Irina, amazing tutorial on integrating OpenAI API with a custom knowledge base! Really excited about the potential of GPTIndex and Langchain. I'd love to see a deep dive comparing AI Agents in Langchain, especially when they're long-running and autonomous. Keep up the fantastic work! 🌟
Thank you for the tutorial... also, to refresh the files details there is a "Refresh" button located just above the Files detail section. It's an icon of a folder with a circular arrow. This will refresh the section without needing to refresh the page.
You don't need to refresh the whole colab page to update the view of the files/folders, but just the refresh button above the directory structure, in the left pane.
Thank you so much, Irina! I copied your source code to Jupyter notebook and create a chatbot in a few minutes! To my surprise, it works! Please give some thumb-ups to this amazing lady. She has spent time to make this solution so easy to use for everyone!
Great work!! Really nice step by step explanation! By the way you can click the refresh button in the file explorer panel (2nd icon) to refresh the files so that they appear.
What could be happening here? I asked how many people were interviewed and the reply was "One person was interviewed". I asked how many times did "It was fun to talk about cooking." appear and it said none (interview4 ends with this quote). Thank you, great video!
Great video Irina !! I was looking for this exact solution and it was the first video of your channel that I followed exactly step by step and it works perfectly end to end It was very clear and well explained. Nice job !! Please continue making this kind of useful videos It was extremely useful for me and extremely detailed. Keep going!
Thank you so much for this information. This is exactly the kind of thing I've looking for. Step by step tutorials for finetining your own AI. This is perfect.
Thanks for the amazing tutorial. BTW, is there any method to increase the output length? I could only get a answer of approximately 160 words (~250 tokens) right now.
Great work, it's quite clear, Seems the llama Index has many updates, I can't recreate your work, would you please make an updated version? thanks a lot~
This just spits out the text related to the query. If I want to augment GPT capabilities with my own data set. what is the best way to do it? For example, using the same example of interview transcription, I should be able to ask the GPT to summarize how the candidate did or whether the interviewee answer was correct for a particular question. Any idea how to go about that? I understand fine-tuning is a possibility but if i have 10,000 interview scripts i want to augment the GPT capabilities with, I am not sure how to go about it. Any help?
Is there a software package that can make an entire openai chatbox GPT4 or even 3.5 just by pointing at a folder of PDFs? We would pay thousands for this right now. The application has to run local on a PC.
Hi Irina! it is such a great tutorial and would be useful for case that I currently work on. I have tried this with my own research data and turns out so good with relevant and decent answer. But I am wondering is it possible to use the GPT 3 Model but not using it's training data or knowledge? So the information/answer produced would be just using custom data that we added to the knowledge base. Your answer would be very appreciated, thanks!
I converted this code to a flask app and it works fine on my local server. However when I deploy to google app engine it fails to return responses. The error is 500 internal server error! any idea or advice is much appreciated.
A few points not mentioned in the videos: Essentially, it is fine-tuning. However, the module for fine-tuning has been pre-written for you. Fine-tuning can only be done with models below GPT3. Currently, Fine-tuning is not available for ChatGPT, GPT3.5, or CPT4. For GPT2 to be effective, you need at least 300M training data. Models with more parameters than GPT3 require even more data to achieve the desired effect
She used about 12kB of text in this demo. Llamaindex built 559kB index from it. And it did the job on text-davinci-003. I'm genuinely interested in the reason behind your training data size needs claim.
Hello! Thank you for the video! Also your secret API key is visible in the first few frames before you blur it! You should delete that API key completely!
Amazing tutorial! Thanks! If you're looking for future tutorial ideas, I'd love to know how to expand on this to create my own API endpoints so my trained chat bot can be made publicly available from my website. I'm not very familiar with Google Collab (or python for that matter - I'm a php/js web developer), so I'll try to do some of my own research on how this might be possible -- but I really enjoyed and easily absorbed the info in this video. Well done. :)
Hi Mikey! Thank you for the suggestion, I definitely need to make a video about that. I think, I'll be able to post it in 3-4 weeks. Though I'll be using NextJS/Typescript because this is what I'm familiar with.
