Packs
4 packscurated
Deploy Azure Infrastructure
Creates databases, caches, and configures authentication, monitoring, and backup.
3 skills · pack
curated
Azure Database Management
For .NET developers managing Azure PostgreSQL and MySQL Flexible Server deployments with the Azure SDK.
6 skills · pack
curated
Build RAG Pipeline with Pinecone
Build a production RAG pipeline and persistent agent memory using Pinecone as the vector database backend.
6 skills · pack
@alirezarezvani
Engineering
37 advanced engineering skills: agent designer, agent workflow designer, RAG architect, database designer + schema designer + SQL assistant, migration architect, observability designer, dependency auditor, changelog generator (with semantic version bumper and hotfix/rollback procedures), API design reviewer, API test suite builder, CI/CD pipeline builder, MCP server builder, skill security auditor
33 skills · pack
Results for “database”
23 skillspinecone-rag
Build production RAG pipelines and persistent agent memory using Pinecone as the vector database backend.
36.2k
zvec
Provides guidance on using the ZVec in-process vector database for efficient similarity search and embedding storage in agent memory systems.
10
weaviate
Search, query, inspect, create, and import data into Weaviate vector database collections using official scripts and references.
42.4k · bundle
ktx
Installs and configures ktx, the open-source context layer for data agents, including database connections, embeddings, agent integration, and context ingestion.
567 · bundle
llm-ops
Guides production AI systems: RAG pipelines, embeddings, vector databases, fine-tuning, prompt engineering, cost estimation, quality evals, and caching.
2
llm-ops
Guides production LLM operations: RAG pipelines, embeddings, vector databases, fine-tuning, prompt engineering, cost estimation, quality evals, and AI architectures.
0 · bundle
More results
pinecone
Provides code examples and best practices for using Pinecone, a managed vector database for production RAG, recommendation, and semantic search applications.
10.4k · bundle
llm-ops
Provides guidance on production AI operations including RAG pipelines, vector databases, embeddings, fine-tuning, prompt engineering, cost estimation, and quality evaluation.
5
chroma
Store and query embeddings with metadata filtering, vector search, and full-text search using an open-source database that scales from notebooks to production.
10.4k · bundle
llm-ops
Implements production LLM operations: RAG pipelines, embeddings, vector databases, fine-tuning, advanced prompt engineering, cost estimation, quality evals, semantic caching, streaming, and agents.
3
llm-ops
Provides guidance and code for production AI workflows including RAG pipelines, vector databases, embedding indexing, prompt engineering, cost estimation, semantic caching, and quality evaluation.
42.4k
qdrant-vector-search
Build production RAG and semantic search systems with a high-performance vector database written in Rust, supporting hybrid search, filtering, and horizontal scaling.
10.4k · bundle
vector-db-ops
Manage vector database operations across Pinecone, Weaviate, Qdrant, and ChromaDB, including embedding generation, index creation, metadata filtering, hybrid search, and production deployment for RAG and similarity search.
10
rag-architect
Design, tune, and evaluate production RAG pipelines with deterministic tools for chunking, pipeline design, and retrieval evaluation.
20.4k · bundle
rag
Builds Retrieval-Augmented Generation systems with document chunking, embedding generation, vector storage, and retrieval pipelines, including evaluation and optimization.
567 · bundle
assessing-vector-and-embedding-weaknesses
Test vector stores for embedding inversion, cross-tenant leakage, and poisoning.
24.6k · bundle
ruvector
Generates and manages vector embeddings for semantic search and RAG retrieval across knowledge bases, with self-learning capabilities.
10
qdrant-scaling-query-volume
Optimizes Qdrant query performance for large limits across multiple shards by using Poisson-distributed subsampling to reduce inter-shard data transfer.
36.2k
mvp
Builds a Streamlit and FastAPI RAG application that lets users upload documents and query them with natural language through LM Studio.
61
chroma
Store and query embeddings with metadata, vector and full-text search, and filtering. Integrates with LangChain and LlamaIndex for RAG and semantic search applications.
2
rag-architect
Designs and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, applying reranking, and evaluating retrieval quality.
10.4k · bundle
surrealdb
Expert guidance for architecting, developing, and operating SurrealDB 3, covering SurrealQL, multi-model data modeling, vector search, security, deployment, performance tuning, SDK integration, and ecosystem tools.
34 · bundle
enterprise-ai
Navigate Oracle Cloud Infrastructure's Enterprise AI services: choose models, build agents with RAG and tools, estimate costs, secure access, and integrate with Oracle Database, APEX, and other platform services.
736 · bundle