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Add Azure OpenAI notebook #191

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346 changes: 346 additions & 0 deletions weaviate-features/hybrid-search/hybrid_search_azure_openai.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Hybrid Search with Azure OpenAI\n",
"\n",
"This recipe will show you how to run hybrid search with embeddings from Azure OpenAI."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Requirements\n",
"\n",
"1. Weaviate cluster\n",
" 1. You can create a 14-day free sandbox on [WCD](https://console.weaviate.cloud/)\n",
" 2. [Embedded Weaviate](https://weaviate.io/developers/weaviate/installation/embedded)\n",
" 3. [Local deployment](https://weaviate.io/developers/weaviate/installation/docker-compose#starter-docker-compose-file)\n",
" 4. [Other options](https://weaviate.io/developers/weaviate/installation)\n",
"\n",
"2. Azure API key. Grab one [here](https://portal.azure.com/)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Import Dependencies, Libraries, and Keys"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip install --q weaviate-client"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import weaviate\n",
"from weaviate.classes.init import Auth\n",
"import weaviate.classes.config as wc\n",
"from weaviate.embedded import EmbeddedOptions\n",
"import weaviate.classes.query as wq\n",
"\n",
"\n",
"import os\n",
"import requests\n",
"import json"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Connect to Weaviate\n",
"\n",
"Only choose one option from the below."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Weaviate Cloud Deployment**"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n"
]
}
],
"source": [
"# WCD_URL = os.environ[\"WEAVIATE_URL\"] # Replace with your Weaviate cluster URL\n",
"# WCD_AUTH_KEY = os.environ[\"WEAVIATE_AUTH\"] # Replace with your cluster auth key\n",
"# AZURE_KEY = os.environ[\"AZURE_API_KEY\"] # Replace with your Azure key\n",
"\n",
"# Weaviate Cloud Deployment\n",
"client = weaviate.connect_to_wcs(\n",
" cluster_url=WCD_URL,\n",
" auth_credentials=weaviate.auth.AuthApiKey(WCD_AUTH_KEY),\n",
" headers={ \"X-Azure-Api-Key\": AZURE_KEY}\n",
")\n",
"\n",
"print(client.is_ready())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Embedded Weaviate**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# AZURE_KEY = os.environ[\"AZURE_API_KEY\"] # Replace with your Azure key\n",
"\n",
"# client = weaviate.WeaviateClient(\n",
"# embedded_options=EmbeddedOptions(\n",
"# version=\"1.28.2\",\n",
"# additional_env_vars={\n",
"# \"ENABLE_MODULES\": \"text2vec-openai\"\n",
"# }),\n",
"# additional_headers={\n",
"# \"X-Azure-Api-Key\": AZURE_KEY\n",
"# }\n",
"# )\n",
"\n",
"# client.connect()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Local Deployment**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# AZURE_KEY = os.environ[\"AZURE_API_KEY\"] # Replace with your Azure key\n",
"\n",
"# client = weaviate.connect_to_local(\n",
"# headers={\n",
"# \"X-Azure-Api-Key\": AZURE_KEY\n",
"# }\n",
"# )\n",
"# print(client.is_ready())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create a collection\n",
"> Collection stores your data and vector embeddings."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Successfully created collection: JeopardyQuestion.\n"
]
}
],
"source": [
"# Note: This will delete your data stored in \"JeopardyQuestion\" and\n",
"# it will require you to re-import again.\n",
"\n",
"# Delete the collection if it already exists\n",
"if (client.collections.exists(\"JeopardyQuestion\")):\n",
" client.collections.delete(\"JeopardyQuestion\")\n",
"\n",
"client.collections.create(\n",
" name=\"JeopardyQuestion\",\n",
"\n",
" vectorizer_config=wc.Configure.Vectorizer.text2vec_azure_openai(\n",
" resource_name=\"azure-xyz\", # name of your resource group\n",
" deployment_id=\"fc...\" # subscription id\n",
" ),\n",
"\n",
" properties=[ # defining properties (data schema) is optional\n",
" wc.Property(name=\"Question\", data_type=wc.DataType.TEXT), \n",
" wc.Property(name=\"Answer\", data_type=wc.DataType.TEXT),\n",
" wc.Property(name=\"Category\", data_type=wc.DataType.TEXT) \n",
" ]\n",
")\n",
"\n",
"print(\"Successfully created collection: JeopardyQuestion.\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Import the Data"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"ename": "WeaviateInsertManyAllFailedError",
"evalue": "Every object failed during insertion. Here is the set of all errors: connection to: Azure OpenAI API failed with status: 401 error: Access denied due to invalid subscription key or wrong API endpoint. Make sure to provide a valid key for an active subscription and use a correct regional API endpoint for your resource.",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mWeaviateInsertManyAllFailedError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[7], line 9\u001b[0m\n\u001b[1;32m 6\u001b[0m jeopardy \u001b[38;5;241m=\u001b[39m client\u001b[38;5;241m.\u001b[39mcollections\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mJeopardyQuestion\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 8\u001b[0m \u001b[38;5;66;03m# Insert data objects\u001b[39;00m\n\u001b[0;32m----> 9\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[43mjeopardy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minsert_many\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 11\u001b[0m \u001b[38;5;66;03m# Note, the `data` array contains 10 objects, which is great to call insert_many with.\u001b[39;00m\n\u001b[1;32m 12\u001b[0m \u001b[38;5;66;03m# However, if you have a milion objects to insert, then you should spit them into smaller batches (i.e. 100-1000 per insert)\u001b[39;00m\n\u001b[1;32m 14\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (response\u001b[38;5;241m.\u001b[39mhas_errors):\n",
