We need to build a simple chat bot in English for customer service for a children illustration book site. Multilingual preferred in Phase Two. Possible architecture for a RAG chatbot: Core flow: User input → Retrieve relevant documents → Generate response using LLM + documents → Return answer Use the most straightforward approach: 1. Use an existing framework- Start with something like LangChain or LlamaIndex. They handle the integration between your retrieval system and LLM, so you don't build everything from scratch. This reduces complexity significantly. 2. Vector database for storage- Store documents as embeddings in a vector database (Pinecone, Weaviate, Milvus, or even simpler options like FAISS for local use). When a user asks a question, convert it to an embedding and find similar documents. 3. Simple retrieval- Query vector database with the user's question to get the top-k most relevant documents. 4. LLM integration- Pass the retrieved documents plus the user's question to an LLM (via OpenAI API, Anthropic, Ollama for local models, etc.) as context. The LLM generates a response grounded in those documents. 5. Basic interface- Start with a simple Python script or lightweight web interface (Flask/FastAPI with a basic frontend). Minimal example structure: - Document ingestion script (loads docs → creates embeddings → stores in vector DB) - Query handler (retrieves docs → calls LLM with context → returns response) - Simple frontend or CLI to interact A system that enables collaborative storytelling through Adaptive Semantic Governance. It extracts and consolidates a story's foundational logic—characters, world rules, cause-and-effect mechanics, and thematic patterns into a dynamic semantic dataset. Adaptation Semantics is the core process: the system's semantic dataset evolves continuously as recent content refines the rules governing the story universe. In short, the content baseline can be changed and expanded by users according to guidelines via chat discourse. Users and generative AI improvise or expand narrative content by page; each addition is validated against this dataset with permissibility feedback. Approved additions integrate canonically and automatically update the semantic framework, ensuring all narrative branches maintain logical consistency and authorial intent while enabling collaborative storytelling to preserve authorial intent. Workflow Summary with Viability Review Step 1: Upload Image & Text (16-32 Pages) Viability: HIGH Step 2: Extract Content, Character & Context (5W1H) Viability: MEDIUM-HIGH Step 3: Human Review & Refinement (Chatbot HTML) Step 4: Consolidate Story Internal Logic to Dataset Viability: MEDIUM Step 5: User Improvises or AI Expands Content Viability: HIGH (But Needs Guardrails) Step 6: Feedback to User on Permissibility Viability: HIGH Step 7: Accept as Canon & Generate Revised Page Text Viability: HIGH (But Assumes Step 6 Passed) Step 8: Incorporate to Dataset Viability: MEDIUM-HIGH (Requires Automation & Care) Proposed Milestones (for discussion) 1. Build VectorDB for one book as one story in 16 pages to consolidate story internal logic (skip step 1 and 2). 2. Demonstrate Adaptive Semantic Governance as dataset works via chatbot discourse with positive or negative feedback to user on permissibility. 3. Demonstrate canon acceptance via continuous expansion of storyline and incorporation to dataset. 4. Build multi datasets for each book and story submitted in cloud with well defined HTML user interface.