Skip to content
All tags

#langchain

5 posts

Reading NCCU Yen-Lung Tsai Generative AI, L08: Retrieval-Augmented Generation (RAG) — Chunk the Text, Embed It, Put the Closest Pieces Back in the Prompt

L06 said a prompt is two things: correct information and clear instructions. RAG lets the computer fetch the information part on its own. Split your documents into chunks, turn chunks and questions into feature vectors with the same model fθ, find the closest few chunks, and drop them into a template: 'Answer {question} based on {retrieved_chunks}.' The code comes in two notebooks: Demo06a builds a vector database with LangChain and FAISS and zips it as faiss_db.zip; Demo06b loads it back, connects an LLM, and wraps it in Gradio. The week-8 assignment is to do the same with your own data.

NTHU NLP RAG Labs + HW4: Building a Cat-Facts RAG Two Ways, with LangChain and by Hand

Each of the two RAG TA sessions builds one version. The first installs Ollama on Colab to run llama3.2:1b and wires up a minimal RAG with LangChain's Chroma, MMR, and retrieval chain. The second uses LangChain only for data prep and writes the rest by hand: chunking, text and vector stores, hybrid BM25 + cosine retrieval merged with RRF, then generation with Llama-3.2-1B-Instruct. HW4 applies the first session's skeleton to 150 cat facts and 150 GPT-5-generated QA pairs. The generator must be Llama3.2-1b and the embedding model jina-embeddings-v2-base-en, and you report recall@1, recall@5, and exact match. Code is 45% of the grade and the report 55%; the report analyzes how prompts, data format, document order, and counterfactual information change the results.

aideep-dive

LangChain v1 Agents: create_agent, Middleware, and the LangGraph Runtime

LangChain v1 provides a high-level agent loop through create_agent, runs it on LangGraph, and treats tools, structured output, and middleware as its extension boundaries.

Local Deep Research Walkthrough: A Privacy-First Deep Research Agent

Local Deep Research is a privacy-first deep research agent built on LangChain + LangGraph, integrating 20+ search engines and 30+ research strategies. Its flagship langgraph_agent_strategy takes the LLM-autonomous tool-calling approach, offering a fundamentally different paradigm from fixed-pipeline RAG graphs.

techproject

DeerFlow: ByteDance's Open-Source Super Agent Harness for Long-Running Research Tasks

DeerFlow is ByteDance's open-source Super Agent Harness built on Python 3.12 + LangGraph. It orchestrates long-running tasks through sandboxes, long-term memory, sub-agents, skills, and a messaging gateway. It hit #1 on GitHub Trending in February 2026, now surpassing 63,000 stars, with support for Telegram/Slack/Feishu, Claude Code integration, and multiple search backends.