Recruiting Coordinator | Talent Acquisition Professional | Candidate Experience Specialist |
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
Future AI Engineer 🤖 | Dreaming in Code, Waking up in Algorithms | Nurturing Neural Networks | Building Tomorrow's Intelligence Today | #AIEngineer
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
https://lnkd.in/gwfbB7_N Welcome to our comprehensive guide on LangChain! In this video, we'll dive deep into the fascinating world of LangChain, exploring its in-built tools and demonstrating how to use them effectively with the agent. 🔍 What You'll Learn: Introduction to LangChain: Understand the basics of LangChain and its applications. Exploring In-Built Tools: A detailed look at the powerful tools built into LangChain with Agent. Practical Examples: Real-world examples to showcase the practical applications of LangChain's tools and agents. 📌 Key Highlights: Simplify your workflow with LangChain's intuitive tools. Enhance your projects with seamless agent integration. Learn from practical examples and real-world scenarios. If you find this video helpful, please give it a thumbs up, share it with your network, and subscribe to our channel for more tutorials and guides. Don’t forget to hit the bell icon to get notified whenever we upload new content! 💬 Let's Connect: If you have any questions or need further assistance, drop a comment below, and we'll be happy to help. Join our community to engage with other LangChain enthusiasts and stay updated on the latest trends. Thank you for watching, and let's get started on mastering LangChain! Git Hub Link https://lnkd.in/gGQ8gxcq #LangChain #InBuiltTools #AgentIntegration #TechTutorial #WorkflowOptimization
If you are looking for a great intro into LangChain, I can recommend you this article by Thirunavukkarasu. Agents are going to be the next big topic in 2025, so get ready to deep dive into libraries like LangChain, LangGraph & Co. https://lnkd.in/ec85KihC
Data Scientist @ e& Egypt | Machine Learning | Deep Learning | LLMs | NLP | Computer Vision | AWS Sagemaker | MLOPS
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
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
In LangChain, text splitters are used to divide large chunks of text into smaller pieces, making it easier to process, analyze, or index the text. Different types of text splitters are suited for different use cases, depending on the structure and nature of the text. #splitters #langchain
I’m thrilled to share my latest blog, “Some of the Useful Functionality Provided by Langchain”! 🎉 In this post, I dive into the functionalities of LangChain that I find most valuable, complete with practical code snippets. From reusing prompts to real-time responses, I’ve explored features that can truly enhance your Langchain experience. I’d love to hear your thoughts and feedback! If you’ve come across any other Langchain features that you find incredibly useful, please share them in the comments. Let’s learn and grow together! 🚀 #Langchain #LangchainAgents #LangchainChain #LLMApplication #LLM #LLMProduct #OpenAI #StructuredOutput #StreamingResponse #Batching
LangChain Memory Types: #LangChain
Data Scientist | Generative AI | Data Engineer | Full-stack Software Engineer | Team Lead | Neo4j Certified Professional | Mentor
🚀 Exploring Langchain and its Powerful Components 🌐 As someone deeply involved in software engineering and data science, I've been diving into the capabilities of Langchain—a framework that's reshaping how we use language models by integrating them with external tools and agents. #Langchain #LLM #AItools #Agents #SoftwareEngineering #DataScience #AIinAction #MachineLearning