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- - + + \ No newline at end of file diff --git a/assets/js/00e0a48e.36781a06.js b/assets/js/00e0a48e.36781a06.js new file mode 100644 index 0000000000..822e5ac087 --- /dev/null +++ b/assets/js/00e0a48e.36781a06.js @@ -0,0 +1 @@ +"use strict";(self.webpackChunksynapseml=self.webpackChunksynapseml||[]).push([[96922],{3905:(e,t,n)=>{n.d(t,{Zo:()=>p,kt:()=>u});var r=n(67294);function a(e,t,n){return t in e?Object.defineProperty(e,t,{value:n,enumerable:!0,configurable:!0,writable:!0}):e[t]=n,e}function s(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter((function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable}))),n.push.apply(n,r)}return n}function i(e){for(var t=1;t=0||(a[n]=e[n]);return a}(e,t);if(Object.getOwnPropertySymbols){var s=Object.getOwnPropertySymbols(e);for(r=0;r=0||Object.prototype.propertyIsEnumerable.call(e,n)&&(a[n]=e[n])}return a}var d=r.createContext({}),l=function(e){var t=r.useContext(d),n=t;return e&&(n="function"==typeof e?e(t):i(i({},t),e)),n},p=function(e){var t=l(e.components);return r.createElement(d.Provider,{value:t},e.children)},c={inlineCode:"code",wrapper:function(e){var t=e.children;return r.createElement(r.Fragment,{},t)}},m=r.forwardRef((function(e,t){var n=e.components,a=e.mdxType,s=e.originalType,d=e.parentName,p=o(e,["components","mdxType","originalType","parentName"]),m=l(n),u=a,g=m["".concat(d,".").concat(u)]||m[u]||c[u]||s;return n?r.createElement(g,i(i({ref:t},p),{},{components:n})):r.createElement(g,i({ref:t},p))}));function u(e,t){var n=arguments,a=t&&t.mdxType;if("string"==typeof e||a){var s=n.length,i=new Array(s);i[0]=m;var o={};for(var d in t)hasOwnProperty.call(t,d)&&(o[d]=t[d]);o.originalType=e,o.mdxType="string"==typeof e?e:a,i[1]=o;for(var l=2;l{n.r(t),n.d(t,{assets:()=>d,contentTitle:()=>i,default:()=>c,frontMatter:()=>s,metadata:()=>o,toc:()=>l});var r=n(83117),a=(n(67294),n(3905));const s={title:"Quickstart - Custom Embeddings and Approximate KNN on GPU",hide_title:!0,status:"stable"},i="Embedding Text with local (per node) NVIDIA TensorRT accelerator and GPU based Aproximate Nearest Neighbor (ANN)",o={unversionedId:"Explore Algorithms/OpenAI/Quickstart - Custom Embeddings and Approximate KNN on GPU",id:"Explore Algorithms/OpenAI/Quickstart - Custom Embeddings and Approximate KNN on GPU",title:"Quickstart - Custom Embeddings and Approximate KNN on GPU",description:"The demo extending existing Azure OpenAI based demo when encoding is processed by OpenAI requests and KNN was using GPU based brute force search. This tutorial shows how to perform fast local embeddings using multilingual E5 text embeddings and fast aproximate Nearest Neighbor search using IVFFlat alcorithm. All tutorial stages accelerated by NVIDIA GPU using NVIDIA TensorRT and Spark Rapids ML. The tutorial folder contains two benchmark notebooks to demonstrate advantages of the presented GPU based approach compare to previos CPU based demo",source:"@site/docs/Explore Algorithms/OpenAI/Quickstart - Custom Embeddings and Approximate KNN on GPU.md",sourceDirName:"Explore Algorithms/OpenAI",slug:"/Explore Algorithms/OpenAI/Quickstart - Custom Embeddings and Approximate KNN on GPU",permalink:"/SynapseML/docs/next/Explore Algorithms/OpenAI/Quickstart - Custom Embeddings and Approximate KNN on GPU",draft:!1,tags:[],version:"current",frontMatter:{title:"Quickstart - Custom Embeddings and Approximate KNN on GPU",hide_title:!0,status:"stable"},sidebar:"docs",previous:{title:"Quickstart - OpenAI Embedding and GPU based KNN",permalink:"/SynapseML/docs/next/Explore Algorithms/OpenAI/Quickstart - OpenAI Embedding and GPU based KNN"},next:{title:"Quickstart - Understand and Search Forms",permalink:"/SynapseML/docs/next/Explore Algorithms/OpenAI/Quickstart - Understand and Search Forms"}},d={},l=[{value:"Step 1: Prepare Environment",id:"step-1-prepare-environment",level:2},{value:"Step 2: Load Input Data",id:"step-2-load-input-data",level:2},{value:"Step 3: Generate Embeddings",id:"step-3-generate-embeddings",level:2},{value:"Step 4: Build the query against embeddings",id:"step-4-build-the-query-against-embeddings",level:2},{value:"Step 5: Build a fast vector index to over review embeddings",id:"step-5-build-a-fast-vector-index-to-over-review-embeddings",level:2},{value:"Step 6: Find top k Nearest Neighbors ON GPU",id:"step-6-find-top-k-nearest-neighbors-on-gpu",level:2},{value:"Step 7: Collect and display results",id:"step-7-collect-and-display-results",level:2}],p={toc:l};function c(e){let{components:t,...n}=e;return(0,a.kt)("wrapper",(0,r.Z)({},p,n,{components:t,mdxType:"MDXLayout"}),(0,a.kt)("h1",{id:"embedding-text-with-local-per-node-nvidia-tensorrt-accelerator-and-gpu-based-aproximate-nearest-neighbor-ann"},"Embedding Text with local (per node) NVIDIA TensorRT accelerator and GPU based Aproximate Nearest Neighbor (ANN)"),(0,a.kt)("p",null,"The demo extending existing ",(0,a.kt)("a",{parentName:"p",href:"https://github.com/microsoft/SynapseML/blob/master/docs/Explore%20Algorithms/OpenAI/Quickstart%20-%20OpenAI%20Embedding%20and%20GPU%20based%20KNN.ipynb"},"Azure OpenAI based demo")," when encoding is processed by OpenAI requests and KNN was using GPU based brute force search. This tutorial shows how to perform fast local embeddings using ",(0,a.kt)("a",{parentName:"p",href:"https://arxiv.org/abs/2402.05672"},"multilingual E5 text embeddings")," and fast aproximate Nearest Neighbor search using IVFFlat alcorithm. All tutorial stages accelerated by NVIDIA GPU using ",(0,a.kt)("a",{parentName:"p",href:"https://developer.nvidia.com/tensorrt"},"NVIDIA TensorRT")," and ",(0,a.kt)("a",{parentName:"p",href:"https://github.com/NVIDIA/spark-rapids-ml"},"Spark Rapids ML"),". The tutorial folder contains two benchmark notebooks to demonstrate advantages of the presented GPU based approach compare to ",(0,a.kt)("a",{parentName:"p",href:"https://github.com/microsoft/SynapseML/blob/master/docs/Explore%20Algorithms/OpenAI/Quickstart%20-%20OpenAI%20Embedding.ipynb"},"previos CPU based demo")),(0,a.kt)("p",null,"The key prerequisites for this quickstart include a working Azure OpenAI resource, and an Apache Spark cluster with SynapseML installed. We suggest creating a Synapse workspace, but currently the notebook was running on Databricks GPU based cluster using Standard_NC24ads_A100_v4 with 6 workers. Databricks Runtime was 13.3 LTS ML (includes Apache Spark 3.4.1, GPU, Scala 2.12) with related ",(0,a.kt)("a",{parentName:"p",href:"https://github.com/microsoft/SynapseML/tree/master/tools/init_scripts"},"init_script")," to install all required packages."),(0,a.kt)("h2",{id:"step-1-prepare-environment"},"Step 1: Prepare Environment"),(0,a.kt)("p",null,"It will imports required libraries and get initial settings"),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},"import torch\nimport sys\nimport pyspark.sql.functions as F\nfrom pyspark.sql.types import StructType, StructField, IntegerType, StringType\nfrom pyspark.ml.linalg import Vectors\nfrom pyspark.ml.linalg import VectorUDT\nfrom spark_rapids_ml.knn import (\n ApproximateNearestNeighbors,\n ApproximateNearestNeighborsModel,\n)\nfrom synapse.ml.hf import HuggingFaceSentenceEmbedder\nfrom synapse.ml.nn import KNN\n")),(0,a.kt)("h2",{id:"step-2-load-input-data"},"Step 2: Load Input Data"),(0,a.kt)("p",null,"It will load public dataset and generate extra syntetic rows if set by size parameter"),(0,a.kt)("p",null,"The loaded dataset has 1000 rows. If you specify ",(0,a.kt)("i",null,"number_of_input_rows")," in ","[1..1000]"," it will cut extra rows if needed"),(0,a.kt)("p",null,"If ",(0,a.kt)("i",null,"number_of_input_rows")," in ","[1000..1000000]"," it will generate extra rows using cross join of original data"),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},'file_path = "wasbs://publicwasb@mmlspark.blob.core.windows.net/fine_food_reviews_1k.csv"\n\ndf = spark.read.options(inferSchema="True", delimiter=",", header=True).csv(file_path)\ndf = df.withColumn(\n "data",\n F.format_string("Title: %s; Content: %s", F.trim(df.Summary), F.trim(df.Text)),\n)\n\n# Size of DF\nnumber_of_input_rows = 100\n\n\n# Check if the row count is less than 10\nif number_of_input_rows <= 0 or number_of_input_rows >= 1000000:\n raise ValueError(f"Limit is {number_of_input_rows}, which should be less than 1M.")