Linsan Yang’s Post

🚀 Building a LangChain RAG pipeline with DSPy enhances LLMs by adding context for more accurate responses without altering model weights. DSPy optimizes LLMs by treating them as programmable modules, and integrating with LangChain allows automated prompt engineering for improved performance. Perfect for advanced, flexible LLM workflows! 🎯

LangChain Series. Part 03: how to chain components for complex tasks using LangChain

We've added support for metadata prefiltering in LangChain for Neo4j vector index. Under the hood we use parallel runtime in a combination with exact nearest neighbor calculation! Let me know how you are using metadata filtering in your applications!

LangChain vs LangGraph: A Tale of Two Frameworks https://lnkd.in/d4MWHneQ

🌟 Facts: 1️⃣ #RAG applications can be built using LangChain. 2️⃣ #LangChain simplifies the development process by integrating most #LLM providers and databases in a unified interface. 3️⃣ The #Neo4j vector index in the LangChain library allows #devs to easily implement advanced vector indexing for efficient storage and retrieval of vector embeddings. Tomaz Bratanic will show us all the above by building a simple #RAG application that can effectively answer questions based on information from a Wikipedia article. Take a look and leave your comments if you've tried it! https://bit.ly/4dyPkT9

I was into innovative concepts around knowledge graphs, RAG, and LangChain. Inspired, I tried an idea for a Retrieval Augmented Generation on a Graph Database. I developed a demo showcasing a question-answering system utilizing Neo4j and LangChain to interact with a knowledge graph. Noteworthy, the entire demo was crafted using an open-source LLM. #Innovation #KnowledgeGraphs #Neo4j #LangChain #QuestionAnswering #OpenSource #LLM

Being a Stage 2 Rollup from Day 1: Etherlink’s Journey https://lnkd.in/gmBA_ZTY

LangChain Fundamentals: Build your First Chain https://lnkd.in/exjHYgpF

Feature walkthrough 👉 Learn how to save time and tokens in Langflow with the Freeze Path feature. 🎥 David Jones-Gilardi https://dtsx.io/42dFi6G

Comparative Analysis of Leading Agentic Frameworks: LangChain, Haystack, AutoGen, CrewAI, Semantic Kernel, and LlamaIndex https://lnkd.in/gMHantKn #llm #AgenticFramework #Agents