Plugins

6 plugins

@lucassantana-dev

Rag from LucasSantana-Dev/forgekit.

2 skills · plugin

Research Papers for RAG

Gather and structure scientific papers for RAG ingestion using Semantic Scholar and BGPT.

9 skills · plugin

Google RAG Platform

For developers using Google's Agent Platform to build RAG applications with Gemini and managed corpora.

4 skills · plugin

Build RAG Pipeline with Pinecone

Build a production RAG pipeline and persistent agent memory using Pinecone as the vector database backend.

6 skills · plugin

AI and ML skills for .NET: technology selection, LLM integration, agentic workflows, RAG pipelines, MCP, and classic ML with ML.NET.

5 skills · plugin

@alirezarezvani

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 · plugin

Results for “rag”

70 skills

Implements production LLM operations: RAG pipelines, embeddings, vector databases, fine-tuning, advanced prompt engineering, cost estimation, quality evals, semantic caching, streaming, and agents.

Provides guidance and code for production AI workflows including RAG pipelines, vector databases, embedding indexing, prompt engineering, cost estimation, semantic caching, and quality evaluation.

Design, operate, and troubleshoot OCI services including OKE, IoT, Functions, and Enterprise AI with OCI Generative AI models, agents, RAG, and cost estimation.

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.

mariadb-corporation

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.

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.

orchestra-research

Sentence Transformers

Generate high-quality sentence and text embeddings for semantic similarity, clustering, and retrieval using 5000+ pre-trained models. Supports multilingual and domain-specific embeddings for RAG and semantic search.

10.4k · bundle

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.

014 API F0515c8f

Reference for configuring and using LangChain4j vector stores, covering setup, search, filtering, and ingestion.

Iterative Retrieval

Progressively refines context retrieval in multi-agent workflows to solve the subagent context problem.

orchestra-research

Build LLM-powered applications with agents, chains, and RAG using a framework that supports multiple providers and 500+ integrations.

10.4k · bundle

Implements machine learning models, embeddings, and AI-powered features with ethical considerations, including model selection, integration, and monitoring.

Augments nearest-neighbour search with graph Laplacian features to retrieve items based on both semantic similarity and structural role.

Assessing Vector And Embedding Weaknesses

Test vector stores for embedding inversion, cross-tenant leakage, and poisoning.

24.6k · bundle

Provides guidance on using the ZVec in-process vector database for efficient similarity search and embedding storage in agent memory systems.

johnalbertini14-glitch

Ingests EPUBs and ebooks into a local vector index, then answers questions and searches passages via a command-line interface.

Azure AI Contentunderstanding Py

Extract semantic content from documents, images, audio, and video using Azure AI Content Understanding SDK for Python.

Search, query, inspect, create, and import data into Weaviate vector database collections using official scripts and references.

42.4k · bundle

Search the web and extract content from URLs using Tavily and Exa APIs via the inference.sh CLI.

Explains dense vector embeddings, their key concepts, common use cases, and best practices for semantic search and RAG applications.

Guides building LLM applications on the Dify platform, covering visual workflows, knowledge bases, agents, and API deployment.

Ingests EPUB and ebook files into a local vector index and provides a command-line interface for asking questions about the books.

Build AI agents, multi-agent teams, and agentic workflows using the Agno framework, with reference files covering agents, teams, workflows, memory, knowledge, and deployment.

Mini Context Graph

Build a persistent, compounding knowledge base that combines a wiki, knowledge graph, and raw source storage for structured retrieval with provenance.

36.2k · bundle

Index local folders and search documents with BM25, vector, or hybrid queries, plus AI answers with citations and a web UI.

Converts code repositories and document directories into semantically-chunked text files optimized for NotebookLM ingestion, with support for config files and incremental processing.

Index local folders and search documents with BM25, vector, or hybrid queries, plus AI answers with citations and a web UI.

Manage a multi-layered knowledge system for ingesting, organizing, syncing, and retrieving knowledge across local files, MCP memory, vector stores, and Git repos.

orchestra-research

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

johnalbertini14-glitch

Index local folders and search documents with BM25, vector, or hybrid queries, plus AI answers with citations and a web UI.

orchestra-research

Build complex AI systems with declarative programming, optimize prompts automatically, and create modular RAG systems and agents using Stanford NLP's DSPy framework.

10.4k · bundle

Index folders of PDFs and other documents into LanceDB for vector search, then query them with semantic search, page filters, verbatim quotes, and cross-document aggregation.

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

Technology Selection

Guides technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI, Microsoft Agent Framework, GitHub Copilot SDK, ONNX Runtime, and OllamaSharp.