I am right now just watch half of the video and this video is like a gold. What an explanation anyone who is a begineer can easily become master and able to know how to use Langchain after that.
00:04 - Introduction to LangChain for building AI applications. 02:11 - LangChain utilizes templates, chains, RAGs, and agents for dynamic AI workflows. 06:27 - LangChain enables effortless integration of LLMs with real-world applications. 08:25 - Creating an API key and setting up a Python development environment. 12:46 - LangChain provides a unified interface for communicating with various LLM APIs. 14:51 - LangChain enhances context management and scalability for AI workflows. 19:13 - Configuring API keys in your environment for OpenAI models. 21:17 - Managing OpenAI account balance for LangChain usage. 25:02 - Using LangChain to create concise responses for engaging Instagram posts. 26:53 - Understanding message types is crucial for building dynamic AI applications. 30:33 - Real-time terminal conversation with an LLM mimicking ChatGPT locally. 32:26 - Implementing dynamic chat history for AI interactions. 36:03 - Integration of AI with Firestore for message storage and retrieval. 37:58 - Setting up Firebase Firestore for application development. 41:43 - Initializing Firestore client for chat message storage. 43:29 - Understanding LangChain's prompt templates enhances application efficiency. 47:24 - LangChain manages prompt templates for better LLM interaction. 49:20 - LangChain enables dynamic message handling and task chaining. 53:00 - Introduction to parallel and conditional chaining in task execution. 54:50 - Exploring LangChain's efficient task chaining with prompt templates. 58:34 - Chains streamline coding by reducing complexity for task management. 1:00:24 - Creating a workflow with runnable Lambdas in LangChain. 1:04:05 - Understanding Runnable Sequence Class for task chaining in LangChain. 1:05:54 - Chaining in LangChain simplifies data handling through various methods. 1:09:33 - Understanding sequential chaining in LangChain with practical examples. 1:11:21 - Using LangChain to analyze movie critiques through parallel tasks. 1:15:03 - Combining results from parallel chains for analysis. 1:16:48 - Implementing parallel and conditional chaining for API interactions. 1:20:31 - Understanding feedback classification and branching logic in LangChain. 1:22:29 - Handling user feedback with appropriate response strategies. 1:26:08 - RAG improves document accessibility for LLMs while addressing context limits. 1:27:57 - RAG enhances LLMs by efficiently retrieving relevant document sections. 1:31:43 - Context windows limit token processing in large documents. 1:33:43 - Chunking enables efficient querying of relevant information based on user prompts. 1:37:30 - Vector embeddings represent relationships between words in multi-dimensional space. 1:39:26 - Understanding embeddings and vector databases for text processing. 1:43:10 - Implementing document loading and embedding in LangChain. 1:44:56 - Setting up local vector stores for document embeddings. 1:48:49 - Chunking text with overlap improves context understanding. 1:50:44 - Embedding and storing texts in a vector database. 1:54:35 - LangChain retrieves top relevant chunks based on similarity scores. 1:56:29 - Understanding the importance of parameter adjustments in search results. 2:00:19 - Storing multiple book documents in a Chroma DB with metadata. 2:02:17 - Load embedded data and query the database for relevant information. 2:06:00 - Integrating user questions with relevant document chunks for accurate LLM responses. 2:07:55 - Understanding Dracula's Castle and its connection to LLMs. 2:11:53 - Agents enhance AI decision-making by autonomously selecting tools for tasks. 2:14:05 - AI agents utilize the REACT pattern for problem-solving. 2:18:10 - Creating an agent enhances LLM capabilities with external tools. 2:20:25 - Creating tools and agents for LangChain applications. 2:24:27 - Creating a Python function to return the current system time. 2:26:46 - Understanding tool integration in LangChain for effective prompt handling. 2:31:10 - React agents efficiently handle complex queries through reasoning and multiple cycles. 2:33:10 - Providing tools enhances LLM efficiency and safety in operations.
absolutely fantastic! For a beginner in AI as well as Agents framework, you have fed the essence of it! Looking forward to the upcoming videos too. Thanks a ton Harish.
This tutorial is very helpful for someone who is just starting out as an AI engineer i.e ME. It is very concise and most importantly up-to-date. Thank you Harish.
