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Community: Updating Azure Retriever and Docs to be Azure AI Search instead of Azure Cognitive Search (#19925)

Browse Source 
Last year Microsoft [changed the
name](https://learn.microsoft.com/en-us/azure/search/search-what-is-azure-search)
of Azure Cognitive Search to Azure AI Search. This PR updates the
Langchain Azure Retriever API and it's associated docs to reflect this
change. It may be confusing for users to see the name Cognitive here and
AI in the Microsoft documentation which is why this is needed. I've also
added a more detailed example to the Azure retriever doc page.

There are more places that need a similar update but I'm breaking it up
so the PRs are not too big 😄 Fixing my errors from the previous PR.

Twitter: @marlene_zw

Two new tests added to test backward compatibility in
`libs/community/tests/integration_tests/retrievers/test_azure_cognitive_search.py`

---------

Co-authored-by: Chester Curme <chester.curme@gmail.com>
This commit is contained in:
Marlene
2024-04-08 16:12:41 +01:00
committed by GitHub
parent 820b713086
commit 2f03bc397e
14 changed files with 402 additions and 209 deletions
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@@ -252,23 +252,23 @@ from langchain_community.vectorstores import AzureCosmosDBVectorSearch
```
## Retrievers
### Azure Cognitive Search
### Azure AI Search
>[Azure Cognitive Search](https://learn.microsoft.com/en-us/azure/search/search-what-is-azure-search) (formerly known as `Azure Search`) is a cloud search service that gives developers infrastructure, APIs, and tools for building a rich search experience over private, heterogeneous content in web, mobile, and enterprise applications.
>[Azure AI Search](https://learn.microsoft.com/en-us/azure/search/search-what-is-azure-search) (formerly known as `Azure Search` or `Azure Cognitive Search` ) is a cloud search service that gives developers infrastructure, APIs, and tools for building a rich search experience over private, heterogeneous content in web, mobile, and enterprise applications.
>Search is foundational to any app that surfaces text to users, where common scenarios include catalog or document search, online retail apps, or data exploration over proprietary content. When you create a search service, you'll work with the following capabilities:
>- A search engine for full text search over a search index containing user-owned content
>- Rich indexing, with lexical analysis and optional AI enrichment for content extraction and transformation
>- Rich query syntax for text search, fuzzy search, autocomplete, geo-search and more
>- Programmability through REST APIs and client libraries in Azure SDKs
>- Azure integration at the data layer, machine learning layer, and AI (Cognitive Services)
>- Azure integration at the data layer, machine learning layer, and AI (AI Services)
See [set up instructions](https://learn.microsoft.com/en-us/azure/search/search-create-service-portal).
See a [usage example](/docs/integrations/retrievers/azure_cognitive_search).
See a [usage example](/docs/integrations/retrievers/azure_ai_search).
```python
from langchain.retrievers import AzureCognitiveSearchRetriever
from langchain.retrievers import AzureAISearchRetriever
```
## Toolkits

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@@ -1,147 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "1edb9e6b",
"metadata": {},
"source": [
"# Azure Cognitive Search\n",
"\n",
">[Azure Cognitive Search](https://learn.microsoft.com/en-us/azure/search/search-what-is-azure-search) (formerly known as `Azure Search`) is a cloud search service that gives developers infrastructure, APIs, and tools for building a rich search experience over private, heterogeneous content in web, mobile, and enterprise applications.\n",
"\n",
">Search is foundational to any app that surfaces text to users, where common scenarios include catalog or document search, online retail apps, or data exploration over proprietary content. When you create a search service, you'll work with the following capabilities:\n",
">- A search engine for full text search over a search index containing user-owned content\n",
">- Rich indexing, with lexical analysis and optional AI enrichment for content extraction and transformation\n",
">- Rich query syntax for text search, fuzzy search, autocomplete, geo-search and more\n",
">- Programmability through REST APIs and client libraries in Azure SDKs\n",
">- Azure integration at the data layer, machine learning layer, and AI (Cognitive Services)\n",
"\n",
"This notebook shows how to use Azure Cognitive Search (ACS) within LangChain."
]
},
{
"cell_type": "markdown",
"id": "074b0004",
"metadata": {},
"source": [
"## Set up Azure Cognitive Search\n",
"\n",
"To set up ACS, please follow the instructions [here](https://learn.microsoft.com/en-us/azure/search/search-create-service-portal).\n",
"\n",
"Please note\n",
"1. the name of your ACS service, \n",
"2. the name of your ACS index,\n",
"3. your API key.\n",
"\n",
"Your API key can be either Admin or Query key, but as we only read data it is recommended to use a Query key."
]
},
{
"cell_type": "markdown",
"id": "0474661d",
"metadata": {},
"source": [
"## Using the Azure Cognitive Search Retriever"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "39d6074e",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"from langchain_community.retrievers import (\n",
" AzureCognitiveSearchRetriever,\n",
")"
]
},
{
"cell_type": "markdown",
"id": "b7243e6d",
"metadata": {},
"source": [
"Set Service Name, Index Name and API key as environment variables (alternatively, you can pass them as arguments to `AzureCognitiveSearchRetriever`)."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "33fd23d1",
"metadata": {},
"outputs": [],
"source": [
"os.environ[\"AZURE_COGNITIVE_SEARCH_SERVICE_NAME\"] = \"<YOUR_ACS_SERVICE_NAME>\"\n",
"os.environ[\"AZURE_COGNITIVE_SEARCH_INDEX_NAME\"] = \"<YOUR_ACS_INDEX_NAME>\"\n",
"os.environ[\"AZURE_COGNITIVE_SEARCH_API_KEY\"] = \"<YOUR_API_KEY>\""
]
},
{
"cell_type": "markdown",
"id": "057deaad",
"metadata": {},
"source": [
"Create the Retriever"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c18d0c4c",
"metadata": {},
"outputs": [],
"source": [
"retriever = AzureCognitiveSearchRetriever(content_key=\"content\", top_k=10)"
]
},
{
"cell_type": "markdown",
"id": "e94ea104",
"metadata": {},
"source": [
"Now you can use retrieve documents from Azure Cognitive Search"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c8b5794b",
"metadata": {},
"outputs": [],
"source": [
"retriever.get_relevant_documents(\"what is langchain\")"
]
},
{
"cell_type": "markdown",
"id": "72eca08e",
"metadata": {},
"source": [
"You can change the number of results returned with the `top_k` parameter. The default value is `None`, which returns all results. "
]
}
],
"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": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}