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},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\")
"
]
},
{
"cell_type": "markdown",
"source": [
"# Install Packages and Setup Variables"
],
"metadata": {
"id": "-zE1h0uQV7uT"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "QPJzr-I9XQ7l"
},
"outputs": [],
"source": [
"!pip install -q llama-index==0.9.21 openai==1.6.0 tiktoken==0.5.2 chromadb==0.4.21 replicate==0.23.1 kaleido==0.2.1 python-multipart==0.0.6 cohere==4.39"
]
},
{
"cell_type": "code",
"source": [
"import os\n",
"\n",
"# Set the \"OPENAI_API_KEY\" and \"REPLICATE_API_TOKEN\" in the Python environment. Will be used by OpenAI client later.\n",
"os.environ[\"OPENAI_API_KEY\"] = \"\"\n",
"os.environ[\"REPLICATE_API_TOKEN\"] = \"\""
],
"metadata": {
"id": "riuXwpSPcvWC"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import nest_asyncio\n",
"\n",
"nest_asyncio.apply()"
],
"metadata": {
"id": "jIEeZzqLbz0J"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# Load a Model"
],
"metadata": {
"id": "Bkgi2OrYzF7q"
}
},
{
"cell_type": "code",
"source": [
"from llama_index.prompts import PromptTemplate\n",
"from llama_index.llms import Replicate\n",
"\n",
"# Use the repicate service to access the LLaMA2-70B chat model\n",
"llm = Replicate(\n",
" model=\"meta/llama-2-70b-chat:2796ee9483c3fd7aa2e171d38f4ca12251a30609463dcfd4cd76703f22e96cdf\",\n",
" is_chat_model=True,\n",
" additional_kwargs={\"max_new_tokens\": 512}\n",
")\n"
],
"metadata": {
"id": "A1yVgic9DeJ6"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# Create a VectoreStore"
],
"metadata": {
"id": "0BwVuJXlzHVL"
}
},
{
"cell_type": "code",
"source": [
"import chromadb\n",
"\n",
"# create client and a new collection\n",
"# chromadb.EphemeralClient saves data in-memory.\n",
"chroma_client = chromadb.PersistentClient(path=\"./mini-llama-articles\")\n",
"chroma_collection = chroma_client.create_collection(\"mini-llama-articles\")"
],
"metadata": {
"id": "SQP87lHczHKc"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from llama_index.vector_stores import ChromaVectorStore\n",
"\n",
"# Define a storage context object using the created vector database.\n",
"vector_store = ChromaVectorStore(chroma_collection=chroma_collection)"
],
"metadata": {
"id": "zAaGcYMJzHAN"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# Load the Dataset (CSV)"
],
"metadata": {
"id": "I9JbAzFcjkpn"
}
},
{
"cell_type": "markdown",
"source": [
"## Download"
],
"metadata": {
"id": "ceveDuYdWCYk"
}
},
{
"cell_type": "markdown",
"source": [
"The dataset includes several articles from the TowardsAI blog, which provide an in-depth explanation of the LLaMA2 model. Read the dataset as a long string."
],
"metadata": {
"id": "eZwf6pv7WFmD"
}
},
{
"cell_type": "code",
"source": [
"!wget https://raw.githubusercontent.com/AlaFalaki/tutorial_notebooks/main/data/mini-llama-articles.csv"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "wl_pbPvMlv1h",
"outputId": "244bb539-f023-46f9-bc25-a05d0e526c14"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"--2024-02-14 14:43:19-- https://raw.githubusercontent.com/AlaFalaki/tutorial_notebooks/main/data/mini-llama-articles.csv\n",
"Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
"Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 173646 (170K) [text/plain]\n",
"Saving to: ‘mini-llama-articles.csv’\n",
"\n",
"\rmini-llama-articles 0%[ ] 0 --.-KB/s \rmini-llama-articles 100%[===================>] 169.58K --.-KB/s in 0.005s \n",
"\n",
"2024-02-14 14:43:19 (31.0 MB/s) - ‘mini-llama-articles.csv’ saved [173646/173646]\n",
"\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## Read File"
],
"metadata": {
"id": "VWBLtDbUWJfA"
}
},
{
"cell_type": "code",
"source": [
"import csv\n",
"\n",
"rows = []\n",
"\n",
"# Load the file as a JSON\n",
"with open(\"./mini-llama-articles.csv\", mode=\"r\", encoding=\"utf-8\") as file:\n",
" csv_reader = csv.reader(file)\n",
"\n",
" for idx, row in enumerate( csv_reader ):\n",
" if idx == 0: continue; # Skip header row\n",
" rows.append( row )\n",
"\n",
"# The number of characters in the dataset.\n",
"len( rows )"
],
"metadata": {
"id": "0Q9sxuW0g3Gd",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "8b475ed3-23b1-43e1-c8fc-027fc26455c3"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"14"
]
},
"metadata": {},
"execution_count": 8
}
]
},
{
"cell_type": "markdown",
"source": [
"# Convert to Document obj"
],
"metadata": {
"id": "S17g2RYOjmf2"
}
},
{
"cell_type": "code",
"source": [
"from llama_index import Document\n",
"\n",
"# Convert the chunks to Document objects so the LlamaIndex framework can process them.\n",
"documents = [Document(text=row[1], metadata={\"title\": row[0], \"url\": row[2], \"source_name\": row[3]}) for row in rows]"
],
"metadata": {
"id": "YizvmXPejkJE"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# Transforming"
],
"metadata": {
"id": "qjuLbmFuWsyl"
}
},
{
"cell_type": "code",
"source": [
"from llama_index.text_splitter import TokenTextSplitter\n",
"\n",
"# Define the splitter object that split the text into segments with 512 tokens,\n",
"# with a 128 overlap between the segments.\n",
"text_splitter = TokenTextSplitter(\n",
" separator=\" \", chunk_size=512, chunk_overlap=128\n",
")"
],
"metadata": {
"id": "9z3t70DGWsjO"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from llama_index.extractors import (\n",
" SummaryExtractor,\n",
" QuestionsAnsweredExtractor,\n",
" KeywordExtractor,\n",
")\n",
"from llama_index.embeddings.huggingface import HuggingFaceEmbedding\n",
"from llama_index.ingestion import IngestionPipeline\n",
"\n",
"# Create the pipeline to apply the transformation on each chunk,\n",
"# and store the transformed text in the chroma vector store.\n",
"pipeline = IngestionPipeline(\n",
" transformations=[\n",
" text_splitter,\n",
" HuggingFaceEmbedding(model_name=\"BAAI/bge-small-en-v1.5\") # Or, OpenAIEmbedding()\n",
" ],\n",
" vector_store=vector_store\n",
")\n",
"\n",
"nodes = pipeline.run(documents=documents, show_progress=True);"
],
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},
"id": "P9LDJ7o-Wsc-",
"outputId": "08a795a9-53e3-4a2b-89d2-c0a8912d66b9"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
"The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
"To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
"You will be able to reuse this secret in all of your notebooks.\n",
"Please note that authentication is recommended but still optional to access public models or datasets.\n",
" warnings.warn(\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
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