@llama_index
Build custom retrievers that beat generic vector search by understanding domain-specific context and jargon ๐ฎ The team at @superlinked shows how to create a Steam games retriever using our custom LlamaIndex integration, combining semantic search with gaming-specific knowledge. ๐ฏ Custom retrievers hardwire domain expertise - perfect for gaming jargon and complex queries like "strategy co-op game with sci-fi elements" โก Multi-field indexing combines name, description, genre into single searchable text for richer understanding ๐ง LlamaIndex BaseRetriever needs just one _retrieve method implementation to plug in any custom backend ๐ @superlinked's InMemoryExecutor delivers sub-millisecond latency for real-time recommendations The guide walks through schema design, vector space configuration, and result processing with practical code examples. We show how combining @superlinked's mixture-of-encoders approach with LlamaIndex's RAG infrastructure creates retrievers that actually understand what users mean, not just what they type. Full tutorial with Colab notebook: https://t.co/hrEbW7wrib