Siddhartha Shrestha’s Post

Super important course to understand the basics of LangChain.

a quick revision about Langchain components which talks about: 1-models and Output Parser 2-Memories and different types of memory in langchain to build powerfull chatbot 3-chains and chains types 5-question answering and QA evaluation 6-finally agent and tools to empower llm ability #langchain , #llm

A brief introduction to Langchain, recommended to anyone who's interested in building LLM Applications!

Advanced #RAG: Multi-Query Retriever Approach! In this brief article, we will explore how to utilize the MultiQueryRetriever method found in the LangChain framework. The code presented here is sourced from an example provided by LangChain. Finally, we will observe and review the various steps and outputs generated on the LangSmith platform. This approach stands as a significant enhancement over the conventional RAG strategy, primarily because it doesn’t rely on a singular set of documents retrieved for an initial query to produce the final output. Instead, it harnesses the power of diversity by retrieving multiple sets of documents based on varied interpretations of the original query. This is particularly advantageous when dealing with queries that are vague or imprecisely formulated. Read the full article by Kamal Dhungana: https://lnkd.in/gn-VZwVR

Completed the Langchain for LLM Application Development. In this sort of course, I have learned different components. - Langchain: Prompts. - Langchain: Memory. - Langchain: Chains. - Langchain: Question and Answer. - Langchain: Agents. - Langchain: Evaluation.

The exploration of LangChain has significantly enhanced my approach to search agent development, facilitating the handling of complex queries and improving the overall user experience. The creation of a search agent using LangChain operates akin to a digital assistant that effectively interprets and responds to user inquiries. Each line of code within LangChain represents a commitment to optimizing information retrieval processes, resulting in more intuitive search functionalities. Engaging with LangChain has reshaped my perspective on search agents, fostering innovation and creativity in coding practices. Ultimately, the process of developing a search agent in LangChain is reminiscent of assembling a puzzle, where each component clarifies user requirements and preferences. #LangChain #SearchAgent #TechInnovation #UserExperience

In this article, we discuss one such framework known as retrieval augmented generation (RAG) along with some tools and a framework called LangChain. Check it out! #SingleStore

In this article, we discuss one such framework known as retrieval augmented generation (RAG) along with some tools and a framework called LangChain. Check it out! #SingleStore

I am removing LangChain in most of my projects! The initial documentation was not so bad, now they made it very complicated to know what you can do and how to do it. Even when you find what you need there is a 50:50 chance it's outdated or what is exposed does not work. There are too many kwargs and you never know what you can pass in as arguments. What about you? Still using it?

Are you a developer for LangChain? This question often comes to mind for many developers. Me too 🤗 What is Distinction between Chains vs Agents: * Chains: Predetermined string of actions * Agents: Using the LLM to decide *which* actions to take