Step-by-step tutorials on building voice and chat AI agents using OpenAI Agents SDK, Realtime API, function calling, multi-agent orchestration, and production deployment patterns.
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Get started with Chroma DB for local vector search — learn to create collections, add documents with auto-embedding, query by similarity, and persist data to disk for rapid AI prototyping.
Understand the four major vector index algorithms — Flat, IVF, HNSW, and Product Quantization — with clear explanations of accuracy vs speed tradeoffs and guidance on tuning parameters.
Build an AI agent that handles volunteer registration, shift scheduling, automated reminders, and appreciation messages for nonprofit organizations and community groups.
Implement text classification systems using structured outputs. Build sentiment analysis, intent detection, and hierarchical category classification with enum constraints, confidence scores, and multi-label support.
Deep dive into LCEL's pipe operator, RunnablePassthrough, RunnableParallel, branching, and fallback patterns for building flexible, declarative AI pipelines in LangChain.
Master LangChain tool creation patterns including the @tool decorator, StructuredTool class, Pydantic input schemas, async tools, and error handling for production-grade agent tools.
Learn how to extract structured data from LLM responses using LangChain output parsers — Pydantic models, JSON parsing, format instructions, and retry parsers for robust extraction.
Master the art of combining vector similarity search with metadata filters, including pre-filter vs post-filter strategies, performance implications, and query design patterns across databases.
