Sunny Savita
Sunny Savita
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LangGraph:13 Corrective RAG for Real Time AI Application #llm #genai #aiagents #ai #langchain #genai
Discover how to implement Corrective Retrieval-Augmented Generation (RAG) using LangGraph for real-time AI applications. This tutorial covers advanced RAG strategies to enhance accuracy, efficiency, and reliability in LLM-powered solutions. Perfect for AI enthusiasts and professionals! #LLM #GenAI
#langchain LangGraph #AIagents #StructuredOutput #GenerativeAI #LangChain #AIautomation #llm #embedding #ai #futureai #generativeai #genai #textgeneration #ragapp #langchain #programminglogic #python #chatbot #openai #gpt #langchainj #rag #crossencoder #transformers #multiretriever #ragfusion #advancerag #llamaindex #langchain #gemini #google #rag #LCEL #langgraph #aiagent #react #aiagents
Don't miss out; learn with me!
P.S. Don't forget to like and subscribe for more AI content!
End-to-End-Langgraph-Course: github.com/sunnysavita10/langgraph-end-to-end
Multimodel RAG Playlis: th-cam.com/video/7CXJWnHI05w/w-d-xo.html&pp=gAQBiAQB
RAG detailed Playlist: th-cam.com/video/wTVTkOb3SZc/w-d-xo.html&pp=gAQBiAQB
GenAI Foundation Playlist: th-cam.com/video/ajWheP8ZD70/w-d-xo.html&pp=gAQBiAQB
Connect with me on Social Media-
LinkedIn : www.linkedin.com/in/sunny-savita/
One to One Call: topmate.io/sunny_savita10
GitHub : github.com/sunnysavita10
Telegram : t.me/aimldlds
มุมมอง: 772

