Building Custom AI Bots
Architecture patterns, tooling decisions, and production considerations for building conversational AI systems with LangChain and modern LLMs.
Architecture Overview
Our standard AI bot architecture follows a Retrieval-Augmented Generation (RAG) pattern. This combines the creative capabilities of large language models with your proprietary data, producing responses that are both accurate and contextual.
Ingestion Layer
Documents are chunked, embedded using OpenAI embeddings, and stored in a vector database (Pinecone or pgvector).
Retrieval Layer
User queries are embedded and matched against the vector store using cosine similarity to find relevant context.
Generation Layer
LangChain chains combine retrieved context with the user query into a structured prompt sent to GPT-4.
Memory Layer
Conversation history is maintained in Redis for multi-turn conversations with configurable context windows.
Tech Stack Decisions
OpenAI GPT-4 Turbo (cost-effective) or GPT-4o (speed-critical applications)
LangChain.js for TypeScript projects, LangChain Python for data-heavy pipelines
pgvector for PostgreSQL-native projects, Pinecone for managed infrastructure
Next.js with Vercel AI SDK for streaming responses and optimistic UI
Redis for conversation memory, response caching, and rate limiting
LangSmith for tracing LLM calls, debugging chains, and tracking costs
Production Considerations
- Implement rate limiting to control API costs and prevent abuse
- Use streaming responses for better perceived performance
- Add guardrails to prevent prompt injection and off-topic responses
- Log all interactions for quality analysis and fine-tuning
- Set up fallback responses when the LLM is unavailable
- Implement human escalation paths for complex queries
- Monitor token usage and set budget alerts per environment
Want to build an AI bot for your business? Get in touch to discuss your use case.