{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "view-in-github"
},
"source": [
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-zE1h0uQV7uT"
},
"source": [
"# Install Packages and Setup Variables\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"id": "QPJzr-I9XQ7l"
},
"outputs": [],
"source": [
"!pip install -q llama-index==0.10.57 openai==1.37.0 llama-index-finetuning llama-index-llms-replicate llama-index-embeddings-huggingface llama-index-embeddings-cohere llama-index-readers-web cohere==5.6.2 tiktoken==0.7.0 chromadb==0.5.5 html2text sentence_transformers pydantic llama-index-vector-stores-chroma==0.1.10 kaleido==0.2.1 replicate==0.23.1"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"id": "riuXwpSPcvWC"
},
"outputs": [],
"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",
"# You can sign up on https://replicate.com/docs/get-started/python and get a token to use for free for this notebook.\n",
"os.environ[\"OPENAI_API_KEY\"] = \"\"\n",
"os.environ[\"REPLICATE_API_TOKEN\"] = \"\"\n",
"os.environ[\"GOOGLE_API_KEY\"] = \"\""
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "jIEeZzqLbz0J"
},
"outputs": [],
"source": [
"# Allows running asyncio in environments with an existing event loop, like Jupyter notebooks.\n",
"\n",
"import nest_asyncio\n",
"\n",
"nest_asyncio.apply()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Bkgi2OrYzF7q"
},
"source": [
"# Load a Model\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "A1yVgic9DeJ6"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/louis/Documents/GitHub/ai-tutor-rag-system/.conda/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
}
],
"source": [
"from llama_index.core.prompts import PromptTemplate\n",
"from llama_index.llms.replicate 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",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0BwVuJXlzHVL"
},
"source": [
"# Create a VectoreStore\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "SQP87lHczHKc"
},
"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=\"./mini-llama-articles\")\n",
"chroma_collection = chroma_client.create_collection(\"mini-llama-articles\")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"id": "zAaGcYMJzHAN"
},
"outputs": [],
"source": [
"from llama_index.vector_stores.chroma import ChromaVectorStore\n",
"\n",
"# Define a storage context object using the created vector database.\n",
"vector_store = ChromaVectorStore(chroma_collection=chroma_collection)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "I9JbAzFcjkpn"
},
"source": [
"# Load the Dataset (CSV)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ceveDuYdWCYk"
},
"source": [
"## Download\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "eZwf6pv7WFmD"
},
"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.\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "wl_pbPvMlv1h",
"outputId": "244bb539-f023-46f9-bc25-a05d0e526c14"
},
"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 169k 100 169k 0 0 921k 0 --:--:-- --:--:-- --:--:-- 926k\n"
]
}
],
"source": [
"!curl -o ./mini-llama-articles.csv https://raw.githubusercontent.com/AlaFalaki/tutorial_notebooks/main/data/mini-llama-articles.csv"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "VWBLtDbUWJfA"
},
"source": [
"## Read File\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "0Q9sxuW0g3Gd",
"outputId": "8b475ed3-23b1-43e1-c8fc-027fc26455c3"
},
"outputs": [
{
"data": {
"text/plain": [
"14"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"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:\n",
" continue\n",
" # Skip header row\n",
" rows.append(row)\n",
"\n",
"# The number of characters in the dataset.\n",
"len(rows)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "S17g2RYOjmf2"
},
"source": [
"# Convert to Document obj\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"id": "YizvmXPejkJE"
},
"outputs": [],
"source": [
"from llama_index.core import Document\n",
"\n",
"# Convert the chunks to Document objects so the LlamaIndex framework can process them.\n",
"documents = [\n",
" Document(\n",
" text=row[1], metadata={\"title\": row[0], \"url\": row[2], \"source_name\": row[3]}\n",
" )\n",
" for row in rows\n",
"]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qjuLbmFuWsyl"
},
"source": [
"# Transforming\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"id": "9z3t70DGWsjO"
},
"outputs": [],
"source": [
