Works Very Well and very low cost (in pennies per PDF). Further savings by 'Persisting the Index' ... Thank You Yet Again! I own you a whole pot of coffee ! 😄
Thank you so much man, just yesterday I was struggling with the same thing, there is no recent content on llamaindex, everything is outdated. This is a live-saver, please continue this series.
Am considering using llamaindex for my ai application project after viewing your wonderfully done video, which is straightforward, simple and absolutely understandable. I am expecting a follow-up video by you on how to deploy llamaindex online for a realistic entrepreneur setting.
i like to learn llamaindex but i wonder if i will just be spreading myself too thin by trying to master both langchain and llamaindex. do you have any advice?
Merci beaucoup pour ce contenu de qualité (comme toutes tes vidéos), vivement la suite ! (j'espère que tu aborderas la création de RAG basé sur des agents)
Love your videos!!! Great content here again. One question: in 30:20 where the index gets created locally, what do the subfolders look like? "image_vector_store", "graph_store"... - does this mean the dataloaders would also split a PDF in plain text, graphs, images and then store the respectivbe embeddings in separate folders? Tried it on my own PDFs but could not make much sense of the index files unfortunately...
Great video. So if I understand correctly, the code example only shows the parsing into documents. So no nodes, embedding/vectorising and persistent storage in a vector DB? Any observations on weak/ strengths in comparison with langchain? The parts upto vector db and the parts from user upto vector DB
what 's the different between (llamaindex for chatbot creation) and (langchain +streamlit ...for pdf bot(the video you did last time) which of them is more suitable if I want to create a chatbot for a company
there is a bit of overlap between the two. but you really can't go wrong with either of them. they are both very reliable and have a great community. it seems to me that llamaindex is focusing a lot more on the data ingestion side and langchain is going more for them overall orchestration of the components. at least for now. the good news is that you can use both :) most of the paradigms are compatible, so you can take advantage of the strengths of each one. in the meantime, i recommend you focus on one of the two and then start implementing features from the other one as needed. you will soon get the core concepts and be able to choose which one is better suited fr a specific project 👍
thanks mate! i'm pretty sure both of us took it from the official docs, tbh 😅 but yeah, i wanted to give a more wholesome presentation of all their offer, not only the open-source part :)
hello there, absolutely. for this particular example, you can just add the model param to the query engine: ```python from llama_index.core import VectorStoreIndex, SimpleDirectoryReader from llama_index.llms.openai import OpenAI documents = SimpleDirectoryReader("data").load_data() index = VectorStoreIndex.from_documents(documents) query_engine = index.as_query_engine(llm=OpenAI(model="gpt-4o-mini")) response = query_engine.query("What is the bootcamp about?") print(response) ``` btw, i am pretty sure that the default model that llamaindex uses with openai is gpt-3.5-turbo. look: github.com/run-llama/llama_index/blob/41643a65bc89cfdb3eb0c11b4f8cb256b02aa21c/llama-index-integrations/llms/llama-index-llms-openai/llama_index/llms/openai/base.py#L78
brother please make a video on RAG (by using the llama index), I have done it already if you need I can send you, so you can save your time for research, Please explain it in your language , please use open source model instead open ai
It is vague at this point - 12:50 "nodes are interconnected creating a network of knowledge". This is very old technique. Obviously embeddings of chunks semantically close to each other will fall in the same area in embedding space. So they are interconnected..How is this any different from chroma db or ANY xyz other vector database in the world? What is different here!
Yes. More on LlamaIndex please. I so appreciate your clear and thoughtful tutorials. Beautifully done!
i appreciate it! absolutely 💪
Yes please make a whole series, especially the application based on usecases. @@alejandro_ao
Thanks for the up-to-date video on Llama Index! It would have been helpful to explicitly mention the deltas from half a few months back.
LLamaindex looks like a survivor. Would love to see some of the advanced new features in your coming tutorials.
totally agree. i am sure they will be shaping the AI app sphere for a long time
Works Very Well and very low cost (in pennies per PDF). Further savings by 'Persisting the Index' ... Thank You Yet Again! I own you a whole pot of coffee ! 😄
Bro literally you are doing such a useful thing please do more videos its very helpful lots of love from student community ❤️
always eager to start building things after watching one of your videos. You really have a talent for explaining things super clear and easy .
Thank you so much man, just yesterday I was struggling with the same thing, there is no recent content on llamaindex, everything is outdated. This is a live-saver, please continue this series.
you can count on that
Am considering using llamaindex for my ai application project after viewing your wonderfully done video, which is straightforward, simple and absolutely understandable. I am expecting a follow-up video by you on how to deploy llamaindex online for a realistic entrepreneur setting.
the moment you say good morning, I feel like I woke up on a flight with pilot announcement, Good stuff btw.
