From af97d58c9ee76deb4644726463dd082cb6a18157 Mon Sep 17 00:00:00 2001 From: Heidi Steen <1612796+HeidiSteen@users.noreply.github.com> Date: Tue, 7 May 2024 15:33:46 -0700 Subject: [PATCH] docs: update docs/integrations/retrievers/azure_ai_search.ipynb (#21160) This is a doc update. It fixes up formatting and product name references. The example code is updated to use a local built-in text file. @mmhangami Please take a look --------- Co-authored-by: Harrison Chase --- .../retrievers/azure_ai_search.ipynb | 166 +++++++++--------- 1 file changed, 80 insertions(+), 86 deletions(-) diff --git a/docs/docs/integrations/retrievers/azure_ai_search.ipynb b/docs/docs/integrations/retrievers/azure_ai_search.ipynb index 15d3210d797..53745cb9731 100644 --- a/docs/docs/integrations/retrievers/azure_ai_search.ipynb +++ b/docs/docs/integrations/retrievers/azure_ai_search.ipynb @@ -7,33 +7,42 @@ "source": [ "# Azure AI Search\n", "\n", - ">[Microsoft Azure AI Search](https://learn.microsoft.com/en-us/azure/search/search-what-is-azure-search) (formerly known as `Azure Cognitive Search`or 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", + "[Azure AI Search](https://learn.microsoft.com/azure/search/search-what-is-azure-search) (formerly known as `Azure Cognitive Search`) is a Microsoft cloud search service that gives developers infrastructure, APIs, and tools for information retrieval of vector, keyword, and hybrid queries at scale.\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 (AI Services)\n", + "`AzureAISearchRetriever` is an integration module that returns documents from an unstructured query. It's based on the BaseRetriever class and it targets the 2023-11-01 stable REST API version of Azure AI Search, which means it supports vector indexing and queries.\n", "\n", - "This notebook shows how to use Azure AI Search (AAS) within LangChain." + "To use this module, you need:\n", + "\n", + "+ An Azure AI Search service. You can [create one](https://learn.microsoft.com/azure/search/search-create-service-portal) for free if you sign up for the Azure trial. A free service has lower quotas, but it's sufficient for running the code in this notebook.\n", + "\n", + "+ An existing index with vector fields. There are several ways to create one, including using the [vector store module](../vectorstores/azuresearch.ipynb). Or, [try the Azure AI Search REST APIs](https://learn.microsoft.com/azure/search/search-get-started-vector).\n", + "\n", + "+ An API key. API keys are generated when you create the search service. If you're just querying an index, you can use the query API key, otherwise use an admin API key. See [Find your API keys](https://learn.microsoft.com/azure/search/search-security-api-keys?tabs=rest-use%2Cportal-find%2Cportal-query#find-existing-keys) for details.\n", + "\n", + "`AzureAISearchRetriever` replaces `AzureCognitiveSearchRetriever`, which will soon be deprecated. We recommend switching to the newer version that's based on the most recent stable version of the search APIs." ] }, { "cell_type": "markdown", - "id": "074b0004", + "id": "f99d4456", "metadata": {}, "source": [ - "## Set up Azure AI Search\n", + "## Install packages\n", "\n", - "To set up AAS, please follow the instructions [here](https://learn.microsoft.com/en-us/azure/search/search-create-service-portal).\n", - "\n", - "Please note you will need\n", - "1. the name of your AAS service, \n", - "2. the name of your AAS 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." + "Use azure-documents-search package 11.4 or later." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ad4521b0", + "metadata": {}, + "outputs": [], + "source": [ + "%pip install --upgrade --quiet langchain\n", + "%pip install --upgrade --quiet langchain-openai\n", + "%pip install --upgrade --quiet azure-search-documents\n", + "%pip install --upgrade --quiet azure-identity" ] }, { @@ -41,12 +50,12 @@ "id": "0474661d", "metadata": {}, "source": [ - "## Using the Azure AI Search Retriever" + "## Import required libraries" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "39d6074e", "metadata": {}, "outputs": [], @@ -63,9 +72,9 @@ "id": "b7243e6d", "metadata": {}, "source": [ - "Set Service Name, Index Name and API key as environment