Відео

Great stuff!

Hey! I really liked your video. I am getting these errors when I run the stt script WebSocketException in LiveClient.start: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: certificate has expired (_ssl.c:1002) Could not open socket: 'LiveClient' object has no attribute '_keep_alive_thread'

16:48 Crazy levels here. As database developer, this is amazing.

Amazing tutorial on this subject. Really appreciate your passion into detailing it in so much depths. Thank you !!!

With the continuous influx of short form content, props to you for making this so interesting to watch. Didn't even realise it was an hour long. Loved every second of it. Thanks!

Could you help me understand what's going on. I'm running this in Docker and keep getting an error when it gets to running the .py: "ModuleNotFoundError: No module named 'langchain_groq'"

👍👍👍

I have the column in my dataset but it still showing key error 💀

Thank you for creating this tutorial it's exactly what I was looking for. Great content!

Brilliantly executed. That graph is incredibly intuitive and information dense.

how can we interrupt the ai??? plsss helpp

how could we interrupt the voicebot can anyone help (pls)

The firstidea that came to me . Is build a personnal shoper apps. Working for a lot brands by saving theireproductds and bring them sells. The consumer can save his face and body for one time and every time he will need to shop it will cn'nect to the personnal shoper apps. Choose the style the needs or only wait for suggestion et voilà, everybody will be happy the consummer the brands and the try on solution company .

Where has this channel been all this while? This is gold. Thanks for the great video!

Where are the Insights?

Hi Sir, what is the best chunking method to process the complex pdfs such as 10K reports. 10K reports will have so many TABLES, How to load those tables to vectorDBs?

How to deal with errors in case of agents? My prompt is this: qry = "Who is the current prime minister of UK? What is the largest prime number that is smaller than his age? Include the name and age of the prime minister in your response." agent.run(qry) It found the prime minister of US Rishi Sunaak. His age 42 years (may be). But, check out the prime number, it arrives at 42. 42 is NOT a prime number. How to deal with such errors? Action Input: "Rishi Sunak age" Observation: 43 years Thought: I need to find the largest prime number that is smaller than 43. Action: Calculator Action Input: "Largest prime number smaller than 43" Observation: Answer: 42 Thought: I now know the final answer. Final Answer: The current prime minister of UK is Rishi Sunak, who is 43 years old. The largest prime number that is smaller than his age is 42. > Finished chain. The current prime minister of UK is Rishi Sunak, who is 43 years old. The largest prime number that is smaller than his age is 42.

Would fine tuning yield better result or is that not guaranteed? Especially if you have large amounts of wirting examples

thanks greg, this was very very easy to understand and insightful

Hi... Any suggestion like how we can handle large chunks s some of the chunks are having token length greater then 4k !!

Theory & Importance of Text Splitting: Context Limits: Language models have limitations on the amount of data they can process at once. Splitting helps by breaking down large texts into manageable chunks. Signal-to-Noise Ratio: Providing focused information relevant to the task improves the model's accuracy and efficiency. Splitting eliminates unnecessary data, enhancing the signal-to-noise ratio. Retrieval Optimization: Splitting prepares data for effective retrieval, ensuring the model can easily access the necessary information for its task. Five Levels of Text Splitting: Level 1: Character Splitting: Concept: Dividing text based on a fixed number of characters. Pros: Simplicity and ease of implementation. Cons: Rigidity and disregard for text structure. Tools: LangChain's CharacterTextSplitter. Level 2: Recursive Character Text Splitting: Concept: Recursively splitting text using a hierarchy of separators like double new lines, new lines, spaces, and characters. Pros: Leverages text structure (paragraphs) for more meaningful splits. Cons: May still split sentences if chunk size is too small. Tools: LangChain's RecursiveCharacterTextSplitter. Level 3: Document Specific Splitting: Concept: Tailoring splitting strategies to specific document types like markdown, Python code, JavaScript code, and PDFs. Pros: Utilizes document structure (headers, functions, classes) for better grouping of similar information. Cons: Requires specific splitters for different document types. Tools: LangChain's various document-specific splitters, Unstructured library for PDFs and images. Level 4: Semantic Splitting: Concept: Grouping text chunks based on their meaning and context using embedding comparisons. Pros: Creates semantically coherent chunks, overcoming limitations of physical structure-based methods. Cons: Requires more processing power and is computationally expensive. Methods: Hierarchical clustering with positional reward, finding breakpoints between sequential sentences. Level 5: Agentic Chunking: Concept: Employing an agent-like system that iteratively decides whether new information belongs to an existing chunk or should initiate a new one. Pros: Emulates human-like chunking with dynamic decision-making. Cons: Highly experimental, slow, and computationally expensive. Tools: LangChain Hub prompts for proposition extraction, custom agentic chunker script. Bonus Level: Alternative Representations: Concept: Exploring ways to represent text beyond raw form for improved retrieval. Methods: Multi-vector indexing (using summaries or hypothetical questions), parent document retrieval, graph structure extraction. Key Takeaways: The ideal splitting strategy depends on your specific task, data type, and desired outcome. Consider the trade-off between simplicity, accuracy, and computational cost when choosing a splitting method. Experiment with different techniques and evaluate their effectiveness for your application. Be mindful of future advancements in language models and chunking technologies. Further Exploration: Full Stack Retrieval website: Explore tutorials, code examples, and resources for retrieval and chunking techniques. LangChain library: Discover various text splitters, document loaders, and retrieval tools. Unstructured library: Explore options for extracting information from PDFs and images. LlamaIndex library: Investigate alternative chunking and retrieval methods. Research papers and articles on text splitting and retrieval.

