llm-app

Pathway LLM App delivers ready-to-deploy AI pipeline templates for building high-accuracy RAG and enterprise search applications at scale. Templates connect to live data sources including local file systems, Google Drive, SharePoint, S3, Kafka, PostgreSQL, and real-time data APIs, automatically syncing changes. Application templates include: Question-Answering RAG App for document Q&A, Live Document Indexing as a vector store service, Multimodal RAG with GPT-4o for extracting tables and charts from PDFs, Unstructured-to-SQL pipeline converting financial documents to SQL queries, Adaptive RAG to reduce token costs, Private RAG using Mistral and Ollama for fully local deployment, Slides AI Search for PowerPoint and PDF indexing, and Video RAG with TwelveLabs for video content search. The framework runs as Docker containers with an HTTP API and optional Streamlit UI, eliminating separate vector databases, caches, or API frameworks by providing built-in vector indexing (via usearch) and hybrid full-text search (via Tantivy), all processing in-memory with caching. Templates scale to millions of pages and deploy on GCP, AWS, Azure, Render, or on-premises. Each template includes a README with setup instructions, and additional code templates are available on the Pathway website.