hybrid-search-implementation

JuanJoseGonGi

Combine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall.

vector-search

jdmorag97-rgb

Implement semantic vector search for construction data. Build AI-powered search using embeddings and vector databases (Qdrant, ChromaDB) for intelligent querying of specifications, standards, and project documents.

rerank

ExoPriors

LLM-powered multi-attribute reranking of candidate sets via pairwise comparison. Supports canonical attributes (clarity, technical_depth, insight), custom prompts, model tier selection, and TopK configuration. Use when the task involves: rerank, rank by clarity, rank by insight, rank by depth, best items, quality tier, LLM judge, pairwise comparison, multi-attribute rank, rerank from sql or list. NOT for: simple SQL sorting (ORDER BY date/upvotes/score -- use scry), semantic search or embedding algebra (use vector-composition), or people identity resolution (use people-graph).

vector-composition

ExoPriors

Compose semantic vectors in Scry -- embed concepts as @handles, search by cosine distance, debias with vector algebra, and diagnose signal loss. Use when the task involves: semantic search, embedding, vector, cosine distance, <=>, "X but not Y", debias, embed this concept, @handle, vibe algebra, concept vector. NOT for: word2vec training, fine-tuning embeddings, local vector databases (FAISS, Pinecone, Chroma), or plain keyword/SQL search (use scry).

chroma

AXGZ21

Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.

RAG Engineer

haniakrim21

Expert in building Retrieval-Augmented Generation (RAG) systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use when: building RAG, vector search, embeddings, semantic search, or document retrieval.

AI Engineer

haniakrim21

Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations. Use PROACTIVELY for LLM features, chatbots, AI agents, or AI-powered applications.

Vector Database Engineer

reikiplanet

Expert in vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similar

semantic-search-cwicr

jdmorag97-rgb

Semantic search in DDC CWICR construction database using vector embeddings. Find similar work items and resources for cost estimation.