Brief

Multiple dev.to articles detail how to build AI agents using LangGraph, a workflow system from LangChain. The posts provide templates for common agent patterns, including Retrieval-Augmented Generation (RAG) for document querying, multi-tool agents that can plan and execute tasks, and human-in-the-loop workflows requiring user review. These templates illustrate LangGraph's architecture with nodes, edges, and state management for creating complex, stateful AI applications. AI

Embeddings are a core concept in AI, transforming text and other data into numerical representations that capture meaning. These numerical vectors allow AI models to understand relationships between words and concepts, enabling functionalities like semantic search and Retrieval-Augmented Generation (RAG). While vector databases like Pinecone, Weaviate, and Chroma are commonly used for storing and querying these embeddings, alternative approaches like BM25 retrieval with tools such as Meilisearch can also be effective for specific use cases, offering simpler operation and lower costs. AI

Retrieval-Augmented Generation (RAG) systems are a crucial technique for enhancing Large Language Models (LLMs) by allowing them to access and utilize external, up-to-date information. RAG addresses LLM limitations such as knowledge cutoffs and context window limits by retrieving relevant data before generating a response. This approach is distinct from fine-tuning, which modifies the model's behavior rather than its knowledge base. Building a RAG system involves two main pipelines: an ingestion pipeline for preparing and storing data, and a retrieval pipeline that fetches context for each user query. AI