ai'"
|
| 1346 |
+
],
|
| 1347 |
+
"application/vnd.google.colaboratory.intrinsic+json": {
|
| 1348 |
+
"type": "string"
|
| 1349 |
+
}
|
| 1350 |
+
},
|
| 1351 |
+
"metadata": {},
|
| 1352 |
+
"execution_count": 34
|
| 1353 |
+
}
|
| 1354 |
+
]
|
| 1355 |
+
},
|
| 1356 |
+
{
|
| 1357 |
+
"cell_type": "markdown",
|
| 1358 |
+
"source": [
|
| 1359 |
+
"# Convert to Document obj"
|
| 1360 |
+
],
|
| 1361 |
+
"metadata": {
|
| 1362 |
+
"id": "wxEStggPdxYs"
|
| 1363 |
+
}
|
| 1364 |
+
},
|
| 1365 |
+
{
|
| 1366 |
+
"cell_type": "code",
|
| 1367 |
+
"source": [
|
| 1368 |
+
"from llama_index import Document\n",
|
| 1369 |
+
"\n",
|
| 1370 |
+
"# Convert the chunks to Document objects so the LlamaIndex framework can process them.\n",
|
| 1371 |
+
"documents = [Document(text=row[1], metadata={\"title\": row[0], \"url\": row[2], \"source_name\": row[3]}) for row in rows]"
|
| 1372 |
+
],
|
| 1373 |
+
"metadata": {
|
| 1374 |
+
"id": "lFvW_886dxKX"
|
| 1375 |
+
},
|
| 1376 |
+
"execution_count": null,
|
| 1377 |
+
"outputs": []
|
| 1378 |
+
},
|
| 1379 |
+
{
|
| 1380 |
+
"cell_type": "code",
|
| 1381 |
+
"source": [
|
| 1382 |
+
"len( documents )"
|
| 1383 |
+
],
|
| 1384 |
+
"metadata": {
|
| 1385 |
+
"colab": {
|
| 1386 |
+
"base_uri": "https://localhost:8080/"
|
| 1387 |
+
},
|
| 1388 |
+
"id": "Njoc3XEVkKkf",
|
| 1389 |
+
"outputId": "02f05737-9dbf-4398-fe21-1f385eacbd13"
|
| 1390 |
+
},
|
| 1391 |
+
"execution_count": null,
|
| 1392 |
+
"outputs": [
|
| 1393 |
+
{
|
| 1394 |
+
"output_type": "execute_result",
|
| 1395 |
+
"data": {
|
| 1396 |
+
"text/plain": [
|
| 1397 |
+
"14"
|
| 1398 |
+
]
|
| 1399 |
+
},
|
| 1400 |
+
"metadata": {},
|
| 1401 |
+
"execution_count": 44
|
| 1402 |
+
}
|
| 1403 |
+
]
|
| 1404 |
+
},
|
| 1405 |
+
{
|
| 1406 |
+
"cell_type": "markdown",
|
| 1407 |
+
"source": [
|
| 1408 |
+
"# Transforming"
|
| 1409 |
+
],
|
| 1410 |
+
"metadata": {
|
| 1411 |
+
"id": "S17g2RYOjmf2"
|
| 1412 |
+
}
|
| 1413 |
+
},
|
| 1414 |
+
{
|
| 1415 |
+
"cell_type": "code",
|
| 1416 |
+
"source": [
|
| 1417 |
+
"from llama_index.text_splitter import TokenTextSplitter\n",
|
| 1418 |
+
"\n",
|
| 1419 |
+
"text_splitter = TokenTextSplitter(\n",
|
| 1420 |
+
" separator=\" \", chunk_size=512, chunk_overlap=128\n",
|
| 1421 |
+
")"
|
| 1422 |
+
],
|
| 1423 |
+
"metadata": {
|
| 1424 |
+
"id": "STACTMUR1z9N"
|
| 1425 |
+
},
|
| 1426 |
+
"execution_count": null,
|
| 1427 |
+
"outputs": []
|
| 1428 |
+
},
|
| 1429 |
+
{
|
| 1430 |
+
"cell_type": "code",
|
| 1431 |
+
"source": [
|
| 1432 |
+
"from llama_index.extractors import (\n",
|
| 1433 |
+
" SummaryExtractor,\n",
|
| 1434 |
+
" QuestionsAnsweredExtractor,\n",
|
| 1435 |
+
" KeywordExtractor,\n",
|
| 1436 |
+
")\n",
|
| 1437 |
+
"from llama_index.embeddings import OpenAIEmbedding\n",
|
| 1438 |
+
"from llama_index.ingestion import IngestionPipeline\n",
|
| 1439 |
+
"\n",
|
| 1440 |
+
"pipeline = IngestionPipeline(\n",
|
| 1441 |
+
" transformations=[\n",
|
| 1442 |
+
" text_splitter,\n",
