This channel is a Godsend, which instilled the fundamentals of Python within me, which helped me to obtain a certification in robotics. You never cease to amaze me. 😊
If you are running this blindly without using Tim's requirements file, please note that due to security `from llama_index.query_engine import PandasQueryEngine` is no longer the right import, try pip installing `llama-index-experimental` and then using the PandasQueryEngine class from `from llama_index.experimental.query_engine import PandasQueryEngine`. This is for py3.10. Finally, the PromptTemplate class is now at `from llama_index.core import PromptTemplate`. The documentation will really help though. Thanks Tim.
To add context in order to refer to previous response like "save the response to my notes", you can add context = " ".join([f"role: {exchange['role']} content: {exchange['content']}" for exchange in st.session_state.messages]) response = agent.query(context + " " + prompt)
This is so well presented. AI appears very scary to ordinary programmers who don't have the deep knowledge of how AI works. This video shows how a programmer can utilize already built models to produce useful agents. Thanks Tim for the video. Kudos.
Amazing video ,tim ...i always wanted a fast an ld easy way to understand llamaindex...now I can build my own project fast ... Thanks a million brother
Very cool. More tutorial on llama usage. This tool will help researchers to manage knowledge. If it can also store image and generate image as answer based on the query's context then it will be more useful. It can be used to build personal library and digital librarian
This is great wow ...🎉i can think of a lot of ideas now for this ...but please guys play safe on this... like wiring your complex project to the net..dev opps are very important regarding that...😅otherwise this is great...❤❤great content Tim..
Thanks, Tim. I've noticed that many of the RAG examples available on TH-cam primarily focus on enhancing the model using PDFs, CSVs, or plain text. However, in practice, a significant portion of business data is stored in relational databases, such as Oracle or SQL Server. Could you provide an example demonstrating how RAG can be applied to data from relational databases?
Hi man, you are the best, I wish if it was about extracting data from text or pdf and also harnessing data from agents LLM to store it in KG and make LLM query from it, all the video I watched about that were poor and not a practical solution, either they doesn't work or poor result or use paid software or don't accumulate data in the KG database with no duplicate... Man you are the one for this project, if you did it I unsure you your channel will be on fire.
@Tim Excellent video. The reason the app wasn't able to save the note (end of video) is because you need to include chat memory / history. The llm has no view of the previous messages.
Thanks for the really nice video! You explained everything in detail, and I loved it! I would like to ask you: Would you say that RAG can be called AI Search Agent? Is there any autonomy in a RAG application, for example, when the model generates an answer from the relevant context? Would you say it’s correct to define RAG as an agent? I'm not criticizing your title, of course. It's just that some describe it as a RAG agent and others as a RAG chatbot, and I'm really confused. Would love to hear your thoughts! Thank you!
great stuff, even though I had to bail on the pdf part because I got some weird stuff going on, it said I have no openai api key and stuff, battled with it for two days and figured out that the code works without that part so... no pdf for me, but everything else works fine :) I will definitely play around with llama-index more
I think that the issue was that the function was not called in openai section so for me, the issue was model was not being used for instance in the 5 or 7 line of code in main the code which ends with input openai is not working because this is not a function which is why the model section near the end was not working so if you want this code to run that I think function needs to be defined for it
This is amazing. Can you create a next video on automation script generation and Sql query generation(for complex schema) using Rag or AI agents. ( but use open source models.)
Very detailled and high quality stuff!! But do you think it's safe to use it without an isolated Docker container? It could potentially damage your system, not?
Great video, but llamaindex did some major changes. Hence, the import statements as represented in the video and download files are incorrect. It is taking some time to figure out the new structure of llamaindex. Does any way have the new import/from statements that llama index now uses?
@TechWithTim can you make a video on how do you go thorough any documentation, what is your mindset where you start, and what flow do you follow. Please and Thank you :)
not sure how long ago this was recorded but the correct import for pandasquery engine as of the latest version of llama-index is: from llama_index.core.query_engine import PandasQueryEngine
This tutorial is super helpful. Thanks Tim. I was able to get the app working. when I ask a question about canada or population, Agent is able to answer the question by looking at the PDF, CSV etc.. But when I ask a question like "what is solar eclipse", the agent is still able to answer the question. How can I prevent it from happening ? I just want answers that are available in the documents.
