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Add Generative Search to Weaviate examples + fix authentication examp…
…les (openai#398) * add Weaviate authentication to client configuration * add a cookbook for generative search * fix name
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examples/vector_databases/weaviate/generative-search-with-weaviate-and-openai.ipynb
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| { | ||
| "cells": [ | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "id": "cb1537e6", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Using Weaviate with Generative OpenAI module for Generative Search\n", | ||
| "\n", | ||
| "This notebook is prepared for a scenario where:\n", | ||
| "* Your data is already in Weaviate\n", | ||
| "* You want to use Weaviate with the Generative OpenAI module ([generative-openai](https://weaviate.io/developers/weaviate/modules/reader-generator-modules/generative-openai)).\n", | ||
| "\n" | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "id": "f1a618c5", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Prerequisites\n", | ||
| "\n", | ||
| "This cookbook only coveres Generative Search examples, however, it doesn't cover the configuration and data imports.\n", | ||
| "\n", | ||
| "In order to make the most of this cookbook, please complete the [Getting Started cookbook](./getting-started-with-weaviate-and-openai.ipynb) firts, where you will learn the essentials of working with Weaviate and import the demo data.\n", | ||
| "\n", | ||
| "Checklist:\n", | ||
| "* completed [Getting Started cookbook](./getting-started-with-weaviate-and-openai.ipynb),\n", | ||
| "* crated a `Weaviate` instance,\n", | ||
| "* imported data into your `Weaviate` instance,\n", | ||
| "* you have an [OpenAI API key](https://beta.openai.com/account/api-keys)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "36fe86f4", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "===========================================================\n", | ||
| "## Prepare your OpenAI API key\n", | ||
| "\n", | ||
| "The `OpenAI API key` is used for vectorization of your data at import, and for running queries.\n", | ||
| "\n", | ||
| "If you don't have an OpenAI API key, you can get one from [https://beta.openai.com/account/api-keys](https://beta.openai.com/account/api-keys).\n", | ||
| "\n", | ||
| "Once you get your key, please add it to your environment variables as `OPENAI_API_KEY`." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "43395339", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# Export OpenAI API Key\n", | ||
| "!export OPENAI_API_KEY=\"your key\"" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "88be138c", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# Test that your OpenAI API key is correctly set as an environment variable\n", | ||
| "# Note. if you run this notebook locally, you will need to reload your terminal and the notebook for the env variables to be live.\n", | ||
| "import os\n", | ||
| "\n", | ||
| "# Note. alternatively you can set a temporary env variable like this:\n", | ||
| "# os.environ[\"OPENAI_API_KEY\"] = 'your-key-goes-here'\n", | ||
| "\n", | ||
| "if os.getenv(\"OPENAI_API_KEY\") is not None:\n", | ||
| " print (\"OPENAI_API_KEY is ready\")\n", | ||
| "else:\n", | ||
| " print (\"OPENAI_API_KEY environment variable not found\")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "91df4d5b", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Connect to your Weaviate instance\n", | ||
| "\n", | ||
| "In this section, we will:\n", | ||
| "\n", | ||
| "1. test env variable `OPENAI_API_KEY` – **make sure** you completed the step in [#Prepare-your-OpenAI-API-key](#Prepare-your-OpenAI-API-key)\n", | ||
| "2. connect to your Weaviate with your `OpenAI API Key`\n", | ||
| "3. and test the client connection\n", | ||
| "\n", | ||
| "### The client \n", | ||
| "\n", | ||
| "After this step, the `client` object will be used to perform all Weaviate-related operations." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "cc662c1b", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import weaviate\n", | ||
| "from datasets import load_dataset\n", | ||
| "import os\n", | ||
| "\n", | ||
| "# Connect to your Weaviate instance\n", | ||
| "client = weaviate.Client(\n", | ||
| " url=\"https://your-wcs-instance-name.weaviate.network/\",\n", | ||
| " # url=\"http://localhost:8080/\",\n", | ||
| " auth_client_secret=weaviate.auth.AuthApiKey(api_key=\"<YOUR-WEAVIATE-API-KEY>\"), # comment out this line if you are not using authentication for your Weaviate instance (i.e. for locally deployed instances)\n", | ||
| " additional_headers={\n", | ||