"from llama_index.core.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(separator=\" \", chunk_size=512, chunk_overlap=128)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 650,
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},
"id": "P9LDJ7o-Wsc-",
"outputId": "08a795a9-53e3-4a2b-89d2-c0a8912d66b9"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Parsing nodes: 100%|██████████| 14/14 [00:00<00:00, 27.58it/s]\n",
"Generating embeddings: 100%|██████████| 108/108 [00:05<00:00, 21.15it/s]\n"
]
}
],
"source": [
"from llama_index.core.extractors import (\n",
" SummaryExtractor,\n",
" QuestionsAnsweredExtractor,\n",
" KeywordExtractor,\n",
")\n",
"from llama_index.embeddings.huggingface import HuggingFaceEmbedding\n",
"from llama_index.core.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(\n",
" model_name=\"BAAI/bge-small-en-v1.5\"\n",
" ), # Or, OpenAIEmbedding()\n",
" ],\n",
" vector_store=vector_store,\n",
")\n",
"\n",
"nodes = pipeline.run(documents=documents, show_progress=True)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "mPGa85hM2P3P",
"outputId": "56c3980a-38a4-40e7-abdd-84ec1f26cb95"
},
"outputs": [
{
"data": {
"text/plain": [
"108"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(nodes)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "OeeG3jxT0taW",
"outputId": "d1938534-9a12-4f5e-b7e1-5fd58d687d60"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" adding: mini-llama-articles/ (stored 0%)\n",
" adding: mini-llama-articles/01877efc-b4a2-4da3-80b3-93cc40b27067/ (stored 0%)\n",
" adding: mini-llama-articles/01877efc-b4a2-4da3-80b3-93cc40b27067/data_level0.bin"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
"To disable this warning, you can either:\n",
"\t- Avoid using `tokenizers` before the fork if possible\n",
"\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" (deflated 57%)\n",
" adding: mini-llama-articles/01877efc-b4a2-4da3-80b3-93cc40b27067/length.bin (deflated 48%)\n",
" adding: mini-llama-articles/01877efc-b4a2-4da3-80b3-93cc40b27067/link_lists.bin (stored 0%)\n",
" adding: mini-llama-articles/01877efc-b4a2-4da3-80b3-93cc40b27067/header.bin (deflated 61%)\n",
" adding: mini-llama-articles/chroma.sqlite3 (deflated 66%)\n"
]
}
],
"source": [
"# Compress the vector store directory to a zip file to be able to download and use later.\n",
"!zip -r vectorstore-bge-embedding.zip mini-llama-articles"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "OWaT6rL7ksp8"
},
"source": [
"# Load Indexes\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "RF4U62oMr-iW"
},
"source": [
"If you have already uploaded the zip file for the vector store checkpoint, please uncomment the code in the following cell block to extract its contents. After doing so, you will be able to load the dataset from local storage.\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "XxPMJ4tq06qx",
"outputId": "8445e40a-b3c6-44ff-dfde-37cd4c73ffa2"
},
"outputs": [],
"source": [
"# !unzip vectorstore.zip"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"id": "mXi56KTXk2sp"
},
"outputs": [],
"source": [
"# Load the vector store from the local storage.\n",
"db = chromadb.PersistentClient(path=\"./mini-llama-articles\")\n",
"chroma_collection = db.get_or_create_collection(\"mini-llama-articles\")\n",
"vector_store = ChromaVectorStore(chroma_collection=chroma_collection)"
]
},
{
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"execution_count": 26,
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"colab": {
"base_uri": "https://localhost:8080/",
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"9c3a689dcc63433eafbc1d1f3941dac5",
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"4d6530cbda8e484796c6c137fd447824",
"220d6c6f192849cc92110bbb628ec19c"
]
},
"id": "RZ5iQ_KkJufJ",
"outputId": "dd6029ee-10ed-4bf8-95d1-88ac5c636c47"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/l7/9qcp7g5x5rl9x8ltw0t85qym0000gn/T/ipykernel_8187/3245113941.py:5: DeprecationWarning: Call to deprecated class method from_defaults. (ServiceContext is deprecated, please use `llama_index.settings.Settings` instead.) -- Deprecated since version 0.10.0.\n",
" service_context = ServiceContext.from_defaults(llm=llm, embed_model=\"local:BAAI/bge-small-en-v1.5\")\n"
]
}
],
"source": [
"from llama_index.core import ServiceContext\n",
"\n",
"# Define a ServiceContext that uses the BGE model for embedding which will be loads from Huggingface.\n",
"# The model will be downloaded in your local machine.\n",
"service_context = ServiceContext.from_defaults(\n",
