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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "view-in-github"
},
"source": [
"<a href=\"https://colab.research.google.com/github/towardsai/ai-tutor-rag-system/blob/main/notebooks/06-Evaluate_RAG.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5BGJ3fxhOk2V"
},
"source": [
"# Install Packages and Setup Variables"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "QPJzr-I9XQ7l",
"outputId": "9949a0e5-8bf2-4ae7-9921-1f9dfbece9ae"
},
"outputs": [],
"source": [
"!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"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "riuXwpSPcvWC"
},
"outputs": [],
"source": [
"import os\n",
"\n",
"# Set the \"OPENAI_API_KEY\" in the Python environment. Will be used by OpenAI client later.\n",
"os.environ[\"OPENAI_API_KEY\"] = \"YOUR_API_KEY\""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "I9JbAzFcjkpn"
},
"source": [
"# Load the Dataset (CSV)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_Tif8-JoRH68"
},
"source": [
"## Download"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4fQaa1LN1mXL"
},
"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."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "-QTUkdfJjY4N"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" % Total % Received % Xferd Average Speed Time Time Time Current\n",
" Dload Upload Total Spent Left Speed\n",
"100 23689 100 23689 0 0 55248 0 --:--:-- --:--:-- --:--:-- 55219\n"
]
}
],
"source": [
"!curl -o ../data/mini-dataset.csv https://raw.githubusercontent.com/AlaFalaki/tutorial_notebooks/main/data/mini-dataset.csv"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "zk-4alIxROo8"
},
"source": [
"## Read File"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "7CYwRT6R0o0I",
"outputId": "6f0f05ae-c92f-45b2-bbc3-d12add118021"
},
"outputs": [
{
"data": {
"text/plain": [
"23632"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import csv\n",
"\n",
"text = \"\"\n",
"\n",
"# Load the file as a JSON\n",
"with open(\"../data/mini-dataset.csv\", mode=\"r\", encoding=\"ISO-8859-1\") as file:\n",
" csv_reader = csv.reader(file)\n",
"\n",
" for row in csv_reader:\n",
" text += row[0]\n",
"\n",
"# The number of characters in the dataset.\n",
"len(text)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "S17g2RYOjmf2"
},
"source": [
"# Chunking"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "STACTMUR1z9N",
"outputId": "8ce58d6b-a38d-48e3-8316-7435907488cf"
},
"outputs": [
{
"data": {
"text/plain": [
"47"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chunk_size = 512\n",
"chunks = []\n",
"\n",
"# Split the long text into smaller manageable chunks of 512 characters.\n",
"for i in range(0, len(text), chunk_size):\n",
" chunks.append(text[i : i + chunk_size])\n",
"\n",
"len(chunks)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"id": "CtdsIUQ81_hT"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"47\n",
"Node ID: node_0\n",
"Text: Meta has once again pushed the boundaries of AI with the release\n",
"of Llama 2, the highly anticipated successor to its groundbreaking\n",
"Llama 1 language model. Boasting a range of cutting-edge features,\n",
"Llama 2 has already disrupted the AI landscape and poses a real\n",
"challenge to ChatGPTÕs dominance. In this article, we will dive into\n",
"the exciting wo...\n"
]
}
],
"source": [
"from llama_index.schema import TextNode\n",
"nodes = [TextNode(text=t,) for i, t in enumerate(chunks)]\n",
"\n",
"# By default, the node ids are set to random uuids. To ensure same id's per run, we manually set them.\n",
"for idx, node in enumerate(nodes):\n",
" node.id_ = f\"node_{idx}\"\n",
"\n",
"print(len(nodes))\n",
"print(nodes[0])"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "OWaT6rL7ksp8"
},
"source": [
"# Save on Chroma"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"id": "mXi56KTXk2sp"
},
"outputs": [],
"source": [
"import chromadb\n",
"\n",
"# create client and a new collection\n",
"# chromadb.EphemeralClient saves data in-memory.\n",
"chroma_client = chromadb.PersistentClient(path=\"../data/mini-chunked-dataset\")\n",
"chroma_collection = chroma_client.create_collection(\"mini-chunked-dataset\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"id": "jKXURvLtkuTS"
},
"outputs": [],
"source": [
"from llama_index.vector_stores import ChromaVectorStore\n",
"from llama_index.storage.storage_context import StorageContext\n",
"\n",
"# Define a storage context object using the created vector database.\n",
"vector_store = ChromaVectorStore(chroma_collection=chroma_collection)\n",
"storage_context = StorageContext.from_defaults(vector_store=vector_store)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"id": "WsD52wtrlESi"
},
"outputs": [
{
"data": {
"text/plain": [
