{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "DjQqNDCr2pd-" }, "source": [ "# Create Vector and store it" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "VVlr85492tGv" }, "outputs": [], "source": [ "# Install requirements\n", "\n", "!pip install -q llama-index==0.12.21 openai==1.59.8 tiktoken==0.8.0 huggingface-hub==0.30 chromadb==0.6.0 llama-index-vector-stores-chroma==0.4.1 llama-index-llms-gemini==0.4.1 llama_index.embeddings.huggingface==0.5.4 llama-index-embeddings-adapter==0.3.0 llama-index-finetuning==0.3.2" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "95E2K6eA13DF" }, "outputs": [], "source": [ "# set variables\n", "import os\n", "from google.colab import userdata\n", "\n", "os.environ[\"OPENAI_API_KEY\"] = userdata.get('openai_api_key')\n", "\n", "os.environ[\"HF_TOKEN\"] = userdata.get('HF_TOKEN2')\n", "HF_TOKEN = userdata.get('HF_TOKEN2')\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "kMFYXB_e09Ge" }, "outputs": [], "source": [ "# Allows running asyncio in environments with an existing event loop, like Jupyter notebooks.\n", "import nest_asyncio\n", "nest_asyncio.apply()" ] }, { "cell_type": "markdown", "metadata": { "id": "R3WwDT3lIYVd" }, "source": [ "# Create a VectoreStore\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "V4VMXXjHsLG7" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "iMLSd1gyIV6k" }, "outputs": [], "source": [ "import chromadb\n", "from llama_index.vector_stores.chroma import ChromaVectorStore\n", "\n", "# create client and a new collection\n", "#chroma_client = chromadb.EphemeralClient() # saves data in-memory.\n", "chroma_path = './azure-architect'\n", "chroma_client = chromadb.PersistentClient(path=chroma_path)\n", "chroma_collection = chroma_client.get_or_create_collection(\"azure-architect\")\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": "GdezQ1N0InWh" }, "source": [ "# Transforming\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ail4lH_az-Ya", "outputId": "080dda89-cba3-498e-d5bb-128dda36ef82" }, "outputs": [ { "data": { "text/plain": [ "8" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import multiprocessing\n", "\n", "cores = multiprocessing.cpu_count() # Count the number of cores in a computer\n", "cores" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 307, "referenced_widgets": [ "d97e7e7d10954d72a7bd1d81239b4ef1", "eda040ce2e4a4079a8d53bede0909769", "98eb0f0d3b354e67828b9c675ac17982", "0fea39c9d8604b26ab84611814652ed5", "b2081440b18d4c7db3d22d6e9caaf472", "8f9a4365d0c643dcac8208cd9ef02be5", "768569b8a4a54282b0320012e222a2a7", "47410abbc107474f9c2ddeebf6cdbd2c", "1b6de75e8a024db6af6ed300d0d0dd71", "0108f805a8e1499882528df7e8baa3bb", "8ed3359d703347d9a6f0281cf66b0fa8", "00a6a2b167ef4bc88a99828d1eb1798b", "09290e0777a74656a59f1fda75295e58", "a156071d2d874a028b182f6232b76b34", "973ee5e95f834505b57f7f8352e0e8b4", "6c7ca30dd3b44dd8ba4e9794352f655a", "411d7051a8ba45f3933b76b09798e03c", "fbfebcb64c75410bbd89c86d731e4c7d", "360e9c4effd84114803609421b4ade28", "76b5003de35347079c8a292ac6646816", "cabc9ad995194e18bcd7276f0890c021", "2ae01f45e1fd469d9e2d16d69c04d18c", "23011c91e7cb4723a3bf2f1196a18154", "236d5c1c5f104a82a89ad77001dab738", "c819b2d719fe472bb792743a3a92b3a9", "b7b5e4abfde54f28bed1d1da0290df6f", "01fc6289cb0c43238c89771dc43ff3bc", "671cf68b78fb4f28a186cf0c872ca333", "46b6ace0407b40b9bde40346c7e5aa89", "8ef2bfa5fa0140ef82cb7170d9d74278", "b36ac79a862b414da11376d1d172f234", "d706181aa7b0485c94a03d0fc5d48299", "f28f21d800594e0ab746c1fe370703a3", "971478c9175a465c816658d7a9164f97", "f33f5680747d43b984c39ac59e064b13", "8e6db0274cde4e3881bdb793f07a54ee", "e3d4ad7225e642c1b8133375832