🗄️ Advanced Backend Support

[jongalloway]

Description

Implement support for additional backend types including vector databases and enterprise search solutions.

New Backend Types

• Azure AI Search: Enhanced implementation (current basic support)
• Qdrant: Vector database integration
• Milvus: Vector similarity search
• OpenSearch: Elasticsearch-compatible search
• Snowflake: Data warehouse integration

Implementation Strategy

• Create backend-specific NuGet packages
• Implement each backend as separate IDataBackend implementations
• Add configuration templates for each backend type
• Create demo scenarios for multi-backend deployments

Priority & Effort

• Priority: High
• Effort: Large
• Timeline: Phase 3 (Months 4-6) - 3-4 months
• Business Value: High - Broader ecosystem compatibility

Dependencies

• Requires 🏗️ Implement Multi-Backend Retrieval Architecture #34 (Multi-Backend Architecture) to be completed
• Benefits from ⚙️ Update Configuration Format Support #36 (Configuration Format Updates)

Notes

The existing AspireDemo already includes Qdrant integration as a proof of concept. This issue would formalize and extend that work to include other backends and create a comprehensive backend ecosystem.

References

• NLWeb June 2025 Analysis - Section: "Advanced Backend Support"
• GitHub Issues Plan - Issue 7

[jongalloway]

Progress note: We already have a Qdrant PoC in the Aspire demo (samples/AspireDemo) with setup docs at samples/AspireDemo/VECTOR_SEARCH_SETUP.md. In addition, semantic embeddings + vector search plumbing landed in commit 159f56b.

Proposal:

• Extract Qdrant into a formal backend implementation (separate package or folder) with clear registration and config templates.
• Split this issue into provider-specific issues for Milvus, OpenSearch, and Snowflake so we can track each independently.
• Keep this issue as the umbrella tracker and link sub-issues.

If you agree, I can open the three sub-issues and outline acceptance criteria for each.

[jongalloway]

Created provider-specific sub-issues to track implementation work:

• Qdrant (formalize from PoC): Backend: Qdrant integration (formalize from PoC) #72
• Milvus: Backend: Milvus integration #73
• OpenSearch: Backend: OpenSearch integration (Vector + KV) #74
• Snowflake Cortex: Backend: Snowflake Cortex integration (vector search) #75

We can iterate on acceptance criteria as we learn from the first one (likely Qdrant).