|
| 1443 |
+
" QuestionsAnsweredExtractor(questions=3, llm=llm),\n",
|
| 1444 |
+
" SummaryExtractor(summaries=[\"prev\", \"self\"], llm=llm),\n",
|
| 1445 |
+
" KeywordExtractor(keywords=10, llm=llm),\n",
|
| 1446 |
+
" OpenAIEmbedding(),\n",
|
| 1447 |
+
" ],\n",
|
| 1448 |
+
" vector_store=vector_store\n",
|
| 1449 |
+
")\n",
|
| 1450 |
+
"\n",
|
| 1451 |
+
"pipeline.run(documents=documents, show_progress=True)"
|
| 1452 |
+
],
|
| 1453 |
+
"metadata": {
|
| 1454 |
+
"id": "CtdsIUQ81_hT",
|
| 1455 |
+
"colab": {
|
| 1456 |
+
"base_uri": "https://localhost:8080/",
|
| 1457 |
+
"height": 385,
|
| 1458 |
+
"referenced_widgets": [
|
| 1459 |
+
"88b29c392c7d403488f81903b2395dc1",
|
| 1460 |
+
"81d6771d5cc74dd49cfccc362d5a3c87",
|
| 1461 |
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"1a021f3baf754210ac8696ecc555d968",
|
| 1462 |
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"35d8b3afc94b4294ac0d5b090a49003c",
|
| 1463 |
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"2980851a0f0141fcb4b54aba07366ad9",
|
| 1464 |
+
"553d09bfb9bc4ceeb0882d5bd6061514",
|
| 1465 |
+
"13d692f33c85490aaf761d9444f6c145",
|
| 1466 |
+
"a3f4c5eb33a64fe895d19a5a52426000",
|
| 1467 |
+
"5b0afc9227e6437ca4ca7a4929f47d7a",
|
| 1468 |
+
"39e87e82d7604abfa03879003898259f",
|
| 1469 |
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"e876a66432c24d39a4630601d0f4ce93",
|
| 1470 |
+
"f67382c8ddf248c4b4eeb1b596284917",
|
| 1471 |
+
"61ebc39888444d448f624f1ae848646a",
|
| 1472 |
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"a692807d09ba4ea89bfdae50821ee518",
|
| 1473 |
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"7fbeb4bc3ea743168a1816b0021e092b",
|
| 1474 |
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"ac3c4e3c9b0c4703b5a9148a70c23e21",
|
| 1475 |
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"165ccb061bb843a4b44896df7b4d15b0",
|
| 1476 |
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"f1a9031e7c3445ee80308888d28f2d66",
|
| 1477 |
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"b15acb0416594d44a41df804e42b4cdf",
|
| 1478 |
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"4aeb5362822f490aa6cff491dded8111",
|
| 1479 |
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"faa803d16fc74ddcbb003485a506569c",
|
| 1480 |
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"4f09405744b04ba79ecb18398b49a389"
|
| 1481 |
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]
|
| 1482 |
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},
|
| 1483 |
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"outputId": "b29154c5-8209-4d0e-b546-2e546e6ceeeb"
|
| 1484 |
+
},
|
| 1485 |
+
"execution_count": null,
|
| 1486 |
+
"outputs": [
|
| 1487 |
+
{
|
| 1488 |
+
"output_type": "display_data",
|
| 1489 |
+
"data": {
|
| 1490 |
+
"text/plain": [
|
| 1491 |
+
"Parsing nodes: 0%| | 0/14 [00:00<?, ?it/s]"
|
| 1492 |
+
],
|
| 1493 |
+
"application/vnd.jupyter.widget-view+json": {
|
| 1494 |
+