\n\nif number_of_input_rows > 1000:\n\n # Cross-join the DataFrame with itself to create n x n pairs for string concatenation (synthetic data)\n cross_joined_df = df.crossJoin(df.withColumnRenamed("data", "data_"))\n\n # Create a new column \'result_vector\' by concatenating the two source vectors\n tmp_df = cross_joined_df.withColumn(\n "result_vector",\n F.concat(F.col("data"), F.lit(". \\n"), F.col("data_")),\n )\n\n # Select only the necessary columns and show the result\n tmp_df = tmp_df.select("result_vector")\n\n # Shuffle the DataFrame with a fixed seed to have close strings spreaded\n seed = 42\n\n df = (\n tmp_df.withColumnRenamed("result_vector", "data")\n .withColumn("id", F.monotonically_increasing_id())\n .orderBy(F.rand(seed))\n )\n\ndf = df.limit(number_of_input_rows).repartition(10).cache()\n\nprint(f"Loaded: {number_of_input_rows} rows")\n')),(0,a.kt)("h2",{id:"step-3-generate-embeddings"},"Step 3: Generate Embeddings"),(0,a.kt)("p",null,'We will first generate embeddings using NVIDIA TensorRT optimized SentenceTransformer. In the demo you can use two fifferent HF models: intfloat/e5-large-v2 or sentence-transformers/all-MiniLM-L6-v2"'),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},'# To create embedder with different models, uncomment the following line\n# embedder = HuggingFaceSentenceEmbedder(modelName="intfloat/e5-large-v2", inputCol="data", outputCol="embeddings", runtime="tensorrt")\nembedder = HuggingFaceSentenceEmbedder(\n modelName="sentence-transformers/all-MiniLM-L6-v2",\n inputCol="data",\n outputCol="embeddings",\n runtime="tensorrt",\n)\n\nembeddings = embedder.transform(df).select("id", "embeddings").cache()\n')),(0,a.kt)("h2",{id:"step-4-build-the-query-against-embeddings"},"Step 4: Build the query against embeddings"),(0,a.kt)("p",null,"Get query embeddings running standard SentenceTransformer just on the driver. Convert embedding results to a data frame"),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},'# Sample query\nqueries = ["desserts", "disgusting"]\nids = [1, 2]\n\n# Create DataFrame directly from the data and schema\nquery_df = spark.createDataFrame(\n list(zip(ids, queries)),\n StructType(\n [\n StructField("id", IntegerType(), nullable=False),\n StructField("data", StringType(), nullable=False),\n ]\n ),\n)\n\nquery_embeddings = embedder.transform(query_df).select("id", "embeddings").cache()\n')),(0,a.kt)("h2",{id:"step-5-build-a-fast-vector-index-to-over-review-embeddings"},"Step 5: Build a fast vector index to over review embeddings"),(0,a.kt)("p",null,"We will use fast NVIDIA Rapids indexer. This KNN implementation will work only on GPU. If you want to use CPU then switch to synapse.ml.nn CPU based KNN implementation"),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},"RUN_ON_GPU = torch.cuda.is_available()\n")),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},'if RUN_ON_GPU:\n rapids_knn_model = (\n ApproximateNearestNeighbors(k=5)\n .setInputCol("embeddings")\n .setIdCol("id")\n .fit(embeddings)\n )\nelse:\n array_to_vector_udf = udf(lambda array: Vectors.dense(array), VectorUDT())\n df_with_vectors = embeddings.withColumn(\n "features", array_to_vector_udf(embeddings["embeddings"])\n )\n knn = (\n KNN()\n .setFeaturesCol("features")\n .setValuesCol("id")\n .setOutputCol("output")\n .setK(10)\n )\n knn_model = knn.fit(df_with_vectors)\n')),(0,a.kt)("h2",{id:"step-6-find-top-k-nearest-neighbors-on-gpu"},"Step 6: Find top k Nearest Neighbors ON GPU"),(0,a.kt)("p",null,"We will use fast ANN ",(0,a.kt)("a",{parentName:"p",href:"https://developer.nvidia.com/blog/accelerated-vector-search-approximating-with-rapids-raft-ivf-flat/"},"IVFFlat algorithm")," from Rapids"),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},'if RUN_ON_GPU:\n (_, _, knn_df) = rapids_knn_model.kneighbors(query_embeddings)\nelse:\n array_to_vector_udf = udf(lambda array: Vectors.dense(array), VectorUDT())\n df_with_vectors = query_embeddings.withColumn(\n "features", array_to_vector_udf(query_embeddings["embeddings"])\n )\n knn_df = knn_model.transform(df_with_vectors)\n')),(0,a.kt)("h2",{id:"step-7-collect-and-display-results"},"Step 7: Collect and display results"),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},'if RUN_ON_GPU:\n result_df = (\n knn_df.withColumn(\n "zipped", F.explode(F.arrays_zip(F.col("indices"), F.col("distances")))\n )\n .select(\n F.col("query_id"),\n F.col("zipped.indices").alias("id"),\n F.col("zipped.distances").alias("distance"),\n )\n .join(df, on="id", how="inner")\n .select("query_id", "id", "data", "distance")\n )\nelse:\n knn_df = knn_df.withColumnRenamed("data", "original_data")\n result_df = (\n knn_df.withColumn("match", F.explode("output"))\n .join(df, df["id"] == F.col("match.value"))\n .select("original_data", F.col("data"), "match.distance")\n )\n\ndisplay(result_df)\n')),(0,a.kt)("h1",{id:"results"},"Results"),(0,a.kt)("p",null,"The goal of this demo is to showcase two acceleration techniques: local (per node) embedding generation and approximate KNN. Compared to the original method, which relies on HTTP requests to the OpenAI model and CPU-based KNN. The new approach is significantly more scalable and provides substantial acceleration, especially for large input datasets."),(0,a.kt)("p",null,"This is the comparison dureation results on 10 T4 GPU nodes for both approaches:"),(0,a.kt)("p",null,(0,a.kt)("img",{parentName:"p",src:"https://mmlspark.blob.core.windows.net/graphics/Documentation/knn_comparison.png",alt:"KNN Comparison"})))}c.isMDXComponent=!0}}]); \ No newline at end of file diff --git a/assets/js/120352d6.0e62ec09.js b/assets/js/120352d6.0e62ec09.js deleted file mode 100644 index cbafdd864c..0000000000 --- a/assets/js/120352d6.0e62ec09.js +++ /dev/null @@ -1 +0,0 @@ -"use strict";(self.webpackChunksynapseml=self.webpackChunksynapseml||[]).push([[89262],{3905:(e,n,t)=>{t.d(n,{Zo:()=>c,kt:()=>m});var r=t(67294);function a(e,n,t){return n in e?Object.defineProperty(e,n,{value:t,enumerable:!0,configurable:!0,writable:!0}):e[n]=t,e}function o(e,n){var t=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);n&&(r=r.filter((function(n){return Object.getOwnPropertyDescriptor(e,n).enumerable}))),t.push.apply(t,r)}return t}function s(e){for(var n=1;n=0||(a[t]=e[t]);return a}(e,n);if(Object.getOwnPropertySymbols){var o=Object.getOwnPropertySymbols(e);for(r=0;r=0||Object.prototype.propertyIsEnumerable.call(e,t)&&(a[t]=e[t])}return a}var l=r.createContext({}),p=function(e){var n=r.useContext(l),t=n;return e&&(t="function"==typeof e?e(n):s(s({},n),e)),t},c=function(e){var n=p(e.components);return r.createElement(l.Provider,{value:n},e.children)},u={inlineCode:"code",wrapper:function(e){var n=e.children;return r.createElement(r.Fragment,{},n)}},d=r.forwardRef((function(e,n){var t=e.components,a=e.mdxType,o=e.originalType,l=e.parentName,c=i(e,["components","mdxType","originalType","parentName"]),d=p(t),m=a,h=d["".concat(l,".").concat(m)]||d[m]||u[m]||o;return t?r.createElement(h,s(s({ref:n},c),{},{components:t})):r.createElement(h,s({ref:n},c))}));function m(e,n){var t=arguments,a=n&&n.mdxType;if("string"==typeof e||a){var o=t.length,s=new Array(o);s[0]=d;var i={};for(var l in n)hasOwnProperty.call(n,l)&&(i[l]=n[l]);i.originalType=e,i.mdxType="string"==typeof e?e:a,s[1]=i;for(var p=2;p{t.r(n),t.d(n,{assets:()=>l,contentTitle:()=>s,default:()=>u,frontMatter:()=>o,metadata:()=>i,toc:()=>p});var r=t(83117),a=(t(67294),t(3905));const o={title:"Quickstart - Understand and Search Forms",hide_title:!0,status:"stable"},s="Tutorial: Create a custom search engine and question-answering system",i={unversionedId:"Explore Algorithms/OpenAI/Quickstart - Understand and Search Forms",id:"Explore Algorithms/OpenAI/Quickstart - Understand and Search Forms",title:"Quickstart - Understand and Search Forms",description:"In this tutorial, learn how to index and query large data loaded from a Spark cluster. You set up a Jupyter Notebook that performs the following actions:",source:"@site/docs/Explore Algorithms/OpenAI/Quickstart - Understand and Search Forms.md",sourceDirName:"Explore Algorithms/OpenAI",slug:"/Explore Algorithms/OpenAI/Quickstart - Understand and Search Forms",permalink:"/SynapseML/docs/next/Explore Algorithms/OpenAI/Quickstart - Understand and Search Forms",draft:!1,tags:[],version:"current",frontMatter:{title:"Quickstart - Understand and Search Forms",hide_title:!0,status:"stable"},sidebar:"docs",previous:{title:"Quickstart - OpenAI Embedding and GPU based KNN",permalink:"/SynapseML/docs/next/Explore Algorithms/OpenAI/Quickstart - OpenAI Embedding and GPU based KNN"},next:{title:"Getting Started",permalink:"/SynapseML/docs/next/Explore Algorithms/Deep Learning/Getting Started"}},l={},p=[{value:"1 - Set up dependencies",id:"1---set-up-dependencies",level:2},{value:"2 - Load data into Spark",id:"2---load-data-into-spark",level:2},{value:"3 - Apply form recognition",id:"3---apply-form-recognition",level:2},{value:"4 - Simplify form recognition output",id:"4---simplify-form-recognition-output",level:2},{value:"5 - Add translations",id:"5---add-translations",level:2},{value:"6 - Translate products to emojis with OpenAI \ud83e\udd2f",id:"6---translate-products-to-emojis-with-openai-",level:2},{value:"7 - Infer vendor address continent with OpenAI",id:"7---infer-vendor-address-continent-with-openai",level:2},{value:"8 - Create an Azure Search Index for the Forms",id:"8---create-an-azure-search-index-for-the-forms",level:2},{value:"9 - Try out a search query",id:"9---try-out-a-search-query",level:2},{value:"10 - Build a chatbot that can use Azure Search as a tool \ud83e\udde0\ud83d\udd27",id:"10---build-a-chatbot-that-can-use-azure-search-as-a-tool-",level:2},{value:"11 - Asking our chatbot a question",id:"11---asking-our-chatbot-a-question",level:2},{value:"12 - A quick double check",id:"12---a-quick-double-check",level:2}],c={toc:p};function u(e){let{components:n,...t}=e;return(0,a.kt)("wrapper",(0,r.Z)({},c,t,{components:n,mdxType:"MDXLayout"}),(0,a.kt)("h1",{id:"tutorial-create-a-custom-search-engine-and-question-answering-system"},"Tutorial: Create a custom search engine and question-answering system"),(0,a.kt)("p",null,"In this tutorial, learn how to index and query large data loaded from a Spark cluster. You set up a Jupyter Notebook that performs the following actions:"),(0,a.kt)("blockquote",null,(0,a.kt)("ul",{parentName:"blockquote"},(0,a.kt)("li",{parentName:"ul"},"Load various forms (invoices) into a data frame in an Apache Spark session"),(0,a.kt)("li",{parentName:"ul"},"Analyze them to determine their features"),(0,a.kt)("li",{parentName:"ul"},"Assemble the resulting output into a tabular data structure"),(0,a.kt)("li",{parentName:"ul"},"Write the output to a search index hosted in Azure Cognitive Search"),(0,a.kt)("li",{parentName:"ul"},"Explore and query over the content you created"))),(0,a.kt)("h2",{id:"1---set-up-dependencies"},"1 - Set up dependencies"),(0,a.kt)("p",null,"We start by importing packages and connecting to the Azure resources used in this workflow."