RAG - Tag - Daily Deep Think https://blog.baifan.site/en/tags/rag/ RAG - Tag - Daily Deep Think Hugo -- gohugo.ioenblog@baifan.site (ByF) blog@baifan.site (ByF)Tue, 03 Feb 2026 14:00:00 +0800 TencentDB Agent Memory: Real Layered Memory for AI Agents https://blog.baifan.site/en/tencentdb-agent-memory-deep-dive/ Tue, 03 Feb 2026 14:00:00 +0800 ByF https://blog.baifan.site/en/tencentdb-agent-memory-deep-dive/

If you’ve spent serious time with AI agents, these scenarios will feel familiar:

  • Re-explaining the project background, coding conventions, and output format preferences in every new session
  • Halfway through a long task, the context fills up with tool logs and the agent starts “forgetting” earlier instructions
  • Wanting the agent to learn your working habits, but having no way to do it except hand-writing a system prompt every time

Memory isn’t about making the AI store everything — it’s about making sure humans never have to repeat themselves. That’s the core idea behind TencentDB Agent Memory.

]]> RAG Complete Guide: Build a Local RAG System from Scratch https://blog.baifan.site/en/rag-system-complete-guide-langchain-ollama-pgvector/ Wed, 27 Aug 2025 14:00:00 +0800 ByF https://blog.baifan.site/en/rag-system-complete-guide-langchain-ollama-pgvector/

Building a Local RAG with LangChain + Ollama + pgvector: A Complete Hands-On Walkthrough from 0 to 1 (with uv Dependency Management and an Interview Guide)

This post is a directly actionable document. Follow it top to bottom and you will build a local RAG (retrieval-augmented generation) system from scratch and understand the key concepts and code. All core scripts come with comments to make learning and interview review easier.

]]> Prompt Engineering: From Prompts to Context Engineering https://blog.baifan.site/en/prompt-engineering-context-management-complete-guide/ Wed, 13 Aug 2025 14:00:00 +0800 ByF https://blog.baifan.site/en/prompt-engineering-context-management-complete-guide/

AI Tutorial: Prompt Engineering

Prompt engineering focuses on designing, optimizing, and strategizing prompts — helping users mobilize the capabilities of large language models more effectively, and pushing their adoption across real-world scenarios and research domains.

A prompt is simply this: you use natural language to tell the model what to do, how to do it, what it may do, and what it must not do. That is all there is to it.

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