Qdrant 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.

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

Explore and analyze codebases using semantic search, dependency graphs, and context artifacts to understand architecture, find functions and types, trace features, and inspect schemas or specs.

Helps Qdrant users optimize and manage vector search deployments through tuning performance, diagnosing search quality, scaling clusters, deploying infrastructure, upgrading versions, and using client SDKs.

Orchestrate security research workflows: annotate codebases, define vulnerability patterns, hunt for bugs, draft structured findings, deduplicate results, generate PoCs, and encode findings into reusable detection modules — all within an agentic audit workspace.

Index PDFs, markdown, and source code into dual Qdrant (semantic) and MeiliSearch (full-text) indexes via the arc CLI, then search across corpora using either vector or keyword queries with AST-aware chunking and git metadata.

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.

Semantically search IEEE, INCOSE, and ISO systems engineering standards. Retrieve relevant knowledge snippets and apply RAG to ground your engineering specifications using a local Python MCP server with Qdrant vector database and OpenAI embeddings.