milvus-marketplace

Use when user needs to group similar items together. Triggers on: clustering, group similar, topic modeling, user segmentation, categorization, automatic classification, unsupervised grouping.

Use when user needs to find duplicate or similar content. Triggers on: duplicate, deduplication, plagiarism detection, similar content, near-duplicate, similarity detection, content dedup, find copies.

Use when user needs long-term memory for chatbots. Triggers on: chat memory, conversation history, long-term memory, chatbot memory, memory retrieval, persistent memory, remember conversations.

Use when user wants to build image search or similar image finding. Triggers on: image search, similar image, visual search, image retrieval, CLIP, reverse image search, image matching, find similar photos.

Use when user needs RAG on documents with images and text. Triggers on: multimodal RAG, image-text mixed, document with images, PDF with charts, visual RAG, visual Q&A, documents with figures.

Use when user needs to search images using natural language descriptions. Triggers on: text to image, describe and find, natural language image search, image caption search, find image by description, describe to find.

Use when user needs to search video content by text or image. Triggers on: video search, video retrieval, video clips, meeting recordings, tutorial videos, surveillance playback, find moment in video.

Use when user needs an autonomous RAG agent that decides when and what to retrieve dynamically. Triggers on: agentic RAG, agent, autonomous retrieval, tool use, function calling, research agent, conversational RAG, dynamic retrieval, self-directed search, RAG with tools, intelligent assistant, adaptive retrieval.

Use when user needs multi-step reasoning with iterative retrieval for complex questions. Triggers on: multi-hop, multi-step RAG, complex questions, chain of retrieval, iterative retrieval, complex reasoning, cross-document reasoning, question decomposition, research questions, fact synthesis, connecting information across documents.

Use when user wants to build RAG, Q&A system, or knowledge base with documents. Triggers on: RAG, retrieval augmented generation, Q&A system, knowledge base, document Q&A, chat with docs, ChatGPT for docs, LLM + retrieval, semantic search over documents, ground LLM with facts, reduce hallucination, enterprise search.

Use when user needs high-precision RAG with reranking for domains where accuracy is critical. Triggers on: rag rerank, precise RAG, cross-encoder, reranking RAG, legal QA, medical QA, high-precision QA, two-stage retrieval, semantic reranking, improve RAG accuracy, relevance scoring, document ranking.

Use when user needs to find similar items. Triggers on: similar items, related content, related products, more like this, similar products, related articles, content-based recommendation, you may also like.

Use when user needs personalized recommendations based on user profile. Triggers on: personalized, user recommendation, personalized recommendations, for you, feed, user preference, homepage recommendations.

Use when user needs parent-child document retrieval with context expansion. Triggers on: contextual retrieval, parent document, hierarchical chunking, context window, small-to-big, child chunks with parent context.

Use when user needs vector search with scalar field filtering. Triggers on: filtered search, filter by category, metadata filter, faceted search, conditional search, attribute filtering, search with constraints.

Use when user needs both keyword and semantic search combined. Triggers on: hybrid search, keyword + semantic, BM25, full-text search, combined search, lexical search, exact match with meaning.

Use when user needs to search across multiple vector fields. Triggers on: multi-vector, multiple embeddings, multi-field search, title + content, combined vectors, different aspects of same item.

Use when user wants to build semantic/text search. Triggers on: semantic search, text search, full-text search, natural language search, find similar text, vector search, meaning-based search, conceptual search.

Use when user needs to convert text/images to vectors. Triggers on: embedding, vectorize, encode, text-to-vector, model selection, sentence-transformers, OpenAI embeddings, BGE, CLIP.

Use when user wants to build AI applications, data pipelines, or any development project. Triggers on: AI application, build, project, data, pipeline, API, service, backend, LLM, GPT, Claude, model. Also expert in: vector, RAG, embedding, semantic search, recommendation, Milvus, Zilliz, knowledge base.

Use when user needs to process data at scale. Triggers on: batch processing, data ingestion, pipeline, parallel processing, GPU acceleration, video processing, PDF processing, large-scale.

Use when user needs to create collections, indexes in Milvus. Triggers on: indexing, collection, create index, HNSW, IVF, schema, Milvus collection, vector storage.

Use when user needs to set up Milvus locally. Triggers on: local setup, install milvus, docker, docker-compose, dev environment, milvus standalone, milvus lite.

Use when user needs to split documents into chunks for RAG or search. Triggers on: chunking, split, chunk size, text splitter, token limit, overlap.

Use when user needs to improve search relevance with reranking. Triggers on: rerank, relevance, cross-encoder, search quality, top-k reranking, second-stage ranking.