Qdrant plugins

Build production-ready data engineering stacks: Airflow DAGs for orchestration, dbt models for transformations, scalable pipelines with Spark on cloud warehouses like BigQuery and Snowflake, Kafka streaming, optimized embeddings for RAG, and vector databases like Pinecone, Weaviate, and pgvector.

Build production LLM applications using expert strategies for context window management via summarization trimming routing and caching, RAG pipelines with chunking embeddings vector stores and agents, observability via Langfuse tracing evaluations, and retrieval optimization workflows.

Index local codebases using Docker, Qdrant, and Ollama to enable semantic search, dependency graph analysis, and deep exploration of architecture, functions, schemas, and feature traces across files.

Manage Qdrant vector databases end-to-end: optimize search speed, relevance, and resource usage; scale clusters for growing workloads; deploy via Docker, local, or cloud; monitor with Prometheus and Grafana; migrate embedding models and upgrade versions without downtime; integrate SDKs in Python, JS/TS, Rust, Go, .NET, and Java.

Index PDFs, markdown files, and source code into Qdrant for semantic vector search and MeiliSearch for full-text keyword search using arc CLI. Perform hybrid conceptual or exact queries on codebases and docs with AST-aware chunking, frontmatter extraction, and git metadata. Manage Docker services, collections, and sync parity locally.

Build TypeDB-powered notebooks for scientific knowledge: ingest literature into a knowledge graph, retrieve via semantic search and dynamic TypeQL queries, analyze with agentic 6-phase curation workflows, index bioskills for composition, and automate browser tasks.

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.