"version_major": 2,
|
| 1495 |
+
"version_minor": 0,
|
| 1496 |
+
"model_id": "88b29c392c7d403488f81903b2395dc1"
|
| 1497 |
+
}
|
| 1498 |
+
},
|
| 1499 |
+
"metadata": {}
|
| 1500 |
+
},
|
| 1501 |
+
{
|
| 1502 |
+
"output_type": "stream",
|
| 1503 |
+
"name": "stdout",
|
| 1504 |
+
"text": [
|
| 1505 |
+
"464\n",
|
| 1506 |
+
"452\n",
|
| 1507 |
+
"457\n",
|
| 1508 |
+
"465\n",
|
| 1509 |
+
"448\n",
|
| 1510 |
+
"468\n",
|
| 1511 |
+
"434\n",
|
| 1512 |
+
"447\n",
|
| 1513 |
+
"455\n",
|
| 1514 |
+
"445\n",
|
| 1515 |
+
"449\n",
|
| 1516 |
+
"455\n",
|
| 1517 |
+
"431\n",
|
| 1518 |
+
"453\n"
|
| 1519 |
+
]
|
| 1520 |
+
},
|
| 1521 |
+
{
|
| 1522 |
+
"output_type": "stream",
|
| 1523 |
+
"name": "stderr",
|
| 1524 |
+
"text": [
|
| 1525 |
+
"100%|ββββββββββ| 108/108 [00:48<00:00, 2.22it/s]\n",
|
| 1526 |
+
"100%|ββββββββββ| 108/108 [01:05<00:00, 1.65it/s]\n",
|
| 1527 |
+
"100%|ββββββββββ| 108/108 [00:48<00:00, 2.22it/s]\n"
|
| 1528 |
+
]
|
| 1529 |
+
},
|
| 1530 |
+
{
|
| 1531 |
+
"output_type": "display_data",
|
| 1532 |
+
"data": {
|
| 1533 |
+
"text/plain": [
|
| 1534 |
+
"Generating embeddings: 0%| | 0/108 [00:00<?, ?it/s]"
|
| 1535 |
+
],
|
| 1536 |
+
"application/vnd.jupyter.widget-view+json": {
|
| 1537 |
+
"version_major": 2,
|
| 1538 |
+
"version_minor": 0,
|
| 1539 |
+
"model_id": "f67382c8ddf248c4b4eeb1b596284917"
|
| 1540 |
+
}
|
| 1541 |
+
},
|
| 1542 |
+
"metadata": {}
|
| 1543 |
+
}
|
| 1544 |
+
]
|
| 1545 |
+
},
|
| 1546 |
+
{
|
| 1547 |
+
"cell_type": "markdown",
|
| 1548 |
+
"source": [
|
| 1549 |
+
"# Load Indexes"
|
| 1550 |
+
],
|
| 1551 |
+
"metadata": {
|
| 1552 |
+
"id": "EV0ll57p46Dc"
|
| 1553 |
+
}
|
| 1554 |
+
},
|
| 1555 |
+
{
|
| 1556 |
+
"cell_type": "code",
|
| 1557 |
+
"source": [
|
| 1558 |
+
"# Create your index\n",
|
| 1559 |
+
"db = chromadb.PersistentClient(path=\"./mini-llama-articles\")\n",
|
| 1560 |
+
"chroma_collection = db.get_or_create_collection(\"mini-llama-articles\")\n",
|
| 1561 |
+
"vector_store = ChromaVectorStore(chroma_collection=chroma_collection)"
|
| 1562 |
+
],
|
| 1563 |
+
"metadata": {
|
| 1564 |
+
"id": "PS215gCGkGD-"
|
| 1565 |
+
},
|
| 1566 |
+
"execution_count": null,
|
| 1567 |
+
"outputs": []
|
| 1568 |
+
},
|
| 1569 |
+
{
|
| 1570 |
+
"cell_type": "code",
|
| 1571 |
+
"source": [
|
| 1572 |
+
"# Create your index\n",
|
| 1573 |
+
"from llama_index import VectorStoreIndex\n",
|
| 1574 |
+
"\n",
|
| 1575 |
+
"index = VectorStoreIndex.from_vector_store(vector_store)"
|
| 1576 |
+
],
|
| 1577 |
+
"metadata": {
|
| 1578 |
+
"id": "HbT3-kRO4Qpt"
|
| 1579 |
+
},
|
| 1580 |
+
"execution_count": null,
|
| 1581 |
+
"outputs": []
|
| 1582 |
+
},
|
| 1583 |
+
{
|
| 1584 |
+
"cell_type": "code",
|
| 1585 |
+
"source": [
|
| 1586 |
+
"query_engine = index.as_query_engine()"
|
| 1587 |
+
],
|
| 1588 |
+
"metadata": {
|
| 1589 |
+
"id": "sb61DWU84bHP"
|
| 1590 |
+
},
|
| 1591 |
+
"execution_count": null,
|
| 1592 |
+
"outputs": []
|
| 1593 |
+
},
|
| 1594 |
+
{
|
| 1595 |
+