"<llama_index.indices.vector_store.base.VectorStoreIndex at 0x17e862410>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from llama_index import VectorStoreIndex\n",
"\n",
"# Add the documents to the database and create Index / embeddings\n",
"index = VectorStoreIndex(nodes=nodes, storage_context=storage_context)\n",
"index"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8JPD8yAinVSq"
},
"source": [
"# Query Dataset"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"id": "mzS13x1ZlZ5X"
},
"outputs": [],
"source": [
"# Define a query engine that is responsible for retrieving related pieces of text,\n",
"# and using a LLM to formulate the final answer.\n",
"query_engine = index.as_query_engine()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "AYsQ4uLN_Oxg",
"outputId": "bf2181ad-27f6-40a2-b792-8a2714a60c29"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The Llama-2 model has three different sizes: 7B, 13B, and 70B.\n"
]
}
],
"source": [
"response = query_engine.query(\"How many parameters LLaMA2 model has?\")\n",
"print(response)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Evaluate the retrieval process and quality of answers\n",
"\n",
"We can evaluate our RAG system with a dataset of questions and associated chunks. Given a question, we can see if the RAG system retrieves the correct chunks of text that can answer the question.\n",
"\n",
"You can generate a synthetic dataset with an LLM such as `gpt-3.5-turbo` or create an authentic and manually curated dataset. \n",
"\n",
"Note that a **well curated dataset will always be a better option**, especially for a specific domain or use case.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In our example, we will generate a synthetic dataset using `gpt-3.5-turbo` to make it simple.\n",
"\n",
"This is the default prompt that the `generate_question_context_pairs` uses:\n",
"\n",
"```python\n",
"DEFAULT_QA_GENERATE_PROMPT_TMPL = \"\"\"\\\n",
"Context information is below.\n",
"\n",
"---------------------\n",
"{context_str}\n",
"---------------------\n",
"\n",
"Given the context information and not prior knowledge.\n",
"generate only questions based on the below query.\n",
"\n",
"You are a Teacher/ Professor. Your task is to setup \\\n",
"{num_questions_per_chunk} questions for an upcoming \\\n",
"quiz/examination. The questions should be diverse in nature \\\n",
"across the document. Restrict the questions to the \\\n",
"context information provided.\"\n",
"\"\"\"\n",
"```\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 47/47 [01:55<00:00, 2.45s/it]\n"
]
}
],
"source": [
"from llama_index.evaluation import generate_question_context_pairs\n",
"from llama_index.llms import OpenAI\n",
"\n",
"llm = OpenAI(model=\"gpt-3.5-turbo\")\n",
"rag_eval_dataset = generate_question_context_pairs(\n",
" nodes,\n",
" llm=llm,\n",
" num_questions_per_chunk=1\n",
")\n",
"# We can save the dataset as a json file for later use.\n",
"rag_eval_dataset.save_json(\"../data/rag_eval_dataset.json\")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"# We can also load the dataset from a previously saved json file.\n",
"from llama_index.finetuning.embeddings.common import (\n",
" EmbeddingQAFinetuneDataset,\n",
")\n",
"rag_eval_dataset = EmbeddingQAFinetuneDataset.from_json(\n",
" \"../data/rag_eval_dataset.json\"\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Evaluation for Hit Rate and Mean Reciprocal Rank (MRR)\n",
"\n",
"We will make use of `RetrieverEvaluator` available in Llama-index. We will measure the Hit Rate and Mean Reciprocal Rank (MRR).\n",
"\n",
"**Hit Rate:**\n",
"\n",
"Think of the Hit Rate like playing a game of guessing. You're given a question and you need to guess the correct answer from a list of options. The Hit Rate measures how often you guess the correct answer by only looking at your top few guesses. If you often find the right answer in your first few guesses, you have a high Hit Rate. So, in the context of a retrieval system, it's about how frequently the system finds the correct document within its top 'k' picks (where 'k' is a number you decide, like top 5 or top 10).\n",
"\n",
"**Mean Reciprocal Rank (MRR):**\n",
"\n",
"MRR is a bit like measuring how quickly you can find a treasure in a list of boxes. Imagine you have a row of boxes and only one of them has a treasure. The MRR calculates how close to the start of the row the treasure box is, on average. If the treasure is always in the first box you open, you're doing great and have an MRR of 1. If it's in the second box, the score is 1/2, since you took two tries to find it. If it's in the third box, your score is 1/3, and so on. MRR averages these scores across all your searches. So, for a retrieval system, MRR looks at where the correct document ranks in the system's guesses. If it's usually near the top, the MRR will be high, indicating good performance.\n",