Irina, thank you for your help? When I ask it irrelevant article questions, it seems to many times provide answers, when it shouldn't. Anyway to ensure it only focuses on my uploaded article?
Thanks it’s really helpful. Capfuls you please let me know if I can use complex data having 100 of parameters (text & numbers) ? If yes in what format the should be uploaded?
Hi Thank you for that content! I am just curious about the files size limit and the importance of the file format in your approach. I have seen that you are using .txt files. I am using pdfs to feed the knowledge base of custom GPTs but I am observing a low accurary in the answers. It seems that the GPT is not looking at all the knowledge base (6 merged pdfs with 7000 pages approx. in total). Do you have any advice?
You can achieve the same result without any external library, GPTIndex just makes it easier for non-professional coders like me 😉. It uses the found chunks as the context to the prompt and not as the answer itself.
@@josehoyos Yeah i have seen Pinecone proposed a lot for this. I did a little test with Pinecone and while it was unfamiliar to me it ended up being dead simple.
Great job! The new data shows a llama_index I went with it because I figured you updated it. But when I construct the index I get and error on line 58 in red it is super()._init_( and it fails. Any helps on this?
Changing index = GPTSimpleVectorIndex(documents, llm_predictor=llm_predictor, prompt_helper=prompt_helper) To service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, prompt_helper=prompt_helper) index = GPTSimpleVectorIndex.from_documents(documents, service_context=service_context) And importing from llama_index import ServiceContext
Absolutely Great video Irina. The colab seems to have llama errors. Was anything changed with the colab? Would love to connect to discuss more. Great tutorials!!
While you're at it, you may want to change the following in the "Define the functions" section of the Colab. Change this --> from langchain import OpenAI To this --> from langchain.chat_models import ChatOpenAI Apparently "from langchain import OpenAI" is old and being deprecated.
@@jlaroche0 Thanks for the feedback Jacques. I tried both recommendations. They seemed to install fine. But still getting error with the following line: -> from llama_index import SimpleDirectoryReader, GPTListIndex, readers, GPTSimpleVectorIndex, LLMPredictor, PromptHelper, ServiceContext
@@jlaroche0 It works now. Thank you so much Jacques. Really appreciate your help. These educational videos are super helpful! Looking forward to more videos!
Hi, when I load and then run your Colab notebook, I get an error - TypeError: __init__() got an unexpected keyword argument 'llm_predictor' when I run the construct_index("context_data/data") code. Any clues on what I'm doing wrong?
Changing index = GPTSimpleVectorIndex(documents, llm_predictor=llm_predictor, prompt_helper=prompt_helper) To service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, prompt_helper=prompt_helper) index = GPTSimpleVectorIndex.from_documents(documents, service_context=service_context) And importing from llama_index import ServiceContext
Very intresting. I noticed you said that you can't share the real interviews in video because there can be private information, which is understandable. However, how do you secure that Open AI doesn't receive this information? I find the biggest problem is how to avoid Open AI getting either user or customer information?
Hi Irina, I am using this one year in the future and the davinci transformer has been deprecated are there any notes on how to handle code on when resources are no longer supported. The Gemini help still results in quite a few errors.
Actually, knowledge bases are different to prompts. It is better. It is closer to quantum computing, because it can search the vector space for that document without having to parse the raw files.
Very nice and easy way thank you !!! I have a question regarding the custom knowledge base … can I implement a prolog knowledge base and put it there or it should be a text type because prolog is a requirement in my school project… I hope you answer and thanks a lot ❤
Thank you for the video! Really cool. I have a question: here you are working on Google Colaboración, but how would you bring this to a website? Is it possible? Is it easy? Greetings from Spain :)
Great tutorial! Thank you so much for going through this is such detail. Can you suggest a resource that explains how to take the chatbot we create and integrate it into a website or web app with a prettier interface?
