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Thank you very much for all your hard work Alejandro, your videos have been absolutely indispensable for creating applications using the power of OpenAI. I am really looking forward to what further tutorials you come up with that can allow us to use as a platform to make magical new tools!

I am learning a lot of how LLMs work in the python ecosystem with your videos, also your solution-centered attitude helps me code less stressed when playing with such complex stuff haha, keep it up! 😃 💯

Qdrant is an open-source vector database. Their free cluster comes with: - 20GB of storage - 1GB of RAM - 0.5 vCPU What other vector databases should I cover? 👇

Very very very great video we are waiting for more such videos

Another great video tutorial, which actually answers my question from your previous video on how to avoid recreating the vector store for the documents whenever the app is launched, especially where the documents are fixed in the application (you do not allow users to upload documents). Muchas gracias.

Thanks for the in depth step by step process to setup qdrant

Thank you for taking time to provide this useful information, Alejandro!!

Great video! Glad you're back!

Great video! Thanks, very well taught, your a good teacher. I'm using Flowise though to implement LangChain, a lot LOT faster plus it allows me to hit production a lot faster.

Excellent piece of work…. This is what I was looking for…. Tons of thanks

Great content and demonstration, thank you so much!

It would be interesting to save vector embeddings in relational databases and then when these embeddings are used at a later point in time to import them in a vector database. Or how to convert embeddings into another “dimensionalaties” removing complexity. Edit: I am using pgvector so pg dumping and restoring does the trick, but reformatting for Pinecone, Chroma and Faiss?

ALEJANDROOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO

Kudos on the excellent video! Your hard work is acknowledged. Could we expect a video about DemoGPT from you?

Hi Alejandro! It was an amazing tutorial, thank you for sharing. Do you know if its possible how to get a summarize a big amount of text or extract an idea of big amount of text? I saw that you ask simple questions, but how about a whole context interpretation?

Hey Alejandro loving your channel and videos so far, please make a series on MLOps if u can. Thankyou in Advance.

Looking forward to see qdrant

Thank you so much for a very nice tutorial. Your clear explanation about each concepts helped to understand the concepts quickly. I have a question also. Say for example, You created embeddings from a text file and stored it in your vector space. But after some months, if you try to create embeddings for the same text file and try to store it in your same vector space. Will it throw any error? or Is there any way to validate this in order to avoid saving same data again and again?. It would be really helpful if you explain this. Looking forward to see more useful videos from you and thanks on behalf of everyone to your gesture.

hi Alejandro! great video! is there any way to determine the ideal chunk size?

thank you heaps for another awesome video, this is very helpful video alternative to pinecone. thanks

Amazing video!!! Thanks for all the shared knowledge. I was wondering if you could tell me if ii si possible to update the information inside the vector database every 30 minutes, so that it contains updated information that users can query. Do you think this is possible in Qdrant?? Thanks

Wow that;s so cool application of vector database. Now with GPT 4 turbo, can it be combined with Quadrant or was it not necessary needed for chatting with documents since we have GPTs?

great video you went over everything in great detail but one thing you did say why did you select "stuff" as a chain type or maybe I missed it?

If I upload more document and run the code, it process the existing text again and even though it was already converted to vector and uploaded to qdrant. How Can I avoid these duplicated

Splendid video. Makes Langchain more appealing to use and learn.❤ Please how do I implement a different LLM(like Zephyr 7B) other than OpenAI? Thanks.

Great tutorial! Thanks for sharing. What if I want to insert some template, with some instructions like "You're a helpful assistant" and "if you don't know the answer say you don't know"? I think I would probably use LangChain PromptTemplate for that but, where would I insert that code?

your video answers my question! What is the vector database? I want to create a location-based database for the city council district travel and food amenities. Thanks for sharing.

Great channel! This might be a silly question but if I build my own project based on your tutorial, can i do that? If yes, how should i credit your work, given that your github repos don't specify a license.

This was awesome video. How can we change it to remember the previous conversations too?

Mb next video about Di id API and Generated video from photo? :)

"Connection was forcibly closed by a peer" -- I'm getting this error when I'm using thunder client and adding curl link and api key as header. What to do?

Great video, thanks for sharing! Is there a way to setup Qdrant quickly and without coding? I'd need to use it in combination with Flowise but even just following your instructions here makes my brain explode lol

Thanks Alex... Any idea if you'll create a community or news letter?

thank you very much!!! 😀

Extraordinario.. gracias!

Awesome video. Is there a way to narrow the token size of the documents accessed if you create your database out of something like 40 PDFs? I get errors saying that the context being passed to OpenAI is much greater than 4096 tokens...

I have a use case where we have list of "intents", and "their definition" explaining what that means. I want to make a QA bot that can answer intelligent questions about this dataset. What should be my strategy converting this data into a vector DB for proper effect ?

Thanks for the great content

Subscribed amazing videos!

Thank you very much for this great video! However, I have a question: How can I update the data? Suppose we want to add a new chapter to our beautiful story. Thank you very much for your help!

Thanks for the program. I am trying to combine read multiple pdf and save the embeddings in drant. If I can append the data.