"cell_type": "code",
|
| 1596 |
+
"source": [
|
| 1597 |
+
"res = query_engine.query(\"How many parameters LLaMA2 model has?\")"
|
| 1598 |
+
],
|
| 1599 |
+
"metadata": {
|
| 1600 |
+
"id": "G32W2LMMCmnv"
|
| 1601 |
+
},
|
| 1602 |
+
"execution_count": null,
|
| 1603 |
+
"outputs": []
|
| 1604 |
+
},
|
| 1605 |
+
{
|
| 1606 |
+
"cell_type": "code",
|
| 1607 |
+
"source": [
|
| 1608 |
+
"res.response"
|
| 1609 |
+
],
|
| 1610 |
+
"metadata": {
|
| 1611 |
+
"colab": {
|
| 1612 |
+
"base_uri": "https://localhost:8080/",
|
| 1613 |
+
"height": 35
|
| 1614 |
+
},
|
| 1615 |
+
"id": "obc20cU5Cxf2",
|
| 1616 |
+
"outputId": "fafff42a-b10d-47e9-8b7f-b0b8d256e31c"
|
| 1617 |
+
},
|
| 1618 |
+
"execution_count": null,
|
| 1619 |
+
"outputs": [
|
| 1620 |
+
{
|
| 1621 |
+
"output_type": "execute_result",
|
| 1622 |
+
"data": {
|
| 1623 |
+
"text/plain": [
|
| 1624 |
+
"'The LLaMA2 model has four different parameter sizes: 7 billion, 13 billion, 34 billion, and 70 billion.'"
|
| 1625 |
+
],
|
| 1626 |
+
"application/vnd.google.colaboratory.intrinsic+json": {
|
| 1627 |
+
"type": "string"
|
| 1628 |
+
}
|
| 1629 |
+
},
|
| 1630 |
+
"metadata": {},
|
| 1631 |
+
"execution_count": 131
|
| 1632 |
+
}
|
| 1633 |
+
]
|
| 1634 |
+
},
|
| 1635 |
+
{
|
| 1636 |
+
"cell_type": "code",
|
| 1637 |
+
"source": [
|
| 1638 |
+
"for src in res.source_nodes:\n",
|
| 1639 |
+
" print(\"Node ID\\t\", src.node_id)\n",
|
| 1640 |
+
" print(\"Title\\t\", src.metadata['title'])\n",
|
| 1641 |
+
" print(\"Text\\t\", src.text)\n",
|
| 1642 |
+
" print(\"Score\\t\", src.score)\n",
|
| 1643 |
+
" print(\"-_\"*20)"
|
| 1644 |
+
],
|
| 1645 |
+
"metadata": {
|
| 1646 |
+
"colab": {
|
| 1647 |
+
"base_uri": "https://localhost:8080/"
|
| 1648 |
+
},
|
| 1649 |
+
"id": "oIAO-saJCzYe",
|
| 1650 |
+
"outputId": "7756d876-a474-42c3-e1a6-a4c042b8fbab"
|
| 1651 |
+
},
|
| 1652 |
+
"execution_count": null,
|
| 1653 |
+
"outputs": [
|
| 1654 |
+
{
|
| 1655 |
+
"output_type": "stream",
|
| 1656 |
+
"name": "stdout",
|
| 1657 |
+
"text": [
|
| 1658 |
+
"Node ID\t cccc8c69-1648-469b-8a2a-ea7f003dbe27\n",
|
| 1659 |
+
"Title\t Fine-Tuning a Llama-2 7B Model for Python Code Generation\n",
|
| 1660 |
+
"Text\t New Llama-2 model In mid-July, Meta released its new family of pre-trained and finetuned models called Llama-2, with an open source and commercial character to facilitate its use and expansion. The base model was released with a chat version and sizes 7B, 13B, and 70B. Together with the models, the corresponding papers were published describing their characteristics and relevant points of the learning process, which provide very interesting information on the subject. For pre-training, 40% more tokens were used, reaching 2T, the context length was doubled and the grouped-query attention (GQA) technique was applied to speed up inference on the heavier 70B model. On the standard transformer architecture, RMSNorm normalization, SwiGLU activation, and rotatory positional embedding