Take your email assistant from basic search to production-grade retrieval. You'll implement the same patterns used by professional AI applications to handle long documents, rank results intelligently, and give your agent multiple tools to choose from.
What You'll Build
An AI email assistant with a sophisticated multi-tool retrieval system that can:
- Search semantically across chunked email content
- Filter emails by precise criteria (sender, date, keywords)
- Fetch full email threads on demand
- Display tool calls transparently in the UI
What You'll Learn
Chunking for Long Content
Break emails into smaller, meaningful pieces using structure-aware text splitting. You'll build a content-addressable embeddings cache that handles chunks efficiently and update your search algorithms to work at the chunk level.
LLM-Powered Reranking
Add a reranking step that uses an LLM to filter search results based on relevance. You'll pass conversation history to the reranker so it understands context, not just the current query.
Multi-Tool Agent Design
Give your agent multiple specialized tools and let it choose the right one. You'll build a filter tool for exact matches and update your system prompt to guide tool selection. The agent learns when to search semantically versus when to filter precisely.
Metadata-First Retrieval
Implement the same pattern coding agents use: browse metadata and snippets first, then fetch full content only when needed. You'll create a getEmails tool that retrieves complete email bodies and entire conversation threads on demand.