"In summary, Hit Rate tells you how often the system gets it right in its top guesses, and MRR tells you how close to the top the right answer usually is. Both metrics are useful for evaluating the effectiveness of a retrieval system, like how well a search engine or a recommendation system works."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"def display_results_retriever(name, eval_results):\n",
" \"\"\"Display results from evaluate.\"\"\"\n",
"\n",
" metric_dicts = []\n",
" for eval_result in eval_results:\n",
" metric_dict = eval_result.metric_vals_dict\n",
" metric_dicts.append(metric_dict)\n",
"\n",
" full_df = pd.DataFrame(metric_dicts)\n",
"\n",
" hit_rate = full_df[\"hit_rate\"].mean()\n",
" mrr = full_df[\"mrr\"].mean()\n",
"\n",
" metric_df = pd.DataFrame(\n",
" {\"Retriever Name\": [name], \"Hit Rate\": [hit_rate], \"MRR\": [mrr]}\n",
" )\n",
"\n",
" return metric_df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then Run the evaluation procedure. We will evaluate the system with different values of top_k and see how the Hit Rate and MRR change."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Retriever Name Hit Rate MRR\n",
"0 Retriever top_2 0.666667 0.606607\n",
" Retriever Name Hit Rate MRR\n",
"0 Retriever top_4 0.744745 0.630631\n",
" Retriever Name Hit Rate MRR\n",
"0 Retriever top_6 0.780781 0.637337\n",
" Retriever Name Hit Rate MRR\n",
"0 Retriever top_8 0.804805 0.640651\n",
" Retriever Name Hit Rate MRR\n",
"0 Retriever top_10 0.828829 0.643037\n"
]
}
],
"source": [
"from llama_index.evaluation import RetrieverEvaluator\n",
"\n",
"# We can evaluate the retievers with different top_k values.\n",
"for i in [2, 4, 6, 8, 10]:\n",
" retriever = index.as_retriever(similarity_top_k=i)\n",
" retriever_evaluator = RetrieverEvaluator.from_metric_names(\n",
" [\"mrr\", \"hit_rate\"], retriever=retriever\n",
" )\n",
" eval_results = await retriever_evaluator.aevaluate_dataset(rag_eval_dataset)\n",
" print(display_results_retriever(f\"Retriever top_{i}\", eval_results))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Evaluation using Relevance and Faithfulness metrics.\n",
"\n",
"Here, we evaluate the answer generated by the LLM. Is the answer using the correct context? Is the answer faithful to the context? Is the answer relevant to the question?\n",
"\n",
"An LLM will answer these questions, more specifically `gpt-4-0125-preview`.\n",
"\n",
"**`FaithfulnessEvaluator`**\n",
"Evaluates if the answer is faithful to the retrieved contexts (in other words, whether there's an hallucination).\n",
"\n",
"**`RelevancyEvaluator`**\n",
"Evaluates whether the retrieved context and answer are relevant to the user question.\n",
"\n",
"\n",
"Now, let's see how the top_k value affects these two metrics."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"top_2 faithfulness_score: 0.9\n",
"top_2 relevancy_score: 0.9\n",
"top_4 faithfulness_score: 1.0\n",
"top_4 relevancy_score: 1.0\n",
"top_6 faithfulness_score: 1.0\n",
"top_6 relevancy_score: 1.0\n",
"top_8 faithfulness_score: 1.0\n",
"top_8 relevancy_score: 1.0\n",
"top_10 faithfulness_score: 1.0\n",
"top_10 relevancy_score: 1.0\n"
]
}
],
"source": [
"from llama_index.evaluation import RelevancyEvaluator, FaithfulnessEvaluator, BatchEvalRunner\n",
"from llama_index import ServiceContext\n",
"from llama_index.llms import OpenAI\n",
"\n",
"for i in [2, 4, 6, 8, 10]:\n",
" # Set Faithfulness and Relevancy evaluators\n",
" query_engine = index.as_query_engine(similarity_top_k=i)\n",
"\n",
" # While we use GPT3.5-Turbo to answer questions, we can use GPT4 to evaluate the answers.\n",
" llm_gpt4 = OpenAI(temperature=0, model=\"gpt-4-0125-preview\")\n",
" service_context_gpt4 = ServiceContext.from_defaults(llm=llm_gpt4)\n",
"\n",
" faithfulness_evaluator = FaithfulnessEvaluator(service_context=service_context_gpt4)\n",
" relevancy_evaluator = RelevancyEvaluator(service_context=service_context_gpt4)\n",
"\n",
" # Run evaluation\n",
" queries = list(rag_eval_dataset.queries.values())\n",
" batch_eval_queries = queries[:20]\n",
"\n",
" runner = BatchEvalRunner(\n",
" {\"faithfulness\": faithfulness_evaluator, \"relevancy\": relevancy_evaluator},\n",
" workers=8,\n",
" )\n",
" eval_results = await runner.aevaluate_queries(\n",
" query_engine, queries=batch_eval_queries\n",
" )\n",
" faithfulness_score = sum(result.passing for result in eval_results['faithfulness']) / len(eval_results['faithfulness'])\n",
" print(f\"top_{i} faithfulness_score: {faithfulness_score}\")\n",
"\n",
" relevancy_score = sum(result.passing for result in eval_results['faithfulness']) / len(eval_results['relevancy'])\n",
" print(f\"top_{i} relevancy_score: {relevancy_score}\")\n"
]
}
],
"metadata": {
"colab": {
"authorship_tag": "ABX9TyNQkVEh0x7hcM9U+6JSEkSG",
"include_colab_link": true,
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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|