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},"%pip install openai==0.28.1\n")),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},'from synapse.ml.core.platform import find_secret\n\ncognitive_key = find_secret(\n secret_name="ai-services-api-key", keyvault="mmlspark-build-keys"\n) # Replace the call to find_secret with your key as a python string. e.g. cognitive_key="27snaiw..."\ncognitive_location = "eastus"\n\ntranslator_key = find_secret(\n secret_name="translator-key", keyvault="mmlspark-build-keys"\n) # Replace the call to find_secret with your key as a python string.\ntranslator_location = "eastus"\n\nsearch_key = find_secret(\n secret_name="azure-search-key", keyvault="mmlspark-build-keys"\n) # Replace the call to find_secret with your key as a python string.\nsearch_service = "mmlspark-azure-search"\nsearch_index = "form-demo-index-5"\n\nopenai_key = find_secret(\n secret_name="openai-api-key-2", keyvault="mmlspark-build-keys"\n) # Replace the call to find_secret with your key as a python string.\nopenai_service_name = "synapseml-openai-2"\nopenai_deployment_name = "gpt-35-turbo"\nopenai_url = f"https://{openai_service_name}.openai.azure.com/"\n')),(0,a.kt)("h2",{id:"2---load-data-into-spark"},"2 - Load data into Spark"),(0,a.kt)("p",null,"This code loads a few external files from an Azure storage account that's used for demo purposes. The files are various invoices, and they're read into a data frame."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},'from pyspark.sql.functions import udf\nfrom pyspark.sql.types import StringType\n\n\ndef blob_to_url(blob):\n [prefix, postfix] = blob.split("@")\n container = prefix.split("/")[-1]\n split_postfix = postfix.split("/")\n account = split_postfix[0]\n filepath = "/".join(split_postfix[1:])\n return "https://{}/{}/{}".format(account, container, filepath)\n\n\ndf2 = (\n spark.read.format("binaryFile")\n .load("wasbs://publicwasb@mmlspark.blob.core.windows.net/form_subset/*")\n .select("path")\n .limit(10)\n .select(udf(blob_to_url, StringType())("path").alias("url"))\n .cache()\n)\n\ndisplay(df2)\n')),(0,a.kt)("img",{src:"https://mmlspark.blob.core.windows.net/graphics/Invoice11205.svg",width:"40%"}),(0,a.kt)("h2",{id:"3---apply-form-recognition"},"3 - Apply form recognition"),(0,a.kt)("p",null,"This code loads the ",(0,a.kt)("a",{parentName:"p",href:"https://microsoft.github.io/SynapseML/docs/Explore%20Algorithms/AI%20Services/Overview/#form-recognizer"},"AnalyzeInvoices transformer")," and passes a reference to the data frame containing the invoices. It calls the pre-built invoice model of Azure Forms Analyzer."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},'from synapse.ml.services.form import AnalyzeInvoices\n\nanalyzed_df = (\n AnalyzeInvoices()\n .setSubscriptionKey(cognitive_key)\n .setLocation(cognitive_location)\n .setImageUrlCol("url")\n .setOutputCol("invoices")\n .setErrorCol("errors")\n .setConcurrency(5)\n .transform(df2)\n .cache()\n)\n\ndisplay(analyzed_df)\n')),(0,a.kt)("h2",{id:"4---simplify-form-recognition-output"},"4 - Simplify form recognition output"),(0,a.kt)("p",null,"This code uses the ",(0,a.kt)("a",{parentName:"p",href:"https://mmlspark.blob.core.windows.net/docs/1.0.4/pyspark/synapse.ml.services.form.html#module-synapse.ml.services.form.FormOntologyTransformer"},"FormOntologyLearner"),", a transformer that analyzes the output of Form Recognizer transformers (for Azure AI Document Intelligence) and infers a tabular data structure. The output of AnalyzeInvoices is dynamic and varies based on the features detected in your content."),(0,a.kt)("p",null,"FormOntologyLearner extends the utility of the AnalyzeInvoices transformer by looking for patterns that can be used to create a tabular data structure. Organizing the output into multiple columns and rows makes for simpler downstream analysis."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},'from synapse.ml.services.form import FormOntologyLearner\n\norganized_df = (\n FormOntologyLearner()\n .setInputCol("invoices")\n .setOutputCol("extracted")\n .fit(analyzed_df)\n .transform(analyzed_df)\n .select("url", "extracted.*")\n .cache()\n)\n\ndisplay(organized_df)\n')),(0,a.kt)("p",null,"With our nice tabular dataframe, we can flatten the nested tables found in the forms with some SparkSQL"),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},'from pyspark.sql.functions import explode, col\n\nitemized_df = (\n organized_df.select("*", explode(col("Items")).alias("Item"))\n .drop("Items")\n .select("Item.*", "*")\n .drop("Item")\n)\n\ndisplay(itemized_df)\n')),(0,a.kt)("h2",{id:"5---add-translations"},"5 - Add translations"),(0,a.kt)("p",null,"This code loads ",(0,a.kt)("a",{parentName:"p",href:"https://microsoft.github.io/SynapseML/docs/Explore%20Algorithms/AI%20Services/Overview/#translation"},"Translate"),', a transformer that calls the Azure AI Translator service in Azure AI services. The original text, which is in English in the "Description" column, is machine-translated into various languages. All of the output is consolidated into "output.translations" array.'),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},'from synapse.ml.services.translate import Translate\n\ntranslated_df = (\n Translate()\n .setSubscriptionKey(translator_key)\n .setLocation(translator_location)\n .setTextCol("Description")\n .setErrorCol("TranslationError")\n .setOutputCol("output")\n .setToLanguage(["zh-Hans", "fr", "ru", "cy"])\n .setConcurrency(5)\n .transform(itemized_df)\n .withColumn("Translations", col("output.translations")[0])\n .drop("output", "TranslationError")\n .cache()\n)\n\ndisplay(translated_df)\n')),(0,a.kt)("h2",{id:"6---translate-products-to-emojis-with-openai-"},"6 - Translate products to emojis with OpenAI \ud83e\udd2f"),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},'from synapse.ml.services.openai import OpenAIPrompt\nfrom pyspark.sql.functions import trim, split\n\nemoji_template = """ \n Your job is to translate item names into emoji. Do not add anything but the emoji and end the translation with a comma\n \n Two Ducks: \ud83e\udd86\ud83e\udd86,\n Light Bulb: \ud83d\udca1,\n Three Peaches: \ud83c\udf51\ud83c\udf51\ud83c\udf51,\n Two kitchen stoves: \u2668\ufe0f\u2668\ufe0f,\n A red car: \ud83d\ude97,\n A person and a cat: \ud83e\uddcd\ud83d\udc08,\n A {Description}: """\n\nprompter = (\n OpenAIPrompt()\n .setSubscriptionKey(openai_key)\n .setDeploymentName(openai_deployment_name)\n .setUrl(openai_url)\n .setMaxTokens(5)\n .setPromptTemplate(emoji_template)\n .setErrorCol("error")\n .setOutputCol("Emoji")\n)\n\nemoji_df = (\n prompter.transform(translated_df)\n .withColumn("Emoji", trim(split(col("Emoji"), ",").getItem(0)))\n .drop("error", "prompt")\n .cache()\n)\n')),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},'display(emoji_df.select("Description", "Emoji"))\n')),(0,a.kt)("h2",{id:"7---infer-vendor-address-continent-with-openai"},"7 - Infer vendor address continent with OpenAI"),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},'continent_template = """\nWhich continent does the following address belong to? \n\nPick one value from Europe, Australia, North America, South America, Asia, Africa, Antarctica. \n\nDont respond with anything but one of the above. If you don\'t know the answer or cannot figure it out from the text, return None. End your answer with a comma.\n\nAddress: "6693 Ryan Rd, North Whales",\nContinent: Europe,\nAddress: "6693 Ryan Rd",\nContinent: None,\nAddress: "{VendorAddress}",\nContinent:"""\n\ncontinent_df = (\n prompter.setOutputCol("Continent")\n .setPromptTemplate(continent_template)\n .transform(emoji_df)\n .withColumn("Continent", trim(split(col("Continent"), ",").getItem(0)))\n .drop("error", "prompt")\n .cache()\n)\n')),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},'display(continent_df.select("VendorAddress", "Continent"))\n')),(0,a.kt)("h2",{id:"8---create-an-azure-search-index-for-the-forms"},"8 - Create an Azure Search Index for the Forms"),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},'from synapse.ml.services import *\nfrom pyspark.sql.functions import monotonically_increasing_id, lit\n\n(\n continent_df.withColumn("DocID", monotonically_increasing_id().cast("string"))\n .withColumn("SearchAction", lit("upload"))\n .writeToAzureSearch(\n subscriptionKey=search_key,\n actionCol="SearchAction",\n serviceName=search_service,\n indexName=search_index,\n keyCol="DocID",\n )\n)\n')),(0,a.kt)("h2",{id:"9---try-out-a-search-query"},"9 - Try out a search query"),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},'import requests\n\nsearch_url = "https://{}.search.windows.net/indexes/{}/docs/search?api-version=2019-05-06".format(\n search_service, search_index\n)\nrequests.post(\n search_url, json={"search": "door"}, headers={"api-key": search_key}\n).json()\n')),(0,a.kt)("h2",{id:"10---build-a-chatbot-that-can-use-azure-search-as-a-tool-"},"10 - Build a chatbot that can use Azure Search as a tool \ud83e\udde0\ud83d\udd27"),(0,a.kt)("img",{src:"https://mmlspark.blob.core.windows.net/graphics/notebooks/chatbot_flow_2.svg",width:"40%"}),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},'import json\nimport openai\n\nopenai.api_type = "azure"\nopenai.api_base = openai_url\nopenai.api_key = openai_key\nopenai.api_version = "2023-03-15-preview"\n\nchat_context_prompt = f"""\nYou are a chatbot designed to answer questions with the help of a search engine that has the following information:\n\n{continent_df.columns}\n\nIf you dont know the answer to a question say "I dont know". Do not lie or hallucinate information. Be brief. If you need to use the search engine to solve the please output a json in the form of {{"query": "example_query"}}\n"""\n\n\ndef search_query_prompt(question):\n return f"""\nGiven the search engine above, what would you search for to answer the following question?