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https://github.com/james-m-jordan/openai-cookbook.git
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6d77a1b93e
* add Weaviate authentication to client configuration * add a cookbook for generative search * fix name
276 lines
9.2 KiB
Plaintext
276 lines
9.2 KiB
Plaintext
{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "cb1537e6",
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"metadata": {},
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"source": [
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"# Using Weaviate with Generative OpenAI module for Generative Search\n",
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"\n",
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"This notebook is prepared for a scenario where:\n",
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"* Your data is already in Weaviate\n",
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"* You want to use Weaviate with the Generative OpenAI module ([generative-openai](https://weaviate.io/developers/weaviate/modules/reader-generator-modules/generative-openai)).\n",
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"\n"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "f1a618c5",
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"metadata": {},
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"source": [
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"## Prerequisites\n",
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"\n",
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"This cookbook only coveres Generative Search examples, however, it doesn't cover the configuration and data imports.\n",
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"\n",
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"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",
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"\n",
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"Checklist:\n",
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"* completed [Getting Started cookbook](./getting-started-with-weaviate-and-openai.ipynb),\n",
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"* crated a `Weaviate` instance,\n",
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"* imported data into your `Weaviate` instance,\n",
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"* you have an [OpenAI API key](https://beta.openai.com/account/api-keys)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "36fe86f4",
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"metadata": {},
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"source": [
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"===========================================================\n",
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"## Prepare your OpenAI API key\n",
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"\n",
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"The `OpenAI API key` is used for vectorization of your data at import, and for running queries.\n",
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"\n",
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"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",
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"\n",
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"Once you get your key, please add it to your environment variables as `OPENAI_API_KEY`."
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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": "43395339",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Export OpenAI API Key\n",
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"!export OPENAI_API_KEY=\"your key\""
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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": "88be138c",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Test that your OpenAI API key is correctly set as an environment variable\n",
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"# 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",
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"import os\n",
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"\n",
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"# Note. alternatively you can set a temporary env variable like this:\n",
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"# os.environ[\"OPENAI_API_KEY\"] = 'your-key-goes-here'\n",
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"\n",
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"if os.getenv(\"OPENAI_API_KEY\") is not None:\n",
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" print (\"OPENAI_API_KEY is ready\")\n",
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"else:\n",
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" print (\"OPENAI_API_KEY environment variable not found\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "91df4d5b",
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"metadata": {},
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"source": [
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"## Connect to your Weaviate instance\n",
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"\n",
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"In this section, we will:\n",
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"\n",
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"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",
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"2. connect to your Weaviate with your `OpenAI API Key`\n",
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"3. and test the client connection\n",
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"\n",
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"### The client \n",
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"\n",
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"After this step, the `client` object will be used to perform all Weaviate-related operations."
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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": "cc662c1b",
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"metadata": {},
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"outputs": [],
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"source": [
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"import weaviate\n",
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"from datasets import load_dataset\n",
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"import os\n",
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"\n",
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"# Connect to your Weaviate instance\n",
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"client = weaviate.Client(\n",
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" url=\"https://your-wcs-instance-name.weaviate.network/\",\n",
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" # url=\"http://localhost:8080/\",\n",
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" 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",
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" additional_headers={\n",
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" \"X-OpenAI-Api-Key\": os.getenv(\"OPENAI_API_KEY\")\n",
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" }\n",
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")\n",
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"\n",
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"# Check if your instance is live and ready\n",
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"# This should return `True`\n",
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"client.is_ready()"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "ceb14da9",
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"metadata": {},
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"source": [
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"## Generative Search\n",
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"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",
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"\n",
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"The way you construct a generative search query is very similar to a standard semantic search query in Weaviate. \n",
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"\n",
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"For example:\n",
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"* search in \"Articles\", \n",
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"* return \"title\", \"content\", \"url\"\n",
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"* look for objects related to \"football clubs\"\n",
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"* limit results to 5 objects\n",
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"\n",
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"```\n",
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" result = (\n",
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" client.query\n",
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" .get(\"Articles\", [\"title\", \"content\", \"url\"])\n",
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" .with_near_text(\"concepts\": \"football clubs\")\n",
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" .with_limit(5)\n",
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" # generative query will go here\n",
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" .do()\n",
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" )\n",
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"```\n",
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"\n",
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"Now, you can add `with_generate()` function to apply generative transformation. `with_generate` takes either:\n",
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"- `single_prompt` - to generate a response for each returned object,\n",
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"- `grouped_task` – to generate a single response from all returned objects.\n"
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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": "51559251",
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"metadata": {},
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"outputs": [],
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"source": [
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"def generative_search_per_item(query, collection_name):\n",
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" prompt = \"Summarize in a short tweet the following content: {content}\"\n",
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"\n",
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" result = (\n",
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" client.query\n",
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" .get(collection_name, [\"title\", \"content\", \"url\"])\n",
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" .with_near_text({ \"concepts\": [query], \"distance\": 0.7 })\n",
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" .with_limit(5)\n",
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" .with_generate(single_prompt=prompt)\n",
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" .do()\n",
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" )\n",
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" \n",
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" # Check for errors\n",
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" if (\"errors\" in result):\n",
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" print (\"\\033[91mYou probably have run out of OpenAI API calls for the current minute – the limit is set at 60 per minute.\")\n",
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" raise Exception(result[\"errors\"][0]['message'])\n",
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" \n",
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" return result[\"data\"][\"Get\"][collection_name]"
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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": "a4604726",
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"metadata": {},
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"outputs": [],
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"source": [
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"query_result = generative_search_per_item(\"football clubs\", \"Article\")\n",
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"\n",
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"for i, article in enumerate(query_result):\n",
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" print(f\"{i+1}. { article['title']}\")\n",
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" print(article['_additional']['generate']['singleResult']) # print generated response\n",
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" print(\"-----------------------\")"
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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": 79,
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"id": "a45ea160",
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"metadata": {},
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"outputs": [],
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"source": [
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"def generative_search_group(query, collection_name):\n",
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" generateTask = \"Explain what these have in common\"\n",
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"\n",
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" result = (\n",
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" client.query\n",
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" .get(collection_name, [\"title\", \"content\", \"url\"])\n",
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" .with_near_text({ \"concepts\": [query], \"distance\": 0.7 })\n",
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" .with_generate(grouped_task=generateTask)\n",
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" .with_limit(5)\n",
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" .do()\n",
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" )\n",
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" \n",
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" # Check for errors\n",
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" if (\"errors\" in result):\n",
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" print (\"\\033[91mYou probably have run out of OpenAI API calls for the current minute – the limit is set at 60 per minute.\")\n",
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" raise Exception(result[\"errors\"][0]['message'])\n",
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" \n",
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" return result[\"data\"][\"Get\"][collection_name]"
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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": "11e0dad2",
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"metadata": {},
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"outputs": [],
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"source": [
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"query_result = generative_search_group(\"football clubs\", \"Article\")\n",
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"\n",
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"print (query_result[0]['_additional']['generate']['groupedResult'])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2007be48",
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"metadata": {},
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"source": [
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"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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]
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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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"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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"pygments_lexer": "ipython3",
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},
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