![Pradip Nichite](/img/default-banner.jpg)
- Видео 96
- Просмотров 1 166 437
Pradip Nichite
Индия
Добавлен 13 дек 2013
Hello, my name is Pradip Nichite. I am a 🚀 Top Rated Plus Data Science Freelancer with 8+ years of experience, specializing in NLP and Back-End Development. Founder of FutureSmart AI, helping clients build custom AI NLP applications using cutting-edge models and techniques. Former Lead Data Scientist at Oracle, primarily working on NLP and MLOps.
💡 As a Freelancer on Upwork, I have earned over $100K with a 100% Job Success rate, creating custom NLP solutions using GPT-3, ChatGPT, GPT-4, and Hugging Face Transformers. Expert in building applications involving semantic search, sentence transformers, vector databases, and more.
💡 As a Freelancer on Upwork, I have earned over $100K with a 100% Job Success rate, creating custom NLP solutions using GPT-3, ChatGPT, GPT-4, and Hugging Face Transformers. Expert in building applications involving semantic search, sentence transformers, vector databases, and more.
Mastering Natural Language to SQL with LangChain and LangSmith | NL2SQL | With Code 👇
Embark on a journey to redefine database querying with "Mastering Natural Language to SQL with LangChain | NL2SQL." This in-depth video guide will navigate you through the revolutionary process of converting natural language queries into SQL commands using LangChain. Here's what we'll cover:
🌟 Introduction to NL2SQL: Understand the basics of translating natural language to SQL with LangChain.
🔨 Building Your First NL2SQL Model: Step-by-step guide on creating a foundational NL2SQL model.
🎯 Incorporating Few-Shot Learning: How to enhance model accuracy using few-shot examples.
🔄 Dynamic Few-Shot Example Selection: Tailor your model's learning process with dynamic example selection for improved ...
🌟 Introduction to NL2SQL: Understand the basics of translating natural language to SQL with LangChain.
🔨 Building Your First NL2SQL Model: Step-by-step guide on creating a foundational NL2SQL model.
🎯 Incorporating Few-Shot Learning: How to enhance model accuracy using few-shot examples.
🔄 Dynamic Few-Shot Example Selection: Tailor your model's learning process with dynamic example selection for improved ...
Просмотров: 34 174
Видео
Building a Conversational Voice Chatbot: OpenAI Speech-to-Text & Text-to-Speech Integration
Просмотров 14 тыс.8 месяцев назад
Explore the cutting-edge world of AI chatbots in this detailed tutorial, where we delve into creating a voice-responsive chatbot utilizing OpenAI's speech-to-text and text-to-speech technologies , all integrated within a Streamlit web application. This guide is perfect for anyone interested in enhancing user interaction through AI and voice recognition. You'll learn how to convert spoken langua...
Master OpenAI's Text-to-Speech & Speech-to-Text: Ultimate Tutorial with Code
Просмотров 5 тыс.8 месяцев назад
Master OpenAI's Text-to-Speech & Speech-to-Text APIs: In-Depth Tutorial with Code Demos. This video guides you through using OpenAI's APIs to create natural-sounding speech in multiple languages and transcribe speech accurately. We cover everything from basic setup and model selection to advanced topics like handling long audio files and using GPT-4 for transcript corrections. Ideal for develop...
GPT-4 Vision: A Comprehensive Tutorial | GPT-4V
Просмотров 2,9 тыс.8 месяцев назад
Explore the capabilities of GPT-4 Turbo Vision in this detailed tutorial. We start with an introduction to GPT-4 Turbo Vision, highlighting its unique features. Learn how to generate image descriptions using image URLs or files, and discover how to interact with the AI by asking questions based on images. We also cover handling complex queries involving multiple images, a useful skill for resea...
LangChain Expression Language (LCEL) | Langchain Tutorial | Code
Просмотров 3,5 тыс.8 месяцев назад
Dive into the world of LangChain Expression Language (LCEL) with our comprehensive tutorial! In this video, we explore the core features of LCEL, focusing on how LangChain elements implement the runnable interface for effective chaining and independent execution. We demonstrate practical uses of the pipe operator for seamless operation chaining and illustrate the power of the invoke method in e...
LangChain Templates Tutorial: Building Production-Ready LLM Apps with LangServe
Просмотров 6 тыс.8 месяцев назад
In this LangChain Templates Tutorial, we dive deep into building production-ready LLM (Language Model) applications with LangServe. 🚀 📌 What You'll Learn: 1. Discover the Power of LangChain Templates: Learn what LangChain Templates are and how they simplify LLM application development. 2. Installation and Execution: Get hands-on guidance on installing and running LangChain Templates, making you...