\n\nQuestion: "{question}"\n\nPlease output a json in the form of {{"query": "example_query"}}\n"""\n\n\ndef search_result_prompt(query):\n search_results = requests.post(\n search_url, json={"search": query}, headers={"api-key": search_key}\n ).json()\n return f"""\n\nYou previously ran a search for "{query}" which returned the following results:\n\n{search_results}\n\nYou should use the results to help you answer questions. If you dont know the answer to a question say "I dont know". Do not lie or hallucinate information. Be Brief and mention which query you used to solve the problem. \n"""\n\n\ndef prompt_gpt(messages):\n response = openai.ChatCompletion.create(\n engine=openai_deployment_name, messages=messages, max_tokens=None, top_p=0.95\n )\n return response["choices"][0]["message"]["content"]\n\n\ndef custom_chatbot(question):\n while True:\n try:\n query = json.loads(\n prompt_gpt(\n [\n {"role": "system", "content": chat_context_prompt},\n {"role": "user", "content": search_query_prompt(question)},\n ]\n )\n )["query"]\n\n return prompt_gpt(\n [\n {"role": "system", "content": chat_context_prompt},\n {"role": "system", "content": search_result_prompt(query)},\n {"role": "user", "content": question},\n ]\n )\n except Exception as e:\n raise e\n')),(0,a.kt)("h2",{id:"11---asking-our-chatbot-a-question"},"11 - Asking our chatbot a question"),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},'custom_chatbot("What did Luke Diaz buy?")\n')),(0,a.kt)("h2",{id:"12---a-quick-double-check"},"12 - A quick double check"),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},'display(\n continent_df.where(col("CustomerName") == "Luke Diaz")\n .select("Description")\n .distinct()\n)\n')))}u.isMDXComponent=!0}}]); \ No newline at end of file diff --git a/assets/js/120352d6.6c87737b.js b/assets/js/120352d6.6c87737b.js new file mode 100644 index 0000000000..dd49e30a58 --- /dev/null +++ b/assets/js/120352d6.6c87737b.js @@ -0,0 +1 @@ +"use strict";(self.webpackChunksynapseml=self.webpackChunksynapseml||[]).push([[89262],{3905:(e,n,t)=>{t.d(n,{Zo:()=>c,kt:()=>m});var r=t(67294);function a(e,n,t){return n in e?Object.defineProperty(e,n,{value:t,enumerable:!0,configurable:!0,writable:!0}):e[n]=t,e}function o(e,n){var t=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);n&&(r=r.filter((function(n){return Object.getOwnPropertyDescriptor(e,n).enumerable}))),t.push.apply(t,r)}return t}function s(e){for(var n=1;n=0||(a[t]=e[t]);return a}(e,n);if(Object.getOwnPropertySymbols){var o=Object.getOwnPropertySymbols(e);for(r=0;r=0||Object.prototype.propertyIsEnumerable.call(e,t)&&(a[t]=e[t])}return a}var l=r.createContext({}),p=function(e){var n=r.useContext(l),t=n;return e&&(t="function"==typeof e?e(n):s(s({},n),e)),t},c=function(e){var n=p(e.components);return r.createElement(l.Provider,{value:n},e.children)},u={inlineCode:"code",wrapper:function(e){var n=e.children;return r.createElement(r.Fragment,{},n)}},d=r.forwardRef((function(e,n){var t=e.components,a=e.mdxType,o=e.originalType,l=e.parentName,c=i(e,["components","mdxType","originalType","parentName"]),d=p(t),m=a,h=d["".concat(l,".").concat(m)]||d[m]||u[m]||o;return t?r.createElement(h,s(s({ref:n},c),{},{components:t})):r.createElement(h,s({ref:n},c))}));function m(e,n){var t=arguments,a=n&&n.mdxType;if("string"==typeof e||a){var o=t.length,s=new Array(o);s[0]=d;var i={};for(var l in n)hasOwnProperty.call(n,l)&&(i[l]=n[l]);i.originalType=e,i.mdxType="string"==typeof e?e:a,s[1]=i;for(var p=2;p{t.r(n),t.d(n,{assets:()=>l,contentTitle:()=>s,default:()=>u,frontMatter:()=>o,metadata:()=>i,toc:()=>p});var r=t(83117),a=(t(67294),t(3905));const o={title:"Quickstart - Understand and Search Forms",hide_title:!0,status:"stable"},s="Tutorial: Create a custom search engine and question-answering system",i={unversionedId:"Explore Algorithms/OpenAI/Quickstart - Understand and Search Forms",id:"Explore Algorithms/OpenAI/Quickstart - Understand and Search Forms",title:"Quickstart - Understand and Search Forms",description:"In this tutorial, learn how to index and query large data loaded from a Spark cluster. You set up a Jupyter Notebook that performs the following actions:",source:"@site/docs/Explore Algorithms/OpenAI/Quickstart - Understand and Search Forms.md",sourceDirName:"Explore Algorithms/OpenAI",slug:"/Explore Algorithms/OpenAI/Quickstart - Understand and Search Forms",permalink:"/SynapseML/docs/next/Explore Algorithms/OpenAI/Quickstart - Understand and Search Forms",draft:!1,tags:[],version:"current",frontMatter:{title:"Quickstart - Understand and Search Forms",hide_title:!0,status:"stable"},sidebar:"docs",previous:{title:"Quickstart - Custom Embeddings and Approximate KNN on GPU",permalink:"/SynapseML/docs/next/Explore Algorithms/OpenAI/Quickstart - Custom Embeddings and Approximate KNN on GPU"},next:{title:"Getting Started",permalink:"/SynapseML/docs/next/Explore Algorithms/Deep Learning/Getting Started"}},l={},p=[{value:"1 - Set up dependencies",id:"1---set-up-dependencies",level:2},{value:"2 - Load data into Spark",id:"2---load-data-into-spark",level:2},{value:"3 - Apply form recognition",id:"3---apply-form-recognition",level:2},{value:"4 - Simplify form recognition output",id:"4---simplify-form-recognition-output",level:2},{value:"5 - Add translations",id:"5---add-translations",level:2},{value:"6 - Translate products to emojis with OpenAI \ud83e\udd2f",id:"6---translate-products-to-emojis-with-openai-",level:2},{value:"7 - Infer vendor address continent with OpenAI",id:"7---infer-vendor-address-continent-with-openai",level:2},{value:"8 - Create an Azure Search Index for the Forms",id:"8---create-an-azure-search-index-for-the-forms",level:2},{value:"9 - Try out a search query",id:"9---try-out-a-search-query",level:2},{value:"10 - Build a chatbot that can use Azure Search as a tool \ud83e\udde0\ud83d\udd27",id:"10---build-a-chatbot-that-can-use-azure-search-as-a-tool-",level:2},{value:"11 - Asking our chatbot a question",id:"11---asking-our-chatbot-a-question",level:2},{value:"12 - A quick double check",id:"12---a-quick-double-check",level:2}],c={toc:p};function u(e){let{components:n,...t}=e;return(0,a.kt)("wrapper",(0,r.Z)({},c,t,{components:n,mdxType:"MDXLayout"}),(0,a.kt)("h1",{id:"tutorial-create-a-custom-search-engine-and-question-answering-system"},"Tutorial: Create a custom search engine and question-answering system"),(0,a.kt)("p",null,"In this tutorial, learn how to index and query large data loaded from a Spark cluster. You set up a Jupyter Notebook that performs the following actions:"),(0,a.kt)("blockquote",null,(0,a.kt)("ul",{parentName:"blockquote"},(0,a.kt)("li",{parentName:"ul"},"Load various forms (invoices) into a data frame in an Apache Spark session"),(0,a.kt)("li",{parentName:"ul"},"Analyze them to determine their features"),(0,a.kt)("li",{parentName:"ul"},"Assemble the resulting output into a tabular data structure"),(0,a.kt)("li",{parentName:"ul"},"Write the output to a search index hosted in Azure Cognitive Search"),(0,a.kt)("li",{parentName:"ul"},"Explore and query over the content you created"))),(0,a.kt)("h2",{id:"1---set-up-dependencies"},"1 - Set up dependencies"),(0,a.kt)("p",null,"We start by importing packages and connecting to the Azure resources used in this workflow."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},"%pip install openai==0.28.1\n")),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},'from synapse.ml.core.platform import find_secret\n\ncognitive_key = find_secret(\n secret_name="ai-services-api-key", keyvault="mmlspark-build-keys"\n) # Replace the call to find_secret with your key as a python string. e.g. cognitive_key="27snaiw..."\ncognitive_location = "eastus"\n\ntranslator_key = find_secret(\n secret_name="translator-key", keyvault="mmlspark-build-keys"\n) # Replace the call to find_secret with your key as a python string.\ntranslator_location = "eastus"\n\nsearch_key = find_secret(\n secret_name="azure-search-key", keyvault="mmlspark-build-keys"\n) # Replace the call to find_secret with your key as a python string.\nsearch_service = "mmlspark-azure-search"\nsearch_index = "form-demo-index-5"\n\nopenai_key = find_secret(\n secret_name="openai-api-key-2", keyvault="mmlspark-build-keys"\n) # Replace the call to find_secret with your key as a python string.\nopenai_service_name = "synapseml-openai-2"\nopenai_deployment_name = "gpt-35-turbo"\nopenai_url = f"https://{openai_service_name}.openai.azure.com/"\n')),(0,a.kt)("h2",{id:"2---load-data-into-spark"},"2 - Load data into Spark"),(0,a.kt)("p",null,"This code loads a few external files from an Azure storage account that's used for demo purposes. The files are various invoices, and they're read into a data frame."