d3186", "ee423eb510ad478395288687d53a3ca0", "8957c557e325401384e154fd12ad5b90", "3ea493d4f2b543678424ed31c052fc9c", "33aeb1bf7e444641ab50a5ae01dd4ea4", "5c32dfbfed2a48c4ba7678e4f6bac2a6", "3573828d2c8b4bb093c2022a2a6be862", "a65e04f2b9b447fbbec86ddc71beeb91", "46e926fef8664a8cb0a8400aa76254c5", "91c8560de3ef459e85792ec37f0bf11e", "adbf72262cc44fc19ff3ff178173aebd", "8651f5c302c54c28a5bec543395b8d4a", "3e84b5c559c3439cad65452c0bd68a5f", "1d6c0b71e00c40ad97e0d1b2d0db1841", "6ae27ec148eb43c18a70c60eba1c7044", "d8321c1c31b444c190cb8135dd35e473", "fe97920f0c3f41c2869c2b10cad3a8f3", "0d6ef4cb4c3c44b69640bacb39554f62", "c88e1a5ad7df4cb58cbbab283b254aaf" ] }, "id": "TibqzAtUf5NI", "outputId": "f2702322-0dc3-49e6-d340-4554c5d754c7" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d97e7e7d10954d72a7bd1d81239b4ef1", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Fetching 4 files: 0%| | 0/4 [00:00,\n", " ,\n", " ]}" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "chroma_collection.peek()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "x5-vgqtHJe1j" }, "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": "code", "execution_count": null, "metadata": { "id": "jjZjEiy771q7" }, "outputs": [], "source": [ "from IPython.display import Markdown, display\n", "def display_response(response):\n", " display(Markdown(f\"{response}\"))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "d1l69Rg1Eo1o" }, "outputs": [], "source": [ "from llama_index.llms.gemini import Gemini\n", "\n", "# Define a query engine that is responsible for retrieving related pieces of text,\n", "# and using a LLM to formulate the final answer.\n", "\n", "llm = Gemini(model=\"models/gemini-2.0-flash\", temperature=1, max_tokens=512)\n", "\n", "query_engine = index.as_query_engine(llm=llm, similarity_top_k=5, embed_model=embed_model)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 761 }, "id": "xWZNTxYe73L8", "outputId": "ba3e3c9f-2e6c-4659-a52d-8987c023b0e6" }, "outputs": [ { "data": { "text/markdown": [ "Azure Functions provides various hosting options for different business needs and application workloads. One can opt for fully serverless options, where you only pay for execution time, or choose to have always-warm instances for faster response times. You can also host functions in an existing App Service plan when you have excess App Service hosting resources. If you desire complete control over your runtime environment and dependencies, you have the option to deploy your functions in customizable containers. These containers can be hosted by Functions, deployed as part of a microservices architecture in Azure Container Apps, or self-hosted in Kubernetes. Running Functions on Container Apps is a good fit for a multitype microservices architecture in a centralized environment, providing consistent network, observability, and billing configurations.\n", "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-----------------\n", "Node ID\t 7bb7cadb-c606-46fe-acf4-eab83041d23d\n", "Title\t Guidance for developing Azure Functions | Microsoft Learn\n", "URL\t https://learn.microsoft.com/en-us/azure/azure-functions/functions-reference?tabs=blob&toc=%2Fazure%2Fdeveloper%2Fjavascript%2Ftoc.json&bc=%2Fazure%2Fdeveloper%2Fjavascript%2Fbreadcrumb%2Ftoc.json&pivots=programming-language-csharp\n", "Score\t 0.5963049277608097\n", "Content Type\t None\n", "-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_\n", "Node ID\t 2befe6e7-6f60-40b5-a303-7ff17bad7076\n", "Title\t Azure Functions – Serverless Functions in Computing | Microsoft Azure\n", "URL\t https://azure.microsoft.com/en-gb/products/functions/\n", "Score\t 