วีดีโอ

LangGraph:12 LangGraph Agent with Human-In-The-Loop, Checkpoints & Breakpoints #llm #genai #aiagents44:22LangGraph:12 LangGraph Agent with Human-In-The-Loop, Checkpoints & Breakpoints #llm #genai #aiagents
LangGraph:12 LangGraph Agent with Human-In-The-Loop, Checkpoints & Breakpoints #llm #genai #aiagents
มุมมอง 65914 วันที่ผ่านมา
we'll explore how to enhance your AI agents using LangGraph with a Human-in-the-Loop (HITL) approach. You'll learn how to incorporate human intervention to refine agent responses, ensuring better accuracy and context understanding. We'll also dive into capturing user feedback to continuously improve model performance. This powerful combination allows you to build AI systems that are not only mo...
LangGraph:11 Building Finance Bot with LangGraph's ReAct Agent #llm #genai #aiagents #langchain #ai30:54LangGraph:11 Building Finance Bot with LangGraph's ReAct Agent #llm #genai #aiagents #langchain #ai
LangGraph:11 Building Finance Bot with LangGraph's ReAct Agent #llm #genai #aiagents #langchain #ai
มุมมอง 90114 วันที่ผ่านมา
In this video, we build a powerful Finance Bot using LangGraph's ReAct Agent. Learn how to integrate real-time financial data, perform complex calculations, and provide intelligent insights for financial queries. Perfect for those exploring AI agents, LangChain, and automating financial tasks with generative AI. #langchain LangGraph #AIagents #StructuredOutput #GenerativeAI #LangChain #AIautoma...
LangGraph:10 Structured Output with LangGraph Agents #llm #genai #aiagents #langchain #ai26:18LangGraph:10 Structured Output with LangGraph Agents #llm #genai #aiagents #langchain #ai
LangGraph:10 Structured Output with LangGraph Agents #llm #genai #aiagents #langchain #ai
มุมมอง 71721 วันที่ผ่านมา
Unlocking the power of LangGraph Agents with structured output to build efficient solutions! In this video, we'll dive deep into leveraging LangGraph Agents for generating structured responses that can seamlessly integrate with real-world applications. Whether you're working on chatbots, automation tools, or complex AI workflows, mastering structured output is key to optimizing performance. We'...
LangGraph:09 End to End Chatbot using LangGraph With Memory #llm #genai #aiagents #langchain #ai46:03LangGraph:09 End to End Chatbot using LangGraph With Memory #llm #genai #aiagents #langchain #ai
LangGraph:09 End to End Chatbot using LangGraph With Memory #llm #genai #aiagents #langchain #ai
มุมมอง 1.4Kหลายเดือนก่อน
In this video, we dive into building a fully functional chatbot using LangGraph, enhanced with memory capabilities for improved user interactions. You'll learn how to set up the chatbot from scratch, implement memory management to retain user context, and utilize advanced features to make your chatbot more conversational and intuitive. We’ll cover: An overview of LangGraph and its architecture ...
LangGraph:08 Adding RAG to LangGraph Workflow | LangGraph Deep Dive #llm #genai #aiagents #langchain37:50LangGraph:08 Adding RAG to LangGraph Workflow | LangGraph Deep Dive #llm #genai #aiagents #langchain
LangGraph:08 Adding RAG to LangGraph Workflow | LangGraph Deep Dive #llm #genai #aiagents #langchain
มุมมอง 1.1Kหลายเดือนก่อน
In this session, we explore how to implement Retrieval-Augmented Generation (RAG) using the LangGraph framework. We’ll break down the RAG workflow, showing how LangGraph can be leveraged to enable efficient, dynamic, and accurate information retrieval paired with powerful generative AI responses. Key points covered: LangGraph for RAG Workflows: How LangGraph’s graph-based structure is ideal for...
LangGraph:07 Code LangGraph From Scratch | LangGraph Deep Dive #llm #genai #ai #aiagents #langchain27:10LangGraph:07 Code LangGraph From Scratch | LangGraph Deep Dive #llm #genai #ai #aiagents #langchain
LangGraph:07 Code LangGraph From Scratch | LangGraph Deep Dive #llm #genai #ai #aiagents #langchain
มุมมอง 818หลายเดือนก่อน
In this session, we’re taking a hands-on approach by building LangGraph from scratch! Get ready to dive into the heart of LangGraph’s codebase, learning the intricacies of its structure and understanding the logic that drives its powerful, graph-based workflows for LLMs and AI agents. Key topics include: What is LangGraph? Understanding its core purpose and how it extends LangChain's capabiliti...
LangGraph:06 Detailed Introduction of LangGraph #llm #genai #ai #aiagents19:49LangGraph:06 Detailed Introduction of LangGraph #llm #genai #ai #aiagents
LangGraph:06 Detailed Introduction of LangGraph #llm #genai #ai #aiagents
มุมมอง 984หลายเดือนก่อน
In this session, we'll dive deep into the LangGraph framework, providing a comprehensive overview of its architecture, key components, and its role in creating robust, scalable workflows. We'll explore how LangGraph enhances traditional LLM operations by introducing graph-based structures that enable advanced data flow, decision-making, and multi-agent system. Key topics include: What is LangGr...
LangGraph:05 Building AI Agent from Scratch Using Python with Custom Tool #llm #genai #ai #aiagents45:31LangGraph:05 Building AI Agent from Scratch Using Python with Custom Tool #llm #genai #ai #aiagents
