Відео

Hi, this video is one of the best, but now langchain changed its modules and classes, please update us with the new video, for eg: simplesequentialchain is not supporting now!!

I really don’t understand the benifit of language chain. Everything it does seems useless. Prompt templates can be done with the python .format() function. I don’t see the need for pinecone… just get a list of tuples [(vector, text/data), …] and just loop over the list, that’s completely free. The agents are cool, but very simple to implement aswell, and that would be better so it could be more efficient for ur use case. The one thing I do like is the chain concept, it lets you define a architecture in a more abstract/clear way, but I don’t like the way the implemented it, it should act more like piping and IPC does because you can have branches and concurrent events.

New subscriber as you giving content a couple layers deeper than I’m seeing from others Love how you start simpler then start adding complexity and other variations

How do you manage the hallucination?

Congrats, great video! Can u share the repo of the code?

This channel knows its stuff! But if you want the best for cold emailing, Mystrika is where it is at. Their unified inbox and automatic bounce detection are unbeatable. Worth every penny!

Awesome breakdown on email automation! Have you tried Mystrika yet? It is far superior to any other tool out there. Sales and lead gen have never been easier for me since I switched.

Yo, great content as always! But if you wanna skyrocket your cold emails, Mystrika is the ultimate tool. Detailed analytics, A/B testing, and an active community of 5000 users. cannot recommend it enough!

Nice one. Whete can I get this notebook?

Wonderful video thanks!

Great work. Thanks for sharing. Maybe you could dive into the langchain expression language in one of your future videos. The piping and runnable is not so clear yet. br

This is one of the best asusual, thanks allot rabbit metrics, this is kept to the point

Build a team for fun. Have a group? 1. CodeCraft Duel: Super Agent Showdown 2. Pixel Pioneers: Super Agent AI Clash 3. Digital Duel: LLM Super Agents Battle 4. Byte Battle Royale: Dueling LLM Agents 5. AI Code Clash: Super Agent Showdown 6. CodeCraft Combat: Super Agent Edition 7. Digital Duel: Super Agent AI Battle 8. Pixel Pioneers: LLM Super Agent Showdown 9. Byte Battle Royale: Super Agent AI Combat 10. AI Code Clash: Dueling Super Agents Edition

note create_pandas_dataframe_agent has been moved to langchain experimental and you have to install if you havent

Hi I created cloud function using storage triggers, but I am not able to see print results in logs.

Content is great, but the title is very misleading. Better: self-correcting SQL-query request generator. And the specific model is less important given the self-correcting feature, which could have been more promoted instead of jumping on the llama3 hype train. Just some food for thought

best

Another video that shows how disappointing AI is. I can easily write those simple queries myself

you can use the result of this chain as a dataset for fine-tuning a model.

Awesome tutorials and tutorial layouts. 👍

great video - btw, I can't download the "schema.py" file from the written tutorial

@rabbitmetrics thank you for the great content. I'm building this pipline as a poc for a project at work. I'm running into an error when in google colab. When running 'from feast import FeatureStore' I get ImportError: cannot import name 'field_validator' from 'pydantic' . Any insights would be appreciated. I experimented with different versions of feast & pydantic to no avail.

Nice tutorial thanks. Just as a note, the link in the description is not clickable.

maybe this is a dumb question, at 7:54 when you say llm=llm in that line, did you define a variable called llm somewhere ?

Wow, this video provides a fascinating insight into Langchain agents and their capabilities! 🤖 I'm excited to learn more about how agents work under the hood of Link Chain and the endless possibilities they offer for businesses investing in technology, data, and analytics. Looking forward to diving deeper into building custom agents and unlocking their potential! 🚀🔍 #LangtuneAgents #AI #Innovation

Nice, but why not use proper BI tools for this? 😂

Will this work with the Redis alternatives now they are persona non grata?

Is it possible to connect directly to the google analytics on the platform?without the need of downloading the dataset?Thank you in advance

Phenomenal thank you 🙏🏽

Phenomenal

good instruction ...

Thanks!

Hi, great tutorial! How would you implement a chat fuctionality? where you can ask follow up questions??

Show de bola!

Dang! all python. Maybe I'll start video using Java and LangChain4J

Wow! What a hot mess! Has it gotten any easier to do this in the past 11 months?

Hello, I just run your script around 05:53 with python3 and pip3. However it says ` Could not import openai python package. Please install it with `pip install openai`. (type=value_error)`. Which version of that dep should I add to get a coherent project with your code?

Thank you.

🎯 Key Takeaways for quick navigation: 00:00 *🛠️ Generación de bases de conocimiento para cadenas SQL basadas en LLM* - La creación de bases de conocimiento estructuradas permite generar consultas SQL más avanzadas. - El uso de técnicas de promoción de cortos proporciona ejemplos de consultas SQL detalladas, reduciendo errores de promoción y acelerando la construcción de productos de datos. - Se presenta una visión general de cómo funciona el proceso, desde la creación de consultas SQL relevantes hasta la generación de cadenas ejecutables basadas en un prompt. 02:17 *📊 Problemas al extraer datos anidados en BigQuery y soluciones propuestas* - Se identifica un problema común al trabajar con datos anidados en BigQuery, especialmente al utilizar el operador UNNEST. - Se muestra cómo abordar este problema mediante la construcción de cadenas SQL personalizadas y la integración de información de esquema detallada. - Se introduce el concepto de promoción de cortos y se demuestra cómo su implementación puede mejorar la robustez del proceso de generación de consultas SQL avanzadas. 05:03 *🔍 Implementación de promoción de cortos con Redis y búsqueda de similitud de vectores* - Se utiliza Redis como almacén de vectores para almacenar ejemplos de consultas SQL necesarios para la promoción de cortos. - Se describe el proceso de búsqueda de similitud de vectores para recuperar consultas relevantes basadas en un prompt específico. - Se muestra cómo utilizar un selector de ejemplos de similitud semántica para realizar la búsqueda de similitud de vectores y seleccionar ejemplos relevantes para la promoción de cortos. Made with HARPA AI

Thank you this is the info I was looking for.

Hi, question, how do you configure it to use gemini-pro and not gpt-4?

You can use create_sql_agent right?

Hi, the code cannot be accessed.

THIS is function-calling but instead of a "json" u get a "sql query". Am i missing something?

What happens if he drops the table when hallucinating

Where can we download the code file?

Good stuff on Apify, fellas! I wonder if you have crossed paths with Mystrika yet? As a user, I can vouch for their superior and comprehensive list of features. You have got to try their tag management system, it is damn neat and super convenient!

What up, Rabbitmetrics! Enjoyed your video on Email Automation. But man, nothing beats Mystrika is unique email warming-up feature for increasing deliverability. Their warmup pool is quality is top-notch and it is free for one email address! Time to upgra

Decent video on Lemlist! But man, are you missing out on Mystrika! Their inbox rotation feature alone has taken my cold email to a whole new level. The scalability is limitless! You got to do a tutorial for the community here.

Hey, good one on Automation with Lemlist! Ever tried Mystrika though? Their cold email features like dynamic email generation and the AI writer are just sublime. Plus, they have got a banging community of 5000 users on FB, youll love it!