LlamaIndex RAGs : Build 'ChatGPT Over Your Data' Using Natural Language.
Просмотров 6 тыс.8 месяцев назад
RAGs is a Streamlit app that lets you create a RAG pipeline from a data source using natural language. - Introduction to RAGs: Understand what RAGs are and how they're revolutionizing the way we interact with data. - Step-by-Step Installation: Follow our easy steps to install RAGs on your system. - Chatbot Creation: Learn how to build a chatbot that can intelligently answer questions using info...
OpenAI Function Calling Explained: Chat Completions & Assistants API
Просмотров 15 тыс.9 месяцев назад
Dive into the world of AI chatbots with our detailed tutorial on OpenAI's Function Calling: Deep Dive into OpenAI APIs: Explore how to use Chat Completions and Assistants APIs for advanced chatbot functionalities. 🛠️ Hands-On Function Integration: Learn step-by-step how to integrate and execute custom functions in your chatbots. 💡 Real-World Application: Watch a practical demonstration with an...
Fine-Tuning GPT-3.5 on Custom Dataset: A Step-by-Step Guide | Code
Просмотров 32 тыс.9 месяцев назад
Dive into the world of GPT-3.5 fine-tuning in this tutorial. Learn how to customize AI models for better accuracy and performance, covering dataset formatting, fine-tuning with OpenAI's API, and practical testing of your model. Code and Dataset: blog.futuresmart.ai/fine-tuning-gpt-35-a-step-by-step-guide If you're curious about the latest in AI technology, I invite you to visit my project, AI D...
Build Chatbot using OpenAI's Latest Assistants API - A Beginner's Guide | Code
Просмотров 38 тыс.9 месяцев назад
This tutorial is a step-by-step guide for beginners eager to explore the capabilities of OpenAI's new Assistants API. In this video, we cover: Fundamental concepts like Assistants, Threads, and Runs. Practical demonstration of building a chatbot using the inbuilt retrieval tool, highlighting its ability to answer queries from an uploaded HTML file. The differences between the new Assistants AP...
Hosting Chroma DB on AWS EC2: Server Setup and Client Connection Tutorial
Просмотров 5 тыс.10 месяцев назад
Discover the advantages of hosting Chroma DB as a server and learn the step-by-step process to set it up on an AWS EC2 instance in this comprehensive tutorial. We'll walk through creating an EC2 instance, installing Docker, running Chroma DB as a Docker image, and connecting to it as a client. Additionally, we'll demonstrate how to run Chroma DB in detach mode to ensure continuous operation eve...
NL2SQL with LlamaIndex: Querying Databases Using Natural Language | Code
Просмотров 21 тыс.10 месяцев назад
Discover LlamaIndex, a powerful toolkit that bridges the gap between LLMs and your external data. In this tutorial, we delve into the concept of Text-to-SQL and demonstrate how you can seamlessly query a database using just natural language. Tackling challenges like managing multiple tables and dynamically indexing table schemas for ChatGPT queries, we've got you covered! AI Demos: www.aidemos....
NLP Roadmap 2024: Step-by-Step Guide | Resources
Просмотров 15 тыс.11 месяцев назад
In this video, we will discuss how you can approach NLP, the step-by-step process you should follow, the resources you can refer to, and the kind of problems you can solve with NLP knowledge. Step 1: Text Pre-Processing Step 2: Text Representation Step 3: Information Extraction Step 4: Deep Learning for NLP Step 5: Transformers and Transfer Learning Step 6: Deploying NLP Models Step 7: Embeddin...