LangGraph:05 Building AI Agent from Scratch Using Python with Custom Tool #llm #genai #ai #aiagents
มุมมอง 1.8Kหลายเดือนก่อน
In this video, we dive deep into building an AI agent from scratch using Python! Whether you're an AI enthusiast or a developer looking to integrate AI-powered tools into your projects, this tutorial will guide you through every step. We'll cover: How to build a custom AI agent from the ground up Integrating custom tools that enhance your agent's capabilities Key concepts like decision-making, ...
LangGraph:04 LangChain ReAct Agent with Custom Tool and Self-Ask Agent with Search | AI Agents #llm52:15LangGraph:04 LangChain ReAct Agent with Custom Tool and Self-Ask Agent with Search | AI Agents #llm
LangGraph:04 LangChain ReAct Agent with Custom Tool and Self-Ask Agent with Search | AI Agents #llm
มุมมอง 2Kหลายเดือนก่อน
In this video, we dive deep into the world of LangChain, focusing on two powerful agent types: the ReAct Agent with Custom Tool and the Self-Ask Agent with Search. Learn how to leverage these agents to build more intelligent, interactive, and efficient AI systems. ReAct Agent: We’ll explore how to create a custom tool within LangChain, enabling agents to react dynamically to the tasks at hand. ...
LangGraph:03 LangChain AI Agents | Tools | Tool Calling Agent | ReAct Agents #genai #llm #aiagent39:15LangGraph:03 LangChain AI Agents | Tools | Tool Calling Agent | ReAct Agents #genai #llm #aiagent
LangGraph:03 LangChain AI Agents | Tools | Tool Calling Agent | ReAct Agents #genai #llm #aiagent
มุมมอง 1.9Kหลายเดือนก่อน
🚀 Welcome to the End-to-End LangGraph Course Syllabus Introduction! Explore LangChain's powerful agents class! Learn how to implement LangChain agents, tool calling agents, and ReAct agents to enhance AI workflows in this essential tutorial. Perfect for AI enthusiasts and developers! #llm #embedding #ai #futureai #generativeai #genai #textgeneration #ragapp #langchain #programminglogic #python ...
LangGraph:02 LangGraph Course Pre-requist | AI Assistant | RAG I LCEL | Tool & Agent #ai #genai #llm42:42LangGraph:02 LangGraph Course Pre-requist | AI Assistant | RAG I LCEL | Tool & Agent #ai #genai #llm
LangGraph:02 LangGraph Course Pre-requist | AI Assistant | RAG I LCEL | Tool & Agent #ai #genai #llm
มุมมอง 2.5K2 หลายเดือนก่อน
🚀 Welcome to the End-to-End LangGraph Course Syllabus Introduction! Welcome to the second video of the LangGraph course! 🚀 In this session, we'll dive deep into the prerequisites you need to master LangGraph, exploring its potential for building AI-powered applications. Whether you're interested in creating simple AI assistants, implementing Retrieval-Augmented Generation (RAG), or chaining com...
LangGraph 01: Syllabus Introduction of End to End LangGraph Course | LangChain #ai #genai #llm10:35LangGraph 01: Syllabus Introduction of End to End LangGraph Course | LangChain #ai #genai #llm
LangGraph 01: Syllabus Introduction of End to End LangGraph Course | LangChain #ai #genai #llm
มุมมอง 3K2 หลายเดือนก่อน
🚀 Welcome to the End-to-End LangGraph Course Syllabus Introduction! Are you ready to master LangGraph and take your AI and LLM skills to the next level? In this video, we’ll walk you through the comprehensive syllabus of our End-to-End LangGraph Course, designed for developers, data scientists, and AI enthusiasts. You'll get a deep dive into LangGraph essentials, learn how to build advanced RAG...
LangChain Expression language(LCEL) for Chaining the Components | All Runnables | Async & Streaming52:49LangChain Expression language(LCEL) for Chaining the Components | All Runnables | Async & Streaming
LangChain Expression language(LCEL) for Chaining the Components | All Runnables | Async & Streaming
มุมมอง 1.7K2 หลายเดือนก่อน
Explore the power of LangChain Expression Language (LCEL) in this comprehensive guide! We'll dive into how LCEL enables seamless chaining of components, making your AI workflows more efficient. Learn about Runnables, async operations, and how to implement streaming for real-time performance. Whether you're a beginner or an advanced user, this video breaks down the essentials of LCEL and how it ...
Langchain Conversation Summary Memory vs Conversation Summary Buffer Memory | Chatbot #ai #llm #rag44:12Langchain Conversation Summary Memory vs Conversation Summary Buffer Memory | Chatbot #ai #llm #rag
Langchain Conversation Summary Memory vs Conversation Summary Buffer Memory | Chatbot #ai #llm #rag
มุมมอง 7812 หลายเดือนก่อน
Langchain Conversation Summary Memory vs Conversation Summary Buffer Memory | Chatbot #ai #llm #rag
Langchain Conversation Entity Memory | Langchain Memory Class | Chat History#ai #llm #yt #chatbot33:00Langchain Conversation Entity Memory | Langchain Memory Class | Chat History#ai #llm #yt #chatbot
Langchain Conversation Entity Memory | Langchain Memory Class | Chat History#ai #llm #yt #chatbot
มุมมอง 7322 หลายเดือนก่อน
Langchain Conversation Entity Memory | Langchain Memory Class | Chat History#ai #llm #yt #chatbot
Langchain Conversation Buffer Memory vs Conversation Buffer Window Memory | Chat History#ai #llm #yt40:45Langchain Conversation Buffer Memory vs Conversation Buffer Window Memory | Chat History#ai #llm #yt
