[Playground] Integration notebooks for bedrock and openai (#284)

* add notebook for bedrock and openai

* update readme

README.md

@@ -29,6 +29,13 @@ The [`notebooks`](notebooks/README.md) folder contains a range of executable Pyt
 - [`question-answering.ipynb`](./notebooks/generative-ai/question-answering.ipynb)
 - [`chatbot.ipynb`](./notebooks/generative-ai/chatbot.ipynb)
 
+### Playground RAG Notebooks
+
+Try out Playground in Kibana with the following notebooks:
+
+- [`OpenAI Example`](./notebooks/playground-examples/openai-elasticsearch-client.ipynb)
+- [`Anthropic Claude 3 Example`](./notebooks/playground-examples/bedrock-anthropic-elasticsearch-client.ipynb)
+
 ### LangChain
 
 - [`question-answering.ipynb`](./notebooks/generative-ai/question-answering.ipynb)

bin/find-notebooks-to-test.sh

@@ -26,7 +26,10 @@ EXEMPT_NOTEBOOKS=(
     "notebooks/integrations/llama3/rag-elastic-llama3-elser.ipynb"
     "notebooks/integrations/llama3/rag-elastic-llama3.ipynb"
     "notebooks/integrations/azure-openai/vector-search-azure-openai-elastic.ipynb"
-    "notebooks/enterprise-search/app-search-engine-exporter.ipynb"
+    "notebooks/enterprise-search/app-search-engine-exporter.ipynb",
+    "notebooks/playground-examples/bedrock-anthropic-elasticsearch-client.ipynb",
+    "notebooks/playground-examples/openai-elasticsearch-client.ipynb",
+
 )
 