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},'from pyspark.sql.functions import udf\nfrom pyspark.sql.types import StringType\n\n\ndef blob_to_url(blob):\n [prefix, postfix] = blob.split("@")\n container = prefix.split("/")[-1]\n split_postfix = postfix.split("/")\n account = split_postfix[0]\n filepath = "/".join(split_postfix[1:])\n return "https://{}/{}/{}".format(account, container, filepath)\n\n\ndf2 = (\n spark.read.format("binaryFile")\n .load("wasbs://publicwasb@mmlspark.blob.core.windows.net/form_subset/*")\n .select("path")\n .limit(10)\n .select(udf(blob_to_url, StringType())("path").alias("url"))\n .cache()\n)\n\ndisplay(df2)\n')),(0,a.kt)("img",{src:"https://mmlspark.blob.core.windows.net/graphics/Invoice11205.svg",width:"40%"}),(0,a.kt)("h2",{id:"3---apply-form-recognition"},"3 - Apply form recognition"),(0,a.kt)("p",null,"This code loads the ",(0,a.kt)("a",{parentName:"p",href:"https://microsoft.github.io/SynapseML/docs/Explore%20Algorithms/AI%20Services/Overview/#form-recognizer"},"AnalyzeInvoices transformer")," and passes a reference to the data frame containing the invoices. It calls the pre-built invoice model of Azure Forms Analyzer."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},'from synapse.ml.services.form import AnalyzeInvoices\n\nanalyzed_df = (\n AnalyzeInvoices()\n .setSubscriptionKey(cognitive_key)\n .setLocation(cognitive_location)\n .setImageUrlCol("url")\n .setOutputCol("invoices")\n .setErrorCol("errors")\n .setConcurrency(5)\n .transform(df2)\n .cache()\n)\n\ndisplay(analyzed_df)\n')),(0,a.kt)("h2",{id:"4---simplify-form-recognition-output"},"4 - Simplify form recognition output"),(0,a.kt)("p",null,"This code uses the ",(0,a.kt)("a",{parentName:"p",href:"https://mmlspark.blob.core.windows.net/docs/1.0.4/pyspark/synapse.ml.services.form.html#module-synapse.ml.services.form.FormOntologyTransformer"},"FormOntologyLearner"),", a transformer that analyzes the output of Form Recognizer transformers (for Azure AI Document Intelligence) and infers a tabular data structure. The output of AnalyzeInvoices is dynamic and varies based on the features detected in your content."),(0,a.kt)("p",null,"FormOntologyLearner extends the utility of the AnalyzeInvoices transformer by looking for patterns that can be used to create a tabular data structure. Organizing the output into multiple columns and rows makes for simpler downstream analysis."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},'from synapse.ml.services.form import FormOntologyLearner\n\norganized_df = (\n FormOntologyLearner()\n .setInputCol("invoices")\n .setOutputCol("extracted")\n .fit(analyzed_df)\n .transform(analyzed_df)\n .select("url", "extracted.*")\n .cache()\n)\n\ndisplay(organized_df)\n')),(0,a.kt)("p",null,"With our nice tabular dataframe, we can flatten the nested tables found in the forms with some SparkSQL"),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},'from pyspark.sql.functions import explode, col\n\nitemized_df = (\n organized_df.select("*", explode(col("Items")).alias("Item"))\n .drop("Items")\n .select("Item.*", "*")\n .drop("Item")\n)\n\ndisplay(itemized_df)\n')),(0,a.kt)("h2",{id:"5---add-translations"},"5 - Add translations"),(0,a.kt)("p",null,"This code loads ",(0,a.kt)("a",{parentName:"p",href:"https://microsoft.github.io/SynapseML/docs/Explore%20Algorithms/AI%20Services/Overview/#translation"},"Translate"),', a transformer that calls the Azure AI Translator service in Azure AI services. The original text, which is in English in the "Description" column, is machine-translated into various languages. All of the output is consolidated into "output.translations" array.'),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},'from synapse.ml.services.translate import Translate\n\ntranslated_df = (\n Translate()\n .setSubscriptionKey(translator_key)\n .setLocation(translator_location)\n .setTextCol("Description")\n .setErrorCol("TranslationError")\n .setOutputCol("output")\n .setToLanguage(["zh-Hans", "fr", "ru", "cy"])\n .setConcurrency(5)\n .transform(itemized_df)\n .withColumn("Translations", col("output.translations")[0])\n .drop("output", "TranslationError")\n .cache()\n)\n\ndisplay(translated_df)\n')),(0,a.kt)("h2",{id:"6---translate-products-to-emojis-with-openai-"},"6 - Translate products to emojis with OpenAI \ud83e\udd2f"),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},'from synapse.ml.services.openai import OpenAIPrompt\nfrom pyspark.sql.functions import trim, split\n\nemoji_template = """ \n Your job is to translate item names into emoji. Do not add anything but the emoji and end the translation with a comma\n \n Two Ducks: \ud83e\udd86\ud83e\udd86,\n Light Bulb: \ud83d\udca1,\n Three Peaches: \ud83c\udf51\ud83c\udf51\ud83c\udf51,\n Two kitchen stoves: \u2668\ufe0f\u2668\ufe0f,\n A red car: \ud83d\ude97,\n A person and a cat: \ud83e\uddcd\ud83d\udc08,\n A {Description}: """\n\nprompter = (\n OpenAIPrompt()\n .setSubscriptionKey(openai_key)\n .setDeploymentName(openai_deployment_name)\n .setUrl(openai_url)\n .setMaxTokens(5)\n .setPromptTemplate(emoji_template)\n .setErrorCol("error")\n .setOutputCol("Emoji")\n)\n\nemoji_df = (\n prompter.transform(translated_df)\n .withColumn("Emoji", trim(split(col("Emoji"), ",").getItem(0)))\n .drop("error", "prompt")\n .cache()\n)\n')),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},'display(emoji_df.select("Description", "Emoji"))\n')),(0,a.kt)("h2",{id:"7---infer-vendor-address-continent-with-openai"},"7 - Infer vendor address continent with OpenAI"),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},'continent_template = """\nWhich continent does the following address belong to? \n\nPick one value from Europe, Australia, North America, South America, Asia, Africa, Antarctica. \n\nDont respond with anything but one of the above. If you don\'t know the answer or cannot figure it out from the text, return None. End your answer with a comma.\n\nAddress: "6693 Ryan Rd, North Whales",\nContinent: Europe,\nAddress: "6693 Ryan Rd",\nContinent: None,\nAddress: "{VendorAddress}",\nContinent:"""\n\ncontinent_df = (\n prompter.setOutputCol("Continent")\n .setPromptTemplate(continent_template)\n .transform(emoji_df)\n .withColumn("Continent", trim(split(col("Continent"), ",").getItem(0)))\n .drop("error", "prompt")\n .cache()\n)\n')),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},'display(continent_df.select("VendorAddress", "Continent"))\n')),(0,a.kt)("h2",{id:"8---create-an-azure-search-index-for-the-forms"},"8 - Create an Azure Search Index for the Forms"),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},'from synapse.ml.services import *\nfrom pyspark.sql.functions import monotonically_increasing_id, lit\n\n(\n continent_df.withColumn("DocID", monotonically_increasing_id().cast("string"))\n .withColumn("SearchAction", lit("upload"))\n .writeToAzureSearch(\n subscriptionKey=search_key,\n actionCol="SearchAction",\n serviceName=search_service,\n indexName=search_index,\n keyCol="DocID",\n )\n)\n')),(0,a.kt)("h2",{id:"9---try-out-a-search-query"},"9 - Try out a search query"),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},'import requests\n\nsearch_url = "https://{}.search.windows.net/indexes/{}/docs/search?api-version=2019-05-06".format(\n search_service, search_index\n)\nrequests.post(\n search_url, json={"search": "door"}, headers={"api-key": search_key}\n).json()\n')),(0,a.kt)("h2",{id:"10---build-a-chatbot-that-can-use-azure-search-as-a-tool-"},"10 - Build a chatbot that can use Azure Search as a tool \ud83e\udde0\ud83d\udd27"),(0,a.kt)("img",{src:"https://mmlspark.blob.core.windows.net/graphics/notebooks/chatbot_flow_2.svg",width:"40%"}),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},'import json\nimport openai\n\nopenai.api_type = "azure"\nopenai.api_base = openai_url\nopenai.api_key = openai_key\nopenai.api_version = "2023-03-15-preview"\n\nchat_context_prompt = f"""\nYou are a chatbot designed to answer questions with the help of a search engine that has the following information:\n\n{continent_df.columns}\n\nIf you dont know the answer to a question say "I dont know". Do not lie or hallucinate information. Be brief. If you need to use the search engine to solve the please output a json in the form of {{"query": "example_query"}}\n"""\n\n\ndef search_query_prompt(question):\n return f"""\nGiven the search engine above, what would you search for to answer the following question?\n\nQuestion: "{question}"\n\nPlease output a json in the form of {{"query": "example_query"}}\n"""\n\n\ndef search_result_prompt(query):\n search_results = requests.post(\n search_url, json={"search": query}, headers={"api-key": search_key}\n ).json()\n return f"""\n\nYou previously ran a search for "{query}" which returned the following results:\n\n{search_results}\n\nYou should use the results to help you answer questions. If you dont know the answer to a question say "I dont know". Do not lie or hallucinate information. Be Brief and mention which query you used to solve the problem. \n"""\n\n\ndef prompt_gpt(messages):\n response = openai.ChatCompletion.create(\n engine=openai_deployment_name, messages=messages, max_tokens=None, top_p=0.95\n )\n return response["choices"][0]["message"]["content"]\n\n\ndef custom_chatbot(question):\n while True:\n try:\n query = json.loads(\n prompt_gpt(\n [\n {"role": "system", "content": chat_context_prompt},\n {"role": "user", "content": search_query_prompt(question)},\n ]\n )\n )["query"]\n\n return prompt_gpt(\n [\n {"role": "system", "content": chat_context_prompt},\n {"role": "system", "content": search_result_prompt(query)},\n {"role": "user", "content": question},\n ]\n )\n except Exception as e:\n raise e\n')),(0,a.kt)("h2",{id:"11---asking-our-chatbot-a-question"},"11 - Asking our chatbot a question"),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},'custom_chatbot("What did Luke Diaz buy?")