Langchain Conversation Buffer Memory vs Conversation Buffer Window Memory | Chat History#ai #llm #yt
มุมมอง 1.2K2 หลายเดือนก่อน
Langchain Conversation Buffer Memory vs Conversation Buffer Window Memory | Chat History#ai #llm #yt
RAG Based Chatbot With Memory(Chat History) | Creating History Aware Retriever | Langchain #ai #rag37:27RAG Based Chatbot With Memory(Chat History) | Creating History Aware Retriever | Langchain #ai #rag
RAG Based Chatbot With Memory(Chat History) | Creating History Aware Retriever | Langchain #ai #rag
มุมมอง 5K3 หลายเดือนก่อน
RAG Based Chatbot With Memory(Chat History) | Creating History Aware Retriever | Langchain #ai #rag
Chatbot Using @LangChain With Memory(Chat History) | LangChain Core | LangSmith1:07:50Chatbot Using @LangChain With Memory(Chat History) | LangChain Core | LangSmith
Chatbot Using @LangChain With Memory(Chat History) | LangChain Core | LangSmith
มุมมอง 4.3K3 หลายเดือนก่อน
Chatbot Using @LangChain With Memory(Chat History) | LangChain Core | LangSmith
Complete @LangChain Essential in 1 shot | LangChain Core | LangServe | LangGraph | LangSmith | Agent1:55:49Complete @LangChain Essential in 1 shot | LangChain Core | LangServe | LangGraph | LangSmith | Agent
Complete @LangChain Essential in 1 shot | LangChain Core | LangServe | LangGraph | LangSmith | Agent
มุมมอง 4.1K3 หลายเดือนก่อน
Complete @LangChain Essential in 1 shot | LangChain Core | LangServe | LangGraph | LangSmith | Agent
Advance RAG 12- Powerful RAG with Merger Retriever and Hypothetical Document Embeddings(HyDE) #ai31:53Advance RAG 12- Powerful RAG with Merger Retriever and Hypothetical Document Embeddings(HyDE) #ai
Advance RAG 12- Powerful RAG with Merger Retriever and Hypothetical Document Embeddings(HyDE) #ai
มุมมอง 1.1K3 หลายเดือนก่อน
Advance RAG 12- Powerful RAG with Merger Retriever and Hypothetical Document Embeddings(HyDE) #ai
Advance RAG 11- Powerful RAG with Sentence Window Retriever using @LlamaIndex and @qdrant #ai #llm39:47Advance RAG 11- Powerful RAG with Sentence Window Retriever using @LlamaIndex and @qdrant #ai #llm
Advance RAG 11- Powerful RAG with Sentence Window Retriever using @LlamaIndex and @qdrant #ai #llm
มุมมอง 1.4K3 หลายเดือนก่อน
Advance RAG 11- Powerful RAG with Sentence Window Retriever using @LlamaIndex and @qdrant #ai #llm
End-to-End RAG With Llama 3.1, Langchain, FAISS and OLlama #ai #llm #llama #huggingface38:04End-to-End RAG With Llama 3.1, Langchain, FAISS and OLlama #ai #llm #llama #huggingface
End-to-End RAG With Llama 3.1, Langchain, FAISS and OLlama #ai #llm #llama #huggingface
มุมมอง 6K3 หลายเดือนก่อน
End-to-End RAG With Llama 3.1, Langchain, FAISS and OLlama #ai #llm #llama #huggingface
Advance RAG 10- Powerful RAG with Parent Document Retriever #ai #llm #openai #gemini51:05Advance RAG 10- Powerful RAG with Parent Document Retriever #ai #llm #openai #gemini
Advance RAG 10- Powerful RAG with Parent Document Retriever #ai #llm #openai #gemini
มุมมอง 1.5K4 หลายเดือนก่อน
Advance RAG 10- Powerful RAG with Parent Document Retriever #ai #llm #openai #gemini
Advance RAG 09- Powerful RAG with Self Querying Retriever #ai #llm #openai45:11Advance RAG 09- Powerful RAG with Self Querying Retriever #ai #llm #openai
Advance RAG 09- Powerful RAG with Self Querying Retriever #ai #llm #openai
มุมมอง 1.9K5 หลายเดือนก่อน
Advance RAG 09- Powerful RAG with Self Querying Retriever #ai #llm #openai
Advance RAG 08- Powerful RAG with Langchain Contextual Compression Retriever #ai #llm #openai57:20Advance RAG 08- Powerful RAG with Langchain Contextual Compression Retriever #ai #llm #openai
Advance RAG 08- Powerful RAG with Langchain Contextual Compression Retriever #ai #llm #openai
มุมมอง 1.8K5 หลายเดือนก่อน
Advance RAG 08- Powerful RAG with Langchain Contextual Compression Retriever #ai #llm #openai
Advance RAG 07 - Flash Reranker for Superfast Reranking29:47Advance RAG 07 - Flash Reranker for Superfast Reranking
Advance RAG 07 - Flash Reranker for Superfast Reranking
มุมมอง 1.6K5 หลายเดือนก่อน
Advance RAG 07 - Flash Reranker for Superfast Reranking
Advance RAG 06- RAG Fusion (Get More Relevant Results for Your RAG) | Reranking With RRF48:59Advance RAG 06- RAG Fusion (Get More Relevant Results for Your RAG) | Reranking With RRF
Advance RAG 06- RAG Fusion (Get More Relevant Results for Your RAG) | Reranking With RRF
มุมมอง 1.8K5 หลายเดือนก่อน
Advance RAG 06- RAG Fusion (Get More Relevant Results for Your RAG) | Reranking With RRF
Advance RAG 05 - Merger Retriever and LongContextReorder | Lost in Middle Phenomenon40:11Advance RAG 05 - Merger Retriever and LongContextReorder | Lost in Middle Phenomenon
Advance RAG 05 - Merger Retriever and LongContextReorder | Lost in Middle Phenomenon
มุมมอง 1.7K5 หลายเดือนก่อน
Advance RAG 05 - Merger Retriever and LongContextReorder | Lost in Middle Phenomenon
Advanced RAG 04 - Reranking with Cross Encoders, and Cohere API19:21Advanced RAG 04 - Reranking with Cross Encoders, and Cohere API
Advanced RAG 04 - Reranking with Cross Encoders, and Cohere API
มุมมอง 2.4K5 หลายเดือนก่อน
Advanced RAG 04 - Reranking with Cross Encoders, and Cohere API