Tech Stack Behind My $100K Upwork Journey as a Freelance Data Scientist
Просмотров 3,7 тыс.Год назад
Tech Stack Behind My $100K Upwork Journey as a Freelance Data Scientist
Journey to $100K and Top 1% on Upwork | Data Science Freelancing |@pradip_nichite
Просмотров 3,6 тыс.Год назад
Journey to $100K and Top 1% on Upwork | Data Science Freelancing |@pradip_nichite
Deploy FastAPI & Open AI ChatGPT on AWS EC2: A Comprehensive Step-by-Step Guide 🚀
Просмотров 6 тыс.Год назад
Deploy FastAPI & Open AI ChatGPT on AWS EC2: A Comprehensive Step-by-Step Guide 🚀
Beginner's Guide to FastAPI & OpenAI ChatGPT API Integration | Code
Просмотров 12 тыс.Год назад
Beginner's Guide to FastAPI & OpenAI ChatGPT API Integration | Code
Mastering LlamaIndex : Create, Save & Load Indexes, Customize LLMs, Prompts & Embeddings | Code
Просмотров 19 тыс.Год назад
Mastering LlamaIndex : Create, Save & Load Indexes, Customize LLMs, Prompts & Embeddings | Code
Using Langchain and Open Source Vector DB Chroma for Semantic Search with OpenAI's LLM | Code
Просмотров 27 тыс.Год назад
Using Langchain and Open Source Vector DB Chroma for Semantic Search with OpenAI's LLM | Code
Semantic Search with Open-Source Vector DB: Chroma DB | Pinecone Alternative | Code
Просмотров 23 тыс.Год назад
Semantic Search with Open-Source Vector DB: Chroma DB | Pinecone Alternative | Code
Connecting Anvil app to external DB and diplaying data in table | Data Grid | Repeating Panels
Просмотров 2 тыс.Год назад
Connecting Anvil app to external DB and diplaying data in table | Data Grid | Repeating Panels
Build AI Web app with Custon UI using Python, Anvil and ChatGPT API| @Anvil-works | Anvil Tutorial
Просмотров 4,7 тыс.Год назад
Build AI Web app with Custon UI using Python, Anvil and ChatGPT API| @Anvil-works | Anvil Tutorial
Chatbot Answering from Your Own Knowledge Base: Langchain, ChatGPT, Pinecone, and Streamlit: | Code
Просмотров 77 тыс.Год назад
Chatbot Answering from Your Own Knowledge Base: Langchain, ChatGPT, Pinecone, and Streamlit: | Code
AWS RDS MySQL with Python: A Step-by-Step Tutorial | Code
Просмотров 10 тыс.Год назад
AWS RDS MySQL with Python: A Step-by-Step Tutorial | Code
LangChain, SQL Agents & OpenAI LLMs: Query Database Using Natural Language | Code
Просмотров 45 тыс.Год назад
LangChain, SQL Agents & OpenAI LLMs: Query Database Using Natural Language | Code
Building a Document-based Question Answering System with LangChain, Pinecone, and LLMs like GPT-4.
Просмотров 58 тыс.Год назад
Building a Document-based Question Answering System with LangChain, Pinecone, and LLMs like GPT-4.
Building a GPT-4 Chatbot using ChatGPT API and Streamlit Chat (with Code)
Просмотров 9 тыс.Год назад
Building a GPT-4 Chatbot using ChatGPT API and Streamlit Chat (with Code)
GPT-4 API: Real-World Use Case & GPT-3 Comparison in Invoice Processing | GPT-4 Demo
Просмотров 3,8 тыс.Год назад
GPT-4 API: Real-World Use Case & GPT-3 Comparison in Invoice Processing | GPT-4 Demo
Using OpenAI's ChatGPT API to Build a Conversational AI Chatbot | Python | Code
Просмотров 23 тыс.Год назад
Using OpenAI's ChatGPT API to Build a Conversational AI Chatbot | Python | Code
How this model work with big query?
Hi sir, am java developer some time i do frontend but to use llms, and all these studf do we need knowledge of ml, ds, ai? Python
this is really great. Can you share your VS code for this tutorial?
What happens if the customer makes the wrong attempt? Repeat the same slot and see how many times it repeats (is there any possibility to set a limit to terminate the lambda )
What happens if the customer makes the wrong attempt? Repeat the same slot and see how many times it repeats (is there any possibility to set a limit fir incorrect attempt?)
Oh man Thank You
Helpful❤
what is token and how it's calculated sir ?
Great tutorial looks like it’s still hallucinating may be llm fine tuning is another option. When u get time can u drop a video
great video! (jeff from chroma)
How to do Multi Level Hierarchical Classification
Sir apne session ke bare me nhi padya h agle video as you said please teach humble request
One of the best , to the point content . Keep it sir really it is very helpful
Can MS Teams be used instead of Streamlit as the user interface? If yes, then how are multiple user sessions handled in there? Can you please consider this topic to make another video?
Yes making video that will explain how to handle sessions for mulitple users. not sure about MS Teams. You can expose your langchain app as api and then you can integrate with any UI
@@FutureSmartAI Another thing which I think would really help people following your playlist - "How to enable data access control based on the user?" I think it's the next thing one would think after creating concurrency. Let's say we are dealing with a HR database of an MNC and we have 2 HRs who are interacting with our bot. X HR works for India office and Y HR works for the US office. Now if X HR asks how many active employees do we have? The answer should be based on the context of their role/dept/team/tower etc.,
Is it possible to use zero-shot for text-2-sql ?