"File \u001b[0;32m/usr/local/lib/python3.11/site-packages/weaviate/syncify.py:23\u001b[0m, in \u001b[0;36mconvert.<locals>.sync_method\u001b[0;34m(self, __new_name, *args, **kwargs)\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(method) \u001b[38;5;66;03m# type: ignore\u001b[39;00m\n\u001b[1;32m 21\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msync_method\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, __new_name\u001b[38;5;241m=\u001b[39mnew_name, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 22\u001b[0m async_func \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mcls\u001b[39m, __new_name)\n\u001b[0;32m---> 23\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_EventLoopSingleton\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_instance\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_until_complete\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 24\u001b[0m \u001b[43m \u001b[49m\u001b[43masync_func\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m 25\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/local/lib/python3.11/site-packages/weaviate/event_loop.py:42\u001b[0m, in \u001b[0;36m_EventLoop.run_until_complete\u001b[0;34m(self, f, *args, **kwargs)\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m WeaviateClosedClientError()\n\u001b[1;32m 41\u001b[0m fut \u001b[38;5;241m=\u001b[39m asyncio\u001b[38;5;241m.\u001b[39mrun_coroutine_threadsafe(f(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloop)\n\u001b[0;32m---> 42\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfut\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/local/Cellar/[email protected]/3.11.6/Frameworks/Python.framework/Versions/3.11/lib/python3.11/concurrent/futures/_base.py:456\u001b[0m, in \u001b[0;36mFuture.result\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 454\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m CancelledError()\n\u001b[1;32m 455\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;241m==\u001b[39m FINISHED:\n\u001b[0;32m--> 456\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__get_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 457\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 458\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTimeoutError\u001b[39;00m()\n",
"File \u001b[0;32m/usr/local/Cellar/[email protected]/3.11.6/Frameworks/Python.framework/Versions/3.11/lib/python3.11/concurrent/futures/_base.py:401\u001b[0m, in \u001b[0;36mFuture.__get_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 399\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception:\n\u001b[1;32m 400\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 401\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception\n\u001b[1;32m 402\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 403\u001b[0m \u001b[38;5;66;03m# Break a reference cycle with the exception in self._exception\u001b[39;00m\n\u001b[1;32m 404\u001b[0m \u001b[38;5;28mself\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
"File \u001b[0;32m/usr/local/lib/python3.11/site-packages/weaviate/collections/data/data.py:386\u001b[0m, in \u001b[0;36m_DataCollectionAsync.insert_many\u001b[0;34m(self, objects)\u001b[0m\n\u001b[1;32m 346\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Insert multiple objects into the collection.\u001b[39;00m\n\u001b[1;32m 347\u001b[0m \n\u001b[1;32m 348\u001b[0m \u001b[38;5;124;03mArguments:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 360\u001b[0m \u001b[38;5;124;03m If every object in the batch fails to be inserted. The exception message contains details about the failure.\u001b[39;00m\n\u001b[1;32m 361\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 362\u001b[0m objs \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 363\u001b[0m (\n\u001b[1;32m 364\u001b[0m _BatchObject(\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 384\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m idx, obj \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(objects)\n\u001b[1;32m 385\u001b[0m ]\n\u001b[0;32m--> 386\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mawait\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_batch_grpc\u001b[38;5;241m.\u001b[39mobjects(objs, timeout\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_connection\u001b[38;5;241m.\u001b[39mtimeout_config\u001b[38;5;241m.\u001b[39minsert)\n\u001b[1;32m 387\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (n_obj_errs \u001b[38;5;241m:=\u001b[39m \u001b[38;5;28mlen\u001b[39m(res\u001b[38;5;241m.\u001b[39merrors)) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 388\u001b[0m logger\u001b[38;5;241m.\u001b[39merror(\n\u001b[1;32m 389\u001b[0m {\n\u001b[1;32m 390\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmessage\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFailed to send \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mn_obj_errs\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m objects in a batch of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(objs)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m. Please inspect the errors variable of the returned object for more information.\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 391\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124merrors\u001b[39m\u001b[38;5;124m\"\u001b[39m: res\u001b[38;5;241m.\u001b[39merrors,\n\u001b[1;32m 392\u001b[0m }\n\u001b[1;32m 393\u001b[0m )\n",
