Packs

2 packs

Migrate Embedding Model in Qdrant

Migrate embedding model in Qdrant without downtime using alias swap and hybrid search.

3 skills · pack

Optimize Qdrant Search Quality

Diagnose and improve Qdrant search relevance by isolating embedding, config, or query issues.

3 skills · pack

Results for “embedding”

38 skills

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

qdrant-model-migration

Guides embedding model migration in Qdrant without downtime, covering alias swap, side-by-side, and hybrid search strategies.

assessing-vector-and-embedding-weaknesses

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

24.6k · bundle

vss-deploy-video-embedding

Deploy and operate the VSS 3.2 GA RT-Embed Video Embedding microservice using Docker Compose, covering GPU prerequisites, REST API usage for file uploads, text/video embeddings, live RTSP streams, Redis/Kafka/OTel integration, and troubleshooting.

qdrant-search-quality

Diagnoses and improves Qdrant search relevance by isolating embedding model, configuration, or query strategy issues.

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

More results

nemotron-retrieval-recipes

Plan, debug, tune, evaluate, export, or deploy public Nemotron embedding and reranking retrieval recipes using the current checkout.

Builds Retrieval-Augmented Generation systems with document chunking, embedding generation, vector storage, and retrieval pipelines, including evaluation and optimization.

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

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

qdrant-search-quality-diagnosis

Diagnoses Qdrant search quality issues by isolating causes like HNSW approximation, quantization, embedding model, or search pipeline problems.

Generates and manages vector embeddings for semantic search and RAG retrieval across knowledge bases, with self-learning capabilities.

Installs and configures ktx, the open-source context layer for data agents, including database connections, embeddings, agent integration, and context ingestion.

Guides production LLM operations: RAG pipelines, embeddings, vector databases, fine-tuning, prompt engineering, cost estimation, quality evals, and AI architectures.

nimoqup046-collab

Guides production AI systems: RAG pipelines, embeddings, vector databases, fine-tuning, prompt engineering, cost estimation, quality evals, and caching.

Designs and implements RAG pipelines, covering document chunking, embedding strategies, hybrid search, answer synthesis with source attribution, and evaluation using RAGAS metrics.

Store and query embeddings with metadata, vector and full-text search, and filtering. Integrates with LangChain and LlamaIndex for RAG and semantic search applications.

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

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

tao-mine-aoi-images

Embeds target and source image parquets, then mines nearest-neighbour source images for augmentation in VCN AOI workflows.

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

openai-automation

Automate OpenAI API operations: generate text and multimodal responses with structured output, create embeddings, generate images, and list models via the Composio MCP integration.

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

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.

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

014-api-f0515c8f

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

Build LLM-powered applications with modular components for chains, agents, memory, and retrieval, supporting Python and JavaScript frameworks.

Build production-ready LLM applications, RAG systems, and intelligent agents with architecture design, model selection, and cost controls.

Design, tune, and evaluate production RAG pipelines with deterministic tools for chunking, pipeline design, and retrieval evaluation.

20.4k · bundle

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

testing-prompt-injection-in-rag-pipelines

Probe RAG applications for prompt injection via poisoned retrieved context and embedding manipulation.

24.6k · bundle

Provides persistent, compounding memory for AI agents across sessions using local markdown files, with optional Supabase-backed semantic search.

Build production-ready LLM applications, advanced RAG systems, and intelligent agents with vector search, multimodal AI, and enterprise integrations.

Chunks a book into canonical retrieval units with heading-aware structure splitting, recursive token targets, and contextual prefixes for downstream RAG ingestion.

Enables fast similarity search and clustering of dense vectors using FAISS, covering index types, GPU acceleration, and integrations with LangChain and LlamaIndex.

lucaspmarie-a11y

Provides guidance on production AI operations including RAG pipelines, vector databases, embeddings, fine-tuning, prompt engineering, cost estimation, and quality evaluation.