ความคิดเห็น

  • @alishaw9999
    @alishaw9999 3 ชั่วโมงที่ผ่านมา

    I also liked the description but could successfully run the code, at his point vectorstore = Chroma.from_documents(chunks,embeddings), i am using colab any suggestions please

  • @sachinkr9764
    @sachinkr9764 12 ชั่วโมงที่ผ่านมา

    @sunny savita not able to find notebook please share in repo

  • @ahmeterdonmez9195
    @ahmeterdonmez9195 วันที่ผ่านมา

    Just 2 words: Extraordinary and Wonderful...

  • @Samurai-hf1un
    @Samurai-hf1un วันที่ผ่านมา

    video Start from here 17:00

  • @abhinayaphalphale
    @abhinayaphalphale 3 วันที่ผ่านมา

    can we detect and create bounding box using llm?

  • @RishabGuggali
    @RishabGuggali 3 วันที่ผ่านมา

    How can we load code files for RAG chatbot

    • @sunnysavita10
      @sunnysavita10 2 วันที่ผ่านมา

      With any data loder from langchian or llamaindex or with any custom code

    • @RishabGuggali
      @RishabGuggali วันที่ผ่านมา

      @ without using langchain

  • @dharanik-m8u
    @dharanik-m8u 3 วันที่ผ่านมา

    Good explanation

  • @thesacredcode233
    @thesacredcode233 4 วันที่ผ่านมา

    Thank you very very much champ🏆🔥

  • @adityakulkarni371
    @adityakulkarni371 4 วันที่ผ่านมา

    @sunny #service_context=ServiceContext.from_defaults(llm=model,embed_model=gemini_embed_model,chunk_size=800,chunk_overlap=20) this code dosent work now its saying this mehtod is deprecated, how can we continue the project?

  • @manikchaudhary2872
    @manikchaudhary2872 4 วันที่ผ่านมา

    I am using gemini key instead of openAI key what changes i need to perform can anybody help me out as i am new to this field.

