RAG
Nine articles, nine patterns. Now build the service that ships: Pydantic config, request-ID middleware, async document ingestion, a unified multi-mode query endpoint, K8s health probes, and a multi-stage Dockerfile.
RAG
Force your RAG pipeline to always return valid, typed data. Combine OpenAI JSON mode, Pydantic v2 schemas, and LangChain output parsers to build a pipeline where every response is schema-validated.
RAG
Implement CRAG: a retrieval pipeline that grades its own results and automatically corrects poor retrievals before generating. Combines LangGraph state machines with a web search fallback.
RAG
Give your RAG system the ability to reason, act, and self-correct. Build a ReAct agent that decides when to retrieve, when to search the web, and when to compute — using LangGraph and custom tools.
RAG
Go beyond flat vector search. Build a knowledge graph from your documents, run multi-hop queries that traverse entity relationships, and combine graph traversal with vector retrieval for richer answers.
RAG
Stop guessing whether your RAG pipeline is good. Use RAGAS to measure faithfulness, answer relevancy, context precision and recall — and learn to interpret the metrics to drive concrete improvements.
RAG
Add token-by-token streaming to your RAG pipeline. Server-Sent Events from FastAPI, a React client with AbortController, and the nuances of streaming structured retrieval responses.
RAG
Build a multi-tenant RAG API where each tenant's documents are fully isolated. FastAPI, per-tenant ChromaDB collections, JWT authentication, and a clean REST interface for document ingestion and querying.
RAG
Strip away every framework and build RAG with raw Python — numpy cosine similarity, direct OpenAI calls, no LangChain. Understand exactly what the abstractions hide before you use them.
RAG
Build a production-ready document Q&A pipeline from scratch. Load PDFs, Word files and web pages, split them into semantic chunks, embed with open-source models, store in a vector DB, and answer questions with full source attribution — zero paid APIs required.
RAG
RAG systems don’t fail at retrieval-they fail at context. As conversations grow, what enters the context window becomes the bottleneck. A context engine manages memory, compression, re-ranking, and token limits, making LLM systems reliable at scale.
AI
Agentic AI transforms automation by enabling autonomous agents to reason, plan, and act independently. By combining advanced models, real-time data, and adaptive memory, it supports dynamic workflows, enhances decision-making, and drives efficient, scalable, and intelligent systems.