im still confused how the LLM knows which tool to call. Is there code magic under the hood that basically has a prompt that calls the LLM to return a decision because I dont see anything explicitly defined in the agents

Thanks Lore for putting together this incredible resource👍🏾👍🏾👍🏾

Your medium articles are gems...we love you

Thanks for this tutorial. I am exploring frameworks for build an AI Agent application. Do you have any comparison between langgraph and Pydandtic AI ?

Thank you for the video Lore. Hope to see more content in future :)

Hello Lore, first time watched the video and absolutely loved it. Please keep it up it is very encouraging for those who are looking to learn. Apart from this, i'm getting a graph recursion limit error when invoking the content_extractor. any idea?

Thank you for the video How do you keep values in the store when using `store.search(("guidlines", user_id))`? If I’m not mistaken, initializing `across_thread_memory = InMemoryStore()` in the next iteration causes the store to lose its values

Hey. Thank you for the tutorial! Is there a way to deploy it on the Front-End(Next.js) or some PHP project?

My latest project involved developing RAG tool to ingest Jira and Wiki. In the process, I learnt some Langchain calls. This will help me to expand my knowledge. Thanks.

Wonderful Lore, can you make a video on how to use the weaviate instance locally using docker?

not sure why i am getting this error. i have a .env file but i get the below error. any guess? TypeError('"Could not resolve authentication method. Expected either api_key or auth_token to be set. Or for one of the `X-Api-Key` or `Authorization` headers to be explicitly omitted"')

I was looking some references to play around with RAG and LLM, and it's been go through with your tutorial which helping so much to started

Thanks. How to stream the result back ? Now its collecting and sending as one response.

Thanks for the demo. For deploying to aws, which components it used. Is this solution is scalble to handle large traffic

Thanks

Very good tutorial.

Very creative. Always learn something new.

Hello 13:00 What code statement(s) are generating the prompt inputs seen in your UI (which you are typing into)?. As you answer, assume others are not running on Apple devices. It's not clear in LangGraph here.

This is a great video! Really helpful for getting started. I appreciate you going over the tools required (Pinecone, AWS) to actually train the model. I’m still working on collecting my corpus, but do you suggestions on ways to productionize a mode, outside of a Jupyter Notebook, perhaps how to host it on a simple website? I want to share my model for others to use and evaluate the responses!

Super interessant!

Very clear explanation provided. Thank you Lore. But I have one question. Can we use custom embeddings instead of openai embeddings? Instead of using vectorizers like text2vec-open ai hugging face transformers etc.? If so how to add text into vector store based on these embeddings?

Please how can i get api of weaviate ???

Great video However, I have found some inconsistencies between the code you are shoeing and the file linked on your description I have managed to work it out, thought it is something to note. Looking forwards to see more of your videos !

Excllent and detailed information good job!

@lorevanoudenhove4946 Running the code at cell in 11:17 part, I get this error: {'error': [{'message': 'update vector: unmarshal response body: json: invalid number literal, trying to unmarshal "\\"rate_limit_exceeded\\"" into Number'}]}

Great video! Just a suggestion: It would help if you could share the ipynb files (e.g. thru collab) from your tutorials. :) Most KZbinrs do it. :)

How do you need to change the code when connecting to a local Weaviate docker image rather than than the cloud sandbox (which is valid only 14 days) ? Secondly, what needs to be changed when I want my own vector database to sit on top of the GPT-3 LLM, as an enhancement rather than a replacement?

Very helpful, thanks. Just curious, but what if I wanted instead of the reply "I don't know" to continue with the usual results from ChatGPT ... if the answer is not in my data, then something more general via the usual gpt model would be shown ?

Hello Lore thanks for video again. I have vectorstore like that "vectorstore = Weaviate(client, "Techs","description", attributes = ["url","author","title","path"])" and i have search in my documents its find 2 similarty data and ai combine them and answer my question So have can i these datas path below the answer need to give all finded documents path in there In this example its find 2 document and must be give 2 path url from there for example : Answer:.... Soruce 1 : {path 1} Source 2 : {path 2}

Great overview thank you so much for putting this together! very helpful!

Hello Lore thanks for video its very helpful, i have a question to you What is the best method to upload a large JSON dataset with over 12,000 entries, each containing 'title', 'description', and 'author' keys, where the 'description' text in each entry averages around 1000 characters, to Weaviate?

Great tutorial Lore! I enjoyed the pace and easiness. Do you have any tutorials about connecting the bot to a website and put it to work?

What open source LLMs can be used with this design?

Super interesting video, very informative, want to learn more creating chatbots!!

Awesome video, Lore!

github?

This is an awesome video. I was able to incorporate a bunch of pdfs into weaviate and make queries, but I wanted to modify the chatbot part of the project a bit. It seems like I can ask a question and get a response, but what if I want to further the conversation and ask another question based on the previous response. Basically, I want the chatbot to remember history so I can make a full conversation as opposed to a question/response. Is there an easy way to implement this because I can imagine a scenario where not only does it have to remember the previous context, but it also has to rerun a similarity search in weaviate.

Thank you for the video, it was very informative. I have a question: I want to upload a very long text to Weaviate. I've installed Weaviate via Docker and wrote the PHP code to post via the API. However, when I try to post a long text, the Weaviate Docker container crashes. Is there a limit for the text size that can be sent to Weaviate, or is there a specific approach I should follow?