Really good tutorial. I wonder on how well this scales with more documents than just a couple. Do you have some experience with the performance of 1k or 10k documents?
Irina, Many use cases. Excellent information. Thank you. Are you able to provide a similar method for creating a generative AI for a closed system that ensures secret or confidential company or government data cannot be leaked?
Thanks for posting this video. The whole demo is great. The only thing that I am not clear about how to pick those input, output sizes, and if some are based on the particular model, how do you obtain those from OpenAI (like the davinci) page, just in more details and a screen split such that you don’t have to toggle around.
hey, any tips on how to fine tune a model based on a very large pdf document without the " " to split prompt/resolution? I thought maybe have a script break down in every question mark? Or is there some other way?
Hi! This library is not available with ChatGPT yet, but you can keep an eye for updates here gpt-index.readthedocs.io/en/latest/how_to/custom_llms.html
One question, Whenever we ask a question......Does it go through the entire Index everytime? And does that cost us a lot of Tokens for each question? Because If that's the case then we would run out of credit if we applied an App like that for users online.
Thanks for the great tutorial! For multiple documents, can you please advise on how i can retrieve the file name where the contextual information is retrieved from?
Hello! Thank you for the helpful tutorial! What would happen if I ask a question in another language? Would this chatbot switch to the language as ChatGPT does? Thanks a lot.
This is great, thank you! When asking questions to the AI, I didn't notice any custom instructions in use. How can you be sure it was answering only using the data given to it in the index? Can you also make more videos for using custom data from other sources, such as databases? How about the ability to categorize? One minor thing: When pronouncing the word "answer", the "w" is actually silent. (My wife is ESL and always asks me to correct her pronunciation, and I ask the same of her when I speak her native tongue.)
The code for 'Construct an index' no longer works. I get the following error msg: You tried to access openai.Embedding, but this is no longer supported in openai>=1.0.0
Hi Irina, thanks for the video. I want to ask how do you limit the model to answer only about your information. I.e., what would happen if the person asks any question out of context (like: "Can I go to Miami for holiday?"), will it reply?. Thanks
Irina, tutorial fantástico. É muito dificil encontrar um material tão simples e que explique como treinar a IA. Muito obrigado por compartilhar o seu conhecimento.
You feed text data files for providing the data to the model, what if I have an excel file or a tabular data file? And, Openai api key is free or it is paid?
I am a researcher and I followed this up until the 'installing the dependencies' part. I have never used Github, but it would be great if this could be covered too. Also, collab didn't let me upload my txt files from github. I received an error saying syntax error. Need a simper method.
Can I implement this locally? Would my knowledge base be private? Can I ingest HTML data? I want to upload technical documentation as a knowledge base so that I can use the prompt to translate the code into a human-readable form or be able to make more sense of the code to make improvements. Because it's proprietary syntax, I want to use the documentation. Thank you for this video!
Learn how to build a full-stack app in this tutorial: ua-cam.com/video/AMc2A5Abj3M/v-deo.html
Irina, amazing tutorial on integrating OpenAI API with a custom knowledge base! Really excited about the potential of GPTIndex and Langchain. I'd love to see a deep dive comparing AI Agents in Langchain, especially when they're long-running and autonomous. Keep up the fantastic work! 🌟
Using ChatGPT to generate sample user interview data: genius 💥
Thank you for the tutorial... also, to refresh the files details there is a "Refresh" button located just above the Files detail section. It's an icon of a folder with a circular arrow. This will refresh the section without needing to refresh the page.
Thank you for the tip!
So happy to have found you this morning! We need more designers that can code and explore AI possibilities like you do!
You don't need to refresh the whole colab page to update the view of the files/folders, but just the refresh button above the directory structure, in the left pane.