\n')),(0,a.kt)("h2",{id:"12---a-quick-double-check"},"12 - A quick double check"),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},'display(\n continent_df.where(col("CustomerName") == "Luke Diaz")\n .select("Description")\n .distinct()\n)\n')))}u.isMDXComponent=!0}}]); \ No newline at end of file diff --git a/assets/js/935f2afb.0c4b2f48.js b/assets/js/935f2afb.0c4b2f48.js new file mode 100644 index 0000000000..d9d45fc11b --- /dev/null +++ b/assets/js/935f2afb.0c4b2f48.js @@ -0,0 +1 @@ +"use strict";(self.webpackChunksynapseml=self.webpackChunksynapseml||[]).push([[80053],{1109:e=>{e.exports=JSON.parse('{"pluginId":"default","version":"current","label":"Next","banner":"unreleased","badge":true,"noIndex":false,"className":"docs-version-current","isLast":false,"docsSidebars":{"docs":[{"type":"link","label":"What is SynapseML?","href":"/SynapseML/docs/next/Overview","docId":"Overview"},{"type":"category","label":"Get Started","items":[{"type":"link","label":"Create a Spark Cluster","href":"/SynapseML/docs/next/Get Started/Create a Spark Cluster","docId":"Get Started/Create a Spark Cluster"},{"type":"link","label":"Install SynapseML","href":"/SynapseML/docs/next/Get Started/Install SynapseML","docId":"Get Started/Install SynapseML"},{"type":"link","label":"Set up Cognitive Services","href":"/SynapseML/docs/next/Get Started/Set up Cognitive Services","docId":"Get Started/Set up Cognitive Services"},{"type":"link","label":"Quickstart - 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The second pipeline shows how to use prebuilt models through the Azure AI Services to solve this problem without training data.","sidebar":"docs"},"Get Started/Set up Cognitive Services":{"id":"Get Started/Set up Cognitive Services","title":"Set up Cognitive Services","description":"In order to use SynapseML\'s OpenAI or Azure AI Services features, specific Azure resources are required. This documentation walks you through the process of setting up these resources and acquiring the necessary credentials.","sidebar":"docs"},"Overview":{"id":"Overview","title":"What is SynapseML?","description":"SynapseML (previously known as MMLSpark), is an open-source library that simplifies the creation of massively scalable machine learning (ML) pipelines. SynapseML provides simple, composable, and distributed APIs for a wide variety of different machine learning tasks such as text analytics, vision, anomaly detection, and many others. 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To make it easier to scale your prompting workflows from a few examples to large datasets of examples we have integrated the Azure OpenAI service with the distributed machine learning library Spark Rapids ML. This integration makes it easy to use the Apache Spark distributed computing framework to process millions of prompts with the OpenAI service. This tutorial shows how to apply large language models to generate embeddings for large datasets of text. 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To make it easier to scale your prompting workflows from a few examples to large datasets of examples we have integrated the Azure OpenAI service with the distributed machine learning library ",(0,a.kt)("a",{parentName:"p",href:"https://github.com/NVIDIA/spark-rapids-ml/"},"Spark Rapids ML"),". This integration makes it easy to use the ",(0,a.kt)("a",{parentName:"p",href:"https://spark.apache.org/"},"Apache Spark"),' distributed computing framework to process millions of prompts with the OpenAI service. This tutorial shows how to apply large language models to generate embeddings for large datasets of text. This demo is based on "Quickstart - OpenAI Embedding" notebook with NVIDIA GPU accelerated KNN.'),(0,a.kt)("p",null,(0,a.kt)("strong",{parentName:"p"},"Note"),": Running the notebook with the demo dataset (Step 4) will generate the same results as CPU based \u201cQuickstart - OpenAI Embedding\u201d notebook. To see GPU acceleration you need to run query against bigger embeddings.\nFor example, running 100K rows dataset will give 6x acceleration and consume less than 10x memory on 2 nodes NVIDIA T4 cluster compare to AMD Epic (Rome) 2 nodes CPU cluster."),(0,a.kt)("h2",{id:"step-1-prerequisites"},"Step 1: Prerequisites"),(0,a.kt)("p",null,"The key prerequisites for this quickstart include a working Azure OpenAI resource, and an Apache Spark cluster with SynapseML installed. We suggest creating a Synapse workspace, but an Azure Databricks, HDInsight, or Spark on Kubernetes, or even a python environment with the ",(0,a.kt)("inlineCode",{parentName:"p"},"pyspark")," package will work. "),(0,a.kt)("ol",null,(0,a.kt)("li",{parentName:"ol"},"An Azure OpenAI resource \u2013 request access ",(0,a.kt)("a",{parentName:"li",href:"https://customervoice.microsoft.com/Pages/ResponsePage.aspx?id=v4j5cvGGr0GRqy180BHbR7en2Ais5pxKtso_Pz4b1_xUOFA5Qk1UWDRBMjg0WFhPMkIzTzhKQ1dWNyQlQCN0PWcu"},"here")," before ",(0,a.kt)("a",{parentName:"li",href:"https://docs.microsoft.com/en-us/azure/cognitive-services/openai/how-to/create-resource?pivots=web-portal#create-a-resource"},"creating a resource")),(0,a.kt)("li",{parentName:"ol"},(0,a.kt)("a",{parentName:"li",href:"https://docs.microsoft.com/en-us/azure/synapse-analytics/get-started-create-workspace"},"Create a Synapse workspace")),(0,a.kt)("li",{parentName:"ol"},(0,a.kt)("a",{parentName:"li",href:"https://docs.microsoft.com/en-us/azure/synapse-analytics/get-started-analyze-spark#create-a-serverless-apache-spark-pool"},"Create a serverless Apache Spark pool"))),(0,a.kt)("h2",{id:"step-2-import-this-guide-as-a-notebook"},"Step 2: Import this guide as a notebook"),(0,a.kt)("p",null,"The next step is to add this code into your Spark cluster. You can either create a notebook in your Spark platform and copy the code into this notebook to run the demo. Or download the notebook and import it into Synapse Analytics"),(0,a.kt)("ol",null,(0,a.kt)("li",{parentName:"ol"},(0,a.kt)("a",{parentName:"li",href:"https://github.com/microsoft/SynapseML/blob/master/notebooks/features/cognitive_services/CognitiveServices%20-%20OpenAI%20Embedding.ipynb"},"Download this demo as a notebook")," (click Raw, then save the file)"),(0,a.kt)("li",{parentName:"ol"},"Import the notebook ",(0,a.kt)("a",{parentName:"li",href:"https://docs.microsoft.com/en-us/azure/synapse-analytics/spark/apache-spark-development-using-notebooks#create-a-notebook"},"into the Synapse Workspace")," or if using Databricks ",(0,a.kt)("a",{parentName:"li",href:"https://docs.microsoft.com/en-us/azure/databricks/notebooks/notebooks-manage#create-a-notebook"},"into the Databricks Workspace")),(0,a.kt)("li",{parentName:"ol"},"Install SynapseML on your cluster. Please see the installation instructions for Synapse at the bottom of ",(0,a.kt)("a",{parentName:"li",href:"https://microsoft.github.io/SynapseML/"},"the SynapseML website"),". Note that this requires pasting an additional cell at the top of the notebook you just imported"),(0,a.kt)("li",{parentName:"ol"},"To run the notebook on Databricks add related init script (/tools/init_scripts/init-rapidsml-cuda-11.8.sh)"),(0,a.kt)("li",{parentName:"ol"},"Connect your notebook to a cluster and follow along, editing and rnnung the cells below.")),(0,a.kt)("h2",{id:"step-3-fill-in-your-service-information"},"Step 3: Fill in your service information"),(0,a.kt)("p",null,"Next, please edit the cell in the notebook to point to your service. In particular set the ",(0,a.kt)("inlineCode",{parentName:"p"},"service_name"),", ",(0,a.kt)("inlineCode",{parentName:"p"},"deployment_name"),", ",(0,a.kt)("inlineCode",{parentName:"p"},"location"),", and ",(0,a.kt)("inlineCode",{parentName:"p"},"key")," variables to match those for your OpenAI service"),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},'from synapse.ml.core.platform import find_secret\n\n# Fill in the following lines with your service information\n# Learn more about selecting which embedding model to choose: https://openai.com/blog/new-and-improved-embedding-model\nservice_name = "synapseml-openai-2"\ndeployment_name_embeddings = "text-embedding-ada-002"\n\nkey = find_secret(\n secret_name="openai-api-key-2", keyvault="mmlspark-build-keys"\n) # please replace this with your key as a string\n\nassert key is not None and service_name is not None\n')),(0,a.kt)("h2",{id:"step-4-load-data"},"Step 4: Load Data"),(0,a.kt)("p",null,"In this demo we will explore a dataset of fine food reviews"),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},'import pyspark.sql.functions as F\n\ndf = (\n spark.read.options(inferSchema="True", delimiter=",", header=True)\n .csv("wasbs://publicwasb@mmlspark.blob.core.windows.net/fine_food_reviews_1k.csv")\n .repartition(5)\n)\n\ndf = df.withColumn(\n "combined",\n F.format_string("Title: %s; Content: %s", F.trim(df.Summary), F.trim(df.Text)),\n)\n\ndisplay(df)\n')),(0,a.kt)("h2",{id:"step-5-generate-embeddings"},"Step 5: Generate Embeddings"),(0,a.kt)("p",null,"We will first generate embeddings for the reviews using the SynapseML OpenAIEmbedding client."