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RAG using Gemini and Elasticsearch (#162)
* Adding Question and answer example using Gemini, Elasticsearch and LangChain * Adding Question and answer example using Gemini, Elasticsearch and LangChain * Update notebooks/integrations/gemini/qa-langchain-gemini-elasticsearch.ipynb Co-authored-by: Max Jakob <[email protected]> * Update notebooks/integrations/gemini/qa-langchain-gemini-elasticsearch.ipynb Co-authored-by: Max Jakob <[email protected]> * Update notebooks/integrations/gemini/qa-langchain-gemini-elasticsearch.ipynb Co-authored-by: Max Jakob <[email protected]> * Update notebooks/integrations/gemini/qa-langchain-gemini-elasticsearch.ipynb Co-authored-by: Max Jakob <[email protected]> * Update notebooks/integrations/gemini/qa-langchain-gemini-elasticsearch.ipynb Co-authored-by: Max Jakob <[email protected]> * Update notebooks/integrations/gemini/qa-langchain-gemini-elasticsearch.ipynb Co-authored-by: Max Jakob <[email protected]> * Suggested changes --------- Co-authored-by: Max Jakob <[email protected]>
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‎notebooks/integrations/gemini/qa-langchain-gemini-elasticsearch.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "2a3143e8-3949-4ecc-905c-8333a43c9c87",
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"metadata": {},
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"source": [
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"# Question Answering using Gemini, Langchain & Elasticsearch\n",
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"\n",
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"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)."
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]
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},
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{
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"cell_type": "markdown",
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"id": "68c5e34d-28f9-4195-9f9c-2a8aec1effe6",
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"metadata": {},
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"source": [
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"## setup\n",
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"\n",
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"* 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",
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"\n",
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"* `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)."
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]
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},
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{
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"cell_type": "markdown",
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"id": "b8e9a58a-942f-4039-96c0-b276d5b8a97f",
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"metadata": {},
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"source": [
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"## Install packages"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "5c4781ec-06a5-48dd-963e-fb832b3f7ca2",
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"metadata": {},
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"outputs": [],
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"source": [
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"pip install -q -U google-generativeai elasticsearch langchain langchain_google_genai"
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]
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},
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{
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"cell_type": "markdown",
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"id": "851db243-ca7d-4a7c-a93b-d22ab149a1bb",
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"metadata": {},
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"source": [
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"## Import packages and credentials"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "26e7f569-c680-447b-9246-b5140ff47b6b",
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"metadata": {},
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"outputs": [],
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"source": [
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"import json\n",
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"import os\n",
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"from getpass import getpass\n",
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"from urllib.request import urlopen\n",
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"\n",
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"from elasticsearch import Elasticsearch, helpers\n",
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"from langchain.vectorstores import ElasticsearchStore\n",
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"from langchain.text_splitter import CharacterTextSplitter\n",
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"from langchain_google_genai import GoogleGenerativeAIEmbeddings\n",
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"from langchain_google_genai import ChatGoogleGenerativeAI\n",
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"from langchain.prompts import ChatPromptTemplate\n",
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"from langchain.schema.output_parser import StrOutputParser\n",
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"from langchain.schema.runnable import RunnablePassthrough"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b2f68db5-21ac-47b0-941b-1d816b586e18",
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"metadata": {},
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"source": [
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"## Get Credentials"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "543d27f4-2c53-4726-a324-716900d72338",
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"metadata": {},
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"outputs": [],
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"source": [
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"os.environ[\"GOOGLE_API_KEY\"] = getpass(\"Google API Key :\")\n",
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"ELASTIC_API_KEY = getpass(\"Elastic API Key :\")\n",
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"ELASTIC_CLOUD_ID = getpass(\"Elastic Cloud ID :\")\n",
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"elastic_index_name = \"gemini-qa\""
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]
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},
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{
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"cell_type": "markdown",
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"id": "e8bd47b9-b946-46d1-ba02-adbda118415a",
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"metadata": {},
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"source": [
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"## Add documents"
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]
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},
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{
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"cell_type": "markdown",
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"id": "bd04e921-206e-4c8b-937a-277d2c5a02e6",
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"metadata": {},
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"source": [
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"### Let's download the sample dataset and deserialize the document."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "44a5b79d-326e-4317-82a3-7918a11ff7b7",
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"metadata": {},
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"outputs": [],
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"source": [