variables (alternatively, you can pass them as arguments to `AzureAISearchRetriever`). The search index name you use determines which documents are queried, so be sure to select the right one. \n", + "## Configure search settings\n", "\n", - "*You may also use `AzureCognitiveSearchRetriever` however this will soon be depreciated. Please switch to `AzureAISearchRetriever` where possible. " + "Set the search service name, index name, and API key as environment variables (alternatively, you can pass them as arguments to `AzureAISearchRetriever`). The search index provides the searchable content. " ] }, { @@ -75,8 +84,8 @@ "metadata": {}, "outputs": [], "source": [ - "os.environ[\"AZURE_AI_SEARCH_SERVICE_NAME\"] = \"\"\n", - "os.environ[\"AZURE_AI_SEARCH_INDEX_NAME\"] = \"\"\n", + "os.environ[\"AZURE_AI_SEARCH_SERVICE_NAME\"] = \"\"\n", + "os.environ[\"AZURE_AI_SEARCH_INDEX_NAME\"] = \"\"\n", "os.environ[\"AZURE_AI_SEARCH_API_KEY\"] = \"\"" ] }, @@ -85,20 +94,21 @@ "id": "057deaad", "metadata": {}, "source": [ - "Create the Retriever\n", + "## Create the retriever\n", "\n", - "`content_key` is the key in the retrieved result to set as the Document page_content.\n", - "`top_k` is the number of number of results you'd like to retrieve. Setting it to None (the default) returns all results. " + "For `AzureAISearchRetriever`, provide an `index_name`, `content_key`, and `top_k` set to the number of number of results you'd like to retrieve. Setting `top_k` to zero (the default) returns all results." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "c18d0c4c", "metadata": {}, "outputs": [], "source": [ - "retriever = AzureAISearchRetriever(content_key=\"content\", top_k=10)" + "retriever = AzureAISearchRetriever(\n", + " content_key=\"content\", top_k=1, index_name=\"langchain-vector-demo\"\n", + ")" ] }, { @@ -117,7 +127,7 @@ "metadata": {}, "outputs": [], "source": [ - "retriever.invoke(\"what is langchain?\")" + "retriever.invoke(\"here is my unstructured query string\")" ] }, { @@ -127,12 +137,14 @@ "source": [ "## Example \n", "\n", - "First let's create an Azure vector store and upload some data to it." + "This section demonstrates using the retriever over built-in sample data. You can skip this step if you already have a vector index on your search service.\n", + "\n", + "Start by providing the endpoints and keys. Since we're creating a vector index in this step, specify a text embedding model to get a vector representation of the text. This example assumes Azure OpenAI with a deployment of text-embedding-ada-002. Because this step creates an index, be sure to use an admin API key for your search service." ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "0b313473", "metadata": {}, "outputs": [], @@ -142,11 +154,16 @@ "from langchain.document_loaders import DirectoryLoader, TextLoader\n", "from langchain.text_splitter import TokenTextSplitter\n", "from langchain.vectorstores import AzureSearch\n", - "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_community.retrievers import AzureAISearchRetriever\n", + "from langchain_openai import AzureOpenAIEmbeddings, OpenAIEmbeddings\n", "\n", - "os.environ[\"OPENAI_API_KEY\"] = \"youropenaiapikey\"\n", - "os.environ[\"AZURE_AI_SEARCH_SERVICE_NAME\"] = \"yourazureaisearchservicename\"\n", - "os.environ[\"AZURE_AI_SEARCH_API_KEY\"] = \"yourazureaisearchapikey\"" + "os.environ[\"AZURE_AI_SEARCH_SERVICE_NAME\"] = \"\"\n", + "os.environ[\"AZURE_AI_SEARCH_INDEX_NAME\"] = \"langchain-vector-demo\"\n", + "os.environ[\"AZURE_AI_SEARCH_API_KEY\"] = \"\"\n", + "azure_endpoint: str = \"\"\n", + "azure_openai_api_key: str = \"\"\n", + "azure_openai_api_version: str = \"2023-05-15\"\n", + "azure_deployment: str = \"text-embedding-ada-002\"" ] }, { @@ -154,19 +171,23 @@ "id": "e889d1dd", "metadata": {}, "source": [ - "We'll use an embedding model from openai to turn our documents into embeddings stored in the Azure AI Search vector store. We'll also set the index name to `langchain-vector-demo`. This will create a new vector store associated with that index name. " + "We'll use an embedding model from Azure OpenAI to turn our documents into embeddings stored in the Azure AI Search vector store. We'll also set the index name to `langchain-vector-demo`. This will create a new vector store associated with that index name. " ] }, { "cell_type": "code", - "execution_count": 16, - "id": "27af8cdb", + "execution_count": null, + "id": "281064b5", "metadata": {}, "outputs": [], "source": [ - "embeddings = OpenAIEmbeddings(model=\"text-embedding-ada-002\")\n", + "embeddings = AzureOpenAIEmbeddings(\n", + " model=azure_deployment,\n", + " azure_endpoint=azure_endpoint,\n", + " openai_api_key=azure_openai_api_key,\n", + ")\n", "\n", - "vector_store = AzureSearch(\n", + "vector_store: AzureSearch = AzureSearch(\n", " embedding_function=embeddings.embed_query,\n", " azure_search_endpoint=os.getenv(\"AZURE_AI_SEARCH_SERVICE_NAME\"),\n", " azure_search_key=os.getenv(\"AZURE_AI_SEARCH_API_KEY\"),\n", @@ -179,41 +200,23 @@ "id": "76c86a34", "metadata": {}, "source": [ - "Next we'll load data into our newly created vector store. \n", - "For this example we load all the text files from a folder named `qna`. We'll split the text in 1000 token chunks with no overlap. Finally the documents are added to our vector store as emeddings." + "Next, we'll load data into our newly created vector store. For this example, we load the `state_of_the_union.txt` file. We'll split the text in 400 token chunks with no overlap. Finally, the documents are added to our vector store as emeddings." ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "f4830b14", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['YWY0NzY1MWYtMTU1Ni00YmEzLTlhNTQtZDQxNWFkMTlkNjMx',\n", - " 'MTUzM2EyOGYtYWE0My00OTIyLWJkNWUtMjVjNTgwMzZlMjcx',\n", - " 'ZGMyMjQ3N2EtNTQ5NC00ZjZhLWIyMzctYjRlZDYzMWUxNGQ4',\n", - " 'OWM5MWQ3YzUtZjFkZS00MGI2LTg1OGMtMmRlYzUwMDc2MzZi',\n", - " 'ZmFiYWVkOGQtNTcwYi00YTVmLWE3ZDEtMWQ3MTAxYjI2NTJj',\n", - " 'NTUwM2ExMjItNTk4Zi00OTg0LTg1ZDItZTZlMGYyMjJiNTIy']" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "loader = DirectoryLoader(\n", - " \"qna/\",\n", - " glob=\"*.txt\",\n", - " loader_cls=TextLoader,\n", - " loader_kwargs={\"autodetect_encoding\": True},\n", - ")\n", + "from langchain_community.document_loaders import TextLoader\n", + "from langchain_text_splitters import CharacterTextSplitter\n", + "\n", + "loader = TextLoader(\"../../modules/state_of_the_union.txt\", encoding=\"utf-8\")\n", + "\n", "documents = loader.load()\n", - "text_splitter = TokenTextSplitter(chunk_size=1000, chunk_overlap=0)\n", + "text_splitter = CharacterTextSplitter(chunk_size=400, chunk_overlap=0)\n", "docs = text_splitter.split_documents(documents)\n", "\n", "vector_store.add_documents(documents=docs)" @@ -224,17 +227,19 @@ "id": "ebb4c433", "metadata": {}, "source": [ - "Next we'll create a retriever similar to the one we created above but we're using the index name associated with our new vector store. In this case that's `langchain-vector-demo`." + "Next, we'll create a retriever. The current `index_name` variable is `langchain-vector-demo` from the last step. If you skipped vector store creation, provide your index name in the parameter. In this query, the top result is returned." ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 21, "id": "7ba2e413", "metadata": {}, "outputs": [], "source": [ - "retriever = AzureAISearchRetriever(content_key=\"content\", top_k=1)" + "retriever = AzureAISearchRetriever(\n", + " content_key=\"content\", top_k=1, index_name=\"langchain-vector-demo\"\n", + ")" ] }, { @@ -247,23 +252,12 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "7edb45e8", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[Document(page_content='\\n# What is Azure OpenAI?\\n\\nThe Azure OpenAI service provides REST API access to OpenAI\\'s powerful language models including the GPT-3, Codex and Embeddings model series. These models can be easily adapted to your specific task including but not limited to content generation, summarization, semantic search, and natural language to code translation. Users can access the service through REST APIs, Python SDK, or our web-based interface in the Azure OpenAI Studio.\\n\\n### Features overview\\n\\n| Feature | Azure OpenAI |\\n| --- | --- |\\n| Models available | GPT-3 base series
Codex series
Embeddings series
Learn more in our [Models](./concepts/models.md) page.|\\n| Fine-tuning | Ada
Babbage
Curie