It doesn't save a note using the previous prompt because I think it is lost. I think you are calling a new instance of the model each time you give a new prompt. So you would have to update or append prompt outside the while loop to get it to remember the entire conversation... But I could definitely be wrong, just my intuition :P
in case if you see this error --ImportError: cannot import name 'OpenAI' from 'llama_index.llms' (unknown location)-- do this --from llama_index.llms.openai import OpenAI-- and for error. --ImportError: cannot import name 'note_engine' from 'note_engine' (/Users/macbookpro/AIAgent/note_engine.py)-- change this. if not os.path.exist(note_file): to this if not os.path.isfile(note_file):
Hi Tim, first of all, great tutorial! I wanted to ask you if you know if it's possible or efficient to use llama index to do RAG over 300k pages of pdfs. I've been researching and a lot of people say that I will have to fine tune the embeddings models and use one from hugging face. Also to make the results better, use metadata. However, I am wondering if using llama index is the correct approach or if I will need to create my own RAG system. Thank you for taking the time to read this.
I did pip install llama-index-experimental so many times and also the upgrade version. I did ' from llama_index.experimental.query_engine import PandasQueryEngine ' and it says ' no module name llama_index.experimental '. Everything sound good but that part doesn't work, anyone? Weird, plz anyone ?
thank god you still aware of the fundamental importance of being free(only by being free can a poor, loser like me can dream of becoming a coder just before rotting completely)
It's really Chilly in here, what's going on ? 🤣🤣🤣 So, are you mitigating the need to have multiple agents with the idea of having 1 Agent that is using the proper tools and Data sources to provide responses? This simplifies the code quite a bit. I am not sure why you didn't create a seperate file for each engine? Also would probably allow a file picker instead of downloading the file file. Are you still going to reduce the chance of Hallucination with this approach? Thanks for Sharing..... Great presentation
Thanks Tim, this is really informative video. Just have a question for this. In your code, the LLM model is OpenAI by default. I tried using a local LLM model such as llama (using "codellama-7b-instruct.Q8_0.gguf' loaded by LlamaCPP), and leave everything else the same as your code. But, it won't produce the desired result as your code shows. Can you have another video using a local LLM model rather than OPENAI that achieves the same functionality of Python AI Agent? Thanks in advance!
Thanks for the information @internetMadeCoder but i have a question i struggle at learning programming languages which makee it frustrating and make the process feel tiring i went online and there something when am learning from the video it seems pretty easy and but when i want to use it to try and solve maybe exercises it feels difficult and also forget of what i learnt the previous days and also how do i work on this and be able to learn better what caan u advice me to do
Hi, could you convert complex PDF documents (with graphics and tables) into an easily readable text format, such as Markdown? The input file would be a PDF and the output file would be a text file (.txt).
Thanks for the great vid. i tried following along but getting too many import errors. i also tried building a new virtual env, then installed the modules from your requirements.txt mentioned in this thread, and that also now doesnt work (llama_index tries to load pkg_resources which is not found).
I have maybe beginner question coming, I wonder if it is possible to make and IA agent that can use both the normal model trained on his dataset, our RAG with provided data source as in this course plus internet search and compile these source in the output ?
Hi guys, I encounted with the following eroor message: from llama_index import PromptTemplate ImportError: cannot import name 'PromptTemplate' from 'llama_index' (unknown location) Please advise
'llama_index' should be 'llama_index.core' for both the import and the pip install. At least, that's what worked for me. So, the pip install is 'pip install llama-index.core pypdf python-dotenv pandas' and the import is 'from llama_index.core.query_engine import PandasQueryEngine'
if you pass context history with the new prompt only then would it be able to save to note. Passing context history to LLM is an integral part of any RAG otherwise it looses context.
is there a way to do this without openAI because if we want to use it at enterprise level, giving data to openAI is not secure. can we use any open source llms and acheive the same?
How to build a Autonomous RAG LLM Agent with Function Calling that is connected with External REST API like Microsoft Graph API ? Can You make a video on this ?
The error message "FileNotFoundError: [Errno 2] No such file or directory: '...config_sentence_transformers.json'" means that the llama_index library is trying to load the specified embedding model ("BAAI/bge-m3") from your local machine, but it can't find the necessary configuration file.
![[TH] 2024 PMGC League | Survival Stage วันที่ 1 | PUBG MOBILE Global Championship](http://i.ytimg.com/vi/FVFT-PFwU80/mqdefault.jpg)
Just a tip for whoever is following along.The code, from llama_index.query_engine needs to be llama_index.core.query_engine.
How did you figured that out bro ? 😮❤
Btw thanks alot
why
@@Al_Miqdad_ because llama_index.query_engine doesn't works, unless you add .core
i love you
@@senorperez looked up the llama documentation
Detailed, no-nonense, topical. One of the best coding channels on youtube. Always looking forward to a new video.
