LangChain
Building a RAG Pipeline with LangChain and Amazon Bedrock
In this post, you'll learn how you can set up and integrate Amazon Bedrock with your LangChain app for an end-to-end RAG pipeline
LangChain
How to extract metadata from PDF and convert to JSON using LangChain and GPT
A task like converting a PDF to JSON used to be complicated but can now be done in a few minutes. In this post, we're going to see how LangChain and GPT can help us achieve this.
LangChain
LangChain vs Semantic Kernel: Which framework is better for your RAG app?
Confused between LangChain and Semantic Kernel? This short post will help you choose the most suitable framework for your RAG app.
LangChain
What is the difference between Embedchain and LangChain?
Another day, another data framework... We can't catch a break! One of the latest kids on the block, Embedchain seems to be gaining popularity, so I took it for a spin and wrote this post to show you how it's different from one of the most popular data frameworks out there, LangChain.
LangChain
Just getting started with LangChain? Here's everything you need to know
Are you just getting started with LangChain? You've come to the right place! This post covers everything you need to know to get started quickly.
LangChain
Chroma: One of the best vector databases to use with LangChain for storing embeddings
The LangChain framework allows you to build a RAG app easily. In this tutorial, see how you can pair it with a great storage option for your vector embeddings using the open-source Chroma DB.
LangChain
A comparison between OpenAI GPTs and its open-source alternative LangChain OpenGPTs
OpenAI GPTs are trending and for a good reason. Anyone can now create their own custom ChatGPT and publish it for everyone to use. But did you know that there's an open-source alternative that does the same thing without the OpenAI platform lock-in?
Programming
How to use Milvus vector database to store and retrieve LLM embeddings using LangChain
This tutorial will guide you through the process of setting up the Milvus Vector Database and querying stored embeddings using the LangChain framework.
LangChain
How to fix the ModuleNotFoundError, AttributeError, and NotImplementedError exceptions in LangChain
Whether you're playing around with LangChain or building the next big RAG LLM app, you'll probably run into one of these issues at some point.
LangChain
LangChain Templates: A powerful collection of customizable chains and agents
Editing and customizing the internals of LangChain agents and chains is not a pleasant experience. LangChain Templates solves this issue by exposing the inner workings as downloadable templates.
General
An introduction to RAG tools and frameworks: Haystack, LangChain, and LlamaIndex
Today, we're going to take a look at Haystack, LangChain, and LlamaIndex and how these tools make it easy for us to build RAG apps.
Programming
How to use LangChain output parsers to structure large language models responses
If you're wondering how you can convert the text returned by an LLM to a Pydantic (JSON) model in your Python app, this post is for you.
Machine Learning
What is the difference between fine-tuning and vector embeddings
In this post, we're going to define what fine-tuning and vector embeddings are and look at which approach could be better suited for your specific use case.
Programming
How to Integrate and Handle LLM Memory using LangChain
LLMs are stateless, meaning they do not have memory that lets them keep track of conversations. However, using LangChain we'll see how to integrate and manage memory easily.
Programming
How to convert natural language text to SQL using LangChain
In this post, we're going to look at how you can use LangChain and OpenAI's GPT model to convert natural language queries to SQL, execute them, and get an answer from your database in plain English.
Programming
What is the difference between LlamaIndex and LangChain
In this post, we'll briefly compare LangChain and LlamaIndex and look at the key features of each solution so that you can choose which is the best suited for your next LLM-powered app.