LangChain’s Post

🤔How to use MyScale within LangChain? Follow this comprehensive guide: https://lnkd.in/gFe2n5HX 🤩You may notice that the default index type has been updated to MSTG, ensuring fast, efficient, and accurate performance across various filtered search ratios. Explore the power of MyScale within LangChain now! #TechIntegration #vectordatabase

Getting started with LLMs and LangChain? Here's a great overview on building a simple Q&A system with Astra DB and LangChain. More resources 👉 https://dtsx.io/3y9ygCG https://dtsx.io/3F3eB7Y

💙 RECIPE: Get ERC-20 token transfer logs with EthersJS 🛠️ Learn how to use EthersJS to fetch ERC-20 token transfer logs with Chainstack nodes in this recipe. Perfect for rookie Web3 developers and analysts eager to track token movements and transaction data. Full recipe 👇 https://bit.ly/3Tc83M5

So you've built a LangChain chain or agent - what next? How do you deploy it? This great blog by @GitMaxd walks through how to do this with LangServe 📃Your first A.I. API endpoint with 🦜LangServe https://lnkd.in/g36AB6wN

In this presentation, Kyryl Truskovskyi explores the confluence of two major trends: the adoption of open-source solutions and the development of LLM-based solutions. Kyryl shares insights on building a stack for LLM and RAG projects using open-source solutions, including Dagster, MinIO, Weaviate, LangChain, and more. https://t.ly/hGli6

How to choose a semantic layer? It can be daunting, but there are eight key considerations to keep in mind as you pick the best one for your organization! Download the complete Practical Guide here: https://bit.ly/3JWSSiX You’ll learn how a semantic layer delivers massive ROI with order of magnitude better query performance, concurrency, cost management, and ease of use.

🐀RAGOps: Advanced Retrieval Strategies with LangChain, Langsmith and Supabase. Implementing advanced retrieval strategies in a notebook is one thing 📈Taking them to production requires a significant ops component Great overview from @Prince_Canuma https://lnkd.in/gUVJ3kcE

LangChain simplifies the creation of advanced RAG pipelines, while Unstructured processes all kinds of unstructured data and delivers it right into your preferred vector store. 🤝 Together, they provide a powerful toolkit for building any RAG application you can think of!

I'm preparing a course for credo-ts (previously Hyperledger Aries) It will be bundled into the existing Hyperledger Fabric training course https://training.kfs.es/ What would you like to learn about Decentralized Identity?

🌟 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