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},'from synapse.ml.services.openai import OpenAIEmbedding\n\nembedding = (\n OpenAIEmbedding()\n .setSubscriptionKey(key)\n .setDeploymentName(deployment_name_embeddings)\n .setCustomServiceName(service_name)\n .setTextCol("combined")\n .setErrorCol("error")\n .setOutputCol("embeddings")\n)\n\ncompleted_df = embedding.transform(df).cache()\ndisplay(completed_df)\n')),(0,a.kt)("h2",{id:"step-6-reduce-embedding-dimensionality-for-visualization"},"Step 6: Reduce Embedding dimensionality for Visualization"),(0,a.kt)("p",null,"We reduce the dimensionality to 2 dimensions using t-SNE decomposition."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},'import pandas as pd\nfrom sklearn.manifold import TSNE\nimport numpy as np\n\ncollected = list(completed_df.collect())\nmatrix = np.array([[r["embeddings"]] for r in collected])[:, 0, :].astype(np.float64)\nscores = np.array([[r["Score"]] for r in collected]).reshape(-1)\n\ntsne = TSNE(n_components=2, perplexity=15, random_state=42, init="pca")\nvis_dims = tsne.fit_transform(matrix)\nvis_dims.shape\n')),(0,a.kt)("h2",{id:"step-7-plot-the-embeddings"},"Step 7: Plot the embeddings"),(0,a.kt)("p",null,"We now use t-SNE to reduce the dimensionality of the embeddings from 1536 to 2. Once the embeddings are reduced to two dimensions, we can plot them in a 2D scatter plot. We colour each review by its star rating, ranging from red for negative reviews, to green for positive reviews. We can observe a decent data separation even in the reduced 2 dimensions."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},'import matplotlib.pyplot as plt\nimport matplotlib\nimport numpy as np\n\ncolors = ["red", "darkorange", "gold", "turquoise", "darkgreen"]\nx = [x for x, y in vis_dims]\ny = [y for x, y in vis_dims]\ncolor_indices = scores - 1\n\ncolormap = matplotlib.colors.ListedColormap(colors)\nplt.scatter(x, y, c=color_indices, cmap=colormap, alpha=0.3)\nfor score in [0, 1, 2, 3, 4]:\n avg_x = np.array(x)[scores - 1 == score].mean()\n avg_y = np.array(y)[scores - 1 == score].mean()\n color = colors[score]\n plt.scatter(avg_x, avg_y, marker="x", color=color, s=100)\n\nplt.title("Amazon ratings visualized in language using t-SNE")\n')),(0,a.kt)("h2",{id:"step-8-build-the-query-against-embeddings"},"Step 8: Build the query against embeddings"),(0,a.kt)("p",null,'Note: The data types of the ID columns in the document and query dataframes should be the same. For some OpenAI models, users should use separate models for embedding documents and queries. These models are denoted by the "-doc" and "-query" suffixes respectively.'),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},'from pyspark.ml import PipelineModel\n\nembedding_query = (\n OpenAIEmbedding()\n .setSubscriptionKey(key)\n .setDeploymentName(deployment_name_embeddings)\n .setCustomServiceName(service_name)\n .setTextCol("query")\n .setErrorCol("error")\n .setOutputCol("embeddings")\n)\n\nquery_df = (\n spark.createDataFrame(\n [\n (\n 0,\n "desserts",\n ),\n (\n 1,\n "disgusting",\n ),\n ]\n )\n .toDF("id", "query")\n .withColumn("id", F.col("id").cast("int"))\n)\n\nembedding_query_df = (\n embedding_query.transform(query_df).select("id", "embeddings").cache()\n)\n')),(0,a.kt)("h2",{id:"step-9-fit-knn-model"},"Step 9: Fit KNN model"),(0,a.kt)("p",null,"Build KNN model using fit method"),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},'from spark_rapids_ml.knn import NearestNeighbors\n\nrapids_knn = NearestNeighbors(k=10)\nrapids_knn.setInputCol("embeddings").setIdCol("id")\n\nrapids_knn_model = rapids_knn.fit(completed_df.select("id", "embeddings"))\n')),(0,a.kt)("h2",{id:"step-10-retrieve-query-results"},"Step 10: Retrieve query results"),(0,a.kt)("p",null,"Find k nearest neighbors"),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},"(_, _, knn_df) = rapids_knn_model.kneighbors(embedding_query_df)\n\ndisplay(knn_df)\n")))}m.isMDXComponent=!0}}]); \ No newline at end of file diff --git a/assets/js/dd3f9ada.97671d28.js b/assets/js/dd3f9ada.97671d28.js new file mode 100644 index 0000000000..f8d6fafdaa --- /dev/null +++ b/assets/js/dd3f9ada.97671d28.js @@ -0,0 +1 @@ +"use strict";(self.webpackChunksynapseml=self.webpackChunksynapseml||[]).push([[17820],{3905:(e,t,n)=>{n.d(t,{Zo:()=>d,kt:()=>u});var r=n(67294);function a(e,t,n){return t in e?Object.defineProperty(e,t,{value:n,enumerable:!0,configurable:!0,writable:!0}):e[t]=n,e}function o(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter((function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable}))),n.push.apply(n,r)}return n}function i(e){for(var t=1;t=0||(a[n]=e[n]);return a}(e,t);if(Object.getOwnPropertySymbols){var o=Object.getOwnPropertySymbols(e);for(r=0;r=0||Object.prototype.propertyIsEnumerable.call(e,n)&&(a[n]=e[n])}return a}var l=r.createContext({}),p=function(e){var t=r.useContext(l),n=t;return e&&(n="function"==typeof e?e(t):i(i({},t),e)),n},d=function(e){var t=p(e.components);return r.createElement(l.Provider,{value:t},e.children)},m={inlineCode:"code",wrapper:function(e){var t=e.children;return r.createElement(r.Fragment,{},t)}},c=r.forwardRef((function(e,t){var n=e.components,a=e.mdxType,o=e.originalType,l=e.parentName,d=s(e,["components","mdxType","originalType","parentName"]),c=p(n),u=a,g=c["".concat(l,".").concat(u)]||c[u]||m[u]||o;return n?r.createElement(g,i(i({ref:t},d),{},{components:n})):r.createElement(g,i({ref:t},d))}));function u(e,t){var n=arguments,a=t&&t.mdxType;if("string"==typeof e||a){var o=n.length,i=new Array(o);i[0]=c;var s={};for(var l in t)hasOwnProperty.call(t,l)&&(s[l]=t[l]);s.originalType=e,s.mdxType="string"==typeof e?e:a,i[1]=s;for(var p=2;p{n.r(t),n.d(t,{assets:()=>l,contentTitle:()=>i,default:()=>m,frontMatter:()=>o,metadata:()=>s,toc:()=>p});var r=n(83117),a=(n(67294),n(3905));const o={title:"Quickstart - OpenAI Embedding and GPU based KNN",hide_title:!0,status:"stable"},i="Embedding Text with Azure OpenAI and GPU based KNN",s={unversionedId:"Explore Algorithms/OpenAI/Quickstart - OpenAI Embedding and GPU based KNN",id:"Explore Algorithms/OpenAI/Quickstart - OpenAI Embedding and GPU based KNN",title:"Quickstart - OpenAI Embedding and GPU based KNN",description:'The Azure OpenAI service can be used to solve a large number of natural language tasks through prompting the completion API. To make it easier to scale your prompting workflows from a few examples to large datasets of examples we have integrated the Azure OpenAI service with the distributed machine learning library Spark Rapids ML. This integration makes it easy to use the Apache Spark distributed computing framework to process millions of prompts with the OpenAI service. This tutorial shows how to apply large language models to generate embeddings for large datasets of text. This demo is based on "Quickstart - OpenAI Embedding" notebook with NVIDIA GPU accelerated KNN.',source:"@site/docs/Explore Algorithms/OpenAI/Quickstart - OpenAI Embedding and GPU based KNN.md",sourceDirName:"Explore Algorithms/OpenAI",slug:"/Explore Algorithms/OpenAI/Quickstart - OpenAI Embedding and GPU based KNN",permalink:"/SynapseML/docs/next/Explore Algorithms/OpenAI/Quickstart - OpenAI Embedding and GPU based KNN",draft:!1,tags:[],version:"current",frontMatter:{title:"Quickstart - OpenAI Embedding and GPU based KNN",hide_title:!0,status:"stable"},sidebar:"docs",previous:{title:"Quickstart - OpenAI Embedding",permalink:"/SynapseML/docs/next/Explore Algorithms/OpenAI/Quickstart - OpenAI Embedding"},next:{title:"Quickstart - Custom Embeddings and Approximate KNN on GPU",permalink:"/SynapseML/docs/next/Explore Algorithms/OpenAI/Quickstart - Custom Embeddings and Approximate KNN on GPU"}},l={},p=[{value:"Step 1: Prerequisites",id:"step-1-prerequisites",level:2},{value:"Step 2: Import this guide as a notebook",id:"step-2-import-this-guide-as-a-notebook",level:2},{value:"Step 3: Fill in your service information",id:"step-3-fill-in-your-service-information",level:2},{value:"Step 4: Load Data",id:"step-4-load-data",level:2},{value:"Step 5: Generate Embeddings",id:"step-5-generate-embeddings",level:2},{value:"Step 6: Reduce Embedding dimensionality for Visualization",id:"step-6-reduce-embedding-dimensionality-for-visualization",level:2},{value:"Step 7: Plot the embeddings",id:"step-7-plot-the-embeddings",level:2},{value:"Step 8: Build the query against embeddings",id:"step-8-build-the-query-against-embeddings",level:2},{value:"Step 9: Fit KNN model",id:"step-9-fit-knn-model",level:2},{value:"Step 10: Retrieve query results",id:"step-10-retrieve-query-results",level:2}],d={toc:p};function m(e){let{components:t,...n}=e;return(0,a.kt)("wrapper",(0,r.Z)({},d,n,{components:t,mdxType:"MDXLayout"}),(0,a.kt)("h1",{id:"embedding-text-with-azure-openai-and-gpu-based-knn"},"Embedding Text with Azure OpenAI and GPU based KNN"),(0,a.kt)("p",null,"The Azure OpenAI service can be used to solve a large number of natural language tasks through prompting the completion API. To make it easier to scale your prompting workflows from a few examples to large datasets of examples we have integrated the Azure OpenAI service with the distributed machine learning library ",(0,a.kt)("a",{parentName:"p",href:"https://github.com/NVIDIA/spark-rapids-ml/"},"Spark Rapids ML"),". This integration makes it easy to use the ",(0,a.kt)("a",{parentName:"p",href:"https://spark.apache.org/"},"Apache Spark"),' distributed computing framework to process millions of prompts with the OpenAI service. This tutorial shows how to apply large language models to generate embeddings for large datasets of text. This demo is based on "Quickstart - OpenAI Embedding" notebook with NVIDIA GPU accelerated KNN.'),(0,a.kt)("p",null,(0,a.kt)("strong",{parentName:"p"},"Note"),": Running the notebook with the demo dataset (Step 4) will generate the same results as CPU based \u201cQuickstart - OpenAI Embedding\u201d notebook. To see GPU acceleration you need to run query against bigger embeddings.