  • @Rits1804-l4r
    @Rits1804-l4r 4 วันที่ผ่านมา

    brother I used tavily api as a tool with react agent instaed of Google serper becz it was giving me an error it still worked , so did you use google serper api

  • @Rits1804-l4r
    @Rits1804-l4r 4 วันที่ผ่านมา

    perfect...what a explanation brother!!!!!!!!!

    • @sunnysavita10
      @sunnysavita10 4 วันที่ผ่านมา

      Glad you liked it

  • @avinashnair5064
    @avinashnair5064 4 วันที่ผ่านมา

    can you upload notes related to all the lectures it will be usful

  • @rkjyoti4167
    @rkjyoti4167 4 วันที่ผ่านมา

    superb

  • @Pratik345-b1y
    @Pratik345-b1y 4 วันที่ผ่านมา

    Your channel is reaching out millions soon !! I feel the best content available across as of now !

  • @redroom07
    @redroom07 4 วันที่ผ่านมา

    Bro, please avoid talking about repetitive things it loses the watchers interest, apart from this everything is perfect ♥️

  • @Rits1804-l4r
    @Rits1804-l4r 5 วันที่ผ่านมา

    He explains very well,.... bro plz Keep uploading video you will definitely succeed

  • @akg_ak
    @akg_ak 7 วันที่ผ่านมา

    I have lots of pdf file

  • @Pratik345-b1y
    @Pratik345-b1y 7 วันที่ผ่านมา

    Best content Man !! Please create a course on Udemy or any platform which will reach out to more people ! !This is best free playlist as of now

  • @traveltales_by_ritu
    @traveltales_by_ritu 7 วันที่ผ่านมา

    Thank you for the amazing explanation. However, the video download thing is not working in my colab. Having multiple issues in {"Author" :yt.author, "Title": yt.title, "Views":yt.views} line. Issue 1 : With Title : KeyError: 'videoDetails'. Tried uninstalling and reinstalling pytube as per stackoverflow, did no work. Issue 2: With Views: int() argument must be a string, a bytes-like object or a real number, not 'NoneType', Did not get proper resultion in internet. Issue 3: If I comment out the above both, getting another issue with any url of youtube. HTTP Error 403: Forbidden Any suggestions will be welcome for solving the above issues.

  • @AymenMessikh-i4x
    @AymenMessikh-i4x 8 วันที่ผ่านมา

    how i can get OPENAI_API_KEY for free just i want tesing my projects

  • @AmitKumar-zt6vo
    @AmitKumar-zt6vo 8 วันที่ผ่านมา

    1.5k views 23 nov 2024

  • @areebahshakeel2409
    @areebahshakeel2409 8 วันที่ผ่านมา

    Agentic rag is my final project. Could you please upload a video on it? It will be very helpful

  • @Jahid_Hasan-J01
    @Jahid_Hasan-J01 8 วันที่ผ่านมา

    Sir please continue 🙏🙏🙏

  • @vagheeshmk3156
    @vagheeshmk3156 8 วันที่ผ่านมา

    Why are you in a hurry ? Seeing the video description, I had hopes you would get into the fundamentals on runnable. But this looks like a Entrance Crash Course where they just repeat the content from the book

  • @sairam-nn5jv
    @sairam-nn5jv 10 วันที่ผ่านมา

    Hi brother Can you suggest best platform to learn GEN AI with practical knowledge Thankyou

  • @santoshbthvr
    @santoshbthvr 10 วันที่ผ่านมา

    Thanks for this session.

    • @sunnysavita10
      @sunnysavita10 9 วันที่ผ่านมา

      Thankyou for appreciating content if anything needs to be added feel free to let me know open for feedback 😊

  • @santoshbthvr
    @santoshbthvr 10 วันที่ผ่านมา

    This is amazing tutorial, Thank you. However video download is not working from youtube, due to some compliance issues. But I have tried on other mp4 file, it worked seamlessly.

