🏗️ Backend Data Source Integration Strategy

[jongalloway]

Backend Data Source Integration Strategy

Overview

Currently, NLWebNet uses a MockDataBackend for demonstration purposes. We need to define and implement a strategy for real-world data backend integration.

Open Questions to Resolve

Data Backend Options

• Database Integration: SQL Server, PostgreSQL, CosmosDB
• Search Engines: Azure AI Search, Elasticsearch, Solr
• Vector Databases: Azure AI Search with vectors, Pinecone, Weaviate
• External APIs: Custom REST APIs, GraphQL endpoints
• File Systems: SharePoint, OneDrive, local file systems

Technical Considerations

• Performance: Query response times, indexing strategies
• Scalability: Concurrent user support, data volume limits
• Security: Authentication, authorization, data privacy
• Schema Design: What format should the schema_object field follow?

Implementation Tasks

Phase 1: Architecture Design

• Define abstract IDataBackend interface extensions
• Design configuration system for multiple backend types
• Plan authentication/authorization integration
• Define data schema standards

Phase 2: Initial Implementations

• Azure AI Search backend implementation
• Basic database backend (Entity Framework)
• File system backend for documents

Phase 3: Advanced Features

• Vector search capabilities
• Multi-backend federation
• Caching strategies
• Performance monitoring

Success Criteria

• Multiple backend implementations available
• Clear configuration documentation
• Performance benchmarks established
• Security best practices implemented

Related Issues

• Will link to LLM integration issue
• Will link to authentication strategy issue

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Labels: enhancement, architecture, backend
Priority: High
Milestone: v0.2.0