This channel is a Godsend, which instilled the fundamentals of Python within me, which helped me to obtain a certification in robotics. You never cease to amaze me. 😊
If you are running this blindly without using Tim's requirements file, please note that due to security `from llama_index.query_engine import PandasQueryEngine` is no longer the right import, try pip installing `llama-index-experimental` and then using the PandasQueryEngine class from `from llama_index.experimental.query_engine import PandasQueryEngine`.
This is for py3.10. Finally, the PromptTemplate class is now at `from llama_index.core import PromptTemplate`. The documentation will really help though.
Thanks Tim.
thank you super helpful!! :)
What about the PDFreaders?
ImportError: cannot import name 'PDFReader' from 'llama_index.readers' (unknown location)
To add context in order to refer to previous response like "save the response to my notes", you can add
context = " ".join([f"role: {exchange['role']} content: {exchange['content']}" for exchange in st.session_state.messages])
response = agent.query(context + "
" + prompt)
Could you specify a timestamp?
5 mins into the video and I am already excited about the future!
For sure it’s super cool!
For windows if './ai/bin/activate' doesn't work then use ' ./ai/Scripts/activate' , that would do the trick^^
This is the gold standard for this kind of coding tutorials.💯 I hope more TH-camrs would be like him. Please keep up the good work.
This is so well presented. AI appears very scary to ordinary programmers who don't have the deep knowledge of how AI works. This video shows how a programmer can utilize already built models to produce useful agents. Thanks Tim for the video. Kudos.
one of the best videos on internet regarding AI agents
thank you very much for your feedback ❤❤❤❤
Amazing video ,tim ...i always wanted a fast an ld easy way to understand llamaindex...now I can build my own project fast ... Thanks a million brother
This is a very good video, very well structured and explained. Thanks a lot !
Very cool. More tutorial on llama usage. This tool will help researchers to manage knowledge. If it can also store image and generate image as answer based on the query's context then it will be more useful. It can be used to build personal library and digital librarian
More ai video 😊 awesome working
Thanks very much!
I love to see you used venv. I find it more practical than other alternatives.
This is great wow ...🎉i can think of a lot of ideas now for this ...but please guys play safe on this... like wiring your complex project to the net..dev opps are very important regarding that...😅otherwise this is great...❤❤great content Tim..
Excellent tutorial. Its clear enough to follow and implement. Keep up your good work.
awesome great explanation i spended days to read the docs i know the efforts you in to generate this content, thanks
I second that, the RAG toolkit is amazing.
Tim you saved my day, you are awesome. I will write in details later how, but for now thanks for the brilliant working code
Thanks, Tim. I've noticed that many of the RAG examples available on TH-cam primarily focus on enhancing the model using PDFs, CSVs, or plain text. However, in practice, a significant portion of business data is stored in relational databases, such as Oracle or SQL Server. Could you provide an example demonstrating how RAG can be applied to data from relational databases?
Hi Tim, i am waiting for your answer please
@@ahmadsaud3531 did you get the answer ?
@@arnav3674 not yet
@@arnav3674 not yet
in next 5 years they can write research papers, if you just give your idea and results. this is mindblowing.
Appreciate you sharing your skills, super helpful for noobs like me.
So RAG stands for Really Awesome Guidance, nice.
Hi Tim - Thanks so much for the video. Great job!!! Would you be able to address not using OpenAI (model, agent) but rather using an open source LLMs?
Best explanation using coding , hats off bro
Hi man, you are the best, I wish if it was about extracting data from text or pdf and also harnessing data from agents LLM to store it in KG and make LLM query from it, all the video I watched about that were poor and not a practical solution, either they doesn't work or poor result or use paid software or don't accumulate data in the KG database with no duplicate... Man you are the one for this project, if you did it I unsure you your channel will be on fire.
@Tim Excellent video.
The reason the app wasn't able to save the note (end of video) is because you need to include chat memory / history. The llm has no view of the previous messages.
Can you explain with code?
Lots of Thanks in the comments section but i owe you another one, THANKS A LOT !!
This looks like a helpful tutorial, hope I can learn something!
Great Topic! It would be awesome if you extend this example with crewai
Tim we need more content like this or a course... and as always awesome work ❤
Thanks for sharing Tim.
