'vector-db' 검색 결과

Hunt vector-store / embedding-layer weaknesses in RAG pipelines (OWASP LLM08 Vector and Embedding Weaknesses) — persistent corpus poisoning that survives across sessions and users (distinct from one-shot indirect prompt injection, which is owned by hunt-llm-ai), cross-tenant vector-database IDOR (unauthenticated or unscoped queries against Pinecone/Weaviate/Chroma/Milvus/Qdrant/pgvector), source-text/metadata leakage in similarity-search results, and retrieval-hijack via adversarial embedding proximity ('SEO poisoning' for RAG). Targets: any app with a shared knowledge base, document upload feeding a chatbot, or a directly reachable vector-DB port. Validate: a second, clean session/account must inherit a poisoned result, or a cross-tenant artifact must be independently verifiable — confabulation is not a finding, same bar as hunt-llm-ai. Use when target is RAG-backed, exposes a vector-DB port, or lets users upload documents that other users' queries later retrieve.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Build and operate LLM applications with guidance on RAG, which uses retrieved documents to inform answers. Covers document indexing, vector database selection, prompt improvement, response evaluation, caching, and estimates of model usage costs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building an assistant that answers from documents
• Choosing a vector database for document search
• Comparing answer quality after prompt changes

Helps design semantic search and RAG, where an AI answers using retrieved documents. Covers embeddings, numerical representations of content, plus document splitting, search indexes, keyword search combinations, and evaluation of retrieval quality.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Design document retrieval for AI answers
• Compare embedding models and document splitting strategies
• Combine semantic search with keyword search

Embedding strategies, ANN algorithms, hybrid search, RAG chunking strategies, and reranking for semantic search and retrieval.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Expert guide for high-performance Vector Databases, Deep RAG architectures, pgvector 0.8+ HNSW, Reciprocal Rank Fusion (RRF), Cross-Encoder Re-ranking, and Late Chunking / Panduan ahli Vector DB, arsitektur Deep RAG, pgvector HNSW, RRF, dan Re-ranking.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Qdrant vector database -- collection management, point operations, payload filtering, named vectors, quantization, recommendations, snapshots

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Weaviate vector database patterns with weaviate-client v3 -- collection management, vectorizer modules, hybrid search, filtering, generative search (RAG), multi-tenancy, batch imports

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Pinecone serverless vector database -- index management, vector operations, metadata filtering, namespaces, hybrid search, inference API

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Chroma vector database -- collection management, automatic embedding, metadata filtering, document storage, query patterns

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Operações de LLM — RAG, embeddings, bancos de dados vetoriais, fine-tuning, prompt engineering avançado, custos de LLM, avaliações de qualidade e arquiteturas de IA para produção.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

LLM Operations -- RAG, embeddings, vector databases, fine-tuning, prompt engineering avancado, custos de LLM, evals de qualidade e arquiteturas de IA para producao.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Sets up vector databases for semantic search including Pinecone, Chroma, pgvector, and Qdrant with embedding generation and similarity search. Use when users request "vector database", "semantic search", "embeddings storage", "Pinecone setup", or "similarity search".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.