|
| 1 | +{ |
| 2 | + "cells": [ |
| 3 | + { |
| 4 | + "cell_type": "markdown", |
| 5 | + "id": "2a3143e8-3949-4ecc-905c-8333a43c9c87", |
| 6 | + "metadata": {}, |
| 7 | + "source": [ |
| 8 | + "# Question Answering using Gemini, Langchain & Elasticsearch\n", |
| 9 | + "\n", |
| 10 | + "This tutorial demonstrates how to use the [Gemini API](https://ai.google.dev/docs) to create [embeddings](https://ai.google.dev/docs/embeddings_guide) and store them in Elasticsearch. We will learn how to connect Gemini to private data stored in Elasticsearch and build question/answer capabilities over it using [LangChian](https://python.langchain.com/docs/get_started/introduction)." |
| 11 | + ] |
| 12 | + }, |
| 13 | + { |
| 14 | + "cell_type": "markdown", |
| 15 | + "id": "68c5e34d-28f9-4195-9f9c-2a8aec1effe6", |
| 16 | + "metadata": {}, |
| 17 | + "source": [ |
| 18 | + "## setup\n", |
| 19 | + "\n", |
| 20 | + "* Elastic Credentials - Create an [Elastic Cloud deployment](https://www.elastic.co/search-labs/tutorials/install-elasticsearch/elastic-cloud) to get all Elastic credentials (`ELASTIC_CLOUD_ID`, `ELASTIC_API_KEY`).\n", |
| 21 | + "\n", |
| 22 | + "* `GOOGLE_API_KEY` - To use the Gemini API, you need to [create an API key in Google AI Studio](https://ai.google.dev/tutorials/setup)." |
| 23 | + ] |
| 24 | + }, |
| 25 | + { |
| 26 | + "cell_type": "markdown", |
| 27 | + "id": "b8e9a58a-942f-4039-96c0-b276d5b8a97f", |
| 28 | + "metadata": {}, |
| 29 | + "source": [ |
| 30 | + "## Install packages" |
| 31 | + ] |
| 32 | + }, |
| 33 | + { |
| 34 | + "cell_type": "code", |
| 35 | + "execution_count": null, |
| 36 | + "id": "5c4781ec-06a5-48dd-963e-fb832b3f7ca2", |
| 37 | + "metadata": {}, |
| 38 | + "outputs": [], |
| 39 | + "source": [ |
| 40 | + "pip install -q -U google-generativeai elasticsearch langchain langchain_google_genai" |
| 41 | + ] |
| 42 | + }, |
| 43 | + { |
| 44 | + "cell_type": "markdown", |
| 45 | + "id": "851db243-ca7d-4a7c-a93b-d22ab149a1bb", |
| 46 | + "metadata": {}, |
| 47 | + "source": [ |
| 48 | + "## Import packages and credentials" |
| 49 | + ] |
| 50 | + }, |
| 51 | + { |
| 52 | + "cell_type": "code", |
| 53 | + "execution_count": null, |
| 54 | + "id": "26e7f569-c680-447b-9246-b5140ff47b6b", |
| 55 | + "metadata": {}, |
| 56 | + "outputs": [], |
| 57 | + "source": [ |
| 58 | + "import json\n", |
| 59 | + "import os\n", |
| 60 | + "from getpass import getpass\n", |
| 61 | + "from urllib.request import urlopen\n", |
| 62 | + "\n", |
| 63 | + "from elasticsearch import Elasticsearch, helpers\n", |
| 64 | + "from langchain.vectorstores import ElasticsearchStore\n", |
| 65 | + "from langchain.text_splitter import CharacterTextSplitter\n", |
| 66 | + "from langchain_google_genai import GoogleGenerativeAIEmbeddings\n", |
| 67 | + "from langchain_google_genai import ChatGoogleGenerativeAI\n", |
| 68 | + "from langchain.prompts import ChatPromptTemplate\n", |
| 69 | + "from langchain.schema.output_parser import StrOutputParser\n", |
| 70 | + "from langchain.schema.runnable import RunnablePassthrough" |
| 71 | + ] |
| 72 | + }, |
| 73 | + { |
| 74 | + "cell_type": "markdown", |
| 75 | + "id": "b2f68db5-21ac-47b0-941b-1d816b586e18", |
| 76 | + "metadata": {}, |
| 77 | + "source": [ |
| 78 | + "## Get Credentials" |
| 79 | + ] |
| 80 | + }, |
| 81 | + { |
| 82 | + "cell_type": "code", |
| 83 | + "execution_count": null, |
| 84 | + "id": "543d27f4-2c53-4726-a324-716900d72338", |
