Mike Taylor’s Post

Unlock the Secret to Smarter Data Management with LangChain! Ever wondered how to efficiently manage and index your data? Imagine a world where data ingestion is seamless and duplicates are a thing of the past. Join us as we unravel the power of LangChain's Indexing API, making your data pipelines not just smarter, but transformative. #openai #llms #generativeai

🚀 Great news! My latest course on Pluralsight, "LangChain Techniques for Complex Datasets," is now live! 🚀 I delve into advanced LangChain techniques that can be used for tricky data retrieval tasks, strategies for working with large datasets, and much more. Here's a short preview of what's covered. You can find a link to the whole course in the comments. #LangChain #DataScience #Pluralsight #RAG #LLMs #byteSolid

Just completed a refresher on langchain with Deeplearning.ai

Here is a blog on Pydantic Output Parsers, yet another type of output parser worth exploring. #llm #genai #langchain

A little coursework during the holiday break. 😀 I love the power of LLMs! ❤️ Achievement unlocked. I highly recommend courses on LangChain by Harrison Chase and Andrew Ng on DeepLearning.AI. Be sure to connect with me if you need help building intelligent chatbots. Look for future Workshop Wednesdays led by me at Applied AI. https://lnkd.in/g4txGRV7 Recursive Awesome Lab651 | Custom Software Development & Process #llms #largelanguagemodels #langchain #chatbot

Completed the DeepLearning.AI short course on LangChain Chat with Your Data!

🐍📰 Build an LLM RAG Chatbot With LangChain In this step-by-step tutorial, you'll leverage LLMs to build your own retrieval-augmented generation (RAG) chatbot using synthetic data with LangChain and Neo4j https://buff.ly/3T1zM0C

First problem encountered. Trying to deploy MinIO as the central data lake of my self-hosted analytics platform, but the default deployment is too much memory for my little remote machine. Will have to specify lower resource allocation for now and then modify the Helm charts once I get a new node.

Your guide to LLM agent reference architecture is here! We teamed up with LangChain to provide: ◆ Common Gen AI design patterns and use cases ◆ In-depth architectural examples ◆ Important considerations to keep in mind Grab your copy https://dtsx.io/3vP8hiP #llm, #datastax , #langchain , #genai

Learnt how to implement Retrieval-Augmented Generation (RAG) for LLMs on diverse datasets, and then built a complete RAG pipeline that involved reading datasets, managing the orchestrator, generating embeddings, managing the database, feeding data to the LLM, and finally receiving answers.