" llm=llm, embed_model=\"local:BAAI/bge-small-en-v1.5\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"id": "jKXURvLtkuTS"
},
"outputs": [],
"source": [
"from llama_index.core import VectorStoreIndex\n",
"\n",
"# Create the index based on the vector store.\n",
"index = VectorStoreIndex.from_vector_store(\n",
" vector_store, service_context=service_context\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8JPD8yAinVSq"
},
"source": [
"# Query Dataset\n"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"id": "8lBu8V7tJ2_8"
},
"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(llm=llm)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"id": "rWAI0jUhJ7qH"
},
"outputs": [],
"source": [
"res = query_engine.query(\"How many parameters LLaMA2 has?\")"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 53
},
"id": "VKK3jMprctre",
"outputId": "06d62444-5e04-4e99-abf0-0ff1c76c0a82"
},
"outputs": [
{
"data": {
"text/plain": [
"'LLaMA2 has four different model sizes: 7 billion, 13 billion, 34 billion, and 70 billion parameters.'"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"res.response"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "nvSmOtqBoCY2",
"outputId": "21a60031-4f39-4d1a-fbfa-ddba7d267936"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Node ID\t 737ac31e-077f-448e-a777-a096ae0550c0\n",
"Title\t Meta's Llama 2: Revolutionizing Open Source Language Models for Commercial Use\n",
"Text\t I. Llama 2: Revolutionizing Commercial Use Unlike its predecessor Llama 1, which was limited to research use, Llama 2 represents a major advancement as an open-source commercial model. Businesses can now integrate Llama 2 into products to create AI-powered applications. Availability on Azure and AWS facilitates fine-tuning and adoption. However, restrictions apply to prevent exploitation. Companies with over 700 million active daily users cannot use Llama 2. Additionally, its output cannot be used to improve other language models. II. Llama 2 Model Flavors Llama 2 is available in four different model sizes: 7 billion, 13 billion, 34 billion, and 70 billion parameters. While 7B, 13B, and 70B have already been released, the 34B model is still awaited. The pretrained variant, trained on a whopping 2 trillion tokens, boasts a context window of 4096 tokens, twice the size of its predecessor Llama 1. Meta also released a Llama 2 fine-tuned model for chat applications that was trained on over 1 million human annotations. Such extensive training comes at a cost, with the 70B model taking a staggering 1720320 GPU hours to train. The context window's length determines the amount of content the model can process at once, making Llama 2 a powerful language model in terms of scale and efficiency. III. Safety Considerations: A Top Priority for Meta Meta's commitment to safety and alignment shines through in Llama 2's design. The model demonstrates exceptionally low AI safety violation percentages, surpassing even ChatGPT in safety benchmarks. Finding the right balance between helpfulness and safety when optimizing a model poses significant challenges. While a highly helpful model may be capable of answering any question, including sensitive ones like \"How do I build a bomb?\", it also raises concerns about potential misuse. Thus, striking the perfect equilibrium between providing useful information and ensuring safety is paramount. However, prioritizing safety to an extreme extent can lead to a model that struggles to effectively address a diverse range of questions. This limitation could hinder the model's practical applicability and user experience. Thus, achieving\n",
"Score\t 0.6191229199592215\n",
"-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_\n",
"Node ID\t c02a107f-b946-48c6-9180-98b058334b92\n",
"Title\t Beyond GPT-4: What's New?\n",