| 85 | + "metadata": {}, |
| 86 | + "outputs": [], |
| 87 | + "source": [ |
| 88 | + "os.environ[\"GOOGLE_API_KEY\"] = getpass(\"Google API Key :\")\n", |
| 89 | + "ELASTIC_API_KEY = getpass(\"Elastic API Key :\")\n", |
| 90 | + "ELASTIC_CLOUD_ID = getpass(\"Elastic Cloud ID :\")\n", |
| 91 | + "elastic_index_name = \"gemini-qa\"" |
| 92 | + ] |
| 93 | + }, |
| 94 | + { |
| 95 | + "cell_type": "markdown", |
| 96 | + "id": "e8bd47b9-b946-46d1-ba02-adbda118415a", |
| 97 | + "metadata": {}, |
| 98 | + "source": [ |
| 99 | + "## Add documents" |
| 100 | + ] |
| 101 | + }, |
| 102 | + { |
| 103 | + "cell_type": "markdown", |
| 104 | + "id": "bd04e921-206e-4c8b-937a-277d2c5a02e6", |
| 105 | + "metadata": {}, |
| 106 | + "source": [ |
| 107 | + "### Let's download the sample dataset and deserialize the document." |
| 108 | + ] |
| 109 | + }, |
| 110 | + { |
| 111 | + "cell_type": "code", |
| 112 | + "execution_count": null, |
| 113 | + "id": "44a5b79d-326e-4317-82a3-7918a11ff7b7", |
| 114 | + "metadata": {}, |
| 115 | + "outputs": [], |
| 116 | + "source": [ |
| 117 | + "url = \"https://raw.githubusercontent.com/ashishtiwari1993/langchain-elasticsearch-RAG/main/data.json\"\n", |
| 118 | + "\n", |
| 119 | + "response = urlopen(url)\n", |
| 120 | + "\n", |
| 121 | + "workplace_docs = json.loads(response.read())" |
| 122 | + ] |
| 123 | + }, |
| 124 | + { |
| 125 | + "cell_type": "markdown", |
| 126 | + "id": "8591dce2-3fe6-4c87-b268-4694bb86e803", |
| 127 | + "metadata": {}, |
| 128 | + "source": [ |
| 129 | + "### Split Documents into Passages" |
| 130 | + ] |
| 131 | + }, |
| 132 | + { |
| 133 | + "cell_type": "code", |
| 134 | + "execution_count": null, |
| 135 | + "id": "3963b0db-80d5-4908-897c-bec6357adc0a", |
| 136 | + "metadata": {}, |
| 137 | + "outputs": [], |
| 138 | + "source": [ |
| 139 | + "metadata = []\n", |
| 140 | + "content = []\n", |
| 141 | + "\n", |
| 142 | + "for doc in workplace_docs:\n", |
| 143 | + " content.append(doc[\"content\"])\n", |
| 144 | + " metadata.append({\n", |
| 145 | + " \"name\": doc[\"name\"],\n", |
| 146 | + " \"summary\": doc[\"summary\"],\n", |
| 147 | + " \"rolePermissions\":doc[\"rolePermissions\"]\n", |
| 148 | + " })\n", |
| 149 | + "\n", |
| 150 | + "text_splitter = CharacterTextSplitter(chunk_size=50, chunk_overlap=0)\n", |
| 151 | + "docs = text_splitter.create_documents(content, metadatas=metadata)" |
| 152 | + ] |
| 153 | + }, |
| 154 | + { |
| 155 | + "cell_type": "markdown", |
| 156 | + "id": "a066d5dc-dbc9-495f-934f-bfe96e0fdeec", |
| 157 | + "metadata": {}, |
| 158 | + "source": [ |
| 159 | + "## Index Documents into Elasticsearch using Gemini Embeddings" |
| 160 | + ] |
| 161 | + }, |
| 162 | + { |
| 163 | + "cell_type": "code", |
| 164 | + "execution_count": null, |
| 165 | + "id": "42ba370a-e4b4-4375-b71a-2aee7c40a330", |
| 166 | + "metadata": {}, |
| 167 | + "outputs": [], |
| 168 | + "source": [ |
| 169 | + "query_embedding = GoogleGenerativeAIEmbeddings(\n", |
| 170 | + " model=\"models/embedding-001\", task_type=\"retrieval_document\"\n", |
| 171 | + ")\n", |
| 172 | + "\n", |
| 173 | + "es = ElasticsearchStore.from_documents(\n", |
| 174 | + " docs,\n", |
| 175 | + " es_cloud_id=ELASTIC_CLOUD_ID,\n", |
| 176 | + " es_api_key=ELASTIC_API_KEY,\n", |
| 177 | + " index_name=elastic_index_name,\n", |
| 178 | + " embedding=query_embedding\n", |
| 179 | + ")" |
| 180 | + ] |
| 181 | + }, |
| 182 | + { |
| 183 | + "cell_type": "markdown", |
