Packs
1 packResults for “vector-database”
26 skills
github pinecone-rag
Build production RAG pipelines and persistent agent memory using Pinecone as the vector database backend.
36.2k
orchestra-research 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
oyi77 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
oyi77 zvec
Provides guidance on using the ZVec in-process vector database for efficient similarity search and embedding storage in agent memory systems.
10
antigravity weaviate
Search, query, inspect, create, and import data into Weaviate vector database collections using official scripts and references.
42.4k · bundle
nimoqup046-collab llm-ops
Guides production AI systems: RAG pipelines, embeddings, vector databases, fine-tuning, prompt engineering, cost estimation, quality evals, and caching.
2
More results
jorcan llm-ops
Guides production LLM operations: RAG pipelines, embeddings, vector databases, fine-tuning, prompt engineering, cost estimation, quality evals, and AI architectures.
0 · bundle
orchestra-research 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
lucaspmarie-a11y llm-ops
Provides guidance on production AI operations including RAG pipelines, vector databases, embeddings, fine-tuning, prompt engineering, cost estimation, and quality evaluation.
5
orchestra-research 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
phoroth 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
mariadb-corporation mariadb-vector
Provides best practices for using MariaDB's built-in vector support for AI workloads, including SQL syntax for vector columns, indexes, distance functions, and RAG patterns.
0
mukul975 assessing-vector-and-embedding-weaknesses
Test vector stores for embedding inversion, cross-tenant leakage, and poisoning.
24.6k · bundle
oyi77 ruvector
Generates and manages vector embeddings for semantic search and RAG retrieval across knowledge bases, with self-learning capabilities.
10
tools-only 014-api-f0515c8f
Reference for configuring and using LangChain4j vector stores, covering setup, search, filtering, and ingestion.
7 · bundle
antigravity 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
jorcan vexor
Enables semantic file search across a codebase using a vector-powered CLI, with integration for Claude and Codex agents.
0 · bundle
lord1egypt qdrant-vector-search
Builds production RAG and semantic search systems with Qdrant, covering collection setup, vector indexing, filtered and hybrid search, and integration with LangChain and LlamaIndex.
2
lord1egypt 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
majiayu000 rag
Builds Retrieval-Augmented Generation systems with document chunking, embedding generation, vector storage, and retrieval pipelines, including evaluation and optimization.
567 · bundle
jeffallan 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
24601 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
alirezarezvani rag-architect
Design, tune, and evaluate production RAG pipelines with deterministic tools for chunking, pipeline design, and retrieval evaluation.
20.4k · bundle
github 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
comeonoliver mvp
Builds a Streamlit and FastAPI RAG application that lets users upload documents and query them with natural language through LM Studio.
61
oracle 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