are used, the context length reaches 4096 tokens, and an Adam optimizer is applied with a cosine learning rate schedule, a weight decay of 0.1 and gradient clipping. The dataset for tuning For our tuning process, we will take a dataset containing about 18,000 examples where the model is asked to build a Python code that solves a given task. This is an extraction of the original dataset [2], where only the Python language examples are selected. Each row contains the description of the task to be solved, an example of data input to the task if applicable, and the generated code fragment that solves the task is provided [3]. Creating the prompt To carry out an instruction fine-tuning, we must transform each one of our data examples as if it were an instruction, outlining its main sections as follows: Output: Fine-tuning the model To carry out this stage, we have used the Google Colab environment, where we have developed a notebook that allows us to run the training in an interactive way and also a Python script to run the training in unattended mode. For the first test runs, a T4 instance with a high RAM capacity is enough, but when it comes to running the whole dataset and epochs, we have opted to use an A100 instance in order to speed up the training and ensure that its execution time is reasonable. In order to be able to\n",
|
| 1661 |
+
"Score\t 0.768167879324151\n",
|
| 1662 |
+
"-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_\n",
|
| 1663 |
+
"Node ID\t 2b138886-4ff7-4c96-9d01-ea883d4f34ed\n",
|
| 1664 |
+
"Title\t Fine-Tuning a Llama-2 7B Model for Python Code Generation\n",
|
| 1665 |
+
"Text\t weights As we mention, we have trained \"modification weights\" on the base model, our final model requires merging the pretrained model and the adapters in a single model. You can find and download the model in my Hugging Face account edumunozsala/llama-27b-int4-python-code-20k. Give it a try! Inferencing or generating Python code And finally, we will show you how you can download the model from the Hugging Face Hub and call the model to generate an accurate result: Thanks to Maxime Labonne for an excellent article [9] and Philipp Schmid who provides an inspiring code [8]. Their articles are a must-read for everyone interested in Llama 2 and model fine-tuning. And it is all I have to mention, I hope you find useful this article and claps are welcome!! You can Follow me and Subscribe to my articles, or even connect to me via Linkedin. The code is available in my Github Repository. References [1] Llama-2 paper [2] Link to the original dataset in the Huggingface hub [3] Link to the used dataset in the Huggingface hub [4] Fine-tuning a GPT - LoRA by Chris Kuo/Dr. Dataman [5] Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, & Weizhu Chen. (2021). LoRA: Low-Rank Adaptation of Large Language Models. arXiv:2106.09685 [6]. QLoRa: Efficient Finetuning of QuantizedLLMs [7] Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning [8] Extended Guide: Instruction-tune Llama 2 by Philipp Schmid. [9] Fine-Tune Your Own Llama 2 Model in a Colab Notebook by Maxime Labonne [10]. My Github Repository\n",
