Weaviate plugins

Build and deploy production-grade LLM applications with LangGraph for agent orchestration, advanced RAG pipelines leveraging vector and hybrid search, prompt engineering patterns, and automated evaluation. Covers embedding model selection, vector index optimization, and multi-agent architectures for document Q&A, chatbots, and semantic search over proprietary data.

Build and evaluate production-grade AI agents using LangGraph, RAG systems, MCP servers, and prompt engineering patterns—with behavioral testing and reliability monitoring.

Accelerate LLM application development with production-ready patterns for context window management, RAG pipelines, prompt caching, observability via Langfuse, and agent architectures.

Perform deep research on videos and web content using Gemini, then automatically generate structured reports, knowledge graphs, and explainer videos with narration and AI-generated visuals.

Design secure multi-tenant RAG/CAG systems by selecting vector databases like Qdrant, Weaviate, Pinecone, PostgreSQL, or Redis, applying chunking strategies (fixed-size, semantic, recursive), and implementing security patterns for tenant isolation, access control, prompt injection prevention, and data classification.

Restore deleted Claude Code sessions, ground code identifiers against project symbols (env vars, DB models, routes, GraphQL, etc.), and recall past conversations via hybrid search — with a deterministic rewriter that catches hallucinated names before they hit disk.