Pinecone plugins

Build production LLM applications with LangGraph agents, RAG pipelines, hybrid search, and advanced prompt engineering. Automate agent architecture design, vector index optimization, and prompt refinement for deploying reliable AI systems.

Build production-ready data pipelines with Apache Airflow and dbt, manage scalable data warehouses, and implement vector search and RAG systems using embedding models and vector databases.

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.

Manage Pinecone vector indexes and build RAG applications from within Claude Code: create and query indexes, upsert vectors, run semantic/hybrid search, and set up document Q&A with Assistants—all via CLI, natural language commands, or an MCP server.

Persists deterministic, team-accessible memory across Claude Code sessions with bi-temporal facts, RBAC, and zero-LLM recall. Installs a local MCP server backed by Postgres, Neo4j, and Pinecone, and automatically records file changes, commands, and session summaries.

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.