|
| 1666 |
+
"Score\t 0.7586440479430622\n",
|
| 1667 |
+
"-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_\n"
|
| 1668 |
+
]
|
| 1669 |
+
}
|
| 1670 |
+
]
|
| 1671 |
+
},
|
| 1672 |
+
{
|
| 1673 |
+
"cell_type": "markdown",
|
| 1674 |
+
"source": [
|
| 1675 |
+
"# No Metadata"
|
| 1676 |
+
],
|
| 1677 |
+
"metadata": {
|
| 1678 |
+
"id": "wvOhbZvl95di"
|
| 1679 |
+
}
|
| 1680 |
+
},
|
| 1681 |
+
{
|
| 1682 |
+
"cell_type": "code",
|
| 1683 |
+
"source": [
|
| 1684 |
+
"from llama_index import Document\n",
|
| 1685 |
+
"\n",
|
| 1686 |
+
"# Convert the chunks to Document objects so the LlamaIndex framework can process them.\n",
|
| 1687 |
+
"documents = [Document(text=row[1]) for row in rows]"
|
| 1688 |
+
],
|
| 1689 |
+
"metadata": {
|
| 1690 |
+
"id": "y5w5ZPbR97iK"
|
| 1691 |
+
},
|
| 1692 |
+
"execution_count": null,
|
| 1693 |
+
"outputs": []
|
| 1694 |
+
},
|
| 1695 |
+
{
|
| 1696 |
+
"cell_type": "code",
|
| 1697 |
+
"source": [
|
| 1698 |
+
"from llama_index.text_splitter import TokenTextSplitter\n",
|
| 1699 |
+
"\n",
|
| 1700 |
+
"text_splitter = TokenTextSplitter(\n",
|
| 1701 |
+
" separator=\" \", chunk_size=512, chunk_overlap=128\n",
|
| 1702 |
+
")"
|
| 1703 |
+
],
|
| 1704 |
+
"metadata": {
|
| 1705 |
+
"id": "WzF8LYgH9-o0"
|
| 1706 |
+
},
|
| 1707 |
+
"execution_count": null,
|
| 1708 |
+
"outputs": []
|
| 1709 |
+
},
|
| 1710 |
+
{
|
| 1711 |
+
"cell_type": "code",
|
| 1712 |
+
"source": [
|
| 1713 |
+
"from llama_index.extractors import (\n",
|
| 1714 |
+
" SummaryExtractor,\n",
|
| 1715 |
+
" QuestionsAnsweredExtractor,\n",
|
| 1716 |
+
" KeywordExtractor,\n",
|
| 1717 |
+
")\n",
|
| 1718 |
+
"from llama_index.embeddings import OpenAIEmbedding\n",
|
| 1719 |
+
"from llama_index.ingestion import IngestionPipeline\n",
|
| 1720 |
+
"\n",
|
| 1721 |
+
"pipeline = IngestionPipeline(\n",
|
| 1722 |
+
" transformations=[\n",
|
| 1723 |
+
" text_splitter\n",
|
| 1724 |
+
" ]\n",
|
| 1725 |
+
")\n",
|
| 1726 |
+
"\n",
|
| 1727 |
+
"nodes = pipeline.run(documents=documents, show_progress=True)"
|
| 1728 |
+
],
|
| 1729 |
+
"metadata": {
|
| 1730 |
+
"colab": {
|
| 1731 |
+
"base_uri": "https://localhost:8080/",
|
| 1732 |
+
"height": 299,
|
| 1733 |
+
"referenced_widgets": [
|
| 1734 |
+
"685e146910634868b154ba03885d8b4c",
|
| 1735 |
+
"fefe61069a1a416cbb512e1f006c82b0",
|
| 1736 |
+
"3cac12c147134eb4b71561f17345712d",
|
| 1737 |
+
"810b0e9e274a433892f87040283b4db9",
|
| 1738 |
+
"11629aeef5a146e79869fded9f603d6d",
|
| 1739 |
+
"dfe6494357d040ac8d51d03069822e41",
|
| 1740 |
+
"190de89f93d048658ee4788ee4af4418",
|
| 1741 |
+
"835fd209022c4086b509bf42084243b2",
|
| 1742 |
+
"4b27da15dad34e839a4a02dab06d3e5a",
|
| 1743 |
+
"afa339155f4b4ffaa5fc70457b6b7a69",
|
| 1744 |
+
"e8d0668d65dd4743b25c1bc74e1d8057"
|
| 1745 |
+
]
|
| 1746 |
+
},
|
