diff --git a/notebooks/integrations/gemini/qa-langchain-gemini-elasticsearch.ipynb b/notebooks/integrations/gemini/qa-langchain-gemini-elasticsearch.ipynb new file mode 100644 index 00000000..9068bb88 --- /dev/null +++ b/notebooks/integrations/gemini/qa-langchain-gemini-elasticsearch.ipynb @@ -0,0 +1,286 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "2a3143e8-3949-4ecc-905c-8333a43c9c87", + "metadata": {}, + "source": [ + "# Question Answering using Gemini, Langchain & Elasticsearch\n", + "\n", + "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)." + ] + }, + { + "cell_type": "markdown", + "id": "68c5e34d-28f9-4195-9f9c-2a8aec1effe6", + "metadata": {}, + "source": [ + "## setup\n", + "\n", + "* 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", + "\n", + "* `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)." + ] + }, + { + "cell_type": "markdown", + "id": "b8e9a58a-942f-4039-96c0-b276d5b8a97f", + "metadata": {}, + "source": [ + "## Install packages" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5c4781ec-06a5-48dd-963e-fb832b3f7ca2", + "metadata": {}, + "outputs": [], + "source": [ + "pip install -q -U google-generativeai elasticsearch langchain langchain_google_genai" + ] + }, + { + "cell_type": "markdown", + "id": "851db243-ca7d-4a7c-a93b-d22ab149a1bb", + "metadata": {}, + "source": [ + "## Import packages and credentials" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "26e7f569-c680-447b-9246-b5140ff47b6b", + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import os\n", + "from getpass import getpass\n", + "from urllib.request import urlopen\n", + "\n", + "from elasticsearch import Elasticsearch, helpers\n", + "from langchain.vectorstores import ElasticsearchStore\n", + "from langchain.text_splitter import CharacterTextSplitter\n", + "from langchain_google_genai import GoogleGenerativeAIEmbeddings\n", + "from langchain_google_genai import ChatGoogleGenerativeAI\n", + "from langchain.prompts import ChatPromptTemplate\n", + "from langchain.schema.output_parser import StrOutputParser\n", + "from langchain.schema.runnable import RunnablePassthrough" + ] + }, + { + "cell_type": "markdown", + "id": "b2f68db5-21ac-47b0-941b-1d816b586e18", + "metadata": {}, + "source": [ + "## Get Credentials" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "543d27f4-2c53-4726-a324-716900d72338", + "metadata": {}, + "outputs": [], + "source": [ + "os.environ[\"GOOGLE_API_KEY\"] = getpass(\"Google API Key :\")\n", + "ELASTIC_API_KEY = getpass(\"Elastic API Key :\")\n", + "ELASTIC_CLOUD_ID = getpass(\"Elastic Cloud ID :\")\n", + "elastic_index_name = \"gemini-qa\"" + ] + }, + { + "cell_type": "markdown", + "id": "e8bd47b9-b946-46d1-ba02-adbda118415a", + "metadata": {}, + "source": [ + "## Add documents" + ] + }, + { + "cell_type": "markdown", + "id": "bd04e921-206e-4c8b-937a-277d2c5a02e6", + "metadata": {}, + "source": [ + "### Let's download the sample dataset and deserialize the document." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "44a5b79d-326e-4317-82a3-7918a11ff7b7", + "metadata": {}, + "outputs": [], + "source": [ + "url = \"https://raw.githubusercontent.com/ashishtiwari1993/langchain-elasticsearch-RAG/main/data.json\"\n", + "\n", + "response = urlopen(url)\n", + "\n", + "workplace_docs = json.loads(response.read())" + ] + }, + { + "cell_type": "markdown", + "id": "8591dce2-3fe6-4c87-b268-4694bb86e803", + "metadata": {}, + "source": [ + "### Split Documents into Passages" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3963b0db-80d5-4908-897c-bec6357adc0a", + "metadata": {}, + "outputs": [], + "source": [ + "metadata = []\n", + "content = []\n", + "\n", + "for doc in workplace_docs:\n", + " content.append(doc[\"content\"])\n", + " metadata.append({\n", + " \"name\": doc[\"name\"],\n", + " \"summary\": doc[\"summary\"],\n", + " \"rolePermissions\":doc[\"rolePermissions\"]\n", + " })\n", + "\n", + "text_splitter = CharacterTextSplitter(chunk_size=50, chunk_overlap=0)\n", + "docs = text_splitter.create_documents(content, metadatas=metadata)" + ] + }, + { + "cell_type": "markdown", + "id": "a066d5dc-dbc9-495f-934f-bfe96e0fdeec", + "metadata": {}, + "source": [ + "## Index Documents into Elasticsearch using Gemini Embeddings" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "42ba370a-e4b4-4375-b71a-2aee7c40a330", + "metadata": {}, + "outputs": [], + "source": [ + "query_embedding = GoogleGenerativeAIEmbeddings(\n", + " model=\"models/embedding-001\", task_type=\"retrieval_document\"\n", + ")\n", + "\n", + "es = ElasticsearchStore.from_documents(\n", + " docs,\n", + " es_cloud_id=ELASTIC_CLOUD_ID,\n", + " es_api_key=ELASTIC_API_KEY,\n", + " index_name=elastic_index_name,\n", + " embedding=query_embedding\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "dbdb2d55-3349-4e95-8087-68f927f0d864", + "metadata": {}, + "source": [ + "## Create a retriever using Elasticsearch" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "17920c1e-9228-42f5-893d-29b666d6f7b2", + "metadata": {}, + "outputs": [], + "source": [ + "query_embedding = GoogleGenerativeAIEmbeddings(\n", + " model=\"models/embedding-001\", task_type=\"retrieval_query\"\n", + ")\n", + "\n", + "es = ElasticsearchStore(\n", + " es_cloud_id=ELASTIC_CLOUD_ID,\n", + " es_api_key=ELASTIC_API_KEY,\n", + " embedding=query_embedding,\n", + " index_name=elastic_index_name\n", + ")\n", + "\n", + "retriever = es.as_retriever(search_kwargs={\"k\": 3})" + ] + }, + { + "cell_type": "markdown", + "id": "3647d005-d70e-4c3a-b784-052b21e9f143", + "metadata": {}, + "source": [ + "## Fromat Docs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "04ee4d3d-09fb-4a35-bc66-8a2951c402a8", + "metadata": {}, + "outputs": [], + "source": [ + "def format_docs(docs):\n", + " return \"\\n\\n\".join(doc.page_content for doc in docs)" + ] + }, + { + "cell_type": "markdown", + "id": "864afb6a-a671-434a-bd30-006c79ccda24", + "metadata": {}, + "source": [ + "## Create a Chain using Prompt Template + `gemini-pro` model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4818aef7-3535-494d-a5d4-16ef6d0581af", + "metadata": {}, + "outputs": [], + "source": [ + "template = \"\"\"Answer the question based only on the following context:\\n\n", + "\n", + "{context}\n", + "\n", + "Question: {question}\n", + "\"\"\"\n", + "prompt = ChatPromptTemplate.from_template(template)\n", + "\n", + "\n", + "chain = (\n", + " {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()} \n", + " | prompt \n", + " | ChatGoogleGenerativeAI(model=\"gemini-pro\", temperature=0.7) \n", + " | StrOutputParser()\n", + ")\n", + "\n", + "chain.invoke(\"what is our sales goals?\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "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": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}