 # Per-version testing exceptions

notebooks/playground-examples/bedrock-anthropic-elasticsearch-client.ipynb

@@ -0,0 +1,202 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Playground: RAG with Anthropic Claude 3 Models via Bedrock & Elasticsearch Python Client\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/elastic/elasticsearch-labs/blob/main/notebooks/playground/bedrock-anthropic-elasticsearch-client.ipynb)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "zQlYpYkI46Ff",
+    "outputId": "83677846-8a6a-4b49-fde0-16d473778814"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+      "poetry 1.6.1 requires build<0.11.0,>=0.10.0, but you have build 1.0.3 which is incompatible.\n",
+      "poetry 1.6.1 requires jsonschema<4.18.0,>=4.10.0, but you have jsonschema 4.19.1 which is incompatible.\u001b[0m\u001b[31m\n",
+      "\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.1\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install -qU elasticsearch \"anthropic[bedrock]\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "GCZR7-zK810e"
+   },
+   "source": [
+    "## Example Integration code\n",
+    "To adapt this example for your use-case:\n",
+    "- Update your connection details in the `es_client`\n",
+    "- Replace the es_query with the query suggested in Playground\n",
+    "- Replace the index_source_fields with the fields you want to use for context, per index. \n",
+    "- Update the prompt to reflect the Plaground prompt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "DofNZ2w25nIr"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This document appears to be a set of company policies and guidelines related to:\n",
+      "\n",
+      "1. A full-time work-from-home policy [Position 1-31]\n",
+      "2. A vacation policy [Position 32-56]\n",
+      "3. A career leveling matrix for software engineers [Position 57-end]\n",
+      "\n",
+      "The work-from-home policy outlines the purpose, scope, eligibility, equipment/resources, workspace requirements, communication expectations, work hours, performance standards, time tracking, confidentiality, and health/well-being considerations for employees working remotely full-time.\n",
+      "\n",
+      "The vacation policy covers vacation accrual rates, scheduling procedures, vacation pay calculations, handling unplanned absences, and payouts for unused vacation time upon termination.\n",
+      "\n",
+      "The career leveling matrix defines the roles, responsibilities, and required skills/experience for three levels of software engineering positions: Junior Software Engineer, Senior Software Engineer, and Principal Software Engineer. It serves as a framework for employee development, performance evaluations, and career progression within the software engineering function.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from elasticsearch import Elasticsearch\n",
+    "from anthropic import AnthropicBedrock\n",
+    "\n",
+    "from getpass import getpass\n",
+    "\n",
+    "# https://www.elastic.co/search-labs/tutorials/install-elasticsearch/elastic-cloud#creating-an-api-key\n",
+    "ELASTIC_API_KEY = getpass(\"Elastic Api Key: \")\n",
+    "\n",
+    "# Update the Elasticsearch client with your own Elasticsearch instance\n",
+    "es_client = Elasticsearch(\n",
+    "    \"http://localhost:9200\",\n",
+    "    api_key=ELASTIC_API_KEY,\n",
+    ")\n",
+    "\n",
+    "completion_client = AnthropicBedrock(\n",
+    "    # Authenticate by either providing the keys below or use the default AWS credential providers, such as\n",
+    "    # using ~/.aws/credentials or the \"AWS_SECRET_ACCESS_KEY\" and \"AWS_ACCESS_KEY_ID\" environment variables.\n",
+    "    aws_access_key=\"AWS_ACCESS_KEY_ID\",\n",
+    "    aws_secret_key=\"AWS_SECRET_ACCESS_KEY\",\n",
+    "    aws_region=\"us-west-2\",\n",
+    ")\n",
+    "\n",
+    "# Update the source fields based on your context field options\n",
+    "index_source_fields = {\"bm25-index\": [\"text\"]}\n",
+    "\n",
+    "\n",
+    "def get_elasticsearch_results(query):\n",
+    "    # Update the query to match your retriever provided in Playground\n",
+    "    es_query = {\n",
+    "        \"retriever\": {\n",
+    "            \"standard\": {\"query\": {\"multi_match\": {\"query\": query, \"fields\": [\"text\"]}}}\n",
+    "        },\n",
+    "        \"size\": 3,\n",
+    "    }\n",
+    "\n",
+    "    result = es_client.search(index=\"bm25-index\", body=es_query)\n",
+    "    return result[\"hits\"][\"hits\"]\n",
+    "\n",
+    "\n",
+    "def create_prompt(question, results):\n",
+    "    context = \"\"\n",
+    "    for hit in results:\n",
+    "        inner_hit_path = f\"{hit['_index']}.{index_source_fields.get(hit['_index'])[0]}\"\n",
+    "\n",
+    "        ## For semantic_text matches, we need to extract the text from the inner_hits\n",
+    "        if \"inner_hits\" in hit and inner_hit_path in hit[\"inner_hits\"]:\n",
+    "            context += \"\\n --- \\n\".join(\n",
+    "                inner_hit[\"_source\"][\"text\"]\n",
+    "                for inner_hit in hit[\"inner_hits\"][inner_hit_path][\"hits\"][\"hits\"]\n",
+    "            )\n",
+    "        else:\n",
+    "            source_field = index_source_fields.get(hit[\"_index\"])[0]\n",
+    "            hit_context = hit[\"_source\"][source_field]\n",
+    "            context += f\"{hit_context}\\n\"\n",
+    "\n",
+    "    # Update the prompt based on your own requirements\n",
+    "    prompt = f\"\"\"\n",
+    "  Instructions:\n",
+    "  \n",
+    "  - You are an assistant for question-answering tasks.\n",
+    "  - Answer questions truthfully and factually using only the information presented.\n",
+    "  - If you don't know the answer, just say that you don't know, don't make up an answer!\n",
+    "  - You must always cite the document where the answer was extracted using inline academic citation style [], using the position.\n",
+    "  - Use markdown format for code examples.\n",
+    "  - You are correct, factual, precise, and reliable.\n",
+    "  \n",
+    "\n",
+    "  Context:\n",
+    "  {context}\n",
+    "\n",
+    "  Question: {question}\n",
+    "  Answer:\n",
+    "  \"\"\"\n",
+    "\n",
+    "    return prompt\n",
+    "\n",
+    "\n",
+    "def generate_completion(user_prompt):\n",
+    "    response = completion_client.messages.create(\n",
+    "        model=\"anthropic.claude-3-sonnet-20240229-v1:0\",\n",
+    "        messages=[\n",
+    "            {\"role\": \"user\", \"content\": user_prompt},\n",
+    "        ],\n",
+    "        max_tokens=256,\n",
+    "    )\n",
+    "\n",
+    "    return response.content\n",
+    "\n",
+    "\n",
+    "if __name__ == \"__main__\":\n",
+    "    question = \"what is this?\"\n",
+    "    elasticsearch_results = get_elasticsearch_results(question)\n",
+    "    context_prompt = create_prompt(question, elasticsearch_results)\n",
+    "    completion = generate_completion(context_prompt)\n",
+    "    print(completion[0].text)"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "include_colab_link": true,
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}