AI Architecture
Self-Querying Retriever: Natural Language to SQL/Metadata Filter Conversion
Empower LLMs to extract semantic search queries and SQL/JSON metadata filters (date range, author, category) from plain English prompts.
Combining Hybrid Filtering with Vector Search
When a user asks 'Show me PDF reports uploaded after 2025 regarding revenue', the self-querying engine translates 'after 2025' into `{ year: { $gt: 2025 } }` before executing vector search.
Related Technical Guides
Deepen your understanding with these closely related production architectures and tutorials:
2026 Frontier Stack: LangGraph Checkpointing, Mem0 Scoped Memory & Prefix Caching FinOps
Architect enterprise AI agents with decoupled lifecycles: prompt KV-cache optimization (90% savings), LangGraph state persistence, and Mem0 long-term memory.
Model Context Protocol (MCP) Guide: Connecting LLMs to Local Databases and Tools
Learn the open-source Model Context Protocol (MCP) standard created by Anthropic and how it turns LLMs into extensible agents connected to your infrastructure.
Prompt Caching Architecture: Slashing LLM API Costs and Latency by 90% via KV-Cache Reuse
Master Anthropic and Gemini Prompt Caching to slash API bills and reduce latency on long documents, system instructions, and multi-turn chats.