0.5875640306714637\n", "Content Type\t None\n", "-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_\n", "Node ID\t 0508adbd-1e4d-4532-b133-9b4e3dde0aca\n", "Title\t Azure Functions – Serverless Functions in Computing | Microsoft Azure\n", "URL\t https://azure.microsoft.com/en-us/products/functions/\n", "Score\t 0.5844802046476505\n", "Content Type\t None\n", "-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_\n", "Node ID\t 5e5171a7-6c4a-4fa0-a8b0-ea72e2835332\n", "Title\t Azure Functions overview | Microsoft Learn\n", "URL\t https://learn.microsoft.com/en-us/azure/azure-functions/functions-overview\n", "Score\t 0.5716228798775332\n", "Content Type\t None\n", "-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_\n", "Node ID\t 59549272-0941-4e02-b526-a51b53c29c7b\n", "Title\t Create a function in Go or Rust using Visual Studio Code - Azure Functions | Microsoft Learn\n", "URL\t https://learn.microsoft.com/en-us/azure/azure-functions/create-first-function-vs-code-other?tabs=go%2Clinux\n", "Score\t 0.5701954049533161\n", "Content Type\t None\n", "-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_\n" ] } ], "source": [ "query = \"When to use Azure Functions instead of App Service? Please explain with details. Use only the context provided\" # Enter your query here, it should be relevant to the crawled websites\n", "res = query_engine.query(query)\n", "display_response(res)\n", "\n", "print(\"-----------------\")\n", "# 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(\"URL\\t\", src.metadata['url'])\n", " print(\"Score\\t\", src.score)\n", " print(\"Content Type\\t\", src.metadata.get(\"contentType\"))\n", " print(\"-_\"*20)" ] }, { "cell_type": "markdown", "metadata": { "id": "5ZPRQy3ZDYS_" }, "source": [ "# Evaluate" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "SxH3t-kMDZju" }, "outputs": [], "source": [ "from huggingface_hub import hf_hub_download\n", "from llama_index.finetuning.embeddings.common import (\n", " EmbeddingQAFinetuneDataset,\n", ")\n", "\n", "# Download the evaluation dataset\n", "hf_hub_download(repo_id=\"vicpada/AzureRAGEval\", filename=\"rag_eval_dataset2.json\", repo_type=\"dataset\", local_dir=\".\")\n", "rag_eval_dataset = EmbeddingQAFinetuneDataset.from_json(\"./rag_eval_dataset2.json\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Q0dFZU4vDOLG" }, "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", " precision = full_df[\"precision\"].mean()\n", " recall = full_df[\"recall\"].mean()\n", " ap = full_df[\"ap\"].mean()\n", " ndcg = full_df[\"ndcg\"].mean()\n", "\n", " metric_df = pd.DataFrame(\n", " {\"Retriever\": [name], \"Hit Rate\": [hit_rate], \"MRR\": [mrr], \"Precision\": [precision], \"Recall\":[recall],\"AP\":[ap],\"NDCG\":[ndcg]}\n", " )\n", "\n", " return metric_df" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "zxu1rAR7DP2S" }, "outputs": [], "source": [ "from llama_index.core.evaluation import RetrieverEvaluator\n", "\n", "metrics = [\"hit_rate\", \"mrr\", \"precision\", \"recall\", \"ap\", \"ndcg\"]\n", "\n", "\n", "# We can evaluate the retievers with different top_k values.\n", "for i in [2,10,20,50,100,200,400]:\n", " retriever = index.as_retriever(\n", " similarity_top_k=i,\n", " embed_model=embed_model\n", " )\n", " retriever_evaluator = RetrieverEvaluator.from_metric_names(\n", " metrics, retriever=retriever\n", " )\n", " eval_results = await retriever_evaluator.aevaluate_dataset(rag_eval_dataset)\n", " print(display_results_retriever(f\"top_{i}\", eval_results))" ] } ], "metadata": { "colab": { "machine_shape": "hm", "provenance": [] }, 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