I was searching about llamaindex yesterday on your RUclips channel
we're in sync 😎
Good video, I have seen few videos that explain this topic well, greetings from Chile
Excellent as usual! And useful as usual. Thanks and stay cool. 😎
thank you brandon! always a pleasure to see you around!
i like to learn llamaindex but i wonder if i will just be spreading myself too thin by trying to master both langchain and llamaindex. do you have any advice?
Merci beaucoup pour ce contenu de qualité (comme toutes tes vidéos), vivement la suite ! (j'espère que tu aborderas la création de RAG basé sur des agents)
Nice & articulate. Thanks for putting this out.
I appreciate it :) expect many more to come
You should become a professor it will benefit thousands of students in your country. Well taught.
Great tutorial!!
Excelente explicação como sempre. Parabéns.👏👏👏
It helped me a lot! Thanks for the video
no worries!
Very precise and much much useful!
hey there, thanks! glad to see you around!
Love your videos!!! Great content here again. One question: in 30:20 where the index gets created locally, what do the subfolders look like? "image_vector_store", "graph_store"... - does this mean the dataloaders would also split a PDF in plain text, graphs, images and then store the respectivbe embeddings in separate folders? Tried it on my own PDFs but could not make much sense of the index files unfortunately...
Thank You✊🏾💎
😌 no problem
Can this setup be implemented within a protected infrastructure? I have sensitive data that I don't want to leave my network
Their recent documents are really really good
they are awesome indeed
Très bonne présentation. Merci
je t'en prie !
can i setup llamIndex on my own server? i dont want to use api or don't want to send data to other's server
Really interested to see a fully open source version of this with hugginface embeddings and models.
coming up!! sorry been super busy with the cohort 😅
Great video. So if I understand correctly, the code example only shows the parsing into documents. So no nodes, embedding/vectorising and persistent storage in a vector DB? Any observations on weak/ strengths in comparison with langchain? The parts upto vector db and the parts from user upto vector DB
I’m waiting for the local install video.
Alejandro Thank you for the clear introduction to LlamaIndex. Instead of using OpenAI API , how can we use a model from hugging face?
definitely, coming up!
@@alejandro_ao thank you! waiting :)
Should we pay for the openai api key? And How
what 's the different between (llamaindex for chatbot creation) and (langchain +streamlit ...for pdf bot(the video you did last time)
which of them is more suitable if I want to create a chatbot for a company
there is a bit of overlap between the two. but you really can't go wrong with either of them. they are both very reliable and have a great community.
it seems to me that llamaindex is focusing a lot more on the data ingestion side and langchain is going more for them overall orchestration of the components. at least for now.
the good news is that you can use both :) most of the paradigms are compatible, so you can take advantage of the strengths of each one.
in the meantime, i recommend you focus on one of the two and then start implementing features from the other one as needed. you will soon get the core concepts and be able to choose which one is better suited fr a specific project 👍
Looking forward to Agentic RAG system build with function calling and etc
i feel like this shirt makes it look like i'm at the beach
it looks cool
😎
looking great bro!
Welldone boss i almost thought you stole it from Krish Naik but your adding the lamaparse made the difference
thanks mate! i'm pretty sure both of us took it from the official docs, tbh 😅
but yeah, i wanted to give a more wholesome presentation of all their offer, not only the open-source part :)
thanks
thank *you*!
Excelente
hola amigo 🙌
Hey AO.. Looks like the default LLM is being used which is Da Vinci. Can we upgrade to GPT4o?
hello there, absolutely. for this particular example, you can just add the model param to the query engine:
```python
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.llms.openai import OpenAI
documents = SimpleDirectoryReader("data").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine(llm=OpenAI(model="gpt-4o-mini"))
response = query_engine.query("What is the bootcamp about?")
print(response)
```
btw, i am pretty sure that the default model that llamaindex uses with openai is gpt-3.5-turbo. look: github.com/run-llama/llama_index/blob/41643a65bc89cfdb3eb0c11b4f8cb256b02aa21c/llama-index-integrations/llms/llama-index-llms-openai/llama_index/llms/openai/base.py#L78
Do you already have a video how to use Llama-Index with local llama3 instead of ChatGPT? Thanks!
Not yet, but coming up next week!
can do langchain tutotrial with open source I was searching and got none in case any please give me the link
brother please make a video on RAG (by using the llama index), I have done it already if you need I can send you, so you can save your time for research, Please explain it in your language , please use open source model instead open ai
It is vague at this point - 12:50 "nodes are interconnected creating a network of knowledge". This is very old technique. Obviously embeddings of chunks semantically close to each other will fall in the same area in embedding space. So they are interconnected..How is this any different from chroma db or ANY xyz other vector database in the world? What is different here!
LlamaIndex is a commercial product, with pricing based on usage... Ok bye. Thanks for a video anyway.
Awesome content as usual
hey sami 🙌
@@alejandro_ao 🎉