Pulled together some really great open-source content from Chris, Brandon and other AI agent creators, plus added my own explanations/analogies from my 2+ years of building over 25+ LangChain apps to make it easily digestible for beginners! Broke down everything from scratch, plus included real projects you can build. Stuck on something? Drop a comment - I'm here to help!
cover 15 mins and you totally crush it my man love the you explaining keep going and we need some other like pydanticai for agents with pineconedb or any other vector db
how to train rag model i have pdfs and want to train rag model, i have already made it from scratch using langchain gpt model and gradio interface and my pdfs converted into vector store fAISS which produce output while querying related to pdf how to know accuracy percentage how good is model. Will you please help me?
We can use google gemini model as well, langchain supports that too, so no need to pay for openai embedding or openai gpt models, you can use google embedding and good gemini model as well
HARSH REALITY OF IT AND TECH JOBS TODAY: It will take you 6 months to learn all this new tech and master it, and by the time you are confident in the stack and start searching the jobs, THE TECHNOLOGY AND THE ENTIRE STACK WILL BE OBSOLETE WITH NO MORE JOBS OR HIRING. I know its a good technology what you are teaching, BUT WHATS THE GUARANTEE IT WILL NOT LEAD TO A LAY OFF? The answer is there is no guarantee.. People dont wont skill anymore, people want a stable job and a constant income. That’s the skill that will put food on your plate.
Amazing dude ... although I implemented various things using Langchain the explaination for every topic was informative and I learnt something new.
Underrated masterpiece with an insane teaching style!
great work!
I am right now just watch half of the video and this video is like a gold. What an explanation anyone who is a begineer can easily become master and able to know how to use Langchain after that.
Bro this is underrated as hell, Awesome, just subscribed.
00:04 - Introduction to LangChain for building AI applications.
02:11 - LangChain utilizes templates, chains, RAGs, and agents for dynamic AI workflows.
06:27 - LangChain enables effortless integration of LLMs with real-world applications.
08:25 - Creating an API key and setting up a Python development environment.
12:46 - LangChain provides a unified interface for communicating with various LLM APIs.
14:51 - LangChain enhances context management and scalability for AI workflows.
19:13 - Configuring API keys in your environment for OpenAI models.
21:17 - Managing OpenAI account balance for LangChain usage.
25:02 - Using LangChain to create concise responses for engaging Instagram posts.
26:53 - Understanding message types is crucial for building dynamic AI applications.
30:33 - Real-time terminal conversation with an LLM mimicking ChatGPT locally.
32:26 - Implementing dynamic chat history for AI interactions.
36:03 - Integration of AI with Firestore for message storage and retrieval.
37:58 - Setting up Firebase Firestore for application development.
41:43 - Initializing Firestore client for chat message storage.
43:29 - Understanding LangChain's prompt templates enhances application efficiency.
47:24 - LangChain manages prompt templates for better LLM interaction.
49:20 - LangChain enables dynamic message handling and task chaining.
53:00 - Introduction to parallel and conditional chaining in task execution.
54:50 - Exploring LangChain's efficient task chaining with prompt templates.
58:34 - Chains streamline coding by reducing complexity for task management.
1:00:24 - Creating a workflow with runnable Lambdas in LangChain.
1:04:05 - Understanding Runnable Sequence Class for task chaining in LangChain.
1:05:54 - Chaining in LangChain simplifies data handling through various methods.
1:09:33 - Understanding sequential chaining in LangChain with practical examples.
1:11:21 - Using LangChain to analyze movie critiques through parallel tasks.
1:15:03 - Combining results from parallel chains for analysis.
1:16:48 - Implementing parallel and conditional chaining for API interactions.
1:20:31 - Understanding feedback classification and branching logic in LangChain.
1:22:29 - Handling user feedback with appropriate response strategies.
1:26:08 - RAG improves document accessibility for LLMs while addressing context limits.
1:27:57 - RAG enhances LLMs by efficiently retrieving relevant document sections.
1:31:43 - Context windows limit token processing in large documents.
1:33:43 - Chunking enables efficient querying of relevant information based on user prompts.
1:37:30 - Vector embeddings represent relationships between words in multi-dimensional space.
1:39:26 - Understanding embeddings and vector databases for text processing.
1:43:10 - Implementing document loading and embedding in LangChain.
1:44:56 - Setting up local vector stores for document embeddings.
1:48:49 - Chunking text with overlap improves context understanding.
1:50:44 - Embedding and storing texts in a vector database.
