Multi-tenant AI chatbot architecture: RAG, Ollama, Celery, Redis, agent modes, CRM integration.
client_id as the fundamental unit, config-driven behavior, the DB schema for clients and agent_modes, and why the system prompt mu…
Loading agent modes per client, composing tone + personality + RAG + capability fragments, defending against prompt injection in a…
One ChromaDB collection per tenant for strict isolation, the document ingestion pipeline (PDF/DOCX to chunks to embeddings), query…
Running Llama 3.1 locally with Ollama, OpenAI-compatible SDK integration, prompt engineering for sales contexts, and latency manag…
WhatsApp's 20-second webhook timeout forces async architecture: acknowledge immediately, process in Celery, retry on failure, and …
The channel adapter pattern isolates WhatsApp, widget, and mobile channel handling from the shared intelligence core. Same LLM, sa…
Agent modes are database-configured feature flags for AI capabilities. Activating lead capture or appointment setting from an admi…
Linking WhatsApp conversations to CRM contacts, LLM-powered lead field extraction, pushing behavioral scores as CRM custom fields,…
Building a production-grade multi-tenant AI chatbot with FastAPI, Ollama, ChromaDB, Celery, and Redis.
Engine-by-engine walkthrough of the BehavioralCompute registry: foundational, cognitive, temporal, memory, simulation, and kernel engines.
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