{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "view-in-github" }, "source": [ "\"Open\n" ] }, { "cell_type": "markdown", "metadata": { "id": "-zE1h0uQV7uT" }, "source": [ "# Install Packages and Setup Variables\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "QPJzr-I9XQ7l", "outputId": "34a040a3-c044-4348-ef4c-d8cc61364c90" }, "outputs": [], "source": [ "!pip install -q llama-index==0.10.57 openai==1.37.0 tiktoken==0.7.0 chromadb==0.5.5 llama-index-vector-stores-chroma==0.1.10 cohere==5.6.2 llama-index-llms-gemini==0.1.11" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "riuXwpSPcvWC" }, "outputs": [], "source": [ "import os\n", "\n", "# Set the following API Keys in the Python environment. Will be used later.\n", "os.environ[\"OPENAI_API_KEY\"] = \"\"\n", "os.environ[\"GOOGLE_API_KEY\"] = \"\"" ] }, { "cell_type": "code", "execution_count": null, "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": null, "metadata": { "id": "9oGT6crooSSj" }, "outputs": [], "source": [ "from llama_index.llms.gemini import Gemini\n", "\n", "llm = Gemini(model=\"models/gemini-1.5-flash\", temperature=1, max_tokens=512)" ] }, { "cell_type": "markdown", "metadata": { "id": "0BwVuJXlzHVL" }, "source": [ "# Create a VectoreStore\n" ] }, { "cell_type": "code", "execution_count": null, "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": null, "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": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "wl_pbPvMlv1h", "outputId": "02651edb-4a76-4bf4-e72f-92219f994292" }, "outputs": [], "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": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "0Q9sxuW0g3Gd", "outputId": "b74eb24b-a956-404a-b343-4f961aca883f" }, "outputs": [], "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": null, "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": null, "metadata": { "id": "9z3t70DGWsjO" }, "outputs": [], "source": [ "from llama_index.core.node_parser 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": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 385, "referenced_widgets": [ "7a469b6821ed458d99a1ed57e72b3d68", "8c556c8c8ce941c6b433780fd4a6ae54", "626b1ba98c374987913a7a4384f19fa1", "a4fad4d11a8941f8b90abb3099e9a090", "c3a4b958e4814294801495226697bce2", "2e939db189424ab7b5f9095932f2c99f", "fd6a36e947ec451a938d266117dab12e", "e4413564a300469d86c3abc567f24701", "64167ae99cd24c729435aefc1ea13519", "2634e510d3c844d88891a98661beb6a9", "6b3d2afb949f4de691ceac601bd96d0e", "8cc800fbe6bc4f4da5dd6b93d4a5143a", "812d5d9b04f74592b850b3eb32f88c04", "ed22c91e813c4351ab1d3eb7e174796c", "de2088a425104f05b52b7a3236c7baa9", "6f9f666836084de7894aa2e65c8dbe07", "63a3dcff335349deacf4abb9b68d76ab", "99eb83f4b8904e20b45573bab84aa5f4", "2c8aef5e8ec848c0a23c72581e5f4b1e", "7d54abb8f3784a789fd042c2ed2dd685", "a1a88448b188407b8e4aa2af86fb9345", "6a4cc229f5774cb0b4d3def7eee8b56e" ] }, "id": "P9LDJ7o-Wsc-", "outputId": "2e27e965-fd4c-4754-94f5-3a6e33a72dea" }, "outputs": [], "source": [ "from llama_index.core.extractors import (\n", " SummaryExtractor,\n", " QuestionsAnsweredExtractor,\n", " KeywordExtractor,\n", ")\n", "from llama_index.embeddings.openai import OpenAIEmbedding\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", " QuestionsAnsweredExtractor(questions=3, llm=llm),\n", " SummaryExtractor(summaries=[\"prev\", \"self\"], llm=llm),\n", " KeywordExtractor(keywords=10, llm=llm),\n", " OpenAIEmbedding(),\n", " ],\n", " vector_store=vector_store,\n", ")\n", "\n", "# Run the transformation pipeline.