Awesome Greg! Best TTS-STT demo yet. Do you have any ideas on how to modify your example for two people having a conversation, and the AI participating as a third person. For example, debate students are debating and want the AI to be the judge to help them improve their debate skills. I would love to hear your thoughts on this. Thanks for this tutorial. I've been looking for this solution since the 90's!

Is there any alternative for Spanish? I cannot find it.

Can this be added to a website?

Your channel is a gem, thank you!

Awesome explanation☺was very helpful

Thanks, Excellent video about chunking strategies👍 Question: Can i store the pulled html table using unstructured in a vector database together with a normal text and asking question (RAG)?.

This was an awesome content

LLAMA PARSE for Semantic Chunking isfree

Nice. But in reality your demo is 3,500-4,000 ms from when you stop speaking to getting a response. It does not match the numbers you are printing...

conversational endpointing is a great idea, but I'd like to see that combined with a small model agent that was only looking for breaks in the conversation and an appropriate time to interject. Maybe with a crude scale for the length of the response. So if the user has a break in the point they're trying to make - we don't want the user interrupted and the conversation moved on - what would be more appropriate would be a simple acknowledgement. But once the point is complete, we would then pass back that we want a longer response.

good to see it

Thanks! How does it compare with assemblyai universal 1? do you know which speech-to-text support arabic with the best accuracy in real time?

Wow! I hadn't even thought about Agentic Chunking! I need to try this. I did some extensive experimentation with chunking on a project at work for a clinical knowledge base and I found that chunking strategies can make the difference between an ok retrieval and an awesome retrieval that works across a higher percentage of queries.

You are amazing!

Awesome video! You do a great job!

what the ___. how good can a tutorial be. such a gem of a video. thx for making this. new to ml and found this very helpful

I'm immensely grateful for your enlightening series on the 5 Levels Of LLM Summarizing. The concept of chunks nearest to centroids representing summaries is brilliant and has offered me a fresh perspective. I eagerly anticipate your insights on AGENTS!

This app won't find my API keys either Groq or Openai though they are there. Too bad. Any suggestions greg?

Thank you, Greg, for this informative video on using LLMs to extract data from text! I found it particularly valuable for its potential application in skill/information extraction from resumes/CVs submitted to large companies. I also noticed a minor error in the original code: """ output = chain.predict_and_parse(text="...")['data'] printOutput(output) """ updated code: """ output = chain.run(text="...")['data'] print(output) """

You're a legend mate. I learn so much in a few minutes of your videos. Thanks for sharing your valuable knowledge and helping shape the world.

i cloned the code but response time is like 12 seconds. 4 weeks past and i'm late to the party

Hey Greg, thanks for the video on structured output! One quick tip - maybe it will help other people, when i run code print(output.content) output ```json [ { "input_industry": "air LineZ", ... and it cannot run next code json.loads(output_content) it has to correct symbol first output_content = output_content.replace("```", "'''") On a separate note, I'm looking for a video about using LangChain for question answering across multiple documents. Any chance you have one in your playlist?

Thanks Greg! Great video on using custom files. Could you share a video about RAGs? I heard there are many types and I'd love to learn which is best for different tasks.

Thank you, Sensei Greg, for this amazing demonstration of LangChain's capabilities!

Great tutorial!

Appreciate the clear explanation of Token Limit

Appreciate it!

Great tutorial!