"Text\t LLM Variants and Meta's Open Source Before shedding light on four major trends, I'd share the latest Meta's Llama 2 and Code Llama. Meta's Llama 2 represents a sophisticated evolution in LLMs. This suite spans models pretrained and fine-tuned across a parameter spectrum of 7 billion to 70 billion. A specialized derivative, Llama 2-Chat, has been engineered explicitly for dialogue-centric applications. Benchmarking revealed Llama 2's superior performance over most extant open-source chat models. Human-centric evaluations, focusing on safety and utility metrics, positioned Llama 2-Chat as a potential contender against proprietary, closed-source counterparts. The development trajectory of Llama 2 emphasized rigorous fine-tuning methodologies. Meta's transparent delineation of these processes aims to catalyze community-driven advancements in LLMs, underscoring a commitment to collaborative and responsible AI development. Code Llama is built on top of Llama 2 and is available in three models: Code Llama, the foundational code model;Codel Llama - Python specialized for Python;and Code Llama - Instruct, which is fine-tuned for understanding natural language instructions. Based on its benchmark testing, Code Llama outperformed state-of-the-art publicly available LLMs (except GPT-4) on code tasks. Llama 2, Llama 2-Chat, and Code Llama are key steps in LLM development but still have a way to go compared to GPT-4. Meta's open access and commitment to improving these models promise transparent and faster LLM progress in the future. Please refer to the LLM and Llama variants below: From LLMs to Multimodal LLMs, like OpenAI's ChatGPT (GPT-3.5), primarily focus on understanding and generating human language. They've been instrumental in tasks like text generation, translation, and even creative writing. However, their scope is limited to text. Enter multimodal models like GPT-4. These are a new breed of AI models that can understand and generate not just text, but also images, sounds, and potentially other types of data. The term \"multimodal\" refers to their ability to process multiple modes or\n",
"Score\t 0.5952622757578383\n",
"-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_\n"
]
}
],
"source": [
"for src in res.source_nodes:\n",
" print(\"Node ID\\t\", src.node_id)\n",
" print(\"Title\\t\", src.metadata[\"title\"])\n",
" print(\"Text\\t\", src.text)\n",
" print(\"Score\\t\", src.score)\n",
" print(\"-_\" * 20)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "iMkpzH7vvb09"
},
"source": [
"# Evaluate\n"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "H8a3eKgKvckU",
"outputId": "a0e0b170-a49e-4c83-fb5a-579046056af5"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 108/108 [04:46<00:00, 2.65s/it]\n"
]
}
],
"source": [
"from llama_index.core.evaluation import generate_question_context_pairs\n",
"from llama_index.llms.gemini import Gemini\n",
"\n",
"# Create questions for each segment. These questions will be used to\n",
"# assess whether the retriever can accurately identify and return the\n",
"# corresponding segment when queried.\n",
"llm = Gemini(model=\"models/gemini-1.5-flash\", temperature=1, max_tokens=512)\n",
"rag_eval_dataset = generate_question_context_pairs(\n",
" nodes, llm=llm, num_questions_per_chunk=1\n",
")\n",
"\n",
"# We can save the evaluation dataset as a json file for later use.\n",
"rag_eval_dataset.save_json(\"./rag_eval_dataset_bge_embedding.json\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JjM95B9Zs29W"
},
"source": [
"If you have uploaded the generated question JSON file, please uncomment the code in the next cell block. This will avoid the need to generate the questions manually, saving you time and effort.\n"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"id": "3sA1K84U254o"
},
"outputs": [],
"source": [
"# from llama_index.finetuning.embeddings.common import (\n",
"# EmbeddingQAFinetuneDataset,\n",
"# )\n",
"# rag_eval_dataset = EmbeddingQAFinetuneDataset.from_json(\n",
"# \"./rag_eval_dataset_bge_embedding.json\"\n",
"# )"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"id": "H7ubvcbk27vr"
},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"\n",
"# A simple function to show the evaluation result.\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": "code",
"execution_count": 34,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "uNLxDxoc2-Ac",
"outputId": "f5762021-37d3-423e-b4d6-23b1066a0e97"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Retriever Name Hit Rate MRR\n",
"0 Retriever top_2 0.6625 0.551042\n",
" Retriever Name Hit Rate MRR\n",
"0 Retriever top_4 0.822917 0.600694\n",
" Retriever Name Hit Rate MRR\n",
"0 Retriever top_6 0.879167 0.611111\n",
" Retriever Name Hit Rate MRR\n",
"0 Retriever top_8 0.902083 0.614199\n",
" Retriever Name Hit Rate MRR\n",
"0 Retriever top_10 0.922917 0.616467\n"
]
}
],
"source": [
"from llama_index.core.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": "code",
"execution_count": null,
"metadata": {
"id": "-DMSFJI8F6jl"
},
"outputs": [],
"source": []
}
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