почему ты все время улыбаешься))) так мило)))
Great video and thanks for answering many of the questions! Looking forward to your future YT on integrating into a website.
Thank you, I'm glad you liked it
@@irina_nik thank you for this video. Do you have a tutorial to show how to integrate it into website/whatsapp? thanks
Miss Irina, thank you. After few days playing around you got me to the point. Merci!
I didn't know that we can do something like this with OpenAI, thanks for the video Irina.
Thank you so much, Irina! I copied your source code to Jupyter notebook and create a chatbot in a few minutes! To my surprise, it works! Please give some thumb-ups to this amazing lady. She has spent time to make this solution so easy to use for everyone!
You are the best at explaining things Irina!! Thank you for taking the time to putting this together.
That was a great tutorial. And I like your approach to explaining why one should not be using only one long prompt etc.
Thanks for this info - it's easier to setup a chatbot than I realized!
I'm glad it's useful for you.
Great explanation. Very explicit and clear instructions. Thank you very much for this.
Nice, already done it but i don't know everything so had to watch this!
It's really nice, I got the insights how we can use the custom knowledge base
This is exactly what I was looking for, thank you!
Great work!! Really nice step by step explanation! By the way you can click the refresh button in the file explorer panel (2nd icon) to refresh the files so that they appear.
Excellent tutorial, well presented and very clear. Thank you …. It works perfectly, unlike many so-called tutorials on YT about AI 😊
What could be happening here? I asked how many people were interviewed and the reply was "One person was interviewed". I asked how many times did "It was fun to talk about cooking." appear and it said none (interview4 ends with this quote). Thank you, great video!
Thanks for the amazing tutorial, simple but impactful.
Great video Irina !!
I was looking for this exact solution and it was the first video of your channel that I followed exactly step by step and it works perfectly end to end
It was very clear and well explained.
Nice job !!
Please continue making this kind of useful videos
It was extremely useful for me and extremely detailed.
Keep going!
Very informative...Thanks 😀
wishing you Lots of love and strength to you.
Thank you so much for this information. This is exactly the kind of thing I've looking for. Step by step tutorials for finetining your own AI. This is perfect.
thank you !!!! this is a great video Irina, keep up the good work !
I love your teaching ❤
Amazing video, very friendly to beginners. Thank you.
i am getting an error while using the code when I ask question. Can someone help me?
Thanks for the amazing tutorial. BTW, is there any method to increase the output length? I could only get a answer of approximately 160 words (~250 tokens) right now.
Even I understood almost everything! Well delivered and interesting content!
Thanks ☺️
This is an incredible video. You did an amazing job. Subscribed
Great work, it's quite clear, Seems the llama Index has many updates, I can't recreate your work, would you please make an updated version? thanks a lot~
Thanks! Now I can fire all my employees and save lots of money!
😎
🤣🤣.... 😅😅.... 😄😄.... 🙂.... 🤔🤔🤔... 😐😐
Seriously 😂😂
@@BwahBwahdon't tell me you are going to fire your employees too😂
@@unitedstarsutopia I'll go one better. I won't have to employ anyone now 😀
This just spits out the text related to the query. If I want to augment GPT capabilities with my own data set. what is the best way to do it? For example, using the same example of interview transcription, I should be able to ask the GPT to summarize how the candidate did or whether the interviewee answer was correct for a particular question. Any idea how to go about that? I understand fine-tuning is a possibility but if i have 10,000 interview scripts i want to augment the GPT capabilities with, I am not sure how to go about it.
Any help?
Is there a software package that can make an entire openai chatbox GPT4 or even 3.5 just by
pointing at a folder of PDFs?
We would pay thousands for this right now.
The application has to run local on a PC.
You can use langchain for that. I'll make more tutorials on that topic
Great !! Keep Going. All the very best !!👍😄
Thank you!!! Your words inspire me for more videos)
Hi Irina! it is such a great tutorial and would be useful for case that I currently work on. I have tried this with my own research data and turns out so good with relevant and decent answer. But I am wondering is it possible to use the GPT 3 Model but not using it's training data or knowledge? So the information/answer produced would be just using custom data that we added to the knowledge base. Your answer would be very appreciated, thanks!