Very cool! Do you have a way to get the size of the embeddings created to understand if those are able to be stored in the current collections box?

the video is great, but why are our old questions not on the screen and the avatars we created for user and client disappeared?

hello! i have been faced with a problem of not getting relationship data using vector databses. any chance of improving it for effecient retrieval ? or can we use different databses like sql, graph or two or more simultaneously for getting realtionship data.

Actually I switched to Qdrant after watching this video on my on premise server. Only issue in facing is duplicatation of content. How to handle that?

But can you create a system where we upload a document and the chatbot takes help from the vector store, and explain according to that its knownledge base embeddings? is it possible ? if so, then please make a dedicated video on it

Great video! Keep up the good work! I am new to this AI programming and I am currently trying to combine 2 of your videos to make an app. This Video and also the video: "Langchain PDF App (GUI) | Create a ChatGPT For Your PDF in Python" I am trying to create an app that has an upload page, where I upload documents as I go and then the other page is where I chat with the AI about the documents I have uploaded. I don't want to manually input every new document I have, I just want to drag and drop the document on the upload page, hit "Upload" and then it should be in the database. However, I am struggling to accomplish this. Can you maybe help me out with a website I should take a look at so that I can figure this out? I would greatly appreciate it! Thank you!

I am developing a chatbot that can work in an offline environment. I'm undecided about Vector db. Do you think Qdrant would be a good choice?

The project works fine in my local tests. But when i upload to Azure i get the error "no module found: qdrant_client". Is there an extra step to make it work online?

Hey!! Can u please suggest how can i integrate this with Microsoft teams Btw great video😉

how can we add stream type response like openAI

Hey, my collection is not created showing Error: ResponseHandlingException: [Errno 99] Cannot assign requested address Can you help to resolve this error?

hi this is an excellent video, thank you. I have a question though, I am new at this so bear with me. In this example you first create a collection in the DB, then you split text into chunks and create the embeddings out of them (so the vector representation in OpenAi format) and then save the vectors into the collection. Then you use a Retrieval chain that uses OpenAI, which uses these vectors you have stored in the DB. My question are: Does openai charge me for using his LLM? Am I actually using the LLM behind the scenes? And the last question is, if the information i am asking is not in the text that has been stored in my DB, will OpenAI try to figure the response out? Do you know if the information stored in my DB is also shared with OpenAI? Thanks

Can we use any open source LLM instead of Open AI?

Great video. I have a question: if i restart the python program, isn't it doing the embeddings again since the get_vector_store function needs to rerun? Is there a way to check whether the embedding exists already?

Not really related to langchain but I just gotta ask, how do you zoom in at 13:51? I don't know how to zoom in/out in a browser using mouse scroll on a Mac.

Can you demonstrate this with PDFs using the new Streamlit Chat component? Also with all use cases, how do we remove a PDF from the vectorstore?

could you do a tutorial on how to build an AI chatbot to use on a website. (Without Botpress and similar websites)

My data is overwriting the previous data is there any way to add more data to database without overwriting the previous one?

But how to upload our own database multiple pdf files?

its been 5 months why not new video, you got a job bro :p

useful

What happened no new video? Looks like you got something big and moved on! All the best

How many indexes can i have?

Good stuff

kindly make video on how to deploy llma-2 on aws with endpoint

Hi, can you make more such content with open source LLMs instead of ChatGPT

The free tier is not as sweet as it was a while back.

Hi I lost my embeddings on qdrant tell how to save the embeddings on qdrant to persistant ??

I watched the videos of chatting with pdfs. When I test the models in HuggingFace without using OpenAi, I get incomplete and truncated answers. What could be the reason for this? I'm new to this field.

Hi, you can Mp , prive ?

thank you very much. what if i want store csv files to vector database?

What about weaviate?

But open ai api key is paid anyways

openai embeddings not free right??

please make tutorial using of free llms api rather than openai 😓😓😓😓😓🙌🙌🙌🙌

Thanks!

Please go back to IDE!!!

😍😍😍🥰💕💕💕💕💕💞💞💞💞💞💞💞💞

Qdrant class doesn't exist.

# create the chain to answer questions qa_chain_instrucEmbed = RetrievalQA.from_chain_type(llm=OpenAI(temperature=0.2), chain_type="stuff", retriever=retriever, return_source_documents=True) here what's the alternative to openai i can't find any

@alejandro_ao great video! Could you possibly make a video on semantic search with langchain it gets very confusing.

Help, I have this error in the same code : vector_store = Qdrant( TypeError: Qdrant.__init__() got an unexpected keyword argument 'embeddings'

First of all, nice video! Helped me a lot! I have a problem here, I created a vectorstore using a big text of legal manual, around 250 pages, converted to txt. At the middle of it, there is a list of 10 cases that can occur in a specific situation. When I ask "Which cases can occur in this specific situation", I get only the first 3 cases. Increasing chunk_size and overlap to 3000 and 500, and LLM to ChatOpenAI to use model gpt-3.5-turbo-16k I could increase it to 7 cases. Increasing even more chunk_size/overlap got me worse (hallucinations) results. Any other ideas of how to solve this? Is there a way to see the return of the vectorstore query that is sent to the OpenAI to see if all the 11 cases are being sent? Btw, just a small correction in your notebook... You're using os.environ['QDRANT_COLLECTION'] when setting and os.getenv['QDRANT_COLLECTION_NAME'] when creating the vectorstore.

😂 Promo-SM