| 184 | + "id": "dbdb2d55-3349-4e95-8087-68f927f0d864", |
| 185 | + "metadata": {}, |
| 186 | + "source": [ |
| 187 | + "## Create a retriever using Elasticsearch" |
| 188 | + ] |
| 189 | + }, |
| 190 | + { |
| 191 | + "cell_type": "code", |
| 192 | + "execution_count": null, |
| 193 | + "id": "17920c1e-9228-42f5-893d-29b666d6f7b2", |
| 194 | + "metadata": {}, |
| 195 | + "outputs": [], |
| 196 | + "source": [ |
| 197 | + "query_embedding = GoogleGenerativeAIEmbeddings(\n", |
| 198 | + " model=\"models/embedding-001\", task_type=\"retrieval_query\"\n", |
| 199 | + ")\n", |
| 200 | + "\n", |
| 201 | + "es = ElasticsearchStore(\n", |
| 202 | + " es_cloud_id=ELASTIC_CLOUD_ID,\n", |
| 203 | + " es_api_key=ELASTIC_API_KEY,\n", |
| 204 | + " embedding=query_embedding,\n", |
| 205 | + " index_name=elastic_index_name\n", |
| 206 | + ")\n", |
| 207 | + "\n", |
| 208 | + "retriever = es.as_retriever(search_kwargs={\"k\": 3})" |
| 209 | + ] |
| 210 | + }, |
| 211 | + { |
| 212 | + "cell_type": "markdown", |
| 213 | + "id": "3647d005-d70e-4c3a-b784-052b21e9f143", |
| 214 | + "metadata": {}, |
| 215 | + "source": [ |
| 216 | + "## Fromat Docs" |
| 217 | + ] |
| 218 | + }, |
| 219 | + { |
| 220 | + "cell_type": "code", |
| 221 | + "execution_count": null, |
| 222 | + "id": "04ee4d3d-09fb-4a35-bc66-8a2951c402a8", |
| 223 | + "metadata": {}, |
| 224 | + "outputs": [], |
| 225 | + "source": [ |
| 226 | + "def format_docs(docs):\n", |
| 227 | + " return \"\\n\\n\".join(doc.page_content for doc in docs)" |
| 228 | + ] |
| 229 | + }, |
| 230 | + { |
| 231 | + "cell_type": "markdown", |
| 232 | + "id": "864afb6a-a671-434a-bd30-006c79ccda24", |
| 233 | + "metadata": {}, |
| 234 | + "source": [ |
| 235 | + "## Create a Chain using Prompt Template + `gemini-pro` model" |
| 236 | + ] |
| 237 | + }, |
| 238 | + { |
| 239 | + "cell_type": "code", |
| 240 | + "execution_count": null, |
| 241 | + "id": "4818aef7-3535-494d-a5d4-16ef6d0581af", |
| 242 | + "metadata": {}, |
| 243 | + "outputs": [], |
| 244 | + "source": [ |
| 245 | + "template = \"\"\"Answer the question based only on the following context:\\n\n", |
| 246 | + "\n", |
| 247 | + "{context}\n", |
| 248 | + "\n", |
| 249 | + "Question: {question}\n", |
| 250 | + "\"\"\"\n", |
| 251 | + "prompt = ChatPromptTemplate.from_template(template)\n", |
| 252 | + "\n", |
| 253 | + "\n", |
| 254 | + "chain = (\n", |
| 255 | + " {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()} \n", |
| 256 | + " | prompt \n", |
| 257 | + " | ChatGoogleGenerativeAI(model=\"gemini-pro\", temperature=0.7) \n", |
| 258 | + " | StrOutputParser()\n", |
| 259 | + ")\n", |
| 260 | + "\n", |
| 261 | + "chain.invoke(\"what is our sales goals?\")" |
| 262 | + ] |
| 263 | + } |
| 264 | + ], |
| 265 | + "metadata": { |
| 266 | + "kernelspec": { |
| 267 | + "display_name": "Python 3 (ipykernel)", |
| 268 | + "language": "python", |
| 269 | + "name": "python3" |
| 270 | + }, |
| 271 | + "language_info": { |
| 272 | + "codemirror_mode": { |
| 273 | + "name": "ipython", |
| 274 | + "version": 3 |
| 275 | + }, |
| 276 | + "file_extension": ".py", |
| 277 | + "mimetype": "text/x-python", |
| 278 | + "name": "python", |
| 279 | + "nbconvert_exporter": "python", |
| 280 | + "pygments_lexer": "ipython3", |
| 281 | + "version": "3.11.6" |
| 282 | + } |
| 283 | + }, |
| 284 | + "nbformat": 4, |
| 285 | + "nbformat_minor": 5 |
| 286 | +} |
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