\n", "nodes = pipeline.run(documents=documents, show_progress=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "mPGa85hM2P3P", "outputId": "c106c463-2459-4b11-bbae-5bd5e2246011" }, "outputs": [], "source": [ "len(nodes)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "23x20bL3_jRb" }, "outputs": [], "source": [ "# Compress the vector store directory to a zip file to be able to download and use later.\n", "!zip -r vectorstore.zip mini-llama-articles" ] }, { "cell_type": "markdown", "metadata": { "id": "OWaT6rL7ksp8" }, "source": [ "# Load Indexes\n" ] }, { "cell_type": "markdown", "metadata": { "id": "xnShapZMdlqD" }, "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": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "SodY2Xpf_kxg", "outputId": "d60906e8-d08c-4f80-fa30-006bcb732f0d" }, "outputs": [], "source": [ "# !unzip vectorstore.zip" ] }, { "cell_type": "code", "execution_count": null, "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)" ] }, { "cell_type": "code", "execution_count": null, "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(vector_store)" ] }, { "cell_type": "markdown", "metadata": { "id": "8JPD8yAinVSq" }, "source": [ "# Query Dataset\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "b0gue7cyctt1" }, "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, similarity_top_k=5)\n", "\n", "res = query_engine.query(\"How many parameters LLaMA2 model has?\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 53 }, "id": "VKK3jMprctre", "outputId": "93cfbd8f-d0ee-4070-b557-5ae1fff4aeeb" }, "outputs": [], "source": [ "res.response" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "465dH4yQc7Ct", "outputId": "85af1ac6-4ece-4c84-ee1d-675cff3080ee" }, "outputs": [], "source": [ "# Show the retrieved nodes\n", "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": "GrqBq8Dfidw6" }, "source": [ "### Trying a different Query\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "MMBQJcPaigA0" }, "outputs": [], "source": [ "res = query_engine.query(\"Does GQA helped LLaMA performance?\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 53 }, "id": "N2QbpT0skT75", "outputId": "c80a09e3-2d1b-464b-bb3e-547c23571b34" }, "outputs": [], "source": [ "res.response" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "f9HPdfMjqsbQ", "outputId": "8ac496a2-90ff-490f-d67c-46ff544faa39" }, "outputs": [], "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": "TmkI8BV8rATi" }, "source": [ "From the articles:\n", "\n", "> [...]The 7 billion model of Llama2 has sufficient NLU (Natural Language Understanding) to create output based on a particular format[...]\n" ] }, { "cell_type": "markdown", "metadata": { "id": "6Wx-IPSMbSwC" }, "source": [ "# No Metadata\n" ] }, { "cell_type": "markdown", "metadata": { "id": "h8QUcGEgeNsD" }, "source": [ "Now, let's evaluate the ability of the query engine independently of the generated metadata, like keyword extraction or summarization.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "oGunPKGRbT6H" }, "outputs": [], "source": [ "from llama_index.core import Document\n", "\n", "documents_no_meta = [Document(text=row[1]) for row in rows]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 331, "referenced_widgets": [ "b10233c49dcc4a2f89de5389309d4fb4", "c617a0bc420b453693bb697a235e50d7", "f14f74d98f824013b562c82fb251ac26", "19f8baa6c24e4c7a8888f73f3cb7e3f8", "19c0bf2b745640b3adf6478738ba02ea", "0258a4a4bdc24404aa005c3b4d1235ee", "8da878f475de494fac3f7acf29e4e7f0", "dc5b9ea6aeea42dfae978e4a8961b03a", "aefce46940904fce9c4e439784cbc28c", "dcfeadeb1cc2483399e8194ec43f2eee", "7cec42608a51413796ec41250e0eed6d", "036ae37776684a46a1a1f9e3c018a87e", "de5e18d6629d4cd0abf9e5c72d07ac73", "29ff5f2d9c114e8bb1b7461dbae2fdb8", "0f79e4f5836f4ebf80af47c8e100b012", "99a5712bb6b64f68b30b9a1dbbc803fb", "24fe3fb4e04546b3a17377d3e6ff61d6", "931b9be975234aa79ae55aa12629f661", "63bf1ccee3ad4101920f74bb2410bfe6", "8f353fecd64a4e18be6fe2eb4fea3f9d", "d190edde40f04461ba066bc7f10b9d31", "3114d5176097487bb1313cd49867680f" ] }, "id": "Hxf4jT6afiZt", "outputId": "48b34670-17cf-494f-9d39-58ae9c47822a" }, "outputs": [], "source": [ "from llama_index.embeddings.openai import OpenAIEmbedding\n", "from llama_index.core.ingestion import IngestionPipeline\n", "\n", "pipeline = IngestionPipeline(\n", " transformations=[\n", " text_splitter,\n", " OpenAIEmbedding(),\n", " ]\n", ")\n", "\n", "nodes_no_meta = pipeline.run(documents=documents_no_meta, show_progress=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "A39Y1Rv6fiXE" }, "outputs": [], "source": [ "from llama_index.core import ServiceContext\n", "\n", "index_no_metadata = VectorStoreIndex(\n", " nodes=nodes_no_meta,\n", " service_context=ServiceContext.from_defaults(\n", " llm=llm,\n", " ),\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "BOpdZdQufiUu" }, "outputs": [], "source": [ "query_engine_no_metadata = index_no_metadata.as_query_engine(llm=llm)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "2U2NIE2Yfz8E" }, "outputs": [], "source": [ "res = query_engine_no_metadata.query(\"Does GQA helped LLaMA performance?\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 125 }, "id": "mxT7_IJ7f1gU", "outputId": "1453e5c3-2637-4d33-f958-832723fd7bea" }, "outputs": [], "source": [ "res.response" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "GD5SQ7VEf2wR", "outputId": "b31499f2-fdb9-41e3-ca93-ccdfced3209f" }, "outputs": [], "source": [ "for src in res.source_nodes:\n", " print(\"Node ID\\t\", src.node_id)\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": null, "metadata": { "id": "H8a3eKgKvckU" }, "outputs": [], "source": [ "from llama_index.core.evaluation import generate_question_context_pairs\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", "\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.json\")" ] }, { "cell_type": "markdown", "metadata": { "id": "eNP3cmiOe_xS" }, "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": null, "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.json\"\n", "# )" ] }, { "cell_type": "code", "execution_count": null, "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": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "uNLxDxoc2-Ac", "outputId": "4084d5d0-21b6-4f0e-aec3-4aab1c8c8c44" }, "outputs": [], "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": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "3ukkWC9R2_0J", "outputId": "ccde96d4-e431-4f9a-f83c-63678de56a93" }, "outputs": [], "source": [ "from llama_index.core.evaluation import (\n", " RelevancyEvaluator,\n", " FaithfulnessEvaluator,\n", " BatchEvalRunner,\n", ")\n", "from llama_index.core import ServiceContext\n", "from llama_index.llms.openai 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, llm=llm)\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-4o\")\n", "\n", " faithfulness_evaluator = FaithfulnessEvaluator(llm=llm_gpt4)\n", " relevancy_evaluator = RelevancyEvaluator(llm=llm_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(\n", " result.passing for result in eval_results[\"faithfulness\"]\n", " ) / 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[\"relevancy\"]) / len(\n", " eval_results[\"relevancy\"]\n", " )\n", " print(f\"top_{i} relevancy_score: {relevancy_score}\")\n", " print(\"-_\" * 10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "1MB1YD1E3EKM" }, "outputs": [], "source": [] } ], "metadata": { "colab": { "authorship_tag": "ABX9TyMPh4RbxOzA/0Wh6s+3gc9P", "include_colab_link": true, "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "codemirror_mode": { "name": 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