An excellent tutorial. Thank you.
I converted this code to a flask app and it works fine on my local server. However when I deploy to google app engine it fails to return responses. The error is 500 internal server error! any idea or advice is much appreciated.
A few points not mentioned in the videos:
Essentially, it is fine-tuning. However, the module for fine-tuning has been pre-written for you.
Fine-tuning can only be done with models below GPT3. Currently, Fine-tuning is not available for ChatGPT, GPT3.5, or CPT4.
For GPT2 to be effective, you need at least 300M training data. Models with more parameters than GPT3 require even more data to achieve the desired effect
She used about 12kB of text in this demo.
Llamaindex built 559kB index from it.
And it did the job on text-davinci-003.
I'm genuinely interested in the reason behind your training data size needs claim.
Hello! Thank you for the video! Also your secret API key is visible in the first few frames before you blur it! You should delete that API key completely!
That smile, that damned smile 😊 And thanks for the nice tutorial btw.
Amazing tutorial! Thanks! If you're looking for future tutorial ideas, I'd love to know how to expand on this to create my own API endpoints so my trained chat bot can be made publicly available from my website. I'm not very familiar with Google Collab (or python for that matter - I'm a php/js web developer), so I'll try to do some of my own research on how this might be possible -- but I really enjoyed and easily absorbed the info in this video. Well done. :)
Hi Mikey! Thank you for the suggestion, I definitely need to make a video about that. I think, I'll be able to post it in 3-4 weeks. Though I'll be using NextJS/Typescript because this is what I'm familiar with.
this response made me subscribe... That would be awesome!
@@irina_nikyou are smart. Can't wait to see you share the typescript/node js version.
Good question Mikey! I have the same question and subscribed to find out from her next video! Thank you!
Irina, thank you for your help? When I ask it irrelevant article questions, it seems to many times provide answers, when it shouldn't. Anyway to ensure it only focuses on my uploaded article?
Thanks it’s really helpful. Capfuls you please let me know if I can use complex data having 100 of parameters (text & numbers) ? If yes in what format the should be uploaded?
Great information irina❤🎉
Hi
Thank you for that content!
I am just curious about the files size limit and the importance of the file format in your approach. I have seen that you are using .txt files. I am using pdfs to feed the knowledge base of custom GPTs but I am observing a low accurary in the answers. It seems that the GPT is not looking at all the knowledge base (6 merged pdfs with 7000 pages approx. in total). Do you have any advice?
How is this GPT-index different from OpenAI's Text Embedding ADA model? Or is it just a wrapper of that model?
great question. would love an answer to this; I can't figure out the difference. they appear to be doing the same thing.
You can achieve the same result without any external library, GPTIndex just makes it easier for non-professional coders like me 😉. It uses the found chunks as the context to the prompt and not as the answer itself.
For embedding you need also a vector database . I wonder if this index solution also performs in a production environment?!
@@josehoyos Yeah i have seen Pinecone proposed a lot for this. I did a little test with Pinecone and while it was unfamiliar to me it ended up being dead simple.
Great job! The new data shows a llama_index
I went with it because I figured you updated it.
But when I construct the index I get and error on line 58 in red it is super()._init_( and it fails.
Any helps on this?
I got the same error as well
I did as well and not sure how to get past it
Changing
index = GPTSimpleVectorIndex(documents, llm_predictor=llm_predictor, prompt_helper=prompt_helper)
To
service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, prompt_helper=prompt_helper)
index = GPTSimpleVectorIndex.from_documents(documents, service_context=service_context)
And importing
from llama_index import ServiceContext
Hi! Thank you for mentioning it. I've updated the code and it should work now 😀
Absolutely Great video Irina. The colab seems to have llama errors. Was anything changed with the colab? Would love to connect to discuss more. Great tutorials!!