Thanks for the really nice video! You explained everything in detail, and I loved it! I would like to ask you: Would you say that RAG can be called AI Search Agent? Is there any autonomy in a RAG application, for example, when the model generates an answer from the relevant context? Would you say it’s correct to define RAG as an agent? I'm not criticizing your title, of course. It's just that some describe it as a RAG agent and others as a RAG chatbot, and I'm really confused. Would love to hear your thoughts! Thank you!
This is brilliant. Thank you.
great stuff, even though I had to bail on the pdf part because I got some weird stuff going on, it said I have no openai api key and stuff, battled with it for two days and figured out that the code works without that part so... no pdf for me, but everything else works fine :)
I will definitely play around with llama-index more
can you please tell how you build the project without open ai key?... I am facing the issue in this only
@@sonalithakur8234I didn't build it without an api_key. I removed the part of code that was meant to read pdf because it was giving me problems.
Yeah same problem for me did anyone figure it out?
I think that the issue was that the function was not called in openai section so for me, the issue was model was not being used for instance in the 5 or 7 line of code in main the code which ends with input openai is not working because this is not a function which is why the model section near the end was not working so if you want this code to run that I think function needs to be defined for it
Very informative video!
Hey Tim,
Can you make a video with mistral model locally loaded rather than using openai API key.
This is amazing. Can you create a next video on automation script generation and Sql query generation(for complex schema) using Rag or AI agents. ( but use open source models.)
Excited to experiment more.
Alright let's go i'll get all hyped up regardless of what will come of it Thanks Tim
Very detailled and high quality stuff!! But do you think it's safe to use it without an isolated Docker container? It could potentially damage your system, not?
Great tutorial Tim!
Great video, but llamaindex did some major changes. Hence, the import statements as represented in the video and download files are incorrect. It is taking some time to figure out the new structure of llamaindex.
Does any way have the new import/from statements that llama index now uses?
@TechWithTim can you make a video on how do you go thorough any documentation, what is your mindset where you start, and what flow do you follow. Please and Thank you :)
not sure how long ago this was recorded but the correct import for pandasquery engine as of the latest version of llama-index is:
from llama_index.core.query_engine import PandasQueryEngine
Thank you for the video. It is interesting and clear
please make a whole series on this
This tutorial is super helpful. Thanks Tim. I was able to get the app working. when I ask a question about canada or population, Agent is able to answer the question by looking at the PDF, CSV etc.. But when I ask a question like "what is solar eclipse", the agent is still able to answer the question. How can I prevent it from happening ? I just want answers that are available in the documents.
Nice one Tim!
Thanks for sharing.
This is really cool stuff awesome video
glad you liked it!
Good vid, needs to be updated, Llama index changing
Did it work for you, I've gotten way too many errors?
It doesn't save a note using the previous prompt because I think it is lost. I think you are calling a new instance of the model each time you give a new prompt. So you would have to update or append prompt outside the while loop to get it to remember the entire conversation... But I could definitely be wrong, just my intuition :P
Makes me want to build my own AI chatbot.
Thank you Tim.
Brilliant!
in case if you see this error --ImportError: cannot import name 'OpenAI' from 'llama_index.llms' (unknown location)-- do this --from llama_index.llms.openai import OpenAI--
and for error. --ImportError: cannot import name 'note_engine' from 'note_engine' (/Users/macbookpro/AIAgent/note_engine.py)--
change this. if not os.path.exist(note_file): to this if not os.path.isfile(note_file):
Thanks
Hey Tim! Is there a way to do this without using an API Key?
awesome video, they are really helpful!
Hi Tim... All your channel is great...! I want to create a RAG Agent but, of one website, do you if is possible?
😊
Dude was forced to go from sublime to vscode. So he made vscode look like sublime.
Hi Tim, first of all, great tutorial! I wanted to ask you if you know if it's possible or efficient to use llama index to do RAG over 300k pages of pdfs. I've been researching and a lot of people say that I will have to fine tune the embeddings models and use one from hugging face. Also to make the results better, use metadata. However, I am wondering if using llama index is the correct approach or if I will need to create my own RAG system. Thank you for taking the time to read this.
Thanks for the video. Can this be done without llama-index and openai? like using the AI on your local PC
Love your content❤
tim is on the rag....
I think this is way easier than the Langchain framework
I did pip install llama-index-experimental so many times and also the upgrade version.
I did ' from llama_index.experimental.query_engine import PandasQueryEngine ' and it says ' no module name llama_index.experimental '.
Everything sound good but that part doesn't work, anyone?
Weird, plz anyone ?
I have the same error, did you get the solution??