"File \u001b[0;32m/usr/local/lib/python3.11/site-packages/weaviate/collections/batch/grpc_batch_objects.py:105\u001b[0m, in \u001b[0;36m_BatchGRPC.objects\u001b[0;34m(self, objects, timeout)\u001b[0m\n\u001b[1;32m 101\u001b[0m elapsed_time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime() \u001b[38;5;241m-\u001b[39m start\n\u001b[1;32m 103\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(errors) \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mlen\u001b[39m(weaviate_objs):\n\u001b[1;32m 104\u001b[0m \u001b[38;5;66;03m# Escape sequence (backslash) not allowed in expression portion of f-string prior to Python 3.12: pylance\u001b[39;00m\n\u001b[0;32m--> 105\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m WeaviateInsertManyAllFailedError(\n\u001b[1;32m 106\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHere is the set of all errors: \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[1;32m 107\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(err \u001b[38;5;28;01mfor\u001b[39;00m err \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mset\u001b[39m(errors\u001b[38;5;241m.\u001b[39mvalues()))\n\u001b[1;32m 108\u001b[0m )\n\u001b[1;32m 109\u001b[0m )\n\u001b[1;32m 111\u001b[0m all_responses: List[Union[uuid_package\u001b[38;5;241m.\u001b[39mUUID, ErrorObject]] \u001b[38;5;241m=\u001b[39m cast(\n\u001b[1;32m 112\u001b[0m List[Union[uuid_package\u001b[38;5;241m.\u001b[39mUUID, ErrorObject]], \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(weaviate_objs)))\n\u001b[1;32m 113\u001b[0m )\n\u001b[1;32m 114\u001b[0m return_success: Dict[\u001b[38;5;28mint\u001b[39m, uuid_package\u001b[38;5;241m.\u001b[39mUUID] \u001b[38;5;241m=\u001b[39m {}\n",
"\u001b[0;31mWeaviateInsertManyAllFailedError\u001b[0m: Every object failed during insertion. Here is the set of all errors: connection to: Azure OpenAI API failed with status: 401 error: Access denied due to invalid subscription key or wrong API endpoint. Make sure to provide a valid key for an active subscription and use a correct regional API endpoint for your resource."
]
}
],
"source": [
"url = 'https://raw.githubusercontent.com/weaviate/weaviate-examples/main/jeopardy_small_dataset/jeopardy_tiny.json'\n",
"resp = requests.get(url)\n",
"data = json.loads(resp.text)\n",
"\n",
"# Get a collection object for \"JeopardyQuestion\"\n",
"jeopardy = client.collections.get(\"JeopardyQuestion\")\n",
"\n",
"# Insert data objects\n",
"response = jeopardy.data.insert_many(data)\n",
"\n",
"# Note, the `data` array contains 10 objects, which is great to call insert_many with.\n",
"# However, if you have a milion objects to insert, then you should spit them into smaller batches (i.e. 100-1000 per insert)\n",
"\n",
"if (response.has_errors):\n",
" print(response.errors)\n",
"else:\n",
" print(\"Insert complete.\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Hybrid Search"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `alpha` parameter determines the weight given to the sparse and dense search methods. `alpha = 0` is pure sparse (bm25) search, whereas `alpha = 1` is pure dense (vector) search. \n",
"\n",
"Alpha is an optional parameter. The default is set to `0.75`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Hybrid Search only\n",
"\n",
"The below query is finding Jeopardy questions about animals and is limiting the output to only two results. Notice `alpha` is set to `0.80`, which means it is weighing the vector search results more than bm25. If you were to set `alpha = 0.25`, you would get different results. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"response = jeopardy.query.hybrid(\n",
" query=\"northern beast\",\n",
" query_properties=[\"question\"],\n",
" alpha=0.8,\n",
" limit=3\n",
")\n",
"\n",
"for item in response.objects:\n",
" print(\"ID:\", item.uuid)\n",
" print(\"Data:\", json.dumps(item.properties, indent=2), \"\\n\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Hybrid Search with a `where` filter\n",
"\n",
"Find Jeopardy questions about elephants, where the category is set to Animals."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"response = jeopardy.query.hybrid(\n",
" query=\"northern beast\",\n",
" alpha=0.8,\n",
" filters=wq.Filter.by_property(\"category\").equal(\"Animals\"),\n",
" limit=3\n",
")\n",
"\n",
"for item in response.objects:\n",
" print(\"ID:\", item.uuid)\n",
" print(\"Data:\", json.dumps(item.properties, indent=2), \"\\n\")"
]
}
],
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"display_name": "Python 3",
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