| 1747 |
+
"id": "hYGkf-Rf-DKd",
|
| 1748 |
+
"outputId": "a1117309-317c-42c0-9b66-8efe3af493f4"
|
| 1749 |
+
},
|
| 1750 |
+
"execution_count": null,
|
| 1751 |
+
"outputs": [
|
| 1752 |
+
{
|
| 1753 |
+
"output_type": "display_data",
|
| 1754 |
+
"data": {
|
| 1755 |
+
"text/plain": [
|
| 1756 |
+
"Parsing nodes: 0%| | 0/14 [00:00<?, ?it/s]"
|
| 1757 |
+
],
|
| 1758 |
+
"application/vnd.jupyter.widget-view+json": {
|
| 1759 |
+
"version_major": 2,
|
| 1760 |
+
"version_minor": 0,
|
| 1761 |
+
"model_id": "685e146910634868b154ba03885d8b4c"
|
| 1762 |
+
}
|
| 1763 |
+
},
|
| 1764 |
+
"metadata": {}
|
| 1765 |
+
},
|
| 1766 |
+
{
|
| 1767 |
+
"output_type": "stream",
|
| 1768 |
+
"name": "stdout",
|
| 1769 |
+
"text": [
|
| 1770 |
+
"510\n",
|
| 1771 |
+
"510\n",
|
| 1772 |
+
"510\n",
|
| 1773 |
+
"510\n",
|
| 1774 |
+
"510\n",
|
| 1775 |
+
"510\n",
|
| 1776 |
+
"510\n",
|
| 1777 |
+
"510\n",
|
| 1778 |
+
"510\n",
|
| 1779 |
+
"510\n",
|
| 1780 |
+
"510\n",
|
| 1781 |
+
"510\n",
|
| 1782 |
+
"510\n",
|
| 1783 |
+
"510\n"
|
| 1784 |
+
]
|
| 1785 |
+
}
|
| 1786 |
+
]
|
| 1787 |
+
},
|
| 1788 |
+
{
|
| 1789 |
+
"cell_type": "code",
|
| 1790 |
+
"source": [
|
| 1791 |
+
"from llama_index import ServiceContext\n",
|
| 1792 |
+
"\n",
|
| 1793 |
+
"index_no_metadata = VectorStoreIndex(\n",
|
| 1794 |
+
" nodes=nodes,\n",
|
| 1795 |
+
" service_context=ServiceContext.from_defaults(llm=OpenAI(model=\"gpt-3.5-turbo\")),\n",
|
| 1796 |
+
")"
|
| 1797 |
+
],
|
| 1798 |
+
"metadata": {
|
| 1799 |
+
"id": "2FR8rgOd-Jt2"
|
| 1800 |
+
},
|
| 1801 |
+
"execution_count": null,
|
| 1802 |
+
"outputs": []
|
| 1803 |
+
},
|
| 1804 |
+
{
|
| 1805 |
+
"cell_type": "code",
|
| 1806 |
+
"source": [
|
| 1807 |
+
"query_engine_no_metadata = index_no_metadata.as_query_engine()"
|
| 1808 |
+
],
|
| 1809 |
+
"metadata": {
|
| 1810 |
+
"id": "HfzZ7Xyx-mbX"
|
| 1811 |
+
},
|
| 1812 |
+
"execution_count": null,
|
| 1813 |
+
"outputs": []
|
| 1814 |
+
},
|
| 1815 |
+
{
|
| 1816 |
+
"cell_type": "code",
|
| 1817 |
+
"source": [
|
| 1818 |
+
"res = query_engine_no_metadata.query(\"How many parameters LLaMA2 model has?\")"
|
| 1819 |
+
],
|
| 1820 |
+
"metadata": {
|
| 1821 |
+
"id": "n8WQJFuLD4FW"
|
| 1822 |
+
},
|
| 1823 |
+
"execution_count": null,
|
| 1824 |
+
"outputs": []
|
| 1825 |
+
},
|
| 1826 |
+
{
|
| 1827 |
+
"cell_type": "code",
|
| 1828 |
+
"source": [
|
| 1829 |
+
"res.response"
|
| 1830 |
+
],
|
| 1831 |
+
"metadata": {
|
| 1832 |
+
"colab": {
|
| 1833 |
+
"base_uri": "https://localhost:8080/",
|
| 1834 |
+
"height": 35
|
| 1835 |
+
},
|
| 1836 |
+
"id": "uZw_S9gNGS17",
|
| 1837 |
+
"outputId": "b7ce3cd0-296d-400a-d12a-6b39dd2c008a"
|
| 1838 |
+
},
|
| 1839 |
+
"execution_count": null,
|
| 1840 |
+
"outputs": [
|
| 1841 |
+
{
|
| 1842 |
+
"output_type": "execute_result",
|
| 1843 |
+
"data": {
|
| 1844 |
+
"text/plain": [
|
| 1845 |
+
"'The context information does not provide any information about the number of parameters in the LLaMA2 model.'"