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"url = \"https://raw.githubusercontent.com/ashishtiwari1993/langchain-elasticsearch-RAG/main/data.json\"\n",
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"\n",
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"response = urlopen(url)\n",
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"\n",
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"workplace_docs = json.loads(response.read())"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8591dce2-3fe6-4c87-b268-4694bb86e803",
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"metadata": {},
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"source": [
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"### Split Documents into Passages"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3963b0db-80d5-4908-897c-bec6357adc0a",
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"metadata": {},
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"outputs": [],
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"source": [
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"metadata = []\n",
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"content = []\n",
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"\n",
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"for doc in workplace_docs:\n",
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" content.append(doc[\"content\"])\n",
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" metadata.append({\n",
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" \"name\": doc[\"name\"],\n",
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" \"summary\": doc[\"summary\"],\n",
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" \"rolePermissions\":doc[\"rolePermissions\"]\n",
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" })\n",
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"\n",
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"text_splitter = CharacterTextSplitter(chunk_size=50, chunk_overlap=0)\n",
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"docs = text_splitter.create_documents(content, metadatas=metadata)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a066d5dc-dbc9-495f-934f-bfe96e0fdeec",
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"metadata": {},
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"source": [
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"## Index Documents into Elasticsearch using Gemini Embeddings"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "42ba370a-e4b4-4375-b71a-2aee7c40a330",
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"metadata": {},
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"outputs": [],
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"source": [
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"query_embedding = GoogleGenerativeAIEmbeddings(\n",
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" model=\"models/embedding-001\", task_type=\"retrieval_document\"\n",
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")\n",
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"\n",
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"es = ElasticsearchStore.from_documents(\n",
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" docs,\n",
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" es_cloud_id=ELASTIC_CLOUD_ID,\n",
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" es_api_key=ELASTIC_API_KEY,\n",
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" index_name=elastic_index_name,\n",
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" embedding=query_embedding\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "dbdb2d55-3349-4e95-8087-68f927f0d864",
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"metadata": {},
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"source": [
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"## Create a retriever using Elasticsearch"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "17920c1e-9228-42f5-893d-29b666d6f7b2",
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"metadata": {},
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"outputs": [],
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"source": [
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"query_embedding = GoogleGenerativeAIEmbeddings(\n",
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" model=\"models/embedding-001\", task_type=\"retrieval_query\"\n",
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")\n",
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"\n",
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"es = ElasticsearchStore(\n",
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" es_cloud_id=ELASTIC_CLOUD_ID,\n",
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" es_api_key=ELASTIC_API_KEY,\n",
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" embedding=query_embedding,\n",
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" index_name=elastic_index_name\n",
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")\n",
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"\n",
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"retriever = es.as_retriever(search_kwargs={\"k\": 3})"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3647d005-d70e-4c3a-b784-052b21e9f143",
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"metadata": {},
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"source": [
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"## Fromat Docs"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "04ee4d3d-09fb-4a35-bc66-8a2951c402a8",
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"metadata": {},
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"outputs": [],
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"source": [
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"def format_docs(docs):\n",
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" return \"\\n\\n\".join(doc.page_content for doc in docs)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "864afb6a-a671-434a-bd30-006c79ccda24",
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"metadata": {},
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"source": [
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"## Create a Chain using Prompt Template + `gemini-pro` model"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4818aef7-3535-494d-a5d4-16ef6d0581af",
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"metadata": {},
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"outputs": [],
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"source": [
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"template = \"\"\"Answer the question based only on the following context:\\n\n",
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"\n",
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"{context}\n",
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"\n",
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"Question: {question}\n",
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"\"\"\"\n",
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"prompt = ChatPromptTemplate.from_template(template)\n",
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"\n",
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"\n",
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"chain = (\n",
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" {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()} \n",
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" | prompt \n",
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" | ChatGoogleGenerativeAI(model=\"gemini-pro\", temperature=0.7) \n",
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" | StrOutputParser()\n",
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")\n",
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"\n",
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"chain.invoke(\"what is our sales goals?\")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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