Azure AI Search Indexing & Document API Setup! 🛠️ | Python Agentic API in Hindi (Part 7) скачать в хорошем качестве

Azure AI Search Indexing & Document API Setup! 🛠️ | Python Agentic API in Hindi (Part 7)

In Part 7 of our Agentic AI series, we move from static storage to active data intelligence. We focus on building the foundational infrastructure required for a Retrieval-Augmented Generation (RAG) system by connecting our FastAPI backend to Azure AI Search. This video covers the transition from simply storing files to preparing them for AI-driven discovery. What we cover in this session: Document Management: Building a FastAPI route to fetch and list metadata (names, sizes) for all blobs in Azure Storage. Schema Design: Using the SearchIndexClient to programmatically define an Azure AI Search Index. Vector Configuration: Why we use 1536 dimensions and how to configure the HNSW (Hierarchical Navigable Small World) algorithm for high-speed semantic retrieval. Field Mapping: The difference between SimpleField (for metadata) and SearchableField (for text content). Infrastructure as Code: Moving away from the Azure Portal to manage your search schema directly through Python for better version control. 🏗️ The Architecture We are building the "Memory" of our AI Agent. By the end of this video, you will have a live Search Index ready to receive vectorized data, and an API to manage your document library. Key Technologies Used: FastAPI (High-performance Python Framework) Azure AI Search SDK (Vector Database & Search Engine) Azure Identity (Passwordless Authentication via DefaultAzureCredential) Azure Blob Storage (Document Repository) 🎓 Why This Matters for 2026 In modern AI architecture, "Search" is no longer just about keywords. We are implementing Hybrid Search capabilities. Understanding how to define these indexes programmatically is a core skill for any Senior AI Solutions Architect. GitHub Repository: [Link to your Repo] Full Playlist: [Link to your Playlist] 🏷️ Tags #AzureAISearch #VectorDatabase #RAG #FastAPI #PythonAI #GenerativeAI #AzureOpenAI #VectorSearch #SemanticSearch #SoftwareArchitecture #CloudComputing