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Found 78 Skills
Build modular Agentic RAG systems with LangGraph, featuring hierarchical indexing, conversation memory, and multi-agent query processing
Comprehensive toolkit for developing with the CocoIndex library. Use when users need to create data transformation pipelines (flows), write custom functions, or operate flows via CLI or API. Covers building ETL workflows for AI data processing, including embedding documents into vector databases, building knowledge graphs, creating search indexes, or processing data streams with incremental updates.
Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.
ADBPG Knowledge Base Management: Create knowledge bases, upload documents, search, Q&A. Triggers: "knowledge base", "document library", "document upload", "knowledge search", "RAG", "Q&A", "embedding", "ADBPG", "AnalyticDB PostgreSQL"
Switch OpenViking's embedding model to a local llama-server (or any OpenAI-compatible embedding endpoint) running inside a bwrap sandbox managed by job-env-manager. Handles the full lifecycle: detect current config, validate the target embedding endpoint, modify ov.conf, delete incompatible vectordb index when dimension changes, restart the openviking-server process in the sandbox, and verify the new collection dimension. Use this skill when the user wants to: (1) switch the OpenViking embedding model, (2) change the embedding dimension, (3) fix EmbeddingRebuildRequiredError after a dimension mismatch, (4) rebuild the vectordb index after an embedding model change, (5) use a local llama-server for OpenViking embeddings. Trigger words: "切换OpenViking embedding", "OpenViking embedding模型", "OpenViking向量化模型", "openviking embedding switch", "change openviking embedding model", "配置openviking embedding", "openviking llama embedding", "bge embedding openviking", "切换向量化模型", "OpenViking模型切换".
Use when working with context management context save
Use when building features that answer questions from private data, documents, policies, or time-sensitive information — RAG architecture, chunking strategies, hybrid search, re-ranking, vector databases, evaluation, agentic RAG, multimodal RAG...
Build RAG systems - embeddings, vector stores, chunking, and retrieval optimization
Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.
Query and export data from seekdb vector database. Supports two search modes: (1) Scalar search - metadata filtering only, (2) Hybrid search - fulltext + semantic search combined. The --query-text parameter is used for BOTH fulltext ($contains) and semantic (query_texts) search simultaneously. Can export results to CSV/Excel.
Build semantic search with Cloudflare Vectorize V2. Covers async mutations, 5M vectors/index, 31ms latency, returnMetadata enum changes, and V1 deprecation. Prevents 14 errors including dimension mismatches, TypeScript types, testing setup. Use when: building RAG or semantic search, troubleshooting returnMetadata, V2 timing, metadata index, dimension errors, vitest setup, or wrangler --json output.
Qdrant vector database: collections, points, payload filtering, indexing, quantization, snapshots, and Docker/Kubernetes deployment.