\nFor example, running 100K rows dataset will give 6x acceleration and consume less than 10x memory on 2 nodes NVIDIA T4 cluster compare to AMD Epic (Rome) 2 nodes CPU cluster."),(0,a.kt)("h2",{id:"step-1-prerequisites"},"Step 1: Prerequisites"),(0,a.kt)("p",null,"The key prerequisites for this quickstart include a working Azure OpenAI resource, and an Apache Spark cluster with SynapseML installed. We suggest creating a Synapse workspace, but an Azure Databricks, HDInsight, or Spark on Kubernetes, or even a python environment with the ",(0,a.kt)("inlineCode",{parentName:"p"},"pyspark")," package will work. "),(0,a.kt)("ol",null,(0,a.kt)("li",{parentName:"ol"},"An Azure OpenAI resource \u2013 request access ",(0,a.kt)("a",{parentName:"li",href:"https://customervoice.microsoft.com/Pages/ResponsePage.aspx?id=v4j5cvGGr0GRqy180BHbR7en2Ais5pxKtso_Pz4b1_xUOFA5Qk1UWDRBMjg0WFhPMkIzTzhKQ1dWNyQlQCN0PWcu"},"here")," before ",(0,a.kt)("a",{parentName:"li",href:"https://docs.microsoft.com/en-us/azure/cognitive-services/openai/how-to/create-resource?pivots=web-portal#create-a-resource"},"creating a resource")),(0,a.kt)("li",{parentName:"ol"},(0,a.kt)("a",{parentName:"li",href:"https://docs.microsoft.com/en-us/azure/synapse-analytics/get-started-create-workspace"},"Create a Synapse workspace")),(0,a.kt)("li",{parentName:"ol"},(0,a.kt)("a",{parentName:"li",href:"https://docs.microsoft.com/en-us/azure/synapse-analytics/get-started-analyze-spark#create-a-serverless-apache-spark-pool"},"Create a serverless Apache Spark pool"))),(0,a.kt)("h2",{id:"step-2-import-this-guide-as-a-notebook"},"Step 2: Import this guide as a notebook"),(0,a.kt)("p",null,"The next step is to add this code into your Spark cluster. You can either create a notebook in your Spark platform and copy the code into this notebook to run the demo. Or download the notebook and import it into Synapse Analytics"),(0,a.kt)("ol",null,(0,a.kt)("li",{parentName:"ol"},(0,a.kt)("a",{parentName:"li",href:"https://github.com/microsoft/SynapseML/blob/master/notebooks/features/cognitive_services/CognitiveServices%20-%20OpenAI%20Embedding.ipynb"},"Download this demo as a notebook")," (click Raw, then save the file)"),(0,a.kt)("li",{parentName:"ol"},"Import the notebook ",(0,a.kt)("a",{parentName:"li",href:"https://docs.microsoft.com/en-us/azure/synapse-analytics/spark/apache-spark-development-using-notebooks#create-a-notebook"},"into the Synapse Workspace")," or if using Databricks ",(0,a.kt)("a",{parentName:"li",href:"https://docs.microsoft.com/en-us/azure/databricks/notebooks/notebooks-manage#create-a-notebook"},"into the Databricks Workspace")),(0,a.kt)("li",{parentName:"ol"},"Install SynapseML on your cluster. Please see the installation instructions for Synapse at the bottom of ",(0,a.kt)("a",{parentName:"li",href:"https://microsoft.github.io/SynapseML/"},"the SynapseML website"),". Note that this requires pasting an additional cell at the top of the notebook you just imported"),(0,a.kt)("li",{parentName:"ol"},"To run the notebook on Databricks add related init script (/tools/init_scripts/init-rapidsml-cuda-11.8.sh)"),(0,a.kt)("li",{parentName:"ol"},"Connect your notebook to a cluster and follow along, editing and rnnung the cells below.")),(0,a.kt)("h2",{id:"step-3-fill-in-your-service-information"},"Step 3: Fill in your service information"),(0,a.kt)("p",null,"Next, please edit the cell in the notebook to point to your service. In particular set the ",(0,a.kt)("inlineCode",{parentName:"p"},"service_name"),", ",(0,a.kt)("inlineCode",{parentName:"p"},"deployment_name"),", ",(0,a.kt)("inlineCode",{parentName:"p"},"location"),", and ",(0,a.kt)("inlineCode",{parentName:"p"},"key")," variables to match those for your OpenAI service"),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},'from synapse.ml.core.platform import find_secret\n\n# Fill in the following lines with your service information\n# Learn more about selecting which embedding model to choose: https://openai.com/blog/new-and-improved-embedding-model\nservice_name = "synapseml-openai-2"\ndeployment_name_embeddings = "text-embedding-ada-002"\n\nkey = find_secret(\n secret_name="openai-api-key-2", keyvault="mmlspark-build-keys"\n) # please replace this with your key as a string\n\nassert key is not None and service_name is not None\n')),(0,a.kt)("h2",{id:"step-4-load-data"},"Step 4: Load Data"),(0,a.kt)("p",null,"In this demo we will explore a dataset of fine food reviews"),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},'import pyspark.sql.functions as F\n\ndf = (\n spark.read.options(inferSchema="True", delimiter=",", header=True)\n .csv("wasbs://publicwasb@mmlspark.blob.core.windows.net/fine_food_reviews_1k.csv")\n .repartition(5)\n)\n\ndf = df.withColumn(\n "combined",\n F.format_string("Title: %s; Content: %s", F.trim(df.Summary), F.trim(df.Text)),\n)\n\ndisplay(df)\n')),(0,a.kt)("h2",{id:"step-5-generate-embeddings"},"Step 5: Generate Embeddings"),(0,a.kt)("p",null,"We will first generate embeddings for the reviews using the SynapseML OpenAIEmbedding client."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},'from synapse.ml.services.openai import OpenAIEmbedding\n\nembedding = (\n OpenAIEmbedding()\n .setSubscriptionKey(key)\n .setDeploymentName(deployment_name_embeddings)\n .setCustomServiceName(service_name)\n .setTextCol("combined")\n .setErrorCol("error")\n .setOutputCol("embeddings")\n)\n\ncompleted_df = embedding.transform(df).cache()\ndisplay(completed_df)\n')),(0,a.kt)("h2",{id:"step-6-reduce-embedding-dimensionality-for-visualization"},"Step 6: Reduce Embedding dimensionality for Visualization"),(0,a.kt)("p",null,"We reduce the dimensionality to 2 dimensions using t-SNE decomposition."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},'import pandas as pd\nfrom sklearn.manifold import TSNE\nimport numpy as np\n\ncollected = list(completed_df.collect())\nmatrix = np.array([[r["embeddings"]] for r in collected])[:, 0, :].astype(np.float64)\nscores = np.array([[r["Score"]] for r in collected]).reshape(-1)\n\ntsne = TSNE(n_components=2, perplexity=15, random_state=42, init="pca")\nvis_dims = tsne.fit_transform(matrix)\nvis_dims.shape\n')),(0,a.kt)("h2",{id:"step-7-plot-the-embeddings"},"Step 7: Plot the embeddings"),(0,a.kt)("p",null,"We now use t-SNE to reduce the dimensionality of the embeddings from 1536 to 2. Once the embeddings are reduced to two dimensions, we can plot them in a 2D scatter plot. We colour each review by its star rating, ranging from red for negative reviews, to green for positive reviews. We can observe a decent data separation even in the reduced 2 dimensions."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},'import matplotlib.pyplot as plt\nimport matplotlib\nimport numpy as np\n\ncolors = ["red", "darkorange", "gold", "turquoise", "darkgreen"]\nx = [x for x, y in vis_dims]\ny = [y for x, y in vis_dims]\ncolor_indices = scores - 1\n\ncolormap = matplotlib.colors.ListedColormap(colors)\nplt.scatter(x, y, c=color_indices, cmap=colormap, alpha=0.3)\nfor score in [0, 1, 2, 3, 4]:\n avg_x = np.array(x)[scores - 1 == score].mean()\n avg_y = np.array(y)[scores - 1 == score].mean()\n color = colors[score]\n plt.scatter(avg_x, avg_y, marker="x", color=color, s=100)\n\nplt.title("Amazon ratings visualized in language using t-SNE")\n')),(0,a.kt)("h2",{id:"step-8-build-the-query-against-embeddings"},"Step 8: Build the query against embeddings"),(0,a.kt)("p",null,'Note: The data types of the ID columns in the document and query dataframes should be the same. For some OpenAI models, users should use separate models for embedding documents and queries. These models are denoted by the "-doc" and "-query" suffixes respectively.'),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},'from pyspark.ml import PipelineModel\n\nembedding_query = (\n OpenAIEmbedding()\n .setSubscriptionKey(key)\n .setDeploymentName(deployment_name_embeddings)\n .setCustomServiceName(service_name)\n .setTextCol("query")\n .setErrorCol("error")\n .setOutputCol("embeddings")\n)\n\nquery_df = (\n spark.createDataFrame(\n [\n (\n 0,\n "desserts",\n ),\n (\n 1,\n "disgusting",\n ),\n ]\n )\n .toDF("id", "query")\n .withColumn("id", F.col("id").cast("int"))\n)\n\nembedding_query_df = (\n embedding_query.transform(query_df).select("id", "embeddings").cache()\n)\n')),(0,a.kt)("h2",{id:"step-9-fit-knn-model"},"Step 9: Fit KNN model"),(0,a.kt)("p",null,"Build KNN model using fit method"),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},'from spark_rapids_ml.knn import NearestNeighbors\n\nrapids_knn = NearestNeighbors(k=10)\nrapids_knn.setInputCol("embeddings").setIdCol("id")\n\nrapids_knn_model = rapids_knn.fit(completed_df.select("id", "embeddings"))\n')),(0,a.kt)("h2",{id:"step-10-retrieve-query-results"},"Step 10: Retrieve query results"),(0,a.kt)("p",null,"Find k nearest neighbors"),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},"(_, _, knn_df) = rapids_knn_model.kneighbors(embedding_query_df)\n\ndisplay(knn_df)\n")))}m.isMDXComponent=!0}}]); \ No newline at end of file diff --git a/assets/js/main.acd258f6.js b/assets/js/main.acd258f6.js new file mode 100644 index 0000000000..e98f8341f6 --- /dev/null +++ b/assets/js/main.acd258f6.js @@ -0,0 +1,2 @@ +/*! 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