KoG Playground is an open-source, browser-based platform designed to simplify the development of retrieval-augmented generation (RAG) agents. It connects to popular vector stores like Pinecone or FAISS, allowing users to ingest text corpora, compute embeddings, and configure retrieval pipelines visually. The interface offers modular components to define prompt templates, LLM backends (OpenAI, Hugging Face), and chain handlers. Real-time logs display token usage and latency metrics for each API call, helping optimize performance and cost. Users can adjust similarity thresholds, re-ranking algorithms, and result fusion strategies on the fly, then export their configuration as code snippets or reproducible projects. KoG Playground streamlines prototyping for knowledge-driven chatbots, semantic search applications, and custom AI assistants with minimal coding overhead.

Local RAG Researcher Deepseek combines Deepseek’s powerful file crawling and indexing capabilities with vector-based semantic search and local LLM inference to create a standalone retrieval-augmented generation (RAG) agent. Users configure a directory to index various document formats—including PDF, Markdown, text, and more—while custom embedding models integrate via FAISS or other vector stores. Queries are processed through local open-source models (e.g., GPT4All, Llama) or remote APIs, returning concise answers or summaries based on the indexed content. With an intuitive CLI interface, customizable prompt templates, and support for incremental updates, the tool ensures data privacy and offline accessibility for researchers, developers, and knowledge workers.