So freaking good
thank god you still aware of the fundamental importance of being free(only by being free can a poor, loser like me can dream of becoming a coder just before rotting completely)
Great video, just one question: What would I have to do if I wanted to use open-source tools instead of the openAI API? Thanks.
Hi There, Great video. I was wondering if this same method would work but the llm was loaded in via llama instead of using openai llm?
It's really Chilly in here, what's going on ? 🤣🤣🤣
So, are you mitigating the need to have multiple agents with the idea of having 1 Agent that is using the proper tools and Data sources to provide responses?
This simplifies the code quite a bit.
I am not sure why you didn't create a seperate file for each engine?
Also would probably allow a file picker instead of downloading the file file.
Are you still going to reduce the chance of Hallucination with this approach?
Thanks for Sharing.....
Great presentation
Hey Tim, Can we integrate matplolib to actually get plots when we are querying the excel file?
Awesome! Hey Tim, can you tell me what theme and font are you using for vscode?
Your vid quality is legit what's your setup?
Thanks Tim, this is really informative video. Just have a question for this. In your code, the LLM model is OpenAI by default. I tried using a local LLM model such as llama (using "codellama-7b-instruct.Q8_0.gguf' loaded by LlamaCPP), and leave everything else the same as your code. But, it won't produce the desired result as your code shows. Can you have another video using a local LLM model rather than OPENAI that achieves the same functionality of Python AI Agent? Thanks in advance!
Thanks for the information @internetMadeCoder but i have a question i struggle at learning programming languages which makee it frustrating and make the process feel tiring i went online and there something when am learning from the video it seems pretty easy and but when i want to use it to try and solve maybe exercises it feels difficult and also forget of what i learnt the previous days and also how do i work on this and be able to learn better what caan u advice me to do
what are your thoughts on using some of the open source LLMs for this, via Ollama?
Hi, could you convert complex PDF documents (with graphics and tables) into an easily readable text format, such as Markdown? The input file would be a PDF and the output file would be a text file (.txt).
Thanks for the great vid. i tried following along but getting too many import errors. i also tried building a new virtual env, then installed the modules from your requirements.txt mentioned in this thread, and that also now doesnt work (llama_index tries to load pkg_resources which is not found).
For me, the agent keeps using the wrong column:
df[df['Country'] == 'Canada']['Population']
despite there being no column named population
Can I use it with my git repos (js, ts on nodejs)? It would be great to build custom, local copilot for coding.
Cool video. Random question, are you ever going to move out of Canada? I know a few that have tried but there are too many hoops.
Yes I’m currently living in Dubai
I have maybe beginner question coming, I wonder if it is possible to make and IA agent that can use both the normal model trained on his dataset, our RAG with provided data source as in this course plus internet search and compile these source in the output ?
Most of the comments here are due to the fact that you need to take the requirements.txt from the git repository and use it with pip install.
Tech with Tim, can you please make something for the forex traders please
Hi guys, I encounted with the following eroor message:
from llama_index import PromptTemplate
ImportError: cannot import name 'PromptTemplate' from 'llama_index' (unknown location)
Please advise
'llama_index' should be 'llama_index.core' for both the import and the pip install. At least, that's what worked for me. So, the pip install is 'pip install llama-index.core pypdf python-dotenv pandas' and the import is 'from llama_index.core.query_engine import PandasQueryEngine'
@@stevenzusack9668 thank you for your response, this just worked for me
Thank you for the video! Did anyone face the issue where the query engine returns only the pandas code and not the pandas output?
Nice
Perhaps extend this to a web based interactive chat that allows a user to choose between different LLM models like the new llama 3 vs chatgpt
if you pass context history with the new prompt only then would it be able to save to note. Passing context history to LLM is an integral part of any RAG otherwise it looses context.
Question : once loading a vector store , how can we output a dataset from the store to be used as a fine tuning object ?
is there a way to do this without openAI because if we want to use it at enterprise level, giving data to openAI is not secure. can we use any open source llms and acheive the same?
How to build a Autonomous RAG LLM Agent with Function Calling that is connected with External REST API like Microsoft Graph API ? Can You make a video on this ?
muy buen video pregunta como hago para que la salida este en formato pandas o dict o list ?
The error message "FileNotFoundError: [Errno 2] No such file or directory: '...config_sentence_transformers.json'" means that the llama_index library is trying to load the specified embedding model ("BAAI/bge-m3") from your local machine, but it can't find the necessary configuration file.
Langchain or llama index, which is ideal
What if ee wnt to use existing index from vectordabases
Let me know if you ever need a volunteer for any future projects.