  • @TanmayBhatnagar-t1h
    @TanmayBhatnagar-t1h 11 วันที่ผ่านมา

    The notebook link is not working. Can you update it please? thank you for the great video and explanation

  • @GauravJain-zo8gt
    @GauravJain-zo8gt 11 วันที่ผ่านมา

    thanks and jai jinendra sir , it was a great explanation from you. thanks for making and uploading this video it really helps me a lot

  • @shashankpandey1966
    @shashankpandey1966 11 วันที่ผ่านมา

    Deploy using streamlit, this will eventually make an end to end project which makes your channel grow

    • @sunnysavita10
      @sunnysavita10 11 วันที่ผ่านมา

      I did in my previous project too for sure will update more end to end projects

  • @shreyasbs2861
    @shreyasbs2861 11 วันที่ผ่านมา

    We need one end to end RAG llm project which includes all advanced RAG concepts and techniques. Its very important for cracking interview and answering all cross question in interview. Its really differentiates from normal RAG project.

    • @sunnysavita10
      @sunnysavita10 11 วันที่ผ่านมา

      I already created a playlist on advanced rag please check and use on your own data

    • @shreyasbs2861
      @shreyasbs2861 9 วันที่ผ่านมา

      @@sunnysavita10 Thank you

  • @muhammadadilnaeem
    @muhammadadilnaeem 11 วันที่ผ่านมา

    Amazing Explanation Sir.

  • @Rr-lh2ru
    @Rr-lh2ru 11 วันที่ผ่านมา

    Sir deep learning playlist

    • @sunnysavita10
      @sunnysavita10 11 วันที่ผ่านมา

      Create in future

    • @Rr-lh2ru
      @Rr-lh2ru 11 วันที่ผ่านมา

      ​@@sunnysavita10 Ok sir

  • @hamzakhanswati9087
    @hamzakhanswati9087 12 วันที่ผ่านมา

    Sir, I have been following you since the first LangChain course you launched on sir krish naik channel, and Alhamdulillah, I am now working as an Gen AI Engineer

    • @sunnysavita10
      @sunnysavita10 11 วันที่ผ่านมา

      congratulations :)

  • @hamzakhanswati9087
    @hamzakhanswati9087 12 วันที่ผ่านมา

    Great sir please also make the video on open AI real time api

  • @Samurai-hf1un
    @Samurai-hf1un 12 วันที่ผ่านมา

    3:12 video starts from here

  • @Samurai-hf1un
    @Samurai-hf1un 12 วันที่ผ่านมา

    Hey sunny, you haven't passed the retriever object in the RAG chain. Can you please elaborate on this??🤔🤔

    • @sunnysavita10
      @sunnysavita10 12 วันที่ผ่านมา

      only in first chain retriever will be required not in other chain understand architecture and code in sync

  • @muhammadimranrafique4778
    @muhammadimranrafique4778 12 วันที่ผ่านมา

    Thank you so much, the corrective rag is so useful.

  • @Ishaheennabi
    @Ishaheennabi 12 วันที่ผ่านมา

    Great sir!

  • @Jahid_Hasan-J01
    @Jahid_Hasan-J01 12 วันที่ผ่านมา

    Thank you sir for giving us this valuable tutorial...God bless you...❤❤❤...i learning many things from You...❤❤❤

    • @sunnysavita10
      @sunnysavita10 12 วันที่ผ่านมา

      keep learning thanks🙂

  • @wewesusui9528
    @wewesusui9528 12 วันที่ผ่านมา

    Code is not available sir

    • @sunnysavita10
      @sunnysavita10 11 วันที่ผ่านมา

      it is there on my github please check

  • @charanpoojary4804
    @charanpoojary4804 12 วันที่ผ่านมา

    great

  • @Jahid_Hasan-J01
    @Jahid_Hasan-J01 13 วันที่ผ่านมา

    Thank you sir...❤❤❤ .. please continue this playlist

  • @charanpoojary4804
    @charanpoojary4804 13 วันที่ผ่านมา

    great

  • @abhishekgupta-fj7wk
    @abhishekgupta-fj7wk 13 วันที่ผ่านมา

    I want to persist the hole conversation in a external databases can you give the idea how to implement in production ready system … please guide

  • @charanpoojary4804
    @charanpoojary4804 13 วันที่ผ่านมา

    boom🔥

  • @FranciscoLopez-ym6gd
    @FranciscoLopez-ym6gd 13 วันที่ผ่านมา

    Amazing as always

  • @charanpoojary4804
    @charanpoojary4804 14 วันที่ผ่านมา

    great

  • @charanpoojary4804
    @charanpoojary4804 14 วันที่ผ่านมา

    Thank you sir