Yes its possible if your tables and column names are self explanatory
Hi sir, I had a query, is it able to map the value asked in the question with the column ? For example - Total budget of mustang ? Will it give the answer for it with correct sql query ? Also how does it work with pattern matching ?
Yes it will be able to decide which columns to use but lets say here `mustang` here user made spelling mistake then even correct sql query wont get returns and you will need to handle it outside
How can we do this task without using openai model and rather open source LLMs ?
Yes you can use open source llm also from langchain_community.llms import Ollama llm = Ollama(model="llama2")
when i create a CloudFormation stacks that i a alo attached IAM role with policy , but it is create a error like rollback_failed. also when i want delete that stack it is showing IAM role invalid or cannot be assumed. Iam role is exits. Please help me on that.
please i need the souce code zip file
Link in the description
I would like to know how to HOST this CUSTOM model in cloud... please? anyone?
hi pradip , your vedios are very useful, can we inegrate like if give any slot value in Amazon lex bot and it will hit API via Lambda and get the response back to the Lex.If yes then please show us how we can achieve
Is it possible to interface with Sybase DB?
Very useful information in the video. Thanks.!
Here how did you get the code that you have been using in the lambda function
very helpful. thank you!
Hi, it can be observed in the "table_details.py" file written in VS code, that there is no mention of Message placeholder to save the chat history while defining the prompt for selected tables. That may cause the chain() module to train on the entire list of tables in the database when the input is "List the names." Will it not? If someone may kindly advise me on this issue. Thank you so much for such an informative session. Cheers!!
No sure exactly what are you asking, you want to limit tables that is being used? db = SQLDatabase.from_uri(f"mysql+pymysql://{db_user}:{db_password}@{db_host}/{db_name}",sample_rows_in_table_info=1,include_tables=['customers','orders'],custom_table_info={'customers':"customer"})
Easy Interpretation!! Kudos
learnt about the helpful langsmith, and also your thought of generating sql query. but when dealing with database column recognizing it's not so precise with this method...looking for knowledge graph based ways. but thanks a lot any way! :)
very useful video
Nice project. How do I obtain the T-SQL query that was generated in this chatbot coding?
Can you make a video on vllm and kubernetes to be used with lang graph
Thank you for the useful video! Can Textract extract text from Bulgarian and return it in Bulgarian language or only English text?
Thank you for this great Video. Wating for next video!!!!!!
Hi Pradip, Thanks for the video. I am getting this exception , Could you please help me to fix? "langchain_core.exceptions.OutputParserException: This output parser can only be used with a chat generation." Instead of OpenAI, I am using Google Palm LLM and Huggingface Embeddings
you need to use llm that support chat messages
Hello Pradip, thanks for your great session. I am learner in AI, I am using sqlcoder 7b-2 model along with langchain and huggingface, here I am stuck with create_extraction_chain_pydantic function, not getting what we can use in place of this function, if u can guide me it will be great help. Thanks in advance.
Best life best view
It's a nice tutorial brother.
if I want to use mic then what should I do?
how to do in node.js?
Thanks for the video, I can understand easily from your explanation.
I don't know why but I have already added all rights and to the right base. Still , when I access it says Invalid authorisation
I tried a model, and api said max 10 candidates
Will it be able to generate charts like if we ask to draw bar chart or line chart?
Yes, you can have one more prompt that can take db results and ask it to convert in format suitable for plotting chart. Then any plotting librt can plot it
This video is just awesome. Thank you. Is there a way I can plot the graph epoch vs accuracy?
It's a great video. Could you please give me the link for the notebook? I want to get the csv file
github.com/PradipNichite/RUclips-Tutorials/blob/main/Langchain_NL2SQL_2024.ipynb
Thanks for amazing video! followed your code bas and all looks good.only problem is create_extraction_chain_pyndantic is not able to extract table name
You can even create seperate prompt and instruct in plain english that output table names as comma seperated values
I am facing the issue deploy on AWS ec2 can u explain 😊
can i give my own aspect and train model accordingly
This is a really nice tutorial and a set of code. Especially the implementation of memory to make it super efficient. I am trying to make some minor changes. I am making use of Snowflake Databases. In this case, the url for connection with sqlalchemy has the database name and schema name as compulsory. This doesn't allow me to access data across multiple schemas for the Chatbot. If I go for multiple db objects, each for one schema, Is it possible to combine them in a single chain for creating the generate_query object??
i need to implement this function calling in vocode, for live calling (voice AI assistant), How can i ?