|
| 1846 |
+
],
|
| 1847 |
+
"application/vnd.google.colaboratory.intrinsic+json": {
|
| 1848 |
+
"type": "string"
|
| 1849 |
+
}
|
| 1850 |
+
},
|
| 1851 |
+
"metadata": {},
|
| 1852 |
+
"execution_count": 134
|
| 1853 |
+
}
|
| 1854 |
+
]
|
| 1855 |
+
},
|
| 1856 |
+
{
|
| 1857 |
+
"cell_type": "code",
|
| 1858 |
+
"source": [
|
| 1859 |
+
"for src in res.source_nodes:\n",
|
| 1860 |
+
" print( src )\n",
|
| 1861 |
+
" print(\"-_\"*20)"
|
| 1862 |
+
],
|
| 1863 |
+
"metadata": {
|
| 1864 |
+
"colab": {
|
| 1865 |
+
"base_uri": "https://localhost:8080/"
|
| 1866 |
+
},
|
| 1867 |
+
"id": "V6Rm7v8eD3xh",
|
| 1868 |
+
"outputId": "88ede9b3-c322-4968-f99d-b27e482ca050"
|
| 1869 |
+
},
|
| 1870 |
+
"execution_count": null,
|
| 1871 |
+
"outputs": [
|
| 1872 |
+
{
|
| 1873 |
+
"output_type": "stream",
|
| 1874 |
+
"name": "stdout",
|
| 1875 |
+
"text": [
|
| 1876 |
+
"Node ID: 895debf4-60ad-4156-8f52-cdddf03b1138\n",
|
| 1877 |
+
"Text: I. Llama 2: Revolutionizing Commercial Use Unlike its\n",
|
| 1878 |
+
"predecessor Llama 1, which was limited to research use, Llama 2\n",
|
| 1879 |
+
"represents a major advancement as an open-source commercial model.\n",
|
| 1880 |
+
"Businesses can now integrate Llama 2 into products to create AI-\n",
|
| 1881 |
+
"powered applications. Availability on Azure and AWS facilitates fine-\n",
|
| 1882 |
+
"tuning and adoption. However,...\n",
|
| 1883 |
+
"Score: 0.852\n",
|
| 1884 |
+
"\n",
|
| 1885 |
+
"-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_\n",
|
| 1886 |
+
"Node ID: a919ca6a-bdda-4d92-a7b2-ab4048cdc0d8\n",
|
| 1887 |
+
"Text: basis. The sharing of codes and weights allows other researchers\n",
|
| 1888 |
+
"to test new approaches in LLMs. The LLaMA models have a range of 7\n",
|
| 1889 |
+
"billion to 65 billion parameters. LLaMA-65B can be compared to\n",
|
| 1890 |
+
"DeepMind's Chinchilla and Google's PaLM. Publicly available unlabeled\n",
|
| 1891 |
+
"data was used to train these models, and training smaller foundational\n",
|
| 1892 |
+
"models requ...\n",
|
| 1893 |
+
"Score: 0.830\n",
|
| 1894 |
+
"\n",
|
| 1895 |
+
"-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_\n"
|
| 1896 |
+
]
|
| 1897 |
+
}
|
| 1898 |
+
]
|
| 1899 |
+
}
|
| 1900 |
+
]
|
| 1901 |
+
}
|