1:54:35 - LangChain retrieves top relevant chunks based on similarity scores.
1:56:29 - Understanding the importance of parameter adjustments in search results.
2:00:19 - Storing multiple book documents in a Chroma DB with metadata.
2:02:17 - Load embedded data and query the database for relevant information.
2:06:00 - Integrating user questions with relevant document chunks for accurate LLM responses.
2:07:55 - Understanding Dracula's Castle and its connection to LLMs.
2:11:53 - Agents enhance AI decision-making by autonomously selecting tools for tasks.
2:14:05 - AI agents utilize the REACT pattern for problem-solving.
2:18:10 - Creating an agent enhances LLM capabilities with external tools.
2:20:25 - Creating tools and agents for LangChain applications.
2:24:27 - Creating a Python function to return the current system time.
2:26:46 - Understanding tool integration in LangChain for effective prompt handling.
2:31:10 - React agents efficiently handle complex queries through reasoning and multiple cycles.
2:33:10 - Providing tools enhances LLM efficiency and safety in operations.
Thanks this helps a lot.
absolutely fantastic! For a beginner in AI as well as Agents framework, you have fed the essence of it! Looking forward to the upcoming videos too. Thanks a ton Harish.
This tutorial is very helpful for someone who is just starting out as an AI engineer i.e ME. It is very concise and most importantly up-to-date. Thank you Harish.
now i understood rag application workflow,thanks
That's exactly what I was looking for 😊
Pulled together some really great open-source content from Chris, Brandon and other AI agent creators, plus added my own explanations/analogies from my 2+ years of building over 25+ LangChain apps to make it easily digestible for beginners!
Broke down everything from scratch, plus included real projects you can build.
Stuck on something? Drop a comment - I'm here to help!
cover 15 mins and you totally crush it my man love the you explaining keep going and we need some other like pydanticai for agents with pineconedb or any other vector db
Wonderful explanation. Found it really helpful. Thank you so much!
This is the tuitorial I was searching recently. I loved the explanation and subscirbed for more.
@@hobbycoding7056 glad it helped you!
great content, neatly explained with examples, very obscure, Thanks!!!
Please do a similar courses on upcoming tech stuffs!!!
Thank you!
Amazing content and articulation/details. So useful. Thanks a lot Harish. Wishing you the best
Thank you 🙂
Thanks sir keep continue such video series
A very nice video, thank you!
Great video thanks for creating awsome videos.
sir need part 2 with full production grage agents using langchain. Thanks you sir
Will upload next few videos by end of month 🙂
@@harish_neel Thanks sir
So i m begginer and i have been learning C# 3 month and i have some issue and i have like this project, who can help to me solve this problem
how to train rag model i have pdfs and want to train rag model, i have already made it from scratch using langchain gpt model and gradio interface and my pdfs converted into vector store fAISS which produce output while querying related to pdf how to know accuracy percentage how good is model. Will you please help me?
That bg
❤
don't we need pay for using openai's keys. i mean we can generate them, sure, but isn't billing details necessary if we wan't some response
@@aniketdey800 yes, when I make the first api call to the model, I’ll explain how to set up billing too during the first chat models section
We can use google gemini model as well, langchain supports that too, so no need to pay for openai embedding or openai gpt models, you can use google embedding and good gemini model as well
I don't know why not use simple formatted string of python instead of prompt template. There is no extra advantage.
True. When building workflows, it's always some prompt repeatedly used, so I'm used to writing prompt templates. But not necessary always
🥇
HARSH REALITY OF IT AND TECH JOBS TODAY:
It will take you 6 months to learn all this new tech and master it, and by the time you are confident in the stack and start searching the jobs, THE TECHNOLOGY AND THE ENTIRE STACK WILL BE OBSOLETE WITH NO MORE JOBS OR HIRING.
I know its a good technology what you are teaching, BUT WHATS THE GUARANTEE IT WILL NOT LEAD TO A LAY OFF? The answer is there is no guarantee..
People dont wont skill anymore, people want a stable job and a constant income. That’s the skill that will put food on your plate.
Bhai AI agent create Karna sekhado
@@BilalAnwar-pk5pf yes, I’ll be covering that towards the end of the course, Bilal
Bro this kiss is for you 💋
Dear Harish Neel, Your content is extremely good, i highly appriciate it , thanks for such a valuable content.