| " \"X-OpenAI-Api-Key\": os.getenv(\"OPENAI_API_KEY\")\n", | ||
| " }\n", | ||
| ")\n", | ||
| "\n", | ||
| "# Check if your instance is live and ready\n", | ||
| "# This should return `True`\n", | ||
| "client.is_ready()" | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "id": "ceb14da9", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Generative Search\n", | ||
| "Weaviate offers a [Generative Search OpenAI](https://weaviate.io/developers/weaviate/modules/reader-generator-modules/generative-openai) module, which generates responses based on the data stored in your Weaviate instance.\n", | ||
| "\n", | ||
| "The way you construct a generative search query is very similar to a standard semantic search query in Weaviate. \n", | ||
| "\n", | ||
| "For example:\n", | ||
| "* search in \"Articles\", \n", | ||
| "* return \"title\", \"content\", \"url\"\n", | ||
| "* look for objects related to \"football clubs\"\n", | ||
| "* limit results to 5 objects\n", | ||
| "\n", | ||
| "```\n", | ||
| " result = (\n", | ||
| " client.query\n", | ||
| " .get(\"Articles\", [\"title\", \"content\", \"url\"])\n", | ||
| " .with_near_text(\"concepts\": \"football clubs\")\n", | ||
| " .with_limit(5)\n", | ||
| " # generative query will go here\n", | ||
| " .do()\n", | ||
| " )\n", | ||
| "```\n", | ||
| "\n", | ||
| "Now, you can add `with_generate()` function to apply generative transformation. `with_generate` takes either:\n", | ||
| "- `single_prompt` - to generate a response for each returned object,\n", | ||
| "- `grouped_task` – to generate a single response from all returned objects.\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "51559251", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "def generative_search_per_item(query, collection_name):\n", | ||
| " prompt = \"Summarize in a short tweet the following content: {content}\"\n", | ||
| "\n", | ||
| " result = (\n", | ||
| " client.query\n", | ||
| " .get(collection_name, [\"title\", \"content\", \"url\"])\n", | ||
| " .with_near_text({ \"concepts\": [query], \"distance\": 0.7 })\n", | ||
| " .with_limit(5)\n", | ||
| " .with_generate(single_prompt=prompt)\n", | ||
| " .do()\n", | ||
| " )\n", | ||
| " \n", | ||
| " # Check for errors\n", | ||
| " if (\"errors\" in result):\n", | ||
| " print (\"\\033[91mYou probably have run out of OpenAI API calls for the current minute – the limit is set at 60 per minute.\")\n", | ||
| " raise Exception(result[\"errors\"][0]['message'])\n", | ||
| " \n", | ||
| " return result[\"data\"][\"Get\"][collection_name]" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "a4604726", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "query_result = generative_search_per_item(\"football clubs\", \"Article\")\n", | ||
| "\n", | ||
| "for i, article in enumerate(query_result):\n", | ||
| " print(f\"{i+1}. { article['title']}\")\n", | ||
| " print(article['_additional']['generate']['singleResult']) # print generated response\n", | ||
| " print(\"-----------------------\")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 79, | ||
| "id": "a45ea160", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "def generative_search_group(query, collection_name):\n", | ||
| " generateTask = \"Explain what these have in common\"\n", | ||
| "\n", | ||
| " result = (\n", | ||
| " client.query\n", | ||
| " .get(collection_name, [\"title\", \"content\", \"url\"])\n", | ||
| " .with_near_text({ \"concepts\": [query], \"distance\": 0.7 })\n", | ||
| " .with_generate(grouped_task=generateTask)\n", | ||
| " .with_limit(5)\n", | ||
| " .do()\n", | ||
| " )\n", | ||
| " \n", | ||
| " # Check for errors\n", | ||
| " if (\"errors\" in result):\n", | ||
| " print (\"\\033[91mYou probably have run out of OpenAI API calls for the current minute – the limit is set at 60 per minute.\")\n", | ||
| " raise Exception(result[\"errors\"][0]['message'])\n", | ||
| " \n", | ||
| " return result[\"data\"][\"Get\"][collection_name]" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "11e0dad2", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "query_result = generative_search_group(\"football clubs\", \"Article\")\n", | ||
| "\n", | ||
| "print (query_result[0]['_additional']['generate']['groupedResult'])" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "2007be48", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Thanks for following along, you're now equipped to set up your own vector databases and use embeddings to do all kinds of cool things - enjoy! For more complex use cases please continue to work through other cookbook examples in this repo." | ||
| ] | ||
| } | ||
| ], | ||
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| "nbformat": 4, | ||
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