While you're at it, you may want to change the following in the "Define the functions" section of the Colab.
Change this --> from langchain import OpenAI
To this --> from langchain.chat_models import ChatOpenAI
Apparently "from langchain import OpenAI" is old and being deprecated.
@@jlaroche0 Thanks for the feedback Jacques. I tried both recommendations. They seemed to install fine. But still getting error with the following line: -> from llama_index import SimpleDirectoryReader, GPTListIndex, readers, GPTSimpleVectorIndex, LLMPredictor, PromptHelper, ServiceContext
@@jlaroche0 It works now. Thank you so much Jacques. Really appreciate your help. These educational videos are super helpful! Looking forward to more videos!
Hi, when I load and then run your Colab notebook, I get an error - TypeError: __init__() got an unexpected keyword argument 'llm_predictor' when I run the construct_index("context_data/data") code. Any clues on what I'm doing wrong?
Did you get the solution?
@@sandipshaw3397 not yet. Have you got the same problem?
Changing
index = GPTSimpleVectorIndex(documents, llm_predictor=llm_predictor, prompt_helper=prompt_helper)
To
service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, prompt_helper=prompt_helper)
index = GPTSimpleVectorIndex.from_documents(documents, service_context=service_context)
And importing
from llama_index import ServiceContext
Great explination, thnka you, may I ask, Is there a limit on how much custom data you can use and would large custom knowledge slow down the chat?
Amazing tutorial.
Is there a way where we can let the model answer the questions faster! It takes nearly 30 seconds to answer the questions.
Great video, thanks!!!
Hey Irina! Thank you for this tutorial, it's a game changer. This is built off GPT 3, how would you go about running it off GPT4? Thanks!
Can the indexing and query code be run locally (interfacing with GPT-3 over the internet of course)?
What IDE?
Thanks worked for me 😇
Very intresting. I noticed you said that you can't share the real interviews in video because there can be private information, which is understandable. However, how do you secure that Open AI doesn't receive this information? I find the biggest problem is how to avoid Open AI getting either user or customer information?
Hi Irina, I am using this one year in the future and the davinci transformer has been deprecated are there any notes on how to handle code on when resources are no longer supported. The Gemini help still results in quite a few errors.
having data in a file and real time embeddings vs embeddings in a db for chatbot for an application (provides information about an application)?
Actually, knowledge bases are different to prompts. It is better. It is closer to quantum computing, because it can search the vector space for that document without having to parse the raw files.
Благодарочка! нижайший поклон! Респект и уважуха)))
Hello dear, could you explain please how to do this on pipedream?
Very nice and easy way thank you !!! I have a question regarding the custom knowledge base … can I implement a prolog knowledge base and put it there or it should be a text type because prolog is a requirement in my school project… I hope you answer and thanks a lot ❤
Thank you for the video! Really cool. I have a question: here you are working on Google Colaboración, but how would you bring this to a website? Is it possible? Is it easy? Greetings from Spain :)
Uanble to use any other file. Even custom text file gives error: 'Rate limit reached'
Great tutorial! Thank you so much for going through this is such detail. Can you suggest a resource that explains how to take the chatbot we create and integrate it into a website or web app with a prettier interface?
Great tutorial. When we send data to OpenAI is that getting used for public training or would it remain private for me ?
Really good tutorial. I wonder on how well this scales with more documents than just a couple. Do you have some experience with the performance of 1k or 10k documents?
im getting a Traceback error when contruct_index. what am I missing?
Irina,
Many use cases. Excellent information. Thank you.
Are you able to provide a similar method for creating a generative AI for a closed system that ensures secret or confidential company or government data cannot be leaked?
