Unlock the Power of Your Microsoft Documents with Azure AI Document Intelligence and LangChain
In the digital age, the ability to query and analyze vast amounts of data efficiently is more crucial than ever. Large Language Models (LLMs) are at the forefront of this capability, especially when combined with techniques such as Retrieval Augmented Generation (RAG). LangChain, a framework designed to power applications with LLMs, simplifies the development of RAG, making it more accessible.
Imagine you possess a substantial collection of Microsoft documents—Word files, PowerPoint presentations, and Excel sheets. You aim to utilize an LLM to easily access and extract information from these files, whether for summarization or data retrieval.
Here's how you can achieve this using LangChain:
- Load the Documents: Start by loading your Microsoft documents into LangChain.
- Transform the Documents: Convert these documents into a into a structured document object.
- Embed: Transform the document into a format that is understandable by an LLM.
- Store the document
- Retrial: query and get the answer from your query
In this section, we'll focus on how to perform the first step of loading documents into LangChain. For guidance on executing the entire process, please refer to my separate comprehensive post.
To begin loading a document into LangChain, you need to utilize the AzureAIDocumentIntelligenceLoader class from LangChain. Before you can employ this class effectively, you must set up a Document Intelligence resource in Azure. You will then use the API key and endpoint associated with that resource to facilitate your operations. Follow these steps to get started:
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