Cushman*
Davinci*
\\\\* available by request. Please open a support request|\\n| Price | [Available here](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/) |\\n| Virtual network support | Yes | \\n| Managed Identity| Yes, via Azure Active Directory | \\n| UI experience | **Azure Portal** for account & resource management,
**Azure OpenAI Service Studio** for model exploration and fine tuning |\\n| Regional availability | East US
South Central US
West Europe |\\n| Content filtering | Prompts and completions are evaluated against our content policy with automated systems. High severity content will be filtered. |\\n\\n## Responsible AI\\n\\nAt Microsoft, we\\'re committed to the advancement of AI driven by principles that put people first. Generative models such as the ones available in the Azure OpenAI service have significant potential benefits, but without careful design and thoughtful mitigations, such models have the potential to generate incorrect or even harmful content. Microsoft has made significant investments to help guard against abuse and unintended harm, which includes requiring applicants to show well-defined use cases, incorporating Microsoft’s principles for responsible AI use, building content filters to support customers, and providing responsible AI implementation guidance to onboarded customers.\\n\\n## How do I get access to Azure OpenAI?\\n\\nHow do I get access to Azure OpenAI Service?\\n\\nAccess is currently limited as we navigate high demand, upcoming product improvements, and Microsoft’s commitment to responsible AI. For now, we\\'re working with customers with an existing partnership with Microsoft, lower risk use cases, and those committed to incorporating mitigations. In addition to applying for initial access, all solutions using the Azure OpenAI service are required to go through a use case review before they can be released for production use.\\n\\nMore specific information is included in the application form. We appreciate your patience as we work to responsibly enable broader access to the Azure OpenAI service.\\n\\nApply here for initial access or for a production review:\\n\\nApply now\\n\\nAll solutions using the Azure OpenAI service are also required to go through a use case review before they can be released for production use, and are evaluated on a case-by-case basis. In general, the more sensitive the scenario the more important risk mitigation measures will be for approval.\\n\\n## Comparing Azure OpenAI and OpenAI\\n\\nAzure OpenAI Service gives customers advanced language AI with OpenAI GPT-3, Codex, and DALL-E models with the security and enterprise promise of Azure. Azure OpenAI co-develops the APIs with OpenAI, ensuring compatibility and a smooth transition from one to the other.\\n\\nWith Azure OpenAI, customers get the security capabilities of Microsoft Azure while running the same models as OpenAI. Azure OpenAI offers private networking, regional availability, and responsible AI content filtering. \\n\\n## Key concepts\\n\\n### Prompts & Completions\\n\\nThe completions endpoint is the core component of the API service. This API provides access to the model\\'s text-in, text-out interface. Users simply need to provide an input **prompt** containing the English text command, and the model will generate a text **completion**.\\n\\nHere\\'s an example of a simple prompt and completion:\\n\\n>**Prompt', metadata={'@search.score': 2.3721094, 'id': 'MDEyNzU0ZDEtOTlmMy00YjE0LWE2YzMtNWI2ZGYxZjBkYzIx', 'content_vector': [3.636302e-05, -0.014039703, -0.0011298007, -0.005913462, -0.016717235, 0.0152605465, -0.003933059, 0.0037596438, -0.026900182, -0.035265736, 0.035598695, 0.0051747127, -0.030132644, -0.014116006, -0.025956802, 0.004467178, 0.022696596, -0.008871927, 0.013366852, -0.0060591307, -0.017272165, 0.00086967775, -0.01308245, -0.0144559, 0.00079510914, 0.004588569, 0.015759982, -0.029882925, 0.0006828228, -0.012666253, 0.018118432, 0.0032931566, -0.013137943, 0.0011601484, 0.02465272, 0.01996357, 0.016606249, -0.009489286, 0.015690617, 0.00049466715, 0.016606249, 0.028662082, 0.019325402, 0.0052891667, -0.015343786, 0.010522841, -0.009385237, -0.01054365, 0.00014501855, 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