Thanks for posting this video. The whole demo is great. The only thing that I am not clear about how to pick those input, output sizes, and if some are based on the particular model, how do you obtain those from OpenAI (like the davinci) page, just in more details and a screen split such that you don’t have to toggle around.
where did the answers come from chat gpt? or the data fed. When I checked the data, I could only find questions?
How do I break the word limit for an answer，Sometimes the answer feels half, not quite ，How can I modify it thank you
How to extend this as a service to use in web / mobile app?
This will take much more time than just playing with API :) I'll make another video about that when I have time.
hey, any tips on how to fine tune a model based on a very large pdf document without the "
" to split prompt/resolution? I thought maybe have a script break down in every question mark? Or is there some other way?
Congrats Irina very clear and nicely explained 👍Which file formats does it support for indexing? Is it only .txt?
Thanks! You can connect other file types with LlamaHub gpt-index.readthedocs.io/en/latest/how_to/data_connectors.html
Thank you very much, very useful tutorial.
Wondering, why did you not use gpt-3.5-turbo - as it is much more inexpensive and probably almost as good?
3:18 to skip the unreasonably long intro
Hey! Great video! Now that Chat GPT api is out do you know if these libraries will work for it? or is this still only a gpt 3.5 method?
Hi! This library is not available with ChatGPT yet, but you can keep an eye for updates here gpt-index.readthedocs.io/en/latest/how_to/custom_llms.html
Can i use this ai, custom knowledge base in chatGPT or in the playground of openAI?
I got this error after ask_ai() "RetryError[]" How can I fix it?
Thank you Irina
One question, Whenever we ask a question......Does it go through the entire Index everytime? And does that cost us a lot of Tokens for each question? Because If that's the case then we would run out of credit if we applied an App like that for users online.
You nailed it ..I ll follow you on Twitter.
How would you adapt this to derive context from dynamic data being generated in a website?
Hi @larina Nik can you check the code again, I think some of libraries are already outdated eg: GPTindex
Thanks for the great tutorial! For multiple documents, can you please advise on how i can retrieve the file name where the contextual information is retrieved from?
Hello! Thank you for the helpful tutorial! What would happen if I ask a question in another language? Would this chatbot switch to the language as ChatGPT does? Thanks a lot.
Could you add this to someone’s website? If so, could you point me to a video you already have on the topic?
This is great, thank you! When asking questions to the AI, I didn't notice any custom instructions in use. How can you be sure it was answering only using the data given to it in the index?
Can you also make more videos for using custom data from other sources, such as databases? How about the ability to categorize?
One minor thing: When pronouncing the word "answer", the "w" is actually silent. (My wife is ESL and always asks me to correct her pronunciation, and I ask the same of her when I speak her native tongue.)
The code for 'Construct an index' no longer works. I get the following error msg: You tried to access openai.Embedding, but this is no longer supported in openai>=1.0.0
Can you set up to read Python code bases?
Hi Irina, thanks for the video. I want to ask how do you limit the model to answer only about your information. I.e., what would happen if the person asks any question out of context (like: "Can I go to Miami for holiday?"), will it reply?.
Thanks
Irina, tutorial fantástico. É muito dificil encontrar um material tão simples e que explique como treinar a IA. Muito obrigado por compartilhar o seu conhecimento.
Hello, nice video. Please let me know how this will ensure our data still will be our data. Will OpenAI won't have access to it now ?
Excellent
You feed text data files for providing the data to the model, what if I have an excel file or a tabular data file?
And, Openai api key is free or it is paid?
I am a researcher and I followed this up until the 'installing the dependencies' part. I have never used Github, but it would be great if this could be covered too. Also, collab didn't let me upload my txt files from github. I received an error saying syntax error. Need a simper method.
Can I implement this locally?
Would my knowledge base be private?
Can I ingest HTML data? I want to upload technical documentation as a knowledge base so that I can use the prompt to translate the code into a human-readable form or be able to make more sense of the code to make improvements. Because it's proprietary syntax, I want to use the documentation. Thank you for this video!
How can i use this as a backend of my website.?